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SMOS brightness temperature assimilation into the Community Land Model

Dominik Rains UGent, Xujun Han, Hans Lievens UGent, Carsten Montzka and Niko Verhoest UGent (2017) HYDROLOGY AND EARTH SYSTEM SCIENCES. 21(11). p.5929-5951
abstract
SMOS (Soil Moisture and Ocean Salinity mission) brightness temperatures at a single incident angle are assimilated into the Community Land Model (CLM) across Australia to improve soil moisture simulations. Therefore, the data assimilation system DasPy is coupled to the local ensemble transform Kalman filter (LETKF) as well as to the Community Microwave Emission Model (CMEM). Brightness temperature climatologies are precomputed to enable the assimilation of brightness temperature anomalies, making use of 6 years of SMOS data (2010-2015). Mean correlation R with in situ measurements increases moderately from 0.61 to 0.68 (11 %) for upper soil layers if the root zone is included in the updates. A reduced improvement of 5% is achieved if the assimilation is restricted to the upper soil layers. Root-zone simulations improve by 7% when updating both the top layers and root zone, and by 4% when only updating the top layers. Mean increments and increment standard deviations are compared for the experiments. The long-term assimilation impact is analysed by looking at a set of quantiles computed for soil moisture at each grid cell. Within hydrological monitoring systems, extreme dry or wet conditions are often defined via their relative occurrence, adding great importance to assimilation-induced quantile changes. Although still being limited now, longer L-band radiometer time series will become available and make model output improved by assimilating such data that are more usable for extreme event statistics.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
remote sensing, soil moisture, land surface modelling, SOIL-MOISTURE RETRIEVALS, BAND MICROWAVE EMISSION, DATA-SETS, SOUTHEAST AUSTRALIA, HYDROLOGICAL MODEL, SURFACE PARAMETERS, WATER-RESOURCES, KALMAN FILTER, SMAP MISSION, CALIBRATION
journal title
HYDROLOGY AND EARTH SYSTEM SCIENCES
Hydrol. Earth Syst. Sci.
volume
21
issue
11
pages
5929 - 5951
Web of Science type
Article
Web of Science id
000416334700002
ISSN
1027-5606
1607-7938
DOI
10.5194/hess-21-5929-2017
language
English
UGent publication?
yes
classification
A1
copyright statement
I have retained and own the full copyright for this publication
id
8542738
handle
http://hdl.handle.net/1854/LU-8542738
date created
2017-12-20 14:21:50
date last changed
2018-01-17 15:41:57
@article{8542738,
  abstract     = {SMOS (Soil Moisture and Ocean Salinity mission) brightness temperatures at a single incident angle are assimilated into the Community Land Model (CLM) across Australia to improve soil moisture simulations. Therefore, the data assimilation system DasPy is coupled to the local ensemble transform Kalman filter (LETKF) as well as to the Community Microwave Emission Model (CMEM). Brightness temperature climatologies are precomputed to enable the assimilation of brightness temperature anomalies, making use of 6 years of SMOS data (2010-2015). Mean correlation R with in situ measurements increases moderately from 0.61 to 0.68 (11 \%) for upper soil layers if the root zone is included in the updates. A reduced improvement of 5\% is achieved if the assimilation is restricted to the upper soil layers. Root-zone simulations improve by 7\% when updating both the top layers and root zone, and by 4\% when only updating the top layers. Mean increments and increment standard deviations are compared for the experiments. The long-term assimilation impact is analysed by looking at a set of quantiles computed for soil moisture at each grid cell. Within hydrological monitoring systems, extreme dry or wet conditions are often defined via their relative occurrence, adding great importance to assimilation-induced quantile changes. Although still being limited now, longer L-band radiometer time series will become available and make model output improved by assimilating such data that are more usable for extreme event statistics.},
  author       = {Rains, Dominik and Han, Xujun and Lievens, Hans and Montzka, Carsten and Verhoest, Niko},
  issn         = {1027-5606},
  journal      = {HYDROLOGY AND EARTH SYSTEM SCIENCES},
  keyword      = {remote sensing,soil moisture,land surface modelling,SOIL-MOISTURE RETRIEVALS,BAND MICROWAVE EMISSION,DATA-SETS,SOUTHEAST AUSTRALIA,HYDROLOGICAL MODEL,SURFACE PARAMETERS,WATER-RESOURCES,KALMAN FILTER,SMAP MISSION,CALIBRATION},
  language     = {eng},
  number       = {11},
  pages        = {5929--5951},
  title        = {SMOS brightness temperature assimilation into the Community Land Model},
  url          = {http://dx.doi.org/10.5194/hess-21-5929-2017},
  volume       = {21},
  year         = {2017},
}

Chicago
Rains, Dominik, Xujun Han, Hans Lievens, Carsten Montzka, and Niko Verhoest. 2017. “SMOS Brightness Temperature Assimilation into the Community Land Model.” Hydrology and Earth System Sciences 21 (11): 5929–5951.
APA
Rains, D., Han, X., Lievens, H., Montzka, C., & Verhoest, N. (2017). SMOS brightness temperature assimilation into the Community Land Model. HYDROLOGY AND EARTH SYSTEM SCIENCES, 21(11), 5929–5951.
Vancouver
1.
Rains D, Han X, Lievens H, Montzka C, Verhoest N. SMOS brightness temperature assimilation into the Community Land Model. HYDROLOGY AND EARTH SYSTEM SCIENCES. 2017;21(11):5929–51.
MLA
Rains, Dominik, Xujun Han, Hans Lievens, et al. “SMOS Brightness Temperature Assimilation into the Community Land Model.” HYDROLOGY AND EARTH SYSTEM SCIENCES 21.11 (2017): 5929–5951. Print.