Data assimilation and downscaling of remotely-sensed satellite precipitation and soil moisture data for hydrologic applications this proposal is built on the on-going effort of developing modern…

This is a federal grant award to Georgia Tech Research Corporation, a non-profit research organization and state institution of higher education associated with the Georgia Institute of Technology. The $397,649.43 grant, awarded by the National Aeronautics and Space Administration (NASA), is for research to develop a joint data assimilation system that can simultaneously incorporate precipitation data from the Global Precipitation Measurement (GPM) mission and soil moisture data from the Soil Moisture Active Passive (SMAP) mission. The goal is to produce high-resolution precipitation, soil moisture, and other environmental data products to improve hydrological modeling and prediction of events like flash floods. This research effort is part of NASA's Precipitation Measurement Missions (PMM) program and builds on the grantee's prior work in data assimilation and satellite data integration. The award does not have a set-aside designation.

Data assimilation and downscaling of remotely-sensed satellite precipitation and soil moisture data for hydrologic applications this proposal is built on the on-going effort of developing modern data assimilation techniques for downscaling of satellite precipitation and soil moisture measurements to improve hydrological modeling and prediction at high spatiotemporal resolutions. The goal of this proposal is to develop a joint data assimilation system to effectively incorporate the merged products of the global precipitation measurement (GPM) and the soil moisture active and passive (smap) missions into a coupled land surface and atmosphere model to better understand catchment-scale hydrologic dynamics such as flash floods. In the past, as part of the PMM science team, we have developed an algorithm that uses the weather research and forecasting (WRF) model with a four-dimensional variational (4D-VAR) data assimilation scheme to directly assimilate and downscale the tropical rainfall measuring mission (trmm) 3B42 precipitation product. Good results were obtained. Furthermore, we developed a onedimensional variational (1D-VAR) data assimilation scheme and linked IT with the coupled WRF-noah model to directly assimilate soil moisture retrieval provided by the soil moisture and ocean salinity (smos) mission. With this effort demonstrated that such soil moisture assimilation significantly improves the soil moisture prediction and thus related hydrologic predictions. Both independent activities are a proof of concept of the potential of joint data assimilation in the current real GPM-smap ERA. This new proposal seeks to make the most of products from the GPM and smap missions. The proposed project will focus on the following new research tasks. First, we will develop a new joint data assimilation system to simultaneously assimilate the precipitation data from the integrated multi-satellite retrievals for GPM (imerg) and soil moisture data from the smap level II radar/radiometer products. Second, using the developed system, we will produce high space-time resolution fields, including precipitation, soil moisture, radiation, humidity, wind, and pressure, and temperature that will serve as inputs to stream flow prediction models. We will use complementary advantages of distributed hydrological models including WRF-hydro and the triangulated irregular network (tin)-based real-time integrated basin simulator (tribs) developed in our group. The outcome of this research will improve knowledge and hydrologically useful products to the basket of efforts of the PMM science team including: (1) a joint data assimilation system that quantifies the relative impact of the GPM and smap on precipitation and soil moisture predictions and (2) a further understanding of the relative importance of the GPM and smap on the parameterization and hydrologic/streamflow predictions of the WRF-hydro and tribs models. The direct deliverables of this project are downscaled rainfall and soil moisture data products from joint data assimilation of satellite precipitation and soil moisture.