92nd American Meteorological Society Annual Meeting (January 22-26, 2012)

Tuesday, 24 January 2012: 2:15 PM
Improving Evapotranspiration Estimation Through Soil Moisture Data Assimilation
Room 352 (New Orleans Convention Center )
Sujay V. Kumar, SAIC, Greenbelt, MD; and C. Peters-Lidard, D. M. Mocko, and Y. Tian

Evapotranspiration (ET) estimates are of critical significance to many end-use applications such as weather and climate prediction, water resources management and agricultural production. Though ET constitutes the largest component of terrestrial water balance, it is difficult to measure directly. The in-situ, point measurements of ET generated through surface meteorological observations are inadequate in providing representations over large spatial domains. As a result, land surface models (LSMs) and land data assimilation systems (LDAS) have been used as a way to generate spatially and temporally continuous estimates of ET globally and regionally. LSMs solve governing equations of soil-vegetation-snowpack medium based on prescribed atmospheric conditions to generate estimates of terrestrial water energy, water and momentum exchanges, including ET. In addition, the model simulations can be constrained with observed land surface states though data assimilation (DA) to further improve the LSM predictions. In this presentation, we describe an assessment of ET estimates from current LDAS systems through comparisons against gridded tower-based ET estimates from the FLUXNET measurements and ET estimates based on MODIS satellite data. We also present an evaluation of the impact of soil moisture data assimilation in ET estimation. Two different retrievals of surface soil moisture retrievals (NASA Level-3 product and the Land Parameter Retrieval Model (LPRM) product from VU Amsterdam) from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) instrument are assimilated into the land surface models. The results indicate that the assimilation of LPRM data help in further improving the ET estimates from LSMs.

Supplementary URL: