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Drought monitoring and early detection have improved greatly in recent decades through the development and refinement of numerous indices and indicators. However, a lack of guidance, based on user experience, exists as to which drought-monitoring tools are most appropriate in a given location. This review paper summarizes the results of targeted user engagement and the published literature to improve the understanding of drought across North America and to enhance the utility of drought-monitoring tools. Workshops and surveys were used to assess and make general conclusions about the perceived performance of drought indicators, indices and impact information used for monitoring drought in the five main Köppen climate types (Tropical, Temperate, Continental, Polar Tundra, Dry) found across Canada, Mexico, and the United States. In Tropical, humid Temperate, and southerly Continental climates, droughts are perceived to be more short-term (less than 6 months) in duration rather than long-term (more than 6 months). In Polar Tundra climates, Dry climates, Temperate climates with dry warm seasons, and northerly Continental climates, droughts are perceived to be more long-term than short-term. In general, agricultural and hydrological droughts were considered to be the most important drought types. Drought impacts related to agriculture, water supply, ecosystem, and human health were rated to be of greatest importance. Users identified the most effective indices and indicators for monitoring drought across North America to be the U.S. Drought Monitor (USDM) and Standardized Precipitation Index (SPI) (or another measure of precipitation anomaly), followed by the Normalized Difference Vegetation Index (NDVI) (or another satellite-observed vegetation index), temperature anomalies, crop status, soil moisture, streamflow, reservoir storage, water use (demand), and reported drought impacts. Users also noted the importance of indices that measure evapotranspiration, evaporative demand, and snow water content. Drought indices and indicators were generally thought to perform equally well across seasons in Tropical and colder Continental climates, but their performance was perceived to vary seasonally in Dry, Temperate, Polar Tundra, and warmer Continental climates, with improved performance during warm and wet times of the year. The drought indices and indicators, in general, were not perceived to perform equally well across geographies. This review paper provides guidance on when (time of year) and where (climate zone) the more popular drought indices and indicators should be used. The paper concludes by noting the importance of understanding how drought, its impacts, and its indicators are changing over time as the climate warms and by recommending ways to strengthen the use of indices and indicators in drought decision making. [ABSTRACT FROM AUTHOR]

This paper analyzes the relative content of CO2 emissions embedded in regional supply chains in four different countries in Latin America: Brazil, Chile, Colombia, and Mexico. We estimate both the trade in value-added (TiVA) and the CO2 content embedded in interregional and foreign exports, mapping the relative intensity of CO2 emission levels on value chains. For that, we applied an inter-regional input-output model to determine the interplay between the CO2 emission embedded in goods of resource-based industries and their linkages with other economic industries, revealing a map of CO2 emissions on trade in value-added trade from a subnational dimension. The main result reveals an interregional dependence, indicating a higher level of embedded CO2 on value-added in each regional economy for resource-based industries, usually intense in CO2 emissions. This finding has considerable implications for the sustainable development goals of these subnational areas, as the spatial concentration of production leads to an unbalanced regional capacity for promoting reductions in CO2 emissions along with value chains. [ABSTRACT FROM AUTHOR]

The noise-like behavior of the geomagnetic anomalies observed in Tlamacas station (volcano Popocatepetl, Mexico), linked to the ionization produced by intensive radon release, are presented in the experimental part of this study. The magnetic field perturbations produced by charge spreading currents within the fair-weather electric field are considered in the theoretical model based on the electrode. The electric charges are generated by the air ionization due to radon emanation. The simulations demonstrated that the ionization of the air leads to magnetic field perturbations of about 0.001–0.1 nT in the ULF (ultra low frequency) range 10−3–10−1 Hz. Magnetic field perturbations can be higher when the radon emanation occurs in a region with terrain irregularities. [ABSTRACT FROM AUTHOR]

What drives countries to realize more integrated policymaking? The co-benefits concept highlights the win–win situations that can arise if one policy measure addresses two or more policy goals, e.g., air quality and health benefits resulting from a climate policy. Scholars have suggested that decision makers, if confronted with the evidence of co-benefits, would update their beliefs and adopt stronger or more ambitious climate policies. In other words, a learning process takes place. This paper looks at the policy processes in two countries, Mexico and Nigeria, as part of the Supporting National Action and Planning (SNAP) initiative under the Climate and Clean Air Coalition (CCAC). The SNAP initiative supports governments with policymaking and implementation for a reduction in short-lived climate pollutants (SLCPs). This paper seeks to reveal how learning processes and their outcomes are influenced by co-benefits as a specific type of information. Looking at an example of how the co-benefits concept is applied in political practice offers valuable insights into how learning is part of the policymaking process and can shape its outcomes, such as national (climate) action plans. [ABSTRACT FROM AUTHOR]

