diff --git a/.github/workflows/update-s3.yml b/.github/workflows/update-s3.yml
new file mode 100644
index 0000000..0283b1c
--- /dev/null
+++ b/.github/workflows/update-s3.yml
@@ -0,0 +1,25 @@
+name: Deploy site to S3
+on:
+ pull_request:
+ push:
+ branches:
+ - main
+
+jobs:
+ update-site:
+ name: Update public site
+ runs-on: ubuntu-latest
+ steps:
+ - name: Checkout
+ uses: actions/checkout@v4
+ - name: Configure AWS credentials
+ uses: aws-actions/configure-aws-credentials@4
+ with:
+ aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
+ aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
+ aws-region: us-west-2
+ - name: Deploy newly built site to S3 + CloudFront
+ run: |
+ aws s3 sync --delete public/ s3://${{ secrets.LTRR_SITE_BUCKET }}/
+ aws cloudfront create-invalidation --distribution-id ${{ secrets.LTRR_SITE_CDN }} --paths '/*'
+
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..e3d2951
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,78 @@
+# Created by https://www.toptal.com/developers/gitignore/api/linux,macos,windows
+# Edit at https://www.toptal.com/developers/gitignore?templates=linux,macos,windows
+
+### Linux ###
+*~
+
+# temporary files which can be created if a process still has a handle open of a deleted file
+.fuse_hidden*
+
+# KDE directory preferences
+.directory
+
+# Linux trash folder which might appear on any partition or disk
+.Trash-*
+
+# .nfs files are created when an open file is removed but is still being accessed
+.nfs*
+
+### macOS ###
+# General
+.DS_Store
+.AppleDouble
+.LSOverride
+
+# Icon must end with two \r
+Icon
+
+
+# Thumbnails
+._*
+
+# Files that might appear in the root of a volume
+.DocumentRevisions-V100
+.fseventsd
+.Spotlight-V100
+.TemporaryItems
+.Trashes
+.VolumeIcon.icns
+.com.apple.timemachine.donotpresent
+
+# Directories potentially created on remote AFP share
+.AppleDB
+.AppleDesktop
+Network Trash Folder
+Temporary Items
+.apdisk
+
+### macOS Patch ###
+# iCloud generated files
+*.icloud
+
+### Windows ###
+# Windows thumbnail cache files
+Thumbs.db
+Thumbs.db:encryptable
+ehthumbs.db
+ehthumbs_vista.db
+
+# Dump file
+*.stackdump
+
+# Folder config file
+[Dd]esktop.ini
+
+# Recycle Bin used on file shares
+$RECYCLE.BIN/
+
+# Windows Installer files
+*.cab
+*.msi
+*.msix
+*.msm
+*.msp
+
+# Windows shortcuts
+*.lnk
+
+# End of https://www.toptal.com/developers/gitignore/api/linux,macos,windows
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..2515a21
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2019-2022 The Arizona Board of Regents on behalf of The University of Arizona
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..a9297d7
--- /dev/null
+++ b/README.md
@@ -0,0 +1,3 @@
+# Dave Meko faculty web site + Time Series course
+
+Automatic maintenance of a static web site in an AWS S3 bucket.
diff --git a/public/Document Scrap 'csaps_m i...'.shs b/public/Document Scrap 'csaps_m i...'.shs
new file mode 100755
index 0000000..05eca33
Binary files /dev/null and b/public/Document Scrap 'csaps_m i...'.shs differ
diff --git a/public/Files_pooled.zip b/public/Files_pooled.zip
new file mode 100644
index 0000000..d9a12a9
Binary files /dev/null and b/public/Files_pooled.zip differ
diff --git a/public/Geos585A_notes2017.zip b/public/Geos585A_notes2017.zip
new file mode 100755
index 0000000..8c81f76
Binary files /dev/null and b/public/Geos585A_notes2017.zip differ
diff --git a/public/Geos585A_notes2019.zip b/public/Geos585A_notes2019.zip
new file mode 100644
index 0000000..d6f0650
Binary files /dev/null and b/public/Geos585A_notes2019.zip differ
diff --git a/public/MekoWeb.bfproject b/public/MekoWeb.bfproject
new file mode 100755
index 0000000..133888e
--- /dev/null
+++ b/public/MekoWeb.bfproject
@@ -0,0 +1,61 @@
+view_main_toolbar: 1
+bmarksearchmode: 0
+snr_scope: 0
+snr_type: 0
+view_cline: 1
+editor_indent_wspaces: 0
+c2e.convert_xml: 1
+snr_dotmatchall: 0
+htmlbar_view: 1
+fb_focus_follow: 1
+adv_open_recursive: 0
+encoding: UTF-8
+autoindent: 1
+e2c.convert_special: 0
+fb_viewmode: 0
+snippets_show_as_menu: 1
+bookmarks_show_mode: 0
+outputb_show_all_output: 0
+autocomplete: 1
+spell_lang: en_ZA
+c2e.convert_symbol: 0
+snr_escape_chars: 0
+view_blockstack: 1
+htmlbar_notebooktab: 0
+last_filefilter:
+spell_insert_entities: 0
+spell_check_default: 1
+savedir: file:///home/david/Data/web
+files: /home/david/Data/web/geos585a.html
+snr_replacetype: 0
+recent_files: file:///home/david/Data/web/geos585a.html
+charmap_block: 1
+e2c.convert_iso: 0
+enable_syntax_scan: 1
+outputb_scroll_mode: 0
+leftpanel_active_tab: 0
+c2e.IE_apos_workaround: 0
+display_right_margin: 0
+editor_tab_width: 3
+view_statusbar: 1
+fb_show_hidden_f: 0
+name: MekoWeb
+view_blocks: 1
+snr_casesens: 0
+c2e.convert_iso: 0
+bookmarks_filename_mode: 0
+wrap_text_default: 0
+opendir: file:///home/david/Data/web
+e2c.convert_xml: 1
+default_mime_type: text/plain
+view_left_panel: 1
+htmlbar_thumbnailwidth: 300
+fb_show_backup_f: 0
+view_line_numbers: 1
+show_mbhl: 1
+adv_open_matchname: 0
+convertcolumn_horizontally: 0
+snr_recursion_level: 0
+e2c.convert_symbol: 0
+e2c.convert_num: 0
+c2e.convert_special: 0
diff --git a/public/contact.html b/public/contact.html
new file mode 100755
index 0000000..e471bb2
--- /dev/null
+++ b/public/contact.html
@@ -0,0 +1,34 @@
+
+
+
+
+ Meko-contact
+
+
+
Contact Info
+
+
+Email: dmeko@LTRR.arizona.edu
+
+
David M. Meko
+Laboratory of Tree-Ring Research
+University of Arizona
+Tucson, AZ 85721
+This course is being taught "Live Online" in Spring Semester 2021. The University of Arizona has policies generally applicable to all graduate courses, whatever the mode. The policies cover such topics as 1) absence and class participation, 2) threatening behavior, 3) accessibility and accommodations, 4) academic integrity, and 5) nondiscrimination and anti-harassment. More information can be found at University Policies.
+
+Analysis tools in the time and frequency domains are introduced in the context of sample time series. I use a dataset of sample time series to illustrate methods. This year the sample dataset comes from an NSF project on hydrologic variability in the Truckee-Carson Basin of California/Nevada. This dataset includes tree-ring chronologies, precipitation records, streamflow records, and time series of snow-water equivalent measured at snow-course stations. You will assemble your own time series for use in the course. Ideally these are from your own research, but in any case should be time series you would like to understand better.
+Back to Top of Page
+
+
+
Overview
+This is an introductory course, with emphasis on practical aspects of time series analysis. Methods are hierarchically introduced -- starting with terminology and exploratory graphics, moving to descriptive statistics, and ending with basic modeling procedures. Topics include detrending, filtering, autoregressive modeling, spectral analysis and regression. You spend the first two weeks installing Matlab on your laptop, getting a basic introduction to Matlab, and assembling your dataset of time series for the course. Twelve topics, or "lessons" are then covered, each allotted a week, or two class periods. Twelve class assignments go along with the topics. Assignments consist of applying methods by running pre-written Matlab scripts (programs) on your time series and interpreting the results.
+
+The course is 3 credits for University of Arizona students, and 1-3 credits for others.
+
+Any time series with a constant time increment (e.g., year, month, day) is a candidate for use in the course. Examples are annual precipitation, monthly mean temperature, and daily cases of COVID-19.
understand basic time series concepts and terminology
+
be able to select time series methods appropriate to goals
+
be able to critically evaluate scientific literature applying the time series methods covered
+
have improved understanding of time series properties of your own dataset
+
be able to concisely summarize results of time series analysis in writing
+
+
+
+
Prerequisites
+
+
An introductory statistics course
+
Access to a laptop computer capable of having Matlab installed on it
+
Permission of the instructor (undergraduates or non-University-of-Arizona students)
+
+
+
+
Other Requirements
+
+
If you are on a University of Arizona (UA) student, you have access to Matlab and required toolboxes through a UA site license as no cost software. No previous experience with Matlab is required, and computer programming is not part of the course.
+
If you are not a University of Arizona student, you may, with the instructor's permission, be able to take the course in Spring 2021 semester as an "iCourse". You must make sure that you have access to Matlab and the required toolboxes (see below) at your location.
+
Access to the internet. There is no paper exchange in the course. Notes and assignments are exchanged electronically and completed assignments are submitted electronically through the University of Arizona Desire2Learn (D2L) system.
+
+
+
+
+Matlab version. I update scripts and functions now and then using the current site-license release of Matlab. For 2021, I am still using MATLAB Version 9.5.0.944444 (R2018b). Beware that cripts and functions used in the course may not run on earlier versions of Matlab.
+
+Install the whole Matlab package (includes all toolboxes) when installing from the U of A site license. Not all of the toolboxes are needed, but this is the easiest installation. If you are not using the site license, keep in mind that my scripts and functions make extensive use of four toolboxes: Statistics, Signal Processing, System Identification, and Curve Fitting.
+
+
+
+
Availability
+
+The course is offered in Spring Semester every other year (2019, 2021, etc.). It is open to graduate students and may also be taken by undergraduate seniors with permission of the instructor. Enrollment is capped at 20 for Spring Semester 2021.
A small number of students not at the University of Arizona can also be accommodated through the "iCourse" path descrbed above.
+Back to Top of Page
+
+
+
+Spring 2021 semester. Class meets twice a week for 75 minute sessions, 9:00-10:15 AM T/Th, over Zoom. The first day of our class is Jan 14 (Thurs). The last day of class is May 4 (Tues). There is no spring break this year, but instead there are "reading days" with no classes. Class will be cancelled for four days this semester: Feb 25 and Mar 9, which are COVID-19 "reading days"; and whichever two days (Tuesday & Thursday) that happen to fall in the the U-of-A Earth Week. I do not yet know the Earth Week schedule for this year. Earth Week is usually around the last week of March.
+
+The schedule typically allows about two weeks for gathering data and becoming familiar with Matlab. After that, one week (two class periods) is devoted to each of the 12 lessons or topics. Class meets on Tuesday and Thursday. A new topic is introduced on Tuesday, and is continued on the following Thursday. Thursday's class ends with an assignment and a demonstration of running the assignment Matlab script on my sample data. The assignment is due (must be uploaded by you to D2L) before class the following Tuesday.
+
+
+Any online students not at the University of Arizona are expected to follow the same schedule of submitting assignments as regular students. In the "live online" mode, students have access to recorded zoom lectures. All students have access to D2L for submitting assignments.
+
+
+
+
+Once we are into into the assignments on data analysis (after first couple of weeks), the class routine is as follows:
+
+
+Tuesday
+
3-minute lightning talk by a student (chosen randomly the previous Thursday).
+
1/2 hour for guided self-assessment, grading, and uploading of graded assignment to D2L
+
Remainder of class time to introduce the next topic
+
+In the lightning talk, the student puts one or more figures from the submitted assignment up on the screen, and describes the time series analyzed and at least one finding from the analysis. Goals of this activity, new in spring 2019, are to 1) expose students to a variety of time series, 2) provide experience in communication of time series methods, and 3) give practical experience in the lightning-talk of briefly and concisely describing research.
+
+
+Thursday
+
Second part of lecture on this week's topic
+
Breakout-room discussion of some point raised by the instructor
+
Description of the next assignment and a trial run on my sample data
+
Random picking (sampling without replacement) of next Tuesday's lightning-talk presenter
+
+
+
+The breakout-room discussion is new for Spring Semester 2021. Students are assigned to breakout rooms and to discuss a specific time series question related to the current topic. After 10 minutes, students return, and one student representing a breakout group reports on their discussion.
+
+You analyze data of your own choosing in the class assignments. As stated in the course overview, there is much flexibility in the choice of time series. I will make suitable time series available, but it is best to focus on your own research data. The first assignment involves running a script that stores the data and metadata you have gathered in Matlab structure variables in a "mat" file, a specific type of storage file readable only by Matlab. Subsequent assignments draw data from the mat file for time series analysis.
+The 12 topics are addressed sequentially over the semester, which covers approximately 15 weeks. About the first two weeks (4-5 class meetings) are used for some introductory material, deciding on and gathering your time series, and readying Matlab on your laptop. Each week after that is devoted to one of the 12 course topics. Each assignment consists of reading a chapter of notes, running an associated Matlab script that applies selected methods of time series analysis to your data, and writing up your interpretation of the results. Assignments require understanding of the lecture topics as well as ability to use the computer and software.
+
+You submit assignments by uploading them to D2L before the Tuesday class when the next topic is introduced. Students self-grade their assignments at the beginning of class on Tuesday. I browse the self-graded assignments the next day, assess the writing in the assignment, and may or may not change the student's self-assessed grade. To find out how to access assignments, click assignment files.
+
+
+
+
Readings
+Readings consist of class notes. There are twelve sets of .pdf notes files , one for each of the course topics. You can access these pdf files through D2L. There are 12 chapters of notes. I put the pdfs up on D2L a couple of weeks before covering the topic in class. More information on the various topics covered in the course can be found through references listed at the end of each chapter of class notes. People from outside the course can also access the full set of notes from last year through the web site.
+
+Grades are based entirely on performance on the assignments, each of which is worth 10 points. There are no exams. The total number of possible points for the 12 topics is 12 x 10 = 120. A grade of "A" required 90-100 percent of the possible points. A grade of "B" requires 80-90 percent. A grade of "C" requires 70-80 percent, and so forth. In each assignment points are subtracted from the maximum of 10 by self-assessment guided by a rubric presented in class. You should mark the number of points earned at the top of each graded assignment, and annotate with reference to the rubric any subtraction of points.
+
+The instructor looks over the self-graded assignments the next day, and may subtract up to an additional point for shortcomings in the writing quality (e.g., too long, incomprehensible, many spelling or grammatical errors).
+
+Assignments, given in class on Thursday, are due (must be uploaded to D2L by you) before the start of class the following Tuesday. The first half hour of Tuesday's meeting period will be dedicated to presentation of a grading rubric, self-assessment of completed assignments, and uploading of self-graded assignments to D2L. This schedule gives you 4 days to complete and upload the assignment to D2L before 9:00 am Tuesday. D2L keeps track of the time the assignment was uploaded, and no penalty is assessed as long as it is uploaded before 9:00 AM on Tuesday of the due date.
+
+A late penalty of 3 points is assessed if the assignment is not submitted to D2L by 9 AM Tuesday. A late penalty of 1 point is assessed if the graded assignment is not uploaded to D2L by 5 AM Wednesday, which is when I begin looking over your self-graded assignments.
+
+If you have some scheduled need to be away from class (e.g., attendance at a conference), you are responsible for uploading your assignment before 9:00 AM the Tuesday it is due, and for uploading the self-graded version by 10:15 AM the same day. In other words, the schedule is the same as for the students who are in class. If an emergency comes up (e.g., you catch COVID) and cannot do the assignment or assessment on schedule, please send me an email and we will reach some accommodation. Otherwise, the late penalties described above will apply.
+
+
+A time series is broadly defined as any series of measurements taken at different times. Some basic descriptive categories of time series are 1) long vs short, 2) even time-step vs uneven time-step, 3) discrete vs continuous, 4) periodic vs aperiodic, 5) stationary vs nonstationary, and 6) univariate vs multivariate. These properties as well as the temporal overlap of multiple series, must be considered in selecting a dataset for analysis in this course. You will analyze your own time series in the course. The first steps are to select those series and to store them in structures in a mat file. Uniformity in storage at the outset is convenient for this class so that attention can then be focused on understanding time series methods rather debugging computer code to ready the data for analysis. A structure is a Matlab variable similar to a database in that the contents are accessed by textual field designators. A structure can store data of different forms. For example, one field might be a numeric time series matrix, another might be text describing the source of data, etc. In the first assignment you will run a Matlab script that reads your time series and metadata from ascii text files you prepare beforehand and stores the data in Matlab structures in a single mat file. In subsequent assignments you will apply time series methods to the data by running Matlab scripts and functions that load the mat file and operate on those structures.
+
+
+ Assignments
+ Select sample data to be use for assignments during the course
+ Read: (1) Notes_1.pdf, (2) "Getting Started", accessible from the MATLAB help menu
+ Answer: Run script geosa1.m and answer questions listed in the file in a1.pdf
+What to Know
+
+
How to distinguish the categories of time series
+
How to start and quit MATLAB
+
How to enter MATLAB commands at command prompt
+
How to create figures in figure window
+
How to export figures to your word processor
+
Difference between MATLAB scripts and functions
+
How to run scripts and functions
+
The form of a MATLAB "structure" variable
+
How to apply the script geosa1.m to get a set of time series and metadata into MATLAB structures
+
+The probability distribution of a time series describes the probability that an observation falls into a specified range of values. An empirical probability distribution for a time series can be arrived at by sorting and ranking the values of the series. Quantiles and percentiles are useful statistics that can be taken directly from the empirical probability distribution. Many parametric statistical tests assume the time series is a sample from a population with a particular population probability distribution. Often the population is assumed to be normal. This chapter presents some basic definitions, statistics and plots related to the probability distribution. In addition, a test (Lilliefors test) is introduced for testing whether a sample comes from a normal distribution with unspecified mean and variance.
+
+
+ Assignments
+ Read: Notes_2.pdf
+ Answer: Run script geosa2.m and answer questions listed in the file in a2.pdf
+What to Know
+
+
Definitions of terms: time series, stationarity, probability density, distribition function, quantile, spread, location, mean, standard deviation, and skew
+
How to interpret the most valuable graphic in time series analysis -- the time series plot
+
How to interpret the box plot, histogram and normal probability plot
+
Parameters and shape of the normal distribution
+
Lilliefors test for normality: graphical description, assumptions, null and alternative hypotheses
+
Caveat on interpretation of significance levels of statistical tests when time series not "random" in time
+
How to apply geosa2.m to check the distribution properties of a time series and test the series for normality
+
+
+
+Autocorrelation refers to the correlation of a time series with its own past and future values. Autocorrelation is also sometimes called lagged correlation or serial correlation, which refers to the correlation between members of a series of numbers arranged in time. Positive autocorrelation might be considered a specific form of persistence, a tendency for a system to remain in the same state from one observation to the next. The likelihood of tomorrow being rainy is greater if today is rainy than if today is dry. Geophysical time series are frequently autocorrelated because of inertia or carryover in the physical system. The slowly evolving low pressure systems in the atmosphere might impart persistence to daily rainfall. The slow drainage of groundwater reserves might impart correlation to successive annual flows of a river. Stored photosynthates might impart correlation to successive annual values of tree-ring indices. Autocorrelation complicates the application of statistical tests by reducing the number of independent observations. Autocorrelation can also complicate the identification of significant covariance or correlation between time series (e.g., precipitation with a tree-ring series). Autocorrelation can be exploited for predictions: an autocorrelated time series is predictable, probabilistically, because future values depend on current and past values. Three tools for assessing the autocorrelation of a time series are (1) the time series plot, (2) the lagged scatterplot, and (3) the autocorrelation function.
