5.8
Data Assimilation of Surface and Satellite Observations to Improve Land Surface Modeling
Jared K. Entin, NASA/GSFC, Greenbelt, MD; and P. R. Houser and B. A. Cosgrove
It is imperative to correctly represent the water and energy balances that exist at the land surface to ensure accurate weather and climate forecasts. Though a land surface model (LSM) can simulate these balances, they are partially limited by the quality of the forcing data they receive. In addition, these models have not been well validated. The Land Data Assimilation System (LDAS) project seeks to address both of these issues.
The LDAS project is a multi-institutional research effort centered on the development of a data assimilation scheme suitable for near-real time and retrospective modeling. Through the use of LSMs as well as terrestrial and space-based observations, this data assimilation scheme will reduce errors in surface fluxes and storage quantities that are often present in LSM simulations. The LDAS currently operates at a 1/8th-degree resolution over the continental United States and makes use of EDAS forcing data, NESDIS/GOES radiation data and Stage IV precipitation data. An LDAS web site -which features a real-time image generator and project information - has been created at http://ldas.gsfc.nasa.gov.
We are using multiple LSMs, including Mosaic and the new Common Land Model (CLM), in the LDAS framework. Out validation of LDAS products centers on the use of observations from various stations, in the United States, where soil moisture and other quantities are regularly measured. These data come from the Oklahoma Mesonet, the Atmospheric Radiation measurement (ARM) network and the Soil Climate Analysis Network (former USDA's Natural Resources Conservation Service).
The second phase of our work involves data assimilation of Tropical Rainfall Measurement Mission (TRMM) observations of the surface brightness temperatures. The lowest frequency, 10 GHz, of the TRMM radiometer, is capable of measuring the surface wetness. During timesteps of the LSM, when TRMM observations are available, the model's conditions are translated into surface microwave brightness temperatures, these are then used in a data assimilation technique to develop an adjustment factor. This factor is used to nudge the model's surface soil moisture conditions towards the true land surface state (as observed by TRMM). This type of data assimilation, in addition to altering the surface soil moisture, can affect the amount of moisture in other soil layers, as well as the latent and sensible heat fluxes.
However, an increasing amount of water in the vegetation canopy can reduce the accuracy of the TRMM observation. Another level of complexity, is that the spatial extent of the TRMM 10GHz footprint is on the order 40 km, so our work has also involved studies to evaluate the performance of data assimilation when the footprint area is occupied by differing amounts and types of vegetation.
Preliminary results show that data assimilation is able to correct for some errors in the precipitation (or other fields) in low vegetated areas (i.e. grassland and agricultural areas). We hope to perform additional runs, using degraded precipitation records, to gauge the potential effectiveness of data assimilation against poor precipitation data. Also, for a select number of sites, addition runs were performed using precipitation data observed at the soil moisture observation sites.
Session 5, Testing and Simulation of Observing Systems: Part 1
Wednesday, 17 January 2001, 1:30 PM-4:45 PM
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