It is expected that spaceborne precipitation estimates during the era of the Global Precipitation Measurement (GPM) mission (available at a resolution of approximately three hours in time and ten kilometers in space) will be useful for a wide range of hydrological applications. This expectation is particularly high for regions where traditional hydrometeorological networks are sparse, such as in Africa and much of Asia. The extent to which GPM-era precipitation estimates will be skillful and useful, however, is unresolved, and a better determination thereof is the primary focus of this research project. Secondary objectives include a refinement of the error characterization of spaceborne precipitation estimates and exploration of ways to improve the predictive skills of spaceborne precipitation estimates by means of integrating them with other remotely sensed information like surface soil moisture and surface temperature. A framework for systematic evaluation of the predictive skill of GPM-era precipitation estimates will be developed and applied to hydrologic applications ranging from event-based river flow forecasting for basins of approximately 10,000 km2 and larger to regional-to-continental scale water cycle variables such as soil moisture and water budget studies. The predictive skill of a variety of precipitation products for forecasts of streamflow and soil moisture content will be objectively assessed relative to a reference standard. Use will be made of available precipitation records based on surface rain gauge and radar data, and infrared, passive and active microwave information observations from spaceborne sensors. This project will yield a quantitative assessment of the predictive skill of GPM-era precipitation estimates and how that may vary with geographical region, seasons, and space and time scales of the hydrologic application. The outcome of this research is not only central to NASA's GPM mission, but also of great interest to the broader hydrometeorology and climate research and operational communities.
