One-Dimensional Data Assimilation Experiment Using 3D Eddy Covariance Heat Flux Observations to Improve Land Surface Modelling

Robert Pipunic, Jeffrey P. Walker, Cathy Trudinger and Andrew Western

Power Point Presentation

Land surface models such as the CSIRO Biosphere Model are often coupled with weather and climate 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, with research focussing on the assimilation of soil moisture measurements. However, variables such as soil moisture typically share a weak or uncertain relationship with the latent and sensible heat fluxes in land surface models. This is often exacerbated by a lack of soil and vegetation property data required to accurately parameterise the models. The assimilation of latent and sensible heat flux observations to improve land surface model predictions of latent and sensible heat fluxes and associated soil moisture and temperature states has received very little attention in the scientific community thus far. In this study, data assimilation was performed using 3D eddy correlation measurements of latent and sensible heat flux, together with meteorological forcing data from south eastern Australia. An Ensemble Kalman Filter assimilation algorithm was applied and results validated against 3D eddy flux data and measured soil moisture and temperature profiles to determine the impact on model estimates of fluxes and states and whether assimilating latent and sensible heat fluxes is an approvement over soil moisture assimilation.