Editorial Type:
Article
Article Type:
Research Article

Estimating Snow Water Storage in North America Using CLM4, DART, and Snow Radiance Data Assimilation

Yonghwan Kwon Department of Geological Sciences, Jackson School of Geosciences, The University of Texas at Austin, Austin, Texas

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Zong-Liang Yang Department of Geological Sciences, Jackson School of Geosciences, The University of Texas at Austin, Austin, Texas

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Long Zhao Department of Geological Sciences, Jackson School of Geosciences, The University of Texas at Austin, Austin, Texas

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Timothy J. Hoar National Center for Atmospheric Research, Boulder, Colorado

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Ally M. Toure Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, and Universities Space Research Association, Columbia, Maryland

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Matthew Rodell Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland

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Print Publication:
01 Nov 2016
DOI:
https://doi.org/10.1175/JHM-D-16-0028.1
Page(s):
2853–2874
Restricted access

Abstract

This paper addresses continental-scale snow estimates in North America using a recently developed snow radiance assimilation (RA) system. A series of RA experiments with the ensemble adjustment Kalman filter are conducted by assimilating the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) brightness temperature TB at 18.7- and 36.5-GHz vertical polarization channels. The overall RA performance in estimating snow depth for North America is improved by simultaneously updating the Community Land Model, version 4 (CLM4), snow/soil states and radiative transfer model (RTM) parameters involved in predicting TB based on their correlations with the prior TB (i.e., rule-based RA), although degradations are also observed. The RA system exhibits a more mixed performance for snow cover fraction estimates. Compared to the open-loop run (0.171 m RMSE), the overall snow depth estimates are improved by 1.6% (0.168 m RMSE) in the rule-based RA whereas the default RA (without a rule) results in a degradation of 3.6% (0.177 m RMSE). Significant improvement of the snow depth estimates in the rule-based RA is observed for tundra snow class (11.5%, p < 0.05) and bare soil land-cover type (13.5%, p < 0.05). However, the overall improvement is not significant (p = 0.135) because snow estimates are degraded or marginally improved for other snow classes and land covers, especially the taiga snow class and forest land cover (7.1% and 7.3% degradations, respectively). The current RA system needs to be further refined to enhance snow estimates for various snow types and forested regions.

Corresponding author address: Zong-Liang Yang, Department of Geological Sciences, Jackson School of Geosciences, The University of Texas at Austin, 1 University Station, C1100, Austin, TX 78712. E-mail: liang@jsg.utexas.edu

Abstract

This paper addresses continental-scale snow estimates in North America using a recently developed snow radiance assimilation (RA) system. A series of RA experiments with the ensemble adjustment Kalman filter are conducted by assimilating the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) brightness temperature TB at 18.7- and 36.5-GHz vertical polarization channels. The overall RA performance in estimating snow depth for North America is improved by simultaneously updating the Community Land Model, version 4 (CLM4), snow/soil states and radiative transfer model (RTM) parameters involved in predicting TB based on their correlations with the prior TB (i.e., rule-based RA), although degradations are also observed. The RA system exhibits a more mixed performance for snow cover fraction estimates. Compared to the open-loop run (0.171 m RMSE), the overall snow depth estimates are improved by 1.6% (0.168 m RMSE) in the rule-based RA whereas the default RA (without a rule) results in a degradation of 3.6% (0.177 m RMSE). Significant improvement of the snow depth estimates in the rule-based RA is observed for tundra snow class (11.5%, p < 0.05) and bare soil land-cover type (13.5%, p < 0.05). However, the overall improvement is not significant (p = 0.135) because snow estimates are degraded or marginally improved for other snow classes and land covers, especially the taiga snow class and forest land cover (7.1% and 7.3% degradations, respectively). The current RA system needs to be further refined to enhance snow estimates for various snow types and forested regions.

Corresponding author address: Zong-Liang Yang, Department of Geological Sciences, Jackson School of Geosciences, The University of Texas at Austin, 1 University Station, C1100, Austin, TX 78712. E-mail: liang@jsg.utexas.edu
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  • Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 2884–2903, doi:10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Anderson, J. L., 2007: Exploring the need for localization in ensemble data assimilation using a hierarchical ensemble filter. Physica D, 230, 99–111, doi:10.1016/j.physd.2006.02.011.

