Meteorological Situations Research Articles

Application of Severe Weather Nowcasting to Case Studies in Air Traffic Management

Effective and time-efficient aircraft assistance and guidance in severe weather environments remains a challenge for air traffic control. Air navigation service providers around the globe could greatly benefit from specific and adapted meteorological information for the controller position, helping to reduce the increased workload induced by adverse weather. The present work proposes a radar-based nowcasting algorithm providing compact meteorological information on convective weather near airports for introduction into the algorithms intended to assist in air-traffic management. The use of vertically integrated liquid density enables extremely rapid identification and short-term prediction of convective regions that should not be traversed by aircraft, which is an essential requirement for use in tactical controller support systems. The proposed tracking and nowcasting method facilitates the anticipation of the meteorological situation around an airport. Nowcasts of centroid locations of various approaching thunderstorms were compared with corresponding radar data, and centroid distances between nowcasted and observed storms were computed. The results were analyzed with Method for the Object-Based Evaluation from the Model Evaluation tools software (MET-10.0.1, Developmental Testbed Center, Boulder, CO, US) and later integrated into an assistance arrival manager software, showing the potential of this approach for automatic air traffic assistance in adverse weather scenarios.

Technical note: Improving the European air quality forecast of the Copernicus Atmosphere Monitoring Service using machine learning techniques

Abstract. Model output statistics (MOS) approaches relying on machine learning algorithms were applied to downscale regional air quality forecasts produced by CAMS (Copernicus Atmosphere Monitoring Service) at hundreds of monitoring sites across Europe. Besides the CAMS forecast, the predictors in the MOS typically include meteorological variables but also ancillary data. We explored first a “local” approach where specific models are trained at each site. An alternative “global” approach where a single model is trained with data from the whole geographical domain was also investigated. In both cases, local predictors are used for a given station in predictive mode. Because of its global nature, the latter approach can capture a variety of meteorological situations within a very short training period and is thereby more suited to cope with operational constraints in relation to the training of the MOS (frequent upgrades of the modelling system, addition of new monitoring sites). Both approaches have been implemented using a variety of machine learning algorithms: random forest, gradient boosting, and standard and regularized multi-linear models. The quality of the MOS predictions is evaluated in this work for four key pollutants, namely particulate matter (PM10 and PM2.5), ozone (O3) and nitrogen dioxide (NO2), according to scores based on the predictive errors and on the detection of pollution peaks (exceedances of the regulatory thresholds). Both the local and the global approaches significantly improve the performances of the raw ensemble forecast. The most important result of this study is that the global approach competes with and can even outperform the local approach in some cases. This global approach gives the best RMSE scores when relying on a random forest model for the prediction of daily mean, daily max and hourly concentrations. By contrast, it is the gradient boosting model which is better suited for the detection of exceedances of the European Union regulated threshold values for O3 and PM10.

Decision criteria for the classification of meteorological phenomena in the weather radar complex of the near-airfield zone

The increasing need to obtain data on the meteorological situation to ensure the safety of aircraft flight actualizes the development of radar systems for remote collection and processing of information, including for solving the problems of classifying dangerous weather phenomena. This determined the primary development of the domestic weather radar complex of the near airfield zone (WR BZ). The article presents the features of the construction of the WR BZ, as well as the main tasks it solves. To classify meteorological phenomena from cloudiness to squall in the weather radar complex of the near airfield zone, it is proposed to take as a basis the criteria tested by weather radar stations of previous generations. These criteria are based on an analysis of the height distribution of reflectivity, taking into account the vertical temperature profile. In addition, a criterion for classifying thunderstorms in the cold period of time has been additionally introduced in the WR BZ. To calibrate the values of the criteria, a mathematical apparatus and special software were developed. To collect statistical data, WR BZ were installed in various climatic regions: the Central and North-Western Federal Districts and the Republic of Crimea. Further, an effective validation of the information received was carried out. At present, WR BZ has passed preliminary, acceptance, certification tests, trial operation, while demonstrating an effective classification of meteorological phenomena, thanks to the correct selection of decision criteria. The article considers the possibility of increasing the reliability and justification of the classification of dangerous meteorological phenomena through the additional use of information on the distribution of altitudes of the specific rate of dissipation of turbulent energy of the atmosphere, as well as an additional set of statistical data in various climatic zones of the European territory of Russia – the Upper Volga Region and the Krasnodar Region.

