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

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.

[1]  N. Verhoest,et al.  Correcting for forecast bias in soil moisture assimilation with the ensemble Kalman filter , 2007 .

[2]  Joanna Joiner,et al.  An error analysis of radiance and suboptimal retrieval assimilation , 2000 .

[3]  R. Koster,et al.  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) , 2007 .

[4]  Lars Isaksen,et al.  Initialisation of Land Surface Variables for Numerical Weather Prediction , 2014, Surveys in Geophysics.

[5]  Michael Durand,et al.  Assimilation of virtual wide swath altimetry to improve Arctic river modeling , 2011 .

[6]  T. Barnett,et al.  Potential impacts of a warming climate on water availability in snow-dominated regions , 2005, Nature.

[7]  L. Isaksen,et al.  A simplified Extended Kalman Filter for the global operational soil moisture analysis at ECMWF , 2013 .

[8]  Victor Zlotnicki,et al.  Time‐variable gravity from GRACE: First results , 2004 .

[9]  Robert M. Parinussa,et al.  Soil Moisture Retrievals From the WindSat Spaceborne Polarimetric Microwave Radiometer , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Paul R. Houser,et al.  An Integrated Hydrologic Modeling and Data Assimilation Framework , 2008 .

[11]  E. Njoku,et al.  The Soil Moisture Active and Passive Mission (SMAP): Science and applications , 2009, 2009 IEEE Radar Conference.

[12]  F. LeMoine,et al.  Resolving mass flux at high spatial and temporal resolution using GRACE intersatellite measurements , 2005 .

[13]  M. Rodell,et al.  Assimilation of GRACE Terrestrial Water Storage Data into a Land Surface Model: Results for the Mississippi River Basin , 2008 .

[14]  Wade T. Crow,et al.  Comparison of adaptive filtering techniques for land surface data assimilation , 2008 .

[15]  Wade T. Crow,et al.  Recent Advances in Land Data Assimilation at the NASA Global Modeling and Assimilation Office , 2009 .

[16]  R. Koster,et al.  Assessing the Impact of Horizontal Error Correlations in Background Fields on Soil Moisture Estimation , 2003 .

[17]  Dara Entekhabi,et al.  Land surface state and flux estimation using the ensemble Kalman smoother during the Southern Great Plains 1997 field experiment , 2006 .

[18]  Wade T. Crow,et al.  An adaptive ensemble Kalman filter for soil moisture data assimilation , 2007 .

[19]  G. Lannoy,et al.  Global Calibration of the GEOS-5 L-Band Microwave Radiative Transfer Model over Nonfrozen Land Using SMOS Observations , 2013 .

[20]  Debbie Clifford,et al.  Global estimates of snow water equivalent from passive microwave instruments: history, challenges and future developments , 2010 .

[21]  D. McLaughlin,et al.  Hydrologic Data Assimilation with the Ensemble Kalman Filter , 2002 .

[22]  D. McLaughlin,et al.  Assessing the Performance of the Ensemble Kalman Filter for Land Surface Data Assimilation , 2006 .

[23]  R. Koster,et al.  Contribution of soil moisture retrievals to land data assimilation products , 2008 .

[24]  J. R. Eyre,et al.  Assimilation of TOVS radiance information through one-dimensional variational analysis , 1993 .

[25]  Seon Ki Park,et al.  Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. III) , 2009 .

[26]  Yann Kerr,et al.  Joint assimilation of surface soil moisture and LAI observations into a land surface model , 2008 .

[27]  Parag S. Narvekar,et al.  Assessment of the NASA AMSR-E SWE Product , 2010, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[28]  Alfred T. C. Chang,et al.  Quantifying the uncertainty in passive microwave snow water equivalent observations , 2005 .

[29]  D. Lettenmaier,et al.  Assimilating remotely sensed snow observations into a macroscale hydrology model , 2006 .

[30]  Witold F. Krajewski,et al.  Analyses of a long‐term, high‐resolution radar rainfall data set for the Baltimore metropolitan region , 2012 .

[31]  Kenneth W. Harrison,et al.  A comparison of methods for a priori bias correction in soil moisture data assimilation , 2012 .

[32]  Jouni Pulliainen,et al.  Mapping of snow water equivalent and snow depth in boreal and sub-arctic zones by assimilating space-borne microwave radiometer data and ground-based observations , 2006 .

[33]  Hongjie Xie,et al.  Toward advanced daily cloud-free snow cover and snow water equivalent products from Terra-Aqua MODIS and Aqua AMSR-E measurements , 2010 .

[34]  T. Oki,et al.  Dynamics of terrestrial water storage change from satellite and surface observations and modeling. , 2010 .

[35]  N. G. Val’es,et al.  CNES/GRGS 10-day gravity field models (release 2) and their evaluation , 2010 .

[36]  Matthias Drusch,et al.  Observation operators for the direct assimilation of TRMM microwave imager retrieved soil moisture , 2005 .

[37]  Thomas J. Jackson,et al.  Validation of Advanced Microwave Scanning Radiometer Soil Moisture Products , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[38]  Jean-Pierre Wigneron,et al.  Monitoring of water and carbon fluxes using a land data assimilation system: a case study for southwestern France , 2010 .

