Intensities of the 2023 and 2024 heatwaves in different local climate zones of the city of Puebla (Mexico)

In 2023 and 2024, Mexico experienced intense heatwaves that affected large areas of the country. This article evaluates the differentiated responses of three events in 2023 and four in 2024 in five local climate zones (LCZ) in the city of Puebla (Mexico). Heatwaves were identified based on the criterion of five consecutive days in which daily temperatures exceeded the 90th percentile, applied to the maximum temperature (27.4 ºC) for diurnal waves and to the minimum temperatures (17.4 ºC) for nocturnal waves. When thermal thresholds were exceeded during both day and night over the same period, waves were classified as circadian. To achieve greater precision, the wave’s intensity was calculated in degree-days and degree-hours. Additionally, the Humidex index was applied to estimate the bioclimatic effects in degree-hours above the preferred Humidex value. The findings indicate that nocturnal waves in the city center are more intense, partly due to the urban heat island effect, and that LCZ with greater vegetation show increased wave intensity when atmospheric humidity is included in the analysis. Thermohygrometric data recorded every 15 min at five meteorological stations throughout 2023, and six months of 2024, were used. Given the limited number of studies in Mexico evaluating the spatially differentiated effects of heatwaves within urban areas, this study adopts a localized approach

Regional frequency analysis of daily rainfall extremes using L-moments approach

Daily extreme precipitation values are among environmental events with the most disastrous consequences for human society. Information on the magnitudes and frequencies of extreme precipitations is essential for sustainable water resources management, planning for weather-related emergencies, and design of hydraulic structures. In the present study, regional frequency analysis of maximum daily rainfalls was investigated for Golestan province located in the northeastern Iran. This study aimed to find appropriate regional frequency distributions for maximum daily rainfalls and predict the return values of extreme rainfall events (design rainfall depths) for the future. L-moment regionalization procedures coupled with an index rainfall methodwere applied to maximum rainfall records of 47 stations across the study area. Due to complex geographicand hydro-climatological characteristics of the region, an important research issue focused on breaking downthe large area into homogeneous and coherent sub-regions. The study area was divided into five homogeneousregions, based on the cluster analysis of site characteristics and tests for the regional homogeneity.The goodness-of-fit results indicated that the best fitting distribution is different for individual homogeneousregions. The difference may be a result of the distinctive climatic and geographic conditions. The estimatedregional quantiles and their accuracy measures produced by Monte Carlo simulations demonstrate that theestimation uncertainty as measured by the RMSE values and 90% error bounds is relatively low when returnperiods are less than 100 years. But, for higher return periods, rainfall estimates should be treated withcaution. More station years, either from longer records or more stations in the regions, would be required forrainfall estimates above T=100 years. It was found from the analyses that, the index rainfall (at-site averagemaximum rainfall) can be estimated reasonably well as a function of mean annual precipitation in Golestanprovince. Index rainfalls combined with the regional growth curves, can be used to estimate design rainfallsat ungauged sites. Overall, it was found that cluster analysis together with the L-moments based regional frequencyanalysis technique could be applied successfully in deriving design rainfall estimates for northeasternIran. The approach utilized in this study and the findings are of great scientific and practical merit, particularlyfor the purpose of planning for weather-related emergencies and design of hydraulic engineering structuresLos valores extremos de precipitación diaria se encuentran entre los sucesos ambientales con consecuencias más desastrosas para la sociedad. La información sobre las magnitudes y frecuencias de las precipitaciones extremas es vital para el manejo sostenible de los recursos hídricos, la planeación de emergencias vinculadas con el clima y el diseño de estructuras hidráulicas. En este trabajo se analiza la frecuencia de precipitaciones diarias máximas en la provincia de Golestán, localizada en el noreste de Irán. Se trataron de encontrar distribuciones de frecuencias regionales adecuadas para precipitaciones máximas diarias y de predecir los valores de retorno de episodios extremos de precipitación (diseño de la profundidad de la precipitación). Se aplicó la regionalización de procedimientos de momentos-L, en conjunto con un método de indización de las precipitaciones, a los registros máximos de precipitación de 47 estaciones en el área de estudio. Debido a las complejas características geográficas e hidroclimatológicas de la región, un aspecto importante de la investigación fue la desagregación del área en subregiones coherentes y homogéneas. Así, se dividió el área de estudio en cinco regiones homogéneas con base en análisis de conglomerados de las características locales y en pruebas de homogeneidad regional. Los resultados de la precisión del ajuste indicaron que la mejor distribución es diferente para cada región homogénea. La diferencia puede deberse a las condiciones climáticas y geográficas distintivas de cada región. Los cuantiles regionales estimados y sus medidas de precisión, obtenidas mediante simulaciones de Monte Carlo, demuestran que la estimación de incertidumbre mediante valores de la raíz cuadrada del error cuadrático medio (RMSE, por sus siglas en inglés) y límites del error estadístico de 90%, es relativamente baja cuando los periodos de retorno son menores de 100 años. Sin embargo, para periodos más largos, las estimaciones de precipitación deben tomarse con cautela. Más años por estación, ya sea por registros más largos o por más estaciones en las regiones, se requerirían para estimaciones de precipitación mayores a T = 100 años. El análisis encontró que el índice de precipitación (promedio in situ de la máxima precipitación) puede estimarse razonablemente bien como una función de la precipitación media anual en la provincia de Golestán. Pueden utilizarse índices de precipitación combinados con curvas de crecimiento regional para calcular precipitaciones de diseños en sitios carentes de sistemas de medición. En general se encontró que el análisis de conglomerados, en conjunto con la técnica de análisis regional de frecuencias basada en momentos-L, puede aplicarse de manera exitosa para obtener estimados de precipitaciones de diseño en el noreste de Irán. El enfoque de este trabajo y sus resultados tienen gran importancia científica y mérito práctico, en particular para la planeación de emergencias relacionadas con el clima y el diseño de estructuras hidráulicas

