Connecting Satellite Observations with Water Cycle Variables Through Land Data Assimilation: Examples Using the NASA GEOS-5 LDAS - The Earth's Hydrological Cycle - page 89

Connecting Satellite Observations with Water Cycle
Variables Through Land Data Assimilation: Examples
Using the NASA GEOS-5 LDAS

Gabri ¨ lle J. M. De Lannoy Rolf H. Reichle Barton A. Forman

• • •

Clara S. Draper • Qing Liu

Received: 7 September 2012 / Accepted: 22 January 2013 / Published online: 15 February 2013
Springer Science+Business Media Dordrecht (outside the USA) 2013

Abstract A land data assimilation system (LDAS) can merge satellite observations (or
retrievals) of land surface hydrological conditions, including soil moisture, snow, and
terrestrial water storage (TWS), into a numerical model of land surface processes. In
theory, the output from such a system is superior to estimates based on the observations or
the model alone, thereby enhancing our ability to understand, monitor, and predict key
elements of the terrestrial water cycle. In practice, however, satellite observations do not
correspond directly to the water cycle variables of interest. The present paper addresses
various aspects of this seeming mismatch using examples drawn from recent research with
the ensemble-based NASA GEOS-5 LDAS. These aspects include (1) the assimilation of
coarse-scale observations into higher-resolution land surface models, (2) the partitioning of
satellite observations (such as TWS retrievals) into their constituent water cycle compo-
nents, (3) the forward modeling of microwave brightness temperatures over land for
radiance-based soil moisture and snow assimilation, and (4) the selection of the most
relevant types of observations for the analysis of a specific water cycle variable that is not
observed (such as root zone soil moisture). The solution to these challenges involves the
careful construction of an observation operator that maps from the land surface model
variables of interest to the space of the assimilated observations.

Keywords Land data assimilation Land surface modeling Satellite remote sensing
Soil moisture Snow Terrestrial water storage Ensemble Kalman filter