Resumo A escassez de dados pluviográficos em Alagoas, similar em muitas regiões do país, faz com que utilizem equações de regressão obtidas em outras regiões do Brasil para calcular o fator R da Equação Universal de Perda de Solo. O estudo tem por objetivos: i) definir uma equação para estimar a erosividade das chuvas baseada no índice EI30 e no coeficiente de chuva Rc, ii) validar o método de imputação de dados para a chuva e erosividade e iii) estimar espacialmente a erosividade nos períodos chuvoso, seco e transição para Alagoas. Utilizaram-se dados pluviométricos mensais de 54 estações no período (1960-2016). A equação utilizada apresentou correlação significativa entre os dados observados e estimados, de acordo com os coeficientes r (93%), R2 (87%) e RMSE (775,2 MJ.mm.ha−1.h−1.ano−1). A Krigagem Ordinária foi o melhor interpolador espacial. A isoerosividade mensal mostrou que os maiores índices de EI30 ocorreram entre abril e julho, período coincidente com a quadra chuvosa do estado. Na erosividade anual, os maiores registros estão situados no Leste Alagoano, próximas ao litoral. Destaque para as estações Satuba, Maceió, São Luiz do Quitunde e Flexeiras, categorizadas entre moderada e forte. Estes resultados auxiliarão no planejamento de práticas conservacionistas, principalmente em áreas de vulnerabilidade.

This study explores the predictability of monthly asthma notifications using models built from different machine learning techniques in Maceió, a municipality with a tropical climate located in the northeast of Brazil. Two sets of predictors were combined and tested, the first containing meteorological variables and pollutants, called exp1, and the second only meteorological variables, called exp2. For both experiments, tests were also carried out incorporating lagged information from the time series of asthma records. The models were trained on 80% of the data and validated on the remaining 20%. Among the five methods evaluated—random forest (RF), eXtreme Gradient Boosting (XGBoost), Multiple Linear Regression (MLR), support vector machine (SVM), and K-nearest neighbors (KNN)—the RF models showed superior performance, notably those of exp1 when incorporating lagged asthma notifications as an additional predictor. Minimum temperature and sulfur dioxide emerged as key variables, probably due to their associations with respiratory health and pollution levels, emphasizing their role in asthma exacerbation. The autocorrelation of the residuals was assessed due to the inclusion of lagged variables in some experiments. The results highlight the importance of pollutant and meteorological factors in predicting asthma cases, with implications for public health monitoring. Despite the limitations presented and discussed, this study demonstrates that forecast accuracy improves when a wider range of lagged variables are used, and indicates the suitability of RF for health datasets with complex time series.

O estudo se baseou no levantamento estatístico de eventos extremos de precipitação (P) no Leste do Nordeste Brasileiro (LNEB), com destaque a cidade de Maceió/AL. Dados de Estações Meteorológicas Automáticas (EMA) do INMET, imagens de satélite e dados de hidroestimador disponibilizado pelo CPTEC/INPE foram usados no estudo. Os resultados obtidos via técnica dos quantis, categorizou as seguintes classes de P(mm): Dia Seco (DS) - P < 2,4mm, Chuva muito fraca (Cmf) - 2,4 ≤ P < 3,8mm, Chuva fraca (Cf) - 3,8 ≤ P < 7,6mm, Chuva Moderada (CM) - 7,6 ≤ P < 14,4mm, Chuva Forte (CF) - 14,4 ≤ P < 39,8mm, Chuva Muito Forte - (CMF) 39,8 ≤ P < 82,8mm e Chuva Extrema (CE) - P ³ 82,8mm. As categorias CMF e CE foram eficientes na associação entre extremos de precipitação e os desastres naturais a partir dos registros de decreto de Estado de Calamidade Pública (ECP). Tal padrão relacional apontou a possibilidade de ocorrência de desastres hidrometeorológicos para a cidade de Maceió/AL. No tocante a distribuição espacial da quantidade de casos de CMF e CE em Alagoas, os municípios com maiores ocorrências foram Japaratinga, Porto de Pedras e Maceió. Especificamente a cidade de Maceió/AL, a região R5 (Ipioca e Pescaria) e R2 foram as que mais se destacaram em comparação as demais regiões.Palavras-chave: MERGE, chuva urbana, quartis, desastres naturais. Evaluation of extreme precipitation events associated with natural disasters A B S T R A C TThe present study mainly aimed to make a statistical survey of extreme precipitation events for the eastern region of the Brazilian Northeast, more specifically for the city of Maceió/AL. For this purpose, punctual data from INMET Automatic Meteorological Stations were used, and spatial data using satellite images and hydro-estimator data provided by CPTEC/INPE. The main results were based on daily precipitation data and quantile technique, it generated the following R(mm) classes: Dry Day R < 2.4mm, Drizzle Rain 2.4 ≤ R < 3, 8mm, Showers 3.8 ≤ R < 7.6mm, Moderate Rain 7.6 ≤ R < 14.4mm, Heavy Rain 14.4 ≤ R < 39.8mm, Strong Rain 39.8 ≤ R < 82.8mm, Extreme Rain R ≥ 82.8mm. The method adopted for classifying extreme rainfall (Strong more Extreme) using quartiles proved to be efficient, made by the association between extreme precipitation events and natural disasters using the records of the State of Public Calamity (SPC) degree, that is, the indicators recognition of SPC and annual accumulated average precipitation, point to the possibility of occurrence of hydrometeorological disasters for the city of Maceió/AL. Regarding the spatial distribution of the number of cases of Strong and Extreme, for Alagoas the municipalities with the most recorded occurrences were Japaratinga, Porto de Pedras, Maceió, among others. For the city of Maceió/AL, the region R5 (Ipioca and Pescaria) and R2 were the ones that presented the most cases of rainfall ranging from very strong to extreme.Keywords: MERGE, urban rainfall, quantiles, natural disasters.

