Atmósfera (Journal)
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Modeling of the PM10 pollutant in Monterrey, Nuevo León, Mexico, with ARIMA, transfer functions and GARCH models
Full text linkParticles of matter smaller than 10 μm (PM10) are significant pollutants due to their impact on health and the environment. This study analyzed daily PM10 data from the Obispado station (Monterrey, Nuevo León, Mexico) provided by the INECC, covering the period from 1997 to 2014. Climatological variables such as precipitation, wind speed (at 2 and 10 m), and atmospheric pressure, obtained from NASA’s POWER project, were included. Data were partitioned into training and testing sets using an 80-20% split, employing 3798 daily observations for training (2002 to 2012) and 950 observations for testing (2012 to 2014). Descriptive analysis and time series decomposition were performed to identify trends and seasonality. ARIMA models, univariate and with transfer functions incorporating meteorological variables, were applied. Augmented Dickey-Fuller and Ljung-Box tests validated the stationarity and residual independence assumptions. The ARIMA (1,1,1)(0,0,0)[365] model with transfer functions outperformed the univariate model. A significant relationship between meteorological variables and PM10 predictions was identified, supporting their use for short-term forecasting (≤ 10 days). Future studies should consider applying multivariate models with additional predictors and geostatistical approaches to improve spatiotemporal characterization
A case study of the sea/land breeze diurnal cycle in the Peninsula and Gulf of Nicoya, Costa Rica: Interactions with local and regional processes
Full text linkThe presence of sea breeze (SB) is analyzed at nine meteorological stations in the northwest of Costa Rica (Peninsula and Gulf of Nicoya, GF); two from the Ticosonde-NAME experiment, University of Costa Rica, and seven from the National Meteorological Institute, for the period from July 1 to September 16, 2004. An objective detection algorithm for SB is applied to hourly data from the stations and sea surface temperature (SST). The algorithm uses temperature gradient and wind direction. Pinilla and Guacalillo stations show 64% of SB on the 78 days analyzed. Liberia (20 km inland) presents 44.9% of SB associated with weak synoptic winds from the east. Puntarenas presents doubtful cases due to wind errors, while the other stations do not present complete records. Some of the non-SB days are dominated, on one hand, by strong synoptic flow from the northeast associated with the low-level Caribbean jet that in turn coincides with the periods of reduced rainfall or mid-summer drought and, on the other hand, by synoptic flow from the southwest associated with the passage of weather systems in the western Caribbean. The algorithm shows a good ability to detect SB despite the poor spatial resolution of SST. Consistent with a typical SB circulation, precipitation at almost all stations is characterized by coastal convective activity and precipitation in the late afternoon and evening hours. The results are encouraging for their potential application to artisanal fishing, agriculture, tourism, and regional air quality, as there are very active ports in the Gulf of Nicoya (Puntarenas and Caldera), points of intense movement of tourist and commercial ships that negatively impact environmental conditions
Regional characterization of ENSO effects on the seasonal rainfall of Sinaloa, Mexico
Full text linkRainfall 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.
Impact assessment of 3D-var data assimilation on simulation of tropical cyclones using WRF
Full text linkThe combination of data from the Advanced Microwave Sounding Unit-A (AMSU-A) and Microwave Humidity Sounder (MHS) satellites provide measurements in frequency channels 23-183 GHz, which allow the estimation of vertical profiles of atmospheric temperature and humidity. These measurements play a significant role in numerical weather prediction models, improving initial conditions during tropical cyclone development. In the present study, measurements from AMSU-A and MHS have been assimilated in the Weather Research and Forecasting (WRF) model through the 3D-variational (3D-var) data assimilation technique using the Gridpoint Statistical Interpolation (GSI) analysis system. The assimilation impact has been assessed on super cyclonic storm Amphan and severe cyclonic storm Nisarga, which formed over the Bay of Bengal (BoB) and the Arabian Sea (AS), respectively. To investigate their impact, a series of experiments are conducted with and without assimilation of AMSU-A and MHS observations from each day’s initial condition for both cyclones. The track and landfall errors of all the experiments are computed against the best track position provided by the India Meteorological Department (IMD). The results indicate that the assimilation of AMSU-A and MHS observations led to an improvement in track errors of about 11 to 35% for Amphan and 6 to 20% for Nisarga for 12 to 72 h lead times. Furthermore, the assimilation of AMSU-A and MHS observations helped to improve the simulation of landfall position and time. The evaluation of maximum sustained surface wind, central pressure, and rainfall against the observations demonstrates the positive impact of the assimilated observations on the performance of the WRF model
Bibliometric analysis of winter meteorological dynamics and atmospheric pollution under climate change conditions
No full textThis 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
Air quality in the Metropolitan Zone of the Valley of Puebla: Comparative evaluation of CAMS and persistence forecasts
