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Home > Newsevents > Training > 2002 > Meteorological >  
   

Course description Met PR
Predictability, diagnostics and seasonal forecasting, 8 - 12 April 2002


 
 

General

This five-day module will start on Monday 8 April at 9.00 and finish Friday 12 April at 13.00. Short descriptions of the contents of the lectures are given below.

Predictability

The predictability of the atmosphere in the medium and extended range will be considered. Theoretical aspects associated with ideas in chaos theory, flow regime diagnosis, and singular vector analysis, will be addressed. There will be a discussion of ensemble techniques, especially those used at the Centre, together with an analysis of specific case-studies of ensemble forecasts. Methods to evaluate the skill of ensemble-based probability forecasts will be studied. The potential user value of ensemble forecasts will be assessed through some idealised and practical examples.

Diagnostics

Despite impressive improvements in our ability to model the atmosphere, forecasts still exhibit systematic and flow-dependent errors as well as random error. Diagnostics software is a crucial component of the forecast system, used to document such errors, and to understand their causes. Lectures on the impact of model resolution, diabatic forcing, and even the general process of deterministic parametrisation, on systematic and flow dependent errors, will be given. The use of the ECMWF reanalysis as a tool for diagnostic analysis will also be studied.

Seasonal Forecasting

Although detailed weather forecasts are not possible beyond a couple of weeks ahead, it is possible to make probabilistic forecasts of large-scale flow anomalies on the seasonal timescale. Predictability on this timescale arises from ocean-atmosphere interaction, typified by El Niño. Lectures will cover the El Niño phenomenon and the ocean circulation. The formulation and performance of the ECMWF coupled ocean-atmosphere seasonal forecast system will be described. The seasonal predictability of tropical cyclones and the MJO, and multi-model seasonal forecasting will be discussed. An overview of the NAO and its predictability on timescales from days to decades will also be given.


 

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