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Connecting Satellite Observations with Water Cycle Variables Through Land Data Assimilation: Examples Using the NASA GEOS-5 LDAS

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Abstract

A land data assimilation system (LDAS) can merge satellite observations (or retrievals) of land surface hydrological conditions, including soil moisture, snow, and terrestrial water storage (TWS), into a numerical model of land surface processes. In theory, the output from such a system is superior to estimates based on the observations or the model alone, thereby enhancing our ability to understand, monitor, and predict key elements of the terrestrial water cycle. In practice, however, satellite observations do not correspond directly to the water cycle variables of interest. The present paper addresses various aspects of this seeming mismatch using examples drawn from recent research with the ensemble-based NASA GEOS-5 LDAS. These aspects include (1) the assimilation of coarse-scale observations into higher-resolution land surface models, (2) the partitioning of satellite observations (such as TWS retrievals) into their constituent water cycle components, (3) the forward modeling of microwave brightness temperatures over land for radiance-based soil moisture and snow assimilation, and (4) the selection of the most relevant types of observations for the analysis of a specific water cycle variable that is not observed (such as root zone soil moisture). The solution to these challenges involves the careful construction of an observation operator that maps from the land surface model variables of interest to the space of the assimilated observations.

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References

  • Albergel C, Calvet J-C, Mahfouf J-F, Rüdiger C, Barbu AL, Lafont S, Roujean J-L, Walker JP, Crapeau M, Wigneron J-P (2010) Monitoring of water and carbon fluxes using a land data assimilation system: a case study for southwestern France. Hydrol Earth Syst Sci 14:1109–1124. doi:10.5194/hess-14-1109-2010

    Google Scholar 

  • Andreadis K, Lettenmaier D (2006) Assimilating remotely sensed snow observations into a macroscale hydrology model. Adv Water Resour 29:872–886

    Article  Google Scholar 

  • Andreadis KM, Clark EA, Lettenmaier DP, Alsdorf DE (2007) Prospects for river discharge and depth estimation through assimilation of swath-altimetry into a raster-based hydrodynamics model. Geophys Res Lett 34:L10403. doi:10.1029/2007GL029721

    Article  Google Scholar 

  • Barnett TP, Adam JC, Lettenmaier DP (2005) Potential impacts of a warming climate on water availability in snow-dominated regions. Nature 438:303–309. doi:10.1038/nature04141

    Article  Google Scholar 

  • Bartalis Z, Wagner W, Naeimi V, Hasenauer S, Scipal K, Bonekamp H, Figa J, Anderson C (2007) Initial soil moisture retrievals from the METOP-A Advanced Scatterometer (ASCAT). Geophys Res Lett 34:L20401. doi:10.1029/2007GL031088

    Article  Google Scholar 

  • Biancamaria S, Durand M, Andreadis KM, Bates PD, Boone A, Mognard NM, Rodríguez E, Alsdorf DE, Lettenmaier DP, Clark EA (2011) Assimilation of virtual wide swath altimetry to improve Arctic river modeling. Remote Sens Environ 115:373–381. doi:10.1016/j.rse.2010.09.008

    Article  Google Scholar 

  • Brasnett B (1999) A global analysis of snow depth for numerical weather prediction. J Appl Meteorol 38:726–740

    Article  Google Scholar 

  • Brown RD, Brasnett B (2010) Canadian Meteorological Centre (CMC) daily snow depth analysis data. Environment Canada, Boulder, Colorado, National Snow and Ice Data Center, available at http://nsidc.org/data/docs/daac/nsidc0447_CMC_snow_depth

  • Bruinsma S, Lemoine J-M, Biancale R, Vales N (2010) CNES/GRGS 10-day gravity field models (release 2) and the evaluation. Adv Space Res 45:587–601

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Crow WT, Reichle RH (2008) Comparison of adaptive filtering techniques for land surface data assimilation. Water Resour Res 44:W08423. doi:10.1029/2008WR006883

    Google Scholar 

  • Crow WT, van den Berg MJ (2010) An improved approach for estimating observation and model error parameters for soil moisture data assimilation. Water Resour Res 46:W12519. doi:10.1029/2010WR009402

