References

HESS

Hydrology and Earth System Sciences

HESS

Hydrol. Earth Syst. Sci.

1607-7938

Copernicus Publications

Göttingen, Germany

10.5194/hess-15-3829-2011

Assimilation of ASCAT near-surface soil moisture into the SIM hydrological model over France

Draper

¹ ³ Mahfouf

J.-F.

¹ Calvet

J.-C.

¹ Martin

¹ Wagner

CNRM/GAME (Météo-France, CNRS), URA 1357, Toulouse, France

Institute of Photogrammetry and Remote Sensing, Vienna University of Technology, Wien, Austria

now at: NASA GSFC/GEST, Greenbelt, USA

21 12 2011

15 12 3829 3841

2011

This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/

This article is available from https://hess.copernicus.org/articles/15/3829/2011/hess-15-3829-2011.html

The full text article is available as a PDF file from https://hess.copernicus.org/articles/15/3829/2011/hess-15-3829-2011.pdf

This study examines whether the assimilation of remotely sensed near-surface soil moisture observations might benefit an operational hydrological model, specifically Météo-France's SAFRAN-ISBA-MODCOU (SIM) model. Soil moisture data derived from ASCAT backscatter observations are assimilated into SIM using a Simplified Extended Kalman Filter (SEKF) over 3.5 years. The benefit of the assimilation is tested by comparison to a delayed cut-off version of SIM, in which the land surface is forced with more accurate atmospheric analyses, due to the availability of additional atmospheric observations after the near-real time data cut-off. However, comparing the near-real time and delayed cut-off SIM models revealed that the main difference between them is a dry bias in the near-real time precipitation forcing, which resulted in a dry bias in the root-zone soil moisture and associated surface moisture flux forecasts. While assimilating the ASCAT data did reduce the root-zone soil moisture dry bias (by nearly 50%), this was more likely due to a bias within the SEKF, than due to the assimilation having accurately responded to the precipitation errors. Several improvements to the assimilation are identified to address this, and a bias-aware strategy is suggested for explicitly correcting the model bias. However, in this experiment the moisture added by the SEKF was quickly lost from the model surface due to the enhanced surface fluxes (particularly drainage) induced by the wetter soil moisture states. Consequently, by the end of each winter, during which frozen conditions prevent the ASCAT data from being assimilated, the model land surface had returned to its original (dry-biased) climate. This highlights that it would be more effective to address the precipitation bias directly, than to correct it by constraining the model soil moisture through data assimilation.

References 1

Albergel, C., Rüdiger, C., Pellarin, T., Calvet, J.-C., Fritz, N., Froissard, F., Suquia, D., Petitpa, A., Piguet, B., and Martin, E.: From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations, Hydrol. Earth Syst. Sci., 12, 1323–1337, <a href="http://dx.doi.org/10.5194/hess-12-1323-2008">https://doi.org/10.5194/hess-12-1323-2008</a>, 2008.

Albergel, C., Rüdiger, C., Carrer, D., Calvet, J.-C., Fritz, N., Naeimi, V., Bartalis, Z., and Hasenauer, S.: An evaluation of ASCAT surface soil moisture products with in-situ observations in Southwestern France, Hydrol. Earth Syst. Sci., 13, 115–124, <a href="http://dx.doi.org/10.5194/hess-13-115-2009">https://doi.org/10.5194/hess-13-115-2009</a>, 2009.

Balsamo, G., Bouyssel, F., and Noilhan, J.: A simplified bi-dimensional variational analysis of soil moisture from screen-level observations in a mesoscale numerical weather-prediction model, Q. J. Ro. Meteorol. Soc., 130, 895–915, 2004.

Balsamo, G., Mahfouf, J.-F., Bélair, S., and Deblonde, G.: A land data assimilation system for soil moisture and temperature: An information content study, J. Hydrometeorology, 8, 1225–1242, 2007.

Berg, A. and Famiglietti, J.: Characterizing regional uncertainty in the initial soil moisture status, Geophys. Res. Lett., 30, 1466, <a href="http://dx.doi.org/10.1029/2003GL017075">https://doi.org/10.1029/2003GL017075</a>, 2003.

Bolten, J., Crow, W., Jackson, T., Zhan, X., and Reynolds, C.: Evaluating the utility of remotely-sensed soil moisture retrievals for operational agricultural drought monitoring, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3, 57–66, 2010.

Boone, A., Calvet, J.-C., and Noilhan, J.: Inclusion of a third soil layer in a land-surface scheme using the force-restore method, J. Appl. Meteorol., 38, 1611–1630, 1999.

Brocca, L., Hasenauer, S., de Rosnay, P., Melone, F., Moramarco, T., Matgen, P., Mart\'inez-Fernández, J., Llorens, P., Latron, J., and Martin, C.: Consistent validation of H-SAF soil moisture satellite and model products against ground measurements for selected sites in Europe, Associated Scientist Activity in the Framework of the Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) Final Report, available at <a href="http://hsaf.meteoam.it/documents/reference/HSAF_AS_09_03_final_report.pdf">http://hsaf.meteoam.it/documents/reference/HSAF_AS_09_03_final_report.pdf</a>, 2010a.