    • Search Google Scholar
    • Export Citation

  • Anderson, J. L., 2009: Spatially and temporally varying adaptive covariance inflation for ensemble filters. Tellus, 61A, 72–83, doi:10.1111/j.1600-0870.2008.00361.x.

    • Search Google Scholar
    • Export Citation

  • Anderson, J. L., Hoar T. , Raeder K. , Liu H. , Collins N. , Torn R. , and Arellano A. , 2009: The Data Assimilation Research Testbed: A community facility. Bull. Amer. Meteor. Soc., 90, 1283–1296, doi:10.1175/2009BAMS2618.1.

    • Search Google Scholar
    • Export Citation

  • Andreadis, K. M., and Lettenmaier D. P. , 2006: Assimilating remotely sensed snow observations into a macroscale hydrology model. Adv. Water Resour., 29, 872–886, doi:10.1016/j.advwatres.2005.08.004.

    • Search Google Scholar
    • Export Citation

  • Andreadis, K. M., and Lettenmaier D. P. , 2012: Implications of representing snowpack stratigraphy for the assimilation of passive microwave satellite observations. J. Hydrometeor., 13, 1493–1506, doi:10.1175/JHM-D-11-056.1.

    • Search Google Scholar
    • Export Citation

  • Brasnett, B. A., 1999: Global analysis of snow depth for numerical weather prediction. J. Appl. Meteor., 38, 726–740, doi:10.1175/1520-0450(1999)038<0726:AGAOSD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Brown, R. D., and Brasnett B. , 2010: Canadian Meteorological Centre (CMC) daily snow depth analysis data. Environment Canada, National Snow and Ice Data Center, updated annually, accessed 1 September 2016. [Available online at https://nsidc.org/data/docs/daac/nsidc0447_CMC_snow_depth/.]

  • Brucker, L., Royer A. , Picard G. , Langlois A. , and Fily M. , 2011: Hourly simulations of the microwave brightness temperature of seasonal snow in Quebec, Canada, using a coupled snow evolution–emission model. Remote Sens. Environ., 115, 1966–1977, doi:10.1016/j.rse.2011.03.019.

    • Search Google Scholar
    • Export Citation

  • Che, T., Li X. , Jin R. , and Huang C. , 2014: Assimilating passive microwave remote sensing data into a land surface model to improve the estimation of snow depth. Remote Sens. Environ., 143, 54–63, doi:10.1016/j.rse.2013.12.009.

    • Search Google Scholar
    • Export Citation

  • Clifford, D., 2010: Global estimates of snow water equivalent from passive microwave instruments: History, challenges and future developments. Int. J. Remote Sens., 31, 3707–3726, doi:10.1080/01431161.2010.483482.

    • Search Google Scholar
    • Export Citation

  • Dechant, C., and Moradkhani M. , 2011: Radiance data assimilation for operational snow and streamflow forecasting. Adv. Water Resour., 34, 351–364, doi:10.1016/j.advwatres.2010.12.009.

    • Search Google Scholar
    • Export Citation

  • Dee, D. P., 1995: On-line estimation of error covariance parameters for atmospheric data assimilation. Mon. Wea. Rev., 123, 1128–1145, doi:10.1175/1520-0493(1995)123<1128:OLEOEC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • De Lannoy, G. J. M., Reichle R. H. , Arsenault K. R. , Houser P. R. , Kumar S. , Verhoest N. E. C. , and Pauwels V. R. N. , 2012: Multiscale assimilation of Advanced Microwave Scanning Radiometer–EOS snow water equivalent and Moderate Resolution Imaging Spectroradiometer snow cover fraction observations in northern Colorado. Water Resour. Res., 48, W01522, doi:10.1029/2011WR010588.

    • Search Google Scholar
    • Export Citation

  • Derksen, C., 2008: The contribution of AMSR-E 18.7 and 10.7 GHz measurements to improved boreal forest snow water equivalent retrievals. Remote Sens. Environ., 112, 2701–2710, doi:10.1016/j.rse.2008.01.001.

    • Search Google Scholar
    • Export Citation

  • Derksen, C., Toose P. , Rees A. , Wang L. , English M. , Walker A. , and Sturm M. , 2010: Development of a tundra-specific snow water equivalent retrieval algorithm for satellite passive microwave data. Remote Sens. Environ., 114, 1699–1709, doi:10.1016/j.rse.2010.02.019.

