Effect of One-Dimensional Field Data Assimilation on Land Surface Model Flux Estimates with Implications for Improved Numerical Weather Prediction

The forecast quality from Numerical Weather Prediction (NWP) models and climate models depends on accurate initialisation. Therefore variables such as latent (LE) and sensible (H) heat flux from the land surface, which provide the lower boundary condition for NWP, need to be as accurate as possible at the beginning of a forecast period. Land Surface Models (LSMs) such as the CSIRO Biosphere Model (CBM) represent the exchange of energy and water between the earth’s surface and lower atmosphere and are used to calculate LE and H. Soil moisture and temperature states of these models help partition incoming energy to the earth’s surface between LE and H. Producing accurate predictions of LE and H is hindered by inaccuracies in LSMs such as uncertain initial model state conditions, errors in model forcing data, errors in model physics and a lack of data for accurately parameterising models.