• Asrar, G., Fuchs M. , Kanemasu E. T. , and Hatfield J. L. , 1984: Estimating absorbed photosynthetic radiation and leaf area index from spectral reflectance in wheat. Agron. J, 76 , 300306.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Benjamin, S. G., and Seaman N. L. , 1985: A simple scheme for improved objective analysis in curved flow. Mon. Wea. Rev, 113 , 11841198.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Black, T. L., 1994: The new NMC Mesoscale Eta Model: Description and forecast experiments. Wea. Forecasting, 2 , 266278.

  • Blackadar, A. K., 1979: High resolution models of the planetary boundary layer. Advances in Environmental Science and Engineering, J. Pfafflin and E. Ziegler, Eds., Gordon and Breach, 50–85.

    • Search Google Scholar
    • Export Citation
  • Box, E. O., Holben B. N. , and Kalb V. , 1989: Accuracy of the AVHRR vegetation index as a predictor of biomass, primary productivity, and net CO2 flux. Vegetation, 30 , 7189.

    • Search Google Scholar
    • Export Citation
  • Brock, F. V., Crawford K. C. , Elliott R. L. , Cuperus G. W. , Stadler S. J. , Johnson H. L. , and Eilts M. D. , 1995: The Oklahoma Mesonet: A technical overview. J. Atmos. Oceanic Technol, 12 , 519.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brooks, H. E., and Douglas A. P. , 1998: Value of weather forecasts for electric utility load forecasting. Preprints, 16th Conf. on Weather Analysis and Forecasting, Phoenix, AZ, Amer. Meteor. Soc., J61–J64.

    • Search Google Scholar
    • Export Citation
  • Capehart, W. J., and Carlson T. N. , 1994: Estimating near-surface soil moisture availability using a meteorologically driven soil-water profile model. J. Hydrol, 160 , 120.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carlson, T. N., and Ripley D. A. , 1990: On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens. Environ, 62 , 241252.

    • Search Google Scholar
    • Export Citation
  • Champeaux, J-L., Arcos D. , Bazile E. , Giard D. , Goutorbe J-P. , Habets F. , Noilhan J. , and Roujean J-L. , 2000: AVHRR-derived vegetation mapping over western Europe for use in numerical weather forecast models. Int. J. Remote Sens, 21 , 11831199.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chang, J-T., and Wetzel P. J. , 1991: Effects of spatial variations of soil moisture and vegetation on the evolution of a prestorm environment: A numerical case study. Mon. Wea. Rev, 119 , 13681390.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, F., and and Coauthors, 1996: Modeling of land-surface evaporation by four schemes and comparison with FIFE observations. J. Geophys. Res, 101 , 72517268.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, T. H., Henderson-Sellers A. , Milly P. C. , Pitman A. J. , and Beljaars A. C. , 1997: Cabauw experimental results from the Project for Intercomparison of Land-Surface Parameterization Schemes. J. Climate, 10 , 11941215.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Colby, F. P. Jr, 1998: A preliminary investigation of temperature errors in operational forecasting models. Wea. Forecasting, 13 , 187205.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Collins, D. C., and Avissar R. , 1994: An evaluation with the Fourier amplitude sensitivity test (FAST) of which land-surface parameters are of greatest importance in atmospheric modeling. J. Climate, 7 , 681703.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Crawford, T. M., Stensrud D. J. , Carlson T. N. , and Capehart W. J. , 2000: Using a soil hydrology model to obtain regionally averaged soil moisture values. J. Hydrometeor., 1, 353–363.

    • Search Google Scholar
    • Export Citation
  • Csiszar, I., and Gutman G. , 1999: Mapping global land surface albedo from NOAA AVHRR. J. Geophys. Res, 104 , 62156228.

