Welcome!
Quick Facts about METEO 815
METEO 815 is a professional, graduate-level course offered by the Department of Meteorology. Analytics is the discovery, interpretation, and communication of patterns by way of statistics, computer programming, and applications that provide decision-making tools. Weather and Climate Analytics (WCA), in particular, uses weather and climate data to provide useful information for improving efficiency, mitigating risks, and optimizing productivity. Why care about WCA? Well, weather and climate affect everyone! Combining weather forecasts and climate predictions with analytics, companies can create better product placements, select efficient transportation routes, provide more lead time on health advisories, and much more.
This course provides practical guidance in the quantitative analysis of large weather and climate datasets for incorporation into a data analytics system. Students will learn a variety of methods for describing environmental data, focusing on bulk characteristics, hypotheses testing, linear modeling, and variability modeling. Furthermore, current data-mining strategies used in creating analysis workflows will be presented. Specific emphasis will be placed on data organization and pre-processing for computational analysis, validating assumptions for a particular analysis technique, identification and resolution of noncompliant data sets, and use of analysis/display software to improve communication of results. Numerous examples and case studies will augment discussion on the various analysis methods, with the goal being to broaden the student’s perspective on the use of weather and climate data in decision-making.
What will you learn in this course?
Meteo 815 seeks to provide guidance on methods used for describing, testing, and modeling environmental data. After successfully completing this course, you will be able to:
- compute descriptive measures and produce figures describing weather and climate datasets;
- formulate and perform hypothesis testing to determine the significance of a prescribed extreme weather event;
- visualize, quantify, and model the relationship between observed and forecast variables in applied problems such as weather-marketplace interactions;
- incorporate current data mining strategies to create analysis workflows for weather and climate data;
- choose the appropriate analysis procedure given a problem and data constraints;
- display weather and climate analyses in a manner that effectively communicates answers to posed questions; and
- demonstrate an appreciation for the role that weather and climate information plays in decision-making processes over a wide range of business and government sectors.
The lessons that comprise this course are:
Lesson 1: Descriptive Statistics (measures of tendency and variability, weather and climate visualizations of probability, common distributions of environmental data, PDFs and CDFs, goodness of fit metrics)
Lessons 2 & 3: Statistical Hypothesis Generation & Testing (normal distributions and the empirical rule, hypothesis testing using atmospheric data, mean paired samples and equivalence using climate observations, chi-squared testing, using statistical software to solve testing problems)
Lesson 4: Correlation (correlation and covariance, considerations using environmental observations, options for 2-D correlation measures, examples and pitfalls of weather and climate correlations, higher dimension approaches and examples)
Lesson 5: Linear Regression Analysis (overview of regression methods, preparing data, procedures to address non-compliant datasets, regression case studies, and best practices)
Lesson 6: ANOVA (single variable analysis of variance, case studies, and best practices, checking dependency of non-environmental responses on weather and climate instigators, practical applications of multi-variable analysis of variance, software approaches for solving real problems)
Lesson 7: Introduction to Data Mining (locating and preparing data for analysis, synthesis techniques, multiple linear regressions, regression trees, choosing an appropriate model, obtaining predictors)
Lesson 8: Data Visualization (examples and best practices for presenting data and analyses, tabular formats, plotting considerations, presentations)
How does this course work?
As with most graduate courses, there is a considerably higher onus on you to take responsibility for your own learning. While lessons present guidance on what you need to learn, much of your actual learning will take place as you engage in directed research and experiment with various examples presented in the text. Following through on these examples and exploring various ways to accomplish prescribed, data-procurement tasks are an absolute necessity, not only to be successful on the lesson's assessment activity but to meet your own learning goals as well.