[39]  Thomas J. Jackson,et al.  Soil moisture retrieval from AMSR-E , 2003, IEEE Trans. Geosci. Remote. Sens..

[40]  Dorothy K. Hall,et al.  Development and evaluation of a cloud-gap-filled MODIS daily snow-cover product , 2010 .

[41]  W. Crow,et al.  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 , 2003 .

[42]  Rasmus Houborg,et al.  Drought Indicators Based on Model Assimilated GRACE Terrestrial Water Storage Observations , 2012 .

[43]  S. Seneviratne,et al.  Investigating soil moisture-climate interactions in a changing climate: A review , 2010 .

[44]  Matthias Drusch,et al.  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 , 2007 .

[45]  Zong-Liang Yang,et al.  Enhancing the estimation of continental-scale snow water equivalent by assimilating MODIS snow cover with the ensemble Kalman filter , 2008 .

[46]  Y. Kerr,et al.  The SMOS Mission: New Tool for Monitoring Key Elements of the Global Water Cycle This satellite mission will use new algorithms to try to forecast weather and estimate climate change from satellite measurements of the Earth's surface. , 2010 .

[47]  Jiancheng Shi,et al.  The Soil Moisture Active Passive (SMAP) Mission , 2010, Proceedings of the IEEE.

[48]  R. Giering,et al.  Carbon cycle data assimilation with a generic phenology model , 2010 .

[49]  Chris Derksen,et al.  Estimating Snow Water Equivalent Using Snow Depth Data and Climate Classes , 2010 .

[50]  Rolf H. Reichle,et al.  Assimilation of passive and active microwave soil moisture retrievals , 2012 .

[51]  Jeffrey P. Walker,et al.  THE GLOBAL LAND DATA ASSIMILATION SYSTEM , 2004 .

[52]  Delwyn Moller,et al.  Estimation of bathymetric depth and slope from data assimilation of swath altimetry into a hydrodynamic model , 2008 .

[53]  R. Koster,et al.  Assessment and Enhancement of MERRA Land Surface Hydrology Estimates , 2011 .

[54]  Rolf Reichle,et al.  Dynamic Approaches for Snow Depth Retrieval From Spaceborne Microwave Brightness Temperature , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[55]  Randal D. Koster,et al.  Global assimilation of satellite surface soil moisture retrievals into the NASA Catchment land surface model , 2005 .

[56]  R. Koster,et al.  Assimilation of GRACE terrestrial water storage into a land surface model: Evaluation and potential value for drought monitoring in western and central Europe , 2012 .

[57]  D. Hall,et al.  Accuracy assessment of the MODIS snow products , 2007 .

[58]  Randal D. Koster,et al.  Bias reduction in short records of satellite soil moisture , 2004 .

[59]  Youngwook Kim,et al.  Developing a Global Data Record of Daily Landscape Freeze/Thaw Status Using Satellite Passive Microwave Remote Sensing , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[60]  S. Swenson,et al.  Post‐processing removal of correlated errors in GRACE data , 2006 .

[61]  Michael Durand,et al.  The Surface Water and Ocean Topography Mission: Observing Terrestrial Surface Water and Oceanic Submesoscale Eddies , 2010, Proceedings of the IEEE.

[62]  Zong-Liang Yang,et al.  Multisensor snow data assimilation at the continental scale: The value of Gravity Recovery and Climate Experiment terrestrial water storage information , 2010 .

[63]  Edward J. Kim,et al.  A blended global snow product using visible, passive microwave and scatterometer satellite data , 2011 .

[64]  Y. Kerr,et al.  L-band Microwave Emission of the Biosphere (L-MEB) Model: Description and calibration against experimental data sets over crop fields , 2007 .

[65]  Li Li,et al.  WindSat soil moisture algorithm and validation , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[66]  G. Gayno,et al.  Implementation of Noah land-surface model advances in the NCEP operational mesoscale Eta model , 2003 .

[67]  Niko E. C. Verhoest,et al.  Satellite-Scale Snow Water Equivalent Assimilation into a High-Resolution Land Surface Model , 2010 .

[68]  William A. Lahoz,et al.  Closing the Gaps in Our Knowledge of the Hydrological Cycle over Land: Conceptual Problems , 2014, Surveys in Geophysics.

[69]  Steven M. Klosko,et al.  Global Mass Flux Solutions from GRACE: A Comparison of Parameter Estimation Strategies - Mass Concentrations Versus Stokes Coefficients , 2010 .

[70]  Yi Y. Liu,et al.  Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals , 2011 .

[71]  Praveen Kumar,et al.  A catchment‐based approach to modeling land surface processes in a general circulation model: 1. Model structure , 2000 .

[72]  Chris Derksen,et al.  Estimating Passive Microwave Brightness Temperature Over Snow-Covered Land in North America Using a Land Surface Model and an Artificial Neural Network , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[73]  W. Wagner,et al.  A Method for Estimating Soil Moisture from ERS Scatterometer and Soil Data , 1999 .