  • Deardorff, J. W., 1978: Efficient prediction of ground surface temperature and moisture, with inclusion of a layer of vegetation. J. Geophys. Res, 83 , 18891903.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dempsey, C. L., Howard K. W. , Maddox R. A. , and Phillips D. H. , 1998: Developing advanced weather technologies for the power industry. Bull. Amer. Meteor. Soc, 79 , 10191035.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dickinson, R. E., and Henderson-Sellers A. , 1988: Modeling tropical deforestation: A study of GCM land-surface parameterizations. Quart. J. Roy. Meteor. Soc, 114 , 439462.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dickinson, R. E., Henderson-Sellers A. , Kennedy P. J. , and Wilson M. F. , 1986: Biosphere-Atmosphere Transfer Scheme (BATS) for the NCAR Community Climate Model. NCAR Tech. Note NCAR/TN-275+STR, 69 pp.

    • Search Google Scholar
    • Export Citation
  • Dudhia, J., 1993: A nonhydrostatic version of the Penn State–NCAR Mesoscale Model: Validation tests and simulation of an Atlantic cyclone and cold front. Mon. Wea. Rev, 121 , 14931513.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ehrlich, D., Estes J. E. , and Singh A. , 1994: Applications of NOAA-AVHRR 1 km data for environmental monitoring. Int. J. Remote Sens, 15 , 145161.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eidenshink, J. C., and Hutchinson J. A. , 1993: AVHRR data set for the conterminous United States. Res. Explor, Water Issue , 8697.

  • Emanuel, K. A., and and Coauthors, 1995: Report of the First Prospectus Development Team of the U.S. Weather Research Program to NOAA and the NSF. Bull. Amer. Meteor. Soc, 76 , 11941208.

    • Search Google Scholar
    • Export Citation
  • Gutman, G., and Ignatov A. , 1995: Global land monitoring from AVHRR: Potential and limitations. Int. J. Remote Sens, 16 , 23012309.

  • Gutman, G., and Ignatov A. , 1998: The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models. Int. J. Remote Sens, 19 , 15331543.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Justice, C. O., and and Coauthors, 1998: The moderate resolution imaging spectroradiometer (MODIS): Land remote sensing for global change research. IEEE Trans. Geosci. Remote Sens, 36 , 12281249.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lawford, R. G., 1999: A midterm report on the GEWEX Continental-Scale International Project (GCIP). J. Geophys. Res, 104, , 1927919292.

  • Los, S. O., and and Coauthors, 2000: A global 9-year biophysical landsurface dataset from NOAA AVHRR data. J. Hydrometeor, 1 , 183199.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Loveland, T. R., Merchant J. W. , Brown J. F. , Ohlen D. O. , Reed B. , and Olsen P. , 1995: Seasonal land cover regions of the United States. Ann. Assoc. Amer. Geogr, 85 , 339355.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mecikalski, J. R., Diak G. R. , Anderson M. C. , and Norman J. M. , 1999: Estimating fluxes on continental scales using remotely sensed data in an atmospheric–land exchange model. J. Appl. Meteor, 38 , 13521369.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Merchant, J. W., Yang L. , and Yang W. , 1994: Validation of continental-scale land cover databases derived from AVHRR data. Proc. Pecora 12 Symp., Bethesda, MD, Amer. Soc. Photogr. Remote Sens., 63–72.

    • Search Google Scholar
    • Export Citation
  • Mitchell, K., and and Coauthors, 2000: Recent GCIP-sponsored advancements in coupled land-surface modeling and data assimilation in the NCEP Eta Mesoscale Model. Preprints, 15th Conf. on Hydrology, Long Beach, CA, Amer. Meteor. Soc., 180–183.

    • Search Google Scholar
    • Export Citation
  • Monteith, J. L., and Unsworth M. H. , 1990: Principles of Environmental Physics. Edward Arnold, 291 pp.

  • National Research Council, 1991: Opportunities in the Hydrologic Sciences. National Academy Press, 368 pp.

  • Niyogi, D. S., Raman S. , and Alapaty K. , 1999: Uncertainty in the specification of surface characteristics, part II: Hierarchy of interaction-explicit statistical analysis. Bound.-Layer Meteor, 91 , 341366.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Noilhan, J., and Planton S. , 1989: A simple parameterization of land surface processes for meteorological models. Mon. Wea. Rev, 117 , 536549.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pitman, A., Pielke R. , Avissar R. , Claussen M. , Gash J. , and Dolman H. , 1999: The role of land surface in weather and climate: Does the land surface matter? IGBP Newsletter, 39 , 411.