In this study, the nowcast models provided by the Python pySTEPS library were evaluated using radar derived rain rate data and the satellite product Split-Window Difference (SWD) based on GOES-16 data, focusing on central Mexico. Initially, we obtained a characterization of the rainfall that occurred in the region using the radar rain rate and the SWD. Subsequently the nowcasts were evaluated using both variables. Two nowcast models were employed from pySTEPS: Extrapolation and S-PROG. The results indicate that average SWD is below 2.5 K, 90 min before the onset of rainfall events, and, on average, the SWD is 2 K during rainfall events. The results from both nowcast models were accurate and produced similar results. The nowcasts performed better when SWD data were used as input, having an average Probability of Detection (PoD) above 70% and a False Alarm Rate (FAR) reaching 40% for the 15-min prediction. The nowcasts were less accurate using the radar rain rate as input for the 15-min forecast, where the PoD was maximum 70% and FAR reaching 40%. However, these nowcasts were more reliable during well-organized precipitation events. In this work, it was determined that the nowcast models provided by pySTEPS can provide valuable rain forecasts using GOES-16 satellite and radar data for the central Mexico region. [ABSTRACT FROM AUTHOR]

Net radiation is an essential forcing of climate in the lower layers of Earth's atmosphere. In this paper, radiation balance is measured in clay soil and green grass, and is compared with three urban materials. These materials: asphalt, concrete and white painted elastomeric polystyrene roofing sheet are widely used in Mexicali, Baja California, México. This study was carried out during August of 2011, the hottest time of the year. The 24-hour average values of net radiation found were: 137.2 W·m-2 for asphalt, 119.1 for concrete, 104.6 for clay soil, 152 for green grass and 29.2 for the polystyrene insulation. The latter two types of materials are likely to be the most effective in reducing urban heat island effects. This variation in the radiation balance has widespread implications for human living conditions, as land cover change tends to be towards surfaces that have higher levels of net radiation. [ABSTRACT FROM AUTHOR]

The residents of Mexico City face serious problems of air pollution. Identifying the most representative scenarios for the transport and dispersion of air pollutants requires the knowledge of the main wind circulation patterns. In this paper, a simple method to recognize and characterize the wind circulation patterns in a given region is proposed and applied to the Mexico City winds (2001-2006). This method uses a lattice wind approach to model the local wind events at the meso-β scale, and hierarchical cluster analysis to recognize their agglomerations in their phase space. Data of the meteorological network of Mexico City was used as input for the lattice wind model. The Ward's clustering algorithm with Euclidean distance was applied to organize the model wind events in seasonal clusters for each year of the period. Comparison of the hourly population trends of these clusters permitted the recognition and detailed description of seven circulation patterns. These patterns resemble the qualitative descriptions of the Mexico City wind circulation modes reported by other authors. Our method, however, permitted also their quantitative characterization in terms of the wind attributes of velocity, divergence and vorticity, and an estimation of their seasonal and annual occurrence probabilities, which never before were quantified. [ABSTRACT FROM AUTHOR]

With buildings being responsible for nearly a quarter of global greenhouse gas emissions, intensive building decarbonization programs are in place worldwide, with unintended consequences for historic buildings. To this end, national and international guidance on energy efficiency for historic buildings advocate for the adoption of a 'whole house approach' that integrates heritage values in energy efficiency plans. Most guidance, though, relies on non-evidence based, pre-assumptions of residents' heritage values. And yet, unless we understand how and why residents negotiate their decisions between energy efficiency, thermal comfort, and heritage conservation, such guidance will not be applicable. Despite the urgency to decarbonize the building stock, research on how inhabitants of old buildings make such decisions is extremely limited. It is also case-study specific, often lacking the required depth. To address this gap, this paper offers the first international, in-depth study on the topic. It does so through a rigorous double-coded, thematic analysis of 59 in-depth semi-structured interviews (totaling 206,771 words) carried out in Greece, Mexico, and the UK. The thematic analysis is combined with system dynamic analysis, essential for unveiling what parameters affect inhabitants' decisions over time. Drawing on theories on the dynamics of social practices, we conclude that the process of decision-making on energy efficiency, thermal comfort improvement, and heritage conservation is a socio-cultural, dynamic practice, the change and continuation of which depends on how the following elements are connected or disconnected: materials (e.g., original features), competencies (e.g., restoration skills), resources (e.g., costs), values, space/environment (e.g., natural light), senses (e.g., thermal comfort), and time (e.g., years living in the house). The connection or disconnection of those elements will depend on (a) the nature of the context (e.g., rural, urban, conservation area); (b) the listing status; (c) age and construction materials of building; (d) local climate; and (e) ownership status. [ABSTRACT FROM AUTHOR]