+
+
+ Assignments
+ Read: Notes_3.pdf
+ Answer: Run script geosa3.m and answer questions listed in the file in a3.pdf
+What to Know
+
+
Definitions: autocorrelation, persistence, serial correlation, autocorrelation function (acf), autocovariance function (acvf), effective sample size
+
How to recognize autocorrelation in the time series plot
+
How to use lagged scatterplots to assess autocorrelation
+
How to interpret the plotted acf
+
How to adjust the sample size for autocorrelation
+
Mathematical definition of the autocorrelation function
+
Terms affecting the width of the computed confidence band of the acf
+
The difference between a one-sided and two-sided test of significant lag-1 autocorrelation
+
How to apply geos3.m to study the autocorrelation of a time series
+
+
+
+The spectrum of a time series summarizes the partitioning of variance of the series to rapid and gradual fluctuations. Rapid fluctuations are those with short wavelength, or high frequency. Gradual fluctuations are those with long-wavelength, or low-frequency The spectrum by definition describes the variance of the series as a function of frequency or wavelength. The object of spectral analysis is to estimate and study the spectrum. The spectrum contains no new information beyond that in the autocovariance function (acvf), and in fact the spectrum can be computed mathematically by transformation of the acvf. But the spectrum and acvf present the information on the variance of the time series from complementary viewpoints. The acf summarizes information in the time domain and the spectrum in the frequency domain.
+
+
+
+ Assignments
+ Read: Notes_4.pdf
+ Answer: Run script geosa4.m and answer questions listed in the file in a4.pdf
+
+Autoregressive-moving-average (ARMA) models are mathematical models of the persistence, or autocorrelation, in a time series. ARMA models are widely used in hydrology, dendrochronology, econometrics, and other fields. There are several possible reasons for fitting ARMA models to data. Modeling can contribute to understanding the physical system by revealing something about the physical process that builds persistence into the series. For example, a simple physical water-balance model consisting of terms for precipitation input, evaporation, infiltration, and groundwater storage can be shown to yield a streamflow series that follows a particular form of ARMA model. ARMA models can also be used to predict behavior of a time series from past values alone. Such a prediction can be used as a baseline to evaluate possible importance of other variables to the system. ARMA models are widely used for prediction of economic and industrial time series. ARMA models can also be used to remove persistence. In dendrochronology, for example, ARMA modeling is applied routinely to generate residual chronologies – time series of ring-width index with no dependence on past values. This operation, called prewhitening, is meant to remove biologically-related persistence from the series so that the residual may be more suitable for studying the influence of climate and other outside environmental factors on tree growth.
+
+ Assignments
+ Read: Notes_5.pdf
+ Answer: Run script geosa5.m and answer questions listed in the file in a5.pdf
+What to Know
+
+
+
The functional form of the simplest AR and ARMA models
+
Why such models are referred to as autoregressive or moving average
+
The three steps in ARMA modeling
+
The diagnostic patterns of the autocorrelation and partial autocorrelation functions for an AR(1) time series
+
Definition of the final prediction error (FPE) and how the FPE is used to select a "best" ARMA model
+
Definition of the Portmanteau statistic, and how it and the acf of residuals can be used to assess whether an ARMA model effectively models the persistence in a series
+
How the principle of parsimony is applied in ARMA modeling
+
Definition of prewhitening
+
How prewhitening affects (1) the appearance of a time series, and (2) the spectrum of a time series
+
How to apply geosa5.m to ARMA-model a time series
+
+
+Trend in a time series is a slow, gradual change in some property of the series over the whole interval under investigation. Trend is sometimes loosely defined as a long term change in the mean, but can also refer to change in other statistical properties. For example, tree-ring series of measured ring width frequently have a trend in variance as well as mean. Years ago a time series was typically decomposed into trend, seasonal or periodic components, and irregular fluctuations, and the various parts were studied separately. Modern analysis techniques frequently treat the series without such routine decomposition, but separate consideration of trend is still often required. One of the most frequent question asked about a time series is whether there is significant trend in mean.
+
+ Assignments
+ Read: Notes_6.pdf
+ Answer: Run script geosa6.m and answer questions listed in the file in a6.pdf
+What to Know
+
+
Definitions: trend, nonstationarity, hypothesis test
+
How to test for a monotonic trend in mean of a time series
+
How to test for a difference of means or variances in two segments of an autocorrelated time series
+
+
+Detrending is the statistical or mathematical operation of removing trend from the series. Detrending is often applied to remove a feature thought to distort or obscure the relationships of interest. In climatology, for example, a temperature trend due to urban warming might obscure a relationship between cloudiness and air temperature. Detrending is also sometimes used as a preprocessing step to prepare time series for analysis by methods that assume stationarity. Many alternative methods are available for detrending. Simple linear trend in mean can be removed by subtracting a least-squares-fit straight line. More complicated trends might require different procedures. For example, the cubic smoothing spline is commonly used in dendrochronology to fit and remove ring-width trend that might not be linear, or not even monotonically increasing or decreasing over time. In studying and removing trend, it is important to understand the effect of detrending on the spectral properties of the time series. This effect can be summarized by the frequency response of the detrending function.
+
+ Assignments
+ Read: Notes_7.pdf
+ Answer: Run script geosa7.m and answer questions listed in the file in a7.pdf
+What to Know
+
+
Definitions: frequency response, spline, cubic smoothing spline
+
Pros and cons of ratio vs difference detrending
+
Interpretation of terms in the equation for the "spline parameter"
+
How to choose a spline interactively from desired frequency response
+
How the spectrum is affected by detrending
+
How to measure the importance of the trend component in a time series
+
How to apply geosa7.m to interactively choose a spline detrending function and detrend a time series
+
+
+
+The estimated spectrum of a time series gives the distribution of variance as a function of frequency. Depending on the purpose of analysis, some frequencies may be of greater interest than others, and it may be helpful to reduce the amplitude of variations at other frequencies by statistically filtering them out before viewing and analyzing the series. For example, the high-frequency (year-to-year) variations in a gauged discharge record of a watershed may be relatively unimportant to water supply in a basin with large reservoirs that can store several years of mean annual runoff. Where low-frequency variations are of main interest, it is desirable to smooth the discharge record to eliminate or reduce the short-period fluctuations before using the discharge record to study the importance of climatic variations to water supply. Smoothing is a form of filtering which produces a time series in which the importance of the spectral components at high frequencies is reduced. Electrical engineers call this type of filter a low-pass filter, because the low-frequency variations are allowed to pass through the filter. In a low-pass filter, the low frequency (long-period) waves are barely affected by the smoothing.
+It is also possible to filter a series such that the low-frequency variations are reduced and the high-frequency variations unaffected. This type of filter is called a high-pass filter. Detrending is a form of high-pass filtering: the fitted trend line tracks the lowest frequencies, and the residuals from the trend line have had those low frequencies removed. A third type of filtering, called band-pass filtering, reduces or filters out both high and low frequencies, and leaves some intermediate frequency band relatively unaffected.
+In this lesson, we cover several methods of smoothing, or low-pass filtering. We have already discussed how the cubic smoothing spline might be useful for this purpose. Four other types of filters are discussed here: 1) simple moving average, 2) binomial, 3) Gaussian, and 4) windowing (Hamming method). Considerations in choosing a type of low-pass filter are the desired frequency response and the span, or width, of the filter.
+
+
+
+ Assignments
+ Read: Notes_8.pdf
+ Answer: Run script geosa8.m and answer questions listed in the file in a8.pdf
+What to Know
+
+
Definitions: filter, filter weights, filter span, low-pass filter, high-pass filter, band-pass filter; frequency response of a filter
+
How the Gaussian filter is related to the Gaussian distribution
+
How to build a simple binomial filter manually (without the computer)
+
How to describe the frequency response function in terms of a system with sinusoidal input and output
+
How to apply geosa8.m to interactively design a Gaussian, binomial or Hamming-window lowpass filter for a time series
+
+
+
+The Pearson product-moment correlation coefficient is probably the single most widely used statistic for summarizing the relationship between two variables. Statistical significance and caveats of interpretation of the correlation coefficient as applied to time series are topics of this lesson. Under certain assumptions, the statistical significance of a correlation coefficient depends on just the sample size, defined as the number of independent observations. If time series are autocorrelated, an effective sample size, lower than the actual sample size, should be used when evaluating significance. Transient or spurious relationships can yield significant correlation for some periods and not for others. The time variation of strength of linear correlation can be examined with plots of correlation computed for a sliding window. But if many correlation coefficients are evaluated simultaneously, confidence intervals should be adjusted (Bonferroni adjustment) to compensate for the increased likelihood of observing some high correlations where no relationship exists. Interpretation of sliding correlations can be also be complicated by time variations of mean and variance of the series, as the sliding correlation reflects covariation in terms of standardized departures from means in the time window of interest, which may differ from the long-term means. Finally, it should be emphasized that the Pearson correlation coefficient measures strength of linear relationship. Scatterplots are useful for checking whether the relationship is linear.
+
+ Assignments
+ Read: Notes_9.pdf
+ Answer: Run script geosa9.m and answer questions listed in the file in a9.pdf
+What to Know
+
+
Mathematical definition of the correlation coefficient
+
Assumptions and hypothesis for significance testing of correlation coefficient
+
How to compute significance level of correlation coefficient and to adjust the significance level for autocorrelation in the individual time series
+
Caveats to interpretation of correlation coefficient
+
Bonferroni adjustment to signficance level of correlation under multiple comparisons
+
Inflation of variance of estimated correlation coefficient when time series autocorrelated
+
Possible effects of data transformation on correlation
+
How to interpret plots of sliding correlations
+
How to apply geosa9.m to analyze correlations and sliding correlations between pairs of time series
+
+
+
+Lagged relationships are characteristic of many natural physical systems. Lagged correlation refers to the correlation between two time series shifted in time relative to one another. Lagged correlation is important in studying the relationship between time series for two reasons. First, one series may have a delayed response to the other series, or perhaps a delayed response to a common stimulus that affects both series. Second, the response of one series to the other series or an outside stimulus may be smeared in time, such that a stimulus restricted to one observation elicits a response at multiple observations. For example, because of storage in reservoirs, glaciers, etc., the volume discharge of a river in one year may depend on precipitation in the several preceding years. Or because of changes in crown density and photosynthate storage, the width of a tree-ring in one year may depend on climate of several preceding years. The simple correlation coefficient between the two series properly aligned in time is inadequate to characterize the relationship in such situations. Useful functions we will examine as alternative to the simple correlation coefficient are the cross-correlation function and the impulse response function. The cross-correlation function is the correlation between the series shifted against one another as a function of number of observations of the offset. If the individual series are autocorrelated, the estimated cross-correlation function may be distorted and misleading as a measure of the lagged relationship. We will look at two approaches to clarifying the pattern of cross-correlations. One is to individually remove the persistence from, or prewhiten, the series before cross-correlation estimation. In this approach, the two series are essentially regarded on equal footing. An alternative is the systems approach: view the series as a dynamic linear system -- one series the input and the other the output -- and estimate the impulse response function. The impulse response function is the response of the output at current and future times to a hypothetical pulse of input restricted to the current time.
+
+ Assignments
+ Read: Notes_10.pdf
+ Answer: Run script geosa10.m and answer questions listed in the file in a10.pdf
+
+Multiple linear regression (MLR) is a method used to model the linear relationship between a dependent variable and one or more independent variables. The dependent variable is sometimes also called the predictand, and the independent variables the predictors. MLR is based on least squares: the model is fit such that the sum-of-squares of differences of observed and predicted values is minimized. MLR is probably the most widely used method in dendroclimatology for developing models to reconstruct climate variables from tree-ring series. Typically, a climatic variable is defined as the predictand and tree-ring variables from one or more sites are defined as predictors. The model is fit to a period -- the calibration period -- for which climatic and tree-ring data overlap. In the process of fitting, or estimating, the model, statistics are computed that summarize the accuracy of the regression model for the calibration period. The performance of the model on data not used to fit the model is usually checked in some way by a process called validation. Finally, tree-ring data from before the calibration period are substituted into the prediction equation to get a reconstruction of the predictand. The reconstruction is a "prediction" in the sense that the regression model is applied to generate estimates of the predictand variable outside the period used to fit the data. The uncertainty in the reconstruction is summarized by confidence intervals, which can be computed by various alternative ways.
+
+ Assignments
+ Read: Notes_11.pdf
+ Answer: Run script geosa11.m (Part 1) and answer questions listed in the file in a11.pdf
+What to Know
+
+
The equation for the MLR model
+
Assumptions for the MLR model
+
Definitions of MLR statistics: coefficient of determination, sums-of-squares terms, overall-F for the regression equation, standard error of the estimate, adjusted R-squared, pool of potential predictors
+
The steps in an analysis of residuals
+
How to apply geosa11.m (part 1) to fit a MLR regression model to predict one variable from a set of several predictor variables
+
+
+
+
+Regression R-squared, even if adjusted for loss of degrees of freedom due to the number of predictors in the model, can give a misleading, overly optimistic view of accuracy of prediction when the model is applied outside the calibration period. Application outside the calibration period is the rule rather than the exception in dendroclimatology. The calibration-period statistics are typically biased because the model is "tuned" for maximum agreement in the calibration period. Sometimes too large a pool of potential predictors is used in automated procedures to select final predictors. Another possible problem is that the calibration period itself may be anomalous in terms of the relationships between the variables: modeled relationships may hold up for some periods of time but not for others. It is advisable therefore to "validate" the regression model by testing the model on data not used to fit the model. Several approaches to validation are available. Among these are cross-validation and split-sample validation. In cross-validation, a series of regression models is fit, each time deleting a different observation from the calibration set and using the model to predict the predictand for the deleted observation. The merged series of predictions for deleted observations is then checked for accuracy against the observed data. In split-sample calibration, the model is fit to some portion of the data (say, the second half), and accuracy is measured on the predictions for the other half of the data. The calibration and validation periods are then exchanged and the process repeated. In any regression problem it is also important to keep in mind that modeled relationships may not be valid for periods when the predictors are outside their ranges for the calibration period: the multivariate distribution of the predictors for some observations outside the calibration period may have no analog in the calibration period. The distinction of predictions as extrapolations versus interpolations is useful in flagging such occurrences.
+
+ Assignments
+ Read: Notes_12.pdf
+ Answer: Run script geosa11.m (Part 2) and answer questions listed in the file in a12.pdf
+What to Know
+
+
Definitions: validation, cross-validation, split-sample validation, mean square error (MSE), root-mean-square error (RMSE); standard error of prediction, PRESS statistic, "hat" matrix, extrapolation vs interpolation
+
Advantages of cross-validation over alternative validation methods
+
How to apply geosa11.m (part 2) for cross-validated MLR modeling of the relationship between a predictand and predictors, including generation of a reconstruction and confidence bands
+
+
+
+
+The Matlab class scripts and user-written functions are zipped in a file called "tsfiles.zip", which students should download from D2L. To get the files, first create an empty folder on your computer. This is where you will store all functions, scripts and data used in the course. Unzip the file there. When you run matlab, be sure that directory is your current matlab working directory. I no longer put the current year's tsfiles.zip on the course web site, but have a version called tsfiles_Stale.zip that people can go to to get the functions, etc., from the previous offering of the course.
+
+Powerpoint lecture outlines & miscellaneous files. Downloadable file other_Stale.zip has miscellaneous files used in lectures from the previous offering of the course. Included are Matlab demo scripts, sample data files, user-written functions used by demo scripts, and powerpoint presentations, as pdfs (lect1a.pdf, lect1b.pdf, etc.) used in on-campus lectures. Students taking the course this semester should not use other_Stale.zip, but instead get the file "other.zip" from D2L contents. I update other.zip over the semester, and add the presentation for the current lecture within a couple of days after that lecture is given. File other.zip for this semester does not exist till after the first lecture, and then is augmented after each lecture. At the end of the semester I revise the online-available other_Stale.zip.
+
+To run the Matlab scripts for the assignments, you must have your data, the class scripts, and the user-written Matlab functions called by the scripts in a single directory on your computer. The name of this directory is unimportant. Under Windows, it might be something like "C:\geos585a\". The functions and scripts provided for the course should not require any tailoring, but some changes can be made for convenience. For example, scripts and functions will typically prompt you for the name of your input data file and present "Spring21" as the default. That is because I've stored the sample data in Spring21.mat. If you want to avoid having to type over "Spring21" with the name of your own data file each time you run the script, edit the matlab script with the Matlab editor/debugger to change one line. In the editor, search for the string "Spring21" and replace it with the name of your .mat storage file (e.g., "Smith2021"), then be sure to re-save the edited script.
+
+
+Notes and assignments are available to those taking the course through D2L. I revise the notes and assignments during the semester, and upload files to D2L at least two weeks before the topic is covered in class. The zipped file "other.zip" contains powerpoints(converted to pdf) and miscellaneous demo files used in class lectures. "other.zip" is built up cumulatively through the semester, and is updated after each class. The powerpoints have feedback from students' submitted assignments, and may be helpful to the correspondence students, who do not have access to the twice-a-week lectures.
+
+I am happy to share my notes, and anyone not taking the course is welcome to download them and modify them for their own purposes. No attribution or acknowledgement of source is requested. Enjoy! Click on the zip file that contains the pdf's of notes for each lecture for the previous semester I taught the course. These are NOT the notes for the current semester. Notes for the current semester are available to registered students only, must be accessed through D2L, and are not finalized till the end of the semester.
+
+
+
+
+The Meko household is made up of Linda, Dave and Matt. Linda's a Program Coordinator in the UA College of Public Health. Matt's is a senor in Howenstein High School. (We are currently dogless, with the sad passing of Silkie and Lolly.) We all love music. Linda pretty much likes all kinds except maybe rap. I'm a nut for buttonbox accordion music, and play in a garage band called "Hector and the Javelinas" with Barry Richards and Bill Mrosek. Matt plays the accoustic bass, and played in the Howenstein High School orchrestra while it existed.
+
+
+
+
+ crn2vec2: .crn file to column vectors of index, sample size, and year
+ [x,s,yr]=crn2vec2(pf1) or [x,s,yr]=crn2vec2;
+ Last revised 2009-8-20
+
+
+ Reads a ".crn" file of tree-ring indices in ITRDB format and converts
+ the indices and associated data into vectors that can be used in Matlab.
+ Input file must be formatted as in ITRDB requirements for crn2vec2 to work.
+
+
+*** IN
+
+ pf1 (1 x ?)s path and file name of .crn file
+ (e.g., 'c:\work\mt100.crn');
+ alternatively, can call with no input arguments and be prompted to
+ point to the .crn file
+
+*** OUT
+
+ x (mx x 1)r tree-ring index, mx years
+ s (mx x 1)r sample size (number of cores) in each year
+ yr (mx x 1)i year vector for x and s
+
+*** REFERENCES -- None
+
+*** UW FUNCTIONS CALLED -- None
+
+*** TOOLBOXES NEEDED -- None
+
+
+ rwl2tsm: ring-width list (rwl) file to time series matrix
+ [X,yrX,nms,T]=rwl2tsm(pf1)
+ Last revised 2009-9-4
+
+
+ Ring-width list (rwl) file to time series matrix.