A community outbreak of Legionnaires' disease caused by outdoor hot tubs for private use in a hotel.

During the period October-November 2017, an outbreak of Legionnaires' disease involving 27 cases occurred in the tourist area of Palmanova (Mallorca, Spain). The majority of cases were reported by the European Centre of Disease Prevention and Control (ECDC) as travel associated cases of Legionnaires' disease (TALD). Most cases belonged to different hotel cluster alerts. No cases were reported among the local population residing in the area. All tourist establishments associated with one or more TALD cases were inspected and sampled by public health inspectors. All relevant sources of aerosol emission detected were investigated and sampled. The absence of active cooling towers in the affected area was verified, by documents and on-site. Samples from hot tubs for private use located on the terraces of the penthouse rooms of a hotel in the area were included in the study. Extremely high concentrations (> 106 CFU/l) of Legionella pneumophila, including the outbreak strain, were found in the hot tubs of vacant rooms of this hotel thus identifying the probable source of infection. Meteorological situation may have contributed to the geographical distribution pattern of this outbreak. In conclusion, hot tubs for private use located outdoors should be considered when investigating community outbreaks of Legionnaires' disease of unclear origin.

An analogues-based forecasting system for Mediterranean marine-litter concentration

Abstract. In this work, we explore the performance of a statistical forecasting system for marine-litter concentration in the Mediterranean Sea. In particular, we assess the potential skills of a system based on the analogues method. The system uses a historical database of marine-litter concentration simulated by a high-resolution realistic model and is trained to identify meteorological situations in the past that are similar to the forecasted ones. Then, the corresponding marine-litter concentrations of the past analogue days are used to construct the marine-litter concentration forecast. Due to the scarcity of observations, the forecasting system has been validated against a synthetic reality (i.e., the outputs from a marine-litter-modeling system). Different approaches have been tested to refine the system, and the results show that using integral definitions for the similarity function, based on the history of the meteorological situation, improves the system performance. We also find that the system accuracy depends on the domain of application being better for larger regions. Also, the method performs well in capturing the spatial patterns but performs worse in capturing the temporal variability, especially the extreme values. Despite the inherent limitations of using a synthetic reality to validate the system, the results are promising, and the approach has potential to become a suitable cost-effective forecasting method for marine-litter concentration.

Melting Alpine Water Towers Aggravate Downstream Low Flows: A Stress‐Test Storyline Approach

AbstractDroughts can lead to extreme low flow situations in rivers, with resulting severe impacts. Upstream snow and ice melt in many of the world's mountain water towers can alleviate the hydrological consequences of drought, yet global warming threatens the cryosphere. To improve the understanding of melt water contributions during drought in the case of future glacier retreat, we developed stress‐test storyline scenarios to model streamflow and tested them in the European river Rhine basin. Meteorological conditions of past drought and low flow years in Europe, 1976, 2003, and 2018, were repeated at three future moments in time, representing nowadays, near future and far future conditions. The latter two conditions were obtained by climate projections under the RCP8.5 scenario. Results show that the low flow situations caused by the meteorological drought situations aggravate in future conditions, more so for the far future and for the year 2003 because of the relatively large glacier ice melt contribution in the past. Summer (July–September) streamflow may decline by 5%–25% far downstream and 30%–70% upstream and the duration of extreme low flow situations may double compared to the selected past drought events. These results are relevant for the Rhine as a major European river but stand exemplary for many other river basins and highlight the importance of cryospheric changes for downstream low flow situations in a changing climate. The stress‐test scenarios allow a glimpse into future extreme low flow events aiding adaptation planning, and might be adapted to include other important low flow drivers.

Construction of a geoecological map of dust particles transfer from the surface of the Shymkent lead (plumbum) factory dump

