Atmósfera <h3>Focus and Scope</h3> <p dir="ltr">Atmosfera is an international, peer-reviewed journal published quarterly from 1988 to 2022. As of 2023, the manuscripts are published according to the continuous publication model. This journal is devoted to original research in the atmospheric sciences, climate change, interactions with the hydrosphere, cryosphere, biosphere and human systems. </p> <p>It is published by the Universidad Nacional Autónoma de México, through the Instituto de Ciencias de la Atmósfera y Cambio Climático.</p> <p>All papers published are Open Access for readers and there are no publication fees for authors. The journal is indexed in Scopus, SCimago, Science Citation Index, LatinIndex, SciELO, among other databases. It has continuously increased its visibility and impact, with an Impact Factor of 2.063 (2022), as determined by the Journal Citation Report (Clarivate/ Web of Science).</p> <!-- WIDGET SCImago - Open Access --> <p><a title="SCImago Journal &amp; Country Rank" href=";tip=sid&amp;exact=no"><img src="" alt="SCImago Journal &amp; Country Rank" border="0" /></a> <img style="float: right; width: 256px; height: 93px;" src="" alt="Atmósfera - CCA UNAM" /></p> <!-- WIDGET SCImago - Open Access --> Instituto de Ciencias de la Atmósfera y Cambio Climático, Universidad Nacional Autónoma de México en-US Atmósfera 0187-6236 <p>Once an article is accepted for publication, the author(s) agree that, from that date on, the owner of the copyright of their work(s) is Atmósfera.</p><p>Reproduction of the published articles (or sections thereof) for non-commercial purposes is permitted, as long as the source is provided and acknowledged.</p><p>Authors are free to upload their published manuscripts at any non-commercial open access repository.</p> Geocoding and spatiotemporal modeling of long-term PM2.5 and NO2 exposure in the Mexican Teachers’ Cohort <p class="p1">Epidemiological studies on air pollution in Mexico often use the environmental concentrations of pollutants as measured by monitors closest to the home of participants as exposure proxies, yet this approach does not account for the space gradients of pollutants and ignores intra-city human mobility. This study aimed to develop high-resolution spatial and temporal models for predicting long-term exposure to PM<sub>2.5</sub> and NO<sub>2</sub> in ~16 500 participants from the Mexican Teachers’ Cohort study. We geocoded the home and work addresses of participants, and used secondary source information on geographical and meteorological variables as well as other pollutants to fit two generalized additive models capable of predicting monthly PM<sub>2.5</sub> and NO<sub>2</sub> concentrations during the 2004-2019 period. Both models were evaluated through 10-fold cross-validation, and showed high predictive accuracy with out-of-sample data and no overfitting (CV-RMSE = 0.102 for PM<sub>2.5</sub> and CV-RMSE = 4.497 for NO<sub>2</sub>). Participants were exposed to a monthly average of 24.38 (6.78) µg m<sup>–3</sup> of PM<sub>2.5</sub> and 28.21 (8.00) ppb of NO<sub>2</sub> during the study period. These models offer a promising alternative for estimating PM<sub>2.5</sub> and NO<sub>2</sub> exposure with high spatiotemporal resolution for epidemiological studies in the Mexico City Metropolitan Area.</p> Karla Cervantes-Martínez Horacio Riojas-Rodríguez Carlos Díaz Avalos Hortensia Moreno-Macías Ruy López-Ridaura Dalia Stern Jorge Octavio Acosta-Montes José Luis Texcalac-Sangrador Copyright (c) 2021 Atmósfera 2023-01-17 2023-01-17 37 191 207 10.20937/ATM.53110 Satellite-based estimation of NO2 concentrations using a machine-learning model: A case study on Rio Grande do Sul, Brazil <p class="p1">Nitrogen dioxide (NO<sub>2</sub>) is one of the most important atmospheric pollutants, affecting human health (increasing susceptibility to respiratory infections) and the environment (soil and water acidification). In many regions of Brazil, NO<sub>2</sub> measurements at ground level meet difficulties because monitoring stations are few and unevenly distributed. Satellite observations combined with machine learning models can mitigate this lack of data. This paper report results from an investigation on the ability of a machine learning approach (a non-linear statistical Random Forest algorithm, hereafter RF) to reconstruct the long-term spatiotemporal ground-level NO<sub>2 </sub>from 2006 to 2019 using as input parameters NO<sub>2</sub> data retrieved from the Ozone Monitoring Instrument (OMI) sensor aboard Aura satellite, besides meteorological covariates and localized ground-level NO<sub>2</sub> measurements. Results show that the RF model predicts NO<sub>2</sub> with an accuracy expressed by an R<sup>2 </sup>= 0.68 correlation based on a 10-fold cross-validation. The model also predicted a mean NO<sub>2</sub> concentration of 18.73 <strong>± </strong>3.86 μg m<sup>–3</sup>. The total NO<sub>2</sub> concentration over the entire region analyzed showed a decreasing trend (2.9 μg m<sup>–3</sup> yr<sup>–1</sup>), being 2006 the year with the higher concentrations and 2017 with the lowest. This study suggests that non-linear statistical algorithm reconstructions using RF can be complementary tools to in situ and satellite observations for NO<sub>2 </sub>mapping.