Assessing Impacts of Integrating MODIS Vegetation Data in the Weather Research and Forecasting (WRF) Model Coupled to Two Different Canopy-Resistance Approaches

The impact of 8-day-averaged data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor—namely, the 1-km leaf area index, absorbed photosynthetic radiation, and land-use data—is investigated for use in the Weather Research and Forecasting (WRF) model for regional weather prediction. These high-resolution, near-real-time MODIS data are hypothesized to enhance the representation of land‐ atmosphere interactions and to potentially improve the WRF model forecast skill for temperature, surface moisture, surfacefluxes, and soil temperature. To test this hypothesis, the impact of using MODIS-based land surfacedataonsurfaceenergyandwaterbudgetswasassessedwithinthe‘‘Noah’’landsurfacemodelwithtwo different canopy-resistance schemes. An ensemble of six model experiments was conducted using the WRF model for a typical summertime episode over the U.S. southern Great Plains that occurred during the International H2O Project (IHOP_2002) field experiment. The six model experiments were statistically analyzed and showed some degree of improvement in surface latent heat flux and sensible heat flux, as well as surface temperature and moisture, after land use, leaf area index, and green vegetation fraction data were replaced by remotely sensed data. There was also an improvement in the WRF-simulated temperature and boundary layer moisture with MODIS data in comparison with the default U.S. Geological Survey land-use and leaf area index inputs. Overall, analysis suggests that recalibration and improvements to both the input data and the land model help to improve estimation of surface and soil parameters and boundary layer moisture and led to improvement in simulating convection in WRF runs. Incorporating updated land conditions provided the most notable improvements, and the mesoscale model performance could be further enhanced when improved land surface schemes become available.

[1]  S. Planton,et al.  A Simple Parameterization of Land Surface Processes for Meteorological Models , 1989 .

[2]  Jordan G. Powers,et al.  A Description of the Advanced Research WRF Version 2 , 2005 .

[3]  J. Berry,et al.  A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species , 1980, Planta.

[4]  Jean-Pierre Wigneron,et al.  An interactive vegetation SVAT model tested against data from six contrasting sites , 1998 .

[5]  Fei Chen,et al.  Development and Evaluation of a Coupled Photosynthesis-Based Gas Exchange Evapotranspiration Model (GEM) for Mesoscale Weather Forecasting Applications , 2009 .

[6]  R. Pielke,et al.  The regional effects of CO2 and landscape change using a coupled plant and meteorological model , 2001 .

[7]  Xubin Zeng,et al.  A global 0.05° maximum albedo dataset of snow‐covered land based on MODIS observations , 2005 .

[8]  R. Leuning.  A critical appraisal of a combined stomatal‐photosynthesis model for C3 plants , 1995 .

[9]  H. Pan,et al.  A two-layer model of soil hydrology , 1984 .

[10]  S. Menon,et al.  Potential impacts of aerosol–land–atmosphere interactions on the Indian monsoonal rainfall characteristics , 2005 .

[11]  C. Knote,et al.  Leaf Area Index Specification for Use in Mesoscale Weather Prediction Systems , 2009 .

[12]  J. Alfieri,et al.  Quantifying the Spatial Variability of Surface Fluxes Using Data from the 2002 International H2O Project , 2009 .

[13]  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 .

[14]  Y. Xue,et al.  Modeling of land surface evaporation by four schemes and comparison with FIFE observations , 1996 .

[15]  A. Guenther,et al.  Evaluating a New Deposition Velocity Module in the Noah Land-Surface Model , 2010 .

[16]  G. Collatz,et al.  Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration: a model that includes a laminar boundary layer , 1991 .

[17]  John E. Kutzbach,et al.  Assessing Global Vegetation–Climate Feedbacks from Observations* , 2006 .

[18]  Jonathan L. Case,et al.  Improving Numerical Weather Predictions of Summertime Precipitation over the Southeastern United States through a High-Resolution Initialization of the Surface State , 2011 .

[19]  M. Ek,et al.  Evaluation of a Photosynthesis-Based Canopy Resistance Formulation in the Noah Land-Surface Model , 2011 .

[20]  Reinder Ronda,et al.  Representation of the canopy conductance in modeling the surface energy budget for low vegetation , 2001 .

[21]  R. Dickinson,et al.  Impacts of land use change on climate , 2010 .

[22]  Sharon Zhong,et al.  Urban and land surface effects on the 30 July 2003 mesoscale convective system event observed in the southern Great Plains , 2006 .

[23]  William J. Massman,et al.  Coupling biochemical and biophysical processes at the leaf level: an equilibrium photosynthesis model for leaves of C3 plants , 1995 .

[24]  R. Betts,et al.  Land use/land cover changes and climate: modeling analysis and observational evidence , 2011 .

[25]  A. Betts,et al.  A new convective adjustment scheme. Part II: Single column tests using GATE wave, BOMEX, ATEX and arctic air‐mass data sets , 1986 .

[26]  A. Robock,et al.  The Global Soil Moisture Data Bank , 2000 .

[27]  Peter D. Blanken,et al.  Estimation of the Minimum Canopy Resistance for Croplands and Grasslands Using Data from the 2002 International H2O Project , 2008 .

[28]  Alan H. Strahler,et al.  Global land cover mapping from MODIS: algorithms and early results , 2002 .

[29]  G. Collatz,et al.  Coupled Photosynthesis-Stomatal Conductance Model for Leaves of C4 Plants , 1992 .

[30]  Xiwu Zhan,et al.  An analytical approach for estimating CO2 and heat fluxes over the Amazonian region , 2003 .

[31]  G. Farquhar,et al.  A hydromechanical and biochemical model of stomatal conductance , 2003 .