The study of horizontal migration of heavy metals is relevant for Shymkent city. In 1934, lead processing began, and sludge waste was stored on the territory of the lead factory. Ameliorative or other structures capable of reducing dust emissions have not been built around the lead dump, allowing lead dust to freely spread over long distances from the SPZ (sanitary protection zone). In this article, the horizontal migration of lead dust was studied based on climatic and calculated data by building a physical model; in addition, a geoecological map was built. This work has practical significance for further ecological and geochemical assessment of soils for the content of heavy metal concentrations, since it visually characterizes the spread of lead dust along the wind rose. To build a geoecological map of the transfer of impurities, we are faced with the task of determining the rate of settling of hovering particles and their distributions depending on the masses of particles and wind speed. To determine the rate of deposition of lead particles in the atmosphere, we need to study the shape of lead, background meteorological situation, particle diameter, solid particle density, medium density and dynamic viscosity of the medium. The removal of dust particles from the surfaces of ash and slag dumps of industrial facilities into the atmosphere under the influence of wind erosion, followed by their deposition on the soil, is one of the most common ways of polluting the territories of industrial facilities. As a result of the calculations, the surface concentration of dust particles was derived and a geoecological map was compiled. Keywords: geoecological map, lead dumps, assessment of dust emissions, horizontal migration of heavy metals, physical model of transport and scattering.

Possible Future Climate Change Impacts on the Meteorological and Hydrological Drought Characteristics in the Jinghe River Basin, China

Revealing the impact of future climate change on the characteristics and evolutionary patterns of meteorological and hydrological droughts and exploring the joint distribution characteristics of their drought characteristics are essential for drought early warning in the basin. In this study, we considered the Jinghe River Basin in the Loess Plateau as the research object. The standardized precipitation index (SPI) and standardized runoff index (SRI) series were used to represent meteorological drought and hydrological drought with monthly runoff generated by the SWAT model. In addition, the evolution laws of the JRB in the future based on Copula functions are discussed. The results showed that: (1) the meteorological drought and hydrological drought of the JRB displayed complex periodic change trends of drought and flood succession, and the evolution laws of meteorological drought and hydrological drought under different spatiotemporal scales and different scenario differ significantly. (2) In terms of the spatial range, the JRB meteorological and hydrological drought duration and severity gradually increased along with the increase in the time scale. (3) Based on the joint distribution model of the Copula function, the future meteorological drought situation in the JRB will be alleviated when compared with the historical period on the seasonal scale, but the hydrological drought situation is more serious. The findings can help policy-makers explore the correlation between meteorological drought and hydrological drought in the background of future climate change, as well as the early warning of hydrological drought.

Sound propagation for a low height impulsive source over an absorbing ground

Noise contours around military training areas are calculated by using: a) the measured source strength for the impulsive sources and b) the calculated sound propagation using a representative set of meteorological situations. For high-energy impulsive sounds the source strength is measured at distances beyond 100 meters, where peak levels are below 154 dB. A linear model is used to correct for the sound propagation from the source to the measurement position. In this way a linear source strength is determined that can be used with linear sound propagation models. Previous results showed that the source strengths at higher frequencies (above 250 to 500 Hz) were overestimated when the impulsive source is on the ground. Apparently, the calculated sound propagation over an absorbing ground, used to determine the source strength, is accounting for too much ground effect. Note that the actual meteorology and ground absorption are measured during the measurements. In this paper an impulsive reference source is used at two heights and the sound propagation is measured and calculated at increasing distances. It is shown that the measured ground effect saturates for higher frequencies. Next, an unknown impulsive source was measured and the effect of saturation was demonstrated.

Solar and Wind Quantity 24 h—Series Prediction Using PDE-Modular Models Gradually Developed according to Spatial Pattern Similarity

The design and implementation of efficient photovoltaic (PV) plants and wind farms require a precise analysis and definition of specifics in the region of interest. Reliable Artificial Intelligence (AI) models can recognize long-term spatial and temporal variability, including anomalies in solar and wind patterns, which are necessary to estimate the generation capacity and configuration parameters of PV panels and wind turbines. The proposed 24 h planning of renewable energy (RE) production involves an initial reassessment of the optimal day data records based on the spatial pattern similarity in the latest hours and their follow-up statistical AI learning. Conventional measurements comprise a larger territory to allow the development of robust models representing unsettled meteorological situations and their significant changes from a comprehensive aspect, which becomes essential in middle-term time horizons. Differential learning is a new unconventionally designed neurocomputing strategy that combines differentiated modules composed of selected binomial network nodes as the output sum. This approach, based on solutions of partial differential equations (PDEs) defined in selected nodes, enables us to comprise high uncertainty in nonlinear chaotic patterns, contingent upon RE local potential, without an undesirable reduction in data dimensionality. The form of back-produced modular compounds in PDE models is directly related to the complexity of large-scale data patterns used in training to avoid problem simplification. The preidentified day-sample series are reassessed secondary to the training applicability, one by one, to better characterize pattern progress. Applicable phase or frequency parameters (e.g., azimuth, temperature, radiation, etc.) are related to the amplitudes at each time to determine and solve particular node PDEs in a complex form of the periodic sine/cosine components. The proposed improvements contribute to better performance of the AI modular concept of PDE models, a cable to represent the dynamics of complex systems. The results are compared with the recent deep learning strategy. Both methods show a high approximation ability in radiation ramping events, often in PV power supply; moreover, differential learning provides more stable wind gust predictions without undesirable alterations in day errors, namely in over-break frontal fluctuations. Their day average percentage approximation of similarity correlation on real data is 87.8 and 88.1% in global radiation day-cycles and 46.7 and 36.3% in wind speed 24 h. series. A parametric C++ executable program with complete spatial metadata records for one month is available for free to enable another comparative evaluation of the conducted experiments.

