Atmósfera (Journal)
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Understanding convective storms in a tropical, high-altitude location with in-situ meteorological observations and GPS-derived water vapor
Get PDFWe investigate convective storms over the Sabana de Bogotá, a high-altitude and densely populated area in the Colombian tropical Andes. Convective events are identified using infrared satellite images and in-situ precipitation data. As expected, convection shows a strong early-afternoon peak during the two rainy seasons. Previous studies hypothesize that early-afternoon westerly winds and their moisture advection from the warmer Magdalena valley are the main explanatory mechanism for intense storms. We find that early-afternoon westerlies are present in 78% of rainy season days, but convective events develop in only 26% of them. Thus, although westerlies seem necessary for convection due to the convergence they generate, they only occasionally generate storms and are therefore not a good predictor. Furthermore, reanalysis data indicate that precipitable water vapor (PWV) at the Magdalena valley is anomalously low during convective days, suggesting that moisture converges locally instead of being advected from the west. Based on composites of surface wind speed, air temperature, surface pressure, and GPS-derived PWV, we identify the most prominent signals associated with deep convection: a weaker than average wind speed throughout the morning, higher than normal values of surface air temperature towards noon, followed by an anomalous steep increase of PWV and wind speed. These features indicate that convection results from a strong diurnal forcing facilitated by convergence of westerly winds, combined with sufficient water vapor convergence, with a timescale of about 3 h. This highlights the relevance of high temporal resolution monitoring of PWV offered by Global Navigational Satellite System stations
Satellite-based estimation of NO2 concentrations using a machine-learning model: A case study on Rio Grande do Sul, Brazil
Get PDFNitrogen dioxide (NO2) 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, NO2 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 NO2 from 2006 to 2019 using as input parameters NO2 data retrieved from the Ozone Monitoring Instrument (OMI) sensor aboard Aura satellite, besides meteorological covariates and localized ground-level NO2 measurements. Results show that the RF model predicts NO2 with an accuracy expressed by an R2 = 0.68 correlation based on a 10-fold cross-validation. The model also predicted a mean NO2 concentration of 18.73 ± 3.86 μg m–3. The total NO2 concentration over the entire region analyzed showed a decreasing trend (2.9 μg m–3 yr–1), 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 NO2 mapping
Crop water use estimation of drip irrigated walnut using ANN and ANFIS models
Get PDFWalnut trees, as well as their fruits, represent an important sector of the agricultural industry and their cultivation significantly contributes to the global economy. Irrigation is a key factor in walnut cultivation and its most important problem is related to accurately estimating the need for irrigation water. Walnut water use was estimated in this study through artificial intelligence methods, namely artificial neural networks (ANN) and the adaptive neuro-fuzzy inference system (ANFIS) using meteorological data in western Turkey, which has semi-arid climatic conditions. Probabilistic scenarios based on maximum, minimum and average temperature, wind speed and sunshine hours over the period 2016-2019 were developed and tested with ANN and ANFIS to estimate walnut evapotranspiration. Results indicate that the optimum performance in the training and testing for ANN and ANFIS was obtained from the fourth scenario with R = 0.95 and two climate parameters: sunshine duration and mean temperature. Both ANN and ANFIS were able to predict crop water use obtaining a high correlation and the minimum number of climatic parameters. Nevertheless, the ANFIS model had a higher predictive capacity, with smaller MSE (0.36 for training and 0.29 for testing) compared to the ANN model
Atlantic and Pacific sea surface temperature correlations with precipitation over northern Mexico
Get PDFThree main sea surface temperature (SST) oscillations in the Atlantic and Pacific oceans have shown to play a key role in modulating rainfall variability over northern Mexico. Nevertheless, only a few studies have explored these teleconnections under a climate classification approach. In this study, the effects of the Pacific Decadal Oscillation (PDO), the Oceanic El Niño Index (ONI) and the Atlantic Multidecadal Oscillation (AMO), over precipitation in dry and semi-dry areas of the Baja California peninsula and the state of Tamaulipas are analyzed for the period 1951-2021. Pearson and Spearman correlations are compared and proven to have equivalent results despite the different physical conditions of the two territories. The results show several statistically significant correlations indicating that for the study regions, the correlation is negative throughout the year between the Standardized Precipitation Index (SPI) and AMO in latitudes above 28º N, while it is negative (positive) during the months of January-April (October-November) in lower latitudes. Simultaneously, the correlation is positive between SPI and ONI/PDO in eastern and western regions of northern Mexico, and it is negative between SPI-ONI and SPI-PDO in the months of August-September over the eastern side. The information generated throughout this study, in conjunction with the understanding of regional climate dynamics, can help to comprehend with greater certainty the effects of these teleconnections
