Multiple spaceborne water cycle observations would aid modeling

Abstract

A land data assimilation system (LDAS) is designed to provide continental‐scale estimates of surface moisture and temperature states—and water and energy flux exchanges with the atmosphere—by integrating micrometeorological forcing data (such as surface precipitation, incoming radiation, wind, air temperature, and humidity) with a land surface model. When measurements of land system states are available, data assimilation approaches can be used to optimally update model states based on the assumed magnitude of modeling and observational errors [e.g., Reichle and Koster, 2005].LDAS estimates have value for water, energy, and biogeochemical studies as well as for the initialization of land surface states, such as soil moisture and temperature, in numerical weather prediction or seasonal climate forecasts. To improve forecast performance, collaborative LDAS projects are currently under way for the North American (NLDAS; http://ldas.gsfc.nasa.gov), European (ELDAS; http://www.knmi.nl/samenw/eldas), and global (GLDAS; http://ldas.gsfc.nasa.gov) domains. Preliminary results demonstrate that the incorporation of LDAS approaches into subseasonal forecast initialization can lead to statistically significant improvements in precipitation predictability [Koster et al., 2004].