Meteorological Training Course Lecture Notes

LECTURE NOTES ARE AVAILABLE FOR THE FOLLOWING MODULES OF THE METEOROLOGICAL TRAINING COURSE

Data Assimilation and the Use of Satellite Data

This module provides an overview of the global observing system, explains the fundamental concepts and notations of data assimilation, outlines the detailed implementation of four important assimilation techniques, describes the current status and use of satellite data, explains the need to control gravity waves in data assimilation, and illustrates the performance of a data assimilation scheme using statistics derived from data.

Numerical Methods and the Adiabatic Formulation of Models

This module examines the types of wave motion described by the linearized atmospheric prediction equations, and gives an introduction to finite difference, finite element and spectral techniques for solving the advection, diffusion and gravity-wave equations. The way that the adiabatic part of large-scale models of the atmosphere is formulated is described. An overview of the state-of-the-art of ocean wave modelling is also given.

Parametrization of Diabatic Processes

This module explains the general aspects of parametrizations and their relation to forecasting errors. It describes the theoretical basis for parametrization schemes of gravity waves, of radiation, of clouds and convective processes, and of the planetary boundary layer and land surface processes. Various models developed for including more physical processes in data assililation schemes are also described.

Predictability, Diagnostics and Seasonal Forecasting

This module considers theoretical ideas in chaos theory, flow-regime diagnosis, and singular-vector analysis. The problem of predictability of atmospheric flow is discussed and the ensemble forecasting techniques used at the Centre are described, with illustrations from specific case studies. The diagnostics used to elucidate forecast errors, and the sensitivity of models to resolution, diabatic forcing etc. are also examined. Lectures also cover monthly and seasonal forecasting, including prediction of the El Niño, methods for initialization, ensemble generation and multi-model techniques.