Use of Satellite-Based Precipitation Observation in Improving the Parameterization of Canopy Hydrological Processes in Land Surface Models

Precipitation exhibits significant spatial variability at scales much smaller than the typical size of climate model grid cells. Neglecting such subgrid-scale variability in climate models causes unrealistic representation of land–atmosphere flux exchanges. It is especially problematic over densely vegetated land. This paper addresses this issue by incorporating satellite-based precipitation observations into the representation of canopy interception processes in land surface models. Rainfall data derived from passive microwave (PM) observations are used to obtain realistic estimates of 1) conditional mean rain rates, which together with the modeled rain rate are used to estimate the rainfall coverage fraction at each model grid cell in this study, and 2) the probability density function (pdf) of rain rates within the rain-covered areas. Both of these properties significantly impact the land–atmosphere water vapor exchanges. Based on the above information, a statistical–dynamical approach is taken to incorporate the representation of precipitation subgrid variability into canopy interception processes in land surface models. The results reveal that incorporation of precipitation subgrid variability significantly alters the partitioning between runoff and total evapotranspiration as well as the partitioning among the three components of evapotranspiration (i.e., canopy interception loss, ground evaporation, and plant transpiration). This further influences soil water, surface temperature, and surface heat fluxes. It is shown that the choice of the rain-rate pdf within rain-covered areas has an effect on the model simulation of land–atmosphere flux exchanges. This study demonstrates that land surface and climate models can substantially benefit from the fine-resolution remotely sensed rainfall observations.

[1]  R. Koster,et al.  Modeling the land surface boundary in climate models as a composite of independent vegetation stands , 1992 .

[2]  A. Dai.  Global Precipitation and Thunderstorm Frequencies. Part II: Diurnal Variations , 2001 .

[3]  S. Ghan,et al.  Influence of Subgrid Variability on Surface Hydrology , 1997 .

[4]  Armin Raabe,et al.  Numerical Investigations on the Influence of Subgrid-Scale Surface Heterogeneity on Evapotranspiration and Cloud Processes , 1996 .

[5]  Roni Avissar,et al.  Conceptual aspects of a statistical‐dynamical approach to represent landscape subgrid‐scale heterogeneities in atmospheric models , 1992 .

[6]  A. Dai.  Global Precipitation and Thunderstorm Frequencies. Part I: Seasonal and Interannual Variations , 2001 .

[7]  T. N. Krishnamurti,et al.  The status of the tropical rainfall measuring mission (TRMM) after two years in orbit , 2000 .

[8]  A. Hahmann.  Representing Spatial Subgrid-Scale Precipitation Variability in a GCM , 2003 .

[9]  Rafael L. Bras,et al.  Estimation of the fractional coverage of rainfall in climate models , 1993 .

[10]  R. Dickinson,et al.  Biosphere-Atmosphere Transfer Scheme (BATS) version le as coupled to the NCAR community climate model. Technical note. [NCAR (National Center for Atmospheric Research)] , 1993 .

[11]  Zeng Qingcun,et al.  A land surface model (IAP94) for climate studies part I: Formulation and validation in off-line experiments , 1997 .

[12]  B. Bonan,et al.  A Land Surface Model (LSM Version 1.0) for Ecological, Hydrological, and Atmospheric Studies: Technical Description and User's Guide , 1996 .

[13]  Feng Gao,et al.  Land boundary conditions from MODIS data and consequences for the albedo of a climate model , 2004 .

[14]  Eric F. Wood,et al.  A land-surface hydrology parameterization with subgrid variability for general circulation models , 1992 .

[15]  Grant W. Petty,et al.  Validation and Intercomparison of SSM/I Rain-Rate Retrieval Methods over the Continental United States , 1998 .

[16]  A. Henderson-Sellers,et al.  An Evaluation of Proposed Representations of Subgrid Hydrologic Processes in Climate Models , 1991 .

[17]  A. Pitman,et al.  Sub-grid scale precipitation in ALCMs: re-assessing the land surface sensitivity using a single column model , 1993 .

