Atmósfera (Journal)
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Satellite data geoprocessing to estimate PM2.5 over the Megalopolis of Central Mexico
Full text linkThe Megalopolis of Central Mexico experiences high levels above the Official Mexican Standard (NOM) of PM2.5, leading to various respiratory diseases ranging from acute symptoms to chronic illnesses such as asthma and lung cancer. It is crucial to measure PM2.5 levels accurately to warn the public about the risks of exposure to particulate matter. Unfortunately, the Megalopolis of Central Mexico has a shortage of monitoring sites, limiting data availability. This study addresses this issue using satellite data to develop a multiple linear regression model. Our model uses aerosol optical depth (AOD), relative humidity (RH), temperature (T), the planetary boundary layer height (PBLH), and the normalized difference vegetation index (NDVI) as independent variables to estimate PM2.5 concentrations in the region under study. The relationship between AOD and PM2.5 concentrations was found to be strongly influenced by RH and T. However, this effect is compensated for by a low PBLH (< 400 m), which enables AOD and PM2.5 measurements to be similar in magnitude. Our findings have important implications for estimating PM2.5 concentrations using satellite data. This study could help improve air quality monitoring in the Megalopolis of Central Mexico by providing more spatial and temporal data on particle concentrations in the atmosphere
Development and evaluation of a bulk three-moment parameterization scheme incorporating the processes of sedimentation and collision-coalescence
Full text linkThere are a few three-moment schemes that consider other processes besides sedimentation. Thus, a performance assessment of these types of schemes due to the combined effect of sedimentation and other microphysical processes is a matter of interest. In this study, a warm rain bulk three-moment parameterized scheme was developed and evaluated through a detailed comparison with a bin microphysical scheme. To evaluate the impact of sedimentation and the combined effect of sedimentation and collision-coalescence on the droplet size distribution (DSD), a rain shaft model was applied to the DSD with different initial values of the shape parameter. For pure sedimentation, a good correspondence was obtained between the three-moment scheme and the explicit model, with a practically perfect coincidence of bulk quantities for larger values of the gamma distribution’s initial shape parameter and, in general, the three-moment parameterization scheme performing much better than the two-moment scheme. The simulations performed for this case confirm (as reported in previous studies) that for pure sedimentation, the three-moment parameterization schemes deliver a physically more complete representation of the evolution of droplet size distribution. The impact of the combined effect of sedimentation and collision-coalescence processes on DSD was also assessed. We could observe that certain differences arise between the parameterized scheme and the spectral model when the collision-coalescence process is incorporated, as the onset of precipitation occurs earlier in the three-moment parameterized scheme. It can be concluded that, in general, the three-moment warm rain bulk microphysics scheme is able to reproduce the results of the reference bin microphysical model
Research on the usability of different machine learning methods in visibility forecasting
Full text linkHaze 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)
A comparison of missing value imputation methods applied to daily precipitation in a semi-arid and a humid region of Mexico
Full text linkClimatological 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
Application of geostatistical models for aridity scenarios in northern Mexico
Full text linkAn annual mean temperature map was calculated using the Kriging interpolation method for the north-central zone of Mexico to obtain the current aridity, as well as possible scenarios for the near and distant future. The altitudinal gradient was estimated by linear regression, and it was used to estimate the mean temperature. Climate Influence Areas (CIA) were obtained by superimposing the official precipitation layer and the annual mean temperature layer using Geographic Information Systems tools. Monthly databases of climatic variables were generated for each CIA and potential evapotranspiration was estimated using the Thorthwaite methodology. The Aridity Index (AI) was calculated and mapped for a base scenario (1970-2000). Subsequently, the aridity behavior of some scenarios was projected and mapped using the global climate models HADGEM 2.0, GFDLCM 3.0, MIP_ESM, and CRNMCM5. Under the best scenario projected, aridity will weaken the humid ecosystems and in the worst scenario, hyper-arid climates will appear in the study region
Performance evaluation of the WRF model in a tropical region: Wind speed analysis at different sites
