HR: 1330h
AN: H12C-1006    [PDF]
TI: A Simplified Land Data Assimilation Scheme (LDAS) for Assimilation of AMSR-E Data and Its Application to CEOP Reference Site: Mongolia
AU: * Mahadevan, P
EM: devan@hydra.t.u-tokyo.ac.jp
AF: Dept of Civil Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8656 Japan
AU: Koike, T
EM: tkoike@hydra.t.u-tokyo.ac.jp
AF: Dept of Civil Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8656 Japan
AB: Soil moisture controls the portioning of land surface heat fluxes into the atmosphere and influences the global and/or regional water cycle. However, improvement in the forecast skill of quantitative/qualitative soil moisture forecasts has been slow. This could be attributed to uncertainties in model physics, parameterization, and initial conditions. With the rapid increase of satellite data, the skill of quantitative/qualitative soil moisture forecasts is expected to be improved by using these data. This paper investigates the method of retrieving spatial distributions and temporal variations of key land surface variables such as soil moisture and soil and canopy temperatures from passive microwave radiance measurements by using the novel application of data assimilation. The Land Surface Scheme (LSS), which forms the heart of the data assimilation algorithm, is a bio-physically based Model (Simplified Biosphere Model2: SiB2) of soil, vegetation, and atmosphere interaction. Our Land Data Assimilation Scheme (LDAS) takes into account both model and observation uncertainties and provides dynamically consistent data product of land surface parameters. The satellite sensor Aqua/AMSR-E measurements, gathered over one of the CEOP reference site: Mongolia, are assimilated into the LSS using our LDAS. However, satellite observations of brightness temperatures are likely be available only over relatively larger spatial scales. In order to integrate spatial heterogeneity effects and pursue the optimal usage of large spatial scale satellite observations, we have introduced a simplified downscaling approach inside the LDAS. An assessment of the experiment results and the impacts of AMSR-E data and the data assimilation concepts in the modelling of land surface processes will be discussed.
DE: 1719 Hydrology
DE: 1866 Soil moisture
DE: 6969 Remote sensing
SC: Hydrology [H]
MN: 2003 Fall Meeting