    Google Scholar 

  • Crow WT, Wood EF (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

    Article  Google Scholar 

  • de Jeu RAM, Wagner W, Holmes TRH, Dolman AJ, Giesen NC, Friesen J (2008) Global soil moisture patterns observed by space borne microwave radiometers and scatterometers. Surv Geophys 29:399–420. doi:10.1007/s10712-008-9044-0

    Article  Google Scholar 

  • De Lannoy GJM, Reichle RH, Houser PR, Pauwels VRN, Verhoest NEC (2007) Correcting for forecast bias in soil moisture assimilation with the ensemble Kalman filter. Water Resour Res 43:W09410. doi:10.1029/2006WR005449

    Google Scholar 

  • De Lannoy GJM, Reichle RH, Houser PR, Arsenault KR, Verhoest NEC, Pauwels VRN (2010) Satellite-scale snow water equivalent assimilation into a high-resolution land surface model. J Hydrometeorol 11:352–369. doi:10.1175/2009JHM1192.1

    Article  Google Scholar 

  • De Lannoy GJM, Reichle RH, Arsenault KR, Houser PR, Kumar SV, Verhoest NEC, Pauwels VRN (2012) Multi-scale assimilation of AMSR-E snow water equivalent and MODIS snow cover fraction in northern Colorado. Water Resour Res 48:W01522. doi:10.1029/2011WR010588

    Google Scholar 

  • De Lannoy GJM, Reichle RH, Pauwels VRN (2013) Global calibration of the GEOS-5 L-band microwave radiative transfer model over land using SMOS observations. J Hydrometeorol. doi:10.1175/JHM-D-12-092.1

  • de Rosnay P, Drusch M, Vasiljevic D, Balsamo G, Albergel C, Isaksen L (2012a) A simplified extended Kalman filter for the global operational soil moisture analysis at ECMWF. Q J R Meteorol Soc. doi:10.1002/qj.2023

  • de Rosnay P, Balsamo G, Albergel C, Munoz Sabater J, Isaksen L (2012b) Initialisation of land surface variables for numerical weather prediction. Surv Geophys. doi:10.1007/s10712-012-9207-x

  • Dee DP et al (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137:553–597. doi:10.1002/qj.828

    Article  Google Scholar 

  • Draper CS, Reichle RH, De Lannoy GJM, Liu Q (2012) Assimilation of passive and active microwave soil moisture retrievals. Geophys Res Lett 39:L04401. doi:10.1029/2011GL050655

    Article  Google Scholar 

  • 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 data set. J Geophys Res 112:D03102. doi:10.1029/2006JD007478

    Google Scholar 

  • Drusch M, Wood EF, Gao H (2005) Observation operators for the direct assimilation of TRMM microwave imager retrieved soil moisture. Geophys Res Let 32:L15403. doi:10.1029/2005GL023623

    Article  Google Scholar 

  • Ducharne A, Koster RD, Suarez MJ, Stieglitz M, Kumar P (2000) A catchment-based approach to modeling land surface processes in a general circulation model, 2: parameter estimation and model demonstration. J Geophys Res 105(20):24823–24838

    Article  Google Scholar 

  • Dunne S, 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

    Google Scholar 

  • Durand M, Margulis S (2008) Effects of uncertainty magnitude and accuracy on assimilation of multi-scale measurements for snowpack characterization. J Geophys Res 113:D02105. doi:10.1029/2007JD008662

    Google Scholar 

  • Durand M, Andreadis KM, Alsdorf DE, Lettenmaier DP, Moller D, Wilson MD (2008) Estimation of bathymetric depth and slope from data assimilation of swath altimetry into a hydrodynamic model. Geophys Res Lett 35:L20401. doi:10.1029/2008GL034150

    Article  Google Scholar 

  • Durand M, Fu LL, Lettenmaier DP, Alsdorf D, Rodríguez E, Esteban-Fernandez D (2010) The Surface Water and Ocean Topography mission: observing terrestrial surface water and oceanic submesoscale eddies. Proc IEEE 98:766–779

