TR32-Database: Database of Transregio 32

[700] - Joint Assimilation of Surface Temperature and L-band Microwave Brightness Temperature in Land Data Assimilation

All available metadata of the dataset are listed below. Some features are available, e.g. download of dataset or additional description file.

Features
Citation
Han, X., Hendricks-Franssen, H., Li, X., Zhang, Y., Montzka, C., Vereecken, H., 2013. Joint Assimilation of Surface Temperature and L-band Microwave Brightness Temperature in Land Data Assimilation. Vadose Zone Journal, 12 (3), 1 - 16. DOI: 10.2136/vzj2012.0072.
Identification
Title(s):Main Title: Joint Assimilation of Surface Temperature and L-band Microwave Brightness Temperature in Land Data Assimilation
Description(s):Abstract: Soil moisture and soil temperature are tightly coupled variables in land surface models. The objective of this study was to evaluate the impact of the joint assimilation of soil moisture and land surface temperature data in a land surface model on soil moisture and soil temperature characterization. Three synthetic tests evaluated the joint assimilation of surface temperature (measured by MODIS) and brightness temperature (from L-band) into the Community Land Model using the local ensemble transform Kalman filter (LETKF). The following three tests were performed for dry and wet conditions: (i) assimilating surface temperature observations only; (ii) assimilating brightness temperature observations only; and (iii) assimilating both surface temperature and brightness temperature observations. The results show that the joint assimilation of surface temperature and brightness temperature results in the best characterization of soil moisture and soil temperature profiles under dry conditions. The assimilation of surface temperature contributed to an improved characterization of soil moisture profiles under dry conditions. For the dry period, brightness temperature assimilation resulted in improved prediction of sensible and latent heat fluxes, whereas surface temperature assimilation improved only the prediction of latent heat flux. Under wet conditions, the joint assimilation scheme cannot outperform the single brightness temperature assimilation. Neither the estimation of soil moisture and soil temperature profiles nor the estimates of the turbulent fluxes were improved by joint assimilation (compared with assimilation of brightness temperature only) under wet conditions.
Identifier(s):DOI: 10.2136/vzj2012.0072
Responsible Party
Creator(s):Author: Xujun Han
Author: Harrie-Jan Hendricks-Franssen
Author: Xin Li
Author: Yanlin Zhang
Author: Carsten Montzka
Author: Harry Vereecken
Publisher:Soil Science Society of America
Topic
TR32 Topic:Remote Sensing
Subject(s):CRC/TR32 Keywords: LAI, Data Assimilation, Ensemble Kalman Filter, MODIS
File Details
File Name:2013_Han_VZJ.pdf
Data Type:Text
Size(s):16 Pages
File Size:6918 kB (6.756 MB)
Date(s):Issued: 2013-01-28
Mime Type:application/pdf
Data Format:PDF
Language:English
Status:Completed
Constraints
Download Permission:OnlyTR32
General Access and Use Conditions:For internal use only
Access Limitations:For internal use only
Licence:TR32DB Data policy agreement
Geographic
North:51.5915435
East:7.4236328
South:50.2058789
West:5.2263672
Measurement Region:RurCatchment
Measurement Location:--RurCatchment--
Specific Informations - Publication
Status:Published
Review:PeerReview
Year:2013
Type:Article
Article Type:Journal
Source:Vadose Zone Journal
Issue:3
Volume:12
Number Of Pages:16
Page Range:1 - 16
Metadata Details
Metadata Creator:Harrie-Jan Hendricks-Franssen
Metadata Created:2013-12-03
Metadata Last Updated:2013-12-03
Subproject:C6
Funding Phase:2
Metadata Language:English
Metadata Version:V40
Dataset Metrics
Page Visits:141
Metadata Downloads:0
Dataset Downloads:4
Dataset Activity
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