Land Data Assimilation: Making the Transition from States to Fluxes (Invited Presentation) (Centennial)

Over the past two decades, land data assimilation (LDA) has progressed from a conceptual stage to full operational implementation within multiple global systems. This talk will briefly discuss this progress and highlight the remaining challenges facing LDA systems. Specifically, while great progress has been made in demonstrating the value of these systems for quantifying surface water and energy states (e.g. soil moisture and soil temperature), prospects for improving associated water and energy fluxes (e.g. evapotranspiration and runoff) are less bright. This, in turn, has hampered the application of LDA for numerical weather prediction (NWP) and hydrologic forecasting applications (that are chiefly reliant on the accuracy of flux estimates). Using examples taken from both hydrologic forecasting and NWP, the talk will clarify the impact of model bias in the strength of soil moisture versus surface flux coupling on the accuracy of LDA flux estimates. For hydrologic forecasting, results suggest a generally tendency for land surface models to under predict the true level of coupling between pre-storm soil moisture and runoff – causing improvements in pre-storm soil moisture accuracy (acquired, for example, via LDA) to be squandered. Conversely, it is found that land models tend to over predict soil moisture versus evapotranspiration coupling – which produces patterns of error that are consistent with negative air temperature forecasting results obtained during the assimilation of satellite soil moisture products into NWP systems. New remote sensing and data analysis techniques for estimating, and correcting, these coupling biases will be presented, and the potential role of these approaches in the long-term evolution of LDA’s will be assessed.