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

This page lists all metadata that was entered for this dataset. Only registered users of the TR32DB may download this file.

Feature
Request downloadRequest download
Full Name:
Affiliation:
eMail:
Purpose of use:
 
I accept the Data protection statement.
Bot check:
Type all characters with this
color
.
 
It is case sensitive.
 
 
↻
 
Submit
Citation
Citation Options
Identification
Title:Main Title: Joint Assimilation of Surface Temperature and L-band Microwave Brightness Temperature in Land Data Assimilation
Description: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:10.2136/vzj2012.0072 (DOI)
Responsible Party
Creators:Xujun Han (Author), Harrie-Jan Hendricks-Franssen (Author), Xin Li (Author), Yanlin Zhang (Author), Carsten Montzka (Author), Harry Vereecken (Author)
Publisher:Soil Science Society of America
Publication Year:2013
Topic
TR32 Topic:Remote Sensing
Related Subproject:C6
Subjects:Keywords: LAI, Data Assimilation, Ensemble Kalman Filter, MODIS
File Details
Filename:2013_Han_VZJ.pdf
Data Type:Text - Article
Size:16 Pages
File Size:6.8 MB
Date:Issued: 28.01.2013
Mime Type:application/pdf
Data Format:PDF
Language:English
Status:Completed
Constraints
Download Permission:Only Project Members
General Access and Use Conditions:For internal use only
Access Limitations:For internal use only
Licence:[TR32DB] Data policy agreement
Geographic
Specific Information - Publication
Publication Status:Published
Review Status:Peer reviewed
Publication Type:Article
Article Type:Journal
Source:Vadose Zone Journal
Source Website:www.soils.org
Issue:3
Volume:12
Number of Pages:16 (1 - 16)
Metadata Details
Metadata Creator:Harrie-Jan Hendricks-Franssen
Metadata Created:03.12.2013
Metadata Last Updated:03.12.2013
Subproject:C6
Funding Phase:2
Metadata Language:English
Metadata Version:V50
Metadata Export
Metadata Schema:
Dataset Statistics
Page Visits:988
Metadata Downloads:0
Dataset Downloads:4
Dataset Activity
Feature
A download is not possibleDownload
top ∧