Editorial Type:
Article
Article Type:
Research Article

The Impact of Rainfall Error Characterization on the Estimation of Soil Moisture Fields in a Land Data Assimilation System

Viviana Maggioni Department of Civil and Environmental Engineering, University of Connecticut, Storrs, Connecticut

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Rolf H. Reichle Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland

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Emmanouil N. Anagnostou Department of Civil and Environmental Engineering, University of Connecticut, Storrs, Connecticut

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Print Publication:
01 Jun 2012
DOI:
https://doi.org/10.1175/JHM-D-11-0115.1
Page(s):
1107–1118
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Abstract

This study presents a numerical experiment to assess the impact of satellite rainfall error structure on the efficiency of assimilating near-surface soil moisture observations. Specifically, the study contrasts a multidimensional satellite rainfall error model (SREM2D) to a simpler rainfall error model (CTRL) currently used to generate rainfall ensembles as part of the ensemble-based land data assimilation system developed at the NASA Global Modeling and Assimilation Office. The study is conducted in the Oklahoma region using rainfall data from a NOAA multisatellite global rainfall product [the Climate Prediction Center (CPC) morphing technique (CMORPH)] and the National Weather Service rain gauge–calibrated radar rainfall product [Weather Surveillance Radar-1988 Doppler (WSR-88D)] representing the “uncertain” and “reference” model rainfall forcing, respectively. Soil moisture simulations using the Catchment land surface model (CLSM), obtained by forcing the model with reference rainfall, are randomly perturbed to represent satellite retrieval uncertainty, and assimilated into CLSM as synthetic near-surface soil moisture observations. The assimilation estimates show improved performance metrics, exhibiting higher anomaly correlation coefficients (e.g., ~0.79 and ~0.90 in the SREM2D nonassimilation and assimilation experiments for root zone soil moisture, respectively) and lower root-mean-square errors (e.g., ~0.034 m3 m−3 and ~0.024 m3 m−3 in the SREM2D nonassimilation and assimilation experiments for root zone soil moisture, respectively). The more elaborate rainfall error model in the assimilation system leads to slightly improved assimilation estimates. In particular, the relative enhancement due to SREM2D over CTRL is larger for root zone soil moisture and in wetter rainfall conditions.

Corresponding author address: Viviana Maggioni, Civil and Environmental Engineering, University of Connecticut, 261 Glenbrook Road, Unit 2037, Storrs, CT 06269. E-mail: viviana@engr.uconn.edu

Abstract

This study presents a numerical experiment to assess the impact of satellite rainfall error structure on the efficiency of assimilating near-surface soil moisture observations. Specifically, the study contrasts a multidimensional satellite rainfall error model (SREM2D) to a simpler rainfall error model (CTRL) currently used to generate rainfall ensembles as part of the ensemble-based land data assimilation system developed at the NASA Global Modeling and Assimilation Office. The study is conducted in the Oklahoma region using rainfall data from a NOAA multisatellite global rainfall product [the Climate Prediction Center (CPC) morphing technique (CMORPH)] and the National Weather Service rain gauge–calibrated radar rainfall product [Weather Surveillance Radar-1988 Doppler (WSR-88D)] representing the “uncertain” and “reference” model rainfall forcing, respectively. Soil moisture simulations using the Catchment land surface model (CLSM), obtained by forcing the model with reference rainfall, are randomly perturbed to represent satellite retrieval uncertainty, and assimilated into CLSM as synthetic near-surface soil moisture observations. The assimilation estimates show improved performance metrics, exhibiting higher anomaly correlation coefficients (e.g., ~0.79 and ~0.90 in the SREM2D nonassimilation and assimilation experiments for root zone soil moisture, respectively) and lower root-mean-square errors (e.g., ~0.034 m3 m−3 and ~0.024 m3 m−3 in the SREM2D nonassimilation and assimilation experiments for root zone soil moisture, respectively). The more elaborate rainfall error model in the assimilation system leads to slightly improved assimilation estimates. In particular, the relative enhancement due to SREM2D over CTRL is larger for root zone soil moisture and in wetter rainfall conditions.

Corresponding author address: Viviana Maggioni, Civil and Environmental Engineering, University of Connecticut, 261 Glenbrook Road, Unit 2037, Storrs, CT 06269. E-mail: viviana@engr.uconn.edu
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Cover Journal of Hydrometeorology
Journal of Hydrometeorology

  • Betts, A. K., and Ball J. H. , 1998: FIFE surface climate and site-average dataset 1987–89. J. Atmos. Sci., 55, 1091–1108.

