Cloud and precipitation parametrization 2: ice and mixed-phase microphysics
This lesson describes the key microphysical processes for ice and mixed-phase cloud and precipitation in the atmosphere, and the uncertainties in parametrization.
Numerical Weather Prediction: Parametrization of diabatic processes - Convection 1: Convection in the context of large-scale circulation
This lesson will take you through what convection is and the phenomena it causes.
Numerical Weather Prediction: Parametrization of diabatic processes - Convection 2: The mass-flux approach and the IFS scheme
This lesson looks at the three classes of parametrization schemes and the main characteristics of the IFS scheme.
Cloud and precipitation parametrization 1: overview and warm-phase microphysics
This lesson describes the key microphysical processes of cloud and precipitation parametrization with a focus on warm-phase processes and how these are used in NWP.
Introduction to the parametrization of sub-grid processes
Sub-grid-scale processes are not explicitly simulated in NWP so must be parameterized. This lesson describes how the parameterization is done at ECMWF and the challenges faced.
An introduction to Data Assimilation
Data assimilation is used in NWP to define ‘optimal' initial conditions for numerical forecasts. In this lesson you will define data assimilation and explore how it is used at ECMWF.
Sources of Uncertainty
All numerical weather prediction models have sources of uncertainty. In this lesson you will learn about these uncertainties and chaotic behaviour, why ensemble prediction is needed, and about ECMWF model set up.
Overview of satellite observations and their role in Numerical Weather Prediction (NWP)
This module will teach you about data sources, the role of satellite observations, satellite data measurements, assimilation, and monitoring of satellite observations.
Using stochastic physics to represent model uncertainty
All numerical weather prediction models have uncertainty. This lesson will explore why there is uncertainty and sources of it and how model uncertainty is represented in the IFS using stochastic physics.
Land surface: introduction to cold processes
Snow’s specific properties impact forecast ranges from a few days to seasonal and climate. In this lesson, you will learn about the role of snow at different time scales.
Numerical Weather Prediction: Parametrization of diabatic processes - case studies (convection)
This lesson contains four case studies which explore the conditions that cause and how to identify deep convection including predictability and forecast errors.
An introduction to single-column modelling
The SCM is a tool to investigate the physical processes of a global model in isolation. This lesson will introduce what a SCM is, its applications and limitations.
Introduction to Cloud Parametrization
This lesson will take you through the basic concepts for the design of a cloud and precipitation microphysics parametrization.