Accurate representation of land surface states is essential to obtaining useful weather, hydrological, and climate model forecasts. However, land surface models are subject to many errors, including from model parameters, mis-specified or missing physical processes, and meteorological forcings or other boundary conditions. These shortcomings can be addressed by land data assimilation, which merges in-situ or satellite-based observations with estimates from land surface models to produce a statistically optimal analysis.

Given on-going land surface model evolution and increasing availability of high-quality observations, land data assimilation has the potential to support Earth System predictions over a wide range of timescales. However, there are still only a limited number of examples of land data assimilation clearly improving forecast skills. This indicates that the community still needs to make considerable progress with coupled and off-line systems.

This session will highlight recent advances in land DA systems including: (i) the use of novel observations; (ii) data assimilation to target novel physical processes (iii) algorithm development; (iv) methodological developments in bias correction and forward operators; (v) incorporation into coupled land-atmosphere systems; and (vi) the use of data assimilation to evaluate and/or improve physical models. In addition, results from the application of LDASs and their impacts on numerical weather prediction, water resources, food security, and ecosystem management, are also strongly encouraged, as well as work involved in transitioning data assimilation from research into operations.

Submitters: Clara R. Draper, NOAA ESRL, Boulder, CO; Sujay V. Kumar, Code 617 (HSL), GSFC, Greenbelt, MD; Andrew Fox, NASA GSFC, Baltimore, MD and Wanshu Nie, Earth and Planetary Sciences, The Johns Hopkins Univ., Hanover, MD

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