Editorial Type:
Article
Article Type:
Research Article

Quantitative Spatiotemporal Evaluation of Dynamically Downscaled MM5 Precipitation Predictions over the Tampa Bay Region, Florida

Syewoon Hwang Department of Agricultural and Biological Engineering, and Water Institute, University of Florida, Gainesville, Florida

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Wendy Graham Department of Agricultural and Biological Engineering, and Water Institute, University of Florida, Gainesville, Florida

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José L. Hernández Department of Agricultural and Biological Engineering, University of Florida, Gainesville, Florida

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Chris Martinez Department of Agricultural and Biological Engineering, University of Florida, Gainesville, Florida

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James W. Jones Department of Agricultural and Biological Engineering, University of Florida, Gainesville, Florida

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Alison Adams Tampa Bay Water, Clearwater, Florida

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Print Publication:
01 Dec 2011
DOI:
https://doi.org/10.1175/2011JHM1309.1
Page(s):
1447–1464
Restricted access

Abstract

This research quantitatively evaluated the ability of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) to reproduce observed spatiotemporal variability of precipitation in the Tampa Bay region over the 1986–2008 period. Raw MM5 model results were positively biased; therefore, the raw model precipitation outputs were bias corrected at 53 long-term precipitation stations in the region using the cumulative distribution function (CDF) mapping approach. CDF mapping effectively removed the bias in the mean daily, monthly, and annual precipitation totals and improved the RMSE of these rainfall totals. Observed daily precipitation transition probabilities were also well predicted by the bias-corrected MM5 results. Nevertheless, significant error remained in predicting specific daily, monthly, and annual total time series. After bias correction, MM5 successfully reproduced seasonal geostatistical precipitation patterns, with higher spatial variance of daily precipitation in the wet season and lower spatial variance of daily precipitation in the dry season. Bias-corrected daily precipitation fields were kriged over the study area to produce spatiotemporally distributed precipitation fields over the dense grids needed to drive hydrologic models in the Tampa Bay region. Cross validation at the 53 long-term precipitation gauges showed that kriging reproduced observed rainfall with average RMSEs lower than the RMSEs of individually bias-corrected point predictions. Results indicate that although significant error remains in predicting actual daily precipitation at rain gauges, kriging the bias-corrected MM5 predictions over a hydrologic model grid produces distributed precipitation fields with sufficient realism in the daily, seasonal, and interannual patterns to be useful for multidecadal water resource planning in the Tampa Bay region.

Corresponding author address: Syewoon Hwang, P.O. Box 110570, Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL 32611-0570. E-mail: aceace111@ufl.edu

Abstract

This research quantitatively evaluated the ability of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) to reproduce observed spatiotemporal variability of precipitation in the Tampa Bay region over the 1986–2008 period. Raw MM5 model results were positively biased; therefore, the raw model precipitation outputs were bias corrected at 53 long-term precipitation stations in the region using the cumulative distribution function (CDF) mapping approach. CDF mapping effectively removed the bias in the mean daily, monthly, and annual precipitation totals and improved the RMSE of these rainfall totals. Observed daily precipitation transition probabilities were also well predicted by the bias-corrected MM5 results. Nevertheless, significant error remained in predicting specific daily, monthly, and annual total time series. After bias correction, MM5 successfully reproduced seasonal geostatistical precipitation patterns, with higher spatial variance of daily precipitation in the wet season and lower spatial variance of daily precipitation in the dry season. Bias-corrected daily precipitation fields were kriged over the study area to produce spatiotemporally distributed precipitation fields over the dense grids needed to drive hydrologic models in the Tampa Bay region. Cross validation at the 53 long-term precipitation gauges showed that kriging reproduced observed rainfall with average RMSEs lower than the RMSEs of individually bias-corrected point predictions. Results indicate that although significant error remains in predicting actual daily precipitation at rain gauges, kriging the bias-corrected MM5 predictions over a hydrologic model grid produces distributed precipitation fields with sufficient realism in the daily, seasonal, and interannual patterns to be useful for multidecadal water resource planning in the Tampa Bay region.

Corresponding author address: Syewoon Hwang, P.O. Box 110570, Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL 32611-0570. E-mail: aceace111@ufl.edu
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Journal of Hydrometeorology

  • Boo, K.-O., Kwon W.-T. , Oh J.-H. , and Baek H.-J. , 2004: Response of global warming on regional climate change over Korea: An experiment with the MM5 model. Geophys. Res. Lett., 31, L21206, doi:10.1029/2004GL021171.

