Development of the Coupled Atmosphere and Land Data Assimilation System (CALDAS) and Its Application Over the Tibetan Plateau

Land surface heterogeneities are important for accurate estimation of land-atmosphere interactions and their feedbacks on water and energy budgets. To physically introduce existing land surface heterogeneities into a mesoscale model, a land data assimilation system was coupled with a mesoscale model (LDAS-A) to assimilate low-frequency satellite microwave observations for soil moisture and the combined system was applied in the Tibetan Plateau. Though the assimilated soil moisture distribution showed high correlation with Advanced Microwave Scanning Radiometer on the Earth Observing System soil moisture retrievals, the assimilated land surface conditions suffered substantial errors and drifts owing to predicted model forcings (i.e., solar radiation and rainfall). To overcome this operational pitfall, the Coupled Land and Atmosphere Data Assimilation System (CALDAS) was developed by coupling the LDAS-A with a cloud microphysics data assimilation. CALDAS assimilated lower frequency microwave data to improve representation of land surface conditions, and merged them with higher frequency microwave data to improve the representation of atmospheric conditions over land surfaces. The simulation results showed that CALDAS effectively assimilated atmospheric information contained in higher frequency microwave data and significantly improved correlation of cloud distribution compared with satellite observation. CALDAS also improved biases in cloud conditions and associated rainfall events, which contaminated land surface conditions in LDAS-A. Improvements in predicted clouds resulted in better land surface model forcings (i.e., solar radiation and rainfall), which maintained assimilated surface conditions in accordance with observed conditions during the model forecast. Improvements in both atmospheric forcings and land surface conditions enhanced land-atmosphere interactions in the CALDAS model, as confirmed by radiosonde observations.

[1]  Guosheng Liu,et al.  A Fast and Accurate Model for Microwave Radiance Calculations , 1998 .

[2]  Simulation of weather systems over Indian region using mesoscale models , 2007 .

[3]  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 .

[4]  C. Justice,et al.  A Revised Land Surface Parameterization (SiB2) for Atmospheric GCMS. Part II: The Generation of Global Fields of Terrestrial Biophysical Parameters from Satellite Data , 1996 .

[5]  G. Evensen.  Data Assimilation: The Ensemble Kalman Filter , 2006 .

[6]  Klaus Scipal,et al.  Assimilation of a ERS scatterometer derived soil moisture index in the ECMWF numerical weather prediction system , 2008 .

[7]  Merritt N. Deeter,et al.  A new satellite retrieval method for precipitable water vapor over land and ocean , 2007 .

[8]  T. Jackson,et al.  Vegetative and Atmospheric Corrections for the Soil Moisture Retrieval from Passive Microwave Remote Sensing Data: Results from the Southern Great Plains Hydrology Experiment 1997 , 2001 .

[9]  Merritt N. Deeter,et al.  New dual‐frequency microwave technique for retrieving liquid water path over land , 2006 .

[10]  Toshio Koike,et al.  Development of a physically-based soil moisture retrieval algorithm for spaceborne passive microwave radiometers and its application to AMSR-E , 2009 .

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

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

[13]  Hiroyuki Tsutsui,et al.  Field-Supported Verification and Improvement of a Passive Microwave Surface Emission Model for Rough, Bare, and Wet Soil Surfaces by Incorporating Shadowing Effects , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[14]  K. Droegemeier,et al.  The Advanced Regional Prediction System (ARPS) – A multi-scale nonhydrostatic atmospheric simulation and prediction model. Part I: Model dynamics and verification , 2000 .

[15]  Jeffrey P. Walker,et al.  An EKF assimilation of AMSR-E soil moisture into the ISBA land surface scheme , 2009 .

[16]  Yangang Liu,et al.  CLOUD PARAMETERIZATIONS, CLOUD PHYSICS, AND THEIR CONNECTIONS: AN OVERVIEW. , 2002 .

[17]  Yann Kerr,et al.  The SMOS Mission: New Tool for Monitoring Key Elements ofthe Global Water Cycle , 2010, Proceedings of the IEEE.

[18]  Leung Tsang,et al.  Theory for thermal microwave emission from a bounded medium containing spherical scatterers , 1977 .

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

[20]  Chunlin Huang,et al.  Experiments of one-dimensional soil moisture assimilation system based on ensemble Kalman filter , 2008 .

[21]  Sharon E. Nicholson,et al.  Land surface processes and Sahel climate , 2000 .

[22]  S. Sorooshian,et al.  Effective and efficient global optimization for conceptual rainfall‐runoff models , 1992 .

[23]  B. Kahn,et al.  Characterization of cloud liquid water content distributions from CloudSat , 2010 .

[24]  Toshio Koike,et al.  Retrieval of Atmospheric Integrated Water Vapor and Cloud Liquid Water Content Over the Ocean From Satellite Data Using the 1-D-Var Ice Cloud Microphysics Data Assimilation System (IMDAS) , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[25]  J. Dudhia,et al.  Four-Dimensional Variational Data Assimilation of Heterogeneous Mesoscale Observations for a Strong Convective Case , 2000 .