+ Reads an rwl file and puts all its ring-width series into a
+ time series matrix X with a corresponding year vector yrX. The columns
+ of X correspond to the individual width series in the order they were
+ stored in their rwl file. The row-cell of string names, nms, has the
+ core ids of the series in X, in the same order as the columns of X. The
+ time series matrix X is filled out with NaNs where data for individual
+ series is missing.
+
+
+
+*** IN
+
+ pf1 (1 x ?)s path and filename of input rwl file(see notes) (e.g.,
+ 'c:\data\'az023.rwl'); if not included, you are prompted to point
+ to the file
+
+*** OUT
+
+ X (mX x nX)r time series matrix, mX years and nX columns
+ yrX (mX x 1)i year vector for X
+ nms {size(X,2) x 1}s col-cell of names of series in X
+ T (nX x 3)i column, first and last years of valid data in each series of X
+
+*** REFERENCES --- none
+
+*** TOOLBOXES NEEDED -- none
+
+*** UW FUNCTIONS CALLED
+
+ rwlinp
+ sov2tsm3
+
+
+ grplot: stacked time series plots of ring-width series from rwl file
+ grplot;
+ Last revised 2008-5-2
+
+
+ Stacked time series plots of ring-width series from rwl file. Up to 8 series
+ plotted per figure windowm, or printed page. Intended to help in deciding on curve-fits
+ for detrending in programs such as ARSTAN. Can process single rwl file (interactive
+ mode), or multiple rlw files(batch mode). Series can be plotted in order as encountered
+ in the rwl file, or re-ordered by age. Series for each page can also be specified if
+ user first stores a pointer variable in a .mat file beforehand.
+
+
+*** IN
+
+ No input arguments
+
+ User prompted for various options, and to point to files for input and
+ output
+
+*** OUT
+
+ No output args.
+
+ Plots of time series appear in one or more figure windows.
+
+ If your input ring-width data is from an .rwl file(s), an ascii .txt file with name
+ xxx_list.txt appears in the working directory. "xxx" is the part of the
+ rwl filename before the period. Thus, if "az024.rwl" is your input,
+ "az024_list.txt" is produced. This .txt file is for surveying file
+ contents and lists each core id and first and last years' measurments
+
+ Other file output is optional, and consists of one or more postscript (.ps) files, one for
+ each input .mat ring-width file. The user can specify the filename in interactive
+ mode. In batch mode, the .ps suffix is assigned to the same prefix as the input
+ .mat file containing the ring-width file.
+
+ In "single" mode you can view the figure windows as the program runs and afterwards.
+ In "batch" mode, figure windows are not saved, but are overwritten from on
+ ring-width file to ring-width file. But in batch mode, have the plots stored in
+ .ps files for later plotting.
+
+*** REFERENCES -- none
+
+*** UW FUNCTIONS CALLED
+
+ eightplt - utility function that plots up to 8 sets of axes on page
+ rwlinp - read rwl file; store data as indexed vector (strung-out-vector) and metadata
+ trailnan --- misc utility
+
+*** TOOLBOXES NEEDED -- none
+
+
+ rwlinp: read rwl-file data and store in a .mat file
+ [pf2,X,yrs,nms]=rwlinp(pf1,path1,path2);
+ Last revised 2009-9-28
+
+
+ Read rwl-file data and store in a .mat file.
+ Utility function to read an rwo file and store the data in an accessible
+ form for MATLAB functions. Data stored as "index-vector", which is each
+ series one after another in a column vector.
+
+
+
+*** INPUT (optionally 0-3 args)
+
+ pf1 (1 x ?)s combined path and filename of .rwl file
+ example: 'd:\jack\data\az033.rwl'
+ path1 (1 x ?)s path to the .rwl file. If this arg is passed, means
+ that pf1 is the filename only
+ Example: 'd:\jack\data\' as path1, and 'az033.rwl' as pf1
+ path2 (1 x ?)s , only if also have pf1 and path1>: path
+ for the output .mat files and .tmp files. If no path2 as argument,
+ default is to the same directory as the .rwl files are in
+
+ Input arguments are optional. There can be 0,1, 2, or 3 input arguments:
+ None: user prompted to clck on names of input and output files
+ One: combined path & filename for the .rwl file
+ Two: first arg is the filename of the .rwl file, and the second is the path
+ Three: path for the output .mat file; this option is convenienent when user
+ wants .mat output files to go to different directories that that
+ of the source .rwl files
+
+
+*** OUTPUT
+
+ pf2 (1 x ?)s path & filename of output .mat file
+ X (? x 1)i column vector of ringwidths stored one core after another
+ in units of hundredths of mm
+ yrs (? x 3)i start year, end year, and row index of start year of each
+ core's ring-width series in X
+ nms {?x1}s ids of each core
+
+ Depending on the number of input arguments, the output .mat
+ file goes to a specified filename or the user is prompted to enter
+ it. The .mat file contains X, yrs and nms, as well as:
+
+ cmask (? x 1)i mask for core; 1==do not mask, 0==mask. cmask is created as all 1's
+ by rwlinp. Subsequent functions may change cmask as cores are deleted from analysis.
+ Fwhen {8 x 4}s history of fits for the data set. Casual user need not be concerned with this
+ or cmask. Fwhen tracks what was the source and target data file, and what and when things were
+ done to the data.
+
+ A .tmp ascii file is also produced listing the core id, and first and last years'
+ data for each core. This file intended for checking that rwlinp.m
+ indeed stores the ring widths properly
+
+
+*** REFERENCES --none
+
+*** UW FUNCTIONS CALLED
+
+ intnan.m -- checks for internal NaNs in a vector
+ trailnan.m -- lops off trailing NaNs from a vector
+
+*** TOOLBOXES NEEDED -- none
+
+
+ sov2tsm: strung-out-vector to time series matrix, with names cell
+ [X,yrX,nms]=sov2tsm3(v,YRS,nms,jpick,tends);
+ Last revised 2008-4-29
+
+
+ Strung-out-vector (SOV) to time series matrix, with names cell.
+ Low-level function to convert ring-width data previously read from an rwl
+ file into MATLAB by rwlinp in to a time series matrix. This function
+ called by rwl2tsm.
+
+
+*** IN
+
+ v (mv x 1)r strung-out-vector (sov) of one or more time series
+ YRS(nsers x 3)i start year, end year, and starting row index of
+ each series in v, where nsers is the number of series in v
+ nms (nsers x ?)s or {nsers x 1}s names of series in v
+ jpick(? x 1)i or [] index to rows of YRS specifying which series in
+ v to include in the tsm. For example, jpick=([1 3 4 7])' would pick
+ only those four series. The series numbers correspond
+ to rows of YRS. See Notes.
+ tends(1 x 2)i, or [] first last year of desired tsm X.
+
+
+*** OUT
+
+ X (mX x nX)r time series matrix, mX years and nX columns
+ yrX (mX x 1)i year vector for X
+ nms {size(X,2) x 1}s names of series in X
+
+*** REFERENCES --- none
+
+*** TOOLBOXES NEEDED -- none
+
+*** UW FUNCTIONS CALLED -- none
+
+
+ trailnan: remove any trailing NaN's from a vector
+ x=trailnan(x);
+ Last revised: 2009-10-05
+
+
+ Utility function used by rwlinp.m and other functions. Most users
+ will not need to call trailnan in their own code
+
+
+*** INPUT
+
+ x (1 x ?)r or (? x 1)r vector, usually a time series
+
+*** OUTPUT
+
+ x (? x 1)r the same vector, but with any trailing NaN's removed
+
+*** REFERENCES -- none
+*** UW FUNCTIONS CALLED -- none
+*** TOOLBOXES NEEDED -- none
+
+
+
+ acf: autocorrelation function and approximate 95% confidence bands
+ [r,SE2,r95]=acf(x,nlags,k);
+ Last revised 2008-4-29
+
+
+ Autocorrelation function and approximate 95% confidence bands. Options available
+ for plotting and for alternative algorithms of calculation.
+
+
+*** INPUT ARGUMENTS
+
+ x (m x 1) time series, length m
+ nlags (1 x 1)i number of lags to compute acf to
+ k (1 x 2) options;
+ k(1) -- plotting
+ ==1 No plotting within function; get this also if no k input
+ argument
+ ==2 Function makes stem plot of acf with CI of +-2 times large-lag
+ standard error
+ k(2) -- alternative algorithms (see Notes)
+ ==1 Uses global means, not means computed separately for subsets of
+ observations, for departures. Computed covariance as sum of products of first k
+ values and last k values from the the mean. Then standardizes so
+ that lag 0 autocorrelation is 1.0 by dividing the vector g by g(1),
+ where g is vector of sums of cross-products at lags 0,1,2,...
+ ==2 Uses subset means and standard deviations in computations
+
+*** OUTPUT ARGUMENTS
+
+ r (1 x nlags)r acf at lags 1 to nlags
+ SE2 (1 x nlags)r two times the large-lag standard error of r
+ r95 (1 x 1)threshold sample r required for significance in one-tailed test at
+ 0.05 alpha (95% signficance). Intended for quick check for positive first-order
+ autocorrelation, the most common form of persistence in many natural.
+ If the sample r(1) does not exceed r95, cannot reject H0 that the
+ population r(1) is zero, at 0.05 alpha.
+
+
+
+*** REFERENCES
+
+ Large-lag standard error after Box, G.E.P., and Jenkins, G.M., 1976, Time series
+ analysis: forecasting and control: San Francisco, Holden Day.
+
+ Confidence interval for r(k) from Haan, C.T., 2002, Statistical methods in Hydrology,
+ second edition: Ames, Iowa, Iowa State University Press.
+
+ Computation formulas for sample r from
+ Wilks, D.S., 1995, Statistical methods in the atmospheric sciences: Academic Press, 467 p.
+
+
+*** UW FUNCTIONS CALLED -- none
+
+
+*** TOOLBOXES NEEDED -- none
+
+
+
+ whit1: fit AR model to a time series using modified AIC criterion, returning model information and residuals
+ [e,k1,vrat,arcs] = whit1(y,nhi,k2);
+ Last revised 2008-4-29
+
+
+ Fit AR model to a time series using modified AIC criterion, returning model information and residuals
+ You specify the highest order AR model to consider. Models up to that order are
+ fit, and the modified (small sample) Akaike Information Criterion (AIC) is used to pick the best model.
+
+
+*** IN **********************
+
+ y (my x 1)r time series, vector; NaNs not allowed
+ nhi (1 x 1)i highest AR order to consider (if k2==1), or the only
+ AR order model to try fitting (if k2=2)
+ k2 (1 x 2)i options
+ k2(1) for order selections
+ ==1 fit models of order 1 to nhi
+ ==2 fit model of order nhi only
+ k2(2) for over-riding selection of null (order = 0) model
+ ==1 if modified AIC computed from original time series with model order 0 is lower than
+ any of the entertained models, return the null model (see notes)
+ ==2 accept the lowest AIC model even if its AIC is not as low as that of the null model
+
+*** OUT **************************
+
+ e (my x 1)r AR residuals, with mean added back in; or, if null model, the
+ original series y
+ k1 - the order of the AR model deemed best by the AIC, or 0 if null model and k2(2)==1
+ vrat - the ratio of variance of AR residuals to variance of original time series y (see notes)
+ arcs the ar coefs and their two-standard errors in a two-row array; row 1 has
+ the coefficients; row two has 2*standard error of the coefficients;
+ arcs==[] if k2==1 and null model has been selected;
+
+*** REFERENCES
+
+ The loss function and AIC are discussed in Ljung, L. 1995. System identification
+ toolbox; for use with MATLAB, The MathWorks, Inc., p. 3-46.
+
+ Akaike H. (1974) A new look at the statistical model identification. IEEE Trans. Autom. Control AC-19, 716-723.
+ Hurvich C. M. and Tsai C. (1989) Regression and time series model selection in small samples. Biometrika 76, 297-307.
+
+
+*** UW FUNCTIONS CALLED
+
+ akaike
+
+
+*** TOOLBOXES NEEDED -- system identification
+
+
+ akaike: Akaike information criterion for order of best ARMA model
+ c=akaike(V,N,m);
+ Last revised 2004-2-17
+
+
+ Akaike information criterion for order of best ARMA model
+
+
+*** INPUT ARGUMENTS
+
+ V (1 x 1)r loss function, or variance of model residuals
+ N (1 x 1)i number of observations
+ m (1 x 1)i number of explanatory variables, or sum of AR and MA orders
+
+*** OUTPUT ARGUMENTS
+
+ c (1 x 2)r Akaike information criterion, with and without correction for small sample bias (see
+ notes); c(1) == with correction; c(2)= without correction
+
+*** REFERENCES
+
+ Akaike H. (1974) A new look at the statistical model identification. IEEE Trans. Autom. Control AC-19, 716-723.
+ Hurvich C. M. and Tsai C. (1989) Regression and time series model selection in small samples. Biometrika 76, 297-307.
+
+
+*** UW FUNCTIONS CALLED -- none
+
+
+*** TOOLBOXES NEEDED
+
+ System identification
+
+
+
+ intnan: check for internal NaN in a vector
+ m=intnan(x)
+ Last revised 2008-4-29
+
+
+ Utility function for checking whether a vector has an internal, or imbedded, NaN
+
+
+*** INPUT
+
+ x(? x 1)r or (1 x ?)r -- a time series
+
+*** OUTPUT
+
+ m(1 x 1)L 1 if x has imbedded NaN, 0 otherwise
+
+*** REFERENCES -- none
+
+
+*** UW FUNCTIONS CALLED -- none
+
+*** TOOLBOXES NEEDED -- none
+
+
+ rwchng: scaled values or scaled first-difference of a time series
+ rwchng(x,k);
+ Last revised 2003-09-10
+
+
+ Scaled values or scaled first-difference of a time series.
+ Written as utility function for skelcrn.
+ The scaled first-difference ring-width or index time series is the
+ change in the series from the previous year expressed as a decimal fraction
+ of the local level of the series. The local level is the values of the series
+ smoothed by a 9-weight Gaussian filter.
+
+
+
+*** INPUT ARGS
+
+ x (mx x 1) input time series, a ring-width series or index series
+ k (1 x 1)i option for first differencing of time series (see notes)
+ ==1 no first differencing ("local" mode in skelcrn)
+ ==2 first differencing
+
+*** OUTPUT ARGS
+
+ y (my x 1) scaled first difference of departures
+ my=mx-1
+
+*** REFERENCES -- none
+
+*** UW FUNCTIONS CALLED -- none
+
+*** TOOLBOXES NEEDED
+ signal processing
+
+
+ rwread3: reads a single .rw file
+ [X,guy,day,pf1]=rwread3(path1,file1);
+ Last revised 1999-2-15
+
+
+ Reads a single .rw file
+ A low-level function called by skelcrn and other
+ UW functions. rwread3.m reads a file of data in ".rw" format,
+ and stores the result in a matrix with the year as column 1 and
+ ring width in column 2
+
+
+*** INPUT ***********************
+
+ path1 (1 x ?)ch path to .rw file
+ file1 (1 x ?)ch filename of .rw file
+
+
+*** OUTPUT ************************
+
+ X (mX x 2)r year in col 1, ring width in col 2
+ guy (1 x ?)ch initials of measurer
+ day (1 x ?)ch day measured
+ pf1 (1 x ?)ch path\filename of the .rw file
+
+*** REFERENCES -- none
+*** UW FUNCTIONS CALLED -- none
+*** TOOLBOXES NEEDED -- none
+
+ skelcrn: skeleton plot from .crn file or .rw file
+ skelcrn;
+ Last revised 2009-9-30
+
+
+ Draws a skeleton plot from chronology indices in a .crn "ITRDB-format"
+ file; a measured ring-width series in a .rw file; or a two-column time series
+ matrix with the year in column 1.
+ The plot is scaled in the x-direction to 100 yr per 20 cm to match
+ with manually drawn plots. The 10% of largest values (index or ring width)
+ indices in each century are marked by small squares on the skeleton plot.
+ For .crn input, a plot of sample size (number of cores, usually) against
+ time is also produced.
+
+ Optionally a ring can be compared to just the flanking rings (adjacent), or
+ to all rings within the local region (local). In "adjacent" mode, ring widths
+ are first-differenced as an intermediate step to emphasize change from
+ previous year. In "local" mode, no first-differencing is done. With the "local" approach,
+ the comparison is centered (comparing with rings before and after). With the
+ adjacent, the comparison can be either centered or backwards (comparing the ring with just the
+ preceding ring).
+
+ A user-prompted scaling factor allows for stretching or shrinking the x axis
+ so that the output of your particular printer exactly matches the scale of manual
+ skeleton plots.
+
+
+*** IN
+
+ No input arguments.
+ User is prompted for three things:
+ 1) type of input (.crn file or .rw file)
+ 2) local or adjacent mode
+ 2) centered or backwards approach to building the plot
+ 3) scaling factor (see notes)
+
+*** OUT
+
+ No output arguments
+
+ Plots are shown in figure windows on the screen. DO NOT WORRY if when you click on
+ a figure window you do not see the entire plots. The printouts will stil be OK.
+ To get a printout, steps depend on your version of matlab. With MATLAB
+ Version 7.5.0.342 (R2007b)follow these steps:
+ '1 Click on a desired figure window to make that window current',...
+ '2 Click File/PrintPreview from the Figure-Window menu',...
+ '3 Click "Center" option -- rectangle at left center of menu...
+ '4 Press "Print" from the Print Preview window',...
+ '6 Press "OK" from the Print Window'};
+
+ Skelcrn is not likely to work with earlier versions of Matlab, as this version of skelcrn was adapted
+ to use some new calling conventions.
+
+*** REFERENCES
+
+ Stokes, M.A., and Smiley, T.L., 1996, An Introduction to Tree-ring Dating:
+ The University of Arizona Press, Tucson, p. 73 pp.
+
+*** UW FUNCTIONS CALLED
+
+ filter1.m -- apply filter to time series
+ rwchng.m -- transforms ring width
+ rwread3.m -- reads a .rw file
+ crn2vec2 -- reads a .crn file and converts data to column vector
+ wtsgaus -- gets Gaussian filter weights
+
+
+*** TOOLBOXES
+
+ statistics
+ signal processing
+
+
+
+ wtsgaus: weights for gaussian filter with specified frequency response
+ b=wtsgaus(p,N);
+ Last revised 2003-3-14
+
+
+ Weights for gaussian filter with specified frequency response
+ Specify te wavelength for the 0.50 respons, and the length of series, get
+ the coefficients, or weights
+
+
+*** INPUT
+
+ p (1 x 1)i period (years) at which filter is to have amp frequency response of 0.5
+ N (1 x 1)i length of the time series (number of observations)
+
+*** OUTPUT
+
+ b (1 x n)r computed weights
+
+
+*** REFERENCES
+
+ WMO 1966, p. 47
+
+*** UW FUNCTIONS CALLED -- NONE
+*** TOOLBOXES NEEDED -- stats
+
+
+ filter1: filter a time series, keeping correct phase and adjusting for end effects
+ [y,ty]=filter1(x,tx,b,k);
+ Last revised 2003-10-31
+
+
+ Filter a time series, keeping correct phase and adjusting for end effects
+
+
+*** INPUT
+
+ x (mx x 1) time series, no NaN allowed
+ tx (mx x 1) time variable
+ b (1 x nb) filter weights -- must be odd number of weights
+ k (1 x 1) option for end effects
+ 1 - truncate (i.e., use only the observed data)
+ 2 - reflect about endpoints -- coded, but not rigorously checked out yet
+ 3 - extend with mean of x
+ 4 - extend with median of x
+
+
+*** OUTPUT *************************
+
+ y (my x 1) filtered version of x. my is fewer than mx if k=1
+ ty (my x 1) time variable corresponding to y
+
+*** REFERENCES
+
+ Mitchell, J.M., Jr., Dzerdzeevskii, B., Flohn, H., Hofmeyr,
+ W.L., Lamb, H.H., Rao, K.N., and Wallen, C.C., 1966, Climatic change,
+ Technical Note 79: Geneva, World Meteorological Organization.
