Courses

Students will learn a variety of methods for accessing appropriate weather and climate datasets available from government and research institutions. Working with very large datasets in a computationally efficient manner will be stressed, as will consideration of factors that affect data reliability.

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.

Students will learn a variety of methods for identifying key temporal patterns in atmospheric datasets, modeling methods based on patterns, trend analyses in climate datasets, advanced modeling methods, frequency domain analyses, and spatial-temporal visualization techniques specific to meteorology.

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.

The goal of this course is to better inform decision-makers on the probability of adverse and advantageous weather events. This course will adopt a case study approach whereby students learn to create a weather and climate analytics analysis and presentation.