Steven J. Greybush Teaching

Meteo 411 | Meteo 460 | Meteo 473 | Meteo 474 | Meteo 527

Meteo 527: Data Assimilation

  • Data Assimilation terminology, mathematical framework, assumptions, and conceptual understanding
  • Data Assimilation methodology, including variational techniques, ensemble kalman filters, and hybrid approaches
  • Application of data assimilation techniques to dynamical systems using computer programming
  • Research frontiers in data assimilation, predictability, and applications to numerical weather prediction

    Data assimilation (DA) is the process of finding the best estimate of the state and associated uncertainty by combining all available information including model forecasts and observations and their respective uncertainties. DA is best known for producing accurate initial conditions for numerical weather prediction (NWP) models, but has been recently adopted for state and parameter estimation for a wide range of dynamical systems across many disciplines such as ocean, land, water, air quality, climate, ecosystem and astrophysics. Taking advantages of improved observing networks, better forecast models and high performing computing, there are two leading types of advanced approaches, namely variational data assimilation through minimization of a cost function, or ensemble-based data assimilation through a Kalman filter. Hybrid techniques, parameter estimation, predictability, and ensemble sensitivity methods will also be covered.
    The material in this course may be relevant to those in engineering, statistics, mathematics, hydrology, earth systems science, atmospheric science, and many other fields that seek to integrate information from observations and models.
    Course Description

    Meteo 411: Synoptic Meteorology

  • Analysis of synoptic-scale and upper-air observations and maps
  • Structure, development, and evolution of synoptic-scale weather systems
  • Norwegian cyclone model conceptual framework
  • Fundamental equations of atmospheric dynamics and assessing vertical motion
  • Role of upper-level flow in development of extratropical cyclones
  • Quasi-geostrophic theory
  • Introduction to numerical weather prediction and predictability
  • Weather forecasting

    Synoptic meteorology is the essential link between dynamical meteorology and weather forecasting, theory and applications, mathematics and weather maps. It is foundational for interpreting weather observations and numerical weather prediction model output. It provides the fundamental conceptual insights to the structure and evolution of mid-latitude weather systems, which are linked to nearly all other aspects of atmospheric science at all scales.
    Course Description

    Meteo 473: Application of Computers to Meteorology

  • Apply numerical algorithms to atmospheric problems
  • Demonstrate new programming knowledge for problem solving
  • Effective teamwork to produce and present a final product
  • Atmospheric data analysis and visualization
  • Documentation of code and results
  • Python programming and the linux environment

    Computer programming is an essential skill, both for academic research and in the workplace. In the world of large datasets, sophisticated model simulations and forecasting tools, it is frequently the only practical way to proceed toward a solution for your application. This course provides a forum for gaining experience with applying algorithms, coding techniques, and visualization tools to practical problems in meteorology. Through the course of the semester one gains appreciation of both the science and art of program design, implementation, and documentation. This journey will take patience, ingenuity, creativity, and teamwork; however the student will be rewarded with not only a valuable skillset of problem solving techniques, but hopefully a sense of accomplishment and enjoyment in their achievements.
    Course Description

    Meteo 474: Computer Methods in Meteorological Analysis and Forecasting

  • Computer methods for statistical analysis and forecasting of the weather
  • Development of data-driven weather foreacsting systems
  • Statistical forecast verification techniques
  • Introduction to data assimilation
  • Artificial Intelligence (AI) and Machine Learning (ML) techniques applied to meteorology
  • Decision trees and neural networks

    Computer Methods of Meteorological Analysis and Forecasting explores the computationally intensive statistical methods used in the development of automated weather analysis and forecasting systems. The focus of the course is on learning to develop and use artificial intelligence, machine learning, and statistical methods to perform data quality control, quantitative analysis of large meteorological data sets, and weather forecasting. Coverage will include the relevant statistical, mathematical, and computational methods including data quality control, ensemble prediction, data assimilation, regression analysis, neural network construction, decision tree growth, and forecast system verification. Students will leave the course with an understanding of how to efficiently develop accurate and robust statistical weather analysis and prediction systems.
    Course Description

    Meteo 460: Weather Risk and Financial Markets

  • Concept of risk and role of weather as driver of economic risk
  • Probabilistic weather forecasting and verification
  • Weather derivatives, options, and valuation
  • Weather and its connection to energy and commodities markets
  • Reinsurance and catastrophe risk management

    This capstone course in the Weather Risk option offers the student numerous opportunities to integrate their foundational knowledge of economics, statistics and the atmosphere to the practical challenges of making money in the market place. The semester-long trading projects provide a continuous learning experience where weather models and quantitative business analyses are used routinely to gain measurable skill in weather risk management. Likewise, the various forecasting exercises are designed to expose students to a variety of medium range weather forecasting challenges they may reasonably encounter working in the weather risk field. Finally, the catastrophe risk project exposes students to the means by which weather risk is incorporated into setting insurance premiums.
    Course Description
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