    Article  Google Scholar 

  • Ek M, Mitchell K, Yin L, Rogers P, Grunmann P, Koren V, Gayno G, Tarpley JD (2003) Implementation of Noah land surface model advances in the NCEP operational mesoscale Eta model. J Geophys Res 108(D22):8851. doi:10.1029/2002JD003296

    Article  Google Scholar 

  • Entekhabi D et al (2010) The Soil Moisture Active and Passive (SMAP) mission. Proc IEEE 98:704–716. doi:10.1109/JPROC.2010.2043918

    Article  Google Scholar 

  • Evensen G (2003) The ensemble Kalman filter: theoretical formulation and practical implementation. Ocean Dyn 53:343–367. doi:10.1007/s10236-003-0036-9

    Article  Google Scholar 

  • Eyre JR, Kelly GA, McNally AP, Andersson E, Persson A (1993) Assimilation of TOVS radiance information through one-dimensional variational analysis. Q J R Meteorol Soc 119:1427–1463. doi:10.1002/qj.49711951411

    Article  Google Scholar 

  • Forman BA, Reichle RH, Rodell M (2012) Assimilation of terrestrial water storage from GRACE in a snow-dominated basin. Water Resour Res 48:W01507. doi:10.1029/2011WR011239

    Google Scholar 

  • Forman BA, Reichle RH, Derksen C (2013) Estimating passive microwave brightness temperature over snow-covered land in North America using a land surface model and an artificial neural network. IEEE Trans Geosci Remote Sens (in press)

  • Foster JL, Sun C, Walker JP, Kelly R, Chang A, Dong J, Powell H (2005) Quantifying the uncertainty in passive microwave snow water equivalent observations. Remote Sens Environ 92(2):187–203

    Article  Google Scholar 

  • Foster JL et al (2011) A blended global snow product using visible, passive microwave and scatterometer satellite data. Int J Remote Sens 32(5):1371–1395. doi:10.1080/01431160903548013

    Article  Google Scholar 

  • Gao Y, Xie H, Lu N, Yao T, Liang T (2010) Toward advanced daily cloud-free snow cover and snow water equivalent products from Terra-Aqua MODIS and Aqua AMSR-E measurements. J Hydrol 385:23–35

    Article  Google Scholar 

  • Hall DK, Riggs GA (2007) Accuracy assessment of the MODIS snow products. Hydrol Process 21:1534–1547

    Article  Google Scholar 

  • Hall DK, Riggs GA, Foster JL, Kumar SV (2010) Development and evaluation of a cloud-gap-filled MODIS daily snow-cover product. Remote Sens Environ 114:496–503

    Article  Google Scholar 

  • Hilker T, Hall FG, Tucker CJ, Coops NC, Black TA, Nichol CJ, Sellers PJ, Barr A, Hollinger DY, Munger JW (2012) Data assimilation of photosynthetic light-use efficiency using multi-angular satellite data: II. Model implementation and validation. Remote Sens Environ 121:287–300

    Article  Google Scholar 

  • Horwath M, Lemoine J-M, Biancale R, Bourgogne S (2011) Improved GRACE science results after adjustment of geometric biases in the Level-1B K-band ranging data. J Geod 85(1):23–38

    Article  Google Scholar 

  • Houborg R, Rodell M, Li B, Reichle RH, Zaitchik BF (2012) Drought indicators based on model assimilated GRACE terrestrial water storage observations. Water Resour Res 48:W07525. doi:10.1029/2011WR011291

    Google Scholar 

  • Jackson TJ et al (2010) Validation of Advanced Microwave Scanning Radiometer soil moisture products. IEEE Trans Geosci Remote Sens 48:4256–4272. doi:10.1109/TGRS.2010.2051035

    Article  Google Scholar 

  • Joiner J, Dee DP (2000) An error analysis of radiance and suboptimal retrieval assimilation. Q J R Meteorol Soc 126:1495–1514. doi:10.1002/qj.49712656514

