Assimilation of Satellite-Derived Skin Temperature Observations into Land Surface Models

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

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Sujay V. Kumar Science Applications International Corporation, Beltsville, and Hydrological Sciences Branch, NASA Goddard Space Flight Center, Greenbelt, Maryland

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Sarith P. P. Mahanama Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, and Goddard Earth Sciences and Technology Center, University of Maryland, Baltimore County, Baltimore, Maryland

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Randal D. Koster Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland

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Q. Liu Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, and Science Applications International Corporation, Beltsville, Maryland

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Abstract

Land surface (or “skin”) temperature (LST) lies at the heart of the surface energy balance and is a key variable in weather and climate models. In this research LST retrievals from the International Satellite Cloud Climatology Project (ISCCP) are assimilated into the Noah land surface model and Catchment land surface model (CLSM) using an ensemble-based, offline land data assimilation system. LST is described very differently in the two models. A priori scaling and dynamic bias estimation approaches are applied because satellite and model LSTs typically exhibit different mean values and variabilities. Performance is measured against 27 months of in situ measurements from the Coordinated Energy and Water Cycle Observations Project at 48 stations. LST estimates from Noah and CLSM without data assimilation (“open loop”) are comparable to each other and superior to ISCCP retrievals. For LST, the RMSE values are 4.9 K (CLSM), 5.5 K (Noah), and 7.6 K (ISCCP), and the anomaly correlation coefficients (R) are 0.61 (CLSM), 0.63 (Noah), and 0.52 (ISCCP). Assimilation of ISCCP retrievals provides modest yet statistically significant improvements (over an open loop, as indicated by nonoverlapping 95% confidence intervals) of up to 0.7 K in RMSE and 0.05 in the anomaly R. The skill of the latent and sensible heat flux estimates from the assimilation integrations is essentially identical to the corresponding open loop skill. Noah assimilation estimates of ground heat flux, however, can be significantly worse than open loop estimates. Provided the assimilation system is properly adapted to each land model, the benefits from the assimilation of LST retrievals are comparable for both models.

Corresponding author address: Rolf H. Reichle, NASA Goddard Space Flight Center, Mail Code 610.1, 8800 Greenbelt Rd., Greenbelt, MD 20771. Email: rolf.reichle@nasa.gov

Abstract

Land surface (or “skin”) temperature (LST) lies at the heart of the surface energy balance and is a key variable in weather and climate models. In this research LST retrievals from the International Satellite Cloud Climatology Project (ISCCP) are assimilated into the Noah land surface model and Catchment land surface model (CLSM) using an ensemble-based, offline land data assimilation system. LST is described very differently in the two models. A priori scaling and dynamic bias estimation approaches are applied because satellite and model LSTs typically exhibit different mean values and variabilities. Performance is measured against 27 months of in situ measurements from the Coordinated Energy and Water Cycle Observations Project at 48 stations. LST estimates from Noah and CLSM without data assimilation (“open loop”) are comparable to each other and superior to ISCCP retrievals. For LST, the RMSE values are 4.9 K (CLSM), 5.5 K (Noah), and 7.6 K (ISCCP), and the anomaly correlation coefficients (R) are 0.61 (CLSM), 0.63 (Noah), and 0.52 (ISCCP). Assimilation of ISCCP retrievals provides modest yet statistically significant improvements (over an open loop, as indicated by nonoverlapping 95% confidence intervals) of up to 0.7 K in RMSE and 0.05 in the anomaly R. The skill of the latent and sensible heat flux estimates from the assimilation integrations is essentially identical to the corresponding open loop skill. Noah assimilation estimates of ground heat flux, however, can be significantly worse than open loop estimates. Provided the assimilation system is properly adapted to each land model, the benefits from the assimilation of LST retrievals are comparable for both models.

Corresponding author address: Rolf H. Reichle, NASA Goddard Space Flight Center, Mail Code 610.1, 8800 Greenbelt Rd., Greenbelt, MD 20771. Email: rolf.reichle@nasa.gov

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  • Aires, F., Prigent C. , and Rossow W. B. , 2004: Temporal interpolation of global surface skin temperature diurnal cycle over land under clear and cloudy conditions. J. Geophys. Res., 109 , D06214. doi:10.1029/2003JD003527.

