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Recent Updates in the SMAP Level-4 Soil Moisture AlgorithmThe NASA Soil Moisture Active Passive (SMAP) mission generates, among other data sets, the Level-4 Soil Moisture (L4_SM) product. The L4_SM data are published with a mean latency of ~2.5 days from the time of observation and provide global, three-hourly, 9-km resolution estimates of surface and root-zone soil moisture and related land surface states and fluxes. The L4_SM algorithm is based on the assimilation of SMAP radiometer brightness temperature observations into the NASA Catchment land surface model using a spatially distributed ensemble Kalman filter (EnKF). In 2018, the L4_SM algorithm was upgraded from Version 3 to Version 4. Underlying the new version is a revised modeling system that includes improved input parameter datasets for land cover, topography, and vegetation height that are based on recent, high-quality, space-borne remote sensing observations. Additionally, SMAP Level-2 soil moisture retrievals and in situ soil moisture measurements were used to calibrate a particular Catchment model parameter that governs the recharge of surface soil moisture from below under non-equilibrium conditions, which brings the model's surface soil moisture more in line with the SMAP Level-2 and in situ soil moisture. Moreover, the calibration of the assimilated SMAP brightness temperatures changed substantially from Version 3 to Version 4, and the "catchment deficit" model variable was removed from the EnKF state vector to avoid degrading the model's groundwater estimates.Considerable effort went into the version upgrade, creating an expectation that the new version is improved over the old version. Indeed, some aspects of the new version are clearly better. However, other aspects are not. In this presentation we summarize the skill of the new and old versions vs. independent in situ measurements and in terms of data assimilation diagnostics, including, for example, the statistics of the (soil moisture) analysis increments and the observation-minus-forecast (brightness temperatures) residuals. We share our experience with trying to improve to the L4_SM product and the lessons learned from the effort.
Document ID
20180007642
Acquisition Source
Goddard Space Flight Center
Document Type
Presentation
Authors
Reichle, Rolf H.
(NASA Goddard Space Flight Center Greenbelt, MD, United States)
Liu, Qing
(Science Systems and Applications, Inc. Lanham, MD, United States)
Koster, Randal D.
(NASA Goddard Space Flight Center Greenbelt, MD, United States)
Ardizzone, Joseph V.
(Science Systems and Applications, Inc. Lanham, MD, United States)
Crow, Wade T.
(Agricultural Research Service Hydrology and Remote Sensing Lab. Beltsville, MD, United States)
De Lannoy, Gabrielle
(Katholieke Univ. te Leuven Belgium)
Kimball, John S.
(Montana Univ. Missoula, MT, United States)
Date Acquired
November 13, 2018
Publication Date
October 22, 2018
Subject Category
Earth Resources And Remote Sensing
Report/Patent Number
GSFC-E-DAA-TN62700
Funding Number(s)
CONTRACT_GRANT: NNG17HP01C
CONTRACT_GRANT: SPEC5732
Distribution Limits
Public
Copyright
Use by or on behalf of the US Gov. Permitted.
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