РАЗРАБОТКА МИКРОПОЛОСКОВОЙ РЕШЕТКИ С НАКЛОННЫМ РАСКРЫВОМ И СНИЖЕННЫМ ЧИСЛОМ ЭЛЕМЕНТОВ РЕГУЛИРОВКИ АМПЛИТУДНО-ФАЗОВОГО РАСПРЕДЕЛЕНИЯ ДЛЯ МАЛОВЫСОТНОЙ МЕТЕОНАВИГАЦИИ БПЛА В АРКТИКЕ

Problem statement. The development of hard-to-reach regions in the absence of ground-based navigation systems and data sources on the meteorological situation can cause an increase in the number of aviation accidents. For small Arctic aviation, there is a need to introduce onboard radar stations that solve various tasks: from ensuring flight safety to analyzing the situation at sea in the area of the escorted vessel. In low-altitude flight conditions, re-reflections from the underlying surface coming along the side lobes of the antenna pattern lead to the identification of false areas dangerous for flight. The paper searches for the requirements for a directional pattern focused on solving the problem of meteorological navigation in polar latitudes. The stages of the development of an antenna with an inclined opening and a deflected main beam of the radiation pattern, which guarantees a very low level of lateral radiation in the lower hemisphere, are presented. Research methods. The mathematical apparatus of the theory of antenna arrays and detection of surface and volume extended radar targets was actively used in the work. Purpose. To substantiate the prospects of using antenna arrays with an inclined opening and a beam to solve meteorological navigation problems. To outline the main stages of the synthesis of a patch antenna, which is characterized by a low level of lateral radiation in the lower hemisphere. Results. The results of modeling are presented, which allow us to obtain requirements for the parameters of antenna systems for meteorological navigation onboard radars operating in polar latitudes. The results of a comparative analysis of an inclined patch antenna array and an antenna array with an opening slope and an inclination of the beam of the opposite sign to reduce the level of lateral radiation are presented. Practical significance. The results of the work can be used in determining the requirements for radiation characteristics, as well as in the development of original designs of patch antenna arrays of reduced cost for small-sized airborne radars of low-altitude carriers in order to increase the probability of correct detection of dangerous weather formations for flight.

Validation of the Aeolus L2B wind product with airborne wind lidar measurements in the polar North Atlantic region and in the tropics

Abstract. During the first 3 years of the European Space Agency's Aeolus mission, the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt, DLR) performed four airborne campaigns deploying two different Doppler wind lidars (DWL) on board the DLR Falcon aircraft, aiming to validate the quality of the recent Aeolus Level 2B (L2B) wind data product (processor baseline 11 and 12). The first two campaigns, WindVal III (November–December 2018) and AVATAR-E (Aeolus Validation Through Airborne Lidars in Europe, May and June 2019), were conducted in Europe and provided first insights into the data quality at the beginning of the mission phase. The two later campaigns, AVATAR-I (Aeolus Validation Through Airborne Lidars in Iceland) and AVATAR-T (Aeolus Validation Through Airborne Lidars in the Tropics), were performed in regions of particular interest for the Aeolus validation: AVATAR-I was conducted from Keflavik, Iceland, between 9 September and 1 October 2019 to sample the high wind speeds in the vicinity of the polar jet stream; AVATAR-T was carried out from Sal, Cape Verde, between 6 and 28 September 2021 to measure winds in the Saharan dust-laden African easterly jet. Altogether, 10 Aeolus underflights were performed during AVATAR-I and 11 underflights during AVATAR-T, covering about 8000 and 11 000 km along the Aeolus measurement track, respectively. Based on these collocated measurements, statistical comparisons of Aeolus data with the reference lidar (2 µm DWL) as well as with in situ measurements by the Falcon were performed to determine the systematic and random errors of Rayleigh-clear and Mie-cloudy winds that are contained in the Aeolus L2B product. It is demonstrated that the systematic error almost fulfills the mission requirement of being below 0.7 m s−1 for both Rayleigh-clear and Mie-cloudy winds. The random error is shown to vary between 5.5 and 7.1 m s−1 for Rayleigh-clear winds and is thus larger than specified (2.5 m s−1), whereas it is close to the specifications for Mie-cloudy winds (2.7 to 2.9 m s−1). In addition, the dependency of the systematic and random errors on the actual wind speed, the geolocation, the scattering ratio, and the time difference between 2 µm DWL observation and satellite overflight is investigated and discussed. Thus, this work contributes to the characterization of the Aeolus data quality in different meteorological situations and allows one to investigate wind retrieval algorithm improvements for reprocessed Aeolus data sets.