+
+*** UW FUNCTIONS NEEDED -- NONE
+
+*** TOOLBOXES NEEDED
+
+ signal processing
+
+
+
+ climgram: box-plot graphs showing monthly distributions of precipitation and temperature
+ [mpcp,npcp,mtmp,ntmp]=climgram(P,T,txtin,windno,koptcolor);
+ Last revised 2009-10-04
+
+
+ Box-plot graphs showing monthly distributions of precipitation and temperature.
+ A figure with two panels, each having 12 box plots, is drawn. The top plot
+ shows the distribution of monthly precipitation; the bottom plot shows the
+ distribution of monthly temperature. The plots convey the same information
+ as the conventional "climatogram", or "climograph", but dislay the variability as
+ well as the mean level of the monthly climate variables.
+
+
+*** IN
+
+ P (? x 13)r monthly precipitation matrix, year in col 1
+ T (? x 13)r monthly temperature matrix, year in col 1
+ txtin{} -- Labeling for plots
+ {1} (1 x ?)s title for plot (will go above top plot). Say,
+ 'Climatogram for Rapid City'
+ {2} (1 x ?)s y axis label for pcp Example
+ 'Precipitation (mm)'
+ {3} (1 x ?)s y axis label for tmp
+ windno: window number for plot
+ koptcolor (1x1)i color option
+ ==1 color
+ ==2 B/W
+
+*** OUT
+
+ mpcp (1 x 12)r means for pcp
+ npcp (1 x 12)n number of years in sample
+ mtmp (1 x 12)r means for tmp
+ ntmp (1 x 12)n number of years in sample
+
+
+*** REFERENCES -- NONE
+
+*** UW FUNCTIONS CALLED --NONE
+
+*** TOOLBOXES NEEDED
+ stats
+
+
+ figsize: figure-window pixel positions from specified fractional screen-width and screen-height
+ [cL,cB,cW,cH]=figsize(fwidth,fheigth);
+ Last Revised 2009-10-04
+
+
+ Figure-window pixel positions from specified fractional screen-width and screen-height.
+ You want to set position property of figure window such that figure window
+ covers a specified fraction of screen regardless of how many pixels
+ are in the x and y directions on your computer.
+
+
+*** INPUT
+
+ fwidth (1 x 1)r desired figure width as decimal fraction of screen width
+ fheight (1 x 1)r desired figure height as decimal fraction of screen height
+
+*** OUTPUT
+
+ cL (1 x 1)i left position as pixel
+ cW (1 x 1)i width in pixels
+ cB (1 x 1)i bottom position as pixel
+ cH (1 x 1)i height in pixelsesult -- structure of results
+
+*** REFERENCES --- none
+
+
+*** UW FUNCTIONS CALLED -- none
+
+*** TOOLBOXES NEEDED -- none
+
+
+ textcorn: Add annotation text to any corner of a figure
+ textcorn(txt,pos,xoffset,yoffset,fsz);
+ Last Revised 2009-10-05
+
+
+ Add annotation text to any corner of a figure.
+ Control over which corner, how far from axes, and font size. One common
+ use is to label subplots as "A", "B", etc.
+
+
+
+*** INPUT
+
+ txt (1 x ?)s or {}s = text of label
+ pos (1 x 2)s position (one of these: "UL","UR","LL" or "LR")
+ xoffset (1 x 1)r how far text is from left or right axis, as percentage
+ of XLim axis property
+ yoffset (1 x 1)r how far text is from bottom or top axis, as percentage
+ of YLim axis property
+ fsz (1 x 1)i font size
+
+*** OUTPUT
+
+ No output, just puts text on the figure
+
+
+*** REFERENCES --- none
+
+*** UW FUNCTIONS CALLED -- none
+
+*** TOOLBOXES NEEDED -- none
+
+
+
+ nonan1: longest continuous block of a time series matrix without missing data
+ [yrgo2,yrsp2]=nonan1(X,yr);
+ Last revised: 2009-10-05
+
+
+ Longest continuous block of a time series matrix without missing data.
+ Finds the longest continuous block of rows of a matrix for which none of
+ the data is NaN. Useful in selecting period for calibration of
+ tree-ring and climate series.
+
+
+*** IN
+
+ X (mX x nX)r time series matrix, mX observations and nX variables
+ yr (mX x 1)i year vector corresponding to X
+
+
+*** OUT
+
+ yrgo2 (1 x 1)i start year of period without any NaN
+ yrsp2 (1 x 1)i end year ...
+
+
+*** REFERENCES -- none
+*** UW FUNCTIONS CALLED -- none
+*** TOOLBOXES NEEDED -- none
+
+
+
+ trimnan: trim any leading and trailing NaNs from a vector time series
+ [x,yrx]=trimnan(x,yrx);
+ Last revised: 2009-10-05
+
+
+ Trim any leading and trailing NaNs from a vector time series.
+
+
+*** INPUT
+
+ x: time series, a col-vector or row-vector
+ yrx: year vector, same size as x
+
+
+*** OUTPUT
+
+ x: trimmed vector of data
+ yrx: trimmed year vector for ouput x
+
+
+*** REFERENCES -- none
+
+*** UW FUNCTIONS CALLED
+
+ trailnan
+
+
+*** TOOLBOXES NEEDED -- none
+
+
+
+ mafilt1: evenly-weighted moving average of an annual time series
+ [y,yry]=mafilt1(x,yr,m,kopt);
+ Last revised 2009-10-05
+
+
+ Evenly-weighted moving average of an annual time series.
+ Option for assigning smoothed value to ending year or middle year of the
+ moving window
+
+
+*** INPUT ARGS
+
+ x (mx x 1)r time series
+ yr (mx x 1)i year vector for x
+ m (1 x 1)i length of moving average (e.g.,number of weights)
+ kopt (1 x 1)i options
+ kopt(1) which year of the m-year period to assign smoothed value
+ ==1 central year ==2 ending year
+
+
+*** OUTPUT ARGS
+
+ y (?x1) smoothed time series
+ yry (? x1) years for y
+
+
+*** REFERENCES -- NONE
+*** UW FUNCTIONS CALLED -- NONE
+*** TOOLBOXES USED -- NONE
+
+
+
+ c132tss: Thirteen-column monthly climate matrix to time-series structure
+ function Result=c132tss(D);
+ Last Revised 2009-10-14
+
+
+ Thirteen-column monthly climate matrix to time-series structure.
+ Reorganized the 13 cols (year and jan-to-dec data) into 3 columns --
+ year, month, data. Reorganized matrix is stored as a field in structure
+ Result. The other fields, designed to hold metadata (e.g., longitude,
+ latitude) are not dealt with by c132tss, whose sole function is
+ reoroganization of the data matrix.
+
+
+*** INPUT
+
+ D (mD x nD)r 13-col climate monthly matrix, year as col 1, Jan-Dec as
+ other cols
+
+*** OUTPUT
+
+ Result -- structure of results
+ .tsm: 3-col matrix (year, month, data value) of reorganized input D
+
+ .... all the other fields (below) are set to dummy values (see notes)
+ Result.units: units of data--> 'null'
+ Result.names: cell with station ids --> {'null'}
+ Result.lat: vector with latitude of stns --> NaN
+ Result.lon: vector with longitude...--> NaN
+ Result.elv: vector with elevation (m)--> NaN
+ Result.type data type (Precip or Tmean) --> 'null'
+ Result.what -- description of fields and history fo generation-->'null'
+ Result.flnames -- cell with names (prefixes) of .mat input files--> {'null'}
+
+*** REFERENCES --- none
+
+*** UW FUNCTIONS CALLED -- none
+
+*** TOOLBOXES NEEDED
+
+ stats
+
+
+
+ suplabel: places text as a title, xlabel, or ylabel on a group of subplots
+ [ax,h]=suplabel(text,whichLabel,supAxes);
+ Copied 2009-10-18 from MATLAB File Exchange (see Notes)
+
+
+ Places text as a title, xlabel, or ylabel on a group of subplots.
+ Function written by Ben Barrowes and made availabe free on MATLAB File
+ Exchange (see Notes). Useful in connection with function subplot when
+ you want a "supertitle" above all subplots.
+
+
+*** INPUT
+
+ text(1 x ?)s text to be placed
+ whichLabel (1 x ?)s specifies where you want text placed
+ Any of 'x', 'y', 'yy', or 't', specifying whether the
+ text is to be the xlable, ylabel, right side y-label,
+ or "supertitle", espectively
+ supAxes is an optional argument specifying the Position of the
+ "super" axes surrounding the subplots.
+ supAxes defaults to [.075 .075 .85 .85]
+ specify supAxes if labels get chopped or overlay subplots
+
+
+*** OUTPUT
+
+ ax = handle to the axis
+ h = handle to the label
+ ax=suplabel(text,whichLabel,supAxes) returns a handle to the axis only
+ suplabel(text) with one input argument assumes whichLabel='x'
+
+*** REFERENCES
+
+ Ben Barrowes wrote this function (see Notes)
+
+*** TOOLBOXES NEEDED -- none
+
+*** UW FUNCTIONS CALLED -- none
+
+
+ arspectrum: spectrum of an AR(1) or AR(2) process
+ Result=arspectrum(phi,varnoise,nsize,delf,kopt);
+ Last Revised 2008-3-4
+
+
+ Spectrum of an AR(1) or AR(2) process.
+ Returns the theoretical spectrum as a function of frequency for
+ frequencies f for an autoregressive order 1 or 2 process whose
+ autoregressive coefficients are in row vector phi. Also requires
+ information on the noise variance and series length.
+
+
+
+*** INPUT
+
+ phi(1 x 2)r coefficients of autoregressive model; handles AR(1), AR(2),
+ or white noise (phi(2)==NaN instructs to use AR(1) --see Notes)
+ varnoise (1 x 1)r noise variance from fit of the AR model -- see Notes
+ nsize (1 x 1)n number of observations in the time series
+ delf (1 x 1)r frequency interval at which spectrum to be computed;
+ will be at frequencies [0:delf:0.5]; modulus of 0.5 and delf must be 0
+ kopt (1 x 2)i options
+ kopt(1): sign convention on AR coefficients
+ ==1 Ljung (positive autocorrelation goes with NEGATIVE phi(1))
+ ==2 Box and Jenkins (positive autocorrelation goes with positive
+ phi(1)
+ kopt(2): terse vs verbose mode
+ ==1 terse, no graphics
+ ==2 verbose, plot of spectrum in figure 1
+
+
+*** OUTPUT
+
+ Result -- structure of results
+ .y spectral estimates at input frequencies Result.f
+ .f col vector of the frequencies
+ .A (1 x 1)r area under theoretical spectrum, cumputed as sum of delf
+ times spectrum
+
+*** REFERENCES
+
+ Box, G.E.P., and Jenkins, G.M., 1976, Time series analysis: forecasting
+ and control: San Francisco, Holden Day, p. 575 pp.
+
+ Chatfield, C., 1975, The analysis of time series: Theory and practice, Chapman and Hall, London, 263 pp.
+
+ Ljung, L. 1995. System identification toolbox; for use with MATLAB, The
+ MathWorks, Inc.
+
+ Wilks, D. S., Statistical Methods in the Atmospheric Sciences, 467 p,
+ Academic Press, 1995 -- p 352-355
+
+*** TOOLBOXES NEEDED
+
+*** UW FUNCTIONS CALLED -- none
+
+
+ specbt1: spectrum of a single time series by Blackman Tukey method
+ [G,null_str]=specbt1(z,M,f,prevwind,sername,kopt,tlab);
+ Last revised 2009-10-22
+
+
+ Compute and optionally plot the sample spectrum with 95% confidence bands.
+ You can interactively control the spectral smoothing through M, the length
+ of the smoothing window. Figure includes white noise spectral line and
+ bandwidth bar.
+
+
+*** IN
+
+ z (mz x 1)r the time series
+ M (1 x 1)i length of lag window used in calculations.
+ f (mf x 1)r frequencies (cycles/yr, in range 0-0.5) for spectral estimates
+ prevwind(1 x 1)i previous figure window
+ sername(1 x ?)s name of series: for annotating title
+ kopt (1 x 2)i or (1x1)i options
+ kopt(1): whether or not to plot within function
+ ==1 interactive plotting (linear y) to select final value of M
+ ==2 no plotting; just compute spectrum given initial M
+ ==3 interactive plotting using semilogy plot
+ kopt(2): null-continuum (see notes)
+ ==1 white noise
+ ==2 red noise
+ tlab (time label for frequency (e.g., years^-1)
+
+*** OUT
+
+ G (mG x 5)r spectrum and related quantities. By column:
+ 1- frequency
+ 2- period (time)
+ 3- spectral estimate
+ 4- lower 95% confi limit
+ 5- upper 95% conf limit
+ null_str: tells what the last col of G is ("white noise" or "red noise"
+
+ Graphics output:
+
+ Figure 1. Spectrum, with 95% confidence interval. Horizontal line marks
+ spectrum of white noise with same variance as z. Horizontal bar marks
+ bandwidth of Tukey window used to smooth autocovariance function
+
+*** REFERENCES
+
+ Chatfield, C. 1975. Time series analysis, theory and practice.
+ Chapman and Hall, London, 263 p.
+
+ Haan C. T. (2002) Statistical methods in Hydrology, second edition.
+ Iowa State University Press, Ames, Iowa.
+
+ Ljung, L. (1987). System Identification: Theory for the User,
+ Prentice-Hall, Englewood Cliffs, NJ.
+
+ Wilks, D.S., 1995, Statistical methods in the atmospheric sciences:
+ Academic Press, 467 p.
+
+
+*** UW FUNCTIONS CALLED
+
+ arspectrum -- for red-noise null continuum
+ specbt1/calcspec -- subfunction that does most of the meaty calculations
+
+*** TOOLBOXES NEEDED
+
+ statistics
+ system identification
+
+
+ corrone: product-moment correlation of one variable with several others
+ r=corrone(y,X);
+ Last revised 2006-9-2
+
+
+ Correlation coefficients between one variable and several other variables.
+ Written for use as an alternative to corrcoef.m when you do not need the
+ full cross-correlation matrix between all variables. Ran about 40 times
+ faster for an example of 1000 time series of length 50 years.
+
+
+*** INPUT
+
+ y (my x 1)r key variable; desire correlations between this and all others
+ X (mX x nX)r other variables
+
+*** OUTPUT
+
+ r (nX x 1)r product moment correlation between y and all series in X
+
+*** REFERENCES -- none
+*** UW FUNCTION CALLED -- none
+*** TOOLBOXES NEEDED -- none
+
+
+ pdgmsim: Exact simulation of Gaussian time series from its periodogram
+ Result=pdgmsim1(x,nsim,mtaper,kopt);
+ Last Revised 2009-10-27
+
+
+ Exact simulation of Gaussian time series from its periodogram.
+ Function coded from Percival and Constantine (2006), using the smoothed-
+ periodogram spectral estimator and cirulant embedding following Dietrich
+ and Newsam(1997). "Exact" refers to the retetion of the statististical
+ properties --- including the spectrum --- of the observed series by the
+ simulations. An option allows final scaling of simulations to have same
+ and variance as the observed series.
+
+
+
+*** INPUT
+
+ x (mx x 1)r time series
+ nsim (1 x 1)n number of simulations desired
+ mtaper (1 x 1)r decimal proportion of series to be tapered. Suggest use mtaper=0.10
+ kopt (1 x `1)i options
+ kopt(1) force simulation means and standard deviations to be same as sample
+ ==1 yes
+ ==2 no
+
+*** OUTPUT
+
+ Result -- structure of results
+ .Y (mx x nsim)r time series matrix of simulated time series
+ .I (mx x 1)r periodogram of x
+ .var (1 x 3)r variance of original series, tapered series, padded and tapered series
+
+*** REFERENCES
+
+
+ Bloomfield, P., 2000. Fourier analysis of time series: an introduction,
+ second edition. John Wiley & Sons, Inc., New York. 261 p.
+
+ Conover, W., 1980, Practical Nonparametric Statistics, 2nd Edition, John
+ Wiley & Sons, New York.
+
+ Deitrich, C.R., and Newsam, G.N., 1997. Fast and exact simulation of
+ stationary Gaussian processes through circulant embedding of the
+ covariance matrix. SIAM Journal on Scientific Computing, 18(4): 1088-1107.
+-- shortened to D&N(1997) in comments
+
+ Ljung, L., 1999. System Identification: Theory for the User (2nd
+ Edition). Prentice-Hall, Inc., New Jersey
+
+ Percival, D.B., and Constantine, W.L.B., 2006. Exact simulation of
+ Gaussian time series from nonparametric spectral estimates with
+ application to bootstrapping. Statistics and Computing, 16: 25-35.
+-- shortened to P&C(2006) in comments
+
+
+*** TOOLBOXES NEEDED
+
+ Statistics
+
+*** UW FUNCTIONS CALLED -- none
+
+
+
+ wtsbinom: weights for binomial filter of desired length or 50% response period
+ b=wtsbinom(a,kopt);
+ Last revised 2003-03-27
+
+
+ Weights for binomial filter of desired length or 50% response period
+
+
+*** INPUT
+
+ a (1 x 1)i desired number of weights or periof of 50% response . See kopt.
+ kopt(1 x 1)i option
+ kopt(1)==1 a is the period for which amp of freq response should be 0.5
+ kopt(1)==2 a is the number of weights (odd, central + both sides desired)
+
+*** OUTPUT
+
+ b (1 x n)r computed weights (see notes)
+
+*** REFERENCES
+
+ Panofsky, H. and Brier, G., 1958. Some applications of statistics to
+ meteorology. The Pennsylvania State University. p. 150.
+
+ Mitchell et al. (1966, p. 47)
+
+*** UW FUNCTIONS CALLED -- NONE
+*** TOOLBOXES NEEDED -- NONE
+
+
+ pdgmraw: Raw periodogram of tapered and padded time series
+ [I,f,v]=pdgmraw(x,mtaper,npad,kopt);
+ Last Revised 2009-12-17
+
+
+ Raw periodogram of tapered and padded time series.
+ Computes raw periodogram after tapering and padding time series. Uses
+ fast Fourier transform (FFT) for discrete Fourier transform (DFT) and
+ computes periodogram such that average ordinate equals variance of
+ original time series.
