Description:

Realistic representation of land surface processes is essential to obtaining accurate hydrologic, weather, and climate model forecasts. However, land surface models are subject to many errors, including from meteorological inputs, model parameters, and mis-specified or missing physical processes. Integration of remotely sensed observations and land surface models can help address these shortcomings, and many government agencies, including NOAA and NASA, are heavily investing in high quality satellite remotely sensed data products of land surface fluxes and states, such as  precipitation, evapotranspiration, snow/ice cover and depth, surface temperatures, vegetation characteristics, soil moisture,  ground water, and irrigation. 

This session will highlight advances in understanding, monitoring, and predicting land surface processes through the application of these remotely sensed data products to enhance land surface modeling. This includes, but is not limited to: (1) development and evaluation of novel remotely sensed data products relevant to land surface modeling, including presentation of novel evaluation methods; (2) use of remotely sensed observations to evaluate and benchmark land surface models, particularly work directed at evaluating model-simulated physical processes and relationships and/or model simulations of specific events; and (3) the direct use of remotely sensed observations in land surface models, including through data assimilation.