Weather Forecasting using Numerical Weather Prediction Models

CSSTEAP Weather Forecasting NWP

Centre for Space Science and Technology Education in Asia and the Pacific (CSSTEAP)

Space Application Centre (SAC)

Indian Space Research Organisation (ISRO)

Mon, Sep 5 - Fri, Sep 16 2022

Text excerpt from course description, for full text see https://www.cssteap.org/

Introduction

Numerical Weather Prediction (NWP) is the quantitative forecast of weather or climate based on amodel or a set of models derived from our “best” understanding of the physical processes that govern the atmosphere or the climate. An NWP model is basically a set of partial differential equations (PDEs) that describe the dynamic and thermodynamic processes in the earth’s environment. The NWP models require initial and boundary conditions that are integrated forward in time to represent and predict the weather. Thanks to the significant developments during past four decades, the Numerical Weather Prediction is now a well recognized discipline of operational sciences that en compasses the elements from various other disciplines such as the computer science, satellite remote sensing, satellite communication, etc.

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Objectives

The overall objectives of this training course is to generate awareness amongst users / researchers / professionals / academicians on fundamentals of numerical weather prediction and data assimilation. The participants will be familiarized with the use of numerical weather prediction models, particularly the world's most widely used model for weather prediction, the Weather Research and Forecasting (WRF). The Mesoscale and Microscale Meteorology (MMM) Division of National Center for Atmospheric Research (NCAR) supports the WRF system to the user community. In addition to this, participants will be made aware of assimilation techniques to make best use of conventional and satellite observations in prediction of extreme weather events.

Find more information on https://www.cssteap.org/ and in the brochure.

Ahmedabad
English
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