- Related Research Areas
- Water & Energy Cycles
This project applies microwave and infrared satellite data to improve model estimations of evaporation over land. These satellite data contain information about near-surface soil and vegetative moisture. The relationships of the measurements to the fluxes is indirect, so analysis of fluxes depends on mathematical or statistical models, which integrate information from other sources. For fluxes over global land areas, bulk formulations for the fluxes are problematic because there are essential parameters for which reliable information is not available, and because the results are highly sensitive to errors in these parameters and to errors in the satellite-provided variables. Data assimilation systems are an attractive option because they have the potential to optimally account for the information content and the error characteristics of all the data sources, while introducing physical constraints with a numerical model. Successful assimilation requires that the model parameterizations and variables are compatible with the satellite measurement sensitivities. When they are incompatible, the satellite information is effectively rejected or misguides the analysis. In this project, we will identify environments and regions where current land surface models are inconsistent with satellite-derived parameters related to evaporation, and hence where modeled evaporative fluxes are likely to be erroneous. This identification will be done employing simple physical and statistical models of expected relationships. For selected environments where inconsistencies are prominent, we will characterize the discrepancies by performing detailed analyses of model variables and satellite and in-situ measurements. The analyses will focus on the surface transfer processes for moisture and energy, and remote sensing phenomenology. The results are intended to provide direction toward improving surface models and their assimilation of data for water vapor flux modeling. This improvement is an essential step toward monitoring and predicting variability of water and energy cycles as manifestations of global change.
Project PI: Alan Lipton/Atmospheric and Environmental Research, Inc.
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