A Hybrid Orographic plus Statistical Model for Downscaling Daily Precipitation in Northern California

Ganesh R. Pandey California Department of Water Resources, Sacramento, California

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Daniel R. Cayan Climate Research Division, Scripps Institution of Oceanography, University of California, San Diego, and U.S. Geological Survey, La Jolla, California

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Michael D. Dettinger U.S. Geological Survey, La Jolla, California

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Konstantine P. Georgakakos Climate Research Division, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, and Hydrologic Research Center, San Diego, California

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Abstract

A hybrid (physical–statistical) scheme is developed to resolve the finescale distribution of daily precipitation over complex terrain. The scheme generates precipitation by combining information from the upper-air conditions and from sparsely distributed station measurements; thus, it proceeds in two steps. First, an initial estimate of the precipitation is made using a simplified orographic precipitation model. It is a steady-state, multilayer, and two-dimensional model following the concepts of Rhea. The model is driven by the 2.5° × 2.5° gridded National Oceanic and Atmospheric Administration–National Centers for Environmental Prediction upper-air profiles, and its parameters are tuned using the observed precipitation structure of the region. Precipitation is generated assuming a forced lifting of the air parcels as they cross the mountain barrier following a straight trajectory. Second, the precipitation is adjusted using errors between derived precipitation and observations from nearby sites. The study area covers the northern half of California, including coastal mountains, central valley, and the Sierra Nevada. The model is run for a 5-km rendition of terrain for days of January–March over the period of 1988–95. A jackknife analysis demonstrates the validity of the approach. The spatial and temporal distributions of the simulated precipitation field agree well with the observed precipitation. Further, a mapping of model performance indices (correlation coefficients, model bias, root-mean-square error, and threat scores) from an array of stations from the region indicates that the model performs satisfactorily in resolving daily precipitation at 5-km resolution.

Corresponding author address: Ganesh Pandey, Dept. of Water Resources, Delta Modeling Section, 1416 Ninth St., Room 215-15, Sacramento, CA 95481.

Email: gpandey@water.ca.gov

Abstract

A hybrid (physical–statistical) scheme is developed to resolve the finescale distribution of daily precipitation over complex terrain. The scheme generates precipitation by combining information from the upper-air conditions and from sparsely distributed station measurements; thus, it proceeds in two steps. First, an initial estimate of the precipitation is made using a simplified orographic precipitation model. It is a steady-state, multilayer, and two-dimensional model following the concepts of Rhea. The model is driven by the 2.5° × 2.5° gridded National Oceanic and Atmospheric Administration–National Centers for Environmental Prediction upper-air profiles, and its parameters are tuned using the observed precipitation structure of the region. Precipitation is generated assuming a forced lifting of the air parcels as they cross the mountain barrier following a straight trajectory. Second, the precipitation is adjusted using errors between derived precipitation and observations from nearby sites. The study area covers the northern half of California, including coastal mountains, central valley, and the Sierra Nevada. The model is run for a 5-km rendition of terrain for days of January–March over the period of 1988–95. A jackknife analysis demonstrates the validity of the approach. The spatial and temporal distributions of the simulated precipitation field agree well with the observed precipitation. Further, a mapping of model performance indices (correlation coefficients, model bias, root-mean-square error, and threat scores) from an array of stations from the region indicates that the model performs satisfactorily in resolving daily precipitation at 5-km resolution.

Corresponding author address: Ganesh Pandey, Dept. of Water Resources, Delta Modeling Section, 1416 Ninth St., Room 215-15, Sacramento, CA 95481.

Email: gpandey@water.ca.gov

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  • Alpert, P., 1986: Mesoscale indexing of the distribution of the orographic precipitation over high mountains. J. Climate Appl. Meteor.,25, 532–545.

    • Crossref
    • Export Citation
  • Bell, R. S., 1978: The forecasting of orographically enhanced rainfall accumulations using 10-level model data. Meteor. Mag.,107, 113–124.

  • Brader, M. J., and W. T. Roach, 1977: Orographic rainfall in warm sectors of depressions. Quart. J. Roy. Meteor. Soc.,103, 269–280.

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

  • Collier, C. G., 1975: A representation of effects of topography on surface rainfall within moving baroclinic disturbances. Quart. J. Roy. Meteor. Soc.,101, 407–422.

    • Crossref
    • Export Citation
  • Dore, A. J., and T. W. Choularton, 1992: A three dimensional model of airflow and orographic rainfall enhancement. Quart. J. Roy. Meteor. Soc.,118, 1041–1056.

