From near-surface to root-zone soil moisture using different assimilation techniques

Journal of Hydrometeorology

Volumn 8, Issue 2, 2007, Pages 194-206

  Sabater, Joaquín Muñoz ^a   Jarlan, Lionel ^a   Calvet, Jean Christophe ^a   Bouyssel, François ^a   De Rosnay, Patricia ^b  

^a CENTRE NATIONAL DE RECHERCHES MÉTÉOROLOGIQUES   (France)

^b CESBIO   (France)

Author keywords

[No Author keywords available]

Indexed keywords

CLIMATE CONDITIONS; DATA ASSIMILATION; KALMAN FILTER; MICROWAVE RADIOMETER; REMOTE SENSING; RHIZOSPHERE; SOIL MOISTURE;

EID: 34248339239     PISSN: 1525755X     EISSN: None     Source Type: Journal    
DOI: 10.1175/JHM571.1     Document Type: Article

References (37)

1. Anderson, J. L., and S. L. Anderson, 1999: A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts. Mon. Wea. Rev., 127, 2741-2758.
2. Balsamo, G., F. Bouyssel, and J. Noilhan, 2004: A simplified bi-dimensional variational analysis of soil moisture from screen-level observations in a mesoscale numerical weather-prediction model. Quart. J. Roy. Meteor. Soc., 130A, 895-915.
3. Balsamo, G., J.-F. Mahfouf, S. Belair, and G. Deblonde, 2006: A global root-zone soil moisture analysis using simulated L-band brightness temperature in preparation for the Hydros satellite mission. J. Hydrometeor., 7, 1126-1146.
4. Beljaars, A. C. M., P. Viterbo, M. J. Miller, and A. K. Betts, 1996: The anomalous rainfall over the United States during 1993: Sensitivity to land surface parameterization and soil moisture anomalies. Mon. Wea. Rev., 124, 362-383.
5. Bouttier, F., 1994: Sur la prévision de la qualité des prévisions météorologiques (On the assessment of the weather forecast quality). Ph.D dissertation, Université Paul Sabatier III, 240 pp. [Available from Université Paul Sabatier, 118 route de Narbonne, 31062 Toulouse CEDEX, France.]
6. Burgers, G., P. J. van Leeuwen, and G. Evensen, 1998: Analysis scheme in the ensemble Kalman filter. Mon. Wea. Rev., 126, 1719-1724.
7. Calvet, J.-C., and J. Noilhan, 2000: From near-surface to root-zone soil moisture using year-round data. J. Hydrometeor., 1, 393-411.
8. 2 enrichment effects using an interactive vegetation SVAT scheme. Agric. For. Meteor., 108, 129-152.
9. Calvet, J.-C., J. Noilhan, and P. Bessemoulin, 1998a: Retrieving the root-zone soil moisture from surface soil moisture or temperature estimates: A feasibility study based on field measurements. J. Appl. Meteor., 37, 371-386.
10. Calvet, J.-C., J. Noilhan, J.-L. Roujean, P. Bessemoulin, M. Cabelguenne, A. Olioso, and J.-P. Wigneron, 1998b: An interactive vegetation SVAT model tested against data from six contrasting sites. Agric. For. Meteor., 92, 73-95.
11. Calvet, J.-C., and Coauthors, 1999: MUREX: A land-surface field experiment to study the annual cycle of the energy and water budgets. Ann. Geophys., 17, 838-854.
12. Deardorff, J. W., 1977: A parameterization of ground-surface moisture content for use in atmospheric prediction models. J. Appl. Meteor., 16, 1182-1185.
13. Deardorff, J. W., 1978: Efficient prediction of ground temperature and moisture with inclusion of a layer of vegetation. J. Geophys. Res., 83, 1889-1903.
14. De Rosnay, P., and Coauthors, 2006: SMOSREX: A long term field campaign experiment for soil moisture and land surface processes remote sensing. Remote Sens. Environ., 102, 377-389.
15. Dirmeyer, P. A., 2000: Using a global soil wetness dataset to improve seasonal climate simulation. J. Climate, 13, 2900-2922.
16. Douville, H., P. Viterbo, J.-F. Mahfouf, and A. C. M. Beljaars, 2000: Evaluation of the optimal interpolation and nudging techniques for soil moisture analysis using FIFE data. Mon. Wea. Rev., 128, 1733-1756.