+
+
+
+*** INPUT
+
+ x (mx x 1)r time series
+ mtaper (1 x 1)r decimal proportion of series to be tapered
+ npad (1 x 1)n desired padded length, a power of 2 at least as large as series-length
+ kopt (1 x `1)i options
+ kopt(1) variance adjustment for tapering and padding
+ ==1 scale variance of tapered and padded variance before DFT
+ ==2 scale periodogram by factors depending on tapering and padding
+
+*** OUTPUT
+
+ I (mI x 1)r periodogram of x, at frequency zero and the Fourier frequencies
+ f (mI x 1)r frequencies for I
+ v (3x1) variances
+ row 1: original series, x
+ row 2: tapered x
+ row 3: tapered and padded x
+ row 4: average periodogram ordinate (average of I)
+
+*** REFERENCES
+
+
+ Bloomfield, P., 2000. Fourier analysis of time series: an introduction,
+ second edition. John Wiley & Sons, Inc., New York. 261 p.
+
+ Ljung, L., 1999. System Identification: Theory for the User (2nd
+ Edition). Prentice-Hall, Inc., New Jersey
+
+*** UW FUNCTIONS -- none
+*** TOOLBOXES
+
+ Statistics
+
+
+
+ dirfiles: cell-list of files with specified suffix(es) in a directory
+ pre=dirfiles(path1,suff,kopt);
+ Last revised 2010-02-18
+
+
+ Cell-list of files with specified suffix(es) in a directory.
+ Finds all files with specified suffixes in a directory and returns the
+ filenames (without suffix) in fields of structure output variable.
+ Example of use would be to make list of all ".rwl" and ".crn" files in
+ some directory.
+
+
+
+*** INPUT
+
+ path1 (1 x ?)s directory of input files
+ suff {1x?}s cell array of suffixes of files for which lists desired (no
+ period)
+ kopt (1 x 1)i options
+ kopt(1): case sensitivity of suffixes
+ ==1 case-insensitive
+ ==2 case-sensitive
+
+*** OUTPUT
+
+ pre. structure with fields x1, x2, ...
+ Each field has a col-cell array of files with the suffix in suff{1}, suff{2}, etc,
+ or is returned empty (see notes)
+
+*** REFERENCES -- NONE
+*** UW FUNCTIONS -- NONE
+*** TOOLBOXES -- NONE
+
+
+
+ Back to Function List
+
diff --git a/public/mflhtml.txt b/public/mflhtml.txt
new file mode 100755
index 0000000..9f9cdf2
--- /dev/null
+++ b/public/mflhtml.txt
@@ -0,0 +1,394 @@
+
+
+ crn2vec2: .crn file to column vectors of index, sample size, and year
+ [x,s,yr]=crn2vec2(pf1) or [x,s,yr]=crn2vec2;
+ Last revised 2008-4-27
+
+
+ Reads a ".crn" file of tree-ring indices in ITRDB format and converts
+ the indices and associated data into vectors that can be used in Matlab.
+ Input file must be formatted as in ITRDB requirements for crn2vec2 to work.
+
+
+*** IN
+
+ pf1 (1 x ?)s path and file name of .crn file
+ (e.g., 'c:\work\mt100.crn');
+ alternatively, can call with no input arguments and be prompted to
+ point to the .crn file
+
+*** OUT
+
+ x (mx x 1)r tree-ring index, mx years
+ s (mx x 1)r sample size (number of cores) in each year
+ yr (mx x 1)i year vector for x and s
+
+*** REFERENCES -- None
+
+*** UW FUNCTIONS CALLED -- None
+
+*** TOOLBOXES NEEDED -- None
+
+
+ rwl2tsm: ring-width list (rwl) file to time series matrix
+ [X,yrX,nms,T]=rwl2tsm(pf1)
+ Last revised 2008-4-30
+
+
+
+*** IN
+
+ pf1 (1 x ?)s path and filename of input rwl file (e.g.,
+ 'c:\data\'az023.rwl'); if not included, you are prompted to point
+ to the file
+
+*** OUT
+
+ X (mX x nX)r time series matrix, mX years and nX columns
+ yrX (mX x 1)i year vector for X
+ nms {size(X,2) x 1}s names of series in X
+ T (nX x 3)i column, first and last years of valid data in each series of X
+
+*** REFERENCES --- none
+
+*** TOOLBOXES NEEDED -- none
+
+*** UW FUNCTIONS CALLED
+
+ rwl2sov
+ sov2tsm3
+
+
+ acf: autocorrelation function and approximate 95% confidence bands
+ [r,SE2,r95]=acf(x,nlags,k);
+ Last revised 2008-4-29
+
+
+ Computes autocorrelation function and related information for a time series.
+
+
+*** INPUT ARGUMENTS
+
+ x (m x 1) time series, length m
+ nlags (1 x 1)i number of lags to compute acf to
+ k (1 x 2) options;
+ k(1) -- plotting
+ ==1 No plotting within function; get this also if no k input
+ argument
+ ==2 Function makes stem plot of acf with CI of +-2 times large-lag
+ standard error
+ k(2) -- alternative algorithms (see Notes)
+ ==1 Uses global means, not means computed separately for subsets of
+ observations, for departures. Computed covariance as sum of products of first k
+ values and last k values from the the mean. Then standardizes so
+ that lag 0 autocorrelation is 1.0 by dividing the vector g by g(1),
+ where g is vector of sums of cross-products at lags 0,1,2,...
+ ==2 Uses subset means and standard deviations in computations
+
+*** OUTPUT ARGUMENTS
+
+ r (1 x nlags)r acf at lags 1 to nlags
+ SE2 (1 x nlags)r two times the large-lag standard error of r
+ r95 (1 x 1)threshold sample r required for significance in one-tailed test at
+ 0.05 alpha (95% signficance). Intended for quick check for positive first-order
+ autocorrelation, the most common form of persistence in many natural.
+ If the sample r(1) does not exceed r95, cannot reject H0 that the
+ population r(1) is zero, at 0.05 alpha.
+
+
+
+*** REFERENCES
+
+ Large-lag standard error after Box, G.E.P., and Jenkins, G.M., 1976, Time series
+ analysis: forecasting and control: San Francisco, Holden Day.
+
+ Confidence interval for r(k) from Haan, C.T., 2002, Statistical methods in Hydrology,
+ second edition: Ames, Iowa, Iowa State University Press.
+
+ Computation formulas for sample r from
+ Wilks, D.S., 1995, Statistical methods in the atmospheric sciences: Academic Press, 467 p.
+
+
+*** UW FUNCTIONS CALLED -- none
+
+
+*** TOOLBOXES NEEDED -- none
+
+
+
+ whit1: fit AR model to a time series using modified AIC criterion, returning model information and residuals
+ [e,k1,vrat,arcs] = whit1(y,nhi,k2);
+ Last revised 2008-4-29
+
+
+ Fit AR model to a time series using modified AIC criterion, returning model information and residuals
+ You specify the highest order AR model to consider. Models up to that order are
+ fit, and the modified (small sample) Akaike Information Criterion (AIC) is used to pick the best model.
+
+
+*** IN **********************
+
+ y (my x 1)r time series, vector; NaNs not allowed
+ nhi (1 x 1)i highest AR order to consider (if k2==1), or the only
+ AR order model to try fitting (if k2=2)
+ k2 (1 x 2)i options
+ k2(1) for order selections
+ ==1 fit models of order 1 to nhi
+ ==2 fit model of order nhi only
+ k2(2) for over-riding selection of null (order = 0) model
+ ==1 if modified AIC computed from original time series with model order 0 is lower than
+ any of the entertained models, return the null model (see notes)
+ ==2 accept the lowest AIC model even if its AIC is not as low as that of the null model
+
+*** OUT **************************
+
+ e (my x 1)r AR residuals, with mean added back in; or, if null model, the
+ original series y
+ k1 - the order of the AR model deemed best by the AIC, or 0 if null model and k2(2)==1
+ vrat - the ratio of variance of AR residuals to variance of original time series y (see notes)
+ arcs the ar coefs and their two-standard errors in a two-row array; row 1 has
+ the coefficients; row two has 2*standard error of the coefficients;
+ arcs==[] if k2==1 and null model has been selected;
+
+*** REFERENCES
+
+ The loss function and AIC are discussed in Ljung, L. 1995. System identification
+ toolbox; for use with MATLAB, The MathWorks, Inc., p. 3-46.
+
+ Akaike H. (1974) A new look at the statistical model identification. IEEE Trans. Autom. Control AC-19, 716-723.
+ Hurvich C. M. and Tsai C. (1989) Regression and time series model selection in small samples. Biometrika 76, 297-307.
+
+
+*** UW FUNCTIONS CALLED -- none
+*** TOOLBOXES NEEDED -- system identification
+
+
+ rwl2sov: read .rwl-file ring widths and store in strung-out-vector format in a .mat file
+ pf2=rwl2sov(pf1,path1,path4);
+ Last revised 2008-4-30
+
+
+ This is a way to get ring-width data into Matlab if the data is in the
+ notorious ".rwl" format. "SOV" is short for "strung-out-vector", which means
+ simply that each ring-width series is stacked one after another in a single column vector.
+
+ Also stored is a matrix with years and row indices that allows retrieval of
+ individual ring-width series; and a string matrix of core ids
+ in the same order as the ring-width series are stored.
+
+ SOV storage is expected by several other functions in the
+ tree-ring toolbox. An example is grplot.m, a function
+ for plotting multiple ring-width series on a page.
+
+
+*** INPUT
+
+ pf1 (1 x ?)s path\filename of .rwl file
+ example: 'd:\jack\data\az033.rwl'
+ path1 (1 x ?)s path to the .rwl file. If this arg is passed, means
+ that pf1 is the filename only
+ Example: 'd:\jack\data\' as path1, and 'az033.rwl' as pf1
+ path4 (1 x ?)s : path
+ for the output .mat files and .txt files. If no path4 as argument,
+ default is to the same directory as the .rwl files are in
+
+ Input arguments are optional. There can be 0,1, 2, or 3 input arguments:
+ None: user prompted to click on names of input and output files
+ One: path\filename for the .rwl file
+ Two: first arg is the filename of the .rwl file, and the second is the path
+ Three: path for the output .mat file; this option is convenienent when user
+ wants .mat output files to go to different directories that that
+ of the source .rwl files
+
+*** OUTPUT *****************************************
+
+ pf2 (1 x ?)s path & filename of output .mat file
+
+ X (? x 1)i column vector of ringwidths stored one core after another
+ in units of hundredths of mm
+ yrs (? x 3)i start year, end year, and row index into X of start year of each
+ core's ring-width series in X
+ nms (? x 8)s identification of each core
+
+
+ If function call with no or just one output argument, a .mat storage file pf2 is made. Otherwise,
+ pf2 is returned as [], and it is assumed the user wants to directly use
+ X, yrs and nms
+ Depending on the number of input arguments, the output .mat
+ file goes to a specified path\filename or the user is prompted to enter
+ the path/filename of the storage file. The .mat stoarge file contains these variables:
+
+ X (? x 1)i column vector of ringwidths stored one core after another
+ in units of hundredths of mm
+ yrs (? x 3)i start year, end year, and row index into X of start year of each
+ core's ring-width series in X
+ nms (? x 8)s identification of each core
+ cmask (? x 1)i mask for core; 1==do not mask, 0==mask. cmask is created as all 1's
+ by rwl2sov. Subsequent functions may change cmask as cores are deleted from analysis.
+ Fwhen {8 x 4}s history of fits for the data set. Casual user need not be concerned with this
+ or cmask. Fwhen tracks what was the source and target data file, and what and when things were
+ done to the data.
+
+ An ascii file "???_list.txt" is also produced in the working directory.
+ "???" is taken from pf1, and is the part of that filename before the suffix. This file lists
+ the ids and first and last years' data for each of the ring-width series read. The numbers in
+ parentheses are the measurements for the first and last years. This file can be used for checking
+ quickly the contents of an rwl file.
+
+*** REFERENCES --none
+
+*** UW FUNCTIONS CALLED
+
+ intnan.m -- checks for internal NaNs in a vector
+ trailnan.m -- lops off trailing NaNs from a vector
+
+*** TOOLBOXES NEEDED -- none
+
+
+ sov2tsm: strung-out-vector to time series matrix, with names cell
+ [X,yrX,nms]=sov2tsm3(v,YRS,nms,jpick,tends);
+ Last revised 2008-4-29
+
+
+
+*** IN
+
+ v (mv x 1)r strung-out-vector (sov) of one or more time series
+ YRS(nsers x 3)i start year, end year, and starting row index of
+ each series in v, where nsers is the number of series in v
+ nms (nsers x ?)s or {nsers x 1}s names of series in v
+ jpick(? x 1)i or [] index to rows of YRS specifying which series in
+ v to include in the tsm. For example, jpick=([1 3 4 7])' would pick
+ only those four series. The series numbers correspond
+ to rows of YRS. See Notes.
+ tends(1 x 2)i, or [] first last year of desired tsm X.
+
+
+*** OUT
+
+ X (mX x nX)r time series matrix, mX years and nX columns
+ yrX (mX x 1)i year vector for X
+ nms {size(X,2) x 1}s names of series in X
+
+*** REFERENCES --- none
+
+*** TOOLBOXES NEEDED -- none
+
+*** UW FUNCTIONS CALLED -- none
+
+
+ akaike: Akaike information criterion for order of best ARMA model
+ c=akaike(V,N,m);
+ Last revised 2-17-04
+
+
+ Akaike information criterion for order of best ARMA model
+
+
+*** INPUT ARGUMENTS
+
+ V (1 x 1)r loss function, or variance of model residuals
+ N (1 x 1)i number of observations
+ m (1 x 1)i number of explanatory variables, or sum of AR and MA orders
+
+*** OUTPUT ARGUMENTS
+
+ c (1 x 2)r Akaike information criterion, with and without correction for small sample bias (see
+ notes); c(1) == with correction; c(2)= without correction
+
+*** REFERENCES
+
+ Akaike H. (1974) A new look at the statistical model identification. IEEE Trans. Autom. Control AC-19, 716-723.
+ Hurvich C. M. and Tsai C. (1989) Regression and time series model selection in small samples. Biometrika 76, 297-307.
+
+
+*** UW FUNCTIONS CALLED -- none
+
+
+*** TOOLBOXES NEEDED
+
+ System identification
+
+
+
+ trailnan: remove any trailing NaN's from a vector
+ x=trailnan(x);
+ Last revised: Meko 2008-4-29
+
+
+ Utility function used by rwlinp.m and other functions. Most users
+ will not need to call trailnan in their own code
+
+
+*** INPUT
+
+ x (1 x ?)r or (? x 1)r vector, usually a time series
+
+*** OUTPUT
+
+ x (? x 1)r the same vector, but with any trailing NaN's removed
+
+*** REFERENCES -- none
+*** UW FUNCTIONS CALLED -- none
+*** TOOLBOXES NEEDED -- none
+
+ intnan: check for internal NaN in a vector
+ m=intnan(x)
+ Last revised 2008-4-29
+
+
+ Utility function for checking whether a vector has an internal, or imbedded, NaN
+
+
+*** INPUT
+
+ x(? x 1)r or ((1 x ?)r -- a time series
+
+*** OUTPUT
+
+ m(1 x 1)L 1 if x has imbedded NaN, 0 otherwise
+
+*** REFERENCES -- none
+
+
+*** UW FUNCTIONS CALLED -- none
+
+*** TOOLBOXES NEEDED -- none
+
+
+
+ Back to Function List
\ No newline at end of file
diff --git a/public/misc.htm b/public/misc.htm
new file mode 100755
index 0000000..3a62025
--- /dev/null
+++ b/public/misc.htm
@@ -0,0 +1,30 @@
+
+
+
+
+ Other
+
+
+
+
+
Miscellaneous
+
+The Meko household is made up of Linda, Dave and Matt. Linda's a Program Coordinator in the UA College of Public Health. Matt's is a senor in Howenstein High School. (We are currently dogless, with the sad passing of Silkie and Lolly.) We all love music. Linda pretty much likes all kinds except maybe rap. I'm a nut for buttonbox accordion music, and play in a garage band called "Hector and the Javelinas" with Barry Richards and Bill Mrosek. Matt plays the accoustic bass, and played in the Howenstein High School Orchestra while it existed.
+
+
+
+
+
+The Meko household is made up of Linda, Dave and Matt. Linda's a Program Coordinator in the UA College of Public Health. Matt's is a senor in Howenstein High School. Music is a something we all love. Matt plays the accoustic bass. Linda and I are KXCI "acoustic alternative" fans. I play buttonbox accordion, dream of playing it like Flaco Jimenez, but play it like Jose Jimenez. Barry Richards, Bill Mrosek and I have a garage band called "Hector and the Javelinas."
+
+Ramblers, still kicking, met again this New Years Day 2002 at Mam-A-Gah in the Tucson Mountains.
+
+To drive the neighors up their fences, I play the accordion, and like to practice in the backyard. This is not the piano accordion, but the diatonic accordion, also called the "button box", or "squeezebox." Mine is a 3-row Hohner keyed in A-D-G.
+I play in a garage band, "Hector and the Javelinas". For some hot button-box accordion music, try Flaco Jimenez, Queen Ida, Sharon Shannon or Ali Bain. My son, Matt, plays bass in the Howenstein High School Orchestra.
+
+
+
+
Decade-to-Century Hydroclimatic Variability in Western North America
+
+Principal Investigators David M. Meko and Malcolm K. Hughes
+
+Funding Agency: NOAA Paleoclimatology
+
+Award: NA 86GP0454
+
+Award Period: 08/01/98 - 11/30/01
+
+
+Two components of this project are the extension of cool-season precipitation information to the millennial time scale and the investigation of the warm-season precipitation signal. As one approach to the warm-season signal we are developing a network of latewood-width chronologies from various parts of western North America. Dave Stahle and Malcolm Cleaveland ( U Arkansas) are collaborators in this research under a related NOAA award.
+
+An extension was granted in 2000 to allow the the research to be expanded to an detailed examination of the latewood width signal in a daily precipitation data set.
+
+Meko, D.M., and Baisan, C.H., 1999, Tree-ring indicators or long-term variability of the North American Monsoon, Poster presentation at the 24th Annual Climate Diagnostics Workshop, Tucson, Arizona, November 1-5, 1999.
+
+Meko, D.M., and Baisan, C.H., 2001, Pilot study of latewood-width of confers as an indicator of variability of summer rainfall in the north American Monsoon region: International J. of Climatology, v. 21, p. 697-708.
+
Earth Science Student Support -- Riparian Dendrochronology
+
+Principal Investigator: David M. Meko
+
+U.S. Geological Survey, Water Resources Division
+
+Award Period: 01/01/00 - 12/31/01
+
+
This is part of a cooperative agreement between the U.S. Geological Survey and the University of Arizona in which graduate students are supported in various types of thesis work. One supported area is "reconstruction of growing conditions of riparian trees using dendrochronological analysis, with an emphasis on reconstructing hydrological time series -- surface-water flow and (or) ground-water discharge -- using advanced statistical techniques.
+
+Kiyomi Morini, o, a PhD candidate in the Department of Geography, is being funded by the agreeement. Kiyomi's research deals with growth and ring-width variations of Freemont cottonwood and other riparian species in southeastern Arizona.