    Article  Google Scholar 

  • Kaminski T, Knorr W, Scholze M, Gobron N, Pinty B, Giering R, Mathieu P-P (2012) Consistent assimilation of MERIS FAPAR and atmospheric CO2 into a terrestrial vegetation model and interactive mission benefit analysis. Biogeosciences 9:3173–3184. doi:10.5194/bg-9-3173-2012

    Article  Google Scholar 

  • Kelly RE (2009) The AMSR-E snow depth algorithm: description and initial results. J Remote Sens Soc Jpn 29:307–317

    Google Scholar 

  • Kerr Y et al (2010) The SMOS mission: new tool for monitoring key elements of the global water cycle. Proc IEEE 98(5):666–687

    Article  Google Scholar 

  • Kim Y, Kimball JS, McDonald KC, Glassy J (2010) Developing a global data record of daily landscape freeze/thaw status using satellite passive microwave remote sensing. IEEE Trans Geosci Remote Sens 49:949–960. doi:10.1109/TGRS.2010.2070515

    Article  Google Scholar 

  • Knorr W, Kaminski T, Scholze M, Gobron N, Pinty B, Giering R, Mathieu P-P (2010) Carbon cycle data assimilation with a generic phenology model. J Geophys Res 115:G04017. doi:10.1029/2009JG001119

    Google Scholar 

  • Knowles KW, Savoie MH, Armstrong RL, Brodzik MJ (2006) Updated 2012. AMSR-E/Aqua daily EASE-grid brightness temperatures, September 2002 through May 2011. National Snow and Ice Data Center, Boulder, Colorado USA. Digital media

  • Koster RD, Suarez MJ, Ducharne A, Stieglitz M, Kumar P (2000) A catchment-based approach to modeling land surface processes in a general circulation model, 1: model structure. J Geophys Res 105(20):24809–24822

    Article  Google Scholar 

  • Kumar SV, Reichle RH, Peters-Lidard CD, Koster RD, Zhan X, Crow WT, Eylander JB, Houser PR (2008a) A land surface data assimilation framework using the Land Information System: description and applications. Adv Water Resour 31:1419–1432. doi:10.1016/j.advwatres.2008.01.013

    Article  Google Scholar 

  • Kumar SV, Peters-Lidard C, Tian Y, Reichle R, Geiger J, Alonge C, Eylander J, Houser P (2008b) An integrated hydrologic modeling and data assimilation framework. IEEE Comput 41:52–59. doi:10.1109/MC.2008.511

    Google Scholar 

  • Kumar SV, Reichle RH, Harrison KW, Peters-Lidard CD, Yatheendradas S, Santanello JA (2012) A comparison of methods for a priori bias correction in soil moisture data assimilation. Water Resour Res 48:W03515. doi:10.1029/2010WR010261

    Google Scholar 

  • Lahoz W, De Lannoy GJM (2013) Closing the gaps in our knowledge of the hydrological cycle over land: conceptual problems. Surv Geophys. doi:10.1007/s10712-013-9221-7

  • Li L, Gaiser P, Jackson T, Bindlish R, Du J (2007) WindSat soil moisture algorithm and validation. In: IEEE international geoscience and remote sensing symposium 23–28 July 2007, pp 1188–1191. doi:10.1109/IGARSS.2007.4423017

  • Li B, Rodell M, Zaitchik BF, Reichle RH, Koster RD, van Dam TM (2012) Assimilation of GRACE terrestrial water storage into a land surface model: evaluation and potential value for drought monitoring in western and central Europe. J Hydrol 446–447:103–115. doi:10.1016/j.jhydrol.2012.04.035

    Article  Google Scholar 

  • Liu Q, Reichle RH, Bindlish R, Cosh MH, Crow WT, de Jeu R, De Lannoy GJM, Huffman GJ, Jackson TJ (2011a) The contributions of precipitation and soil moisture observations to the skill of soil moisture estimates in a land data assimilation system. J Hydrometeorol 12:750–765. doi:10.1175/JHM-D-10-05000.1

    Article  Google Scholar 

  • Liu YY, Parinussa RM, Dorigo WA, de Jeu RAM, Wagner W, van Dijk AIJM, McCabe MF, Evans JP (2011b) Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals. Hydrol Earth Syst Sci 15(2):425–436. doi:10.5194/hess-15-425-2011