    • Search Google Scholar
    • Export Citation

  • Ding, K. H., Tsang L. , and Shih S. E. , 2001: Monte Carlo simulations of particle positions for densely packed multispecies sticky particles. Microwave Opt. Technol. Lett., 30, 187–192, doi:10.1002/mop.1261.

    • Search Google Scholar
    • Export Citation

  • Dobson, M. C., Ulaby F. T. , Hallikainen M. T. , and El-Rayes M. A. , 1985: Microwave dielectric behavior of wet soil—Part II: Dielectric mixing models. IEEE Trans. Geosci. Remote Sens., GE-23, 35–46, doi:10.1109/TGRS.1985.289498.

    • Search Google Scholar
    • Export Citation

  • Dong, J., Walker J. P. , Houser P. R. , and Sun C. , 2007: Scanning multichannel microwave radiometer snow water equivalent assimilation. J. Geophys. Res., 112, D07108, doi:10.1029/2006JD007209.

    • Search Google Scholar
    • Export Citation

  • Durand, M., and Margulis S. A. , 2006: Feasibility test of multifrequency radiometric data assimilation to estimate snow water equivalent. J. Hydrometeor., 7, 443–457, doi:10.1175/JHM502.1.

    • Search Google Scholar
    • Export Citation

  • Durand, M., and Margulis S. A. , 2007: Correcting first-order errors in snow water equivalent estimates using a multifrequency, multiscale radiometric data assimilation scheme. J. Geophys. Res., 112, D13121, doi:10.1029/2006JD008067.

    • Search Google Scholar
    • Export Citation

  • Durand, M., Kim E. J. , and Margulis S. A. , 2008: Quantifying uncertainty in modeling snow microwave radiance for a mountain snowpack at the point-scale, including stratigraphic effects. IEEE Trans. Geosci. Remote Sens., 46, 1753–1767, doi:10.1109/TGRS.2008.916221.

    • Search Google Scholar
    • Export Citation

  • Durand, M., Kim E. J. , and Margulis S. A. , 2009: Radiance assimilation shows promise for snowpack characterization. Geophys. Res. Lett., 36, L02503, doi:10.1029/2008GL035214.

    • 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, 10 143–10 162, doi:10.1029/94JC00572.

    • Search Google Scholar
    • Export Citation

  • Flores, A. N., Ivanov V. Y. , Entekhabi D. , and Bras R. L. , 2009: Impact of hillslope-scale organization of topography, soil moisture, soil temperature, and vegetation on modeling surface microwave radiation emission. IEEE Trans. Geosci. Remote Sens., 47, 2557–2571, doi:10.1109/TGRS.2009.2014743.

    • Search Google Scholar
    • Export Citation

  • Foster, J. L., Sun C. , Walker J. P. , Kelly R. , Chang A. , Dong J. , and Powell H. , 2005: Quantifying the uncertainty in passive microwave snow water equivalent observations. Remote Sens. Environ., 94, 187–203, doi:10.1016/j.rse.2004.09.012.

    • Search Google Scholar
    • Export Citation

  • Gaspari, G., and Cohn S. E. , 1999: Construction of correlation functions in two and three dimensions. Quart. J. Roy. Meteor. Soc., 125, 723–757, doi:10.1002/qj.49712555417.

    • Search Google Scholar
    • Export Citation

  • Gent, P. R., and Coauthors, 2011: The Community Climate System Model version 4. J. Climate, 24, 4973–4991, doi:10.1175/2011JCLI4083.1.

    • Search Google Scholar
    • Export Citation

  • Hall, D. K., Salomonson V. V. , and Riggs G. A. , 2006: MODIS/Terra snow cover daily L3 global 0.05deg CMG, version 5. National Snow and Ice Data Center, accessed 1 September 2016. [Available online at http://nsidc.org/data/docs/daac/modis_v5/mod10c1_modis_terra_snow_daily_global_0.05deg_cmg.gd.html.]

  • Huang, C., Li X. , Lu L. , and Gu J. , 2008: Experiments of one-dimensional soil moisture assimilation system based on ensemble Kalman filter. Remote Sens. Environ., 112, 888–900, doi:10.1016/j.rse.2007.06.026.