Drought Analysis and Impact of Drought on Crop Yield in Nevşehir City

Turkey is one of the most sensitive regions to climate change. Drought events triggered by climate change occur frequently in the region and cause important environmental problems. The agricultural sector and crop yield are among the areas that are adversely affected. In this study, meteorological and hydrological drought situation and precipitation trend is studied with SPI and SPEI methods by using Nevşehir Center and Ürgüp stations. Besides, the severity and duration of long-lasting strong droughts in the region are studied. The most important negative effects of drought events are seen in the agricultural sector on crop production. In order to analyze this effect, the yield values of agricultural products grown in the region were obtained from Turkish Statistical Institute (TUIK) and standardized with Z-Score method. Afterwards, the impact of drought events on the yield of agricultural products grown in the region are examined

Assimilation of Water Vapor Retrieved from Radar Reflectivity Data through the Bayesian Method

This work describes the implementation of an updated radar reflectivity assimilation scheme with the three-dimensional variational (3D-Var) system of Weather Research and Forecast (WRF). The updated scheme, instead of the original scheme assuming the relative humidity to a fixed value where radar reflectivity is higher than a threshold, assimilates pseudo water vapor retrieved by the Bayesian method, which would be consistent with clouds/precipitations provided by the model in theory. To verify the effect of the updated scheme to the improvement of precipitation simulation, a convective case in Wenquan County and the continuous monthly simulation with contrasting experiments in Xinjiang were performed. The test of single reflectivity observation demonstrates that the water vapor retrieved by the Bayesian method is consistent with the meteorological situation around. In the convective case, both the updated and original scheme results show that the assimilation of pseudo water vapor can adjust to the environmental conditions of water vapor and temperature. This can improve the hourly precipitation forecast skill more than the contrasting experiment, which was designed to only assimilate conventional observations and radar radial velocity data. In the continuous monthly experiments, the updated scheme reveals that the analysis of water vapor is more reasonable, and obtains a better precipitation forecast skill for 6 h accumulated precipitation than the contrasting experiments.

Towards Ensemble-Based Kilometer-Scale Climate Simulations over the Third Pole Region

The Tibetan Plateau and its surrounding mountains have an average elevation of 4,400 m and a glaciated area of $$\sim $$ 100,000 $$\hbox {km}^{2}$$ giving it the name “Third Pole (TP) region”. The TP is the headwater of many major rivers in Asia that provide fresh water to hundreds of millions of people. Climate change is altering the energy and water cycle of the TP at a record pace but the future of this region is highly uncertain due to major challenges in simulating weather and climate processes in this complex area. The Convection-Permitting Third Pole (CPTP) project is a Coordinated Regional Downscaling Experiment (CORDEX) Flagship Pilot Study (FPS) that aims to revolutionize our understanding of climate change impacts on the TP through ensemble-based, kilometer-scale climate modeling. Here we present the experimental design and first results from multi-model, multi-physics ensemble simulations of three case studies. The five participating modeling systems show high performance across a range of meteorological situations and are close to having ”observational quality” in simulating precipitation and near-surface temperature. This is partly due to the large differences between observational datasets in this region, which are the leading source of uncertainty in model evaluations. However, a systematic cold bias above 2000 m exists in most modeling systems. Model physics sensitivity tests performed with the Weather Research and Forecasting (WRF) model show that planetary boundary layer (PBL) physics and microphysics contribute equally to model uncertainties. Additionally, larger domains result in better model performance. We conclude by describing high-priority research needs and the next steps in the CPTP project.

