HR: 14:30h
AN: H23D-03 [Abstracts]
TI: A comparison of soil moisture predicted from NCEP operational forecast and assimilation systems and retrieved from AMSR-E
AU: * Meng, J
EM: Jesse.Meng@noaa.gov
AF: SAIC, 4600 POWDER MILL RD, BELTSVILLE, MD 20705, United States
AU: * Meng, J
EM: Jesse.Meng@noaa.gov
AF: NOAA/NCEP/EMC, 5200 AUTH RD ROOM 207, CAMP SPRINGS, MD 20746, United
States
AU: Ek, M
EM: Michael.Ek@noaa.gov
AF: NOAA/NCEP/EMC, 5200 AUTH RD ROOM 207, CAMP SPRINGS, MD 20746, United
States
AU: Zhan, X
EM: Xiwu.Zhan@noaa.gov
AF: NOAA/NESDIS, 5200 AUTH RD ROOM 701, CAMP SPRINGS, MD 20746, United States
AU: Liu, J
EM: Jicheng.Liu@noaa.gov
AF: NOAA/NESDIS, 5200 AUTH RD ROOM 701, CAMP SPRINGS, MD 20746, United States
AB:
Accurate assessment of global and regional soil moisture is critical in numerical weather and climate
prediction systems because of their regulation of water and energy fluxes between the land surface and
atmosphere over a variety of spatial and temporal scales. Since in-situ measurement of soil moisture is not
available on global or regional scales, current coupled atmosphere-land forecast systems are relying on their
land surface models to predict soil moisture, and the water and energy fluxes between the land surface and
atmosphere. It is now widely acknowledged that different land surface models often spin up to rather different
climatologies of soil moisture. The accuracy of the predicted soil moisture is not assessed on regular basis.
Soil moisture retrieved from remote sensing platforms, e.g., the Advanced Microwave Scanning Radiometer
(AMSR-E), have shown the potential to 'observe' soil moisture on
the global scale. Even though its accuracy and uncertainty is yet to be determined, remotely-sensed soil
moisture provides the first global picture of this crucial hydrological property and its spatial and temporal
variations.
In this study, soil moisture predicted from the NCEP operational global and regional forecast and assimilation
systems, and that retrieved from AMSR-E will be compared. This diagnosis can provide information on how
the prediction systems as well as the retrieval might be improved so that they can be used in actual global and
regional land data assimilation systems.
DE: 1800 HYDROLOGY
DE: 1843 Land/atmosphere interactions (1218, 1631, 3322)
DE: 1866 Soil moisture
SC: Hydrology [H]
MN: 2009 Joint Assembly