    • Search Google Scholar
    • Export Citation
  • Andreadis, K., and Lettenmaier D. , 2006: Assimilating remotely sensed snow observations into a macroscale hydrology model. Adv. Water Resour., 29 , 872886.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Balsamo, G., Mahfouf J-F. , and Belair S. , 2007: A land data assimilation system for soil moisture and temperature: An information content study. J. Hydrometeor., 8 , 12251242.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bloom, S., and Coauthors, 2005: Documentation and validation of the Goddard Earth Observing System (GEOS) Data Assimilation System—Version 4. NASA Tech. Doc. Vol. 10460626, NASA Tech. Rep. Series on Global Modeling and Data Assimilation, Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, 187 pp. [Available online at http://gmao.gsfc.nasa.gov/pubs/docs/Bloom168.pdf].

    • Search Google Scholar
    • Export Citation
  • Boni, G., Entekhabi D. , and Castelli F. , 2001: Land data assimilation with satellite measurements for the estimation of surface energy balance components and surface control on evaporation. Water Resour. Res., 37 , 17131722.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bosilovich, M., Radakovich J. , da Silva A. , Todling R. , and Verter F. , 2007: Skin temperature analysis and bias correction in a coupled land–atmosphere data assimilation system. J. Meteor. Soc. Japan, 85A , 205228.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Caparrini, F., Castelli F. , and Entekhabi D. , 2004: Variational estimation of soil and vegetation turbulent transfer and heat flux parameters from sequences of multisensor imagery. Water Resour. Res., 40 , W12515. doi:10.1029/2004WR003358.

    • Search Google Scholar
    • Export Citation
  • Castelli, F., Entekhabi D. , and Caporali E. , 1999: Estimation of surface heat flux and an index of soil moisture using adjoint-state surface energy balance. Water Resour. Res., 35 , 31153125.

    • Crossref
    • 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 , 137149.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Crow, W. T., and Reichle R. H. , 2008: Comparison of adaptive filtering techniques for land surface data assimilation. Water Resour. Res., 44 , W08423. doi:10.1029/2008WR006883.

    • Search Google Scholar
    • Export Citation
  • Dee, D. P., 2005: Bias and data assimilation. Quart. J. Roy. Meteor. Soc., 131 , 33233343. doi:10.1256/qj.05.137.

  • De Lannoy, G. J. M., Reichle R. H. , Houser P. R. , Pauwels V. R. N. , and Verhoest N. E. C. , 2007: Correcting for forecast bias in soil moisture assimilation with the ensemble Kalman filter. Water Resour. Res., 43 , W09410. doi:10.1029/2006WR005449.

    • Search Google Scholar
    • Export Citation
  • Drusch, M., 2007: 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 dataset. J. Geophys. Res., 112 , D03102. doi:10.1029/2006JD007478.

    • Search Google Scholar
    • Export Citation
  • Dunne, S., and Entekhabi D. , 2006: Land surface state and flux estimation using the ensemble Kalman smoother during the Southern Great Plains 1997 field experiment. Water Resour. Res., 42 , W01407. doi:10.1029/2005WR004334.

    • Search Google Scholar
    • Export Citation
  • Ek, M., Mitchell K. , Yin L. , Rogers P. , Grunmann P. , Koren V. , Gayno G. , and Tarpley J. D. , 2003: Implementation of Noah land-surface model advances in the NCEP operational mesoscale Eta model. J. Geophys. Res., 108 , 8851. doi:10.1029/2002JD003296.

    • Search Google Scholar
    • Export Citation
  • Entekhabi, D., Reichle R. H. , Koster R. D. , and Crow W. T. , 2010: Performance metrics for soil moisture retrievals and application requirements. J. Hydrometeor., 11 , 832840.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Finkelstein, P. L., and Sims P. F. , 2001: Sampling error in eddy correlation flux measurements. J. Geophys. Res., 106 , (D4). 35033509.

