Optimal Precipitation Estimation for Land Surface Modeling
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.