Module 2: Numerical Weather Prediction

About this module

This module, led by Prof. Tomoki Ushiyama, introduces the fundamentals of Numerical Weather Prediction (NWP) and describes how atmospheric observations, data assimilation and numerical models are combined to produce weather forecasts. It introduces the roles of global and regional NWP models, including the use of higher-resolution regional models for rainfall prediction and hydrological applications. 

The module also introduces ensemble forecasting as a means of understanding forecast uncertainty and reliability, and provides examples of its application to rainfall and flood forecasting. Finally, it provides a basic overview of emerging AI/ML approaches to weather forecasting and their relationship with conventional physics-based NWP. 

Learning Objectives

By the end of this module, participants should be able to:

• describe the basic concept and main components of Numerical Weather Prediction;
• explain the roles of atmospheric observations, data assimilation and numerical models in producing weather forecasts;
• recognize the advantages of regional NWP models for higher-resolution rainfall prediction and hydrological applications;
• describe the basic concept of ensemble forecasting and its use in understanding forecast uncertainty and reliability; and
• recognize the main characteristics, opportunities and limitations of AI/ML-based weather forecasting in relation to physics-based NWP.