Thailand is under threat from climate change, where extreme climate events are expected to intensify and increase in the coming decades. The objective is to assess extreme drought and rainfall events in Thailand based on climate modeling through an ensemble for future projections of extreme climate indices. The climate indices used were Consecutive Dry Days (CDD), Maximum Number of Consecutive Summer Days (CSU), Consecutive Wet Days (CWD), Warm Spell Duration Index (WSDI), and Maximum Number of Consecutive Wet Days (WW) derived from simulations of an ensemble composed of six models from the Intergovernmental Panel on Climate Change (IPCC) via the Coupled Model Intercomparison Project Phase 6 (CMIP6) using Artificial Neural Networks (ANN) with the backpropagation method. The projections were based on three scenarios: historical (20th century); intermediate forcing (RCP 4.5) and high forcing (RCP 8.5). The results of the climate indices pointed to significant regional differences in Thailand. Historically, the CDD indicated 35 consecutive dry days in the northern (N) and northeastern (NE) parts of Thailand, whereas the southern region showed CDD values of fewer than 10 consecutive dry days. In the R4.5 scenario, a meridional pattern emerged in CDD, increasing from east (E) to west (W). In the R8.5 scenario, the number of consecutive dry days increased across the entire country. The WSDI stood out in both the R4.5 and R8.5 scenarios, with an increase in the duration of warm spells in Thailand. The CSU did not perform satisfactorily in the scenarios adopted. Historically, the CWD indicated consecutive wet days in the N and NE, whereas in the R4.5 and R8.5 scenarios, this was observed only in the Central and Southern regions. Historically, the maximum number of consecutive rainy days varied in the NE and South via WW. In the R4.5 and R8.5 scenarios, there was a significant increase in the maximum number of consecutive rainy days across Thailand. Projections based on climate indices indicate that Thailand needs to adopt mitigation measures across its regions to neutralize the impacts of extreme drought and rainfall events on socioeconomic sectors, particularly in tourism, industry, agricultural production, and food security for its population.

This study assessed the influence of meteorological conditions on particulate matter (PM) concentrations in Campo Grande, Brazil, from May to December 2021. Using statistical analyses, including Pearson’s correlation coefficient and multivariate regression, we analyzed secondary data on PM2.5 and PM10 concentrations and meteorological variables from the Federal University of Mato Grosso do Sul’s Physics Department. Daily PM concentrations complied with Brazil’s National Ambient Air Quality Standards (PQAr). The PM2.5/PM10 ratios averaged 0.436 (hourly) and 0.442 (daily), indicating a mix of fine and coarse particles. Significant positive correlations were found with temperature, while relative humidity showed a negative correlation, reducing PM levels through deposition. Wind speed had no significant impact. Meteorological influences suggest that air quality management should be tailored to regional conditions, particularly addressing local emission sources like vehicular traffic and biomass burning.

Several studies investigated the occurrence of fires in Africa with numerical modeling or applied statistics; however, only a few studies focused on the influence of El Niño on the fire risk using a coupled model. The study aimed to assess the influence of El Niño on wildfire dynamics in Africa using the SPEEDY-HYCOM model. El Niño events in the Eastern Tropical Pacific were classified via sea surface temperature (SST) anomaly based on a predefined climatology between 1961 and 2020 for the entire time series of SST, obtaining linear anomalies. The time series of the SST anomalies was created for the region between 5° N and 5° S and 110° W and 170° W. The events were defined in three consecutive 3-month periods as weak, moderate, and strong El Niño conditions. The Meteorological Fire Danger Index (MFDI) was applied to detect fire hazards. The MFDI simulated by the SPEEDY-HYCOM model for three El Niño categories across different lagged months revealed relevant distinctions among the categories. In the case of ‘Weak’, the maximum variability of fire risk observed at time lags (0, -3, -6, and -9 months) was primarily in Congo, Gabon, and Madagascar. The ‘Moderate’ pattern had similar characteristics to ‘Weak’ except for the lag-6 months and its occurrence in the equatorial zone of Africa. ‘Strong’ showed a remarkable impact in East Africa, resulting in high fire risk, regardless of time lags. Precipitation and evaporation simulations (SPEEDY-HYCOM) indicated that El Niño categories in Africa need particular attention in the central, southern, and southeastern regions emphasizing the significance of lag-0 and lag-6 (evaporation) as well as lag-0, lag-6, and lag-9 (precipitation). The SPEEDY-HYCOM coupled model in conjunction with the MFDI was efficient in assessing climate variabilities in Africa during El Niño events. This model allows the analysis and prediction of wildfire risks based on El Niño events, providing crucial information for wildfire management and prevention. Its simulations uncover significant variations in risks among different El Niño categories and lagged months, contributing to the understanding and mitigation of this environmental challenge.