+
+
+
+
+
Research Topics
+
+
+
Discriminating annual rings from other growth bands
+
Circumferential variations in ring width and implications for sampling needs
+
Associations between ring-width patterns and hydrologic setting of trees
+
Datability of riparian tree species
+
Hydrologic signal in ring width of riparian tree species
+
+
+Principal Investigator: David M. Meko
+
+Funding Agency: National Science Foundation
+
+Award Period: 09/01/00 - 08/31/03
+
+
+The long version of the title is "Collaborative Research: Reconstruction of Drought and Streamflow over the Coterminous United States from Tree Rings, with Extensions into Mexico and Canada." The objective of this study is to improve the application of tree-ring data to the reconstruction of regional-scale streamflow variations. Of particular interest is improvement in accuracy of reconstruction of high-flow anomalies.
+
+Collaborators are Connie Woodhouse (NOAA), Ed Cook (Lamont Doherty), and Manu Lall (Utah State).
+
+
+
+
Research Topics
+
+
+
Statistical treatment of nonlinearity
+
Meshing basins of different scale in a reconstruction
+
Gridding applied to streaflow data
+
Exploration of robust regression methods
+
+
+
+
Publications
+
+
+
Meko, D.M., Therrell, M.D., Baisan, C.H., and Hughes, M.K., 2001, Sacramento River flow reconstructed to A.D. 869 from tree rings: J. of the American Water Resources Association, v. 37, no. 4, p. 1029-1040.
+
+
Meko, D.M., 2001, Reconstructed Sacramento River system runoff from tree rings, Report prepared for the California Department of Water Resources under Agreement No. B81923-SAP # 4600000193: Tucson, Arizona, Available from: California Department of Water Resources, P.O. Box 942836, Room 1601, Sacramento, CA 94236-0001.
+
+
+
+Principal Investigators: Katherine K. Hirschboeck and David M. Meko
+
+Funding Agency: Salt River Project
+
+Award Period: 11/01/03 - 02/11/05
+
+
+The long version of the title is "A Tree-Ring Based Hydroclimatic Assessment of Synchronous Extreme Streamflow Episodes in the Upper Colorado and Salt-Verde River Basins". The objective of this study is improved knowledge of the likelihood of drought jointly affecting runoff in two key sources of water to the Salt River Project.
+
+Principal Investigators: Scott St. George and David M. Meko
+
+Funding Agency: Manitoba Hydro
+
+Award Period: 9/01/03 - 09/01/07
+
+
+The long version of the title is "The Frequency, Severity and Causes of Extreme Droughts in the Winnipeg River Basin". This is a grant that PhD student Scott St. George brought with him to the University of Arizona. The Winnipeg River basin, which straddles Manitoba, Ontario and Minnesota, directly influences the production of about 4,500 megawatts of hydropower. An objective is to determine if low flows are predictable and periodic and associated with specific atmospheric circulation patterns.
+
+Principal Investigators: David M. Meko
+
+Funding Agency: Subcontract from University of Arkansas (Main project funded by CALFED)
+
+Award Period: 11/01/04 - 05/31/09
+
+
+The long version of the title is "Dendrohydrology of Blue Oak in the Central Valley of California. The project is a subcontract from the University of Arkansas and contributes to a multi-institutional project "Hydroclimatic Reconstruction and Ancient Blue Oak Mapping over the Drainage Basin of SanFrancisco Bay". The larger project is headed by David W. Stahle. Co-investigators are Dan Cayan, Mike Dettinger and Kelly Redmond. Some objectives of this study are 1)improved climatic reconstructions of streamflow and precipitation, 2) improved understanding of the ecology of ancient blue oak, and 3) testing of a predictive model for presence of ancient blue oak.
+
+
+ A summary of my recent work on the project is in the powerpoint "spacemode.ppt" at
+Meko blue oak
+
+Kelly Redmond and John Abatzoglou, Western Regional Climate Center, have put together a comprehensive analysis of the climate signal in the blue oak chronologies at
+Blue Oak WRCC
+
+
+
+
+
+
+
+
Research Topics
+
+
+
Site-interpolated precipitation reconstruction
+
Regional precipitation reconstruction
+
Spatial gradients in precipitation anomalies
+
Natural flow reconstructon
+
Spatial resolution of Reconstruction of tree-site-interpolated precipitation
+
Optimizing the hydrologic signal in reconstructions
+
+
+Principal Investigators: Kathy Jacobs, Bonnie Colby, David Meko, Peter Troch, and Connie Woodhouse
+
+Post-doc on tree-ring portion: Kiyomi Morino
+
+
+Funding: U.S. Bureau of Reclamation
+
+Award Period: 1/01/09 - 12/31/10
+
+
+The long version of the project title is "Integrating Improved Water Supply Predictive Capacity And Response Into Lower Colorado Basin Policy And Management". This project further develops research partnerships and extends four prior years of work on a multi-pronged approach to preparing for and responding to variable Colorado River supply reliability in the Lower Basin. General areas of work include: a) demonstrating potential strategies for enhanced use of climate information in Bureau of Reclamation river system modeling and river operations including use of paleoclimatology, climate forecasts and climate change predictions to improve predictive capacity/reduce system vulnerability for the Colorado River system broadly, with particular focus on the Lower Basin and the Central Arizona Project; b) evaluating specific management tools designed to translate improved predictive capacity into enhanced supply reliability for water users, including market-based tools that can be used by federal and state agencies, municipalities, irrigation districts, tribes and other stakeholders; and c) engaging with other research groups and key stakeholders to tailor research foci, approaches and findings for applications of interest to Reclamation.
+ Principal Investigators: David Meko and Amy McCoy
+
+Funding Agency: National Park Service, Desert Southwest Cooperative Ecosystem Studies Unit
+
+Award Period: 09/30/2007 - 09/30/2009
+
+
+The long version of the title is "Riparian Thresholds: Using Dendrochronological Metrics to Evaluate Change". The main objective is to pioneer the use of Netleaf Hackberry (Celtis reticulata) as a dendrochronological indicator of riparian health and riparian climate/growth relationships.
+
+
+
+
Research Topics
+
+
+
Develop hackberry chronologies from trees located on public and private land in the Santa Cruz, San Pedro, and Gila River watersheds
+
Explore dependency of ring width on tree age/size and the possibility of developing regional curves summarizing the dependency
+
Explore density differences in the early and late wood in each ring to measure for irregularities in wood density development trends as related to winter and monsoon precipitation patterns
+
Map water/tree growth relationships through time and space along the river
+
Support and extend the tree ring water availability analysis by utilizing a simple regional water budget to understand how the system's water budget may have varied over the recent past beyond available stream gauging records
+
+
+
+Principal Investigators: Connie Woodhouse, Chris Castro, Steve Leavitt, David Meko, and Ramzi Touchan
+
+Funding Agency: NSF
+
+Award Period: 09/01/08 - 08/31/11
+
+
+The long version of the title is "An Investigation of North American Monsoon (NAM) Variability using Instrumental and Tree-Ring Data". A main objective of this study is improved understanding of the long-term variability of the North American Monsoon through use of partial-widthand stable isotope tree-ring data. The project includes both new and existing tree-ring collections.
+
+
+
+
Research Topics
+
+
+
Document monsoon variability, decades to centuries
+
Study covariation of NAM with winter precipitation variability
+
Study links of NAM with ocean/atmosphere circulation
+
Examine The consistency of downscaled general circulation model (GCM) simulations with paleoclimatic evidence
+of NAM variability
+
+
+Principal Investigators: Ramzi Touchan and David Meko
+
+Funding Agency: NSF Paleoclimatology
+
+Award Period: 09/01/08 - 08/31/11
+
+
+The long version of the title is "Spatiotemporal Drought Variability of the Near East and Eastern Mediterranean from Tree Rings". A main objective of this study is to develop new tree-ring chronologies from the Near East and Eastern Mediterranean and apply them to study the spatio-temporal variability of drought
+
+
+
+
Research Topics
+
+
+
Complete development of the tree-ring proxy network necessary for multi-century drought reconstructions of the eastern Mediterranean and Near East (Turkey, Greece, Cyprus, and Lebanon), by geographically and temporally extending and enhancing the existing tree-ring dataset.
+
Study patterns of large-scale spatiotemporal climate variations impacting the Middle East via integration of these newly-developed proxy data into spatially-complete gridded climate field reconstructions of drought for the entire Eurasian-Mediterranean Rim, and identify the relationship between regional drought, radiative forcing, and atmosphere-ocean dynamics.
+
+
+
+Principal Investigators: Ramzi Touchan and David Meko
+
+Funding Agency: NSF-Paleo Pespectives on Climate Change (P2C2)
+
+Award Period: 06/01/2011-05/31/2016
+
+
+The long version of the title is "Collaborative Research: Past and Future Drought Variability in the
+Mediterranean Basin". A main objective of this study is to develop new tree-ring chronologies from North Africa and apply them to study the spatio-temporal variability of drought
+
+
+
+
Research Topics
+
+
+
Develop tree-ring chronologies from Morocco, Algeria, Tunisia and Libya
+
Build networks of tree-ring chronologies over the Medierranean using previously developed and new chronologies
+
Apply the networks to study the large-scale spatiotemporal climate variations impacting the Middle East
+
Identify relationships between regional drought, radiative forcing, and atmosphere-ocean dynamics
+
Generate reconstructed time series of streamflow and other variables of use to resource managers
+
+
+
+Principal Investigators: David Meko, Leonid Agafonov, and Irina Panyushkina
+
+Funding Agency: CRDF Global (Joint Russia-US research program)
+
+Award Period: 04/01/2013-03/31/2015, with follow-up to 06/30/2016
+
+
+The long version of the title is "Millennial context for recent hydroclimatic changes in Ob River basin from tree
+rings". A main objective of this study is long-term information on variability of Ob River, Siberia, inflow to the Arctic Ocean. Additional funding was obtained after 03/31/2015 to fund a trip to Russia by Panyushkina and a trip to LTRR by Agafonov to develop journal articles
+
+
+
+
Research Topics
+
+
+
Collect tree-ring samples along Ob River, and develop width chronologies from living trees
+
Collect tree-ring samples of remnant wood from banks of the Ob River
+
Identify the hydroclimatic signal in the tree-ring data
+
Apply the tree-ring data to develop an extended time series of Ob River annual flows
+
+
+Principal Investigators: David Meko and Ramzi Touchan
+
+Funding Agency: NSF-Hydrologic Sciences
+
+Award Period: 05/01/2015-04/30/2018
+
+
+The long version of the title is "Collaborative Research: Interannual and seasonal variability of snowpack in the Sierra Nevada from tree rings". A main objective is to develop generally applicable tree-ring measures for study of variability of snowpack parameters. Collaborators on the project are researchers from the Hydrologic Research Center, San Diego, CA (Eylon Shamir, Rochelle Campbell, and Kosta Georgakakos)
+
+
+
+
Research Topics
+
+
+
Develop chronologies of tree-ring width and cell morphology for the North Fork of the American River (NFAR), California
+
Identify the signals for snowpack variables in the chronologies
+
Apply the chronologies to reconstruct snowpack variables in the NFAR
+
Develop dendro-snow methodology transferable to other basins
+
+
University of Arizona Southern California Tree-Ring Study
+
+Principal Investigators: David Meko and Connie Woodhouse
+
+Funding Agency: California Department of Water Resources
+
+Award Period: 07/01/2015-12/31/2017
+
+[Though this project is ended, we are still working on publications]
+
+
+The main objective is to provide information on the variability of water supply to Southern California. Tasks include field collections of tree-ring samples, development of chronologies, statistical reconstruction of annual flows and precipitation, and interpretation of reconstructions. Dr. Erica Bigio is a post-doc with major responsibilities on the project.
+
+
+
+
+
Research Topics
+
+
+
Develop new tree-ring chronologies from Southern California and the Colorado River Basin
+
Reconstruct annual flows of specified rivers in Southern California
+
Update reconstruction of annual flow of the Colorado River at Lees Ferry
+
Place instrumental droughts and wet periods in long-term perspective
+
Document any cyclic tendencies in moisture
+
Document covariation of local (S. California) and regional (Northern California, Upper Colorado River Basin) sources of water
+
+
+Principal Investigator: David Meko
+
+Funding Agency: US Civilian Research and Development Foundation (CRDF Global)
+
+Award Period: 04/25/2018-04/24/2019
+
+
+The long version of the title is "Ob River Flood History from West Siberia Tree Rings". A main objective of this study is identifying signatures in tree rings for floods along the Ob. A secondary objective is improvement of accuracy of a previous reconstructions of annual flows with information from a West Siberia network of temperature-sensitive trees. Key individuals on the project are Dr. Irina Panyushkina (senior personnel) and Leonid Agafonov (Russian collaborator). Most of the funding is for a 10-day visit of Meko, Panyushkina and a graduate student (Julie Edwards, a student of Kevin Anchukaitis, has volunteered!).
+
+
+
+
Research Topics
+
+
+
Cell anatomical examination of Salix and Populus tree-ring samples for flood signatures
+
Search for archival historical documentation of floods
+
Statistical reconstruction of floods and annual flows from tree rings
+
+The main objective is to provide information on the variability of hydroclimate of the Carson-Truckee River Basin over decades to centuries. Collaboration is with Franco Biondi (University of Nevada, Reno). Tasks include application of modern tree-ring data, including cell anatomical data, in the framework of a water balance model in producing field reconstructions of water-balance parameters over the basin. MS candidate Anabel Winitsky joined the project in Fall Semester, 2020.
+
+
+
+
+
Research Topics
+
+
+
Investigate hydrology of the Truckee-Carson Basin (California/Nevada)
+
Develop new tree-ring cell-anatomy time series (Biondi)
+
Calibrate a water-balance model to the study basin
+
Reconstruct fields of soil moisture, snowmelt, runoff, and other variables from tree rings.
+
Document moisture regimes and cyclic tendencies
+
Develop a web-based tool to encourage wide use of project data
+
Collaborative Research: Water and energy fluxes to the Arctic Ocean using tree-ring data and hydrological modeling
+
+Principal Investigators: Irina Panyushkina, David Meko, and David Frank
+
+Funding Agency: National Science Foundation Office of Polar Programs - Arctic Natural Sciences
+
+Award Period: 09/01/2019-07/31/2022
+
+
+
+The main objective is to provide information on the variability of freshwater and heat inflow to the Arctic Ocean from the Yenisei River. NSF collaboration is with a team of scientists from the University of New Hampshire (UNH). Tasks include field collections of tree-ring data, application of those data and existing tree-ring data to reconstruction of fluxes of heat and water, and integration of tree-ring data in a water-balance modeling and database framework that will more broadly encourage dendrohydrologic studies of Arctic Basins. MS candidate Richie Thaxton joined the project in Fall Semester, 2020.
+
+
+
+
+
Research Topics
+
+
+
Hydroclimatic studies of the Yenisei Basin, Siberia
+
Field collections and tree-ring chronology development, including cell anatomy
+
Reconstruction of fields of water-balance variables over the the Yenisei Basin
+
Development of a new research tool for exploring the hydrologic signal in tree-ring networks
+
+
+
+
+
+
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+publications
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+#!/bin/sh
+
+# Setup a working direcory for MATLAB directly under the user's home
+# directory containg all the necessary files for the Applied Time
+# Series Analysis course.
+
+# Copyright (C) 2009 Laboratory of Tree-Ring Research
+
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU General Public License for more details.
+
+# You should have received a copy of the GNU General Public License
+# along with this program. If not, see .
+
+# Written by Martin Munro.
+
+# Pattern for the path as originally hard-coded into the .m and other files
+datdir="[Cc]:\\\\[Gg]eos585a\\\\"
+
+# Default name of input data file hard-coded into the .m and other files
+datfile="spring09"
+
+# The following shell variables can be overriden from the environment........
+
+# The default directory for input data
+if [ "x$SETUP_WORKING_DIRECTORY" = "x" ]; then
+ workingdirectory=geos585a
+else
+ workingdirectory=$SETUP_WORKING_DIRECTORY
+fi
+
+# The default data file name
+if [ "x$SETUP_DATA_FILENAME" != "x" ]; then
+ datafilename=$SETUP_DATA_FILENAME
+elif [ "x$USER" != "x" ]; then
+ datafilename=$USER
+else
+ datafilename="${datfile}_copy"
+fi
+
+# Name of the program to uncompress pkzip files
+if [ "x$SETUP_UNZIP_PROGRAM" = "x" ]; then
+ unzipprogram=unzip
+else
+ unzipprogram=$SETUP_UNZIP_PROGRAM
+fi
+
+# Name of the local curl program (for downloading)
+if [ "x$SETUP_CURL_PROGRAM" = "x" ]; then
+ curlprogram=curl
+else
+ curlprogram=$SETUP_CURL_PROGRAM
+fi
+
+# Name of the local wget program (for downloading)
+if [ "x$SETUP_WGET_PROGRAM" = "x" ]; then
+ wgetprogram=wget
+else
+ wgetprogram=$SETUP_WGET_PROGRAM
+fi
+
+# Name of the pkzip-compressed archive containing all the files
+if [ "x$SETUP_ZIP_FILENAME" = "x" ]; then
+ zipfilename=tsfiles.zip
+else
+ zipfilename=$SETUP_ZIP_FILENAME
+fi
+
+# URL used to test downloads & as the root of the file retrieval URL
+if [ "x$SETUP_BASE_FILE_URL" = "x" ]; then
+ baseurl="http://www.ltrr.arizona.edu/~dmeko/"
+else
+ baseurl=$SETUP_BASE_FILE_URL
+fi
+
+# URL used to automatically download the .zip file
+if [ "x$SETUP_ZIP_FILE_URL" = "x" ]; then
+ zipfileurl="$baseurl/$zipfilename"
+else
+ zipfileurl=$SETUP_ZIP_FILE_URL
+fi
+
+# Make sure an unadorned name really is in the current directory
+(unset CDPATH) > /dev/null 2>&1 && unset CDPATH
+
+# Start in the user's home directory
+cd > /dev/null 2>&1 ||
+{
+ echo "*** Unable to change to the home directory." >&2
+ exit 1
+}
+
+# Check for the unzip program
+$unzipprogram -v > /dev/null 2>&1 ||
+{
+ echo "*** Cannot find the $unzipprogram to uncompress .zip files." >&2
+ exit 1
+}
+
+# Check for a pre-existing directory
+if [ -d $workingdirectory ]; then
+ echo "*** A directory called $workingdirectory already exists." >&2
+ echo "If this is an old working directory for this course," >&2
+ echo "try re-naming it to something else, then try again." >&2
+ exit 1
+fi
+
+# Has the user already downloaded the zip file?
+if [ -r $zipfilename ]; then
+ echo "Found the zipped file $zipfilename." >&2
+ setup_has_zipfile=YES
+fi
+
+# Check for file download tools.
+if $curlprogram $baseurl > /dev/null 2>&1 ; then
+ echo "Found the $curlprogram program (for downloading files)." >&2
+ setup_has_curl=YES
+fi
+if $wgetprogram --version > /dev/null 2>&1 ; then
+ echo "Found the $wgetprogram program (for downloading files)." >&2
+ setup_has_wget=YES
+fi
+
+# Give up if there's no way to find the .zip file
+if [ "x$setup_has_zipfile" = "x" ] && [ "x$setup_has_curl" = "x" ] && [ "x$setup_has_wget" = "x" ]; then
+ echo "*** Found neither the file $zipfilename in your home directory," >&2
+ echo "nor any way to automatically download it from the Internet at" >&2
+ echo "$zipfileurl" >&2
+ exit 1
+fi
+
+# Try downloading the .zip file if it's not alredy present
+if [ "x$setup_has_zipfile" = "x" ] && [ "x$setup_has_curl" != "x" ] &&
+ $curlprogram $zipfileurl > $zipfilename ; then
+ setup_has_zipfile=YES
+fi
+if [ "x$setup_has_zipfile" = "x" ] && [ "x$setup_has_wget" != "x" ] &&
+ $wgetprogram --no-clobber $zipfileurl ; then
+ setup_has_zipfile=YES
+fi
+if [ "x$setup_has_zipfile" = "x" ]; then
+ echo "*** Was unable to download the file $zipfilename from the Internet at" >&2
+ echo "$zipfileurl" >&2
+ exit 1
+fi
+# Note that wget will always use the filename from the URL,
+# which may conflict with $zipfilename. Investigate this if the download works,
+# but subsequent steps can't find the .zip file.