17. Eagleman, J. R., and W. C. Lin, 1976: Remote sensing of soil moisture by a 21 cm passive radiometer. J. Geophys. Res., 81, 3660-3666.
18. Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99, 10 143-10 162.
19. Evensen, G. 2003: The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean Dyn., 53, 343-367.
20. François, C., A. Quesney, and C. Ottlé, 2003: Sequential assimilation of ERS-1 SAR data into a coupled land surface-hydrological model using an extended Kalman filter. J. Hydrometeor., 4, 473-487.
21. Giard, D., and E. Bazile, 2000: Implementation of a new assimilation scheme for soil and surface variables in a global NWP model. Mon. Wea. Rev., 128, 997-1015.
22. Gibelin, A.-L., J.-C. Calvet, J.-L. Roujean, L. Jarlan, and S. Los, 2006: Ability of the land surface model ISBA-A-gs to simulate leaf area index at the global scale: Comparison with satellites products. J. Geophys. Res., 111, D18102, doi:10.1029/2005JD006691.
23. Houser, P. R., W. J. Shuttleworth, J. S. Famiglietti, H. V. Gupta, K. H. Syed, and D. C. Goodrich, 1998: Integration of soil moisture remote sensing and hydrologic modeling using data assimilation. Water Resour. Res., 34, 3405-3420.
24. Kalman, R. E., 1960: A new approach to linear filtering and prediction problems. Trans. ASME J. Basic Eng., 82, 35-45.
25. Kerr, Y. K., P. Waldteufel, J.-P. Wigneron, J. M. Martinuzzi, J. Font, and M. Berger, 2001: Soil moisture retrieval from space: The Soil Moisture and Ocean Salinity (SMOS) mission. IEEE Trans. Geosci. Remote Sens., 39, 1729-1735.
26. Koster, R. D., and M. J. Suarez, 2003: Impact of land surface initialization on seasonal precipitation and temperature prediction. J. Hydrometeor., 4, 408-423.
27. Miller, R. N., E. F. Carter, and S. T. Blue, 1999: Data assimilation into nonlinear stochastic models. Tellus, 51A, 167-194.
28. Njoku, E. G., T. J. Jackson, V. Lakshmi, T. K. Chan, and S. V. Nghiem, 2003: Soil moisture retrieval from AMSR-E. IEEE Trans. Geosci. Remote Sens., 41, 215-229.
29. Noilhan, J., and S. Planton, 1989: A simple parameterization of land surface processes for meteorological models. Mon. Wea. Rev., 117, 536-549.
30. Noilhan, J., and J.-F. Mahfouf, 1996: The ISBA land surface parameterisation scheme. Global Planet. Change, 13, 145-159.
31. Reichle, R. H., and R. D. Koster, 2003: Assessing the impact of horizontal error correlations in background fields on soil moisture estimation. J. Hydrometeor., 4, 1229-1242.
32. Reichle, R. H., J. P. Walker, R. D. Koster, and P. R. Houser, 2002: Extended versus ensemble Kalman filtering for land data assimilation. J. Hydrometeor],, 3, 728-740.
33. Seuffert, G., H. Wilker, P. Viterbo, J.-F. Mahfouf, M. Drusch, and J.-C. Calvet, 2003: Soil moisture analysis combining screen-level parameters and microwave brightness temperature: A test with field data. Geophys. Res. Lett., 30, 1498, doi:10.1029/2003GLO17128.
34. Shukla, J., and Y. Mintz, 1982: Influence of land-surface evapotranspiration on the Earth's climate. Science, 215, 1498-1501.
35. Teunissen, P. J. G., 2000: Adjustment Theory: An Introduction. Delft University Press, 194 pp.
36. Wagner, W., K. Scipal, C. Pathe, D. Gerten, W. Lucht, and B. Rudolf, 2003: Evaluation of the agreement between the first global remotely sensed soil moisture data with model and precipitation data. J. Geophys. Res., 108, 4611-4626.
37. Wigneron, J.-P., A. Chanzy, J.-C. Calvet, A. Olioso, and Y. Kerr, 2002: Modeling approaches to assimilating L-band passive microwave observations over land surfaces. J. Geophys. Res., 107, 4219, doi:10.1029/2001JD000958.