    • Search Google Scholar
    • Export Citation

  • Jackson, T. J., and Schmugge T. J. , 1991: Vegetation effects on the microwave emission of soils. Remote Sens. Environ., 36, 203–212, doi:10.1016/0034-4257(91)90057-D.

    • Search Google Scholar
    • Export Citation

  • Jin, Y. Q., 1994: Electromagnetic Scattering Modelling for Quantitative Remote Sensing. World Scientific, 348 pp.

  • Kirchner, P. B., Bales R. C. , Molotch N. P. , Flanagan J. , and Guo Q. , 2014: LiDAR measurement of seasonal snow accumulation along an elevation gradient in the southern Sierra Nevada, California. Hydrol. Earth Syst. Sci., 18, 4261–4275, doi:10.5194/hess-18-4261-2014.

    • Search Google Scholar
    • Export Citation

  • Knowles, K. W., Savoie M. H. , Armstrong R. L. , and Brodzik M. J. , 2006: AMSR-E/Aqua daily EASE-grid brightness temperatures. National Snow and Ice Data Center, accessed 1 September 2016, doi:10.5067/RRR4WWORG070.

  • Kwon, Y., Toure A. M. , Yang Z.-L. , Rodell M. , and Picard G. , 2015: Error characterization of coupled land surface–radiative transfer models for snow microwave radiance assimilation. IEEE Trans. Geosci. Remote Sens., 53, 5247–5268, doi:10.1109/TGRS.2015.2419977.

    • Search Google Scholar
    • Export Citation

  • Langlois, A., Royer A. , Derksen C. , Montpetit B. , Dupont F. , and Goïta K. , 2012: Coupling the snow thermodynamic model SNOWPACK with the microwave emission model of layered snowpacks for subarctic and arctic snow water equivalent retrievals. Water Resour. Res., 48, W12524, doi:10.1029/2012WR012133.

    • Search Google Scholar
    • Export Citation

  • Mätzler, C., 1987: Applications of the interaction of microwaves with the natural snow cover. Remote Sens. Rev., 2, 259–387, doi:10.1080/02757258709532086.

    • Search Google Scholar
    • Export Citation

  • Mätzler, C., 1998: Improved Born approximation for scattering of radiation in a granular medium. J. Appl. Phys., 83, 6111–6117, doi:10.1063/1.367496.

    • Search Google Scholar
    • Export Citation

  • Mätzler, C., Schanda E. , and Good W. , 1982: Towards the definition of optimum sensor specifications for microwave remote sensing of snow. IEEE Trans. Geosci. Remote Sens., GE-20, 57–66, doi:10.1109/TGRS.1982.4307521.

    • Search Google Scholar
    • Export Citation

  • Moradkhani, H., Sorooshian S. , Gupta H. V. , and Houser P. R. , 2005: Dual state–parameter estimation of hydrological models using ensemble Kalman filter. Adv. Water Resour., 28, 135–147, doi:10.1016/j.advwatres.2004.09.002.

    • Search Google Scholar
    • Export Citation

  • Mott, R., Scipión D. , Schneebeli M. , Dawes N. , Berne A. , and Lehning M. , 2014: Orographic effects on snow deposition patterns in mountainous terrain. J. Geophys. Res. Atmos., 119, 1363–1385, doi:10.1002/2013JD019880.

    • Search Google Scholar
    • Export Citation

  • Niu, G.-Y., and Yang Z.-L. , 2007: An observation-based formulation of snow cover fraction and its evaluation over large North American river basins. J. Geophys. Res., 112, D21101, doi:10.1029/2007JD008674.

    • Search Google Scholar
    • Export Citation

  • Oleson, K. W., and Coauthors, 2010: Technical description of version 4.0 of the Community Land Model (CLM). NCAR Tech. Note NCAR/TN-478+STR, 257 pp., doi:10.5065/D6FB50WZ.

  • Paloscia, S., and Pampaloni P. , 1988: Microwave polarization index for monitoring vegetation growth. IEEE Trans. Geosci. Remote Sens., 26, 617–621, doi:10.1109/36.7687.

    • Search Google Scholar
    • Export Citation

  • Piani, C., Weedon G. P. , Best M. , Gomes S. M. , Viterbo P. , Hagemann S. , and Haerter J. O. , 2010: Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models. J. Hydrol., 395, 199–215, doi:10.1016/j.jhydrol.2010.10.024.