  • Bloom, S., and Coauthors, 2005: Documentation and validation of the Goddard Earth Observing System (GEOS) Data Assimilation System: Version 4. Tech. Rep. Series on Global Modeling and Data Assimilation NASA/TM-2005-104606, Vol. 26, 187 pp.

  • Boone, A., and Coauthors, 2004: The Rhône-Aggregation Land Surface Scheme intercomparison project: An overview. J. Climate, 17, 187–208.

    • Search Google Scholar
    • Export Citation

  • Bowling, L. C., and Coauthors, 2003: Simulation of high latitude hydrological processes in the Torne–Kalix basin: PILPS Phase 2(e): 1: Experiment description and summary intercomparisons. Global Planet. Change, 38, 1–30.

    • Search Google Scholar
    • Export Citation

  • Crow, W. T., and Wood E. F. , 2003: 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.

    • Search Google Scholar
    • Export Citation

  • Crow, W. T., and Van Loon E. , 2006: Impact of incorrect model error assumptions on the sequential assimilation of remotely sensed surface soil moisture. J. Hydrometeor., 7, 421–432.

    • Search Google Scholar
    • Export Citation

  • Dorigo, W. A., Van Oevelen P. , Wagner W. , Drusch M. , Mecklenburg S. , Robock A. , and Jackson T. , 2011: A new international network for in situ soil moisture data. Eos, Trans. Amer. Geophys. Union, 92, 141–142.

    • Search Google Scholar
    • Export Citation

  • Drusch, M., Scipal K. , de Rosnay P. , Balsamo G. , Andersson E. , Bougeault P. , and Viterbo P. , 2009: Towards a Kalman Filter based soil moisture analysis system for the operational ECMWF Integrated Forecast System. Geophys. Res. Lett., 36, L10401, doi:10.1029/2009GL037716.

    • Search Google Scholar
    • Export Citation

  • Entekhabi, D., and Coauthors, 2010a: The Soil Moisture Active Passive (SMAP) mission. Proc. IEEE, 98, 704–716.

  • Entekhabi, D., Reichle R. H. , Koster R. D. , and Crow W. T. , 2010b: Performance metrics for soil moisture retrievals and application requirements. J. Hydrometeor., 11, 832–840.

    • Search Google Scholar
    • Export Citation

  • Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99 (C5),10 143–10 162.

    • Search Google Scholar
    • Export Citation

  • Fulton, R. A., 1998: WSR-88D polar-to-HRAP mapping. Hydrologic Research Laboratory, Office of Hydrology, National Weather Service Tech. Memo., 34 pp.

  • Hossain, F., and Anagnostou E. N. , 2005: Using a multi-dimensional satellite rainfall error model to characterize uncertainty in soil moisture fields simulated by an offline land surface model. Geophys. Res. Lett., 32, L15402, doi:10.1029/2005GL023122.

    • Search Google Scholar
    • Export Citation

  • Hossain, F., and Anagnostou E. N. , 2006a: A two-dimensional satellite rainfall error model. IEEE Trans. Geosci. Remote Sens., 44, 1511–1522.

    • Search Google Scholar
    • Export Citation

  • Hossain, F., and Anagnostou E. N. , 2006b: Assessment of a multidimensional satellite rainfall error model for ensemble generation of satellite rainfall data. Geosci. Remote Sens. Lett., 3, 419–423.

    • Search Google Scholar
    • Export Citation

  • Joyce, R. J., Janowiak J. E. , Arkin P. A. , and Xie P. P. , 2004: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydrometeor., 5, 487–503.

    • Search Google Scholar
    • Export Citation

  • Koster, R. D., Suarez M. J. , Ducharne A. , Stieglitz M. , and Kumar P. , 2000: A catchment-based approach to modeling land surface processes in a general circulation model 1. Model structure. J. Geophys. Res., 105 (D20), 24 809–24 822.

    • Search Google Scholar
    • Export Citation

  • Li, F., Crow W. T. , and Kustas W. P. , 2010: Towards the estimation root-zone soil moisture via the simultaneous assimilation of thermal and microwave soil moisture retrievals. Adv. Water Resour., 33, 201–214.

    • Search Google Scholar
    • Export Citation

  • Lin, C., Vasić S. , Kilambi A. , Turner B. , and Zawadzki I. , 2005: Precipitation forecast skill of numerical weather prediction models and radar nowcasts. Geophys. Res. Lett., 32, L14801, doi:10.1029/2005GL023451.

    • Search Google Scholar
    • Export Citation

  • Liu, Q., and Coauthors, 2011: The contributions of precipitation and soil moisture observations to the skill of soil moisture estimates in a land data assimilation system. J. Hydrometeor., 12, 750–765.