+
+# Create what will be the course working directory
+mkdir $workingdirectory > /dev/null 2>&1 ||
+{
+ echo "*** Unable to create to the $workingdirectory directory." >&2
+ exit 1
+}
+
+# Change to the working directory, or clean up
+cd $workingdirectory > /dev/null 2>&1 ||
+{
+ echo "*** Unable to change to the $workingdirectory directory." >&2
+ rmdir $workingdirectory > /dev/null 2>&1
+ exit 1
+}
+
+# Unzip the .zip archive within the working directory, or clean up
+$unzipprogram ../$zipfilename > /dev/null 2>&1 ||
+{
+ echo "*** Unable to unzip $zipfilename in the $workingdirectory directory." >&2
+ cd .. > /dev/null 2>&1
+ # Dangerous, so display any errors
+ rm -rf $workingdirectory
+ exit 1
+}
+
+# Search & replace the hard-coded working directory & file paths in the files
+ourworking=`pwd`
+for f in * ; do
+ modifiedf="${f}_setback"
+ if sed "s|$datdir|$ourworking/|g" $f | sed "s|datfile='$datfile'|datfile='$datafilename'|" > $modifiedf ; then
+ mv $modifiedf $f > /dev/null 2>&1 || { echo "Warning: kept old version of $f" >&2 ; }
+ else
+ echo "Warning: could not edit $f" >&2
+ fi
+done
+# Note that if this last step didn't work we just leave whatever mess was produced.
\ No newline at end of file
diff --git a/public/setupgeos585a.sh b/public/setupgeos585a.sh
new file mode 100755
index 0000000..7a875e6
--- /dev/null
+++ b/public/setupgeos585a.sh
@@ -0,0 +1,192 @@
+#!/bin/sh
+
+# Setup a working direcory for MATLAB directly under the user's home
+# directory containg all the necessary files for the Applied Time
+# Series Analysis course.
+
+# Copyright (C) 2009 Laboratory of Tree-Ring Research
+
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU General Public License for more details.
+
+# You should have received a copy of the GNU General Public License
+# along with this program. If not, see .
+
+# Written by Martin Munro.
+
+# Pattern for the path as originally hard-coded into the .m and other files
+datdir="[Cc]:\\\\[Gg]eos585a\\\\"
+
+# Default name of input data file hard-coded into the .m and other files
+datfile="spring09"
+
+# The following shell variables can be overriden from the environment........
+
+# The default directory for input data
+if [ "x$SETUP_WORKING_DIRECTORY" = "x" ]; then
+ workingdirectory=geos585a
+else
+ workingdirectory=$SETUP_WORKING_DIRECTORY
+fi
+
+# The default data file name
+if [ "x$SETUP_DATA_FILENAME" != "x" ]; then
+ datafilename=$SETUP_DATA_FILENAME
+elif [ "x$USER" != "x" ]; then
+ datafilename=$USER
+else
+ datafilename="${datfile}_copy"
+fi
+
+# Name of the program to uncompress pkzip files
+if [ "x$SETUP_UNZIP_PROGRAM" = "x" ]; then
+ unzipprogram=unzip
+else
+ unzipprogram=$SETUP_UNZIP_PROGRAM
+fi
+
+# Name of the local curl program (for downloading)
+if [ "x$SETUP_CURL_PROGRAM" = "x" ]; then
+ curlprogram=curl
+else
+ curlprogram=$SETUP_CURL_PROGRAM
+fi
+
+# Name of the local wget program (for downloading)
+if [ "x$SETUP_WGET_PROGRAM" = "x" ]; then
+ wgetprogram=wget
+else
+ wgetprogram=$SETUP_WGET_PROGRAM
+fi
+
+# Name of the pkzip-compressed archive containing all the files
+if [ "x$SETUP_ZIP_FILENAME" = "x" ]; then
+ zipfilename=tsfiles.zip
+else
+ zipfilename=$SETUP_ZIP_FILENAME
+fi
+
+# URL used to test downloads & as the root of the file retrieval URL
+if [ "x$SETUP_BASE_FILE_URL" = "x" ]; then
+ baseurl="http://www.ltrr.arizona.edu/~dmeko/"
+else
+ baseurl=$SETUP_BASE_FILE_URL
+fi
+
+# URL used to automatically download the .zip file
+if [ "x$SETUP_ZIP_FILE_URL" = "x" ]; then
+ zipfileurl="$baseurl/$zipfilename"
+else
+ zipfileurl=$SETUP_ZIP_FILE_URL
+fi
+
+# Make sure an unadorned name really is in the current directory
+(unset CDPATH) > /dev/null 2>&1 && unset CDPATH
+
+# Start in the user's home directory
+cd > /dev/null 2>&1 ||
+{
+ echo "*** Unable to change to the home directory." >&2
+ exit 1
+}
+
+# Check for the unzip program
+$unzipprogram -v > /dev/null 2>&1 ||
+{
+ echo "*** Cannot find the $unzipprogram to uncompress .zip files." >&2
+ exit 1
+}
+
+# Check for a pre-existing directory
+if [ -d $workingdirectory ]; then
+ echo "*** A directory called $workingdirectory already exists." >&2
+ echo "If this is an old working directory for this course," >&2
+ echo "try re-naming it to something else, then try again." >&2
+ exit 1
+fi
+
+# Has the user already downloaded the zip file?
+if [ -r $zipfilename ]; then
+ echo "Found the zipped file $zipfilename." >&2
+ setup_has_zipfile=YES
+fi
+
+# Check for file download tools.
+if $curlprogram $baseurl > /dev/null 2>&1 ; then
+ echo "Found the $curlprogram program (for downloading files)." >&2
+ setup_has_curl=YES
+fi
+if $wgetprogram --version > /dev/null 2>&1 ; then
+ echo "Found the $wgetprogram program (for downloading files)." >&2
+ setup_has_wget=YES
+fi
+
+# Give up if there's no way to find the .zip file
+if [ "x$setup_has_zipfile" = "x" ] && [ "x$setup_has_curl" = "x" ] && [ "x$setup_has_wget" = "x" ]; then
+ echo "*** Found neither the file $zipfilename in your home directory," >&2
+ echo "nor any way to automatically download it from the Internet at" >&2
+ echo "$zipfileurl" >&2
+ exit 1
+fi
+
+# Try downloading the .zip file if it's not alredy present
+if [ "x$setup_has_zipfile" = "x" ] && [ "x$setup_has_curl" != "x" ] &&
+ $curlprogram $zipfileurl > $zipfilename ; then
+ setup_has_zipfile=YES
+fi
+if [ "x$setup_has_zipfile" = "x" ] && [ "x$setup_has_wget" != "x" ] &&
+ $wgetprogram --no-clobber $zipfileurl ; then
+ setup_has_zipfile=YES
+fi
+if [ "x$setup_has_zipfile" = "x" ]; then
+ echo "*** Was unable to download the file $zipfilename from the Internet at" >&2
+ echo "$zipfileurl" >&2
+ exit 1
+fi
+# Note that wget will always use the filename from the URL,
+# which may conflict with $zipfilename. Investigate this if the download works,
+# but subsequent steps can't find the .zip file.
+
+# Create what will be the course working directory
+mkdir $workingdirectory > /dev/null 2>&1 ||
+{
+ echo "*** Unable to create to the $workingdirectory directory." >&2
+ exit 1
+}
+
+# Change to the working directory, or clean up
+cd $workingdirectory > /dev/null 2>&1 ||
+{
+ echo "*** Unable to change to the $workingdirectory directory." >&2
+ rmdir $workingdirectory > /dev/null 2>&1
+ exit 1
+}
+
+# Unzip the .zip archive within the working directory, or clean up
+$unzipprogram ../$zipfilename > /dev/null 2>&1 ||
+{
+ echo "*** Unable to unzip $zipfilename in the $workingdirectory directory." >&2
+ cd .. > /dev/null 2>&1
+ # Dangerous, so display any errors
+ rm -rf $workingdirectory
+ exit 1
+}
+
+# Search & replace the hard-coded working directory & file paths in the files
+ourworking=`pwd`
+for f in * ; do
+ modifiedf="${f}_setback"
+ if sed "s|$datdir|$ourworking/|g" $f | sed "s|datfile='$datfile'|datfile='$datafilename'|" > $modifiedf ; then
+ mv $modifiedf $f > /dev/null 2>&1 || { echo "Warning: kept old version of $f" >&2 ; }
+ else
+ echo "Warning: could not edit $f" >&2
+ fi
+done
+# Note that if this last step didn't work we just leave whatever mess was produced.
\ No newline at end of file
diff --git a/public/spacemode.ppt b/public/spacemode.ppt
new file mode 100755
index 0000000..3fa67f4
Binary files /dev/null and b/public/spacemode.ppt differ
diff --git a/public/staticdirs/images/department_logo.svg b/public/staticdirs/images/department_logo.svg
new file mode 100644
index 0000000..7c1cbfb
--- /dev/null
+++ b/public/staticdirs/images/department_logo.svg
@@ -0,0 +1,303 @@
+
+
+
diff --git a/public/staticdirs_index.html b/public/staticdirs_index.html
new file mode 100644
index 0000000..e6f4f81
--- /dev/null
+++ b/public/staticdirs_index.html
@@ -0,0 +1 @@
+Home
Home
\ No newline at end of file
diff --git a/public/toolbox.html b/public/toolbox.html
new file mode 100755
index 0000000..2491314
--- /dev/null
+++ b/public/toolbox.html
@@ -0,0 +1,198 @@
+
+
+
+
+ Tree-Ring Matlab Toolbox
+
+
+
+
+
+
+
+
+Matlab is a commercial software package for numeric computation, graphics, visualization, and high-level programming. Matlab is available from The MathWorks, Inc. The basic Matlab package has many functions generally useful for tree-ring studies. Matlab "toolboxes" with specialized functions can also purchased from The MathWorks, Inc. Toolboxes are available for signal processing, mapping, image processing, neural networks, statistics, and many other topics.
+
+The "Tree-Ring Toolbox" is not a commercially available Matlab toolbox, but is a collection of Matlab functions I have written to do various tasks in tree-ring analysis. For example, the function crn2vec2 reads an ITRDB-formatted ".crn" file and returns the site chronology, years, and sample size as matlab vectors.
+
+Back to Top of Page
+
+
+
+
Requirements
+To use the Tree-Ring Toolbox, you must have Matlab installed on your computer or network. I use the University of Arizona Matlab site license, which has the most recent Matlab release. The functions may not work on older versions. Depending on which functions you intend to use, you may also need to have one or more "Matlab toolboxes" (e.g., statistics) installed. The comment section of each function lists which toolboxes are required. Some functions requre statistics, signal processing or spline toolboxes.
+
+To use the Tree-Ring Toolbox, you must its functions in a directory on your Matlab path. My functions usually call on other utility functions I wrote. The best method to be sure you don't call an "unknown" function is to download the whole set of Tree-Ring Toolbox functions. I have zipped these into a file File_pooled.zip, which you should unzip into a directory on your Matlab path. I recommend keeping those functions by themselves in a dedicated folder. Put that folder first on your Matlab path, so that no other functions you might have by the same name, or not obsolete versions of the same function, are used. Alternatively, move your data to the dedicated folder and run programs with that folder as the current working directory.
+
+I distribute these function freely. I require no acknowledgement of the source. Go ahead and modify them as you wish. I welcome any comments on the functions in the function list. But due to time constraints, I cannot promise anything more than grudging cooperation in updating or correcting functions when I'm in the mood.
To find out what's in the Tree-Ring Matlab Toolbox, check out the function list, , which give a brief description of what the function does. I suggest you read the comment section at the beginning of any function before using the function. My opening comments are organized as follows:
+
function name with 1-line description
+
syntax of the function call
+
date of most recent revision
+
extended description of what the function does
+
Input requirements
+
Output
+
References
+
UW Functions Called (other functions I wrote)
+
Matlab toolboxes required (e.g., signal processing)
+
+
+Among other things, the comment secti0ons defines the input and output arguments. I use a shorthand code for such data. The best way of describing this code is by way of example:
+
+(1x1)r real number, scalar
+(3x2)r matrix (3 x 2) of real numbers
+(3x1)i column vector (3 elements) of integers
+(1x8)s character string of length 8
+(1x1)L logical scalar
+
+
+
+Matlab also has data types called "cells" and "structures", which are best found out about by reading the Matlab documentation. My code for cells uses braces. For example,
+the code below indicates a 1 x 3 "cell" variable containing a 2x2 integer matrix, a 1x5 character string and a 1x10 row vector of real numbers.
+
+
+{1x3}
+ (2 x 2)i
+ (1 x 5)s
+ (1 x 10)r
+
+
+Among the files included is seascorr, which has been published in Computers & Geosciences. A pdf of the paper and a zip of sample data are included for seascorr. Sample data are also included for function skelcrn.
+
+Back to Top of Page
+
+
+
+
List of Functions
+
+
+
+
+
+
+
Tree-ring and climate-data analysis
+
+
climgram -- climogram of monthly mean precipitation and temperature
+
grplot -- grouped plots of ring width series, one below the other
+
lockdown -- graphical & statistic check of cross-dating of cores in rlw file or of ring-width series with site chronology
+
seascorr -- identify seasonal climatic signal in annual tree-ring series
+
skelcrn -- skeleton plot from chronology (e.g., crn file) or ring-width series
+
+
+
+
+
IO, formatting and plotting of tree-ring data
+
+
crn2vec2 -- crn file to column vectors of index, sample size, and year
+
rwlids1 -- check core ids in an rwl file and build a renaming template
+
rwlids2 -- substitue ids in an rwl file
+
rwlids3 -- high-grade an overloaded mat file of ring widths; first of two function (second is rwlids4) used to high-grade a collection
+ of measured ring widths to emphasize long, highly intercorrelated series
+ and evenness of sample coverage.
+
rwlids4 -- trim an rwl file by deleting indicated series.
+
rwl2tsm --convert .rwl file to time series matrix
+
rwlinp --- load rwl series as indexed vector into Matlab
+
tsplotss1 --time series plots of sample size from two sets of ring widths (for comparison of two chronologies)
+
tsplotss2 --- Time series plot of 2 alternative site chronologies from matrices of core indices (for comparison of two chronologies)
+
+
+
+
+
+
IO and formatting of climate data
+
+
c132tss --thirteen-column monthly climate matrix to time-series structure
+
+
+
+
+
Time series analysis
+
+
acf -- autocorrelation function and confidence limits
+
akaike --akaike information criterion for fitting AR models
+
arspectrum -- spectrum of an AR(1) or AR(2) process
+
corrdiff -- test difference of two non-zero sample correlation coefficients
+
corrone -- product-moment correlation of one variable with several others (vector with columns of a matrix)
+
mafilt1 -- evenly-weighted moving average of an annual time series
+
mannken1 -- Mann-Kendall nonparametric test for trend
+
pdgmraw -- raw periodogram of a time series
+
pdgmsim -- exact simulation of Gaussian time series from its periodogram
+
quenouille --confidence interval for cross-correlation of autocorrelated series
+
specbt1 -- spectrum of a single time series by Blackman Tukey method
+
sseff -- effective (adjusted for autocorrelation) sample size of a time series
+
splinep -- spline smoothing parameter for a desired amplitude of frequency response at a specified wavelength (paramter in as define in Matlab curve fitting toolbox function csaps)
+
whit1 -- fit an AR model to time series and prewhiten series with the model
+
+
+
+
+
Utility: low level functions called by other functions
+
+test that two independent correlations are estimates of the same population correlation
+
eightplt -- eight plots on a page (used by grplot)
+
figsize -- figure-window pixel positions from specified fractional screen-width and screen-height
+
filter1 -- low-pass filter a time series using specified weights
+
firstlst -- find first and last years of valid data in columns of a time series matrix
+
intnan --check for imbedded NaN in a time series
+
nonan1 --longest continuous block of a time series matrix without missing data
+
menucell --menu for selecting elements of a cell array
+
OStell --find out the operating system of the computer
+
ranktie --get ranks for values in a time series that might contain ties
+
rwchng --scaled values or first-difference of a time series (used by lockdown)
+
rwread3 --reads a single .rw file (used by skelcrn)
+
shiftcor --UNFINISHED function to be called by lockdown for the yet-to-be-implemented mode of searching for the best dating spot for a floating set of ring widths
+
sov2tsm --convert strung-out-vector to time series matrix (used by rwlinp); this version does not handle names cell
+
sov2tsm3 --convert strung-out-vector to time series matrix (used by rwlinp); this version handles names cell
+
suplabel --places text as a title, xlabel, or ylabel on a group of subplots
+
textcorn -- Add annotation text to any corner of a figure
+
trailnan -- remove any trailing NaN from a time series
+
trimnan -- remove any leading or trailing NaN from a time series
+
wtsbinom -- weights for binomial filter with specified frequency response
+
wtsgaus -- weights for Gaussian filter with specified frequency response
+
+find first and last years of valid data in a time series matrix
+
+1968 - 1972 Pennsylvania State University, BS Meteorology
+1972 - 1974 University of Arizona, MS Atmospheric Sciences
+1975 - 1981 University of Arizona, Ph.D. Hydrology
+Back to Top of Page
+
+
+
+
Employment History
+2012-present, RESEARCH PROFESSOR
+ Laboratory of Tree-Ring Research, Univ. of Arizona, Tucson, Arizona
+2004-2012, ASSOCIATE RESEARCH PROFESSOR
+ Laboratory of Tree-Ring Research, Univ. of Arizona, Tucson, Arizona
+1994-2004, PRINCIPAL RESEARCH SPECIALIST
+ Laboratory of Tree-Ring Research, Univ. of Arizona, Tucson, Arizona
+1991 - 1994, ADJUNCT ASSISTANT PROFESSOR
+ Laboratory of Tree-Ring Research, Univ. of Arizona, Tucson, Arizona
+1988 - 1991, RESEARCH ASSOCIATE Laboratory of Tree-Ring Research, University of Arizona
+1986 - 1988, HYDROLOGIST / ASSISTANT DIRECTOR
+ Dept. of Water Resources, Tohono O'odham Nation, Sells, Arizona
+1981 - 1986, RESEARCH ASSOCIATE
+ Laboratory of Tree-Ring Research, Univ. of Arizona, Tucson, Arizona
+Back to Top of Page
+
+
+
+
Associations and Committees
+American Geophysical Union
+Tree-Ring Society
+University of Arizona Committee on Global Change
+Back to Top of Page
+
+
+
+
Research Interests
+I use tree-ring data data to study the variability of climatic and hydrologic systems on time scales of decades to centuries. The spatial scale ranges from the small watershed to the continent. I hope the research contributes to the ability of society to make intelligent choices in the management of its natural resources. Basins that are the subjects of current projects are the Yenisei (Siberia), the Carson-Truckee (Nevada-California), and American River (California). Funding comes from three divisions of the National Science Foundation: Hydrological Sciences; Paleoperspectives on Climate Change (P2C2); and Arctic Natural Sciences.I am also interested in time series methods for reconstructing streamflow and analyzing reconstructions. I write functions and distribute them through my toolbox.
