Meteorological Situations Research Articles

Summer daily dynamics of the vertical structure of atmospheric aerosol over Baikal coastal area from ground-based lidar measurements

ABSTRACT The spatial structure of atmospheric aerosol over Baikal in summer is studied on a regular basis with the use of ground-based LOSA-M2 lidar measurements at the Boyarsky scientific station of the Institute of Physical Material Science, Siberian Branch, Russian Academy of Sciences (51.83°N, 106.06°E, 456 m a.s.l.), Republic of Buryatia, Russia. The lake is located in a basin surrounded by mountain ranges on all side; its water volume is large. The lidar data are analysed along with the regional meteorological situation and meteorological parameters of the atmosphere. The daily dynamics of aerosol distribution over the atmospheric is examined for the period from 2015 to 2023 accounting the effect of different air masses in the region. Three main typical air circulation types are identified for the coastal zone of southern Baikal in summer, which determine the atmospheric aerosol generation and transport. The first and most common type relates to breeze circulation, where the main changes in the spatial structure of aerosol occur in the 2–3-km layer and are determined by changes in the transport direction within a breeze cell during the day. The main feature of this type is a decrease in the altitude of an air layer which contains most of aerosol in the lower atmosphere up to 1 km. The second type refers to southwestern transport under the presence of an anticyclone over the region. The maximal altitude of the aerosol layers attains 7 km in this case. The most complex and dynamic changes in the daily spatial structure of atmospheric aerosol occurred under transport of cyclones and associated atmospheric fronts over Lake Baikal. Under this third type, the altitude of the aerosol layers decreases throughout the day within the altitude range from the surface to 3–4 km.

Observed flash drought to persist in future over southern Africa

Southern Africa has experienced multiple occurrences of drought episodes, which is projected to persist in the future, considering all climate scenarios. Despite the documented change in a meteorological, agricultural, and hydrological drought situation in the region, few studies are yet to explore the changes in flash drought (hereafter; FD), which is characterized by a rapid reduction in root-zone soil moisture and more substantial intensification in few days to weeks. Here, we analyze the observed FD and related underlying drivers during the past 34 years. Also, we estimate the future changes in FD using the severity-duration magnitude matrix under the middle of the road (SSP2-4.5) and business as usual (SSP5-8.5), scenario. Lastly, the study investigates the role of anthropogenic warming using the fraction of attributable risk (FAR) approach and possible bivariate return periods of FD events. Our results demonstrate that the region has experienced multiple occurrences up to 72 pentads from 1980 to 2014. Underlying mechanisms revealed the compounding influence of Vapor Pressure Deficit (VPD), Potential Evapotranspiration (PET), and precipitation deficit that have a significant impact on the abrupt onset and rapid intensification of FD events and other hot extremes over the SAF region. Under a high emission scenario, the region will experience FD duration lasting for 30 days with >40 % severity projected to impact the region. Anthropogenic climate change and land use and land cover changes remain the most dominant drivers altering the FD events over the SAF region. The return period of FD events under the SSP5-8.5 scenario shows that the SAF region will witness multiple FD events of up to 80 pentads in the far future. These findings reinforce the need to limit the emission of greenhouse gases. Sustained warming of the climate will exacerbate the extreme events and other compounding factors, thus affecting the livelihoods of humans and life-supporting strata.

Deep causal inference for understanding the impact of meteorological variations on traffic

Understanding the causal impact of meteorological variations on traffic conditions (e.g., traffic flow and speed) is crucial for effective traffic prediction and management, as well as the mitigation of adverse weather effects on traffic. However, many existing studies focused on establishing associations between meteorological situations and traffic, rather than delving into causal relationships, especially with deep learning techniques. Consequently, the ability to identify specific meteorological conditions that significantly contribute to traffic congestion or delays is still limited. To address this issue, this study proposes the Meteorological-Traffic Causal Inference Variational Auto-Encoder Model (MT-CIVAE) to estimate the causal impact of fine-grained meteorological variations (e.g., rain and temperature) on traffic. Specifically, MT-CIVAE is based on the Variational Auto-Encoder and consists of an encoder to recover the distribution of latent confounders and a decoder to estimate the conditional probabilities of treatments. Transformer encoder layers are incorporated to analyze the spatial and temporal correlations of historical traffic data to further enhance the inference capability. To evaluate the effectiveness of the proposed approach for causal inference, real-world traffic flow and speed datasets collected from California, along with corresponding fine-grained meteorological datasets, are employed. The counterfactual analysis is conducted using artificially generated meteorological conditions as treatments, which allows for the simulation of hypothetical meteorological scenarios and the evaluation of their potential impact on traffic conditions. This study develops deep learning methods for assessing the causal impact of meteorological variations on traffic dynamics, offering explanations and insights that can assist transportation institutions in guiding post-meteorology traffic management strategies.