    • Crossref
    • Export Citation
  • Elliot, R. D., and R. W. Shafer, 1962: The development of a quantitative relationship between orographic precipitation and airmass parameters for use in forecasting and cloud seeding evaluation. J. Appl. Meteor.,1, 218–228.

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

    • Crossref
    • Export Citation
  • Giorgi, F., G. T. Bates, and S. J. Nieman, 1993: The multiyear surface climatology of a regional atmospheric model over the western United States. J. Climate,6, 75–95.

    • Crossref
    • Export Citation
  • Gocho, Y., 1978: Numerical experiment of orographic heavy rainfall due to a stratiform cloud. J. Meteor. Soc. Japan,56, 405–422.

    • Crossref
    • Export Citation
  • Hay, L. E., 1996: Assessment of an orographic precipitation model in southwestern Colorado. Ph.D. thesis, University of Colorado, 127 pp. [Available from Department of Geography, University of Colorado, Boulder, CO 80309.].

  • ——, and G. J. McCabe, 1998: Verification of the Rhea-orographic-precipitation model. J. Amer. Water. Res. Assoc.,34 (1), 103–112.

    • Crossref
    • Export Citation
  • Hill, F. F., K. A. Browning, and M. J. Bader, 1981: Radar and raingage observations of orographic rain over south Wales. Quart. J. Roy. Meteor. Soc.,107, 643–670.

    • Crossref
    • Export Citation
  • Isakson, A., 1996: Rainfall distribution over central and southern Israel induced by large-scale moisture flux. J. Appl. Meteor.,35, 1063–1075.

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

    • Crossref
    • Export Citation
  • Kuligowski, R. J., and A. P. Barros, 1998: Experiments in short-term precipitating forecasting using artificial neural networks. Mon. Wea. Rev.,126, 470–482.

    • Crossref
    • Export Citation
  • Olson, D. A., N. V. Junker, and B. Korty, 1995: Evaluation of 33 years of quantitative precipitation forecasting at the NMC. Wea. Forecasting,10, 498–511.

  • Pandey, G. R., D. R. Cayan, M. D. Dettinger, and K. P. Georgakakos, 1998: Downscaling of precipitation distributions within the Sierra Nevada from global atmospheric fields. Proc. 17th Conf. on Hydrology, Dallas, TX, Amer. Meteor. Soc., 87–89.

  • ——, ——, and K. P. Georgakakos, 1999: Precipitation structure in the Sierra Nevada of California during winter. J. Geophys. Res.,104, 12019–12030.

    • Crossref
    • Export Citation
  • Rango, A. L., and P. V. Hobbs, 1994: Ice particle concentrations and precipitation development in small continental cumuliform clouds. Quart. J. Roy. Meteor. Soc.,120, 573–601.

    • Crossref
    • Export Citation
  • Rhea, J. O., 1978: Orographic precipitation model for hydrometeorological use. Atmospheric Paper 278, Colorado State University, Fort Collins, CO, 198 pp. [Available from Department of Atmospheric Sciences, Colorado State University, Fort Collins, CO 80523.].

  • Robichaud, A. J., and G. L. Austin, 1988: On the modeling of warm orographic rain by the seeder–feeder effect. Quart. J. Roy. Meteor. Soc.,114, 967–988.

    • Crossref
    • Export Citation
  • Rogers, E., T. L. Black, D. G. Deaven, G. J. DiMego, Q. Zhao, M. Bladwin, N. W. Junker, and Y. Lin, 1996: Changes to operational early Eta analysis/forecast system at the National Centers for Environmental Prediction. Wea. Forecasting,11, 391–413.

    • Crossref
    • Export Citation
  • Sinclair, M. R., 1994: A diagnostic model for estimating orographic precipitation. J. Appl. Meteor.,33, 1163–1175.

    • Crossref
    • Export Citation
  • Speers, P., and C. F. Mass, 1986: Diagnosis and prediction of precipitation in regions of complex terrain. Washington State Dept. of Transportation Rep. WA-RD 91.1, 159 pp. [Available from Washington State Department of Transportation Library, P.O. Box 47425, 310 Maple Park Ave. S.E., Olympia, WA 98504.].

  • Speers-Hayes, P., 1991: Prediction of precipitation in western Washington state. Washington State Dept. of Transportation Rep. WA-RD 231.1, 65 pp. [Available from Washington State Department of Transportation Library, P.O. Box 47425, 310 Maple Park Ave. S.E., Olympia, WA 98504.].

  • Wallace, J. M., and P. V. Hobbs, 1977: Atmospheric Science; An Introductory Survey. Academic Press, 467 pp.

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