A quantitative study of extreme rainfall intensity and occurrence in northern Algeria

This paper examines the characteristics associated with the spatiotemporal evolution of extreme precipitation, assesses its recurrence frequency, and predicts future return levels over northern Algeria. The study employs extreme precipitation indices in conjunction with the application of extreme value theory to a rainfall dataset spanning from 1982 to 2022. The study focused on modeling the index that demonstrated the highest percentage of significant positive trends at the α = 0.05 significance level. This was accomplished through the utilization of the Mann-Kendall test and the generalized extreme value distribution. Subsequently, the model was validated using the Kolmogorov-Smirnov fit test. The results revealed that the northeastern region of the study area experienced a more pronounced increase in rainfall intensity compared to the southern and western regions. Significant trends in precipitation intensity were observed over time. Notably, the index of days with rainfall exceeding 20 mm demonstrated the highest percentage of positive trends, with 88% of meteorological stations exhibiting an upward trend. Furthermore, a strong correlation was identified between the index of days with rainfall exceeding 20 mm and the very wet days index, particularly in the high plateaus and western region. This finding supports the hypothesis that extreme rainfall patterns are becoming more frequent in the region

Physical processes of fog in the Brazilian Northeast: Forecast by PAFOG and FogVIS models

A specific model for low-visibility forecasting in the Brazilian Northeast (BNE) has not been developed; therefore, the German Parameterized Fog (PAFOG) model was adapted for the region. Additionally, Fog Visibility (FogVIS), a simple equation-based tool, was developed and requires further testing. From 2008 to 2020, Meteorological Aerodrome Report and Terminal Aerodrome Forecast surface data were collected via the Meteorology Network of the Brazilian Air Force Application Programming Interface, identifying 218 fog events across three airports: Maceió (32 events), Recife (1 event), and Campina Grande (185 events). GOES satellite images were accessed from the Center for Weather Forecasting and Climate Studies database, and synoptic and thermodynamic analyses were performed using ERA5 reanalysis data. Humidity from nearby water sources (lagoon for Maceió, dam for Campina Grande) was a primary factor in fog formation. PAFOG demonstrated strong predictive performance for Maceió and Recife’s single brief events, especially in 12-h forecasts, particularly when fog events were preceded or followed by mist or light rain. In contrast, FogVIS often aligned closely with the observed visibility range and provided complementary results 18 hours in advance for Campina Grande’s events, which were more intense but less associated with rain or mist, and also showed higher Fog Stability Index results. Both models demonstrated efficiency, with PAFOG excelling in Maceió and FogVIS in Campina Grande, highlighting the applicability and accuracy of both models in predicting fog for the BNE