[18]  D. Entekhabi,et al.  Regional and seasonal estimates of fractional storm coverage based on station precipitation observations , 1994 .

[19]  Eric F. Wood,et al.  One-dimensional statistical dynamic representation of subgrid spatial variability of precipitation in the two-layer variable infiltration capacity model , 1996 .

[20]  G. Bonan,et al.  Influence of Subgrid-Scale Heterogeneity in Leaf Area Index, Stomatal Resistance, and Soil Moisture on Grid-Scale Land–Atmosphere Interactions , 1993 .

[21]  Elfatih A. B. Eltahir,et al.  Modeling the Biosphere–Atmosphere System: The Impact of the Subgrid Variability in Rainfall Interception , 2000 .

[22]  C. Collier.  The application of a continental-scale radar database to hydrological process parametrization within Atmospheric General Circulation Models , 1993 .

[23]  W. Shuttleworth,et al.  Macrohydrology ― the new challenge for process hydrology , 1988 .

[24]  E. Anagnostou,et al.  Overland Precipitation Estimation from TRMM Passive Microwave Observations , 2001 .

[25]  R. Dickinson,et al.  A fine-mesh land approach for general circulation models and its impact on regional climate , 2001 .

[26]  A. Pitman,et al.  Land‐surface schemes for future climate models: Specification, aggregation, and heterogeneity , 1992 .

[27]  S. Ghan,et al.  A subgrid parameterization of orographic precipitation , 1995 .

[28]  P. S. Eagleson,et al.  Land Surface Hydrology Parameterization for Atmospheric General Circulation models Including Subgrid Scale Spatial Variability , 1989 .

[29]  Robert E. Dickinson,et al.  Adjustment of GCM precipitation intensity over the United States , 1998 .

[30]  A. Henderson-Sellers,et al.  Sensitivity of regional climates to localized precipitation in global models , 1990, Nature.

[31]  Ann Henderson-Sellers,et al.  Biosphere-atmosphere transfer scheme(BATS) version 1e as coupled to the NCAR community climate model , 1993 .

[32]  R. Dickinson,et al.  The Common Land Model , 2003 .

[33]  Emmanouil N. Anagnostou,et al.  Use of passive microwave observations in a radar rainfall-profiling algorithm , 2001 .

[34]  Emmanouil N. Anagnostou,et al.  Regional Differences in Overland Rainfall Estimation from PR-Calibrated TMI Algorithm , 2005 .

[35]  R. Reynolds,et al.  The NCEP/NCAR 40-Year Reanalysis Project , 1996, Renewable Energy.

[36]  R. Dickinson,et al.  Comparison of precipitation observed over the Continental United States to that simulated by a climate model , 1996 .

[37]  Ralph Ferraro,et al.  Next generation of NOAA/NESDIS TMI, SSM/I, and AMSR‐E microwave land rainfall algorithms , 2003 .

[38]  Soroosh Sorooshian,et al.  A stochastic precipitation disaggregation scheme for GCM applications , 1994 .

[39]  Roger A. Pielke,et al.  A parameterization of heterogeneous land surfaces for atmospheric numerical models and its impact on regional meteorology , 1989 .

[40]  Ralph Ferraro,et al.  The Development of SSM/I Rain-Rate Retrieval Algorithms Using Ground-Based Radar Measurements , 1995 .

[41]  Robert E. Dickinson,et al.  Simulating fluxes from heterogeneous land surfaces: Explicit subgrid method employing the biosphere‐atmosphere transfer scheme (BATS) , 1994 .

[42]  J. Ramirez,et al.  A Statistical–Dynamical Parameterization of Interception and Land Surface–Atmosphere Interactions , 2000 .

[43]  A. Dolman,et al.  The parametrization of rainfall interception in GCMs , 1992 .

[44]  R. Dickinson,et al.  Impact of new land boundary conditions from Moderate Resolution Imaging Spectroradiometer (MODIS) data on the climatology of land surface variables , 2004 .

[45]  Dara Entekhabi,et al.  The Implementation and Validation of Improved Land-Surface Hydrology in an Atmospheric General Circulation Model , 1993 .