    Google Scholar 

  • Margulis SA, McLaughlin D, Entekhabi D, 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

    Google Scholar 

  • Muñoz Sabater J, Rüdiger C, Calvet J-C, Fritz N, Jarlan L, Kerr Y (2008) Joint assimilation of surface soil moisture and LAI observations into a land surface model. Agric For Meteorol 148:1362–1373

    Article  Google Scholar 

  • Njoku EG, Jackson TJ, Lakshmi V, Chan TK, Nghiem SV (2003) Soil moisture retrieval from AMSR-E. IEEE Trans Geosci Remote Sens 41:215–229. doi:10.1109/TGRS.2002.808243

    Article  Google Scholar 

  • Pan M, Wood EF (2006) Data assimilation for estimating the terrestrial water budget using a constrained ensemble Kalman filter. J Hydrometeorol 7:534–547

    Article  Google Scholar 

  • Pan M, Wood EF, Wojcik R, McCabe MF (2008) Estimation of regional terrestrial water cycle using multi-sensor remote sensing observations and data assimilation. Remote Sens Environ 112:1282–1294. doi:10.1016/j.rse.2007.02.039

    Article  Google Scholar 

  • Parinussa RM, Holmes TRH, de Jeu RAM (2012) Soil moisture retrievals from the WindSat spaceborne polarimetric microwave radiometer. IEEE Trans Geosci Remote Sens. doi:10.1109/TGRS.2011.2174643

  • Pulliainen J (2006) Mapping of snow water equivalent and snow depth in boreal and subarctic zones by assimilating space-borne microwave radiometer data and ground-based observations. Remote Sens Environ 101:257–269

    Article  Google Scholar 

  • Reichle RH (2012) The MERRA-land data product (Version 1.1). NASA Global Modeling and Assimilation Office, Office Note No. 3. Available at http://gmao.gsfc.nasa.gov/pubs/

  • Reichle RH, Koster RD (2003) Assessing the impact of horizontal error correlations in background fields on soil moisture estimation. J Hydrometeorol 4:1229–1242

    Article  Google Scholar 

  • Reichle RH, Koster RD (2004) Bias reduction in short records of satellite soil moisture. Geophys Res Lett 31:L19501. doi:10.1029/2004GL020938

    Article  Google Scholar 

  • Reichle RH, Koster RD (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

    Article  Google Scholar 

  • Reichle RH, Entekhabi D, McLaughlin DB (2001) Downscaling of radiobrightness measurements for soil moisture estimation: a four-dimensional variational data assimilation approach. Water Resour Res 37:2353–2364. doi:10.1029/2001WR000475

    Article  Google Scholar 

  • Reichle RH, McLaughlin DB, Entekhabi D (2002a) Hydrologic data assimilation with the ensemble Kalman filter. Mon Weather Rev 130:103–114

    Article  Google Scholar 

  • Reichle RH, Walker JP, Koster RD, Houser PR (2002b) Extended vs. ensemble Kalman filtering for land data assimilation. J Hydrometeorol 3:728–740

    Article  Google Scholar 

  • Reichle RH, Koster RD, Liu P, Mahanama SPP, Njoku EG, 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

    Google Scholar 

  • Reichle RH, Crow WT, Koster RD, Sharif H, Mahanama SPP (2008a) Contribution of soil moisture retrievals to land data assimilation products. Geophys Res Lett 35:L01404. doi:10.1029/2007GL031986

    Article  Google Scholar 

  • Reichle RH, Crow WT, Keppenne CL (2008b) An adaptive ensemble Kalman filter for soil moisture data assimilation. Water Resour Res 44:W03423. doi:10.1029/2007WR006357

    Google Scholar 

  • Reichle RH, Bosilovich MG, Crow WT, Koster RD, Kumar SV, Mahanama SPP, Zaitchik BF (2009) Recent advances in land data assimilation at the NASA global modeling and assimilation office. In: Park SK, Xu L (eds) Data assimilation for atmospheric, oceanic and hydrologic applications. Springer, New York, pp 407–428. doi:10.1007/978-3-540-71056-1

