Optimal Precipitation Estimation for Land Surface Modeling

June 29, 2012 | By | Add a Comment

X. Feng, P. Houser, R. MacCracken, 2013-2015: Optimal Precipitation Estimation for Land Surface Modeling . NASA-ROSES-2012 A20: PMM: Precipitation Measurement Missions Science Team (NNH12ZDA001N-PMM), $467,349, 1 mo/yr (GMU), 1/01/2013 – 12/31/2015, Sponsor POC: Dr. Ramesh K. Kakar (202/358-0240, ramesh.k.kakar@nasa.gov).

Summary: Precipitation is the most influential meteorological forcing variable for land surface modeling, providing moisture for processes such as runoff, biogeochemical cycling, evaporation, transpiration, groundwater recharge, and soil moisture. Accurate knowledge of precipitation characteristics and patterns is critical for predicting all land states and fluxes on time scales ranging from minutes to years. Moreover, timely and reliable precipitation information on recent, current and future timescales is vital for weather and climate forecasts, water management, agriculture, droughts and floods monitoring. Unfortunately, precipitation estimates from rain gauge, ground-based radar, satellite and numerical models have significant uncertainties and these can be amplified when exposed to highly non-linear land model physics. Therefore, we propose to optimally merge precipitation estimates from different data sources to produce the best estimate of precipitation that minimizes the land surface simulation errors. Specifically, we will conduct merging experiments using the National Aeronautics and Space Administration (NASA) Land Information System (LIS) framework that integrates atmospheric forcing, land and vegetation parameters into a state-of-art land surface modeling system. Moreover, we will also utilize the recently emerging long-term, high-resolution NASA Earth System Data Records (ESDRs) estimates of temperature, soil moisture, evapotranspiration, and runoff that will be used to train the optimal merging of precipitation from several sources. This project is expected to develop the high temporal and spatial resolution combined precipitation data can be used with hydrological models and to determine the impact of precipitation uncertainties on the model output. The proposal work is submitted to NASA Research Opportunities in Space and Earth Science (ROSES) Precipitation Measurement Mission (PMM) Program in response to the Funding Opportunity for FY2012.


Filed in: Rejected Support

Dr. Paul R. Houser

About the Author (Author Profile)

Dr. Houser in an internationally recognized expert in local to global land surface-atmospheric remote sensing, in-situ observation and numerical simulation, development and application of hydrologic data assimilation methods, scientific integrity and policy, and global water and energy cycling. He received his B.S. and Ph.D. degrees in Hydrology and Water Resources from the University of Arizona in 1992 and 1996 respectively. Dr. Houser's previous experience includes internships at the U.S. Geological Survey and at Los Alamos National Laboratory. Dr. Houser joined the NASA-GSFC Hydrological Sciences Branch and the Data Assimilation Office (DAO/GMAO) in 1997, served as manager of NASA’s Land Surface Hydrology Program, and served as branch head of the Hydrological Science Branch. In 2005, he joined the George Mason University Climate Dynamics Program and the Geography and Geoinformation Sciences Department as Professor of Global Hydrology, and formed CREW (the Center for Research for Environment and Water). Dr. Houser has also teamed with groundwater development and exploration companies (EarthWater Global and Geovesi) and has served as Science Advisor to the U.S. Bureau of Reclamation. Dr. Houser has led numerous scientific contributions, including the development of Land Data Assimilation Systems (LDAS), the Hydrospheric States Mission (Hydros/SMAP), the Land Information System (LIS), the NASA Energy and Water cycle Study (NEWS), and the Water Cycle Solutions Network (WaterNet).

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