Sistema Regional de Información
en línea para Revistas Científicas de América Latina,
el Caribe, España y Portugal

We 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.

Wind power plants are vulnerable to abrupt weather changes caused by thunderstorms associated with lightning activity and accompanying severe wind gusts and rapid wind direction changes. Due to the damages that such phenomena may cause, the knowledge of the relationship between storm systems and the produced wind field is essential during the construction and operation phase of a plant. In the first part of this study, the relationship between severe wind gusts and lightning activity in a power plant in Greece is investigated. Wind data are measured at the wind turbines for a 3-year period (2012-2014); the corresponding lightning data come from the ZEUS lighting detection network. Wind gusts are well correlated to lightning strikes. This correlation is maximized during winter when well organized weather systems affect the area and minimized in summer as a result of local storms due to thermal instability. The second part of the study focuses on the development of an artificial neural network (ANN) model in order to forecast these two parameters in a 1-h ahead horizon based on wind speed, wind direction, and maximum observed wind gust measured at the nacelle of a wind turbine and four other variables, namely CAPE, TTI, wind speed at the 500 hPa isobaric level, and the 0-6 km vertical wind shear. The proposed model could be considered as a promising tool in simulating the occurrence both of wind gusts and lightning flashes, providing a relatively good evidence of the possibility of occurrence of such events.

This study uses the data of Indian Air Force (IAF) Lightning Detection System (LDS) network to prepare climatological plots of lightning over India and to formulate location-specific thunderstorm (TS) guidance for a total of 12 Indian airports. The analysis of climatological plots reveals that there is a distinct warm-season preponderance of lightning strikes over Indian subcontinent, with pre-monsoon months receiving the maximum lightning. The most probable time of occurrence being 12:00-14:00 UTC during all the seasons across the country. Location-specific TS guidance not only signifies the most probable direction of occurrence of TS with respect to the airport, but also clearly brings out the favorable direction of movement. Hence, the same can be judiciously used as nowcasting aid. Further, the characteristic features of lightning, like surges in flash rate, can be objectively used to define a predictor for nowcasting severe weather associated with a TS cloud. The study of these surges in lightning flash rate vis a vis the occurrence of strong surface winds (SSW) > 60 km h–1 over Delhi National Capital Region (NCR) (Hindan Airport observations), indicated that there is an increase in the number of lightning flashes prior to the occurrence of SSW. Within 45 min of their occurrence, 77.5% of SSW are preceded by surges in flash rates; however, the probability of detection of the event with a lead time of 15 to 45 min is around 71%.

Haze pollution, mainly characterized by low visibility, is one of the main environmental problems currently faced by China. Accurate haze forecasts facilitate the implementation of preventive measures to control the emission of air pollutants and thereby mitigate haze pollution. However, it is not easy to accurately predict low visibility events induced by haze, which requires not only accurate prediction for weather elements, but also refined and real-time updated source emission inventory. In order to obtain reliable forecasting tools, this paper studies the usability of several popular machine learning methods, such as support vector machine (SVM), k-nearest neighbor, and random forest, as well as several deep learning methods, on visibility forecasting. Starting from the main factors related to visibility, the relationships between wind speed, wind direction, temperature, humidity, and visibility are discussed. Training and forecasting were performed using the machine learning methods. The accuracy of these methods in visibility forecasting was confirmed through several parameters (i.e., root-mean-square error, mean absolute error, and mean absolute percentage error). The results show that: (1) among all meteorological parameters, wind speed was the best at reflecting the visibility change patterns; (2) long short-term memory recurrent neural networks (LSTM RNN), and gated recurrent unit (GRU) methods perform almost equally well on short-term visibility forecasts (i.e., 1, 3, and 6 h); (3) a classical machine learning method (i.e., the SVM) performs well in mid- and long-term visibility forecasts; (4) machine learning methods also have a certain degree of forecast accuracy even for long time periods (e.g., 7 2h).

A flow climatology was established for the Metropolitan Area of Querétaro (MAQ) in central Mexico, by analyzing four years (2014-2017) of back-trajectories generated using the HYSPLIT model. Two flow regimes were found: one from June until September (rainy regime); the other from December to May (dry regime). October and November were considered transition months. Northeasterly flows were present throughout the year; in contrast, trajectories from the southwest were much less frequent and observed mainly during the dry regime. An analysis of the wind fields from the North American Regional Reanalysis (NARR) database for a longer period of time (1979-2019) suggests that these results are representative of the average conditions of the atmosphere at the study site. Some of the northeasterly trajectories observed originate within a dessert region of the state of Querétaro, where several limestone mines are located. During the dry regime and transition months some clusters originate at the industrial area in Guanajuato, which includes the Salamanca refinery. As air transport of pollutants follow these paths, this analysis could be useful for identifying regional sources that affect the MAQ and possibly increase its air pollution load. In fact, the variability of criteria pollutants concentrations matched the flow regimes described above.

