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Satellite-scale snow water equivalent assimilation into a high-resolution land surface model

Gabriëlle De Lannoy UGent, Rolf H Reichle, Paul R Houser, Kristi R Arsenault, Niko Verhoest UGent and Valentijn Pauwels UGent (2010) JOURNAL OF HYDROMETEOROLOGY. 11(2). p.352-369
abstract
Four methods based on the ensemble Kalman filter (EnKF) are tested to assimilate coarse-scale (25 km) snow water equivalent (SWE) observations (typical of passive microwave satellite retrievals) into finescale (1 km) land model simulations. Synthetic coarse-scale observations are assimilated directly using an observation operator for mapping between the coarse and fine scales or, alternatively, after disaggregation (regridding) to the finescale model resolution prior to data assimilation. In either case, observations are assimilated either simultaneously or independently for each location. Results indicate that assimilating disaggregated finescale observations independently (method 1D-F1) is less efficient than assimilating a collection of neighboring disaggregated observations (method 3D-Fm). Direct assimilation of coarse-scale observations is superior to a priori disaggregation. Independent assimilation of individual coarse-scale observations (method 3D-C1) can bring the overall mean analyzed field close to the truth, but does not necessarily improve estimates of the finescale structure. There is a clear benefit to simultaneously assimilating multiple coarse-scale observations (method 3D-Cm) even as the entire domain is observed, indicating that underlying spatial error correlations can be exploited to improve SWE estimates. Method 3D-Cm avoids artificial transitions at the coarse observation pixel boundaries and can reduce the RMSE by 60% when compared to the open loop in this study.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
PREDICTION, ANALYSIS SCHEME, UNCERTAINTY, INFORMATION-SYSTEM, SOIL-MOISTURE ESTIMATION, ENSEMBLE KALMAN FILTER, IMPLEMENTATION, COVER, MODIS, FRAMEWORK
journal title
JOURNAL OF HYDROMETEOROLOGY
J. Hydrometeorol.
volume
11
issue
2
pages
352 - 369
Web of Science type
Article
Web of Science id
000277601000007
JCR category
METEOROLOGY & ATMOSPHERIC SCIENCES
JCR impact factor
2.185 (2010)
JCR rank
21/68 (2010)
JCR quartile
2 (2010)
ISSN
1525-755X
DOI
10.1175/2009JHM1192.1
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
946474
handle
http://hdl.handle.net/1854/LU-946474
date created
2010-05-17 08:37:43
date last changed
2011-04-27 15:02:22
@article{946474,
  abstract     = {Four methods based on the ensemble Kalman filter (EnKF) are tested to assimilate coarse-scale (25 km) snow water equivalent (SWE) observations (typical of passive microwave satellite retrievals) into finescale (1 km) land model simulations. Synthetic coarse-scale observations are assimilated directly using an observation operator for mapping between the coarse and fine scales or, alternatively, after disaggregation (regridding) to the finescale model resolution prior to data assimilation. In either case, observations are assimilated either simultaneously or independently for each location. Results indicate that assimilating disaggregated finescale observations independently (method 1D-F1) is less efficient than assimilating a collection of neighboring disaggregated observations (method 3D-Fm). Direct assimilation of coarse-scale observations is superior to a priori disaggregation. Independent assimilation of individual coarse-scale observations (method 3D-C1) can bring the overall mean analyzed field close to the truth, but does not necessarily improve estimates of the finescale structure. There is a clear benefit to simultaneously assimilating multiple coarse-scale observations (method 3D-Cm) even as the entire domain is observed, indicating that underlying spatial error correlations can be exploited to improve SWE estimates. Method 3D-Cm avoids artificial transitions at the coarse observation pixel boundaries and can reduce the RMSE by 60\% when compared to the open loop in this study.},
  author       = {De Lannoy, Gabri{\"e}lle and Reichle, Rolf H and Houser, Paul R and Arsenault, Kristi R and Verhoest, Niko and Pauwels, Valentijn},
  issn         = {1525-755X},
  journal      = {JOURNAL OF HYDROMETEOROLOGY},
  keyword      = {PREDICTION,ANALYSIS SCHEME,UNCERTAINTY,INFORMATION-SYSTEM,SOIL-MOISTURE ESTIMATION,ENSEMBLE KALMAN FILTER,IMPLEMENTATION,COVER,MODIS,FRAMEWORK},
  language     = {eng},
  number       = {2},
  pages        = {352--369},
  title        = {Satellite-scale snow water equivalent assimilation into a high-resolution land surface model},
  url          = {http://dx.doi.org/10.1175/2009JHM1192.1},
  volume       = {11},
  year         = {2010},
}

Chicago
De Lannoy, Gabriëlle, Rolf H Reichle, Paul R Houser, Kristi R Arsenault, Niko Verhoest, and Valentijn Pauwels. 2010. “Satellite-scale Snow Water Equivalent Assimilation into a High-resolution Land Surface Model.” Journal of Hydrometeorology 11 (2): 352–369.
APA
De Lannoy, G., Reichle, R. H., Houser, P. R., Arsenault, K. R., Verhoest, N., & Pauwels, V. (2010). Satellite-scale snow water equivalent assimilation into a high-resolution land surface model. JOURNAL OF HYDROMETEOROLOGY, 11(2), 352–369.
Vancouver
1.
De Lannoy G, Reichle RH, Houser PR, Arsenault KR, Verhoest N, Pauwels V. Satellite-scale snow water equivalent assimilation into a high-resolution land surface model. JOURNAL OF HYDROMETEOROLOGY. 2010;11(2):352–69.
MLA
De Lannoy, Gabriëlle, Rolf H Reichle, Paul R Houser, et al. “Satellite-scale Snow Water Equivalent Assimilation into a High-resolution Land Surface Model.” JOURNAL OF HYDROMETEOROLOGY 11.2 (2010): 352–369. Print.