Current status and prospects of research on safety situation awareness of high speed railway operation environment

PurposeThe safety of high-speed rail operation environments is an important guarantee for the safe operation of high-speed rail. The operating environment of the high-speed rail is complex, and the main factors affecting the safety of high-speed rail operating environment include meteorological disasters, perimeter intrusion and external environmental hazards. The purpose of the paper is to elaborate on the current research status and team research progress on the perception of safety situation in high-speed rail operation environment and to propose directions for further research in the future.Design/methodology/approachIn terms of the mechanism and spatio-temporal evolution law of the main influencing factors on the safety of high-speed rail operation environments, the research status is elaborated, and the latest research progress and achievements of the team are introduced. This paper elaborates on the research status and introduces the latest research progress and achievements of the team in terms of meteorological, perimeter and external environmental situation perception methods for high-speed rail operation.FindingsBased on the technical route of “situational awareness evaluation warning active control,” a technical system for monitoring the safety of high-speed train operation environments has been formed. Relevant theoretical and technical research and application have been carried out around the impact of meteorological disasters, perimeter intrusion and the external environment on high-speed rail safety. These works strongly support the improvement of China’s railway environmental safety guarantee technology.Originality/valueWith the operation of CR450 high-speed trains with a speed of 400 km per hour and the application of high-speed train autonomous driving technology in the future, new and higher requirements have been put forward for the safety of high-speed rail operation environments. The following five aspects of work are urgently needed: (1) Research the single factor disaster mechanism of wind, rain, snow, lightning, etc. for high-speed railways with a speed of 400 kms per hour, and based on this, study the evolution characteristics of multiple safety factors and the correlation between the high-speed driving safety environment, revealing the coupling disaster mechanism of multiple influencing factors; (2) Research covers multi-source data fusion methods and associated features such as disaster monitoring data, meteorological information, route characteristics and terrain and landforms, studying the spatio-temporal evolution laws of meteorological disasters, perimeter intrusions and external environmental hazards; (3) In terms of meteorological disaster situation awareness, research high-precision prediction methods for meteorological information time series along high-speed rail lines and study the realization of small-scale real-time dynamic and accurate prediction of meteorological disasters along high-speed rail lines; (4) In terms of perimeter intrusion, research a multi-modal fusion perception method for typical scenarios of high-speed rail operation in all time, all weather and all coverage and combine artificial intelligence technology to achieve comprehensive and accurate perception of perimeter security risks along the high-speed rail line and (5) In terms of external environment, based on the existing general network framework for change detection, we will carry out research on change detection and algorithms in the surrounding environment of high-speed rail.

Analysis of informativeness of features of classification of dangerous weather events based on radar observation results

One of the crucial factors affecting the safety and regularity of state and civil aviation flights is the meteorological situation. The European territory of Russia is most characterized by dangerous meteorological phenomena associated with cumulonimbus clouds: shower, thunderstorms, hail, accompanied by high atmospheric turbulence. Currently, meteorological radar stations are an indispensable source of information about the weather situation for air transport. The criteria for the classification of meteorological phenomena used in modern radar stations are formed for each event separately and are based on knowledge only about the altitude distribution of radar reflectivity and air temperature, despite the fact that radar data assess the wind characteristics of the atmosphere. It is shown that optimization of the classification criteria for the mentioned meteorological phenomena should be realized by generalization of the criteria and their construction in accordance with the theory of statistical hypothesis distinction, as well as by additional use of information on atmospheric turbulence. Based on the analysis of radar signals reflected from the meteorological events of shower, thunderstorm, and hail, probability distributions of reflectivity and specific dissipation rate of turbulent energy were obtained. Statistical analysis of probability distribution densities was carried out for: the maximum value of reflectivity Zmax, its dependence on height H(Zmax), as well as the maximum specific dissipation rate of turbulent energy EDRmax and the value H(EDRmax). The classification criterion based on the maximum probability functional was chosen to determine the structure of classification algorithms and decision rules. At the same time under the acceptable confidence is accepted the value of the probability of correct classification not lower than 0.8. For the accepted criterion the decision thresholds are constructed and the complete matrices of classification probabilities are calculated. The results of calculations showed that the worst informativeness in the classification of dangerous meteorological events of cumulonimbus cloudiness have parameters H(Zmax), H(EDRmax). Parameters Zmax, EDRmax have greater separating ability, but even for them the confidence of classification is unacceptable. In the article to increase the confidence of classification the joint use of features in the form of multivariate probability distribution densities of information parameters was applied. The best results are achieved when three p(Zmax,H(Zmax),EDRmax) and four p(Zmax,H(Zmax),EDRmax,H(EDRmax)) features are used. In the probability matrices for these cases, the maximum and acceptable at 0.8 level of probabilities of correct classification are achieved. Thus, the expansion of the feature space due to atmospheric turbulence is justified in the problem under consideration. These results will be refined with increasing observation time and will vary for different climatic zones. In general, the decision thresholds for classifying dangerous meteorological events of cumulonimbus cloudiness should be adaptive.