Using a hybrid approach for wind power forecasting in Northwestern Mexico
Get PDFWind energy is an important renewable source that has been considerably developed recently. In order to obtain successful 24-h lead-time wind power forecasts for operational and commercial uses, a combination of physical and statistical models is desirable. In this paper, a hybrid methodology that employs a numerical weather prediction model (Weather Research and Forecasting) and a neural network (NN) algorithm is proposed and assessed. The methodology is applied to a wind farm in northwestern Mexico, a region with high wind potential where complex geography adds large uncertainty to wind energy forecasts. The energy forecasts are then evaluated against actual on-site power generation over one year and compared with two reference models: decision trees (DT) and support vector regression (SVR). The proposed method exhibits a better performance with respect to the reference methods, showing an hourly normalized mean absolute percentage error of 6.97%, which represents 6 and 13 percentage points less error in wind power forecasts than with DT and SVR methods, respectively. Under strong synoptic forcing, the NN wind power forecast is not very accurate, and novel approaches such as hierarchical algorithms should be employed instead. Overall, the proposed model is capable of producing high-quality wind power forecasts for most weather conditions prevailing in this region and demonstrates a good performance with respect to similar models for medium-term wind power forecasts
Variability, cycles, and trends of mean air temperature north of Colombia
Get PDFClimate variability is of global interest due to its socioeconomic and environmental effects on the world’s population. In Colombia, temperature changes affect food security, especially for the most vulnerable people in the Caribbean region. We analyzed monthly air temperature in northeastern Colombia (Cesar, La Guajira, and Magdalena departments). We reconstructed time series with missing data using nonlinear principal component analysis. Subsequently, temporal variability, associations with events of climatic variability, and temporal trends were evaluated. Periodicity analyses indicate the dominance of annual variability, although statistically significant associations with periods between 3 and 7 years show the influence of El Niño-Southern Oscillation (ENSO) events. The Spearman correlation coefficient with N = 360 and 95% significance shows a better association with the Multivariate ENSO Index (rsp mean = 0.38) and the Southern Oscillation Index (rsp mean = –0.32). The multi-year monthly analysis shows positive trends, with maximum values between March (1.04 ºC month–1), and June (1.07 ºC month–1) in the valley of the Cesar department, and a minimum in March, at the northernmost La Guajira (0.2 ºC month–1)
Particulate matter air pollution effects on pulmonary tuberculosis activation in a semi-desert city on the US-Mexican border
Get PDFIn this paper we assessed the association (relative risk, RR) between the exposure to PM10 and PM2.5 (as a continuous variable and as categories of low or high pollution exposure) on the incidence of pulmonary tuberculosis (PTB) in Mexicali, Baja California, Mexico. We used a weekly, lagged multiple Poisson regression model. We observed a 10-week delayed effect for PM10 and PM2.5 in all PTB cases and in male cases with PTB. An 11-week delayed effect occurred in the female PTB cases. For all the PTB cases, the RR rose by 2.4% (95% CI: 2.1, 2.6, p<0.10) for each 10 µg/m3 increase of PM10 in the continuous exposure and by 3.6% (CI: 3.3, 4.0, p<0.05) in the high pollution exposure category, and by 3.2% (CI: 2.9, 3.4, p<0.05) for each 10 µg/m3 increase of PM2.5 in the continuous exposure and by 3.9% (CI: 3.6, 4.3, p<0.05) in the high pollution exposure category. In men, the RR rose by 2.8% (CI: 2.5, 3.1, p<0.10) for each 10 µg/m3 increase of PM10 in the continuous exposure and by 4.6% (CI: 4.2, 5.0, p<0.05) in the high pollution exposure category, and by 3.4% (CI: 3.1, 3.7, p<0.05) for each 10 µg/m3 increase of PM2.5 in the continuous exposure and by 4.2% (CI: 3.8, 4.6, p<0.05) in the high pollution exposure category. In women, the RR rose by 5.1% (CI: 4.7, 5.5, p<0.05) for each 10 µg/m3 increase of PM10 in the continuous exposure and by 5.3% (CI: 4.7, 5.8, p<0.10) in the high pollution exposure category, and by 4.3% (CI: 3.8, 4.8, p<0.10) for each 10 µg/m3 increase of PM2.5 in the continuous exposure and by 5.3% (CI: 4.8, 5.9, p<0.10) in the high pollution exposure category. PM air pollution appears to associate with the incidence of PTB in the population of Mexicali