Wind speed and global radiation forecasting based on differential, deep and stochastic machine learning of patterns in 2-level historical meteo-quantity sets

AbstractAccurate forecasting of wind speed and solar radiation can help operators of wind farms and Photo-Voltaic (PV) plants prepare efficient and practicable production plans to balance the supply with demand in the generation and consumption of Renewable Energy (RE). Reliable Artificial Intelligence (AI) forecast models can minimize the effect of wind and solar power fluctuations, eliminating their intermittent character in system dispatch planning and utilization. Intelligent wind and solar energy management is essential in load scheduling and decision-making processes to meet user requirements. The proposed 24-h prediction schemes involve the beginning detection and secondary similarity re-evaluation of optimal day-data sequences, which is a notable incremental improvement against state-of-the-art in the consequent application of statistical AI learning. 2-level altitude measurements allow the identification of data relationships between two surface layers (hill and lowland) and adequate interpretation of various meteorological situations, whose differentiate information is used by AI models to recognize upcoming changes in the mid-term day horizon. Observations at two professional meteorological stations comprise specific quantities, of which the most valuable are automatically selected as input for the day model. Differential learning is a novel designed unconventional neurocomputing approach that combines derivative components produced in selected network nodes in the weighted modular output. The complexity of the node-stepwise composed model corresponds to the patterns included in the training data. It allows for representation of high uncertain and nonlinear dynamic systems, dependent on local RE production, not substantially reducing the input vector dimensionality leading to model over simplifications as standard AI does. Available angular and frequency time data (e.g., wind direction, humidity, and irradiation cycles) are combined with the amplitudes to solve reduced Partial Differential Equations (PDEs), defined in network nodes, in the periodical complex form. This is a substantial improvement over the previous publication design. The comparative results show better efficiency and reliability of differential learning in representing the modular uncertainty and PDE dynamics of patterns on a day horizon, taking into account recent deep and stochastic learning. A free available C++ parametric software together with the processed meteo-data sets allow additional comparisons with the presented model results.

Does the meteorological origin of heat waves influence their impact on health? A 6-year morbidity and mortality study in Madrid (Spain)

BackgroundIn Spain, two synoptic-scale conditions influence heat wave formation. The first involves advection of warm and dry air masses carrying dust of Saharan origin (North African Dust (NAF) = 1). The second entails anticyclonic stagnation with high insolation and stability (NAF) = 0). Some studies show that the meteorological origin of these heat waves may affect their impact on morbidity and mortality. ObjectiveTo determine whether the impact of heat waves on health outcomes in Madrid (Spain) during 2013–2018 varied by synoptic-scale condition. MethodologyOutcome data consist of daily mortality and daily hospital emergency admissions (morbidity) for natural, circulatory, and respiratory causes. Predictors include daily maximum and minimum temperatures and daily mean concentrations of NO2, PM10, PM2.5, NO2, and O3. Analyses adjust for insolation, relative humidity, and wind speed. Generalized linear models were performed with Poisson link between the variables controlling for trend, seasonality, and auto-regression in the series. Relative Risks (RR) and Attributable Risks (AR) were determined. The RRs for mortality attributable to high temperatures were similar regardless of NAF status. For hospital admissions, however, the RRs for hot days with NAF = 0 are higher than for days with NAF = 1. We also found that atmospheric pollutants worsen morbidity and mortality, especially PM10 concentrations when NAF = 1 and O3 concentrations when NAF = 0. ResultsThe effect of heat waves on morbidity and mortality depends on the synoptic situation. The impact is greater under anticyclonic stagnation conditions than under Saharan dust advection. Further, the health impact of pollutants such as PM10 and O3 varies according to the synoptic situation. ConclusionsBased on these findings, we strongly recommend prevention plans to include data on the meteorological situation originating the heat wave, on a synoptic-scale, as well as comprehensive preventive measures against the compounding effect of high temperatures and pollution.

