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An Indirect Data Assimilation Scheme for Deep Soil Temperature in the Pleim-Xiu Land Surface Model
Citation:
PLEIM, J. E. AND R. C. GILLIAM. An Indirect Data Assimilation Scheme for Deep Soil Temperature in the Pleim-Xiu Land Surface Model. JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY. American Meteorological Society, Boston, MA, 48(7):1362-1376, (2009).
Impact/Purpose:
National Exposure Research Laboratory′s (NERL′s) Atmospheric Modeling Division (AMD) conducts research in support of EPA′s mission to protect human health and the environment. AMD′s research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the Nation′s air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.
Description:
The Pleim-Xiu land surface model (PX LSM) has been improved by the addition of a 2nd indirect data assimilation scheme. The first, which was described previously, is a technique where soil moisture in nudged according to the biases in 2-m air temperature and relative humidity between the model and observation based analyses. The new technique involves nudging the deep soil temperature in the soil temperature force-restore (FR) model according to model bias in the 2-m air temperature only during nighttime.
