Object Based Analysis and Seamless prediction
The research group on Object-based Analysis and SEamless prediction (OASE) consists of scientists at the Meteorological Institute, University of Bonn (MIUB), the Leibniz-Institute for Tropospheric Research (IfT) in Leipzig and the German Weather Service (DWD) in Offenbach a. M. funded by the Hans Ertel Centre for Weather Research programme (HErZ)
The proposed project cluster within Theme 1 of the Ertel Centre for Weather Research will address seamless prediction of convective events from nowcasting to daily predictions by combining radar/satellite compositing and tracking with high-resolution model-based ensemble generation and prediction. An object-based approach to weather analysis will be used to better understand and efficiently characterize and quantify the process structure and life cycles of severe weather events. This methodology will also be exploited both for the development of novel tracking and tracking-based nowcasting strategies, and for the generation and initialisation of the model prediction ensembles.
The project cluster is designed for a total duration of 12 years, and initially comprises four work packages. The primary activity (AtmDynPred-A) during the first phase of four years will be the development of a near-realtime remote sensing-driven (primarily radar- and satellite-based) dual 3D observation-microphysics composite covering Germany. The composite will provide both gridded observations and estimated microphysics. The observation and the microphysics composite are intertwined via forward operators and estimated inverse relations, which also provide uncertainties for ensuing model ensemble initialisations. An object-based analysis (AtmDynPred-B) will condense the extensive information contained in the 3D distributions of observables and related microphysics in the form of physical descriptors as proxies for the underlying processes. A statistical-physical analysis identifies the governing physical processes and related observables for the formation and evolution especially of severe weather events. 3D scale-space tracking will monitor the development of convective events over the course of their lifetime using a Lagrangian approach encompassing primarily the microphysical quantities. The object-based perspective also offers a promising strategy for the validation of atmospheric models that aims at the causes and better understanding of differences between modeled and observed convective systems. Observation-based nowcasting (AtmDynPred-C) will exploit the dual-composite based 3D feature detection and tracking to generate of a set of predictions (observation-based ensemble) for severe weather events. Both the dual-composite and the observation-based ensemble will be the starting point for model-based predictions (AtmDynPred-D) via the initialisation of high-resolution model runs for extended warnings.
The project cluster will closely connect to the project cluster within Theme 2 (data assimilation) by providing in near-realtime most recent remote-sensing 3D observational fields of convection-related observables including their error characteristics. A close natural link exists with the project cluster in Theme 3 (model development) and Theme 4 (climate monitoring and diagnostics) via the development of the microphysics composite. The project cluster will finally contribute to an eventual project cluster in Theme 5 by the provision of 4D weather information as basis for visualizations.