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References

  • Ashbaugh LL, Myrup LO, Flocchini RG. 1984. A principal components analysis of sulfur concentrations in the Western United States. Atmospheric Environment 18: 783791.
  • Buell CE. 1975. The topography of empirical orthogonal functions. [preprints, Fourth Conference on Probability and Statistics in Atmospheric Science]. Tallahassee, FL, American Meteorological Society 188193.
  • Cannon AJ, Lord ER. 2000. Forecasting summertime surface-level ozone concentrations in the Lower Fraser Valley of British Columbia: An ensemble neural network approach. Journal of the Air and Waste Management Association 50: 322339.
  • Cannon AJ, McKendry IG. 1999. Forecasting all India summer monsoon rainfall using regional circulation principal components: A comparison between neural network and multiple regression models. International Journal of Climatology 19: 15611578.
  • Carlson RE. 1990. Heat stress, plant-available soil moisture, and corn yield in Iowa: A short- and long-term view. Journal of Production Agriculture 3: 293297.
  • Cavasos T. 1997. Downscaling large-scale circulation to local winter rainfall in North-eastern Mexico. International Journal of Climatology 17: 10691082.
  • Craddock JM, Flood CR. 1969. Eigenvectors for representing the 500 mb geopotential surface over the Northern Hemisphere. Quarterly Journal of the Royal Meteorological Society 95: 576593.
  • Easterling DR. 1999. Development of regional climate scenarios using a downscaling approach. Climatic Change 41: 615634.
  • Eberhart R, Dobbins B. 1990. Neural Network PC Tools: A Practical Guide. Academic Press: San Diego, CA.
  • FSL/NCDC. 1997. Radiosonde data of North America, 1946–1996. Available from http://www.ncdc.noaa.gov/.
  • Gardner MW, Dorling SR. 1998. Artificial neural networks (the multi layer perceptron)—A review of applications in the atmospheric sciences. Atmospheric Environment 32: 26272636.
  • Giorgi F, Marinucci MR, Visconti G. 1998. Use of a limited-area model nested in a general circulation model for regional climate simulation over Europe. Journal of Geophysical Research 95: 1841318431.
  • Gong X, Richman MB. 1995. On the application of cluster analysis to growing season precipitation data in North America east of the Rockies. Journal of Climate 8: 897931.
  • Hewitson BC, Crane RG. 1992. Large-scale atmospheric controls on local precipitation in tropical Mexico. Geophysical Research Letters 19: 18351838.
  • Heyen H, Zorita E, von Storch H. 1996. Statistical downscaling of monthly mean North Atlantic air-pressure to sea level anomalies in the Baltic Sea. Tellus 48A: 312323.
  • Hornik K, Stinchcombe H, White H. 1989. Multilayer feedforward networks are universal approximators. Neural Networks 2: 359366.
  • Hughes JP, Guttorp P. 1994. A class of stochastic models for relating synoptic atmospheric patterns to regional hydrologic phenomena. Water Resources Research 30: 15351546.
  • Huth R. 1999. Statistical downscaling in central Europe: Evaluation of methods and potential predictors. Climate Research 13: 91101.
  • Kalkstein LS, Tan G, Skindlov JA. 1987. An evaluation of three clustering procedures for use in synoptic climatological classification. Journal of Climate and Applied Meteorology 26: 717730.
  • Karl TR, Wang WC, Schlesinger ME, Knight RW, Portman D. 1990. A method of relating general circulation model simulated climate to observed local climate. Part I: Seasonal statistics. Journal of Climate 3: 10531079.
  • Karl JK, Knight RW, Easterling DR, Quayle RO. 1996. Indicies of climate change for the United States. Bulletin of the American Meteorological Society 77: 279292.
  • Kidson JW, Thompson CS. 1998. A comparison of statistical and model-based downscaling techniques for estimating local climate variations. Journal of Climate 11: 735753.
  • Kilsby CG, Cowpertwait PSP, O'Connell PE, Jones PD. 1998. Predicting rainfall statistics in England and Wales using atmospheric circulation variables. International Journal of Climatology 18: 523539.
  • McGregor GR. 1996. Identification of air quality affinity areas in Birmingham, UK. Applied Geography 16: 109122.
  • Mendelsohn R, Nordhaus WD, Shaw D. 1994. The impact of global warming on agriculture: A Ricardian analysis. The American Economic Review 84: 753771.
  • NCDC/NREL. 1993. Solar and meteorological surface observation network (SAMSON). Available from http://www.ncdc.noaa.gov/. Data sets were used from CD ROM.
  • Orlanski I. 1975. A rational subdivision of scales for atmospheric processes. Bulletin of the American Meteorological Society 56: 527530.
  • Overland JE, Preisendorfer RW. 1982. A significance test for principal components applied to a cyclone climatology. Monthly Weather Review 110: 14.
  • Pryor SC, Davies TD, Hoffer TE, Richman MB. 1995. The influence of synoptic scale meteorology on transport of urban air to remote locations in the southwestern United States of America. Atmospheric Environment 29: 16091618.
  • Richman MB. 1986. Rotation of principal components. Journal of Climatology 6: 293335.
  • Richman MB, Gong X-F. 1993. Relationships between the definition of the hyperplane width and the fidelity of the resulting PC loading patterns. In Proceedings of the Seventeenth Annual Climate Diagnostics Workshop. US Department of Commerce, Washington, D.C.; 372379.
  • Rounsevell MD, Evans SP, Bullock P. 1999. Climate change and agricultural soils: Impacts and adaptation. Climatic Change 43: 683709.
  • Smit B, McNabb D, Smithers J. 1996. Agricultural adaptation to climatic variation. Climatic Change 33: 729.
  • Solman SA, Nuñez MN. 1999. Local estimates of global climate change: A statistical downscaling approach. International Journal of Climatology 19: 835861.
  • Stratheropoulos M, Vassiliadis N, Pappa A. 1998. Principal component and canonical correlation analysis for examining air pollution and meteorological data. Atmospheric Environment 32: 10871095.
  • Ward JH. 1963. Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association 58: 236244.
  • Warner B, Misra M. 1996. Understanding neural networks as statistical tools. The American Statistician 50: 284293.
  • Weichert A, Bürger G. 1998. Linear versus nonlinear techniques in downscaling. Climate Research 10: 8393.
  • White DM, Richman MB, Yarnal B. 1991. Climate regionalization and rotation of principal components. International Journal of Climatology 11: 125.
  • Wigley TM, Jones PD, Briffa KR, Smith G. 1990. Obtaining sub-grid scale information from coarse resolution general circulation model output. Journal of Geophysical Research 95: 19431953.
  • Wilks DS. 1995. Statistical Methods in the Atmospheric Sciences. Academic Press: San Diego, CA.
  • Willmott CJ. 1981. On the validation of models. Physical Geography 2: 184194.
  • Winkler JA, Palutikof JP, Andresen JA, Goodess CM. 1997. The simulation of daily temperature time series from GCM output. Part II: sensitivity analysis of an empirical transfer function methodology. Journal of Climate 10: 25142532.
  • Xopalski E, Luterbacher J, Burkard R, Patrikas I, Maheras P. 2000. Connection between the large-scale 500 hPa geopotential height fields and precipitation over Greece during wintertime. Climate Research 14: 129146.