Using Synthetic Satellite Images for Automatic Monitoring of Nwp Fields: Operational Applications

Within the new meteorological workstation project NinJo, a comprehensive Automatic Monitoring and Alerting system (AutoMON) has been introduced at the Deutscher Wetterdienst (DWD). This system is able to permanently monitor significant weather situations in observations and model forecasts, and it automatically alerts the forecaster in case of critical (hazardous) weather events. Furthermore, it monitors the quality of Numerical Weather Prediction (NWP) fields in comparison to observations using various techniques. Synthetic satellite images are generated from the nonhydrostatic Limited-Area Model LME of the DWD using a process-based modelling approach. The approach is based on the radiative transfer model code RTTOV-7 developed at the European Centre for Medium Range Weather Forecasts (ECMWF). Infrared (10.8 μm) and Water Vapour (6.2 μm) channels as they would be seen by the Meteosat-8 SEVIRI radiometers are used as calculated from LME using profiles of pressure, temperature, specific humidity, cloud liquid water, cloud ice, cloud cover, ozone, and some surface properties. Within AutoMON, synthetic satellite images allow a large-scale evaluation of the quality of vertically integrated NWP model output against observations. Operational applications for the automatic and objective comparison of synthetic and observed Meteosat-8 satellite images as developed at the DWD are presented. In order to quantify deviations between synthetic and actual satellite images, statistical and image comparison techniques are used. Difference images, correlation between images, and a field of displacement vectors are calculated. The forecaster is automatically alerted in case a predefined level of disagreement between synthetic model output and the latest observed satellite image is reached. The comparison products can be displayed along with highlighted differences, correlations, and displacements. The system is demonstrated based on selected weather episodes. Calculation of such deviations and tracking of the development of deviations with time allows an immediate assessment about which model run is closest to reality and may therefore be most reliable for the next hours considering the current observational situation. The system therefore helps the meteorologist to evaluate the quality of simulations with respect to their value for the current weather forecast. Weather situations that may develop in a way that has not been predicted by models can be recognised at an early stage.