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.

The present work presents the study of wind mapping in the state of Alagoas, northeastern Brazil, using the mesoscale model Weather Research Forecast (WRF), version (4.1.2), and approximations of atmospheric stability and terrain slope. The simulations were run daily for a period of 8 years (2007 - 2015), with a horizontal resolution of 5 km and a resolution refinement to 250 meters. They were statistically validated with data from 5 surface anemometric stations located in the following cities: Água Branca (hinterland), Girau do Ponciano (wild), Palmeira dos Índios (wild), Maragogi (coast) and Roteiro (coast). The order of magnitude and similar wind pattern results for the 5km and 250m resolutions indicate good representation by the WRF atmospheric model, as well as validating the proposed downscaling method. Being the moderate correlation indices, BIAS between -1.89 to 1.76 m/s and REQM between 1.94 to 3.82 m/s for wind speed and predominant wind direction varying between East/Southeast. The spatial comparison of the results with simulations of the WAsP model reinforces that the inclusion of atmospheric stability and slope made it possible to represent with greater clarity the effect of topography and surface roughness, which is crucial for reducing the cascade of uncertainties in the wind energy system. The refinement of the output of the WRF model highlighted the promising areas for wind use in the mountainous regions of the cities of Água Branca and Palmeira dos Índios, with annual intensity above 8m/s for 100m of height. El trabajo muestra el estudio del mapeo del viento en el estado de Alagoas, noreste del Brasil, utilizando el modelo de mesoescala Weather Research Forecast (WRF), versión (4.1.2), y aproximaciones de estabilidad atmosférica y pendiente del terreno. Las simulaciones se realizaron diariamente durante el período 8 años (2007 - 2015), con resolución horizontal de 5km y un refinamiento de resolución 250m. Fueron validados estadísticamente con datos de 5 estaciones anemométricas de superficie ubicadas en las siguientes ciudades: Água Branca (interior), Girau do Ponciano (salvaje), Palmeira dos Índios (salvaje), Maragogi (costa) y Roteiro (costa). El orden de magnitud y los resultados similares del patrón de viento para las resoluciones de 5km y 250m indican una buena representación del modelo WRF, además de validar el método de reducción de escala propuesto. Siendo los índices de correlación moderados, BIAS entre -1.89 a 1.76m/s y REQM entre 1.94 a 3.82m/s para la velocidad del viento y la dirección predominante del viento variando entre Este/Sureste. La comparación espacial de los resultados con simulaciones del modelo WAsP refuerza que la inclusión de la estabilidad atmosférica y la pendiente permitieron representar con claridad el efecto de la topografía y la rugosidad de la superficie, siendo crucial para reducir la cascada de incertidumbres en la energía eólica. El refinamiento de la salida del modelo WRF destacó las áreas promisorias para el uso del viento en las regiones montañosas de las ciudades de Água Branca y Palmeira dos Índios, con intensidad anual mayor 8m/s para 100m de altura. O presente trabalho apresenta o estudo do mapeamento eólico no estado de Alagoas, nordeste do Brasil, utilizando o modelo mesoescala Weather Research Forecast (WRF), versão (4.1.2), e aproximações de estabilidade atmosférica e declividade do terreno. As simulações foram executadas diariamente por um período de 8 anos (2007 - 2015), com resolução horizontal de 5 km e realizado um refinamento da resolução para 250 metros. Foram validadas estatisticamente com dados de 5 estações anemométricas de superfície situadas nas seguintes cidades: Água Branca (sertão), Girau do Ponciano (agreste), Palmeira dos Índios (agreste), Maragogi (litoral) e Roteiro (litoral). A ordem de grandeza e os resultados dos padrões do vento semelhantes para as resoluções de 5km e 250m, indica boa representação pelo modelo atmosférico WRF, como também, valida o método downscaling proposto. Sendo os índices de correlação moderada, BIAS entre -1,89 a 1,76 m/s e REQM entre 1,94 a 3,82 m/s para velocidade do vento e direção predominante do vento variando entre Leste/Sudeste. A comparação espacial dos resultados com simulações do modelo WAsP reforça que a inclusão da estabilidade atmosférica e declividade possibilitou representar com maior clareza o efeito da topografia e rugosidade superficial, o que é crucial para redução da cascata de incertezas no sistema de energia eólica. O refinamento da saída do modelo WRF ressaltaram as áreas promissoras para aproveitamento eólico nas regiões serranas das cidades de Água Branca e Palmeira dos Índios, com intensidade anual acima de 8m/s para 100m de altura.