Assimilation of Remote Sensing Data into Land-surface Models: the Importance of Uncertainty

Darren Ghent, Heiko Balzter and Jorg Kaduk

Department of Geography, University of Leicester, Leicester, LEI 7RH, UK.

{djg20, hb91,jk61}@le.ac.uk

Abstract: Land-surface models calculate the surface to atmosphere fluxes of heat, water and carbon; and are crucial elements of General Circulation Models (GCMs). Much variation however, exists in their parameterization and representation of physical processes, leading to uncertainty in how climate change influences the land surface on a regional or global scale. A key variable in the calculation of the surface energy budget is land-surface temperature (LST), which influences the partitioning of downward radiant energy into ground, sensible and latent heat fluxes. Furthermore, LST can be applied in the prediction of live fuel moisture content, a critical variable determining fire ignition and propagation; and is crucial to soil moisture - climate feedbacks. Reductions in the uncertainty in model predicted soil moisture and surface energy fluxes are achievable by constraining LST simulations with remote sensing data through the process of assimilation. An often used data assimilation mechanism is the Ensemble Kalman Filter (EnKF), which is a variant of the Kalman Filter sequential assimilation method, taking a Monte Carlo approach. Of key importance to the performance of the filter are the determination of both the uncertainty in the observation source and the size of the ensemble. Results presented here indicate significantly different assimilated LST can emerge as a consequence of changes made to either of these two factors.

Keywords: data assimilation; Ensemble Kalman Filter; land- surface modelling

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