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

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

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

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

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

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

Regional characterization of ENSO effects on the seasonal rainfall of Sinaloa, Mexico

Rainfall seasonality is of paramount relevance for the northwestern Mexican ecosystems. Among other factors, it is annually driven by the North American Monsoon. An outstanding yet irregular and changing factor that affects rainfall seasonality is the El Niño Southern Oscillation (ENSO) and its two phases, El Niño and La Niña, which can change the seasonal rainfall patterns. Here, we characterized spatially seasonal rainfall patterns of three physiographic regions of Sinaloa and adjacent states in northwestern Mexico. The covariances between El Niño and La Niña phases and their respective summer and winter rainfall amounts were estimated in each station within their regions. The magnitude of covariance was also differentiated among regions and characterized spatially. A multivariate analysis was performed to attain a simultaneous perspective of the rainfall-related variables. We detected differences among regions for the measured rainfall-related variables; altitude and longitude explained most of its spatial variation. Winter rainfall increased in all stations of El Niño and La Niña occurrence. El Niño decreased rainfall in most stations for summer, whilst La Niña increased rainfall in summer. Summer rainfall covariance with El Niño and La Niña was differentiated among regions. Latitude and longitude were correlated with the covariation between El Niño and La Niña and winter rainfall. Altitude correlated to the interaction of summer rainfall and La Niña and El Niño. Multivariate analysis segregated regions on the variation of winter, annual rainfall, number of rainfall events, and rainfall seasonality.

Regionalization of precipitation in Guatemala in climatology and El Niño-Southern Oscillation in its Niño, Niña, and neutral phases

The regionalization of precipitation is a vital tool for understanding hydrological phenomena, particularly in the context of the El Niño-Southern Oscillation (ENSO) phases (El Niño, La Niña, neutral, and climatology) in Guatemala. This study introduces a novel framework that defines previously diffuse regional boundaries, revealing the dynamic nature of precipitation patterns across the country. The findings demonstrate how regional boundaries shift in response to ENSO phases, as well as under climatology and neutral conditions. These insights highlight the importance of considering dynamic regionalization to accurately analyze climatic impacts and precipitation variability, providing a foundation for more effective climate adaptation strategies

Performance evaluation of random forest and boosted tree in rainfall-runoff process modeling for sub-basins of Lake Urmia

This study aimed to develop rainfall-runoff (P-Q) modeling using machine learning models in the sub-basins of Lake Urmia, Iran. In this research, chronological records of hydrological parameters and meteorological inputs at a regional scale were analyzed using Random Forest (RF) and Boosted Tree (BT) heuristic methods. This study compared the performance of these two models for the Urmia Basin over the period from 1976 to 2019. The results showed that the RF model provided better estimates in Akhula, Daryan, and Ghermez Gol stations in the eastern sub-basin and Miandoab, Pole Ozbak, Abajalu Sofla, Nezam Abad, and Pole Bahramlu stations in the western sub-basin. In contrast, the BT model performed better at Pole Senikh, Shishvan, Gheshlagh Amir, Shirin Kandi, and Khormazard stations in the eastern sub-basin and Babarud, Keshtiban, and Yalghoz Aghaj stations in the western sub-basin. Additionally, the time series analysis showed changes in yearly rainfall frequency and a decreasing trend in flow discharge in most years. These findings highlight a significant reduction in inflow to Lake Urmia over the past 43 years, with a particularly sharp decline in recent years

Effect of green corridors on the mitigation of the urban heat island (UHI) in the district of Lince, Lima, Peru

In Peru, various studies on canopy urban heat islands (CUHI) show their effects on the health and habitability of cities. In the case of Lima, various effects have been analyzed. However, the analysis of the mitigation mechanisms of urban heat islands is still limited. Therefore, information on the mitigation potential of such measures is also limited. The main objective of this work is to evaluate the effect of green corridors on Arequipa Avenue in the city of Lima with respect to the mitigation of CUHI. The work was carried out by recording temperatures and relative humidity between August and September 2022, using fixed stations, through determined observation points, and numerical atmospheric modelling. The results show that the maximum temperatures reached in areas without a corridor are higher than those in areas with a green corridor by up to 5.4 ºC. On the other hand, there are significant differences in the intensity of CUHI between areas inside and outside the corridor. CUHI values within the corridor, ranging from 0.4 to 0.6 ºC, are lower than those found outside, indicating the positive effect of the green corridors in mitigating the CUHI impact.  The analysis of the data allows us to evaluate the effectiveness of this intervention proposed in the Strategic evaluation of measures to reduce the urban heat island in the Province of Lima, prepared by the Metropolitan Municipality of Lima