Recent developments and potential applications of opportunistic sensing data in operational precipitation products at Deutscher Wetterdienst

High-quality precipitation analyses serve several applications spanning from weather prediction, flood forecasting, and drought monitoring to disaster management and climate change studies and are therefore amongst the key operational products of National Meteorological and Hydrological Services (NMHS). In Germany, the Deutscher Wetterdienst (DWD) provides regional to global scale quantitative precipitation estimates (QPE) for real-time applications as well as climatological analyses. Exemplary DWD precipitation products are the radar-based QPE of RADOLAN and RADKLIM, the Global Precipitation Climatology Centre’s (GPCC) gridded products based on interpolated station data and the solely satellite-retrieved precipitation estimate GIRAFE of the EUMETSAT Satellite Application Facility on Climate Monitoring (CM-SAF). All these products rely on ground-based precipitation measurements, either directly in the interpolation scheme, for the adjustment of indirect measurements, or for validation purposes.

DWD’s operational radar-based QPE product RADOLAN uses ground-based observations to adjust remote-sensing information to quantitative in situ precipitation values. Due to the relatively low network density, however, a significant fraction of local heavy precipitation events is missed by classical pluviometers. Opportunistic sensors (OS) - not originally designed for high-quality hydrometeorological observations - such as commercial microwave links (CML) and private weather stations (PWS) increase the density of ground-based sensors tremendously. Adding abundant OS information promises a better capture of extreme events and thus an improved input for flood forecasting applications. The drawback, however, is the lower data quality and reliability requiring extensive quality control and new ways of data management and processing.

Within the project HoWa-PRO funded by the German Federal Ministry of Education and Research DWD has established a pre-operational framework for real-time radar-based QPE including CML data with the aim of providing improved precipitation estimates in a timely manner to the flood forecasting centers of the German federal states. Different merging algorithms as well as AI-based QPE estimates have been implemented and tested. We present case study results along with a reprocessing of approximately 1.5 years of data.

OS data may constitute an important additional source of information for national and global operational data products, but still it is barely used. The reason lies in the limited availability of data from networks operated by private companies. We will discuss further potential applications and benefits of OS data and present best practice examples and initiatives like the Global Microwave Link Data Collection Initiative (GMDI) bridging the gap between NMHS, science, and the private sector aiming at working together towards better environmental monitoring.