    Chapter  Google Scholar 

  • Reichle RH, Koster RD, De Lannoy GJM, Forman BA, Liu Q, Mahanama SPP, Toure A (2011) Assessment and enhancement of MERRA land surface hydrology estimates. J Clim 24:6322–6338. doi:10.1175/JCLI-D-10-05033.1

    Article  Google Scholar 

  • Rienecker MM et al (2008) The GEOS-5 data assimilation system—documentation of versions 5.0.1 and 5.1.0, and 5.2.0. NASA Tech. Rep. Series on Global Modeling and Data Assimilation, NASA/TM-2008-104606, vol 27, 92 pp. Available at http://gmao.gsfc.nasa.gov/pubs/

  • Rienecker MM et al (2011) MERRA—NASA’s Modern-Era Retrospective analysis for Research and Applications. J Clim 24:3624–3648. doi:10.1175/JCLI-D-11-00015.1

    Article  Google Scholar 

  • Rodell M et al (2003) The global land data assimilation system. Bull Am Meteorol Soc 85:381–394. doi:10.1175/BAMS-85-3-381

    Article  Google Scholar 

  • Rodell M, Velicogna I, Famiglietti JS (2009) Satellite-based estimates of groundwater depletion in India. Nature 460:999–1002. doi:10.1038/nature08238

    Article  Google Scholar 

  • Rowlands DD, Luthcke SB, Klosko SM, Lemoine FGR, Chinn DS, McCarthy JJ, Cox CM, Anderson OB (2005) Resolving mass flux at high spatial and temporal resolution using GRACE intersatellite measurements. Geophys Res Lett 32:L04310. doi:10.1029/2004GL021908

    Article  Google Scholar 

  • Rowlands DD, Luthcke SB, McCarthy JJ, Klosko SM, Chinn DS, Lemoine FG, Boy J-P, Sabaka TJ (2010) Global mass flux solutions from GRACE: a comparison of parameter estimation strategies—mass concentrations versus Stokes coefficients. J Geophys Res 115:B01403. doi:10.1029/2009JB006546

    Google Scholar 

  • Sahoo AK, De Lannoy GJM, Reichle RH, Houser PR (2012) Assimilation and downscaling of satellite observed soil moisture over the little river experimental watershed in Georgia, USA. Adv Water Resour 52:19–33. doi:10.1016/j.advwatres.2012.08.007

  • Schaefer GL, Cosh MH, Jackson TJ (2007) The USDA Natural Resources Conservation Service Soil Climate Analysis Network (SCAN). J Atmos Ocean Technol 24:2073–2077. doi:10.1175/2007JTECHA930.1

    Article  Google Scholar 

  • Seneviratne SI, Corti T, Davin EL, Hirschi M, Jaeger EB, Lehner I, Orlowsky B, Teuling AJ (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

    Article  Google Scholar 

  • Smith AB et al (2012) The Murrumbidgee soil moisture monitoring network data set. Water Resour Res 48:W07701. doi:10.1029/2012WR011976

    Google Scholar 

  • Stieglitz M, Ducharne A, Koster RD, Suarez M (2001) The impact of detailed snow physics on the simulation of snow cover and subsurface thermodynamics at continental scales. J Hydrometeorol 2:228–242

    Article  Google Scholar 

  • Stöckli R, Rutishauser T, Baker I, Liniger MA, Denning AS (2011) A global reanalysis of vegetation phenology. J Geophys Res 116:G03020. doi:10.1029/2010JG001545

    Google Scholar 

  • Sturm M, Holmgren J, Liston GE (1995) A seasonal snow cover classification system for local to global applications. J Clim 8:1261–1283

    Article  Google Scholar 

  • Sturm M, Taras B, Liston GE, Derksen C, Jonas T, Lea J (2010) Estimating snow water equivalent using snow depth data and climate classes. J Hydrometeorol 11:1380–1394

    Article  Google Scholar 

  • Su H, Yang Z-L, Niu G-Y, Dickinson RE (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