Air-quality monitoring and analysis are initial parts of a comprehensive strategy to prevent air pollution in cities. In such a context, statistical tools play an important role in determining the time-series trends, locating areas with high pollutant concentrations, and building predictive models. In this work, we analyzed the spatio-temporal behavior of the pollutant PM10 in the Monterrey Metropolitan Area (MMA), Mexico during the period 2010–2018 by applying statistical analysis to the time series of seven environmental stations. First, we used experimental variograms and scientific visualization to determine the general trends and variability in time. Then, fractal exponents (the Hurst rescaled range and Higuchi algorithm) were used to analyze the long-term dependence of the time series and characterize the study area by correlating that dependence with the geographical parameters of each environmental station. The results suggest a linear decrease in PM10 concentration, which showed an annual cyclicity. The autumn-winter period was the most polluted and the spring-summer period was the least. Furthermore, it was found that the highest average concentrations are located in the western and high-altitude zones of the MMA, and that average concentration is related in a quadratic way to the Hurst and Higuchi exponents, which in turn are related to some geographic parameters. Therefore, in addition to the results for the MMA, the present paper shows three practical statistical methods for analyzing the spatio-temporal behavior of air quality. [ABSTRACT FROM AUTHOR]

In recent decades, the United States has experienced changing patterns of extreme temperature. Although much progress has been made, delineating the change in synoptic surface temperature variability (SSTV) and understanding its potential causes remain to be pursued. In this study, we seek to provide a quantitative description of the change in SSTV in the past seven decades across the US and its potential relevant physical factors. To achieve this goal, we develop a spatiotemporally local analysis method based on the ensemble empirical mode decomposition that bypasses the stationary assumption and makes it possible to continuously track the change in SSTV in the spatiotemporal domain. We have found that the change in SSTV across the US is spatially inhomogeneous and temporally non-uniform. The change in the SSTV amplitude ranges from −36% to 39% across the continental United States, Northern Mexico, and surrounding oceans. Higher altitudes and surrounding regions generally see an increase in variability, while elsewhere over land, a reduction is observed, creating a three-band zonal structure across the continental United States. Generally, increases in variability are observed in the subtropics. The shape of the spatiotemporal evolution of SSTV implies that the topography of the United States may play an important role in altering synoptic-scale variability. [ABSTRACT FROM AUTHOR]

Assessments of the efficiency of grazing systems, in terms of productivity and ecological sustainability, are necessary in view of the increased demand for animal protein. In this study, the methane (CH4) emissions (sniffer methodology), dry matter (DM) yield, paddock chemical composition (AOAC and Van Soest methods), nutrient intake (dry matter, DMI; crude protein, CPI; metabolizable energy, MEI), daily milk yield (DMY), body condition score (BCS), and body weight (BW) of cattle, in intensive silvopastoral systems (ISPSs) and monoculture systems (MSs), in the tropics of Mexico were evaluated. In the ISPS, CH4 emissions (18% less) and DMY were lower than in the MS. Cows from MSs tend to disperse across higher values of CH4 emissions per kg of DMI, as well as higher milk production, while cows from the ISPS were dispersed over a higher intake (DMI, CPI, and MEI) and lower CH4 emissions. There were no differences between systems in paddock DM yield, chemical composition, cows' BCS, and BW, regardless of whether it was the dry (April to May) and rainy (September to October) season. Based on the results obtained in this study, ISPSs contribute to the mitigation of methane emissions in cattle; forage and animal production variables in both systems were similar, with a lower use of imported inputs in the ISPS. [ABSTRACT FROM AUTHOR]

Primary artisanal mercury (Hg) mining in Mexico continues to proliferate unabated, while official Hg exports have declined in recent years amid speculation of a rising black market trade. In this paper, an assessment of primary Hg mining in Mexico was conducted, with a focus on four sites in Querétaro State. Atmospheric Hg concentrations were measured at two of those sites. In addition, trade data was examined, including Hg exports from Mexico and imports by countries that have a large artisanal gold mining (AGM) sector. Results showed that while annual Hg production in Mexico has ramped up in recent years, official Hg exports reduced from 307 tonnes in 2015 to 63 tonnes in 2019. Since 2010, mercury exports to Colombia, Peru and Bolivia have represented 77% of Mexico's total Hg trade. As the large majority of Hg trade with these countries is apparently destined for the AGM sector, which is contrary to Article 3 of the Minamata Convention, there is evidence that increased international scrutiny has led to an increase in unregulated international transfers. Atmospheric Hg concentrations at the mines show dangerously high levels, raising concern over the risk of significant health impacts to miners and other community members. [ABSTRACT FROM AUTHOR]