  • Garand, L., 2003: Toward an integrated land-ocean surface skin temperature analysis from the variational assimilation of infrared radiances. J. Appl. Meteor., 42 , 570583.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hollinger, D. Y., and Richardson A. D. , 2005: Uncertainty in eddy covariance measurements and its application to physiological models. Tree Physiol., 25 , 873885.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jin, M., 2004: Analysis of land skin temperature using AVHRR observations. Bull. Amer. Meteor. Soc., 85 , 587600.

  • Jin, M., Dickinson R. E. , and Vogelmann A. M. , 1997: A comparison of CCM2–BATS skin temperature and surface-air temperature with satellite and surface observations. J. Climate, 10 , 15051524.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kalma, J. D., McVicar T. R. , and McCabe M. F. , 2008: Estimating land surface evaporation: A review of methods using remotely sensed surface temperature data. Surv. Geophys., 29 , 421469. doi:10.1007/s10712-008-9037-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Keppenne, C. L., 2000: Data assimilation into a primitive-equation model with a parallel ensemble Kalman filter. Mon. Wea. Rev., 128 , 19711981.

    • Crossref
    • 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 , 2480924822.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumar, P., and Kaleita A. L. , 2003: Assimilation of near-surface temperature using extended Kalman filter. Adv. Water Resour., 26 , 7993.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumar, S. V., Reichle R. H. , Peters-Lidard C. D. , Koster R. D. , Zhan X. , Crow W. T. , Eylander J. B. , and Houser P. R. , 2008: A land surface data assimilation framework using the land information system: Description and applications. Adv. Water Resour., 31 , 14191432. doi:10.1016/j.advwatres.2008.01.013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lakshmi, V., 2000: A simple surface temperature assimilation scheme for use in land surface models. Water Resour. Res., 36 , 36873700.

  • Mahfouf, J-F., Bergaoui K. , Draper C. , Bouyssel F. , Taillefer F. , and Taseva L. , 2009: A comparison of two off-line soil analysis schemes for assimilation of screen level observations. J. Geophys. Res., 114 , D08105. doi:10.1029/2008JD011077.

    • Search Google Scholar
    • Export Citation
  • Margulis, S. A., McLaughlin D. , Entekhabi D. , and Dunne S. , 2002: Land data assimilation and estimation of soil moisture using measurements from the southern Great Plains 1997 field experiment. Water Resour. Res., 38 , 1299. doi:10.1029/2001WR001114.

    • Search Google Scholar
    • Export Citation
  • McNider, R. T., Song A. J. , Casey D. M. , Wetzel P. J. , Crosson W. L. , and Rabin R. M. , 1994: Toward a dynamic–thermodynamic assimilation of satellite surface temperature in numerical atmospheric models. Mon. Wea. Rev., 122 , 27842787.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meng, C. L., Li Z-L. , Zhan X. , Shi J. C. , and Liu C. Y. , 2009: Land surface temperature data assimilation and its impact on evapotranspiration estimates from the Common Land Model. Water Resour. Res., 45 , W02421. doi:10.1029/2008WR006971.

    • Search Google Scholar
    • Export Citation
  • Minnis, P., and Khaiyer M. M. , 2000: Anisotropy of land surface skin temperature derived from satellite data. J. Appl. Meteor., 39 , 11171129.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pan, M., and Wood E. F. , 2006: Data assimilation for estimating the terrestrial water budget using a constrained ensemble Kalman filter. J. Hydrometeor., 7 , 534547.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pinheiro, A. C. T., Privette J. L. , Mahoney R. , and Tucker C. J. , 2004: Directional effects in a daily AVHRR land surface temperature dataset over Africa. IEEE Trans. Geosci. Remote Sens., 42 , 19411954. doi:10.1109/TGRS.2004.831886.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reichle, R. H., and Koster R. D. , 2003: Assessing the impact of horizontal error correlations in background fields on soil moisture estimation. J. Hydrometeor., 4 , 12291242.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reichle, R. H., and Koster R. D. , 2004: Bias reduction in short records of satellite soil moisture. Geophys. Res. Lett., 31 , L19501. doi:10.1029/2004GL020938.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reichle, R. H., McLaughlin D. , and Entekhabi D. , 2002a: Hydrologic data assimilation with the ensemble Kalman filter. Mon. Wea. Rev., 130 , 103114.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reichle, R. H., Walker J. P. , Koster R. D. , and Houser P. R. , 2002b: Extended versus ensemble Kalman filtering for land data assimilation. J. Hydrometeor., 3 , 728740.

