Project Details
Projekt
Observation-based weather analysis and Nowcasting (QPN)
Applicants
Dr. Cintia Carbajal Henken; Professor Dr. Clemens Simmer; Privatdozentin Dr. Silke Trömel, Ph.D.
Subject Area
Atmospheric Science
Hydrogeology, Hydrology, Limnology, Urban Water Management, Water Chemistry, Integrated Water Resources Management
Hydrogeology, Hydrology, Limnology, Urban Water Management, Water Chemistry, Integrated Water Resources Management
Term
since 2018
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 320397309
Most radar-based nowcasting methods assume, that the evolution of precipitation fields is primarily governed by advection. Thus, major deficits relate to the neglect of lifecycles of precipitating cells including potential initiations of new cells during the forecast period. In Phase I, we will extend advection-based nowcasting by including lifecycle effects and follow two strategies: In a first step, trends in rainfall intensity, size and shape of the precipitating cells observed during previous time steps will be extrapolated in time. In a second step polarimetric radar signatures indicative for potential changes in precipitation generation will be exploited for refinements of the approach. E.g. strong, vertically-organized enhancements of differential reflectivity ZDR, so-called ZDR-columns suggest strong convective updrafts with ensuing precipitation enhancement. Also, opposing gradients in ZDR and reflectivity ZH – an indication of on-going droplet size sorting – are indications of weaker but still relevant updrafts. In stratiform rain, ongoing surface precipitation enhancement is often preceded 30 – 60 minutes before by a strengthening of the dendritic growth layer as e.g. indicated by strong vertical ZH gradients combined with enhanced specific differential phase KDP in the layer between -10°C and -15°C. For both approaches, P2 will derive an ensemble of nowcasted QPE fields (QPN) based upon the QPE fields provided by P1 while exploiting the 3D multi-sensor composite provided by C1. The ensemble will reflect uncertainties with respect to cell location based on the standard deviation of the cell tracks, and with respect to intensity based upon the accuracy of QPE fields from P1 and the distinctness and predictive power of polarimetric process signatures. The QPN ensemble will serve as the prime input for P4 (Flood Prediction) and will also be used in P3 (Seamless Prediction).
DFG Programme
Research Units
