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Institute of Physics (IOP)

Roland at Fudan University in Shanghai April 2018

Data Assimilation and Inverse Problems

Our Division for Data Assimilation (FE12) of the German Weather Service (DWD), a part of the German Ministery of Transport and Digital Infrastructure (BMVI), is working on data assimilation for numerical weather prediction (NWP). Our main task is to provide

This includes ensemble data assimilation for the ensemble prediction systems ICON-EPS (40/20km resolution) as well as COSMO-DE-EPS (2.8/2.2km resolution) and a mean (best) state estimator for the deterministic runs (13/6.5km and 2.8/2.2km). A core goal is to replace COSMO by the ICON-LAM model with 2/1km resolution over central Europe operationally in about 2020/21. We run a hybrid ensemble-variational data assimilation scheme (EnVar) globally and a Local Ensemble Transform Kalman Filter (4D-LETKF) for our reginal data assimilation.

Data Assimilation includes the use of a broad varity of both direct and remote sensing measurements from

Geostationary satellites and polar orbiting satellites are used operationally, while a lot of research is going into the better use of hyperspectral observations (many thousand frequencies per observed atmospheric column) in particular over land and in cloudy situations. The observation and reconstruction of snow, ice, sea surface temperature, land surface temperature, coverage, emissivity and soil moisture is a very active area of research. Also, the observation and data assimilation of clouds and convective processes with high-impact phenomena such as thunderstorms, heavy rain and wind gusts with lead times from minutes to days is a special focus of our research.

The research of our group at the University of Reading, UK, is concerned with inverse problems and data assimilation in three areas:

These are extremely exciting areas scientifically and very important for society, for example for air traffic control, severe weather warnings and national energy supply, in medicine by medical imaging and for many industrial and environmental questions.

Workshop and Seminar News


For more news see IP News and IP Events!
Other News News RP 2017, News RP 2016.


My group consists of approximately 35-45 researchers on data assimilation and inverse problems in Frankfurt/Offenbach and Reading (UK), see group.


My Christian Blog

Thinking about faith and life has always been a passion for me. I have become a Christian and have started to explore the world as someone who follows Jesus - that has turned out to be quite an adventure and highly exciting. In my daily blog I explore thoughts and arguments about faith, and monitor how faith works on a daily basis: Jesus Network


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Recent publications can be found on publications. A book Inverse Modeling by Nakamura and Potthast with an introduction into data assimilation and inverse problems has recently appeared at IOP.

Working in an operational center, our focus is to develop state-of-the-art inversion methods which can be run in a reliable way on a supercomputer in near real-time. It includes codes on scattering of waves, propagation of light and radiation, tomography, large-scale optimization and uncertainty quanitfication, ensemble and particle methods.

(Our supercomputers at Deutscher Wetterdienst are no 99 and no 130 on the TOP-500 Supercomputer List Web)

However, the development of insight into the scientific problems we need to solve is an indispensible ingredient of our daily work. Part of this insight is based on mathematical analysis and the testing of computational methods for purpuse-built small-scale demonstration systems.

COST Action 1303 “TOPROF” Website;

Editorial Board


Prof. Dr. Roland Potthast
Deutscher Wetterdienst (DWD)
Frankfurter Strasse 135
63067 Offenbach, Germany

Professor for Applied Mathematics
Department of Mathematics and Statistics,
Whiteknights, PO Box 220,
Reading RG6 6AX, UK

Web Statistics, for last 30 days on my webpages (3.5.2018)

Visitors Visits Page Views Robots
Sum ~ 16.800 ~ 18.000 ~ 91.000 ~ 701.000

including, and