The pervasiveness of "stacking" between traditional and clean fuels/stoves is moving the household clean cooking research and policy agenda from programs that are centered on one-fuel or stove, to multi-clean fuel/device interventions that could result in a more effective displacement of traditional biomass stoves. However, there is little recognition and knowledge of the benefits realistic clean-stacking cooking solutions can have on indoor air quality and health. In this paper, particulate matter (PM2.5) and carbon monoxide (CO) concentration levels that are associated with stove-stacking options (Patsari-U-shaped open fire (U-type), Patsari-liquefied petroleum gas (LPG) stove, U-type-LPG, and Patsari-U-type-LPG) common within rural Mexico were evaluated while using a controlled cooking cycle (CCC) from the Purepecha Highlands, which mimics the most common dishes prepared daily within the region as well as their sequential arrangement. The results confirms that the WHO Interim Target-1 (IT1) of 35 μg/m3 for PM2.5 is exceeded whenever woodburning open fires are part of the stacking combination (Patsari-U-type 107 ± 9 μg/m3, U-type-LPG 131 ± 55 μg/m3 and Patsari-U-type-LPG 107 ± 22 μg/m3). However, well-designed and properly operated woodburning chimney stoves, such as the Patsari can meet the IT1, either used exclusively (21 ± 8 μg/m3) or as a "clean stacking" option with LPG (24 ± 5 μg/m3). Given stove stacking patterns, evaluating the health and environmental consequences of stove transitions while assuming the total replacement of traditional fires by clean cooking options will lead to misplaced expectations, and programs should evaluate more realistic "clean-stacking" options. [ABSTRACT FROM AUTHOR]

This paper examines the ability of the Weather Research and Forecasting model forecast to simulate moisture and precipitation during the North American Monsoon GPS Hydrometeorological Network field campaign that took place in 2017. A convective-permitting model configuration performs daily weather forecast simulations for northwestern Mexico and southwestern United States. Model precipitable water vapor (PWV) exhibits wet biases greater than 0.5 mm at the initial forecast hour, and its diurnal cycle is out of phase with time, compared to observations. As a result, the model initiates and terminates precipitation earlier than the satellite and rain gauge measurements, underestimates the westward propagation of the convective systems, and exhibits relatively low forecast skills on the days where strong synoptic-scale forcing features are absent. Sensitivity analysis shows that model PWV in the domain is sensitive to changes in initial PWV at coastal sites, whereas the model precipitation and moisture flux convergence (QCONV) are sensitive to changes in initial PWV at the mountainous sites. Improving the initial physical states, such as PWV, potentially increases the forecast skills. [ABSTRACT FROM AUTHOR]

This paper describes the use of citizen science-derived data for the creation of a land-use regression (LUR) model for particulate matter (PM2.5 and PMcoarse) for a vulnerable community in Imperial County, California (CA), near the United States (US)/Mexico border. Data from the Imperial County Community Air Monitoring Network community monitors were calibrated and added to a LUR, along with meteorology and land use. PM2.5 and PMcoarse were predicted across the county at the monthly timescale. Model types were compared by cross-validated (CV) R2 and root-mean-square error (RMSE). The Bayesian additive regression trees model (BART) performed the best for both PM2.5 (CV R2 = 0.47, RMSE = 1.5 µg/m3) and PMcoarse (CV R2 = 0.65, RMSE = 8.07 µg/m3). Model predictions were also compared to measurements from the regulatory monitors. RMSE for the monthly models was 3.6 µg/m3 for PM2.5 and 17.7 µg/m3 for PMcoarse. Variable importance measures pointed to seasonality and length of roads as drivers of PM2.5, and seasonality, type of farmland, and length of roads as drivers of PMcoarse. Predicted PM2.5 was elevated near the US/Mexico border and predicted PMcoarse was elevated in the center of Imperial Valley. Both sizes of PM were high near the western edge of the Salton Sea. This analysis provides some of the initial evidence for the utility of citizen science-derived pollution measurements to develop spatial and temporal models which can make estimates of pollution levels throughout vulnerable communities. [ABSTRACT FROM AUTHOR]

Due to its geographical position, Mexico is exposed annually to cold fronts and tropical cyclones, registering extremely high values that are atypical in the series of maximum annual flows. Univariate mixed probability distribution functions have been developed based on the theory of extreme values, which require techniques to determine their parameters. Therefore, this paper explores a function that considers three populations to analyze maximum annual flows. According to the structure of the Generalized Extreme-Value Distribution (GEV), the simultaneous definition of nine parameters is required: three of location, three of scale, and three of probability of occurrence. Thus, the use of a meta-heuristic technique was proposed (harmonic search). The precision of the adjustment was increased through the optimization of the parameters, and with it came a reduction in the uncertainty of the forecast, particularly for cyclonic events. It is concluded that the use of an extreme value distribution (Type I) structured with three populations and accompanied by the technique of harmonic search improves the performance in respect to classic techniques for the determination of its parameters. [ABSTRACT FROM AUTHOR]