    • Search Google Scholar
    • Export Citation

  • Chen, F., and Dudhia J. , 2001a: Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon. Wea. Rev., 129, 569–585.

    • Search Google Scholar
    • Export Citation

  • Chen, F., and Dudhia J. , 2001b: Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part II: Preliminary model validation. Mon. Wea. Rev., 129, 587–604.

    • Search Google Scholar
    • Export Citation

  • Christensen, J. H., and Christensen O. B. , 2003: Severe summertime flooding in Europe. Nature, 421, 805–806.

  • Christensen, J. H., and Christensen O. B. , 2004: Intensification of extreme European summer precipitation in a warmer climate. Global Planet. Change, 44, 107–117.

    • Search Google Scholar
    • Export Citation

  • Colle, B. A., and Mass C. F. , 1996: An observational and modeling study of the interaction of low-level southwesterly flow with the Olympic Mountains during COAST IOP 4. Mon. Wea. Rev., 124, 2152–2175.

    • Search Google Scholar
    • Export Citation

  • Colle, B. A., and Mass C. F. , 2000: The 5–9 February 1996 flooding event over the Pacific Northwest: Sensitivity studies and evaluation of the MM5 precipitation forecasts. Mon. Wea. Rev., 128, 593–617.

    • Search Google Scholar
    • Export Citation

  • Colle, B. A., Westrick K. J. , and Mass C. F. , 1999: Evaluation of MM5 and Eta-10 precipitation forecasts over the Pacific Northwest during the cool season. Wea. Forecasting, 14, 137–154.

    • Search Google Scholar
    • Export Citation

  • Colle, B. A., Mass C. F. , and Westrick K. J. , 2000: MM5 precipitation verification over the Pacific Northwest during the 1997–99 cool seasons. Wea. Forecasting, 15, 730–744.

    • Search Google Scholar
    • Export Citation

  • Colle, B. A., Olson J. B. , and Tongue J. S. , 2003: Multiseason verification of the MM5. Part II: Evaluation of high-resolution precipitation forecasts over the northeastern United States. Wea. Forecasting, 18, 458–480.

    • Search Google Scholar
    • Export Citation

  • Deutsch, C. V., and Journel A. G. , 1998: GSLIB, Geostatistical Software Library and User’s Guide. 2nd ed. Oxford University Press, 139–148.

    • Search Google Scholar
    • Export Citation

  • Dudhia, J., 1996: A multi-layer soil temperature model for MM5. Preprints, Sixth PSU/NCAR Mesoscale Model Users Workshop, Boulder, CO, National Center for Atmospheric Research, 49–50.

    • Search Google Scholar
    • Export Citation

  • Easterling, D. R., Meehl G. A. , Parmesan C. , Changnon S. A. , Karl T. R. , and Mearns L. O. , 2000: Climate extremes: Observations, modeling, and impacts. Science, 289, 2068–2074.

    • Search Google Scholar
    • Export Citation

  • Enke, W., and Spekat A. , 1997: Downscaling climate model outputs into local and regional weather elements by classification and regression. Climate Res., 8, 195–207.

    • Search Google Scholar
    • Export Citation

  • Fowler, H. J., and Kilsby C. G. , 2007: Using regional climate model data to simulate historical and future river flows in northwest England. Climatic Change, 80, 337–367.

    • Search Google Scholar
    • Export Citation

  • Fowler, H. J., Blenkinsop S. , and Tebaldi C. , 2007a: Linking climate change modeling to impacts studies: Recent advances in downscaling techniques for hydrological modeling. Int. J. Climatol., 27, 1547–1578.

    • Search Google Scholar
    • Export Citation

  • Fowler, H. J., Ekström M. , Blenkinsop S. , and Smith A. P. , 2007b: Estimating change in extreme European precipitation using a multimodel ensemble. J. Geophys. Res., 112, D18104, doi:10.1029/2007JD008619.

    • Search Google Scholar
    • Export Citation

  • Frei, C., Schöll R. , Fukutome S. , Schmidli J. , and Vidale P. L. , 2006: Future change of precipitation extremes in Europe: Intercomparison of scenarios from regional climate models. J. Geophys. Res., 111, D06105, doi:10.1029/2005JD005965.

