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.
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.
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.
This module will teach you about data sources, the role of satellite observations, satellite data measurements, assimilation, and monitoring of satellite observations.
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.
This lesson contains four case studies which explore the conditions that cause and how to identify deep convection including predictability and forecast errors.