    • Search Google Scholar
    • Export Citation
  • Price, J. C., 1993: Estimating leaf area index from satellite data. IEEE Trans. Geosci. Remote Sens, 31 , 727734.

  • Rabin, R. M., Stadler S. , Wetzel P. J. , Stensrud D. J. , and Gregory M. , 1990: Observed effects of landscape variability on convective clouds. Bull. Amer. Meteor. Soc, 71 , 272280.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reed, B. C., and Yang L. , 1997: Seasonal vegetation characteristics of the United States. GeoCarto, 12 , 6571.

  • Schwartz, M. D., and Karl T. R. , 1990: Spring phenology: Nature's experiment to detect the effect of “green-up” on surface maximum temperatures. Mon. Wea. Rev, 118 , 883890.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sellers, P. J., and and Coauthors, 1995: Remote sensing of the land surface for studies of global change: Models–algorithms–experiments. Remote Sens. Environ, 51 , 326.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sellers, P. J., and and Coauthors, 1996: The ISLSCP Initiative I global datasets: Surface boundary conditions and atmospheric forcings for land–atmosphere studies. Bull. Amer. Meteor. Soc, 77 , 19872005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sud, Y. C., Shukla J. , and Mintz Y. , 1988: Influence of land surface roughness on atmospheric circulation and precipitation: A sensitivity study with a general circulation model. J. Appl. Meteor, 27 , 10361054.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vogelmann, J. E., Sohl T. , and Howard S. M. , 1998: Regional characterization of land cover using multiple sources of data. Photogr. Eng. Remote Sens, 64 , 4557.

    • Search Google Scholar
    • Export Citation
  • Wetzel, P. J., and Chang J-T. , 1988: Evapotranspiration from nonuniform surfaces: A first approach for short-term numerical weather prediction. Mon. Wea. Rev, 116 , 600621.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wetzel, P. J., and Boone A. , 1995: A parameterization for land–atmosphere–cloud exchange (PLACE): Documentation and testing of a detailed process model of the partly cloudy boundary layer over heterogeneous land. J. Climate, 8 , 18101837.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wetzel, P. J., Atlas D. , and Woodward R. H. , 1984: Determining soil moisture from geosynchronous satellite infrared data: A feasibility study. J. Climate Appl. Meteor, 23 , 375391.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 1995: Statistical Methods in the Atmospheric Sciences. Academic Press, 467 pp.

  • Wilks, D. S., 1997: Forecast value: Prescriptive decision studies. Economic Value of Weather and Climate Forecasts, R. W. Katz and A. H. Murphy, Eds., Cambridge University Press, 109–145.

    • Search Google Scholar
    • Export Citation
  • Wydick, J. E., Davies P. A. , and Gruber A. , 1987: Estimation of broadband planetary albedo from operational narrowband satellite measurements. NOAA Tech. Rep. NESDIS 27, 32 pp.

    • Search Google Scholar
    • Export Citation
  • Yin, Z., and Williams T. H. L. , 1997: Obtaining spatial and temporal vegetation data from Landsat MSS and AVHRR/NOAA satellite images for a hydrologic model. Photogr. Eng. Remote Sens, 63 , 6977.

    • Search Google Scholar
    • Export Citation
  • Zhang, D., and Anthes R. A. , 1982: A high resolution model of the planetary boundary layer—sensitivity tests and comparisons with SESAME-79 data. J. Appl. Meteor, 21 , 15941609.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhangshi, Y., and Williams T. H. L. , 1997: Obtaining spatial and temporal vegetation data from Landsat MSS and AVHRR/NOAA satellite images for a hydrological model. Photogr. Eng. Remote Sens, 63 , 6977.

