4th workshop on assimilating satellite cloud and precipitation observations for NWP

Thanks to their explicit microphysical parameterization, non-hydrostatic Cloud Resolving Models (CRM) allow realistic representations of non linear diabatic processes. Forecast errors of thermodynamical variables and hydrometeors can be computed specifically in cloudy and precipitating conditions by applying e.g geographical masks to ensemble of forecasts obtained with such CRMs and by...

Data assimilation schemes blend observational data, with limited coverage, with a short term forecast to produce an analysis, which is meant to be the best estimate of the atmospheric state. Appropriately specifying error statistics is necessary to obtain an optimal analysis. However, observations often measure a higher resolution state than coarse resolution model grids can describe. Hence,...

The quality of a numerical weather prediction (NWP) system depends on the reliability of its forecasts in both its deterministic and probabilistic configurations. Forecast skill then is affected by the accuracy of the NWP model and its physical parametrizations, as well as by initial condition errors, which include the contributions from observation errors, both random and systematic. It...

In the presence of clouds and precipitation, there is a greater need for information on all state variables in a context where both data assimilation and remote sensing become more complicated, because:

1) Clouds, and to a lesser extent precipitation, shut atmospheric windows at optical (UV to IR) and upper-microwave frequencies while also introducing challenges to observation simulation,...

Roland Potthast, Anne Walter, Andreas Rhodin, Nora Schenk, Liselotte Bach, Takemasa Miyoshi, Shunji Kotsuki, Peter Jan van Leeuwen

We discuss the development of non-linear filtering methods for very high-dimensional systems. In this talk, non-linear filtering is developed in the framework of the convective-scale ensemble data assimilation system ICON-KENDA of DWD with upcoming 2km...