</p> Adriana Becerra-Rondón Jorge Ducati Rafael Haag Copyright (c) 2022 Atmósfera 2023-01-17 2023-01-17 37 175 190 10.20937/ATM.53116 The influence of snow cover on Ozone Monitor Instrument formaldehyde observations <p>Formaldehyde (HCHO) is measured from space using backscattered ultraviolet sun-light. Because of HCHO’s short lifetime, space-based observations of HCHO can serve as a proxy for volatile organic compounds, helping to characterize their global emissions and distributions. HCHO satellite observations rely on Air Mass Factor (AMF) calculations to transform fitted slant columns into vertical column densities. Most HCHO satellite products do not explicitly consider the presence of snow on the ground during the calculation of AMFs. In this study, we leverage information from the MODIS bidirectional reflectance distribution function (BRDF), MODIS snow cover information, and the Interactive Multisensor Snow and Ice Mapping System to evaluate the impact of ground snow on Ozone Monitoring Instrument (OMI) HCHO retrievals. We focus our analysis on the year 2005. We compare AMFs computed using daily MODIS BRDF to AMFs computed using OMI’s surface reflectance climatology, the baseline for NASA’s OMHCHO product. Over snow-covered regions, both sets of AMFs show significant differences. We observe two different behaviors. Regions with permanent snow cover (Greenland and Antarctica) show smaller AMFs calculated with MODIS BRDF than with the OMI climatology resulting in a 6% median annual increase of HCHO VCDs. Over regions with seasonal snow cover, the situation is more complex with more variability in the differences during the year. For example, a February 2005 case study over Europe shows that the NASA OMHCHO VCDs (calculated using the OMI Lambertian climatology) are on average 16% larger than HCHO columns retrieved using daily MODIS BRDF information.</p> CareyAnne Howlett Gonzalo González Abad Christopher Chan Miller Caroline Rebecca Nowlan Zolal Ayazpour Lei Zhu Copyright (c) 2022 Atmósfera 2023-01-17 2023-01-17 37 159 174 10.20937/ATM.53134 Spatio-temporal co-variability of air pollutants and meteorological variables over Haqel and Jeddah, Saudi Arabia <p class="p1">This study presents a first simultaneous trend and magnitude assessment of air pollutants (CO, H<sub>2</sub>S, SO<sub>2</sub>, NO<sub>2</sub>, NO, NO<sub>x</sub>, O<sub>3</sub> and PM<sub>10</sub>) and meteorological variables (rainfall [RF], relative humidity [RH], atmospheric pressure [PR], temperature [TC], wind speed [WS], and wind direction [WD]) in the city of Haqel and at four different locations in the city of Jeddah, Saudi Arabia, for a continuous 5-year period (2008-2012). The spatio-temporal co-variations of air pollutants in terms of their diurnal, weekly, seasonal and annual cycles, and their relationship with meteorological conditions, along with the estimates of the weekend effect, are described. A decreasing annual trend was observed for most air pollutants analyzed except for O<sub>3</sub> and PM<sub>10</sub>. The CO, NO<sub>2</sub>, NO and NO<sub>x </sub>displayed a strong weekend effect. A percentile-based change analysis displayed an increase in concentrations for O<sub>3</sub> (PM<sub>10</sub>) in the lower (higher) percentiles from the first to second half of the study period. The study identified 12 cyclonic weather events during the 5-year time period associated with high PM<sub>10 </sub>concentrations (&gt; 500 µg m<sup>–3</sup>) relative to a mean value of 102 µg m<sup>–3</sup>, with a standard deviation value of 179 µg m<sup>–3</sup>. The study also analyzed the impacts of several mid-latitude anti-cyclonic events on air pollutant concentrations and found a significant change in air pollutant concentrations (CO, SO<sub>2</sub>, NO<sub>2</sub>, NO, NO<sub>x</sub>, O<sub>3 </sub>and PM<sub>10</sub>) and meteorological variables (RH, PR, TC, WS, and WD) associated with stagnant upper air conditions during the atmospheric blocking.