    Google Scholar 

  • Su H, Yang Z-L, Dickinson RE, Wilson CR, 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

    Article  Google Scholar 

  • Swenson S, Wahr J (2006) Post-processing removal of correlated errors in GRACE data. Geophys Res Lett 33:L08402. doi:10.1029/2005GL025285

    Google Scholar 

  • Tang Q, Gao H, Yeh P, Oki T, Su F, Lettenmaier DP (2010) Dynamics of terrestrial water storage change from satellite and surface observations and modeling. J Hydrometeorol 11:156–170

    Article  Google Scholar 

  • Tedesco M, Narvekar PS (2010) Assessment of the NASA AMSR-E SWE product. IEEE J Sel Top Appl Earth Obs Remote Sens 3:141–159

    Article  Google Scholar 

  • Tedesco M, Reichle R, Löw A, Markus T, Foster JL (2010) Dynamic approaches for snow depth retrieval from spaceborne microwave brightness temperature. IEEE Trans Geosci Remote Sens 48(4):1955–1967

    Article  Google Scholar 

  • Trenberth KE, Asrar G (2013) Challenges and opportunities in water cycle research: WCRP contributions. Surv Geophys. doi:10.1007/s10712-012-9214-y

  • Wagner W, Lemoine G, Rott H (1999) A method for estimating soil moisture from ERS scatterometer and soil data. Remote Sens Environ 70:191–207. doi:10.1016/S0034-4257(99)00036-X

    Article  Google Scholar 

  • Wahr J, Swenson S, Zlotnicki V, Velicogna I (2004) Time-variable gravity from GRACE: first results. Geophys Res Lett 31:L11501. doi:10.1029/2004GL019779

    Article  Google Scholar 

  • Wigneron J-P et al (2007) L-band microwave emission of the biosphere (L-MEB) model: description and calibration against experimental data sets over crop fields. Remote Sens Environ 107:639–655

    Article  Google Scholar 

  • Zaitchik BF, Rodell M, Reichle RH (2008) Assimilation of GRACE terrestrial water storage data into a land surface model: results for the Mississippi River basin. J Hydrometeorol 9:535–548. doi:10.1175/2007JHM951.1

    Article  Google Scholar 

  • Zhou Y, McLaughlin D, Entekhabi D (2006) Assessing the performance of the ensemble Kalman filter for land surface data assimilation. Mon Weather Rev 134:2128–2142

    Article  Google Scholar 

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Acknowledgments

The authors thank the organizers of the ISSI Workshop on “The Earth’s Hydrological Cycle” held February 6–10, 2012 and two anonymous reviewers for their efforts. The research was supported by the NASA program on The Science of Terra and Aqua, the NASA Soil Moisture Active Passive mission, the NASA Postdoctoral Program, and the NASA High-End Computing program.

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Authors and Affiliations

  1. Global Modeling and Assimilation Office (Code 610.1), NASA Goddard Space Flight Center, 8800 Greenbelt Road, Greenbelt, MD, 20771, USA

    Rolf H. Reichle, Gabriëlle J. M. De Lannoy, Clara S. Draper & Qing Liu

  2. Universities Space Research Association, Columbia, MD, USA

    Gabriëlle J. M. De Lannoy & Clara S. Draper

  3. Department of Civil and Environmental Engineering, University of Maryland, College Park, MD, USA

    Barton A. Forman

  4. Science Systems and Applications, Inc., Lanham, MD, USA

    Qing Liu

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  1. Rolf H. Reichle
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  2. Gabriëlle J. M. De Lannoy
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  3. Barton A. Forman
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  4. Clara S. Draper
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  5. Qing Liu
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Correspondence to Rolf H. Reichle.

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Reichle, R.H., De Lannoy, G.J.M., Forman, B.A. et al. Connecting Satellite Observations with Water Cycle Variables Through Land Data Assimilation: Examples Using the NASA GEOS-5 LDAS. Surv Geophys 35, 577–606 (2014). https://doi.org/10.1007/s10712-013-9220-8

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  • DOI: https://doi.org/10.1007/s10712-013-9220-8

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