    • Search Google Scholar
    • Export Citation

  • Picard, G., Brucker L. , Roy A. , Dupont F. , Fily M. , and Royer A. , 2013: Simulation of the microwave emission of multi-layered snowpacks using the Dense Media Radiative Transfer theory: The DMRT-ML model. Geosci. Model Dev., 6, 1061–1078, doi:10.5194/gmd-6-1061-2013.

    • Search Google Scholar
    • Export Citation

  • Qian, T., Dai A. , Trenberth K. E. , and Oleson K. W. , 2006: Simulation of global land surface conditions from 1948 to 2004: Part I: Forcing data and evaluations. J. Hydrometeor., 7, 953–975, doi:10.1175/JHM540.1.

    • Search Google Scholar
    • Export Citation

  • Raeder, K., Anderson J. L. , Collins N. , Hoar T. J. , Kay J. E. , Lauritzen P. H. , and Pincus R. , 2012: DART/CAM: An ensemble data assimilation system for CESM atmospheric models. J. Climate, 25, 6304–6317, doi:10.1175/JCLI-D-11-00395.1.

    • Search Google Scholar
    • Export Citation

  • Rees, A., Lemmetyinen J. , Derksen C. , Pulliainen J. , and English M. , 2010: Observed and modelled effects of ice lens formation on passive microwave brightness temperatures over snow covered tundra. Remote Sens. Environ., 114, 116–126, doi:10.1016/j.rse.2009.08.013.

    • Search Google Scholar
    • Export Citation

  • Reichle, R. H., Koster R. D. , De Lannoy G. J. M. , Forman B. A. , Liu Q. , Mahanama S. P. P. , and Toure A. , 2011: Assessment and enhancement of MERRA land surface hydrology estimates. J. Climate, 24, 6322–6338, doi:10.1175/JCLI-D-10-05033.1.

    • Search Google Scholar
    • Export Citation

  • Riggs, G. A., Hall D. K. , and Salomonson V. V. , 2006: MODIS snow products user guide to collection 5. MODIS Doc., 80 pp. [Available online at https://nsidc.org/data/docs/daac/modis_v5/dorothy_snow_doc.pdf.]

  • Rodell, M., and Houser P. R. , 2004: Updating a land surface model with MODIS derived snow cover. J. Hydrometeor., 5, 1064–1075, doi:10.1175/JHM-395.1.

    • Search Google Scholar
    • Export Citation

  • Rosenfeld, S., and Grody N. , 2000: Anomalous microwave spectra of snow cover observed from Special Sensor Microwave/Imager measurements. J. Geophys. Res., 105, 14 913–14 925, doi:10.1029/1999JD900486.

    • Search Google Scholar
    • Export Citation

  • Roy, A., Picard G. , Royer A. , Montpetit B. , Dupont F. , Langlois A. , Derksen C. , and Champollion N. , 2013: Brightness temperature simulations of the Canadian seasonal snowpack driven by measurements of the snow specific surface area. IEEE Trans. Geosci. Remote Sens., 51, 4692–4704, doi:10.1109/TGRS.2012.2235842.

    • Search Google Scholar
    • Export Citation

  • Sharma, D., Das Gupta A. , and Babel M. S. , 2007: Spatial disaggregation of bias-corrected GCM precipitation for improved hydrologic simulation: Ping River basin, Thailand. Hydrol. Earth Syst. Sci., 11, 1373–1390, doi:10.5194/hess-11-1373-2007.

    • Search Google Scholar
    • Export Citation

  • Sturm, M., Holmgren J. , and Liston G. E. , 1995: A seasonal snow cover classification system for local to regional applications. J. Climate, 8, 1261–1283, doi:10.1175/1520-0442(1995)008<1261:ASSCCS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Su, H., Yang Z.-L. , Niu G.-Y. , and Dickinson R. E. , 2008: Enhancing the estimation of continental-scale snow water equivalent by assimilating MODIS snow cover with the ensemble Kalman filter. J. Geophys. Res., 113, D08120, doi:10.1029/2007JD009232.

    • Search Google Scholar
    • Export Citation

  • Su, H., Yang Z.-L. , Dickinson R. E. , Wilson C. R. , and Niu G.-Y. , 2010: Multisensor snow data assimilation at the continental scale: The value of Gravity Recovery and Climate Experiment terrestrial water storage information. J. Geophys. Res., 115, D10104, doi:10.1029/2009JD013035.