In this study, we evaluated the performance of the Brazilian Global Atmospheric Model (BAM), in its version 2.2.1, in the representation of the surface variables solar radiation, temperature (maximum, minimum, and average), and wind speed. Three experiments were carried out for the period from 2016 to 2022 under three different aerosol conditions (constant (CTE), climatological (CLIM), and equal to zero (ZERO)), discarding the first year as a spin-up period. The observations came from a high-resolution gridded analysis that provides Brazil with robust data based on observations from surface stations on a daily scale from 1961 to 2020; therefore, combining the BAM outputs with the observations, our intercomparison period took place from 2017 to 2020, for three timescales: daily, 10-day average, and monthly, targeting different applications. In its different simulations, BAM overestimated solar radiation throughout Brazil, especially in the Amazon; underestimated temperature in most of the northeast, southeast, and south regions; and overestimated in parts of the north and mid-west; while wind speed was only not overestimated in the Amazon region. In relative terms, the simulations with constant aerosol showed better performance than the others, followed by climatological conditions and zero aerosol. The dexterity indices applied in the intercomparison between BAM and observations indicate that BAM needs adjustments and calibration to better represent these surface variables. Where model deficiencies have been identified, these can be used to drive model development and further improve the predictive capabilities.

O ozônio (O3) representa menos de 1% dos gases da atmosfera terrestre, entretanto, é indispensável para a vida na Terra, devido sua influência no balanço energético do planeta filtrando a Radiação Ultravioleta (RUV) do tipo UV-B, nociva à saúde. O objetivo geral deste trabalho é investigar o impacto da coluna total de ozônio sobre a variabilidade da RUV na América do Sul, utilizando dados diários de RUV e Coluna Total de Ozônio (CTO) do sensor Ozone Monitoring Instrument (OMI) da Aura/NASA (National Aeronautics and Space Administration). Na análise mensal, o valor médio da coluna total de ozônio para o decênio de 2005 a 2014 apresentou uma maior variabilidade no trimestre SON na região da América do Sul. A Análise de Componentes Principais mostrou que nos meses JJA e SON apresenta-se uma correlação direta entre CTO e RUV. As análises dos casos estudados comprovaram que apenas os baixos níveis de ozônio não são determinantes para os altos valores de RUV. Os meses entre julho e outubro apresentam grandes áreas no da América do Sul com forte correlação inversa apresentando regiões estatisticamente significantes. Os resultados exprimem a relação entre a CTO e RUV que dependente da sazonalidade nas diferentes regiões, entre outros fatores. Spatio-temporal variability of total ozone column and ultraviolet radiation: assessment of relationship in South America A B S T R A C T Ozone (O3) represents less than 1% of the earth's atmosphere gases, however, it is indispensable for life on Earth, due to its influence on the planet's energy balance as well as filtering out harmful UV-B type UVR-B the health. The general objective of this work is to investigate the impact of the total ozone column on UVR variability in South America, using daily Aura/NASA Ozone Monitoring Instrument (OMI) sensor and Total Column Ozone (TOC) data daily. National Aeronautics and Space Administration). In the monthly analysis, the average value of the total ozone column for the decade from 2005 to 2014 showed a greater variability in the SON quarter in the South American region. The Analisys Principals Components pattern showed that in the JJA and SON months, there is a direct correlation between TOC and RUV. Analyzes of the case studies have shown that only low ozone levels are not determinant for high UVR values. The months between July and October present large areas in southern South America with strong inverse correlation showing statistically significant regions. The results express that the relationship between TOC and UVR depends on seasonality and different regions, among other factors. Keywords: Solar Radiation, OMI Sensor, Daily Erythmic Dose