    • Search Google Scholar
    • Export Citation

  • Maggioni, V., Reichle R. H. , and Anagnostou E. N. , 2011: The effect of satellite rainfall error modeling on soil moisture prediction uncertainty. J. Hydrometeor., 12, 413–428.

    • Search Google Scholar
    • Export Citation

  • Nijssen, B., and Coauthors, 2003: Simulation of high latitude hydrological processes in the Torne-Kalix basin: PILPS Phase 2(e): 2: Comparison of model results with observations. Global Planet. Change, 38, 31–53.

    • Search Google Scholar
    • Export Citation

  • Parajka, J., Naeimi V. , Blöschl G. , Wagner W. , Merz R. , and Scipal K. , 2006: Assimilating scatterometer soil moisture data into conceptual hydrologic models at the regional scale. Hydrol. Earth Syst. Sci., 10, 353–368, doi:10.5194/hess-10-353-2006.

    • Search Google Scholar
    • Export Citation

  • Peters-Lidard, C. D., and Coauthors, 2007: High-performance Earth system modeling with NASA/GSFC’s Land Information System. Innovations Syst. Software Eng., 3 (3), 157–165.

    • Search Google Scholar
    • Export Citation

  • Reichle, R. H., and Koster R. D. , 2005: Global assimilation of satellite surface soil moisture retrievals into the NASA Catchment land surface model. Geophys. Res. Lett., 32, L02404, doi:10.1029/2004GL021700.

    • Search Google Scholar
    • Export Citation

  • Reichle, R. H., McLaughlin D. , and Entekhabi D. , 2002: Hydrological data assimilation with the ensemble Kalman filter. Mon. Wea. Rev., 130, 103–114.

    • Search Google Scholar
    • Export Citation

  • Reichle, R. H., Koster R. D. , Liu P. , Mahanama S. P. P. , Njoku E. G. , and Owe M. , 2007: Comparison and assimilation of global soil moisture retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and the Scanning Multichannel Microwave Radiometer (SMMR). J. Geophys. Res., 112, D09108, doi:10.1029/2006JD008033.

    • Search Google Scholar
    • Export Citation

  • Reichle, R. H., Crow W. T. , and Keppenne C. L. , 2008: An adaptive ensemble Kalman filter for soil moisture data assimilation. Water Resour. Res., 44, W03423, doi:10.1029/2007WR006357.

    • Search Google Scholar
    • Export Citation

  • Rienecker, M. M., and Coauthors, 2008: The GEOS-5 Data Assimilation System—Documentation of versions 5.0.1, 5.1.0, and 5.2.0. Tech. Rep. Series on Global Modeling and Data Assimilation NASA/TM-2008-104606, Vol. 27, 101 pp. [Available online at http://gmao.gsfc.nasa.gov/pubs/docs/Rienecker369.pdf.]

  • Robinson, D. A., and Coauthors, 2008: Soil moisture measurements for ecological and hydrological watershed scale observatories: A review. Vadose Zone J., 7, 358–389, doi:10.2136/vzj2007.0143.

    • Search Google Scholar
    • Export Citation

  • Robock, A., Vinnikov K. Ya , Srinivasan G. , Entin J. K. , Hollinger S. E. , Speranskaya N. A. , Liu S. , and Namkhai A. , 2000: The global soil moisture data bank. Bull. Amer. Meteor. Soc., 81, 1281–1299.

    • Search Google Scholar
    • Export Citation

  • Schmugge, T. J., Kustas W. P. , Ritchie J. C. , Jackson T. J. , and Rango A. , 2002: Remote sensing in hydrology. Adv. Water Resour., 25, 1367–1385.

    • Search Google Scholar
    • Export Citation

  • Seneviratne, S. I., Corti T. , Davin E. L. , Hirschi M. , Jaeger E. B. , Lehner I. , Orlowsky B. , and Teuling A. J. , 2010: Investigating soil moisture–climate interactions in a changing climate: A review. Earth-Sci. Rev., 99, 125–161, doi:10.1016/j.earscirev.2010.02.004.

    • Search Google Scholar
    • Export Citation

  • Rodell, M., and Coauthors, 2004: The Global Land Data Assimilation System. Bull. Amer. Meteor. Soc., 85, 381–394.

  • Walker, J. P., Willgoose G. R. , and Kalma J. D. , 2004: In situ measurement of soil moisture: A comparison of techniques. J. Hydrol., 293, 85–99.

    • Search Google Scholar
    • Export Citation
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