    • Search Google Scholar
    • Export Citation

  • Su, H., Yang Z.-L. , Niu G.-Y. , and Wilson C. R. , 2011: Parameter estimation in ensemble based snow data assimilation: A synthetic study. Adv. Water Resour., 34, 407–416, doi:10.1016/j.advwatres.2010.12.002.

    • Search Google Scholar
    • Export Citation

  • Swenson, S. C., and Lawrence D. M. , 2012: A new fractional snow-covered area parameterization for the Community Land Model and its effect on the surface energy balance. J. Geophys. Res., 117, D21107, doi:10.1029/2012JD018178.

    • Search Google Scholar
    • Export Citation

  • Toure, A. M., Goïta K. , Royer A. , Kim E. J. , Durand M. , Margulis S. A. , and Lu H. , 2011: A case study of using a multilayered thermodynamical snow model for radiance assimilation. IEEE Trans. Geosci. Remote Sens., 49, 2828–2837, doi:10.1109/TGRS.2011.2118761.

    • Search Google Scholar
    • Export Citation

  • Toure, A. M., Rodell M. , Yang Z.-L. , Beaudoing H. , Kim E. , Zhang Y. , and Kwon Y. , 2016: Evaluation of the snow simulations from the Community Land Model, version 4 (CLM4). J. Hydrometeor., 17, 153–170, doi:10.1175/JHM-D-14-0165.1.

    • Search Google Scholar
    • Export Citation

  • Tsang, L., and Kong J. A. , 2001: Scattering of Electromagnetic Waves: Advanced Topics. Vol. 3, Wiley-Interscience, 413 pp.

  • Tsang, L., Chen C. , Chang A. , Guo J. , and Ding K. , 2000: Dense Media Radiative Transfer theory based on quasicrystalline approximation with applications to passive microwave remote sensing of snow. Radio Sci., 35, 731–749, doi:10.1029/1999RS002270.

    • Search Google Scholar
    • Export Citation

  • Tsang, L., Xu P. , and Chen K. S. , 2008: Third and fourth Stokes parameters in polarimetric passive microwave remote sensing of rough surfaces over layered media. Microwave Opt. Technol. Lett., 50, 3063–3069, doi:10.1002/mop.23892.

    • Search Google Scholar
    • Export Citation

  • Ulaby, F. T., Moore R. K. , and Fung A. K. , 1981: Microwave Remote Sensing: Active and Passive. Vol. 1, Addison-Wesley, 456 pp.

  • Vrugt, J. A., Gupta H. V. , Nualláin B. Ó. , and Bouten W. , 2006: Real-time data assimilation for operational ensemble streamflow forecasting. J. Hydrometeor., 7, 548–565, doi:10.1175/JHM504.1.

    • Search Google Scholar
    • Export Citation

  • Wegmüller, U., and Mätzler C. , 1999: Rough bare soil reflectivity model. IEEE Trans. Geosci. Remote Sens., 37, 1391–1395, doi:10.1109/36.763303.

    • Search Google Scholar
    • Export Citation

  • Wiesmann, A., and Mätzler C. , 1999: Microwave emission model of layered snowpacks. Remote Sens. Environ., 70, 307–316, doi:10.1016/S0034-4257(99)00046-2.

    • 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, 130–148, doi:10.1175/2008JHM1042.1.

    • Search Google Scholar
    • Export Citation

  • Zhang, Y.-F., Hoar T. J. , Yang Z.-L. , Anderson J. L. , Toure A. M. , and Rodell M. , 2014: Assimilation of MODIS snow cover through the Data Assimilation Research Testbed and the Community Land Model version 4. J. Geophys. Res. Atmos., 119, 7091–7103, doi:10.1002/2013JD021329.

    • Search Google Scholar
    • Export Citation

  • Zhao, L., Yang Z.-L. , and Hoar T. J. , 2016: Global soil moisture estimation by assimilating AMSR-E brightness temperatures in a coupled CLM4–RTM–DART system. J. Hydrometeor., 17, 2431–2454, doi:10.1175/JHM-D-15-0218.1.

    • Search Google Scholar
    • Export Citation
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