A Stochastic Precipitation Generator Conditioned on ENSO Phase: A Case Study in Southeastern South America

Martin O. Grondona Instituto Nacional de Tecnología Agropecuaria, Castelar, Argentina

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Guillermo P. Podestá Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida

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Mario Bidegain Dirección Nacional de Meteorología, Montevideo, Uruguay

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Monica Marino Servicio Meteorológico Nacional, Buenos Aires, Argentina

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Hugo Hordij Servicio Meteorológico Nacional, Buenos Aires, Argentina

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Abstract

Stochastic precipitation generators can produce synthetic daily rainfall series with statistical characteristics similar to those of historical data. Typically, parameters of precipitation generators have been fit using all historical data for a given period. This approach, however, fails to capture differences in the precipitation process associated with an El Niño–Southern Oscillation (ENSO) signal. Stochastic precipitation generators conditioned on the ENSO phase were developed to address this problem. Precipitation models with a range of parameterization schemes were tested in six locations in central-eastern Argentina and western Uruguay (southeastern South America), an important agricultural region with a clear ENSO precipitation signal in October–March. Conditional precipitation models (occurrence, intensity, or both) were superior to simple models in 24 of the 36 locations/months analyzed. Graphic diagnostics showed that conditional occurrence models successfully captured differences in the number and persistence of wet days among ENSO phases. Similarly, conditional intensity models improved noticeably the agreement between theoretical and empirical distributions of daily rainfall amounts. Conditional precipitation generators can be linked to other process models (e.g., crop models) to derive realistic assessments of the likely consequences of ENSO-related variability. Conditional stochastic precipitation generators, therefore, can be useful tools to translate ENSO forecasts into likely regional impacts on sectors of interest.

* Current affiliation: Zeneca Semillas S.A.I.C., Balcarce Research Station, Balcarce, Argentina.

Corresponding author address: Guillermo Podestá, University of Miami RSMAS-MPO, 4600 Rickenbacker Cswy., Miami, FL 33149-1098.

Abstract

Stochastic precipitation generators can produce synthetic daily rainfall series with statistical characteristics similar to those of historical data. Typically, parameters of precipitation generators have been fit using all historical data for a given period. This approach, however, fails to capture differences in the precipitation process associated with an El Niño–Southern Oscillation (ENSO) signal. Stochastic precipitation generators conditioned on the ENSO phase were developed to address this problem. Precipitation models with a range of parameterization schemes were tested in six locations in central-eastern Argentina and western Uruguay (southeastern South America), an important agricultural region with a clear ENSO precipitation signal in October–March. Conditional precipitation models (occurrence, intensity, or both) were superior to simple models in 24 of the 36 locations/months analyzed. Graphic diagnostics showed that conditional occurrence models successfully captured differences in the number and persistence of wet days among ENSO phases. Similarly, conditional intensity models improved noticeably the agreement between theoretical and empirical distributions of daily rainfall amounts. Conditional precipitation generators can be linked to other process models (e.g., crop models) to derive realistic assessments of the likely consequences of ENSO-related variability. Conditional stochastic precipitation generators, therefore, can be useful tools to translate ENSO forecasts into likely regional impacts on sectors of interest.

* Current affiliation: Zeneca Semillas S.A.I.C., Balcarce Research Station, Balcarce, Argentina.

Corresponding author address: Guillermo Podestá, University of Miami RSMAS-MPO, 4600 Rickenbacker Cswy., Miami, FL 33149-1098.

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  • Akaike, H., 1974: A new look at the statistical model identification. IEEE Trans. Autom. Control,19, 716–723.

  • Barnston, A. G., and Coauthors, 1994: Long-lead seasonal forecasts—Where do we stand? Bull. Amer. Meteor. Soc.,75, 2097–2114.

  • Bidegain, M., and M. Caffera, 1989: La variación de los días con precipitación sobre el Uruguay y el fenómeno El Niño-Oscilación del Sur (Variability of rain days over Uruguay and the El Niño-Southern Oscillation phenomenon). Proc. Third Int. Conf. on Southern Hemisphere Meteorology and Oceanography, Buenos Aires, Argentina, Amer. Meteor. Soc.

  • Boote, K. J., J. W. Jones, and N. B. Pickering, 1996: Potential uses and limitations of crop models. Agron. J.,88, 704–716.

  • Bruhn, J. A., W. E. Fry, and G. W. Fick, 1980: Simulation of daily weather data using theoretical probability distributions. J. Appl. Meteor.,19, 1029–1036.

