Roland Potthast Webhome

Our department FE1 for Numerical Weather Prediction (NWP) of the German Weather Service (DWD) consists of about 85 scientists at DWD headquarters in Offenbach close to Frankfurt/Main and in Potsdam close to Berlin, about 35 of them on funded R&D projects. We are currently restructuring into four sections:

• Data Assimilation and Predictability (FE11),
• Observations Modeling and Verification (FE12),
• Numerical Modeling (FE13),
• Physical Processes and Dispersion (FE14).

DWD is a part of the German Ministery of Transport and Digital Infrastructure (BMVI), but our work is carried out in intensive cooperation with

• the Max-Planck-Institute for Meteorology MPI-M, see also ICON,
• the German Climate Computing Center DKRZ,
• the Karlsruhe Institute of Technology KIT, in particular with ICON-ART,
• ETH Zurich, in particular C2SM,
• the weather services of the COSMO Consortium (Italy, Switzerland, Russia, Poland, Romania, Greece and Israel),
• the Meteorological Service of the German Armee Geo BW, see also PDF,
• the Hans-Ertel Center HErZ research branches at the Universities of Munich, Bonn, Frankfurt, Hamburg, Berlin

and further international partners such as the

• European Centre for Medium Range Weather Forecasting ECMWF located in Reading, Bologna and Bonn.
• EUMETSAT, in particular the NWP-SAF Consortium with the Partners MetOffice (UK), Meteo-France, ECMWF and DWD.

We are working on modeling and data assimilation for numerical weather prediction (NWP) and earth system simulation (ESM). Our main task is to provide operational data assimilation and forecasting with

• ICON-global, i.e. a global NWP-model with 13km resolution, 90 layers 75km height, run every 3 hours 24/7,
• ICON-EU, i.e. its mesoscale two-way-nesting area over Europe with 6.5km resolution and the
• ICON-D2, i.e. high-resolution convection-permitting analysis and forecasts over central Europe with 2km resolution, 24km height analysis every hour 24/7, forecasts every three hours.

We prepare a rapid update cycle (RUC) with forecasts every hour in integration with Nowcasting techniques. This system is called

• ICON-RUC and SINFONY.

Our development and services include ensemble data assimilation for the ensemble prediction systems

• ICON-EPS global with 40 km resolution and 40 members and
• ICON-EU-EPS with 20km resolution over Europe with 40 members as well as
• ICON-D2-EPS with 2km resolution over central Europe, 40 members.

We run a

• hybrid ensemble-variational data assimilation scheme (EnVar) globally, i.e. a variational data assimilation scheme, coupled with an Localized Ensemble Transform Kalman Filter (LETKF).
• For ICON-D2 we employ the four-dimensional version of the Localized Ensemble Transform Kalman Filter (4D-LETKF).

Further,

• Particle filters, in particular the Localized Adaptive Particle Filter LAPF and Localized Mixture Coefficients Particle Filter LMCPF are available for global and regional scale and are being used for research.
• EnVar for convective-scale ICON-D2 is under development,
• 4D-EnVAR for both global and regional systems
• an ultra-rapid data assimilation scheme (URDA) based on our ensemble and also
• a coarse EnVar (cEnVar) using our global ensemble for regional ensemble data assimilation world-wide are being developed and tested.

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

• Ground Stations and Ships (SYNOP),
• Radio Sondes (TEMP) and dropsondes,
• Buoys,
• Air Planes (AMDAR, AIREP, ACAR, …),
• Atmospheric Motion Vectors (AMV),
• Scatterometers (SCAT),
• Infrared Sounders (IR),
• Microwave Sounders (MW) and Microwave Radiometers (MWR),
• LIDAR including Clound Bottom Height (CBH), Cloud Top Height (CTH), Backscatter Profiles, Line-of-Sight Winds (AEOLUS), Ceilometers,
• RADAR including RADAR Radial Winds, RADAR Reflectivity and RADAR Dual Polarization,
• GPS/GNSS including Radio Occultations (RO), Zenith Total Delay (ZTD), Slant Total Delay (STD),
• Cameras,
• Cars.

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:

• numerical weather prediction (NWP),
• cognitive neuroscience / neural field theory (NFT),
• inverse scattering problems / remote sensing.

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.

• April 14-16, 2021: ECMWF workshop: Machine learning for numerical weather predictions and climate services Webpage, talk on Intelligent Cloud Observation Operators
• April 4-7, 2021: Hans-Ertel Centre Yearly Meeting, Area Expert for Data Assimilation

• March 24, 2021: Webinar Intelligent camera cloud operators, Introductory Talk https://reuniwatt.com/en/2021/02/02/webinar-series-intelligent-camera-cloud-operators-for-numerical-weather-prediction/
• March 16, 2021: ICCARUS ICON-Seamless Prediction from Weather to Climate, Talk on DA Concepts
• March 8, 2021: ICCARUS ICON-COSMO-CLM-ART User Seminar, Solicited Survey Talk on Data Assimilation Innovation, Webpage

• Jan 14, 2021: Campus for Christ Karlsruhe, Open Seminar Talk on Science, History and Faith
• Jan 8, 2021: International Symposium on Data Assimilation ISDA online, Invited talk on feature data assimilation Webpage.

For more news see IP News and IP Events!
Previous News News RP 2020, News RP 2019, News RP 2018, News RP 2017, News RP 2016.

Since October 2020 I am heading the department on Numerical Weather Prediction (NWP) with about 85 researchers. There are four division heads to lead sections in this department of approximately 20-25 researchers each. Currently I am still head of data assimilation with approximately 40 researchers on data assimilation and inverse problems in Frankfurt/Offenbach and Reading (UK), see group.

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

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;

• Frontiers in Applied Mathematics and Statistics - Dynamical Systems
• Mathematical Inverse Problems