Low-cost sensors have been used considerably to characterize air pollution in the last few years. This study involves the usage of this technology for the first time to assess PM2.5 pollution at four cities on the U.S.–Mexico border. These cities in the Lower Rio Grande Valley Region of South Texas are Brownsville, Edinburg, Weslaco, and Port Isabel. A year-long sampling campaign was undertaken from 1 March 2021 to 31 March 2022. TSI BlueSky™ Air Quality Monitors were deployed concurrently at 11 different locations in these four cities. Twenty-four-hour PM2.5 concentrations from these sensors were then compared with ambient PM2.5 data available at the Texas Commission on Environmental Quality (TCEQ) Continuous Ambient Monitoring Station (CAMS) sites to elucidate spatial and temporal variability in the pollutant concentrations at the neighborhood level. The results indicate low to moderate spatial heterogeneity in the PM2.5 concentrations throughout the region. Our findings suggest that low-cost sensors in combination with CAMS sites have the potential to aid community monitoring for real-time spatiotemporal PM2.5 pollution patterns. [ABSTRACT FROM AUTHOR]

The forests of northern Mexico and the southwestern United States have been subjected to warmer temperatures, persistent drought, and more intense and widespread wildfire. Tree-ring data from four conifer species native to these borderlands forests are compared with regional and large-scale precipitation and temperature data. These species include Abies durangensis, Pinus arizonica, Pinus cembroides, and Pseudotsuga menziesii. Twelve detrended and standardized ring-width chronologies are derived for these four species, all are cross-correlated during their common interval of 1903–2000 (r = 0.567 to 0.738, p < 0.01), and all load positively on the first principal component of radial growth, which alone represents 56% of the variance in the correlation matrix. Correlation with monthly precipitation and temperature data for the study area indicates that all four species respond primarily to precipitation during the cool season of autumn and winter, October–May (r = 0.71, p < 0.01, 1931–2000), and to temperature primarily during the late spring and early summer, January–July (r 0 −0.67, p < 0.01, 1931–2000), in spite of differences in phylogeny and microsite conditions. The instrumental climate data for the region indicate that warmer conditions during the January–July season most relevant to radial growth are beginning to exceed the warmest episode of the 20th century in both intensity and duration. The strong negative correlation between temperature and tree growth indicates that these four conifer species may be challenged by the warmer temperatures forecast in the coming decades for the borderlands region due to anthropogenic forcing. This information could constitute a baseline to analyze the impact of climate change in other regions of Mexico and the USA, where conifer species are of great ecological and socioeconomical importance. [ABSTRACT FROM AUTHOR]

Some of the effects of climate change may be related to a change in patterns of rainfall intensity or scarcity. Therefore, humanity is facing environmental challenges due to an increase in the occurrence and intensity of droughts. The forecast of droughts can be of great help when trying to reduce the adverse effects that the scarcity of water brings, particularly in agriculture. When evaluating the conditions of water scarcity, as well as in the identification and characterization of droughts, the use of predictive models of drought indices could be a very useful tool. In this research, the utility of Artificial Neural Networks with exogenous inputs was tested, with the aim of predicting the monthly Standardized Precipitation Index in 4 regions (Semi-desert, Highlands, Canyons and Mountains) of north-central México using predictor data from 1979 to 2014. The best model was found using the scaled conjugate gradient backpropagation algorithm as the optimization method and was set to the following architecture: 6-25-1 network. The correlation coefficient of predicted and observed Standardized Precipitation Index values for the test dataset was between 0.84 and 0.95. As a result, the Artificial Neural Network models performed successfully in predicting Standardized Precipitation Index at the four analyzed regions. The developed and tested Artificial Neural Network models in this research suggest remarkable prediction abilities of the monthly Standardized Precipitation Index in the study region. [ABSTRACT FROM AUTHOR]

Assessing adaptive capacity to climate change is a complex task since it is a multidimensional component. There has been considerable discrepancy between the dimensions or elements that compose it. This study aimed to analyze the relevant dimensions and indicators that allow estimation of the adaptive capacity to climate change and to propose a set of indicators that will enable their application to assessment at the level of agricultural producers. A systematic review of scientific literature on evaluating or measuring adaptive capacity to climate change was carried out. Subsequently, the indicators were analyzed and selected through a coincidence analysis and were calibrated through a multicriteria evaluation with relevant actors in the southern Mexico, state of Chiapas. In total, 329 indicators were identified and analyzed. As a result, 19 indicators were selected and then grouped into six dimensions: economic resources, human resources, infrastructure for production and marketing, institutionality, social capital, and natural resources. These represent the 14 specific dimensions with the greatest potential to contribute to the estimation of adaptive capacity to climate change. The dimensions and indicators can be applied to assess the adaptive capacity of farmers in Mexico at a national or regional scale and specifically by producer types. [ABSTRACT FROM AUTHOR]

