Paper Info
Title
Impact Of Model Coupling Bias On Water Flux Estimates Acquired From A Land Data Assimilation System
Abstract
Land data assimilation (DA) systems have a well-documented track record of utilizing satellite observations to enhance the estimation of land surface states (e.g., soil moisture and soil temperature). However, evidence that land DA can also improve water and energy flux estimates (e.g., evapotranspiration and runoff) is much less compelling. As a result, the role of land DA in flux-based applications like numerical weather prediction and hydrologic forecasting remains somewhat uncertain. Here we hypothesize that the source of this problem is bias in the representation of state/flux coupling strength (e.g., soil moisture/evapotranspiration and soil moisture/runoff) within existing land surface models. These coupling biases effectively prevent improvements in land surface states (realized during land DA) from propagating into enhanced flux estimates. Prospects for utilizing satellite remote sensing to eliminate these biases and enhance the positive impact of land DA are discussed.
Year
DOI
Venue
2020
10.1109/IGARSS39084.2020.9324701
IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM
Keywords
DocType
Citations 
data assimilation, soil moisture, evapotranspiration, microwave remote sensing, thermal remote sensing
Conference
0
PageRank 
References 
Authors
0.34
0
1
Name
Order
Citations
PageRank
Wade T. Crow100.68