</p> Syeda Batool Tazeem Ahmed Nabeel Hussain Athar Copyright (c) 2022 Atmósfera 2023-01-17 2023-01-17 37 131 158 10.20937/ATM.53100 Comparison of two air quality models in complex terrain near seashore <p class="p1">Air pollution is the most important environmental problem in Zonguldak, Turkey due to excessive coal combustion and thermal power plant emissions. The city center is located on a complex terrain near the Black Sea shore. There exist some previous studies about PM<sub>10</sub> pollution in this area, but none of them is related to the spatial distribution of the pollutant. This air quality modeling study aims to fill this gap in the literature. Firstly, a PM<sub>10</sub> emission inventory has been prepared for point, line, and area sources for the year 2011, when bituminous coal was the principal fuel for domestic heating in houses and to generate electricity in thermal power plants, therefore particulate matter (PM<sub>10</sub>) was the leading air pollutant. Emission inventory calculations revealed that 2710.2 t of PM<sub>10</sub> have been emitted to the atmosphere from all sources in the study area. Then, the air quality modeling has been performed for PM<sub>10</sub> by using two air quality models: AERMOD and CALPUFF. According to the modeling results, PM<sub>10</sub> pollution levels may pose a health threat to the inhabitants of Zonguldak. The maximum PM<sub>10</sub> concentrations predicted by the CALPUFF model were higher than that of AERMOD. Predicted values plus background concentration were validated against the PM<sub>10</sub> measurements by using fractional bias, index of agreement, geometric mean bias, and geometric mean-variance. According to the model performance analysis, CALPUFF showed slightly better performance as compared to AERMOD.</p> Özgür Zeydan Aykan Karademir Copyright (c) 2022 Atmósfera 2023-01-17 2023-01-17 37 113 130 10.20937/ATM.53118 Research on the usability of different machine learning methods in visibility forecasting <p class="p1">Haze pollution, mainly characterized by low visibility, is one of the main environmental problems currently faced by China. Accurate haze forecasts facilitate the implementation of preventive measures to control the emission of air pollutants and thereby mitigate haze pollution. However, it is not easy to accurately predict low visibility events induced by haze, which requires not only accurate prediction for weather elements, but also refined and real-time updated source emission inventory. In order to obtain reliable forecasting tools, this paper studies the usability of several popular machine learning methods, such as support vector machine (SVM), k-nearest neighbor, and random forest, as well as several deep learning methods, on visibility forecasting. Starting from the main factors related to visibility, the relationships between wind speed, wind direction, temperature, humidity, and visibility are discussed. Training and forecasting were performed using the machine learning methods. The accuracy of these methods in visibility forecasting was confirmed through several parameters (i.e., root-mean-square error, mean absolute error, and mean absolute percentage error). The results show that: (1) among all meteorological parameters, wind speed was the best at reflecting the visibility change patterns; (2) long short-term memory recurrent neural networks (LSTM RNN), and gated recurrent unit (GRU) methods perform almost equally well on short-term visibility forecasts (i.e., 1, 3, and 6 h); (3) a classical machine learning method (i.e., the SVM) performs well in mid- and long-term visibility forecasts; (4) machine learning methods also have a certain degree of forecast accuracy even for long time periods (e.g., 7 2h).</p> Wu Wen Lei Li P. W. Chan Yuan-Yuan Liu Min Wei Copyright (c) 2021 Atmósfera 2023-01-17 2023-01-17 37 99 111 10.20937/ATM.53053 Nowcasting severity of thunderstorm associated with strong wind flow over Indian Subcontinent: Resource lightning surge <p class="p1">This study uses the data of Indian Air Force (IAF) Lightning Detection System (LDS) network to prepare climatological plots of lightning over India and to formulate location-specific thunderstorm (TS) guidance for a total of 12 Indian airports. The analysis of climatological plots reveals that there is a distinct warm-season preponderance of lightning strikes over Indian subcontinent, with pre-monsoon months receiving the maximum lightning. The most probable time of occurrence being 12:00-14:00 UTC during all the seasons across the country. Location-specific TS guidance not only signifies the most probable direction of occurrence of TS with respect to the airport, but also clearly brings out the favorable direction of movement. Hence, the same can be judiciously used as nowcasting aid. Further, the characteristic features of lightning, like surges in flash rate, can be objectively used to define a predictor for nowcasting severe weather associated with a TS cloud. The study of these surges in lightning flash rate vis a vis the occurrence of strong surface winds (SSW) &gt; 60 km h<sup>–1</sup> over Delhi National Capital Region (NCR) (Hindan Airport observations), indicated that there is an increase in the number of lightning flashes prior to the occurrence of SSW. Within 45 min of their occurrence, 77.5% of SSW are preceded by surges in flash rates; however, the probability of detection of the event with a lead time of 15 to 45 min is around 71%.