The lockdown measures implemented due to the SARS-CoV-2 pandemic to reduce the epidemic curve, in most cases, have had a positive impact on air quality indices. Our study describes the changes in the concentration levels of PM 2.5 and PM 10 during the lockdown and post-lockdown in Victoria, Mexico, considering the following periods: before the lockdown (BL) from 16 February to 14 March, during the lockdown (DL) from 15 March to 2 May, and in the partial lockdown (PL) from 3 May to 6 June. When comparing the DL period of 2019 and 2020, we document a reduction in the average concentration of PM 2.5 and PM 10 of −55.56% and −55.17%, respectively. Moreover, we note a decrease of −53.57% for PM 2.5 and −51.61% for PM 10 in the PL period. When contrasting the average concentration between the DL periods of 2020 and 2021, an increase of 91.67% for PM 2.5 and 100.00% for PM 10 was identified. Furthermore, in the PL periods of 2020 and 2021, an increase of 38.46% and 31.33% was observed for PM 2.5 and PM 10 , respectively. On the other hand, when comparing the concentrations of PM 2.5 in the three periods of 2020, we found a decrease between BL and DL of −50.00%, between BL and PL a decrease of −45.83%, and an increase of 8.33% between DL and PL. In the case of PM 10 , a decrease of −48.00% between BL and DL, −40.00% between BL and PL, and an increase of 15.38% between the DL and PL periods were observed. In addition, we performed a non-parametric statistical analysis, where a significant statistical difference was found between the DL-2020 and DL-2019 pairs ( x 2 = 1.204) and between the DL-2021 and DL-2019 pairs ( x 2 = 0.372), with a p < 0.000 for PM 2.5 , and the contrast between pairs of PM 10 (DL) showed a significant difference between all pairs with p < 0.01 . [ABSTRACT FROM AUTHOR]

This study presents experimental and theoretical results of winter dew harvest in México City in terms of condensation rate. A simplified theoretical model based on a steady-state energy balance on a radiator-condenser was fitted, as a function of the ambient temperature, the relative humidity and the wind velocity. A glass sheet and aluminum sheet white-painted were used as samples over the outdoor experiments. A good correlation was obtained between the theoretical and experimental data. The experimental results show that there was condensation in 68% of the winter nights on both condensers. The total winter condensed mass was 2977 g/m2 and 2888 g/m2 on the glass sheet and aluminum sheet white-painted, respectively. Thus, the condensed mass on the glass was only 3% higher than that on the painted surface. The maximum nightly dew harvests occurred during December, which linearly reduced from 50 g/m2 night to 22 g/m2 night as the winter months went by. The condensation occurred from 1:00 a.m. to 9:00 a.m., with maximum condensation rates between 6:00 a.m. and 7:00 a.m. The dew harvest can provide a partial alternative to the winter water shortage in certain locations with similar climates to the winter in Mexico City, as long as pollution is not significant. [ABSTRACT FROM AUTHOR]

Data from the annual, seasonal, and hourly behavior of the criteria air pollutants CO, NO2, SO2, O3, and PM10 in three Mexican metropolitan areas (the Mexico City Metropolitan Area (MCMA), Guadalajara Metropolitan Area (GMA), and Monterrey Metropolitan Area (MMA)) over the period 2000-2011 were analyzed; and compliance with Mexican air quality standards was evaluated, highlighting causes of specific episodes of high and low concentrations. Data analyzed were collected from automatic air-monitoring networks located in the MCMA (32 stations), GMA (8 stations), and MMA (5 stations). In the MCMA and MMA, correlations between wind direction and concentrations of SO2 suggest that there was a considerable contribution of trans-boundary transport from outside of these areas. Analysis of annual trends revealed large reductions of CO in the MCMA, and SO2 in the three metropolitan areas. However, the annual mean concentration of O2 increased by 47% and 42% in the GMA and MMA, respectively, from 2000 to 2011, but decreased by 13% in the MCMA from 2005 to 2010. The annual mean concentration of PM10 in the MMA was about 58% and 76% higher than that in the MCMA and GMA, respectively, from 2001 to 2010. [ABSTRACT FROM AUTHOR]

Land use land cover (LULC) significantly impacts local circulation in the Mexico Basin, particularly wind field circulations such as gap winds, convergence lines, and thermally induced upslope/downslope wind. A case study with a high-pressure system over the Mexico Basin isolates the influence of large-scale synoptic forcing. Numerical simulations reveal a wind system composed of meridional circulation and a zonal component. Thermal pressure gradients between the Mexico basin and its colder surroundings create near-surface convergence lines as part of the meridional circulation. Experiments show that the intensity and organization of meridional circulations and downslope winds increase when LULC changes from natural and cultivated land to urban. Zonal circulation exhibits a typical circulation pattern with the upslope flow and descending motion in the middle of the basin. Large values of moist static energy are near the surface where air parcels pick up energy from the surface either as fluxes of enthalpy or latent heat. Surface heat fluxes and stored energy in the ground are drivers of local circulation, which is more evident in zonal circulation patterns. [ABSTRACT FROM AUTHOR]