    • Crossref
    • 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
  • Reichle, R. H., Bosilovich M. G. , Crow W. T. , Koster R. D. , Kumar S. V. , Mahanama S. P. P. , and Zaitchik B. F. , 2009: Recent advances in land data assimilation at the NASA Global Modeling and Assimilation Office. Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications, S.-K. Park and L. Xu, Eds., Springer, 407–428, doi:10.1007/978-3-540-71056-1.

    • Search Google Scholar
    • Export Citation
  • Rodell, M., and Coauthors, 2004: The Global Land Data Assimilation System. Bull. Amer. Meteor. Soc., 85 , 381394.

  • Rossow, W. B., and Schiffer R. A. , 1991: ISCCP cloud data products. Bull. Amer. Meteor. Soc., 72 , 220.

  • Rossow, W. B., and Schiffer R. A. , 1999: Advances in understanding clouds from ISCCP. Bull. Amer. Meteor. Soc., 80 , 22612287.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seuffert, G., Wilker H. , Viterbo P. , Mahfouf J-F. , Drusch M. , and Calvet J-C. , 2003: Soil moisture analysis combining screen-level parameters and microwave brightness temperature: A test with field data. Geophys. Res. Lett., 30 , 1498. doi:10.1029/2063GL017128.

    • Search Google Scholar
    • Export Citation
  • Sini, F., Boni G. , Caparrini F. , and Entekhabi D. , 2008: Estimation of large-scale evaporation fields based on assimilation of remotely sensed land temperature. Water Resour. Res., 44 , W06410. doi:10.1029/2006WR005574.

    • Search Google Scholar
    • Export Citation
  • Slater, A., and Clark M. , 2006: Snow data assimilation via an ensemble Kalman filter. J. Hydrometeor., 7 , 478493.

  • Trigo, I. F., and Viterbo P. , 2003: Clear-sky window channel radiances: A comparison between observations and the ECMWF model. J. Appl. Meteor., 42 , 14631479.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Van den Hurk, B. J. J. M., Jia L. , Jacobs C. , Menenti M. , and Li Z. L. , 2002: Assimilation of land surface temperature data from ATSR in an NWP environment—A case study. Int. J. Remote Sens., 23 , 51935209.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vrugt, J. A., Gupta H. V. , Bastidas L. A. , Bouten W. , and Sorooshian S. , 2003: Effective and efficient algorithm for multiobjective optimization of hydrologic models. Water Resour. Res., 39 , W1214. doi:10.1029/2002WR001746.

    • Search Google Scholar
    • Export Citation
  • Walker, J. P., Willgoose G. R. , and Kalma J. D. , 2001: One-dimensional soil moisture profile retrieval by assimilation of near-surface observations: A comparison of retrieval algorithms. Adv. Water Resour., 24 , 631650.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wan, Z., and Li Z-L. , 1997: A physics-based algorithm for retrieving land-surface emissivity and temperature from EOS/MODIS data. IEEE Trans. Geosci. Remote Sens., 35 , 980996.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zaitchik, B. F., and Rodell M. , 2009: Forward-looking assimilation of MODIS-derived snow-covered area into a land surface model. J. Hydrometeor., 10 , 130148. doi:10.1175/2008JHM1042.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhou, Y., McLaughlin D. , and Entekhabi D. , 2006: Assessing the performance of the ensemble Kalman filter for land surface data assimilation. Mon. Wea. Rev., 134 , 21282142.

    • Crossref
    • Search Google Scholar
    • Export Citation
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