Improvement of the vertical mixing in chemistry transport modeling based on a 1.5-order turbulence kinetic energy-based eddy diffusivity closure scheme

In this study, firstly, three first-order local closure vertical diffusion schemes used in multiple mesoscale chemical transport models (CTM) were embedded in the CHIMERE CTM model and tested over a one-year simulation covering the whole France. Three model configurations present a fair reproduction of pollutant concentrations both in urban and rural areas, indicating it is an effective way to reproduce the dispersion of pollutants in chemistry transport modeling. However, it cannot be expected to significantly improve the vertical mixing under the first-order closure scheme. Secondly, a 1.5-order turbulence kinetic energy-based eddy diffusivity closure scheme called as the new eddy diffusion (NED) is implemented in CHIMERE to describe more realistic diffusion processes near the surface. A fifteen-day simulation encompassing a winter pollution episode was performed for three major cities with horizontal resolution of 1.67 km and the first layer height at 12 m, respectively. The NED scheme improved NO2 simulations at most urban sites compared to the initial Kz diffusion scheme (IKD). Taking the root mean square error as evaluation criteria, the average improvements are 18.8%, 24.5% and 9.5% for NO2 simualtion in Paris, Lyon and Bordeaux respectively. For the model performance of PM2.5 and PM10 simulations in the urban areas of Paris, the improvements are 13.5% and 19.1%, respectively. Overall, preliminary outcomes of this study are encouraging. The simulation with more sophisticated and realistic eddy viscosities are better than for IKD that is widely used in CTMs, but we need to realize that this is only a fifteen-day simulation for three cities, for further research, longer periods are needed with a greater variety of meteorological situations to prove the universality of the NED scheme.

Cleaner energy for sustainable future using hybrid photovoltaics-thermoelectric generators system under non-static conditions using machine learning based control technique

In addition to the load demand, the temperature difference between the hot and cold sides of the thermoelectric generator (TEG) module determines the output power for thermoelectric generator systems. Maximum power point tracking (MPPT) control is needed to track the optimal global power point as operating conditions change. The growing use of electricity and the decline in the use of fossil fuels have sparked interest in photovoltaic-TEG system utilization in the energy sector. Thermoelectric generation systems are meant to recover waste heat as a green energy supply. Concentrated solar can overcome the drawbacks of inefficient power generation. The feasibility of employing a machine learning and metaheuristic-based control strategy to yield maximum power from a hybrid photovoltaic and thermoelectric generator system under various operating situations is examined in this study. The output of both TEG and PV modules is affected by the environment; PV panels create heat as a result of shade and wind speed. Maximum energy harvesting of PV-TEG under non-uniform temperature settings is proposed in this paper using a feed-forward neural network (FFNN) trained by a squirrel search optimization (SQS). TEG systems have several local maxima due to this non-uniform state. MPPT algorithms based on gradients are unlikely to discover actual GMPP in the majority of cases. The unique SQSFFNN is evaluated under non-uniform temperature distribution and variable load and temperature circumstances as a possible answer to this non-linear issue. Certain advances are made in this study by addressing concerns of global maximum power point tracking with non-uniform temperature distribution, low efficiency, higher settling and tracking time, and oscillations. Particle swarm optimization, Cuckoo search optimization (CSA), CSA-FFNN, and grey wolf optimization algorithms are compared to the outcomes. Four experiments are carried out under various meteorological situations. Experiments and MATLAB/SIMULINK are used to validate and prove the results. The experimental results, comparisons with existing techniques, and statistical data show that the suggested SQDFFNN technique achieves a greater performance, distinguishing PV-TEG as a cleaner source of electrical power generation.

Spatial assessment of the inland excess water presence on subsurface drained areas in the Körös Interfluve (Hungary)

Due to extreme meteorological and soil hydrological situations the agricultural production security is highly unpredictable. To release the extent and duration of inland excess water (IEW) inundations or two-phase soil conditions during the period intended for cultivation, subsurface drainage (SD) has been used as a best practice in several countries. SD interventions took place between 1960’s and 1990 in Hungary. After 1989, land ownership conditions changed, thus professional operation and the necessary maintenance of the SD networks designed as a complex system became insignificant. In this paper, our aim was to present the IEW hazard in one of the most equipped areas by SD in Hungary. The occurrence frequency of IEW inundations in drained and non-drained (control) areas in different time intervals were compared. According to our results, we could state that the frequency of IEW on the subsurface drained areas was moderately lower in only a few periods compared to the control areas. IEW hazard of the arable areas at the Körös Interfluve was classified as nonhazarded in 52.7% of the area. Another 38.2% were moderately hazarded, 8.26% of the lands were meanly hazarded and less than 1% were highly hazarded area by IEW.