This work provides an assessment of the two most intense seasonal droughts that occurred over the Balsas River Basin (BRB) in the period 1980–2017. The detection of the drought events was performed using the 6 month scale standardized precipitation–evapotranspiration index (SPEI-6) and the 6 month standardized precipitation index (SPI-6) in October. Both indices were quite similar during the studied period, highlighting the larger contribution of precipitation deficits vs. temperature excess to the drought occurrence in the basin. The origin of the atmospheric water arriving to the BRB (1 May 1980–31 October 2017) was investigated by using a Lagrangian diagnosis method. The BRB receives moisture from the Caribbean Sea and the rest of the tropical Atlantic, the Gulf of Mexico, the eastern north Pacific and from three terrestrial evaporative sources: the region north of BRB, the south of BRB and the BRB itself. The terrestrial evaporative source of the BRB itself is by far the main moisture source. The two most intense drought events that occurred in the studied period were selected for further analysis. During the severe drought of 2005, the summertime sea surface temperature (SST) soared over the Caribbean Sea, extending eastward into a large swathe of tropical North Atlantic, which was accompanied by the record to date of hurricane activity. This heating generated a Rossby wave response with westward propagating anticyclonic/cyclonic gyres in the upper/lower troposphere. A cyclonic low-level circulation developed over the Gulf of Mexico and prevented the moisture from arriving to the BRB, with a consequent deficit in precipitation. Additionally, subsidence also prevented convection in most of the months of this drought period. During the extreme drought event of 1982, the Inter Tropical Convergence Zone (ITCZ) remained southern and stronger than the climatological mean over the eastern tropical Pacific, producing an intense regional Hadley circulation. The descent branch of this cell inhibited the development of convection over the BRB, although the moisture sources increased their contributions; however, these were bounded to the lower levels by a strong trade wind inversion. [ABSTRACT FROM AUTHOR]

Statistical time series forecasting is a useful tool for predicting air pollutant concentrations in urban areas, especially in emerging economies, where the capacity to implement comprehensive air quality models is limited. In this study, a general multiple regression with seasonal autoregressive moving average errors model was estimated and implemented to forecast maximum ozone concentrations with a short time resolution: overnight, morning, afternoon and evening. In contrast to a number of short-term air quality time series forecasting applications, the model was designed to explicitly include the effects of meteorological variables on the ozone level as exogenous variables. As the application location, the model was constructed with data from five monitoring stations in the Monterrey Metropolitan Area of Mexico. The results show that, together with structural stochastic components, meteorological parameters have a significant contribution for obtaining reliable forecasts. The resulting model is an interpretable, useful and easily implementable model for forecasting ozone maxima. Moreover, it proved to be consistent with the general dynamics of ozone formation and provides a suitable platform for forecasting, showing similar or better performance compared to models in other existing studies. [ABSTRACT FROM AUTHOR]

The seasonal and diurnal variability of the wind resource in Northern Mexico is examined. Fourteen weather stations were grouped according to the terrain morphology and weather systems that affect the region to evaluate the impact on wind ramps and high wind persistent events. Four areas driven by weather systems seasonality are identified. Wind power ramps and persistent generation events are produced by cold fronts in winter, while mesoscale convective systems and local circulations are dominant in summer. Moreover, the 2013 wind forecast of the Rapid Refresh Model (RAP) and the North American Mesoscale Forecast System (NAM) forecast systems were also assessed. In general, both systems have less ability to predict mesoscale events and local circulations over complex topography, underestimating strong winds and overestimating weak winds. Wind forecast variations in the mesoscale range are smoother than observations due to the effects of spatial and temporal averaging, producing fewer wind power ramps and longer lasting generation events. The study carried out shows the importance of evaluating operational models in terms of wind variability, wind power ramps and persistence events to improve the regional wind forecast. The characteristics of weather systems and topography of Mexico requires model refinements for proper management of the wind resource. [ABSTRACT FROM AUTHOR]

In atmospheric modeling, an accurate representation of land cover is required because such information impacts water and energy budgets and, consequently, the performance of models in simulating regional climate. This study analyzes the impact of the land cover data on an operational weather forecasting system using the Weather Research and Forecasting (WRF) model for central Mexico, with the aim of improving the quality of the operative forecast. Two experiments were conducted using different land cover datasets: a United States Geological Survey (USGS) map and an updated North American Land Change Monitoring System (NALCMS) map. The experiments were conducted as a daily 120 h forecast for each day of January, April, July, and September of 2012, and the near-surface temperature, wind speed, and hourly precipitation were analyzed. Both experiments were compared with observations from meteorological stations. The statistical analysis of this study showed that wind speed and near-surface temperature prediction may be further improved with the updated and more accurate NALCMS dataset, particularly in the forecast covering 48 to 72 h. The Root Mean Square Error (RMSE) of the average wind speed reached a maximum reduction of up to 1.2 m s−1, whereas for the near-surface temperature there was a reduction of up to 0.6 °C. The RMSE of the average hourly precipitation was very similar between both experiments, however the location of precipitation was modified. [ABSTRACT FROM AUTHOR]