</p> Shreya Pandit Savitesh Mishra Ashish Mittal Anil Kumar Devrani Copyright (c) 2022 Atmósfera 2023-01-17 2023-01-17 37 85 98 10.20937/ATM.53042 Time series trend analysis of rainfall and temperature over Kolkata and surrounding region <p class="p1">Studies of temperature and rainfall long-term variability in the context of climate change are important, particularly in regions where rainfed agriculture is predominant. Long-term trends of temperature and rainfall have been determined for Kolkata, India (a tropical region) using gridded monthly data from the Global Precipitation and Climate Centre (GPCC v. 7) with 0.5º <strong>× </strong>0.5º resolution for the period 1901 to 2014. Precipitation concentration index, coefficient of variation, and rainfall anomaly have been calculated and Palmer drought severity index has been analyzed. Furthermore, the Mann-Kendall test and Sen’s slope estimate have been used to detect time series trend. Annual temperature and rainfall have increased at a rate of 0.0082ºC yr<sup>–1</sup> and 0.03 mm yr<sup>–1</sup>, respectively. Most months show statistically significant increasing trends for temperature and rainfall. Rainfall with high precipitation concentration index (16-20) has been observed for the period 1951-1975 and 1976-2000. The number of years with dry conditions has increased. However, the intensity of dryness is very close to zero. The information from this study will be helpful for farmers to plan for resilient farming.</p> Arijit De Srishty Shreya Neel Sarkar Animesh Maitra Copyright (c) 2021 Atmósfera 2023-01-17 2023-01-17 37 71 84 10.20937/ATM.53059 Distribution and spatio-temporal variation of temperature and precipitation in Sierra de Otontepec Ecological Reserve, Veracruz, Mexico, through GIS modeling <p class="p1">Sierra de Otontepec Ecological Reserve is an isolated mountain in the Coastal Plain of the Gulf of Mexico with high scientific relevance. However, it lacks climatological information to support the studies of the ecosystems that have been carried out in recent years. GIS modeling characterized its climatology, and climate variability scenarios were created for the period 1981-2010. From temperature and precipitation data recorded <em>in situ</em>, vertical gradients of both variables were obtained in relation to the orography of the zone. The records were correlated with data from nearby weather stations so that by using a DEM, it was possible to obtain raster layers with a resolution of <span class="s1">15 meters </span>per pixel (MPP) for temperature in the summer and winter seasons; and for precipitation in the rainy and dry seasons; the annual value was also calculated for both variables. The climatic variability detected in the zone indicates a gradual increase in air temperature over time and a spatial variation in the distribution of precipitation. The spatial resolution of the modeling is precisely adjusted to the relief of the mountain, allowing the flora and fauna elements found within each pixel to be analyzed with a good level of detail. This work represents the most viable and effective alternative to estimate temperature and precipitation values in mountain systems lacking climatological stations, demonstrating that beyond providing climate information, which is increasingly necessary for ecosystems, it is possible to model their Spatio-temporal dynamics to understand the complex climate variability of our days.