Cloud Hydrometeors Tracking in Severe Weather using LEO Small Satellite Fleet
The LEO small satellite fleet concept requires an extended capability for microwave radiance assimilation under conditions of rapidly evolving mesoscale convection and widely varying cloud opacity, along with assimilation processing in real time. To achieve this capability an all-weather data assimilation technique based on iterative eXtended Kalman Filtering (XKF) and use of the Unified Microwave Radiative Transfer (UMRT) model is being developed to facilitate tracking of the hydrometric (i.e., cloud and precipitation) state variables. The technique will provide effective assimilation of LEO constellation data into a regional-scale numerical weather prediction (NWP) model. A state-dependent background error covariance model based on a rapidly computable covariance function separable in the vertical and horizontal coordinates, along with a baseline covariance lookup library, are critical components to the all-weather assimilation cycle. The background error covariance model will function within a scalable local 3DVar iterative assimilation step. A demonstration of the above hydrometric tracking system using simulated microwave radiance data for Hurricane Sandy in the framework of LEO CubeSat fleet will be presented, along with plans for validating the proposed background error covariance model. The degree to which the WRF NWP model is able to lock on to simulated hurricane radiance data and maintain hydrometric locking status will be demonstrated.