This paper analyzes the relation between COVID-19, air pollution, and public transport mobility in the Mexico City Metropolitan Area (MCMA). We test if the restrictions to economic activity introduced to mitigate the spread of COVID-19 are associated with a structural change in air pollution levels and public transport mobility. Our results show that mobility in public transportation was significantly reduced following the government’s recommendations. Nonetheless, we show that the reduction in mobility was not accompanied by a reduction in air pollution. Furthermore, Granger-causality tests show that the precedence relation between public transport mobility and air pollution disappeared as a product of the restrictions. Thus, our results suggest that air pollution in the MCMA seems primarily driven by industry and private car usage. In this regard, the government should redouble its efforts to develop policies to reduce industrial pollution and private car usage.

Mexican temperate forests are among the most biodiverse in the world. At present, they face anthropogenic pressures and climatic changes. Quercus candicans is a canopy-dominant, widely distributed species common in the moist habitats of these ecosystems. Its ecological importance, habitat vulnerability, and wide distribution make it a useful model of the vulnerability of Mexican tree forest species to climate change. We used ecological niche modeling to estimate future climatic suitability for this species and its potential range shifts under two emissions scenarios and three-time frames. We also identified areas where novel climates could arise and where predictions should be interpreted cautiously. Additionally, we analyzed how climatic suitability could change across the national protected areas system. In both emissions scenarios, areas with suitable climatic conditions were predicted to experience a net reduction of more than 40% by 2070. This corresponds to more than 100 000 km2 becoming climatically unsuitable. In the national protected areas, we forecast a contraction of approximately 30%. Climatic novelty increased considerably in the higher emissions scenario (RCP 8.5), accounting for 10% of the Mexican temperate mountains, compared to 1% on RCP 4.5. Areas of expansion of suitability not intersected by novel climates occur in areas highly affected by land-use change and other anthropogenic pressures. Effective protection of temperate forests’ tree species such as Q. candicans would need to allow migrations across altitudinal gradients, as areas of stability and expansion of climatic suitability are forecasted to occur at higher altitude sections of mountain ranges.

ABSTRACT There are few three-moment schemes that consider other processes besides sedimentation. Then, a performance assessment of these type of schemes due 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 rainshaft model was applied to 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 initial shape parameter of the gamma distribution, 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 more physically complete representation of the droplet size distribution evolution. 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 coalescence collision 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.

Validation of gridded precipitation products (GPP) increases the users’ confidence and highlights possible improvements in the algorithms to handle complex rain-forming processes. We evaluated the skill of three GGPs (CHIRPS-v2, CHELSA, and TerraClimate) in estimating the rain gauge observations and compared the precipitation trends derived from these products across the East and Southern Africa (ESA) region. We used Taylor diagrams and Kling-Gupta Efficiency (KGE) to assess the accuracy. A modified Mann-Kendal test and a Sen’s slope estimator were utilized to determine the trends’ significance and magnitude, respectively. The three GPPs had varied performance over temporal and altitudinal ranges. The skill of the three GPPs, at a monthly scale, was generally high but showed lower performance at elevations over 1500 masl, especially during the October-November-December (OND) season. The three GPPs performed equally well between the 1001 – 1500 masl elevation range. CHELSA-v2.1 was most accurate at 0-500 masl but had the lowest skill in both 501 – 1000 and above 1500 masl elevations, which caused over-estimation of the annual and seasonal precipitation trends over mountainous terrain and large inland water bodies. The quantified precipitation trends revealed high spatial-temporal variability. Generally, the skill and precipitation trends derived from CHIRPS-v2 and TC data showed substantial convergence except in Tanzania. Our results emphasize the importance of validating climate datasets to avoid error propagation in different models and applications. Moreover, we demonstrate that new or higher-resolution precipitation data are not always accurate since an algorithm update can introduce artifacts or biases.

Statistical and synoptic studies of spring dust storms over the Arabian Peninsula (AP) were performed using surface observations from 27 surface stations and meteorological data from the NCEP/NCAR reanalysis data set for the period 1978-2008. The study showed that, spatially, the northern and eastern AP are the regions most affected by dust storms and that, temporally, the study period can be divided into two subperiods: before and after 1995, with a pronounced increase before 1995 and a smooth increase (decrease) after 1995 with respect to dust (dust storms) types. The synoptic study reveals three main atmospheric systems: frontal systems over the northern region, Red Sea Trough ()-related systems over the western region, and thermal low systems over the eastern region. Additionally, the synoptic study shows that all the atmospheric systems are associated with a favorable pressure (geopotential) gradient area and that the shape and strength of the maximum wind and upper-layer atmospheric regimes are suitable for completely integrating the atmospheric layers. Moreover, the southern thermal low is a common synoptic component of dust-related atmospheric systems, but its effect is particularly pronounced in the atmospheric system of the eastern region.