[26]  A. Waldvogel,et al.  Hail and Raindrop Size Distributions from a Swiss Multicell Storm , 1975 .

[27]  S. Boussetta,et al.  Development of a coupled land–atmosphere satellite data assimilation system for improved local atmospheric simulations , 2008 .

[28]  IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 34. NO. 4, JULY 1996 Universal Multifractal Scaling of Synthetic , 1996 .

[29]  Patrick Minnis,et al.  Determination of ice water path in ice‐over‐water cloud systems using combined MODIS and AMSR‐E measurements , 2006 .

[30]  Keiji Imaoka,et al.  Improvement of the AMSR-E Algorithm for Soil Moisture Estimation by Introducing a Fractional Vegetation Coverage Dataset Derived from MODIS Data , 2009 .

[31]  Christopher W. O'Dell,et al.  Cloud Liquid Water Path from Satellite-Based Passive Microwave Observations: A New Climatology over the Global Oceans , 2008 .

[32]  Roger G. Barry,et al.  Cloud cover analysis with Arctic AVHRR data: 1. Cloud detection , 1989 .

[33]  Jeremy S. Pal,et al.  Teleconnections of soil moisture and rainfall during the 1993 midwest summer flood , 2002 .

[34]  M. Xue,et al.  The Advanced Regional Prediction System (ARPS) – A multi-scale nonhydrostatic atmospheric simulation and prediction tool. Part II: Model physics and applications , 2001 .

[35]  Dong Huang,et al.  Determination of cloud liquid water distribution using 3D cloud tomography , 2008 .

[36]  Hiroyuki Tsutsui,et al.  Improving the AMSR-E Soil Moisture Algorithm of the University of Tokyo through Field Experiments and Parameters Optimization , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[37]  Wade T. Crow,et al.  Role of Subsurface Physics in the Assimilation of Surface Soil Moisture Observations , 2009 .

[38]  Ying-Hwa Kuo,et al.  Variational Assimilation of Precipitable Water Using a Nonhydrostatic Mesoscale Adjoint Model. Part I: Moisture Retrieval and Sensitivity Experiments , 1996 .

[39]  Gyu-Ho Lim,et al.  Variational assimilation of slant-path wet delay measurements from a hypothetical ground-based GPS network. Part I: Comparison with precipitable water assimilation , 2003 .

[40]  H. D. Orville,et al.  Bulk Parameterization of the Snow Field in a Cloud Model , 1983 .

[41]  Xiangde Xu,et al.  A New Integrated Observational System Over the Tibetan Plateau , 2008 .

[42]  Qin Li,et al.  Emission of rough surfaces calculated by the integral equation method with comparison to three-dimensional moment method simulations , 2003, IEEE Trans. Geosci. Remote. Sens..

[43]  L. C. Henyey,et al.  Diffuse radiation in the Galaxy , 1940 .

[44]  Elizaveta Zabolotskikh,et al.  Atmospheric Water Vapor and Cloud Liquid Water Retrieval Over the Arctic Ocean Using Satellite Passive Microwave Sensing , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[45]  Hui Lu,et al.  Development of a Satellite Land Data Assimilation System Coupled With a Mesoscale Model in the Tibetan Plateau , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[46]  Guoping Zhang,et al.  ATOVS data assimilation in two different heavy rainfalls , 2011, International Conference on Information Science and Technology.

[47]  Richard K. Moore,et al.  Radar remote sensing and surface scattering and emission theory , 1986 .

[48]  William B. Rossow,et al.  Cloud Vertical Structure and Its Variations from a 20-Yr Global Rawinsonde Dataset , 2000 .

[49]  Nazzareno Pierdicca,et al.  Satellite-Based Retrieval of Precipitable Water Vapor Over Land by Using a Neural Network Approach , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[50]  J. Marshall,et al.  THE DISTRIBUTION WITH SIZE OF AGGREGATE SNOWFLAKES , 1958 .

[51]  Miller,et al.  The Anomalous Rainfall over the United States during July 1993: Sensitivity to Land Surface Parameterization and Soil Moisture Anomalies , 1996 .

[52]  F. Ulaby,et al.  Microwave Dielectric Behavior of Wet Soil-Part II: Dielectric Mixing Models , 1985, IEEE Transactions on Geoscience and Remote Sensing.

[53]  D. Lawrence,et al.  Regions of Strong Coupling Between Soil Moisture and Precipitation , 2004, Science.

[54]  Development of a TRMM/TMI Algorithm for Precipitation in the Tibetan Plateau by Considering Effects of Land Surface Emissivity. , 2001 .

[55]  J. Marshall,et al.  THE DISTRIBUTION OF RAINDROPS WITH SIZE , 1948 .