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| Titel |
Comparing the ensemble and extended Kalman filters for in situ soil moisture assimilation with contrasting conditions |
| VerfasserIn |
D. Fairbairn, A. L. Barbu, J.-F. Mahfouf, J.-C. Calvet, E. Gelati |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 19, no. 12 ; Nr. 19, no. 12 (2015-12-16), S.4811-4830 |
| Datensatznummer |
250120865
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| Publikation (Nr.) |
 copernicus.org/hess-19-4811-2015.pdf |
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| Zusammenfassung |
Two data assimilation (DA) methods are compared for their ability to
produce an accurate soil moisture analysis using the
Météo-France land surface model: (i) SEKF, a simplified
extended Kalman filter, which uses a climatological background-error
covariance, and (ii) EnSRF, the ensemble square root filter, which uses an
ensemble background-error covariance and approximates random rainfall
errors stochastically. In situ soil moisture observations at
5 cm depth are assimilated into the surface layer and 30 cm deep observations
are used to evaluate the root-zone analysis on 12 sites in south-western France
(SMOSMANIA network). These sites differ in terms of climate and soil texture.
The two methods perform similarly and improve on the open loop.
Both methods suffer from incorrect
linear assumptions which are particularly degrading to the analysis
during water-stressed conditions: the EnSRF by a dry bias and the SEKF
by an over-sensitivity of the model Jacobian between the surface and
the root-zone layers. These problems are less severe for the
sites with wetter climates. A simple bias correction
technique is tested on the EnSRF. Although this reduces the bias,
it modifies the soil moisture fluxes and
suppresses the ensemble spread, which degrades the analysis
performance. However, the EnSRF flow-dependent background-error
covariance evidently captures seasonal variability in the soil
moisture errors and should exploit planned improvements in the model
physics.
Synthetic twin experiments demonstrate that when there is
only a random component in the precipitation forcing errors, the
correct stochastic representation of these errors enables the EnSRF to
perform better than the SEKF. It might therefore be possible for the EnSRF to perform better than the
SEKF with real data, if the rainfall uncertainty was accurately captured. However, the simple rainfall error model
is not advantageous in our real experiments. More realistic rainfall error models are suggested. |
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