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

Martin O. GrondonaInstituto Nacional de Tecnología Agropecuaria, Castelar, Argentina

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

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Mario BidegainDirección Nacional de Meteorología, Montevideo, Uruguay

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Monica MarinoServicio Meteorológico Nacional, Buenos Aires, Argentina

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Hugo HordijServicio Meteorológico Nacional, Buenos Aires, Argentina

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Print Publication:
15 Aug 2000
DOI:
https://doi.org/10.1175/1520-0442(2000)013<2973:ASPGCO>2.0.CO;2
Page(s):
2973–2986
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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.

Email: gpodesta@rsmas.miami.edu

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.

Email: gpodesta@rsmas.miami.edu

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