An analysis of the seasonal precipitation forecasts in South America using wavelets

Abstract

A post-processing technique was applied to statistically correct the seasonal rainfall forecasts over South America (SA). The aim of this work was to reduce errors in the seasonal climate simulations obtained from the Centro de Previsão de Tempo e Estudos Climáticos (CPTEC) atmospheric general circulation model (AGCM) which was run with different deep cumulus convection parameterizations. One of the main contributions of this study is the discussion of the super-ensemble approach to reduce errors in the seasonal rainfall prediction for SA. A novel aspect here is the use of the wavelet technique to compare forecast and observed time series by investigating their time-frequency structures. This methodology has not yet been applied to super-ensemble model validations. The statistical algorithm used in the superensemble technique was based on the linear multiple regression method. The time series of the super-ensemble forecast (FCT), arithmetic averaged forecast (MEM) and individual model forecasts and the observed (OBS) ones for selected areas of SA were compared by calculating the root mean square errors (RMSEs) and by applying the wavelet technique on these time series. In general, for the analysed areas we obtained a super-ensemble skill superior to that for the MEM. The wavelet analysis proved to be very useful to compare forecast and observed time series. In fact, differences and similarities among the time series such as the dominant scale of variability and the time location of the largest variances in the time series were detected with the wavelet analyses.

Monthly values of the standardized precipitation index in the State of São Paulo, Brazil: trends and spectral features under the normality assumption

“…The corresponding p-value should be close to 0.5. In addition, Radziejewski and Kundzewicz (2004) states that even when no significant trend has been detected by statistical tests, this cannot ensure the absence of Considering these last two statements, it becomes important to verify that according to the Pettitt test (Table 4) the beginning of a (decreasing) trend in the SPI series of Campinas and Ribeirão Preto (months of October; p--value < 0.10) was detected as being the years of 1983/84. The same feature is observed in the series of Jundiaí and Pindorama (months of October), although with p-values greater than 0.20.…”

“…Consequently, this sort of spectral analysis has allowed us to observe the variance peaks in the frequency domain and also to verify how those peaks vary in time. Detailed explanation of the continuous wavelet transform (CWT) used in this study can be found in Torrence and Compo (1998), Grinsted et al (2004), Reboita et al (2006), Blain (2009), Pezzi and Kayano (2009) and, Kayano and Sansigolo (2009). The wavelet analysis (including its statistical significance testing) was estimated based on the computational procedure described by Torrence and Compo (1998) and available at http://paos.colorado.edu/research/wavelets (accessed at November 30, 2010).…”

Oceanic SACZ produces an abnormally wet 2021/2022 rainy season in South America

“…However, it should be noted that the model slightly underestimates the extremes. The oceanic SACZ cases are adequately simulated, considering that the precipitation field is one of the most difficult to simulate with high precision and often needs to be corrected after its simulation 19 . The satisfactory skill of the model is good and can be seen by comparing both the simulated northern cases (Fig.…”

Sensitivity of South America Climate to Positive Extremes of Antarctic Sea Ice

“…However, studies of weather and climate conditions and their local and remote agents must consider the large latitudinal extent and the various forms of the South American topography, which allows the development and performance of several atmospheric systems, reflecting in a climate heterogeneity (spacetime) and a very regionalized precipitation regime. Thus, the climate analysis of changes over South America resulting from the increased ASI was performed spatially, in the domain of the continent, and spectrally, through time series for six regions (R1 to R6) with distinct climate regime (Ratisbona 1976, Pezzi & Kayano 2008, namely: Northwestern South America (R1), Brazilian Amazon (R2), Northeast Brazil (R3), South Northeast Brazil (R4), Southeastern Brazil (R5) and Southern Brazil (R6) (Figure 1).…”