Full text linkIn this study, the performance of the mesoscale Weather Research and Forecasting (WRF) model is evaluated using combinations of three planetary boundary layers (PBL) (YSU, ACM2, and MYJ) and three land surface model (LSM) schemes (RUC, Noah and Noah-MP) in order to identify the optimal parameters for the determination of wind speed in a tropical region. The state of Bahia in Brazil is selected as the location for the case study and simulations are performed over a period of eight months between 2015 and 2016 (dry and rainy seasons). The results of the simulations are compared with observational data obtained from three towers equipped with anemometers at heights of 80, 100, 120 and 150 m, strategically placed at each site and evaluated with statistical indices: MB, RMSE, MAGE, IOA, R, Fac2 and standard deviation. Overestimation of wind speed is observed in the simulations, despite similarities between the simulated and observed wind directions. In addition, the accuracies of simulations corresponding to sites that are closer to the ocean are observed to be lower. The most accurate wind speed estimates are obtained corresponding to Mucugê, which is located farthest from the ocean. Finally, analysis of the results obtained from each tower accounting for periods with higher and lower precipitation reveals that the combination of the PBL-YSU scheme with the LSM-RUC scheme yields the best results
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
Full text linkForecast 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
Relationship between rainfall and streamflow in the La Plata Basin: Annual cycles, interdecadal and multidecadal variability
Full text linkThe aim of this study is to understand the interaction between rainfall and streamflow variability in the La Plata basin (LPB) along a wide range of timescales. The LPB is divided in six sub-basins associated to the main regional rivers (Paraguay, Paraná, Uruguay and Iguazú). The amplification of the streamflow response is addressed in order to evaluate to what extent river discharges variability can be explained by precipitation fluctuations. Mean annual cycles corresponding to the period 1931-2010 and to each decade of this interval are analyzed. Streamflow interdecadal changes are observed in most of the gauging stations. In addition, an 11-year moving-average filter is applied to the normalized annual time series. Results exhibit a considerable higher percentage of explained variance in the streamflow filtered series, highlighting the predominance of medium and low frequencies variability present in these compared to those of precipitation. Consistently, river discharges show higher spectral density in the interdecadal/multidecadal frequencies compared to precipitation analysis. A simple statistical approach to advance in the understanding of the complex rainfall-streamflow physical relationship is addressed with promising results: streamflow spectrums are derived directly from the precipitation spectrum, transformed by a “basin” operator, characteristic of the basin itself. It is assumed that watersheds act on precipitation as spatiotemporal integrators operating as low-pass filters, like a moving average. Streamflow power spectrums are simulated assuming that the underlying process is an autoregressive moving average. Considering the sub-basin areal-averaged precipitation time series as the only input, results show that simulated streamflow spectrums fit effectively the observations at the sub-basin scale.
Comparison of two air quality models in complex terrain near seashore
Full text linkAir 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 PM10 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 PM10 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 (PM10) was the leading air pollutant. Emission inventory calculations revealed that 2710.2 t of PM10 have been emitted to the atmosphere from all sources in the study area. Then, the air quality modeling has been performed for PM10 by using two air quality models: AERMOD and CALPUFF. According to the modeling results, PM10 pollution levels may pose a health threat to the inhabitants of Zonguldak. The maximum PM10 concentrations predicted by the CALPUFF model were higher than that of AERMOD. Predicted values plus background concentration were validated against the PM10 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
Solar PV technologies selection for the design of photovoltaic installations in Mexico based on the analysis of meteorological satellite data from the region
Full text linkMexico’s expansive territory spans diverse climatic conditions, which directly influences the selection of commercial photovoltaic technologies. This study utilizes solar irradiance, temperature, and cloud index data (derived from satellite sources) to generate a suitability map for commercial solar panel technologies through the Analytical Hierarchy Process-Geographical Information Systems methodology. The map illustrates that chalcopyrites and cadmium telluride emerge as the most suitable technologies in 47.12% of the national territory. Following closely behind is amorphous silicon, covering 30.45%, while monocrystalline and polycrystalline silicon account for 22.43%. The primary objective of this paper is to guide the proper selection of solar panel technology types that align optimally with Mexico’s climatic conditions. This strategic approach aims to strengthen the planning and viability of photovoltaic solar energy projects nationwide