  • Buckland, S. T., K. P. Burnham, and N. H. Augustin, 1997: Model selection: An integral part of inference. Biometrics,53, 603–618.

  • Bürger, G., 1997: On the disaggregation of climatological means and anomalies. Climate Res.,8, 183–194.

  • Coe, R., and R. D. Stern, 1982: Fitting models to daily rainfall data. J. Appl. Meteor.,21, 1024–1031.

  • Díaz, A. F., C. D. Studzinski, and C. R. Mechoso, 1998: Relationships between precipitation anomalies in Uruguay and southern Brazil and sea surface temperature in the Pacific and Atlantic Oceans. J. Climate,11, 251–271.

  • Gabriel, K. R., and J. Neumann, 1962: A Markov chain model for daily rainfall occurrences at Tel Aviv. Quart. J. Roy. Meteor. Soc.,88, 90–95.

  • Geng, S., F. W. T. Penning de Vries, and I. Supit, 1985: A simple method for generating daily rainfall data. Agric. For. Meteor.,36, 363–376.

  • Georgakakos, K. P., and M. L. Kavvas, 1987: Precipitation analysis, modeling and prediction in hydrology. Rev. Geophys.,25, 163–178.

  • Gregory, J. M., T. M. L. Wigley, and P. D. Jones, 1993: Application of Markov models to two area-average daily precipitation series and interannual variability in seasonal totals. Climate Dyn.,8, 299–310.

  • Grimm, A., V. R. Barros, and M. E. Doyle, 2000: Climate variability in southern South America associated with El Niño and La Niña events. J. Climate,13, 35–58.

  • Hall, A. J., C. M. Rebella, C. M. Ghersa, and J. P. Culot, 1992: Field-crop systems of the Pampas. Ecosystems of the World, Field Crop Ecosystems, C. J. Pearson, Ed., Elsevier, 413–449.

  • Hutchinson, M. F., 1987: Methods of generation of weather sequences. Agricultural Environments: Characterization, Classification and Mapping, A. H. Bunting, Ed., CAB International, 149–157.

  • Johnson, G. L., C. L. Hanson, S. P. Hardegree, and E. B. Ballard, 1996: Stochastic weather simulation: Overview and analysis of two commonly used models. J. Appl. Meteor.,35, 1878–1896.

  • Jones, P. G., and P. K. Thornton, 1993: A rainfall generator for agricultural applications in the tropics. Agric. For. Meteor.,63, 1–9.

  • Katz, R. W., 1981: On some criteria for estimating the order of a Markov chain. Technometrics,23, 243–249.

  • ——, and M. B. Parlange, 1993: Effects of an atmospheric index of atmospheric circulation on stochastic properties of precipitation. Water Resour. Res.,29, 2335–2344.

  • ——, and ——, 1996: Mixtures of stochastic processes: Application to statistical downscaling. Climate Res.,7, 185–193.

  • ——, and ——, 1998: Overdispersion phenomenon in stochastic modeling of precipitation. J. Climate,11, 591–601.

  • Kumar, A., and M. P. Hoerling, 1997: Interpretations and implications of the observed inter-El Niño variability. J. Climate,10, 83–91.

  • Lanzante, J. R., 1996: Resistant, robust and nonparametric techniques for the analysis of climate data: Theory and examples, including applications to historical radiosonde station data. Int. J. Climatol.,16, 1197–1226.

  • Latif, M., and Coauthors, 1998: A review of the predictability and prediction of ENSO. J. Geophys. Res.,103, 14 375–14 393.

  • Lettenmaier, D., 1995: Stochastic modeling of precipitation with applications to climate model downscaling. Analysis of Climate Variability: Applications of Statistical Techniques, H. von Storch and A. Navarra, Eds., Springer-Verlag, 197–212.

  • Livezey, R. E., and W. Y. Chen, 1983: Statistical field significance and its determination by Monte Carlo techniques. Mon. Wea. Rev.,111, 46–59.

  • MacDonald, I. L., and W. Zucchini, 1997: Hidden Markov and other models for discrete-valued time series. Statistics and Applied Probability, Meteor. Monogr., No. 70, Chapman and Hall, 236 pp.

  • Meyers, S. D., J. J. O’Brien, and E. Thelin, 1999: Reconstruction of monthly SST in the tropical Pacific Ocean during 1868–1993 using adaptive climate basis functions. Mon. Wea. Rev.,127, 1599–1612.