Convective ensembles promise to increase forecast accuracy while at the same time providing information on the probability of the forecast. A vast number of different methods of ensemble creation have been developed over time. Here, initial conditions and model error uncertainties are represented by a convective-allowing ensemble with more than 50 members. The results are analyzed using one case study with relatively high precipitation over Ensenada, Baja California, Mexico. The ensemble members are perturbed using random initial perturbations, breeding, and the Stochastic Kinetic Energy Backscatter parameterization (SKEBS) within the Weather Research and Forecasting (WRF) model. The aim is to improve the high-resolution ensemble design provided in a previous study for the same region by maximizing the spread of an ensemble with low member count. To this end, a comparative analysis of the members is performed using perturbation growth rates and information entropy. In addition, a comparative verification is performed using observations from one automatic meteorological station and satellite-derived precipitation data. It was found that the growth rates and the one-dimensional power spectral density of the initial perturbation fields are clustered depending on each member's origin and the methods used to generate the breeding members. An inverse relationship was observed between these two variables, which can be useful for selecting appropriate initial condition perturbations. The dynamical injections of energy, introduced as perturbations to the numerical fields by the SKEBS method, were essential to maintain positive growth rates during the simulation period. Evaluation of the information entropy suggests that a selection of a set of members generated by the SKEBS method is best for increasing the ensemble spread while saving computer resources. [ABSTRACT FROM AUTHOR]

We examined the diurnal cycle of the rainfall in a coastal tropical mountainous region in central Veracruz State, Mexico (18°–21° N, 95.5°–98.5° W), featuring a striking topographic gradient running from sea level at the Gulf of Mexico coast to 5000 m above sea level (m.a.s.l.) in less than 100 km horizontal distance. During the summer, this unique location leads to regular the interaction between the easterly moisture inflow and the mountainous barrier. Over the complex terrain, forced ascent leads the occurrence of maximum rainfall during the afternoon (16–19 local time, LT ≈ 1½ hours ahead of solar time in summer), first along the slope and later over the coast. Along the coastal plain, the precipitation continues until the early morning consistent with there being convergence between land breezes and the trade winds. Observations obtained during a measurement campaign from 28 June to 3 July 2015, indicate that during the early evening downslope winds move against easterly flow, likely due to katabatic outflows previously observed over the region. These features are confirmed using spatial (0.88°) and temporal (30 min) resolution CMORPH rainfall estimates, since we observed evening episodes initiating along the slope during the afternoon (14–17 LT) moving later towards the coast. [ABSTRACT FROM AUTHOR]

In many countries of the world, floods continue to cause extensive damage to people and properties. This is also the case of Mexico, where meteorological phenomena cause flooding every year. In order to mitigate continuous losses and damages, crucial tools like hazard maps are essential for prevention. This review article analyzes the main reasons for the shortcomings on disasters caused by floods in Mexico. We argue that strong linking between the realm of technical hazard mappings and local governance as an integrated approach to manage disasters can be a basis for a new prevention policy in Mexico. This consideration is achieved through the description of the available information on the meteorological events that have caused major damage in recent years and the analysis of the interventions carried out at decision-making level by the government and the national civil protection system. The application that hazard maps have in the world and their usefulness is also discussed. From the analysis carried out it emerges that the inefficiency of the system in preventing damage caused by floods in Mexico is due to both the lack of high-level expertise in hazard maps design and the lack of their use in decision-making policies at the local level. [ABSTRACT FROM AUTHOR]

In this work, we report the economic impact of exposure to high ozone concentrations on four important crops in the area of influence of the Mexico City Megalopolis. Estimated yield losses were as follows: maize: 3%; oats: 26%; beans: 14%; sorghum: 15%. The information needed to estimate the impact of air pollution in Mexico is decidedly deficient. Regarding ozone, the coverage provided by the monitoring networks is strongly focused on urban monitoring and its consistency over time is highly irregular. Apart from the Mexico City Metropolitan Area (MCMA) and less than a handful of other cities, the quality of the data is poor. Ozone in rural areas can be estimated with air quality models. However, these models depend on a high-resolution emissions inventory, which has only been done through validation processes in the MCMA. With these limitations, we set out to estimate the economic impact of exposure to ozone in these crops with a varying degree of sensitivity to ozone in the city belt of Central Mexico. To this end, we developed a procedure that makes optimal use of the sparse information available for construction of AOT40 (accumulated exposure over the threshold of 40 ppb) exceedance maps for the 2011 growing season. We believe that, due to the way in which we dealt with the sparse information and the uncertainty regarding the available data, our findings lie on the safe side of having little knowledge such that they may be useful to decision-makers. We believe that this procedure can be extended to the rest of the country, and that it may be useful to developing countries with similar monitoring and modeling capacities. In addition, these impacts are not evenly distributed in the region and sometimes they were greater in municipalities that have a higher index of poverty. Air pollution arriving from urban areas increases the social inequalities to which these already vulnerable populations are exposed. [ABSTRACT FROM AUTHOR]