SMOS brightness temperature assimilation into the Community Land Model
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 Ka...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2017-11-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | https://www.hydrol-earth-syst-sci.net/21/5929/2017/hess-21-5929-2017.pdf |
| _version_ | 1811253789098770432 |
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| author | D. Rains X. Han H. Lievens H. Lievens C. Montzka N. E. C. Verhoest |
| author_facet | D. Rains X. Han H. Lievens H. Lievens C. Montzka N. E. C. Verhoest |
| author_sort | D. Rains |
| collection | DOAJ |
| description | 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 <i>R</i> 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. |
| first_indexed | 2024-04-12T16:56:33Z |
| format | Article |
| id | doaj.art-476410d770284341a7fff3af8cd49d6d |
| institution | Directory Open Access Journal |
| issn | 1027-5606 1607-7938 |
| language | English |
| last_indexed | 2024-04-12T16:56:33Z |
| publishDate | 2017-11-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Hydrology and Earth System Sciences |
| spelling | doaj.art-476410d770284341a7fff3af8cd49d6d2022-12-22T03:24:11ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382017-11-01215929595110.5194/hess-21-5929-2017SMOS brightness temperature assimilation into the Community Land ModelD. Rains0X. Han1H. Lievens2H. Lievens3C. Montzka4N. E. C. Verhoest5Laboratory of Hydrology and Water Management, Ghent University, Ghent, BelgiumForschungszentrum Jülich GmbH, Institute of Bio- and Geosciences: Agrosphere (IBG-3), Jülich, GermanyLaboratory of Hydrology and Water Management, Ghent University, Ghent, BelgiumGlobal Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD, USAForschungszentrum Jülich GmbH, Institute of Bio- and Geosciences: Agrosphere (IBG-3), Jülich, GermanyLaboratory of Hydrology and Water Management, Ghent University, Ghent, BelgiumSMOS (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 <i>R</i> 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.https://www.hydrol-earth-syst-sci.net/21/5929/2017/hess-21-5929-2017.pdf |
| spellingShingle | D. Rains X. Han H. Lievens H. Lievens C. Montzka N. E. C. Verhoest SMOS brightness temperature assimilation into the Community Land Model Hydrology and Earth System Sciences |
| title | SMOS brightness temperature assimilation into the Community Land Model |
| title_full | SMOS brightness temperature assimilation into the Community Land Model |
| title_fullStr | SMOS brightness temperature assimilation into the Community Land Model |
| title_full_unstemmed | SMOS brightness temperature assimilation into the Community Land Model |
| title_short | SMOS brightness temperature assimilation into the Community Land Model |
| title_sort | smos brightness temperature assimilation into the community land model |
| url | https://www.hydrol-earth-syst-sci.net/21/5929/2017/hess-21-5929-2017.pdf |
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