Brocca, L., Melone, F., Moramarco, T., Wagner, W., and Hasenauer, S.: ASCAT soil wetness index validation through in situ and modeled soil moisture data in central Italy, Remote Sens. Environ., 114, 2745–2755, 2010b.

Calvet, J.-C., Fritz, N., Froissard, F., Suquia, D., Petitpa, A., and Piguet, B.: In situ soil moisture observations for the CAL/VAL of SMOS: the SMOSMANIA network, 2007 International Geoscience and Remote Sensing Symposium, Proceedings, 2007.

Canellas, C.: Intercomparison de pluviomèmetres, Metéo-France Note de la Direction de la Production, 2005.

Crow, W. and Wood, E.: The assimilation of remotely sensed soil brightness temperature imagery into a land surface model using Ensemble Kalman filtering: a case study based on ESTAR measurements during SGP97, Adv. Water Resour., 26, 137–149, 2003.

Crow, W., Miralles, D., and Cosh, M.: A quasi-global evaluation system for satellite surface soil moisture retrievals, IEEE Transactions on Geoscience and Remote Sensing, 48, 2516–2527, 2009.

Dee, D.: Bias and data assimilation, Q. J. Roy. Meteorol. Soc., 131, 3323–3343, 2005.

De Lannoy, G., Reichle, R., Houser, P., Pauwels, V., and Verhoest, N.: Correcting for forecast bias in soil moisture assimilation with the Ensemble Kalman Filter, Water Resour. Res., 43, W09410, <a href="http://dx.doi.org/10.1029/2006WR005449">https://doi.org/10.1029/2006WR005449</a>, 2007.

de Rosnay, P., Drusch, M., Balsamo, G., Beljaars, A., Isaksen, L., Vasiljevic, D., Albergel, C., and Scipal, K.: Advances in land data assimilation at ECMWF, ECMWF/GLASS Workshop on Land Surface Modelling and Data Assimilation and the Implications for Predictability, 9–12 November 2009, ECMWF, Reading, UK, 2009.

Dharssi, I., Bovis, K. J., Macpherson, B., and Jones, C. P.: Operational assimilation of ASCAT surface soil wetness at the Met Office, Hydrol. Earth Syst. Sci., 15, 2729–2746, <a href="http://dx.doi.org/10.5194/hess-15-2729-2011">https://doi.org/10.5194/hess-15-2729-2011</a>, 2011.

Draper, C., Mahfouf, J.-F., and Walker, J.: An EKF assimilation of AMSR-E soil moisture into the ISBA land surface scheme, J. Geophys. Res., 114, D20104, <a href="http://dx.doi.org/10.1029/2008JD011650">https://doi.org/10.1029/2008JD011650</a>, 2009.

Draper, C., Mahfouf, J.-F., and Walker, J.: Root-zone soil moisture from the assimilation of screen-level variables and remotely sensed soil moisture, J. Geophys. Res., 116, D02127, <a href="http://dx.doi.org/10.1029/2010JD013829">https://doi.org/10.1029/2010JD013829</a>, 2011.

Drusch, M.: Initializing numerical weather prediction models with satellite-derived surface soil moisture: Data assimilation experiments with ECMWF's Integrated Forecast System and the TMI soil moisture data set, J. Geophys. Res., 112, D03102, <a href="http://dx.doi.org/10.1029/2006JD007478">https://doi.org/10.1029/2006JD007478</a>, 2007.

Entekhabi, D., Njoku, E., Houser, P., Spencer, M., Doiron, T., Kim, Y., Smith, J., Girard, R., Bélair, S., Crow, W., Jackson, T., Kerr, Y., Kimball, J., Koster, R., McDonald, K., O'Neill, P., Pultz, T., Running, S., Shi, J., Wood, E., and van Zyl, J.: The hydrosphere state (Hydros) satellite mission: An earth system pathfinder for global mapping of soil moisture and land freeze/thaw, IEEE Transactions on Geoscience and Remote Sensing, 42, 2184–2195, 2004.

Habets, F., Boone, A., Champeaux, J.-L., Etchevers, P., Franchistéguy, L., Leblois, E., Ledoux, E., Le Moigne, P., Martin, E., Morel, S., Noilhan, J., and Quintana-Segu\`{i}, P.: The SAFRAN-ISBA-MODCOU hydrometeorological model applied over France, J. Geophys. Res., 113, D06113, <a href="http://dx.doi.org/10.1029/2007JD008548">https://doi.org/10.1029/2007JD008548</a>, 2008.

Houser, P., Shuttleworth, W., Famiglietti, J., Gupta, H., Syed, K., and Goodrich, D.: Integration of soil moisture remote sensing and hydrologic modeling using data assimilation, Water Resour. Res., 34, 3405–3420, 1998.

Kerr, Y., Waldteufel, P., Wigneron, J., Martinuzzi, J., Font, J., and Berger, M.: Soil moisture retrieval from space: The Soil Moisture and Ocean Salinity (SMOS) mission, IEEE Transactions on Geoscience and Remote Sensing, 39, 1729–1735, 2001.

