Abstract
The improvement of probabilistic assessments of extreme air temperature events is a major goal for agrometeorological studies. The regional frequency analysis based on L-moments (RFA-Lmom) has been successfully used to improve the study of hydrometeorological variables such as extreme rainfall events. This study investigated the hypothesis that the RFA-Lmom can be applied to extreme maximum (Tmax) and minimum (Tmin) air temperature data. The RFA-Lmon was calculated considering its original algorithm (multiplicative approach) and a new procedure referred as to additive approach. The suitability of both approaches was evaluated through Monte Carlo experiments, which simulated homogeneous and heterogeneous groups of Tmin and Tmax series, and through a case study based on weather stations situated in the state of São Paulo, Brazil. The results found in this study indicated that the RFA-Lmom can be applied to Tmax and Tmin data in tropical/subtropical regions such as the state of São Paulo. In addition, the additive approach consistently outperformed the multiplicative approach. Both discordance and heterogeneous measures presented their best performances when calculated under this new approach. The original goodness-of-fit measure may also be replaced by its bivariate extension when the group is formed by more than 15 series.
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