    • Search Google Scholar
    • Export Citation
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Value of Incorporating Satellite-Derived Land Cover Data in MM5/PLACE for Simulating Surface Temperatures

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  • 1 NOAA/National Severe Storms Laboratory, Norman, Oklahoma
  • | 2 Center for Advanced Land Management Information Technologies, University of Nebraska—Lincoln, Lincoln, Nebraska
  • | 3 Mesoscale Atmospheric Processes Branch, NASA Goddard Space Flight Center, Greenbelt, Maryland
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Abstract

The Parameterization for Land–Atmosphere–Cloud Exchange (PLACE) module is used within the Fifth-Generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) to determine the importance of individual land surface parameters in simulating surface temperatures. Sensitivity tests indicate that soil moisture and the coverage and thickness of green vegetation [as manifested by the values of fractional green vegetation coverage (fVEG) and leaf area index (LAI)] have a large effect on the magnitudes of surface sensible heat fluxes. The combined influence of LAI and fVEG is larger than the influence of soil moisture on the partitioning of the surface energy budget. Values for fVEG, albedo, and LAI, derived from 1-km-resolution Advanced Very High Resolution Radiometer data, are inserted into PLACE, and changes in model-simulated 1.5-m air temperatures in Oklahoma during July of 1997 are documented. Use of the land cover data provides a clear improvement in afternoon temperature forecasts when compared with model runs with monthly climatological values for each land cover type. However, temperature forecasts from MM5 without PLACE are significantly more accurate than those with PLACE, even when the land cover data are incorporated into the model. When only the temperature observations above 37°C are analyzed, however, the simulations from the high-resolution land cover dataset with PLACE significantly outperform MM5 without PLACE. Previous land surface models have simply used (at best) climatological values of these crucial land cover parameters. The ability to improve model simulations of surface energy fluxes and the resultant temperatures in a diagnostic sense provides promise for future attempts at ingesting satellite-derived land cover data into numerical models. These model improvements would likely be most helpful in predictions of extreme temperature events (during drought or extremely wet conditions) for which current numerical weather prediction models often perform poorly. The potential value of real-time land cover information for model initialization is substantial.

Corresponding author address: Dr. David J. Stensrud, National Severe Storms Laboratory, 1313 Halley Circle, Norman, OK 73069. Email: david.stensrud@nssl.noaa.gov

Abstract

The Parameterization for Land–Atmosphere–Cloud Exchange (PLACE) module is used within the Fifth-Generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) to determine the importance of individual land surface parameters in simulating surface temperatures. Sensitivity tests indicate that soil moisture and the coverage and thickness of green vegetation [as manifested by the values of fractional green vegetation coverage (fVEG) and leaf area index (LAI)] have a large effect on the magnitudes of surface sensible heat fluxes. The combined influence of LAI and fVEG is larger than the influence of soil moisture on the partitioning of the surface energy budget. Values for fVEG, albedo, and LAI, derived from 1-km-resolution Advanced Very High Resolution Radiometer data, are inserted into PLACE, and changes in model-simulated 1.5-m air temperatures in Oklahoma during July of 1997 are documented. Use of the land cover data provides a clear improvement in afternoon temperature forecasts when compared with model runs with monthly climatological values for each land cover type. However, temperature forecasts from MM5 without PLACE are significantly more accurate than those with PLACE, even when the land cover data are incorporated into the model. When only the temperature observations above 37°C are analyzed, however, the simulations from the high-resolution land cover dataset with PLACE significantly outperform MM5 without PLACE. Previous land surface models have simply used (at best) climatological values of these crucial land cover parameters. The ability to improve model simulations of surface energy fluxes and the resultant temperatures in a diagnostic sense provides promise for future attempts at ingesting satellite-derived land cover data into numerical models. These model improvements would likely be most helpful in predictions of extreme temperature events (during drought or extremely wet conditions) for which current numerical weather prediction models often perform poorly. The potential value of real-time land cover information for model initialization is substantial.

Corresponding author address: Dr. David J. Stensrud, National Severe Storms Laboratory, 1313 Halley Circle, Norman, OK 73069. Email: david.stensrud@nssl.noaa.gov

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