Comparison of meteorological indices for drought assessment and monitoring: A case study from the Tafna watershed, Northwestern Algeria

Drought is a complex phenomenon that includes meteorological, agricultural, and hydrological aspects. It is characterized by an extended period of insufficient rainfall that adversely impacts civilization‘s economic, social, and environmental aspects. This study compares four meteorological indices, including the Standardized Precipitation Index (SPI), China-Z index (CZI), Modified China Z Index (MCZI), and Z-Score Index (ZSI), to identify the most suitable drought indices (DIs) for assessing drought in the Tafna watershed. Monthly rainfall data from 14 stations (1970-2019) were used to calculate drought events on 1-, 3-, and 12-month time scales. On the 1- and 3-month time scales, the frequency of total drought for SPI-1 and CZI-1 is higher than for MCZI-1 and ZSI-1. At the 12-month time scale, all DIs showed the same frequency of total drought for most stations. The highest Pearson correlation coefficient is observed between SPI-1 and CZI-1 on a 1-month scale, with values exceeding 0.939 across all stations. Additionally, 57 and 71% of stations exhibit the highest correlation between SPI and CZI, with coefficients exceeding 0.963 and 0.999 at 3- and 12-month scales, respectively. Most stations do not show any trend (increase or decrease) using the Mann-Kendall trend test in all index values at 1- and 3-month scales. On a 12-month scale, most stations showed an increase in the values of all DIs. The results reported in this study provide valuable insights that can enhance the management of water resources and improve preparedness for drought events in the Tafna watershed, particularly in the context of climate change

Bibliometric analysis of winter meteorological dynamics and atmospheric pollution under climate change conditions

This study aims to systematically evaluate the evolution of scientific research on the impacts of climate change on winter meteorological events and atmospheric pollution through a bibliometric analysis. Utilizing data from the Scopus database and the Bibliometrix package in R, the analysis investigates publication trends, international collaboration, and thematic developments from 1980 to 2024. The objectives are to identify key research areas, influential contributors, and emerging patterns within this interdisciplinary field. Results indicate a strong and sustained growth in scholarly output, with an annual increase of 12.48% and an average of 43.28 citations per publication, reflecting the rising global interest and relevance of this topic. Collaboration networks reveal robust partnerships among researchers from the United States, China, and Europe, though regional disparities persist—particularly in Eastern Europe. Thematic clustering and multiple correspondence analysis (MCA) identify three dominant research areas: statistically driven climate studies, investigations of seasonal weather dynamics, and analyses of extreme winter events. The findings highlight the field’s intellectual structure and underscore the need for expanded international cooperation and increased research efforts in underrepresented regions. This analysis provides valuable insights for future research and policymaking in the field of climate and atmospheric sciences

Evaluation of air quality in Puebla, Mexico: A wavelet transform and predictive modeling approach

This article presents a detailed analysis of air pollutant dynamics in Puebla City, Mexico, using data collected between 2016 and 2024. The research examines the daily variation of five main pollutants: ozone (O3), particulate matter smaller than 10 microns (PM10), particulate matter smaller than 2.5 microns (PM2.5), sulfur dioxide (SO2), and nitrogen dioxide (NO2). To identify significant trends and seasonal patterns, the Mann-Kendall test, innovative trend analysis (ITA), and wavelet transform were applied. The results indicate statistically significant upward trends in O3, SO2, and NO2 concentrations, while PM10 and PM2.5 levels have exhibited a sustained decrease throughout the study period. The scalogram analysis highlights seasonal energy concentrations of SO2, potentially linked to industrial activity and meteorological conditions. Additionally, the Prophet forecasting model was used to estimate PM2.5 and PM10 levels from 2022 to 2024, achieving better performance over longer time horizons. This study is particularly relevant given the urban growth and industrial activity in Puebla, factors that can contribute to the deterioration of air quality and affect the health of the population. The identification of trends and patterns in air pollution is essential for the implementation of mitigation strategies and public policies aimed at improving air quality in the region