Seasonal variation of atmospheric bulk deposition along an urbanization gradient in Nuevo Leon, Mexico
Get PDFBulk deposition was studied along an urbanization gradient in the state of Nuevo Leon, Mexico. During a yearlong period seven sites within the Metropolitan Area of Monterrey (MAM) and two rural sites (Allende and Linares) were monitored, with the purpose of characterizing deposition and identifying possible patterns between sites. A total of 32 rainfall events were collected. An average pH of 7.15 ± 0.02 was found, which indicates the presence of neutralizing substances in rainwater, as well as an average Electrical Conductivity of 153.96 ± 6.83 μS/cm. The annual accumulated deposition follows the descending order Ca> K> Mg> Fe> Zn> Mn> Cu> Cd> Ni and does not show significant differences between urban and rural areas, with the exception of Ca (p = 0.017). The Principal Component Analysis shows that metals (Cu, Zn, Ni, Mn, and Cd) represent an important pathway in the deposition phenomena and this behavior is maintained through the urbanization gradient, which denotes that the rural areas could be connected to the air basin of the MAM. Seasonal deposition showed that Zn, Fe, Cd, Cu, Ni, Mn Ca, and Mg are higher during autumn and K during winter. Enrichment Factors shows that Zn and Cd were highly enriched, Cu and Ni were moderately enriched, and Ca, K, and Mn were not enriched. Finally, backward trajectories for rural sites showed that only for Allende site a possible carry-over of pollutants is observed during the summer, since the wind currents come preferably from the northern part of the MAM
Assessment of bioclimatic sensitive spatial planning in a Turkish city, Eskisehir
Get PDFThe city of Eskişehir is located in the Central Anatolia Region of Turkey, where harsh continental climatic characteristics are prevalent (i.e., cold winters and hot summers). In recent years, quality and quantity of studies on bioclimatic comfort have increased both all over the world and in Turkey. Outdoor bioclimatic comfort conditions are counted amongst the indicators of human quality of life in urban environments, together with other physical, social and economic features such as air quality, GDP, and possibilities of social activities. The calculated values representing bioclimatic comfort conditions have been used instead of individual mean values of some climatic elements, in order to provide an insight of the liveability of a city. The aim of the present research study is to determine: (1) hourly bioclimatic comfort conditions in the Eskişehir city center during sultry summer days, considering bioclimatic comfort values calculated according to 12-year data from urban, sub-urban and rural areas using the physiological equivalent temperature (PET) index and RayMan software for the calculation of solar radiation fluxes on individuals in the hottest five months of the year; (2) the spatial distribution of these comfort values in decades (10-day intervals) using Geographic Information Systems and raster maps, taking into consideration elevation and land use; and (3) which urban design and planning principles could be adopted to deal with adverse thermal comfort conditions triggered by the urban heat island (UHI) effect. The results of the study indicate that the poorest comfort conditions are provided in urban areas, while rural areas are more advantageous in terms of comfort conditions. New bioclimatic-sensitive urban design principles are taken into consideration to create more comfortable areas from the bioclimatic perspective (i.e., windier and less humid sites open to the prevalent wind direction and out of heat stress)
Spatial and temporal changes of land uses and its relationship with surface temperature in western Iran
Get PDFA split-window algorithm has been used in the Ilam dam watershed to determine the relationship between land surface temperature (LST) and types of land use. Landsat satellite images of the TM sensor for 1990, 1995, 2000, 2005 and 2010 and Landsat 8 (OLI Sensor) for 2015 and 2018 are used. After geometric and radiometric corrections of satellite images, land use maps are extracted by using the fuzzy ARTMAP method. An accuracy assessment showed that the highest value of the kappa coefficient was 94% with a total accuracy of 0.95 for 2015, and the lowest kappa coefficient value was 87% with a total accuracy of 0.9 for 1990. The high values of these coefficients indicate the acceptable accuracy of using Landsat’s remote sensing data for land use detection. The most important land use change is related to dense forest and sparse forest land uses, with decreases of 20.07 and 17.04%, respectively. The minimum LST measures in 1990, 2010, and 2018 in dense forest are 21.27, 30.55 and 33.82 ºC, respectively. The maximum LSTs for the sparse forest land use in 1990 and 2010 are 52.48 and 56.09, and 56.10 ºC for the dense forest land use in 2018. As a result, the average LST in agricultural lands was lower than in sparse forest and rangeland;, which is mainly due to the high moisture content and the greater evapotranspiration rate. Land use/land cover variations from 1990 to 2018 show that all land uses have experienced an increase in LST