Cloud Hydrometeors Tracking in Severe Weather using LEO Small Satellite Fleet

Observations of tropospheric convection with temporal resolution of order 15-30 minutes and ~15-30 km spatial resolution are required for improved forecasting and monitoring of severe mesoscale weather events. To achieve this goal the Precipitation and All-weather Temperature and Humidity (PATH) mission has been identified by the NRC decadal survey in 2007 based on a microwave array spectrometer. More recently, a constellation of ~20-30 Low-Earth Orbit (LEO) sensors based upon CubeSat specifications has been identified as a potential cost-competitive option to a geostationary microwave sensor to achieve PATH goals. To this end a 3U CubeSat microwave temperature sounder/imager is being developed at CU to operate at the 118.7503 GHz O2 resonance with a spatial resolution of ~16 km from a ~410 km altitude orbit.

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.