From c4128abb8d67c4232990e52a1bafb9af125e4659 Mon Sep 17 00:00:00 2001 From: Dave Meko Date: Fri, 8 Dec 2023 16:27:20 -0700 Subject: [PATCH] changes to geos585.html and vita.html --- public/geos585a.html | 160 +++++++++++++++++++++---------------------- public/vita.html | 6 +- 2 files changed, 82 insertions(+), 84 deletions(-) diff --git a/public/geos585a.html b/public/geos585a.html index 1a25a5b..42a5606 100644 --- a/public/geos585a.html +++ b/public/geos585a.html @@ -8,12 +8,12 @@ -

GEOS 585A, Applied Time Series Analysis

- - +

GEOS 585A, Applied Time Series Analysis

+ + @@ -58,18 +58,16 @@

GEOS 585A, Applied Time Series Analysis

David M. Meko

Laboratory of Tree-Ring Research, , Room 417, Bryant Bannister Tree-Ring Building (Bldg #45B)

-Email: dmeko@LTRR.arizona.edu

-Phone: (520) 621-3457

-Fax: (520) 621-8229

-Office hours Friday, 1:00-6:00 PM (please email to schedule zoom meeting) +Email: dmeko@arizona.edu

+Office hours Wednesday, 1:00-3:00 PM (please email to schedule zoom meeting)

Back to Top of Page

- -

Course Mode and University Policies

-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. -

+ +

Course Mode and University Policies

+This course was last taught in Spring Semester 2021, and is now being revamped by revision of the notes and moving from Matlab to R as the supporting software. I hope to have this transition completed by fall semester, 2025. The University of Arizona has policies generally applicable to all graduate courses. 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. +

Back to Top of Page @@ -79,8 +77,8 @@

GEOS 585A, Applied Time Series Analysis

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. -

+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.

@@ -100,7 +98,7 @@

GEOS 585A, Applied Time Series Analysis

Prerequisites

    -
  1. An introductory statistics course +
  2. An introductory statistics course
  3. Access to a laptop computer capable of having Matlab installed on it
  4. Permission of the instructor (undergraduates or non-University-of-Arizona students)
@@ -108,15 +106,15 @@

GEOS 585A, Applied Time Series Analysis

Other Requirements

    -
  1. 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. -
  2. 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. +
  3. 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. +
  4. 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.
  5. 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.

- +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.

@@ -149,39 +147,39 @@

GEOS 585A, Applied Time Series Analysis

Syllabus

Calendar

- -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. -

    - + +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. +

    + Back to Top of Page @@ -192,7 +190,7 @@

    Calendar

    Assignments

    -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.

    +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. @@ -205,17 +203,17 @@

    Calendar

    Grades

    -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. -

    - +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. +

    + Back to Top of Page @@ -252,7 +250,7 @@

    Lessons

  • Probability distribution

    -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. +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

    @@ -275,9 +273,9 @@

    Lessons

    -
  • Autocorrelation

    - -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. +

  • Autocorrelation

    + +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

    @@ -302,8 +300,8 @@

    Lessons

  • Spectrum

    - -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. + +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.

    @@ -357,8 +355,8 @@

    Lessons

    -
  • Trend

    -

    +

  • Trend

    +

    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

    @@ -402,8 +400,8 @@

    Lessons

  • Filtering

    -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. +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.

    @@ -513,8 +511,8 @@

    Lessons

  • 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 -Back to Top of Page -

    +Back to Top of Page +

    Back to Top of Page @@ -548,9 +546,9 @@

    Tailoring the Matlab Scripts

    Notes and Assignments

    -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. +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.


    diff --git a/public/vita.html b/public/vita.html index 3fb26a2..fa57f62 100644 --- a/public/vita.html +++ b/public/vita.html @@ -68,19 +68,19 @@

    Employment History

    Associations and Committees

    American Geophysical Union
    Tree-Ring Society
    -University of Arizona Committee on Global Change
    +American Water Resources Association
    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 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. I am also interested in time series methods for reconstructing streamflow and analyzing reconstructions. I write functions and distribute them through my toolbox.

    Back to Top of Page

    Teaching

    -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. +Before retiring, I taught a 3-credit course (1-credit online) in Applied Time Series Analysis every other year. That course made use of Matlab through a University of Arizona site license to give students access to the software. I am now rewriting scripts and function for the course in R, with the intent of making the course available again by 2025. For the past decade I have also 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.

    Back to Top of Page