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Quick Facts About METEO 825
METEO 825 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 forecast systems of weather and climate variables for incorporation into decision-making systems. Students will learn a variety of methods for prognostic modeling of categorical and continuous variables, measuring forecast accuracy, and assessing results through Monte Carlo simulations. Ensemble environmental forecasting techniques will also be presented. Specific emphasis will be placed on the strengths and limitations of each technique, validating assumptions for particular forecast methods, and assessing the results of the weather or climate model using a variety of statistical techniques. Numerous examples and case studies will augment discussion of the techniques, with the goal being to grow the student's knowledge of weather and climate forecasting and its usage in decision-making.
What will you learn in this course?
Meteo 825 seeks to provide guidance on predictive methods and assessments for weather and climate variables. After successfully completing this course, you will be able to:
- understand how the components of the weather and climate prediction enterprise link together to produce forecasts;
- select and use the appropriate metrics for measuring the performance of a weather or climate forecast system and its usefulness in a particular decision-making context;
- select, develop, and test an appropriate analytics-based forecast system for a given weather or climate impacted decision;
- communicate weather and climate forecasts and their level of uncertainty to decision makers; and
- demonstrate an appreciation for the role that weather and climate forecast systems play in the decision-making process over a wide range of industries.
The lessons that comprise this course are:
Lesson 1: An Introduction to Weather and Climate Prediction (physics-based weather forecasting overview, solving conservation equations, Numerical Weather Prediction (NWP) model initialization, comparison of NWP to Global Climate Models (GCMs))
Lesson 2: Measuring Forecast Accuracy (exploit measurements of forecast system skill, quantify the skill of a weather or climate forecast system, compute the value of a forecast system as it pertains to socio-economic impact)
Lesson 3: Statistical Forecast Methods (strengths and limitations of statistical and hybrid predictions, regression and categorization predictions of environmental variables, decision charts for selecting models, Model Output Statistics (MOS) approach to NWP, testing and visualizing techniques of weather forecasts)
Lesson 4: Forecasting Categorical Variables (tree methods including Classification and Regression Tree (CART) analysis, building and termination rules, logistic regression)
Lesson 5: Forecasting Continuous Variables (simple regression of weather and climate variables, advanced regression, fitting seasonal cycles, CART)
Lesson 6: Ensemble Overview (strengths and limitations, types of ensemble forecasts, and verifying ensembles)
Lesson 7: Monte Carlo Simulations (estimating uncertainties, drawing samples from a distribution fit of a weather or climate variable, advantages and disadvantages)
Lesson 8: Communicating Predictions (Analytical Dashboards) (examples and best practices for presenting weather and climate forecasts, quantifying results, 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 in the lesson's assessment activity, but to meet your own learning goals as well.