    • Search Google Scholar
    • Export Citation

  • Gaudet, B., and Cotton W. R. , 1998: Statistical characteristics of a real-time precipitation forecasting model. Wea. Forecasting, 13, 966–982.

    • Search Google Scholar
    • Export Citation

  • Goovaerts, P., 1997: Geostatistics for Natural Resources Evaluation. Oxford University Press, 496 pp.

  • Grell, G. A., Dudhia J. , and Stauffer D. R. , 1994: A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5). NCAR Tech. Note NCAR/TN-398+STR, 121 pp.

    • Search Google Scholar
    • Export Citation

  • Haan, T. C., 1977: Statistical Methods in Hydrology. The Iowa State University Press, 303–305.

  • Hewitson, B. C., and Crane R. G. , 1996: Climate downscaling: Techniques and application. Climate Res., 7, 85–95.

  • Hong, J.-S., 2003: Evaluation of the high-resolution model forecasts over the Taiwan area during GIMEX. Wea. Forecasting, 18, 836–846.

    • Search Google Scholar
    • Export Citation

  • Hong, S.-Y., and Pan H.-L. , 1996: Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon. Wea. Rev., 124, 2322–2339.

    • Search Google Scholar
    • Export Citation

  • Ines, A. V. M., and Hansen J. W. , 2006: Bias-correction of daily GCM rainfall for crop simulation studies. Agric. For. Meteor., 138, 44–53.

    • Search Google Scholar
    • Export Citation

  • Isaaks, E. H., and Srivastava R. M. , 1989: An Introduction to Applied Geostatistics. Oxford University Press, 592 pp.

  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437–471.

  • Kiehl, J. T., Hack J. J. , Bonan G. B. , Boville B. A. , Briegleb B. P. , Williamson D. L. , and Rasch P. J. , 1996: Description of the NCAR Community Climate Model (CCM3). NCAR Tech. Note NCAR/TN-420+STR, 152 pp.

    • Search Google Scholar
    • Export Citation

  • Kistler, R., and Coauthors, 2001: The NCEP–NCAR 50-year reanalysis: Monthly means CD-ROM and documentation. Bull. Amer. Meteor. Soc., 82, 247–267.

    • Search Google Scholar
    • Export Citation

  • Kotroni, V., and Lagouvardos K. , 2001: Precipitation forecast skill of different convective parameterization and microphysical schemes: Application for the cold season over Greece. Geophys. Res. Lett., 28, 1977–1980.

    • Search Google Scholar
    • Export Citation

  • Leander, R., Buishand T. A. , van den Hurk B. J. J. M. , de Wit M. J. M. , 2008: Estimated change in flood quartiles of the river Meuse from resampling of regional climate model output. J. Hydrol., 351, 331–343.

    • Search Google Scholar
    • Export Citation

  • Lim, Y.-K., Shin D. W. , Cocke S. , LaRow T. E. , Schoof J. T. , O’Brien J. J. , and Chassignet E. P. , 2007: Dynamically and statistically downscaled seasonal simulations of maximum surface air temperature over the southeastern United States. J. Geophys. Res., 112, D24102, doi:10.1029/2007JD008764.

    • Search Google Scholar
    • Export Citation

  • Maurer, E. P., and Hidalgo H. G. , 2008: Utility of daily vs. monthly large-scale climate data: An intercomparison of two statistical downscaling methods. Hydrol. Earth Syst. Sci., 12, 551–563.

    • Search Google Scholar
    • Export Citation

  • Maurer, E. P., Hidalgo H. G. , Das T. , Dettinger M. D. , and Cayan D. R. , 2010: The utility of daily large-scale climate data in the assessment of climate change impacts on daily streamflow in California. Hydrol. Earth Syst. Sci., 14, 1125–1138.

    • Search Google Scholar
    • Export Citation

  • McGregor, J. L., 1997: Regional climate modeling. Meteor. Atmos. Phys., 63, 105–117.

  • Murphy, A. H., 1988: Skill scores based on the mean square error and their relationships to the correlation coefficient. Mon. Wea. Rev., 116, 2417–2424.

    • Search Google Scholar
    • Export Citation

  • Murphy, J., 1999: An evaluation of statistical and dynamical techniques for downscaling local climate. J. Climate, 12, 2256–2284.

  • Pal, J. S., Giorgi F. , and Bi X. , 2004: Consistency of recent European summer precipitation trends and extremes with future regional climate projections. Geophys. Res. Lett., 31, L13202, doi:10.1029/2004GL019836.

