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Precipitation Extremes Analysis over the Brazilian Northeast via Logistic Regression

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摘要 This work diagnosed the precipitation extremes over the Brazilian Northeast (NEB) based on logistic regression for obtaining associations between precipitation extremes and the meteorological variables by Odd Ratio (OR). Data of ten meteorological variables to the NEB (North (NNEB), East (ENEB), South (SNEB) and Semiarid (SANEB)) were used daily. The OR results evidenced that the outgoing longwave radiation was the key variable on the precipitation extremes detection in three sub-regions: ENEB with 2.91 times (95% confidence interval (CI): 2.11, 4.02), NNEB with 3.63 times (95% CI: 1.93, 6.83), and SANEB with 5.40 times (95% CI: 3.04, 9.61);while on SNEB, it was relative humidity with 3.88 times (95% CI: 2.89, 5.20) more chance to favor the precipitation extremes. The maximum temperature, zonal wind component, evaporation, specific humidity and RH also had influence on these extremes. Goodness-of-fit and ROC analysis demonstrated that all models had a good fit and good predictive capability. This work diagnosed the precipitation extremes over the Brazilian Northeast (NEB) based on logistic regression for obtaining associations between precipitation extremes and the meteorological variables by Odd Ratio (OR). Data of ten meteorological variables to the NEB (North (NNEB), East (ENEB), South (SNEB) and Semiarid (SANEB)) were used daily. The OR results evidenced that the outgoing longwave radiation was the key variable on the precipitation extremes detection in three sub-regions: ENEB with 2.91 times (95% confidence interval (CI): 2.11, 4.02), NNEB with 3.63 times (95% CI: 1.93, 6.83), and SANEB with 5.40 times (95% CI: 3.04, 9.61);while on SNEB, it was relative humidity with 3.88 times (95% CI: 2.89, 5.20) more chance to favor the precipitation extremes. The maximum temperature, zonal wind component, evaporation, specific humidity and RH also had influence on these extremes. Goodness-of-fit and ROC analysis demonstrated that all models had a good fit and good predictive capability.
出处 《Atmospheric and Climate Sciences》 2014年第1期53-59,共7页 大气和气候科学(英文)
基金 CAPES for doctoral financial support George Pedra and Naurinete Barreto by several contributions for this article.P.S.Lucio is sponsored by a PQ2 grant(Proc.302493/2007-7)from CNPq(Brazil).
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