Heat Flux Data Assimilation for Improved Land Surface Modelling A One-Dimensional Field Data Case Study

Robert Pipunic, Jeffrey Walker, Cathy Trudinger and Andrew Western

Land surface models such as the CSIRO Biosphere Model are often coupled with weather and climate forecast models to provide a continuous feedback of latent and sensible heat flux values as the lower boundary condition for weather and climate forecasting. However, these flux estimates are typically poor due to approximations of complex physical processes and errors in model forcing data and parameters. Hence the technique of data assimilation is commonly applied to improve latent and sensible heat flux prediction, traditionally using soil moisture or screen level (2m above ground) relative humidity and temperature measurements. Yet these variables typically share a weak or indirect relationship with latent and sensible heat fluxes. To overcome this limitation latent and sensible heat flux data are directly assimilated to improve both land surface model predictions of latent and sensible heat fluxes and soil moisture and temperature states. This is demonstrated in a data assimilation study using 3D eddy correlation measurements of latent and sensible heat flux, together with meteorological forcing data from Kyeamba Creek in the Murrumbidgee catchment in NSW for a one year period (2005 to 2006). An Ensemble Kalman Filter assimilation algorithm is applied and results analysed to determine the assimilation impact on model estimates of fluxes and states under various conditions.

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