+
+I teach a 3-credit course (1-credit online) in Applied Time Series Analysis every other year. Each year I guest lecture in the Introductory Dendrochronology course and the Dendrochronology Intensive Summer Courses; both are regular course offerings at LTRR. For the past decade I have co-taught (with Ramzi Touchan) an international summer course called Tree Rings, Climate, and Natural Resources Summer School in various countries. This course is organized in collaboration with a host institution.
+
+
+Publications are listed below in chronological order. In the same year, publicaitons are ordered alphabetically by first author. To access pdfs of these, download this zip. You can then extract the desired pdf. The name of the pdf has my name and year (e.g., Meko1981.pdf) or the names of the first two authors and year (e.g., Meko&Stockton1980.pdf) if two or more authors. Sometimes there will be a letter after the year (e.g., Meko&Basian2001a). Ignore that letter, as it is important only to my internal referencing.
+
+A few listed publications are not in the zip. This missing files are usually from a long time ago (before routine use of pdfs) or are for book chapters.
+
+
+Stockton, C.W., and Meko, D.M., 1975, A long-term history of drought occurrence in western United States as inferred from tree rings: Weatherwise, v. 28, no. 6, p. 245-249.
+
+Meko, D.M., Stockton, C.W., and Boggess, W.R., 1980, A tree-ring reconstruction of drought in southern California: Water Resources Bulletin, v. 16, no. 4, p. 594-600.
+
+Meko, 1981, Applications of Box-Jenkins Methods of time-series analysis to reconstruction of drought from tree rings, Ph.D. Dissertation, University of Arizona.
+
+Stockton, C.W., and Meko, D.M., 1983, Drought recurrence in the Great Plains as reconstructed from long-term tree-ring records: J. of Climate and Applied Climatology, v. 22, no. 1, p. 17-29.
+
+Meko, D.M., and Stockton, C.W., 1984, Secular variations in streamflow in the western United States: J. of Climate and Applied Meteorology, v. 23, no. 6, p. 889-897.
+
+Stockton, C.W., Boggess, W.R., and Meko, D.M., 1985, Climate and tree rings, in Hecht, A.D., ed., Paleoclimate analysis and modeling: John Wiley & Sons, p. 71-150.
+
+Meko, D.M., Stockton, C.W., and Blasing, T.J., 1985, Periodicity in tree rings from the corn belt: Science, v. 229, p. 381-384.
+
+
+
+
+Meko, D.M., Hughes, M.K., and Stockton, C.W., 1991, Climate change and climate variability: The paleo record, in Managing Water Resources in the West under Conditions of Climate Uncertainty: National Academy Press, p. 71-100.
+
+Meko, 1992, Dendroclimatic evidence from the Great Plains of the United States, in Bradley, R.S., and Jones, P.D., eds., Climate since A.D. 1500: London, Routledge, p. 312-330.
+
+Meko, 1992, Spectral properties of tree-ring data in the United States Southwest as related to El Nino/Southern Oscillation, in Diaz, H.F., and Markgraf, V., eds., El Nino, Historical and Paleoclimatic Aspects of the Southern Oscillation: Cambridge: Cambridge University Press, p. 227-241.
+
+Meko, Cook, E.R., Stahle, D.W., Stockton, C.W., and Hughes, M.K., 1993, Spatial patterns of tree-growth anomalies in the United States and southeastern Canada: J. of Climate, v. 6, p. 1773-1786.
+
+Cook, E.R., Briffa, K.R., Meko, D.M., Graybill, D.A., and Funkhouser, G., 1995, The "segment length curse" in long tree-ring chronology development for palaeolclimatic studies: The Holocene, v. 5, no. 2, p. 229-237.
+
+Meko, D.M., and Graybill, D.A., 1995, Tree-ring reconstruction of Upper Gila River discharge: Water Resources Bulletin, v. 31, no. 4, p. 605-616.
+
+Meko, D. M., Stockton, C.W., and Boggess, W.R., 1995, The tree-ring record of severe sustained drought: Water Resources Bulletin, v. 31, no. 5, p. 789-801.
+
+
+
+Dean, J.S., Meko, D.M., and Swetnam, T.W., editors, 1996, : Tree Rings, Environment and Humanity, Proceedings of the International Conference, 17-21 May, 1994: Tucson, Arizona, USA, Radiocarbon, p. 889 pp.
+
+Cook, E.R., Meko, D.M., Stahle, D.W., and Cleaveland, M.K., 1996, Tree-ring reconstructions of past drought across the coterminous United States: Tests of a regression method and calibration/verification results, in Dean, J.S., Meko, D.M., and Swetnam, T.W., eds., Tree rings, environment and humanity: Tucson, Arizona, USA, Radiocarbon, p. 155-169.
+
+Sieg, C.H., Meko, D.M., DeGaetano, A.D., and Ni, W., 1996, Dendroclimatic potential in the northern Great Plains, in Dean, J.S., Meko, D.M., and Swetnam, T.W., eds., Tree rings, environment and humanity; Proceedings of the International Conference, Tucson, Arizona, 17-21 May, 1994: Tucson, Radiocarbon, p. 295-302.
+
+Cook, E.R., Meko, D.M., and Stockton, C.W., 1997, A new assessment of possible solar and lunar forcing of the bi-decadal drought rhythm in the western United States: Journal of Climate, v. 10, p. 1343-1356.
+
+Meko, D.M., 1997, Dendroclimatic reconstruction with time varying subsets of tree indices: Journal of Climate, v. 10, p. 687-696.
+
+Woodhouse, C. A., and Meko, D.M., 1997, Number of winter precipitation days reconstructed from southwestern tree rings: J. of Climate, v. 10, p. 2663-2669.
+
+Dettinger, M.D., Cayan, D.R., Diaz, H.F., and Meko, D.M., 1998, North-south precipitation patterns in western North America on interannual-to-decadal timescales: Journal of Climate, v. 11, p. 3095-3111.
+
+Touchan, R., Meko, D.M., and Hughes, M.K., 1999, A 396 year reconstruction of precipitation in southern Jordan: Journal of the American Water Resources Association, v. 35, no. 1, p. 49-59.
+
+Cook, E.R., Meko, D.M., Stahle, D.W., and Cleaveland, M.K., 1999, Drought reconstructions for the continental United States: J. of Climate, v. 12, p. 1145-1162.
+
+
+
+
+
+Meko, D.M., Therrell, M.D., Baisan, C.H., and Hughes, M.K., 2001, Sacramento River flow reconstructed to A.D. 869 from tree rings: J. of the American Water Resources Association, v. 37, no. 4, p. 1029-1040.
+
+Meko, D.M., and Baisan, C.H., 2001, Pilot study of latewood-width of confers as an indicator of variability of summer rainfall in the north American Monsoon region: International J. of Climatology, v. 21, p. 697-708
+
+Meko, D.M., 2001, Reconstructed Sacramento River system runoff from tree rings, Report prepared for the California Department of Water Resources under agreement No. B81923-SAP # 4600000193: Tucson, Arizona, Available from: California Department of Water Resources, P.O. Box 942836, Room 1601, Sacramento, CA 94236-0001.
+
+Benson L., Kashgarian M., Rye R., Lund S., Paillet F., Smoot J., Kester C., Mensing S., Meko D. and Lindstrom S., 2002, Holocene multidecadal and multicentennial droughts affecting northern California and Nevada. Quaternary Science Reviews 21, 659-682.
+
+Skirvin S. M., Marsh S. E., McClaran M. P. and Meko D. M., 2003, Climate Spatial Variability and Data Resolution in a Semi-Arid Watershed, Southeastern Arizona. Journal of Arid Environments 54(4), 667-686.
+
+Cook E. R., Woodhouse C., Eakin C. M., Meko D. M. and Stahle D. W., 2004, Long-term aridity changes in the western United States. Science 306, 1015-1018.
+
+Meko D. M. and Woodhouse C. A., (2005) Tree-ring footprint of joint hydrologic drought in Sacramento and Upper Colorado River Basins, western USA. J. of Hydrology 308, 196-213.
+
+Meko D. M. (2006) Tree-ring inferences on water-level fluctuations of Lake Athabasca. Canadian Water Resources Journal 31(4), 229-248.
+
+
+Meko D. M., Woodhouse C. A., Baisan C. H., Knight T., Lukas J. J., Hughes M. K. and Salzer M. W. (2007). Medieval drought in the Upper Colorado River Basin. Geophys. Res. Lett. 34(10, L10705), 10.1029/2007GL029988.
+
+Shamir, E., D. M. Meko, and N. E. Graham and Konstantine P. Georgakakos (2007), Hydrologic model framework for water resources planning in Santa Cruz River, Southern Arizona, J. Am. Water Resour. Assoc., 43 (5), 1155-1170.
+
+St. George, S., D. M. Meko., M. P. Girardin, G. M. MacDonald, E. Nielsen, G. T. Pederson, D. J. Sauchyn, J. C. Tardif, and E. Watson (2009), The tree-ring record of drought on the Canadian Prairies, 22, 689–710.
+
+St. George, S., D. M. Meko., and E. R. Cook (2010), The seasonality of precipitation signal embedded within the North American Drought Atlas, The Holocene, 20 (6), 983-988,
+doi:10.1177/0959683610365937.
+
+
+
+
+
+Griffin, D., D. M. Meko, R. Touchan, S. W. Leavitt, and C. A. Woodhouse (2011), Latewood chronology development for summer-moisture reconstruction in the US Southwest, Tree Ring Res., 67 (2), 87-101.
+
+Meko, D. M., D. W. Stahle, D. Griffin, and T. A. Knight (2011), Inferring precipitation-anomaly gradients from tree rings, Quatern. Int., 235, 89-100.
+
+
+Meko, D. M., R. Touchan, and K. A. Anchukaitis (2011), Seascorr: a MATLAB program for identifying the seasonal climate signal in an annual tree-ring time series, Computers
+& Geosciences, 37, 1234-1241.
+
+
+Meko, D. M., and C. A. Woodhouse (2011), Application of streamflow reconstruction to water resources management, in Dendroclimatology, Progress and Prospects, Develop-
+ments in Paleoenvironmental Research, vol. 11, edited by M. K. Hughes, T. W. Swetnam, and H. F. Diaz, pp. 231-261, Springer Netherlands.
+
+Stambaugh, M. C., R. P. Guyette, E. R. McMurry, E. R. Cook, D. M. Meko, and A. R. Lupo (2011), Drought duration and frequency in the U.S. Corn Belt during the last millennium
+(AD 9922004), Agr. Forest Meteor., 151, 154-162.
+
+Meko, D. M., C. A. Woodhouse, and K. Morino (2012), Dendrochronology and links to streamflow, J. Hydrol., 412-413, 200-209
+
+Griffin, D., C. A. Woodhouse, D. M. Meko, D. W. Stahe, H. L. Faulstich, C. Carillo, R. Touchan, C. Castro, and S. Leavitt (2013), North American monsoon precipitation recon-
+structed from tree-ring latewood, Geophys. Res. Lett., 40, 1-5, doi:10.1002/grl.50184.
+
+Meko, D. M., R. Touchan, J. Villanueva-Dı́az, D. Griffin, C. A. Woodhouse, C. L. Castro, C. Carillo, and S. W. Leavitt (2013), Sierra San Pedroe Mártir, Baja California, cool-season precipitation reconstructed from earlywood width of Abies concolor tree rings, J. Geophys. Res-Biogeo, 118, 1660-1673, doi:10.1002/2013JG002408.
+
+Stahle, D. W., R. D. Griffin, D. M. Meko, M. D. Therrell, J. R. Edmondson, M. K. Cleaveland, L. N. Stahle, D. J. Burnette, J. T. Abatzoglou, K. T. Redmond, M. D. Dettinger, and D. R. Cayan (2013), The ancient blue oak woodlands of California: longevity and hydroclimatic history, Earth Interact., 17 (12), 1–23, doi:10.1175/2013EI000518.1.
+
+Williams, A. P., C. D. Allen, A. K. Macalady, D. Griffin, C. A. Woodhouse, D. M. Meko, T. W. Swetnam, S. A. Rauscher, R. Seager, H. D. Grissino-Mayer, J. S. Dean, E. R. Cook,
+C. Gangodagamage, M. Cai, and N. G. McDowell (2013), Temperature as a potent driver of regional forest drought stress and tree mortality, Nature Clim. Change, 3 (3), 292–-297.
+
+Woodhouse, C. A., D. M. Meko, D. Griffin, and C. L. Castro (2013), Tree rings reveal multi-season drought variability in the lower Rio Grande basin, USA, Water Resour. Res.,
+49 (2), 844-850, doi:10.1002/wrcr.20098.
+
+Ault, T. R., J. E. Cole, J. T. Overpeck, G. T. Pederson, and D. M. Meko (2014), Assessing the risk of persistent drought in the future using climate model simulations and paleoclimate data, J. Climate, doi:10.1175/JCLI-D-12-00282.1.
+
+Touchan, R., Christou, A. K., & Meko, D. M. (2014). Six centuries of May-July precipitation in Cyprus from tree rings. Climate Dynamics , 1-12.
+
+Meko, D. M., Friedman, J. M., Touchan, R., Edmondson, J. R., Griffin, E. R., & Scott, J. A. (2015). Alternative standardization approaches to improving streamflow reconstructions with ring-width indices of riparian trees. Holocene, 25(7), 1093-1101.
+
+Meko, D. M., & Piovano, E. L. (2015). Editorial note -- special issue Advances in Paleohydrology Research and Applications. Journal of Hydrology, 529, Part 2, 431-432.
+
+
+
+
+Agafonov, L. I., Meko, D. M., & Panyushkina, I. P. (2016). Reconstruction of Ob river, Russia, discharge from ring widths of floodplain trees. Journal of Hydrology, 543, 198-207. http://authors.elsevier.com/sd/article/S0022169416305856
+
+Coulthard, B., Smith, D. J., & Meko, D. M. (2016). Is worst-case scenario streamflow drought underestimated in British Columbia? A 332-year perspective for the south coast, derived from tree-rings. Journal of Hydrology, 534, 205-218.
+
+Touchan, R., Kherchouche, D., Oudjehih, B., Touchan, H., Slimani, S., & Meko, D. M. (2016). Dendroclimatology and wheat production in Algeria. Journal of Arid Environments, 124, 102-110.
+
+Touchan, R., Shishov, V. V., Tychkov, I. I., Sivrikaya, F., Attieh, J., Ketmen, M., ... Meko, D. M. (2016). Elevation-layered dendroclimatic signal in Eastern Mediterranean tree rings. Environ. Res. Lett., 11(044020).
+
+Babushkina, E. A., Belokopytova, L. V., Grachev, A. M., Meko, D. M., & Vaganov, E. A. (2017). Variation of the hydrological regime of Bele-Shira closed basin in Southern Siberia and its reflection in the radial growth of Larix sibirica. Regional Environmental Change, 1-13.
+
+Coulthard, B. L., R. Touchan, K. J. Anchukaitis, D. M. Meko, , and F. Sivrikaya (2017), Tree growth and vegetation activity at the ecosystem-scale in the eastern Mediterranean, Environ. Res. Lett., 12 (084008), doi:https://doi.org/10.1088/1748-9326/aa7b26.
+
+Panyushkina, I. P., D. M. Meko, M. G. Macklin, W. H. J. Toonen, N. S. Mukhamdiev, V. G. Konovalov, N. A. Ashikbaev, and A. O. Sagitov (2018), Runoff variations in Lake Balkhash Basin, Central Asia, 1779 to 2015, inferred from tree rings, Clim. Dynam., doi:https://doi.org/10.1007/s00382-018-4072-z.
+
+Szejner, P., W. E. Wright, S. Belmecheri, D. Meko, S. W. Leavitt, J. R. Ehleringer, and R. K. Monson (2018), Disentangling seasonal and interannual legacies from inferred patterns of forest water and carbon cycling using treering stable isotopes, Global Change Biol., 24 (11), 5332–5347, doi:10.1111/gcb.14395.
+
+Biondi, F., & Meko, D. M. (2019). Long-term hydroclimatic patterns in the Truckee-Carson Basin of the eastern Sierra Nevada, USA. Water Resour. Res., 55, 5559–5574. https://doi.org/10.1029/2019WR024735.
+
+Chen, F., Shang, H., Panyushkina, I., Meko, D., Yu, S., Yuan, Y., &, Chen, F. (2019). Tree-ring reconstruction of Lhasa River streamflow reveals 472 years of hydrologic change on southern Tibetan Plateau. J. Hydrol., 572, 169–178. https://doi.org/10.1016/j.jhydrol.2019.02.054.
+
+Chen, F., Shang, H., Panyushkina, I., Meko, D., Li, J., Yuan, Y., Yu, S., Chen, F., He, D., & Luo, X. (2019). 500-yr tree-ring reconstruction of Salween River streamflow related to the history of water supply in Southeast Asia. Clim. Dynam., 53 (11), 6595–6607. https://doi.org/10.1007/s00382-019-04948-1.
+
+Kedziora, W., Touchan, R., Meko, D., Szyc, K., Wojtan, R., Bijak, Sz., & Tomusiak, R. (2019). The 2019 International Summer School "Tree Rings, Climate, Natural Resources, and Human Interactions," Warsaw, Poland. Dendrochronologia, 58(125631). https://doi.org/10.1016/j.dendro.2019.125631.
+
+Fletcher, T., Touchan, R., Lepley, K., Rouini, N., Bloye, R., Tremarelli, T. S., Peña, K., Meko, D. M. (2019). Two reconstructions of August-July precipitation for Central Northern Arizona from tree rings. Tree Ring Res., 75 (2), 115–126. http://dx.doi.org/10.3959/1536-1098-75.2.116.
+
+Panyushkina, I. P., M. G. Macklin, W. H. J. Toonen, and D. M. Meko (2019), Water supply and ancient society in the Lake Balkhash Basin: runoff variations along the historical Silk Road, in Socio-Environmental Dynamics along the Historical Silk Road, Springer International Publishing, doi:https://doi.org/10.1007/978-3-030-00728-7, chapter in open access ebook.
+
+Meko, D. M., I. P. Panyushkina, L. A. Agafonov, and J. A. Edwards (2020), Impact of high flows of an Arctic river on ring widths of floodplain trees, The Holocene, pp. 1–10, doi:/doi.org/10.1177/0959683620902.
+
+Shamir, E., D. M. Meko, R. Touchan, K. S. Lepley, R. Campbell, R. N. Kaliff, and K. P. Georgakakos (2020), Snowpack and soil water content–related hydrologic indices and their association with radial growth of conifers in the Sierra Nevada, California, J. Geophys. Res-Biogeo, 125 (e2019JG005331), 1–21, doi:https://doi.org/10.1029/2019JG005331.
+
+Woodhouse, C. A., D. M. Meko, and E. R. Bigio (2020), A long view of Southern California water supply: perfect droughts revisited, J. Am. Water Resour. Assoc., pp. 1–18, doi:https://doi.org/10.1111/1752-1688.12822.
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