Full text linkBackground on air quality in the Metropolitan Zone of the Valley of Puebla shows that suspended particles smaller than 10 micrometers (PM10) and smaller than 2.5 micrometers (PM2.5) represent a health risk. Puebla’s automatic air quality monitoring system measures PM10 and PM2.5 at five stations in the municipalities of Puebla and Coronango. These measurements allow for determining the Air and Health Index according to the NOM-172-SEMARNAT-2019 standard for these pollutants. The advancement of global pollutant modeling techniques represents an opportunity for air quality management in areas with scarce terrestrial measurements. However, it is necessary to validate global forecasts with ground measurements from georeferenced monitoring stations to reduce uncertainties and determine reliability. The Copernicus Atmospheric Monitoring Service (CAMS) forecast allows atmospheric pollution exploration processes in the study region. This study presents an analysis of the CAMS forecast against the Persistence forecast. The results show that the persistence forecast performs better than the CAMS forecast in general, both for PM10 and for PM2.5. However, using the CAMS forecast for a preliminary evaluation of the prediction of PM2.5 is feasible due to its acceptable values in the comparison criteria of the dichotomous statistics ACCURACY, probability of detection (POD), false alarm rate (FAR), probability of false detection (POFD), success ratio (SR), threat score (TS), equitable threat score (ETS), Heidke skill score (HSS), and odds ratio skill score (ORSS). This work provides valuable insights to both the population and decision-makers, aiding in the enhancement of air quality management and public health strategies
Spatial variation of climate change indices in the state of Chiapas, Mexico
Full text linkAlthough climate change is evidenced by a steady increase in global temperature, several indicators have been defined and are represented by mathematical expressions called indices, which are identified, recorded, and compared to demonstrate variations in climate change. However, using these indices requires: (1) timely evaluation, and (2) determining their spatial variation over a given region. However, there are only a few studies on the spatial trend of these indices, which is important considering that the impacts of climate change, as well as the factors that determine them, are not spatially homogeneous. Therefore, information from the historical series (1969-2009) of 16 meteorological stations, distributed in Chiapas, Mexico, was used. To determine the spatial variation of the climatic indices, each index was associated with 25 environmental variables through multiple linear regressions defined by the stepwise procedure. According to the results, the environmental variables with the greatest significant influence (p < 0.001) were mean annual temperature, mean annual runoff, real evapotranspiration, mean minimum temperature, and mean annual isotherms. On the other hand, the variables not used in the models were: highest insolation in May, soil moisture regimes, hydrogeology, biotic provinces, and physiographic provinces. The results of multiple linear regression models defined high R2 values (from 0.72 to 0.97), and the resulting mapping shows that each index defined a particular spatial variation. We conclude that, for the purpose of evidencing climate change, the process followed in this work can be used to determine the variation of this type of index in other regions
Evaluation of air quality in Puebla, Mexico: A wavelet transform and predictive modeling approach
Full text linkThis 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
Modeling climate change impacts on the Nazas River basin: Future aridity and land use change
Full text linkThis study assessed potential aridity shifts in the Nazas River basin (NRB) using the De Martonne Aridity Index, historical data (1970-2000), and projections from four climate models under two shared socioeconomic pathways (SSP1-2.6 W m–2 and SSP5-8.5 W m–2) and two time horizons (2021-2040 and 2041-2060). Additionally, anthropogenic disturbances were analyzed by comparing changes in land use and vegetation cover (LUVC) between 2003 and 2018. Results show that 64% of the NRB is currently arid. Climate models project increasing aridity, but only the CNRM-CM6-1 model shows statistically significant changes under SSP1 (2041-2060) and SSP5 (for both periods). The CNRM-CM6-1 model under the SSP5 (2041-2060) scenario projects an increase in aridity of up to 81% (54% for Mediterranean semiarid climate and 27% for semiarid climate), suggesting that 29% of the NRB, mainly in the upper part, could be threatened by increased aridity. The most severe aridity category projected was semiarid, with no expectation of reaching hyper-arid conditions in the medium term. LUVC changes between 2003 and 2018 showed no statistically significant differences. However, human settlements expanded with an annual growth rate of 6.6%, comparable to that of some major cities in Asia. Although covering a small area of the basin, human settlements play a significant role in radiative forcing. The increase in productive lands, mainly croplands, could impact CO2 retention and soil hydrodynamic processes, increasing susceptibility to erosion and the likelihood of desertification
Satellite precipitation product assessment and correction technique selection at sub-basin scale for maximum annual events. Case study: Acaponeta River basin
Full text linkSatellite precipitation products (SPP) are increasingly being used for detailed hydrological studies due to scarce and discontinuous precipitation observations at different spatial and temporal scales. However, to evaluate its full utility, it is necessary to assess and correct the bias between estimated and observed precipitation (OP). The aim of this paper is to evaluate the CHIRPSv2.0 product for maximum annual events and different climatological conditions based on in-situ observations, using statistical metrics and selecting from linear scaling (LS), local intensity scaling (LOCI) and power transformation (PT) the appropriate bias correction technique (CT), at point and sub-basin scale, improving the maximum annual precipitation records for the period 2001-2020 in the Acaponeta River basin, Mexico. Previous applications of bias CT have focused on broader temporal scales rather than specific maximum events. Differences in the performance of the correction methods were identified between point and sub-basin scales. PT presented a good performance at the point scale, in contrast to percentual bias (PBIAS), which resulted in a great overestimation at the sub-basin scale in the upper zone for the average and dry years, while for the wet year, it overestimated in the lower part. Although LS and LOCI generally observed a good PBIAS reduction at the gauge stations, LS overestimated at the sub-basin scale overall. LOCI showed better SPP corrections in the middle and lower zones and a wider range of overestimation for the upper basins in the middle and wet years. The corrected annual maximum estimated values for the revised period are useful for hydrological analysis in the context of flood risk assessment