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 Nino-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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 2973-2986 |
| Number of pages | 14 |
| Journal | Journal of Climate |
| Volume | 13 |
| Issue number | 16 |
| State | Published - Aug 2000 |
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ASJC Scopus subject areas
- Atmospheric Science
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A stochastic precipitation generator conditioned on ENSO phase : A case study in southeastern South America. / Grondona, M. O.; Podesta, Guillermo P; Bidegain, M.; Marino, M.; Hordij, H.
In: Journal of Climate, Vol. 13, No. 16, 08.2000, p. 2973-2986.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A stochastic precipitation generator conditioned on ENSO phase
T2 - A case study in southeastern South America
AU - Grondona, M. O.
AU - Podesta, Guillermo P
AU - Bidegain, M.
AU - Marino, M.
AU - Hordij, H.
PY - 2000/8
Y1 - 2000/8
N2 - 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 Nino-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.
AB - 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 Nino-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.
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UR - http://www.scopus.com/inward/citedby.url?scp=0034253169&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:0034253169
VL - 13
SP - 2973
EP - 2986
JO - Journal of Climate
JF - Journal of Climate
SN - 0894-8755
IS - 16
ER -