[74]  D. Entekhabi,et al.  Land data assimilation and estimation of soil moisture using measurements from the Southern Great Plains 1997 Field Experiment , 2001 .

[75]  Richard Kelly,et al.  The AMSR-E Snow Depth Algorithm: Description and Initial Results , 2009 .

[76]  Wade T. Crow,et al.  An improved approach for estimating observation and model error parameters in soil moisture data assimilation , 2010 .

[77]  Jeffrey P. Walker,et al.  Extended versus Ensemble Kalman Filtering for Land Data Assimilation , 2002 .

[78]  B. Brasnett,et al.  A Global Analysis of Snow Depth for Numerical Weather Prediction , 1999 .

[79]  R. Giering,et al.  Consistent assimilation of MERIS FAPAR and atmospheric CO2 into a terrestrial vegetation model and interactive mission benefit analysis , 2011 .

[80]  S. Schubert,et al.  MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications , 2011 .

[81]  M. Durand,et al.  Effects of uncertainty magnitude and accuracy on assimilation of multiscale measurements for snowpack characterization , 2008 .

[82]  Ricardo Todling,et al.  The GEOS-5 Data Assimilation System-Documentation of Versions 5.0.1, 5.1.0, and 5.2.0 , 2008 .

[83]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .

[84]  M. Rodell,et al.  Assimilation of terrestrial water storage from GRACE in a snow‐dominated basin , 2011 .

[85]  J. D. Tarpley,et al.  Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model , 2003 .

[86]  N. Verhoest,et al.  Multi-Scale Assimilation of AMSR-E Snow Water Equivalent and MODIS Snow Cover Fraction , 2011 .

[87]  J. Lemoine,et al.  Improved GRACE science results after adjustment of geometric biases in the Level-1B K-band ranging data , 2010 .

[88]  P. Houser,et al.  Assimilation and downscaling of satellite observed soil moisture over the Little River Experimental Watershed in Georgia, USA , 2013 .

[89]  D. McLaughlin,et al.  Downscaling of radio brightness measurements for soil moisture estimation: A four‐dimensional variational data assimilation approach , 2001 .

[90]  Sujay V. Kumar,et al.  Multiscale assimilation of Advanced Microwave Scanning Radiometer–EOS snow water equivalent and Moderate Resolution Imaging Spectroradiometer snow cover fraction observations in northern Colorado , 2012 .

[91]  W. Wagner,et al.  Initial soil moisture retrievals from the METOP‐A Advanced Scatterometer (ASCAT) , 2007 .

[92]  Max J. Suarez,et al.  The Impact of Detailed Snow Physics on the Simulation of Snow Cover and Subsurface Thermodynamics at Continental Scales , 2001 .

[93]  Jon Holmgren,et al.  A Seasonal Snow Cover Classification System for Local to Global Applications. , 1995 .

[94]  Wade T. Crow,et al.  A land surface data assimilation framework using the land information system : Description and applications , 2008 .

[95]  Geir Evensen,et al.  The Ensemble Kalman Filter: theoretical formulation and practical implementation , 2003 .

[96]  D. Lettenmaier,et al.  Prospects for river discharge and depth estimation through assimilation of swath‐altimetry into a raster‐based hydrodynamics model , 2007 .

[97]  E. Wood,et al.  Data Assimilation for Estimating the Terrestrial Water Budget Using a Constrained Ensemble Kalman Filter , 2006 .

[98]  M. Mccabe,et al.  Estimation of regional terrestrial water cycle using multi-sensor remote sensing observations and data assimilation , 2008 .

[99]  R. Houborg,et al.  Drought indicators based on model‐assimilated Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage observations , 2012 .

[100]  J. Famiglietti,et al.  Satellite-based estimates of groundwater depletion in India , 2009, Nature.

[101]  W. Wagner,et al.  Global Soil Moisture Patterns Observed by Space Borne Microwave Radiometers and Scatterometers , 2008 .

[102]  A. B. Smith,et al.  The Murrumbidgee soil moisture monitoring network data set , 2012 .

[103]  Wade T. Crow,et al.  The Contributions of Precipitation and Soil Moisture Observations to the Skill of Soil Moisture Estimates in a Land Data Assimilation System , 2011 .

[104]  Mark A. Liniger,et al.  A global reanalysis of vegetation phenology , 2011 .

[105]  Ghassem R. Asrar,et al.  Challenges and Opportunities in Water Cycle Research: WCRP Contributions , 2014, Surveys in Geophysics.

[106]  Jun Kong,et al.  Analysis and Semantic Querying in Large Biomedical Image Datasets , 2008, Computer.

[107]  Thomas Hilker,et al.  Data assimilation of photosynthetic light-use efficiency using multi-angular satellite data: II Model implementation and validation , 2012 .

[108]  R. Koster,et al.  A catchment-based approach to modeling land surface processes in a general circulation model , 2000 .

[109]  T. Jackson,et al.  The USDA Natural Resources Conservation Service Soil Climate Analysis Network (SCAN) , 2007 .