</p> Victor Soto José Luis Alanís Juan Manuel Pech Jorge Luis Chagoya Copyright (c) 2022 Atmósfera 2023-01-17 2023-01-17 37 53 70 10.20937/ATM.53124 A comparison of missing value imputation methods applied to daily precipitation in a semi-arid and a humid region of Mexico <p class="p1">Climatological data with unreliable or missing values is an important area of research, and multiple methods are available to fill in missing data and evaluate data quality. Our study aims to compare the performance of different methods for estimating missing values explicitly designed for precipitation and multipurpose hydrological data. The climate variable used for the analysis was daily precipitation. We considered two different climate and orographic regions to evaluate the effects of altitude, precipitation regime, and percentage of missing data on the Mean Absolute Error of imputed values and performed a homogeneity evaluation of meteorological stations. We excluded meteorological stations with more than 25% missing data from the analysis. In the semi-arid region, ReddPrec (optimal for nine stations) and GCIDW (optimal for eight stations) were the best-performing methods for the 23 stations, with average MAE values of 1.63 mm/day and 1.46 mm/day, respectively. In the humid region, GCIDW was optimal in ~59% of stations, EM in ~24%, and ReddPrec in ~17%, with average MAE values of ~6.0 mm/day, 6.5 mm/day, and ~9.8 mm/day, respectively. This research makes a valuable contribution to identifying the most appropriate methods to impute daily precipitation in different climatic regions of Mexico based on efficiency indicators and homogeneity evaluation.</p> Juan Manuel Navarro Céspedes Jesús Horacio Hernández Pedro Camilo Alcántara Concepción Jorge Luis Morales Martínez Gilberto Carreño Aguilera Francisco Padilla Copyright (c) 2022 Atmósfera 2023-01-17 2023-01-17 37 33 52 10.20937/ATM.53095 A breviary of Earth’s climate changes using Stephan-Boltzmann law <p class="p1"><span class="s1">Earth’s surface temperature has oscillated greatly throughout time. From near total freezing during the “snowball Earth” (2.9) Ga to an ice-free world in the Paleocene-Eocene thermal maximum 55 (Ma). These changes have been forced by internal (e.g., changes in the chemical composition of the atmosphere) or external (e.g., changes in solar irradiance) drivers that varied through time. Thus, if we understand how the radiation budget evolved at different times, we can approximate past global climate, a fundamental comparison to situate current climate change in the context Earth’s history. Here I present an analytical framework employing a simple energy balance derived from the Stephan-Boltzmann law, that allows for quick comparison between drivers of global temperature at multiple times during the history of our planet. My results show that current rates of increase in global temperature are at least four times faster than any previous warming event.</span></p> Guillermo Murray-Tortarolo Copyright (c) 2021 Atmósfera 2023-01-17 2023-01-17 37 17 32 10.20937/ATM.53102 Comparison of forecasting accuracy for the Madden Julian Oscillation (MJO) and Convectively Coupled Equatorial Waves (CCEW) using Tropical Rainfall Measuring Mission (TRMM) and ERA-Interim precipitation forecast data for Indonesia <p class="p1">Forecast data from the Tropical Rainfall Measuring Mission (TRMM) and the ERA-Interim reanalysis of the European Centre for Medium-Range Weather Forecasts (ECMWF) were analyzed using the second-order autoregressive method AR(2) and space-time spectral analysis methods, respectively. Our analysis revealed contrasting results for predicting the Madden Julian Oscillation (MJO) and convectively coupled equatorial waves (CCEW) over Indonesia. We used the same 13-year series of daily TRMM 3B42 V7 and ERA-Interim reanalysis model datasets from the ECMWF for precipitation forecasts. Three years (2016 to 2018) of the filtered 3B42 and ERA-Interim forecast data were then used to evaluate forecast accuracy by looking at correlation coefficients for forecast leads from day +1 through day +7. The results show that rainfall estimation data from 3B42 provides better results for the shorter forecast leads, particularly for MJO, equatorial Rossby (ER), mixed Rossby-gravity (MRG), and inertia-gravity phenomena in zonal wavenumber 1 (IG1), but gives a poor correlation for Kelvin waves for all forecast leads. A consistent correlation for all waves was achieved from the filtered ERA-Interim precipitation forecast model, and although this was quite weak for the first forecast leads it did not reach a negative correlation in the later forecast leads except for IG1. Furthermore, the Taylor diagram was also examined to complement forecasting skills for both data sources, with the result that residual error for the filtered ERA-Interim precipitation forecast was quite small during all forecast leads and for all wave types. These findings prove that the ERA-Interim precipitation forecast model remains as an adequate precipitation model in the tropics for MJO and CCEW forecasting, specifically in Indonesia.</p> Ida Pramuwardani Hartono Hartono Sunarto Sunarto Arhasena Sopaheluwakan Copyright (c) 2021 Atmósfera 2023-01-17 2023-01-17 37 1 15 10.20937/ATM.53009