NDLI: Comparison of different model error treatments and assimilation schemes in land surface temperature assimilation

Content Provider	IEEE Xplore Digital Library
Author	Yu Shanshan Xin Xiaozhou Liu Qinhuo
Copyright Year	2012
Description	Author affiliation: State Key Laboratory of Remote Sensing Science, the Institute of Remote Sensing of Application, CAS, Beijing, 100101, China (Yu Shanshan; Xin Xiaozhou; Liu Qinhuo)
Abstract	In this study, land surface temperature (LST) is assimilated by using Common Land Model (CoLM) and Ensemble Kalman Filter (EnKF). To found the most reasonable method on model error treatment and remote sensed land surface temperature assimilation, some methods on the model ensemble generation and the construction of observe operators are compared. Though experiments show the two methods have similar result, forcing and parameter perturbation can generate ensemble more reasonable compared with perturbing only state variables, and is more easily to achieve in realistic assimilation. In observe operator comparison, by the component temperature decomposition method, the land surface temperature of remote sensing can update ground surface temperature in the CoLM model directly, which has more obvious physical meaning than other observe operator. A synthetic experiment also shows this method have the best result in the comparison.
Starting Page	994
Ending Page	997
File Size	391862
Page Count	4
File Format	PDF
ISBN	9781467311601
ISSN	21536996
e-ISBN	9781467311595
e-ISBN	9781467311588
DOI	10.1109/IGARSS.2012.6351234
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-22
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Land surface temperature Land surface Temperature sensors Remote sensing Data models Temperature distribution Predictive models Latent and sensible heat fluxes Common Land Model Data assimilation Ensemble Kalman Filter land surface temperature
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A dual-pass data assimilation scheme for estimating surface fluxes with FY3A-VIRR land surface temperature

Estimation of Regional Soil Moisture by Assimilating Multi-Sensor Passive Microwave Remote Sensing Observations based on Ensemble Kalman Filter

Estimation of sensible and latent heat flux by assimilating MODIS LST products

Can Data Assimilation Improve Short-Term Prediction of Land Surface Variables?

Comparison Experiments of Different Model Error Schemes in Ensemble Kalman Filter Soil Moisture Assimilation

Using ensemble Kalman filter to assimilate land surface temperature and evapotranspiration

Assimilation of latent and sensible heat flux data into a land surface model (2004)

Remote sensing of surface sensible and latent heat fluxes

Correlation Analysis of Urban Land Surface Temperature and Fluxes Based on Remote Sensing Technology

Comparison of different model error treatments and assimilation schemes in land surface temperature assimilation

Similar Documents

A dual-pass data assimilation scheme for estimating surface fluxes with FY3A-VIRR land surface temperature

Estimation of Regional Soil Moisture by Assimilating Multi-Sensor Passive Microwave Remote Sensing Observations based on Ensemble Kalman Filter

Estimation of sensible and latent heat flux by assimilating MODIS LST products

Can Data Assimilation Improve Short-Term Prediction of Land Surface Variables?

Comparison Experiments of Different Model Error Schemes in Ensemble Kalman Filter Soil Moisture Assimilation

Using ensemble Kalman filter to assimilate land surface temperature and evapotranspiration

Assimilation of latent and sensible heat flux data into a land surface model (2004)

Remote sensing of surface sensible and latent heat fluxes

Correlation Analysis of Urban Land Surface Temperature and Fluxes Based on Remote Sensing Technology

Comparison of different model error treatments and assimilation schemes in land surface temperature assimilation