[32]  A. Pitman.  The evolution of, and revolution in, land surface schemes designed for climate models , 2003 .

[33]  J. Noilhan,et al.  Sensitivity study and validation of a land surface parameterization using the HAPEX-MOBILHY data set , 1990 .

[34]  H. Pan,et al.  Nonlocal Boundary Layer Vertical Diffusion in a Medium-Range Forecast Model , 1996 .

[35]  J. Dudhia,et al.  Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity , 2001 .

[36]  Jimy Dudhia,et al.  Impacts of the Lowest Model Level Height on the Performance of Planetary Boundary Layer Parameterizations , 2012 .

[37]  E. Vivoni,et al.  Observed relation between evapotranspiration and soil moisture in the North American monsoon region , 2008 .

[38]  Roger A. Pielke,et al.  The regional effects of CO2 and landscape change using a coupled plant and meteorological model , 2001 .

[39]  R. Ceulemans,et al.  Stomatal conductance of forest species after long-term exposure to elevated CO2 concentration: a synthesis. , 2001, The New phytologist.

[40]  M. Ek,et al.  The Influence of Atmospheric Stability on Potential Evaporation , 1984 .

[41]  Fei Chen,et al.  Evaluation of the Noah Land Surface Model Using Data from a Fair-Weather IHOP_2002 Day with Heterogeneous Surface Fluxes , 2008 .

[42]  E. Mlawer,et al.  Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave , 1997 .

[43]  Zong-Liang Yang,et al.  The Project for Intercomparison of Land-surface Parameterization Schemes (PILPS) Phase 2(c) Red–Arkansas River basin experiment:: 1. Experiment description and summary intercomparisons , 1998 .

[44]  Nadine Gobron,et al.  Monitoring biosphere vegetation 1998–2009 , 2010 .

[45]  J. Dudhia.  Numerical Study of Convection Observed during the Winter Monsoon Experiment Using a Mesoscale Two-Dimensional Model , 1989 .

[46]  P. Blanken,et al.  NCAR/CU Surface, Soil, and Vegetation Observations during the International H2O Project 2002 Field Campaign , 2007 .

[47]  R. Betts,et al.  The impact of new land surface physics on the GCM simulation of climate and climate sensitivity , 1999 .

[48]  D. Randall,et al.  A Revised Land Surface Parameterization (SiB2) for Atmospheric GCMS. Part I: Model Formulation , 1996 .

[49]  U. C. Mohanty,et al.  Possible relation between land surface feedback and the post‐landfall structure of monsoon depressions , 2009 .

[50]  Xubin Zeng,et al.  Moderate Resolution Imaging Spectroradiometer bidirectional reflectance distribution function-based albedo parameterization for weather and climate models , 2007 .

[51]  Roger L. King,et al.  Interpretation of the relationship between skin temperature and vegetation fraction: Effect of subpixel soil temperature variability , 2008 .

[52]  Xubin Zeng,et al.  Sensitivity of the NCEP/Noah land surface model to the MODIS green vegetation fraction data set , 2006 .

[53]  Song‐You Hong,et al.  The WRF Single-Moment 6-Class Microphysics Scheme (WSM6) , 2006 .

[54]  Fei Chen,et al.  Effect of Land–Atmosphere Interactions on the IHOP 24–25 May 2002 Convection Case , 2006 .

[55]  Thomas J. Jackson,et al.  meeting summary: GEWEX/BAHC International Workshop on Soil Moisture Monitoring, Analysis, and Prediction for Hydrometeorological and Hydroclimatological Applications , 2001 .

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

[57]  J. Dudhia,et al.  A Revised Approach to Ice Microphysical Processes for the Bulk Parameterization of Clouds and Precipitation , 2004 .

[58]  H. Pan,et al.  Interaction between soil hydrology and boundary-layer development , 1987 .

[59]  K. Mitchell,et al.  Impact of Atmospheric Surface-layer Parameterizations in the new Land-surface Scheme of the NCEP Mesoscale Eta Model , 1997 .

[60]  M. Ek,et al.  A formulation for boundary-layer cloud cover , 1991 .

[61]  Zong-Liang Yang,et al.  Quantifying parameter sensitivity, interaction, and transferability in hydrologically enhanced versions of the Noah land surface model over transition zones during the warm season , 2010 .

[62]  Sethu Raman,et al.  Comparison of Four Different Stomatal Resistance Schemes Using FIFE Observations , 1997 .

[63]  D. Baldocchi.  A lagrangian random-walk model for simulating water vapor, CO2 and sensible heat flux densities and scalar profiles over and within a soybean canopy , 1992 .

[64]  Fei Chen,et al.  Land Surface Heterogeneity in the Cooperative Atmosphere Surface Exchange Study (CASES-97). Part I: Comparing Modeled Surface Flux Maps with Surface-Flux Tower and Aircraft Measurements , 2003 .

[65]  Seungbum Hong,et al.  Relationship between Vegetation Biophysical Properties and Surface Temperature Using Multisensor Satellite Data , 2007 .

[66]  A. Betts.  A new convective adjustment scheme. Part I: Observational and theoretical basis , 1986 .

[67]  I. E. Woodrow,et al.  A Model Predicting Stomatal Conductance and its Contribution to the Control of Photosynthesis under Different Environmental Conditions , 1987 .

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

[69]  J. Dudhia,et al.  Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part II: Preliminary Model Validation , 2001 .

[70]  K. Mitchell,et al.  Assessment of the Land Surface and Boundary Layer Models in Two Operational Versions of the NCEP Eta Model Using FIFE Data , 1997 .

[71]  Steven E. Koch,et al.  An Overview of the International H2O Project (IHOP_2002) and Some Preliminary Highlights , 2004 .