Ledoux, E., Girard, G., Marsily, G. D., and Deschenes, J.: Unsaturated Flow Hydrologic Modeling: Theory and Practice, chap. Spatially distributed modeling: Conceptual approach, coupling surface water and ground-water, pp. 435–454, NATO ASI Series C, vol. 275, 1989.

Liu, Q., Reichle, R., Bindlish, R., Cosh, M., Crow, W., de Jeu, R., De Lannoy, G., Huffman, G., and Jackson, T.: The contributions of precipitation and soil moisture observations to the skill of soil moisture estimates in a land data assimilation system, J. Hydrometeorology, 12, <a href="http://dx.doi.org/10.1175/JHM-D-10-05000.1">https://doi.org/10.1175/JHM-D-10-05000.1</a>, 2011.

Mahfouf, J.-F.: Amelioration des statistiques des erreurs d'ebauche pour l'analyse de l'etat hydrique des sols, in: Ateliers de Modélisation de l'Atmosphère 2010, Toulouse, 26-28 January 2010, at <a href="http://www.cnrm.meteo.fr/ama2010/resumes_courts/resume_69.html">http://www.cnrm.meteo.fr/ama2010/resumes_courts/resume_69.html</a>, last accessed June 2010.

Mahfouf, J.-F., Bergaoui, K., Draper, C., Bouyssel, C., Taillefer, F., and Taseva, L.: A comparison of two off-line soil analysis schemes for assimilation of screen-level observations, J. Geophys. Res., 114, D08105, <a href="http://dx.doi.org/10.1029/2008JD011077">https://doi.org/10.1029/2008JD011077</a>, 2009.

Masson, V., Champeaux, J.-L., Chauvin, F., Meriguet, C., and Lacaze, R.: A global database of land surface parameters at 1-km resolution in meteorological and climate models, J. Climate, 16, 1261–1282, 2003.

Muñoz Sabater, J., Jarlan, L., Calvet, J.-C., Bouyssel, F., and de Rosnay, P.: From near-surface to root-zone soil moisture using different assimilation techniques, J. Hydrometeorology, 8, 194–206, 2007.

Naeimi, V., Scipal, K., Bartalis, Z., Hasenauer, S., and Wagner, W.: An improved soil moisture retrieval algorithm for ERS and METOP scatterometer observations, IEEE Transactions on Geoscience and Remote Sensing, 47, 1999–2013, 2009.

Noilhan, J. and Mahfouf, J.-F.: The ISBA land surface parameterisation scheme, Global Planet. Change, 13, 145–159, 1996.

Noilhan, J. and Planton, S.: A simple parameterization of land surface processes for meteorological models, Mon. Weather Rev., 117, 536–549, 1989.

Owe, M., de Jeu, R., and Walker, J.: A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index, IEEE Transactions on Geoscience and Remote Sensing, 39, 1643–1654, 2001.

Quintana-Segu\'i, P., Le Moigne, P., Durand, Y., Martin, E., Habets, F., Baillon, M., Canellas, C., Franchistéguy, L., and Morel, S.: Analysis of near-surface atmospheric variables: Validation of the SAFRAN analysis over France, J. Appl. Meteorol. Climatol., 47, 92–107, 2008.

Reichle, R. and Koster, R.: Bias reduction in short records of satellite soil moisture, Geophys. Res. Lett., 31, L19501, <a href="http://dx.doi.org/10.1029/2004GL020938">https://doi.org/10.1029/2004GL020938</a>, 2004.

Reichle, R. and Koster, R.: Global assimilation of satellite surface soil moisture retrievals into the NASA Catchment Land Model, Geophys. Res. Lett., 32, L02404, <a href="http://dx.doi.org/10.1029/2004GL021700">https://doi.org/10.1029/2004GL021700</a>, 2005.

Reichle, R., Koster, R., Dong, J., and Berg, A.: Global soil moisture from satellite observations, land surface models, and ground data: Implications for data assimilation, J. Hydrometeorology, 5, 430–442, 2004.

Thirel, G., Regimbeau, F., Martin, E., Noilhan, J., and Habets, F.: Short- and medium-range hydrological ensemble forecasts over France, Atmos. Sci. Lett., 11, 72–77, 2010.

van den Hurk, B., Ettema, J., and Viterbo, P.: Analysis of soil moisture changes in Europe during a single growing season in a new ECMWF soil moisture assimilation system, J. Hydrometeorology, 9, 116–131, 2008.

Vidal, J.-P., Martin, E., Franchistéguy, L., Baillon, M., and Soubeyroux, J.-M.: A 50-year high-resolution atmospheric reanalysis over France with the Safran system, Int. J. Climatol., 30, 1627–1644, 2010.

Wagner, W., Lemoine, G., and Rott, H.: A method for estimating soil moisture from ERS scatterometer and soil data, Remote Sens. Environ., 70, 191–207, 1999.

Wagner, W., Bartalis, Z., Naeimi, V., Park, S.-E., Figa-Saldaña, J., and Bonekamp, H.: Status of the MetOp ASCAT soil moisture product, 2010 International Geoscience and Remote Sensing Symposium, Proceedings, 2010.