Climatic suitability variations for climbing bean cultivation under climate change scenarios in Cundinamarca, Colombia

Climate change is expected to modify the current suitable areas for bean cultivation, driven by regional shifts in temperature and precipitation. Despite the economic and food security importance of beans in Colombia, there is a lack of knowledge about how ongoing and future climate change may reshape their agroclimatic suitability across the country. This study aimed to assess the potential future shifts in suitable areas for climbing bean (Phaseolus vulgaris L.) cultivation in the department of Cundinamarca under projected climate change scenarios. Suitable areas were categorized into A1 (best conditions), A2 (moderate constraints), A3 (strong restrictions), and N1 (not suitable), based on a decision tree with defined altitude, temperature, and precipitation intervals. The period 1981-2010 was utilized as the present climate, and the future periods 2011-2040, 2041-2070, and 2071-2100 were considered under the climate change scenarios RCP 4.5 and RCP 8.5. Information from the Global Climate Model CCSM4 from the National Center for Atmospheric Research was used to identify new potential areas and changes in optimal zones under future scenarios. The forecast for Cundinamarca indicates that under the RCP 4.5 scenario, the total suitable area decreases slightly by 3.8%, while the A1 zone expands, especially in cooler highland regions. In a high-emission future (the RCP 8.5 scenario), the total suitable area declines more sharply (by 14.8% between 2071 and 2100), while the unsuitable area increases by 6.5%. The expansion of A3 zones by up to 13.3% in the early and mid-21st century reflects the downgrading of currently optimal or moderate areas to low suitability due to rising temperatures, particularly in the Llanos foothills and the Magdalena slopes subregions

Assessment of aerosol remote sensing uncertainty in urban centers of Latin America

Satellite-derived aerosol optical depth (AOD) is a key indicator for expanding spatial coverage in air quality studies, particularly for estimating PM2.5 concentrations. However, validation of high-resolution AOD products remains limited in Latin American urban environments, which are characterized by complex aerosol dynamics and sparse ground-based monitoring. In this study, we evaluated the performance of MAIAC C6.1 AOD in six densely populated Latin American cities (São Paulo, Santiago, Buenos Aires, Medellín, La Paz, and Mexico City) from 2015 to 2022, using the AERONET network as a reference. MAIAC C6.1 performance was also compared with the previous version (C6.0) and MODIS DT to analyze differences in spatial resolution and assess performance improvements. A lack of ground-level AOD was observed, especially in Medellín and São Paulo, along with low average levels (AOD < 0.2) in La Paz and Buenos Aires. MAIAC C6.1 performance showed notable variability depending on the spatial window size, although no considerable impact was seen in the temporal window. Two site groups were identified: (i) La Paz and Buenos Aires, with lower AOD levels, lower performance, and positive bias; and (ii) São Paulo, Santiago, and Mexico City, with higher AOD levels, better performance, and negative bias. MAIAC C6.1 showed improvement in bias reduction, but no significant changes in R2 or RMSE compared to C6.0. Compared to MODIS DT, MAIAC C6.1 exhibited greater accuracy and lower bias, with MODIS overestimating AOD across all sites. Despite advances with high-resolution products, limitations in data coverage and uncertainty persist, especially in urban Latin American areas

Feasibility assessment of machine learning for predicting heatwaves in Bangladesh

As extreme weather phenomena, heatwaves bring severe risks to human health, society, and ecosystems. Over the past few years, Bangladesh has experienced heatwaves that are becoming more frequent and intense. Early warning systems (EWS) can help to minimize the potential damage from these events by providing sufficient time for thorough and effective preparation. Traditionally, numerical weather prediction (NWP) is employed for heatwave forecasting, but it is both expensive and time-consuming. This study explores the potential of using machine learning as a faster and more cost-effective alternative to NWP. Specifically, we focus on building an artificial neural network (ANN) to predict heatwaves three days in advance over Bangladesh. Our model utilizes 28 features to predict a binary target value (0 for no heatwave, 1 for heatwave). The results are promising, with the model achieving an accuracy of 91% in distinguishing heatwave and non-heatwave days. This suggests that machine learning can be a valuable tool for large-scale heatwave prediction in Bangladesh