Remotely sensed forcing data and the Global Land Data Assimilation System

Houser, Paul R.; Rodell, Matthew; Gottschalck, Jon; Meng, C. J.; Cosgrove, B.; Entin, Jared

doi:10.1109/igarss.2002.1026131

Cited by 1 publication

(1 citation statement)

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“…Despite all these detailed studies, however, none have directly evaluated both GLDAS and NLDAS using observations over CONUS. One study that did compare early versions of both systems (Jambor et al, 2002) demonstrated the benefit of satellitebased precipitation used in conjunction with model precipitation in GLDAS while using the gauge-based precipitation in NLDAS as the evaluation dataset. Overall, GLDAS's advancements in land data assimilation and GRACE-based ET estimation significantly advanced the ability of LSMs to estimate ET, subject to observational constraints.…”

Section: Global Land Data Assimilation Systemmentioning

confidence: 99%

Estimating evapotranspiration with land data assimilation systems

Peters‐Lidard

Kumar²,

Mocko³

et al. 2011

Hydrological Processes

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Advancements in both land surface models (LSM) and land surface data assimilation, especially over the last decade, have substantially advanced the ability of land data assimilation systems (LDAS) to estimate evapotranspiration (ET). This article provides a historical perspective on international LSM intercomparison efforts and the development of LDAS systems, both of which have improved LSM ET skill. In addition, an assessment of ET estimates for current LDAS systems is provided along with current research that demonstrates improvement in LSM ET estimates due to assimilating satellite-based soil moisture products. Using the Ensemble Kalman Filter in the Land Information System, we assimilate both NASA and Land Parameter Retrieval Model (LPRM) soil moisture products into the Noah LSM Version 3.2 with the North American LDAS phase 2 (NLDAS-2) forcing to mimic the NLDAS-2 configuration. Through comparisons with two global reference ET products, one based on interpolated flux tower data and one from a new satellite ET algorithm, over the NLDAS2 domain, we demonstrate improvement in ET estimates only when assimilating the LPRM soil moisture product.

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Section: Global Land Data Assimilation Systemmentioning

confidence: 99%