    • Search Google Scholar
    • Export Citation

  • Schmidli, J., Frei C. , and Vidale P. L. , 2006: Downscaling from GCM precipitation: A benchmark for dynamical and statistical downscaling methods. Int. J. Climatol., 26, 679–689.

    • Search Google Scholar
    • Export Citation

  • Schmidt, N., Luther M. E. , and Johns R. , 2004: Climate variability and estuarine water resources: A case study from Tampa Bay, Florida. Coastal Manage. J., 32, 101–116.

    • Search Google Scholar
    • Export Citation

  • Schoof, J. T., Shin D. W. , Cocke S. , LaRow T. E. , Lim Y.-K. , and O’Brien J. J. , 2009: Dynamically and statistically downscaled seasonal temperature and precipitation hindcast ensembles for the southeastern USA. Int. J. Climatol., 29, 243–257.

    • Search Google Scholar
    • Export Citation

  • Sun, H., and Furbish D. J. , 1997: Annual precipitation and river discharges in Florida in response to El Niño- and La Niña-sea surface temperature anomalies. J. Hydrol., 199, 74–87.

    • Search Google Scholar
    • Export Citation

  • Varis, O., Kajander T. , and Lemmelä R. , 2004: Climate and water: From climate models to water resources management and vice versa. Climate Change, 66, 321–344.

    • Search Google Scholar
    • Export Citation

  • Wang, Y., Leung L. R. , McGregor J. L. , Lee D. K. , Wang W. C. , Ding Y. , and Kimura F. , 2004: Regional climate modeling: Progress, challenges, and prospects. J. Meteor. Soc. Japan, 82, 1599–1628.

    • Search Google Scholar
    • Export Citation

  • Westrick, K. J., Storck P. , and Mass C. F. , 2002: Description and evaluation of a hydrometeorological forecast system for mountainous watersheds. Wea. Forecasting, 17, 250–262.

    • Search Google Scholar
    • Export Citation

  • Wilby, R. L., Hay L. E. , Gutowski W. J. Jr., Arritt R. W. , Takle E. S. , Pan Z. , Leavesley G. H. , and Clark M. P. , 2000: Hydrological responses to dynamically and statistically downscaled climate model output. Geophys. Res. Lett., 27, 1199–1202.

    • Search Google Scholar
    • Export Citation

  • Wood, A. W., Maurer E. P. , Kumar A. , and Lettenmaier D. P. , 2002: Long-range experimental hydrologic forecasting for the eastern United States. J. Geophys. Res., 107, 4429, doi:10.1029/2001JD000659.

    • Search Google Scholar
    • Export Citation

  • Wood, A. W., Leung L. R. , Sridhar V. , and Lettenmaier D. P. , 2004: Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Climatic Change, 62, 189–216.

    • Search Google Scholar
    • Export Citation

  • Xian, G., Crane M. , and Su J. , 2007: An analysis of urban development and its environmental impact on the Tampa Bay watershed. J. Environ. Manage., 85, 965–976.

    • Search Google Scholar
    • Export Citation

  • Yang, M.-J., and Tung Q. C. , 2003: Evaluation of rainfall forecasts over Taiwan by four cumulus parameterization schemes. J. Meteor. Soc. Japan, 81, 1163–1183.

    • Search Google Scholar
    • Export Citation

  • Yu, Z., and Coauthors, 1999: Simulating the river-basin response to atmospheric forcing by linking a mesoscale meteorological model and a hydrologic model system. J. Hydrol., 218, 72–91.

    • Search Google Scholar
    • Export Citation

  • Zehnder, J. A., 2002: Simple modifications to improve fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model performance for the Phoenix, Arizona, metropolitan area. J. Appl. Meteor., 41, 971–979.

    • Search Google Scholar
    • Export Citation

  • Zhong, S., and Fast J. , 2003: An Evaluation of the MM5, RAMS, and Meso-Eta models at subkilometer resolution using VTMX field campaign data in the Salt Lake Valley. Mon. Wea. Rev., 131, 1301–1322.

    • Search Google Scholar
    • Export Citation

  • Zhong, S., In H. J. , Bian X. , Charney J. , Heilman W. , and Potter B. , 2005: Evaluation of real-time high-resolution MM5 predictions over the Great Lakes region. Wea. Forecasting, 20, 63–81.

    • Search Google Scholar
    • Export Citation
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