Terrestrial Data Assimilation

Abstract

Accurate assessment of the spatial and temporal Variation of terrestrial system storages (energy and mass) is essential for addressing a wide variety of highly socially relevant science, education, application, and management issues. Improved land-surface State estimates find direct application in agriculture, forest ecology, civil engineering, water resources management, crop System modelling, rainfall-runoff prediction, atmospheric process studies, and climate and ecosystem prediction. Data assimilation is a method by which observations and modelling are combined to create a continuous dataset in Space and time, devoid of gaps. Pioneered for the atmosphere in operational Numerical Weather Prediction (NWP) centres (the National Centers for Environmental Prediction, Kalnay et al. 1996; the European Centre for Medium-Range Weather Forecasts, Gibson et al. 1994; the NASA Data Assimilation Office, Schubert et al. 1993), the method is nowbeing applied at the land surface. Spatially and temporally variable rainfall and available energy, combined with land-surface heterogeneity, cause complex variations in all processes related to surface hydrology between the scales of conventional measurement networks. The characterisation of the spatial and temporal variability of water and energy cycles are critical to improve our understanding of land-surface/atmosphere interaction and the impact of land-surface processes on climate extremes. Because the accurate knowledge of these processes and their variability is important for weather and climate predictions, most NWP centres have incorporated land-surface schemes in their models. However, errors in the NWP forcing accumulate in the surface water and energy Stores, leading to incorrect surface water and energy partitioning and related processes. This has motivated the NWP centres to impose ad hoc corrections to the land-surface states to prevent this drift.