  • Moschini, R. C., M. O. Grondona, and D. Vila, 1996: Influencia del ENSO sobre la distribución de las probabilidades condicionales diarias de precipitación en algunas estaciones de la región pampeana (ENSO influence on the distribution of conditional precipitation probabilities in some stations in the Pampas). Actas del VII Congreso Argentino y VII Congreso Latinoamericano e Ibérico de Meteorología, Buenos Aires, Argentina, Centro Argentino de Meteorólogos, 307–308.

  • Pickering, N. B., J. W. Hansen, J. W. Jones, C. M. Wells, V. K. Chan, and D. C. Godwin, 1994: WeatherMan: A utility for managing and generating daily weather data. Agron. J.,86, 332–337.

  • Pisciottano, G., A. Díaz, G. Cazes, and C. R. Mechoso, 1994: El Niño–Southern Oscillation impact on rainfall in Uruguay. J. Climate,7, 1286–1302.

  • Prohaska, F., 1976: The climate of Argentina, Paraguay and Uruguay. Climates of Central and South America, Vol. 12, World Survey of Climatology, W. Schwerdtfeger, Ed., Elsevier, 13–112.

  • Racsko, P., L. Szeidl, and M. Semenov, 1991: A serial approach to local stochastic weather models. Ecol. Modell.,57, 27–41.

  • Richardson, C. W., 1981: Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resour. Res.,17, 182–190.

  • Ropelewski, C. F., and M. S. Halpert, 1987: Global and regional scale patterns associated with the El Niño/Southern Oscillation. Mon. Wea. Rev.,115, 1606–1626.

  • ——, and ——, 1989: Precipitation patterns associated with the high index phase of the Southern Oscillation. J. Climate,2, 268–284.

  • ——, and ——, 1996: Quantifying Southern Oscillation-precipitation relationships. J. Climate,9, 1043–1059.

  • Schimmelpfennig, D., 1996: Uncertainty in economic models of climate-change impact. Climatic Change,33, 213–234.

  • Semenov, M. A., R. J. Brooks, E. M. Barrow, and C. W. Richardson, 1998: Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climates. Climate Res.,10, 95–107.

  • Stern, R. D., and R. Coe, 1984: A model fitting analysis of daily rainfall data. J. Roy. Stat. Soc.,147, 1–34.

  • Trenberth, K., 1997a: Short-term climate variations: Recent accomplishments and issues for future progress. Bull. Amer. Meteor. Soc.,78, 1081–1096.

  • ——, 1997b: The definition of El Niño. Bull. Amer. Meteor. Soc.,78, 2771–2777.

  • Venables, W. N., and B. D. Ripley, 1997: Modern Applied Statistics with S-Plus. 2d ed. Springer, 548 pp.

  • Vila, D. A., and M. O. Grondona, 1996: Estudio preliminar sobre las relaciones entre el ENSO y la frecuencia de días con Iluvia en la Pampa húmeda (Preliminary study on associations between ENSO and the frequency of rain days in the Pampas). Actas VII Congreso Argentino y VII Congreso Latinoamericano e Ibérico de Meteorología, Buenos Aires, Argentina, Centro Argentino de Meteorólogos, 309–310.

  • Wallis, T. W. R., and J. F. Griffiths, 1995: An assessment of the weather generator (WXGEN) used in the erosion/productivity impact calculator (EPIC). Agric. For. Meteor.,73, 115–133.

  • Wilks, D. S., 1989: Conditioning stochastic daily precipitation models on total monthly precipitation. Water Resour. Res.,25, 1429–1439.

  • ——, 1992: Adapting stochastic weather generation algorithms for climate change studies. Climatic Change,22, 67–84.

  • ——, 1996: Statistical significance of long-range “optimal climate normal” temperature and precipitation forecasts. J. Climate,9, 827–839.

  • ——, 1997: Resampling hypothesis tests for autocorrelated fields. J. Climate,10, 65–82.

  • Woolhiser, D. A., 1992: Modeling daily precipitation—Progress and problems. Statistics in the Environmental and Earth Sciences, A. T. Walden and P. Guttorp, Eds., Edward Arnold, 71–89.

  • ——, T. O. Keefer, and K. T. Redmond, 1993: Southern Oscillation effects on daily precipitation in the southwestern United States. Water Resour. Res.,29, 1287–1295.

  • Zwiers, F. W., 1987: Statistical considerations for climate experiments. Part II: Multivariate tests. J. Climate Appl. Meteor.,26, 477–487.

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