Improving Error Estimates for Evaluating Satellite-Based Atmospheric CO2 Measurement Concepts through Numerical Simulations

To assess the accuracy of satellite monitoring of anthropogenic CO2 emissions, inversions of satellite data in SWIR are usually combined with the assimilation of the total CO2 column into a Kalman filter that reconstructs the sources and sinks of atmospheric CO2. To provide error estimates of the total CO2 column for multi-month assimilation experiments of simulated satellite data, we parametrise these errors using linear regressions. These regression are obtained from a database that links meteorological situations, albedos, and aerosols to the errors in the inversion of the total CO2 column based on simulated satellite data for those conditions. The errors in this database are explicitly computed using the Bayesian estimation formalism, and the linear regressions are optimised by selecting appropriate predictors and predictants. For different levels of measurement noise, error simulations are performed over a period of several months using the albedo and aerosol data from MODIS.

Assessment of flight range and transmission of video information when monitoring emergency situations from an unmanned aircraft in difficult meteorological conditions

The purpose of research. Modern unmanned aerial vehicles of various types provide the ability to perform various tasks of operational information collection, monitoring the state of the environment, technological facilities and territories, updating data on these facilities, as well as for reconnaissance and monitoring their condition. The purpose of the study is to establish the maximum flight range and distance necessary for the organization of a communication channel through which the video stream and flight control commands using the KAM-16 Turbo ¾ method will be transmitted between the ground control complex and the unmanned aerial vehicle in a difficult meteorological situation.Methods are based on the basics of radio electronics, diagnostics and forecasting of the technical condition of aircraft. The methods of multicriteria analysis, parametric and structural synthesis were used. The principles of transmitting video images from drones used to monitor emergency situations were studied. A critical assessment of the maximum flight range of an unmanned aerial vehicle in a difficult meteorological situation was carried out.Results. Analytical formulas for determining the energy potential of a data transmission channel in line of sight are presented. Graphs of the dependence of the energy reserve in the data transmission channel between the unmanned aerial vehicle and the ground control system are presented, which make it possible to determine the maximum distance for transmitting video images in Full HD quality in the 2.4 GHz band. Calculations were carried out on the basis of complex analytical expressions and graphical dependences of the flight range of a micro UAV on the speed of tailwind and headwind were constructed. The maximum headwind speed is calculated, which does not allow the use of micro UAVs in blizzard conditions.Conclusion. A promising area of study of the use of UAV in emergency monitoring are micro-class copters capable of detecting victims by transmitting video information from a thermal imager during search and rescue operations in difficult weather conditions, such as snowstorms.

Investigating meteorological wet and dry transitions in the Dutch Meuse River basin

The Netherlands has traditionally focused on managing flood risk. However, the frequent occurrence of droughts in recent years has brought attention to managing both extremes. Transitions between these opposite extremes pose additional challenges to water management, requiring a trade-off between water storage during dry periods and flood control during wet periods. In this study, we develop a framework to define wet and dry meteorological events and study their transitions using timeseries of meteorological data namely, precipitation, temperature and potential evapotranspiration. The magnitudes of event characteristics are retained, which presents a different approach to the normalized climate indices (like the Standardized Precipitation Index) commonly used in literature. We apply this framework to the Dutch part of the Meuse River basin in northwestern Europe using climate observations between 1951 and 2022. Our analysis shows a statistically significant increase in the amount of water lost from potential evapotranspiration compared to water gained from precipitation between April and September of the water year and an increase in the length of this drying period over the past decades. Such trends in the drying period are related to variability in potential evapotranspiration caused by rising temperatures in the region, indicating the potential for increased water shortage in Spring and Summer due to future temperature increases. We also identify abrupt transitions between opposite extreme events where there is a lack of water at the end of the second event as meteorological situations that challenge water management due to overlapping impacts like flash flooding, less time for water storage, and reduced water availability. We see such conditions occur in 6% of the wet-dry transitions and 20% of the dry-wet transitions, highlighting meteorological scenarios to which the hydrological response of the catchment can be simulated to increase our understanding of the combined risk of floods and droughts.

Comparing the simulated influence of biomass burning plumes on low-level clouds over the southeastern Atlantic under varying smoke conditions

Abstract. Biomass burning plumes are frequently transported over the southeast Atlantic (SEA) stratocumulus deck during the southern African fire season (June–October). The plumes bring large amounts of absorbing aerosols and enhanced moisture, which can trigger a rich set of aerosol–cloud–radiation interactions with climatic consequences that are still poorly understood. We use large-eddy simulation (LES) to explore and disentangle the individual impacts of aerosols and moisture on the underlying stratocumulus clouds, the marine boundary layer (MBL) evolution, and the stratocumulus-to-cumulus transition (SCT) for three different meteorological situations over the southeast Atlantic during August 2017. For all three cases, our LES shows that the SCT is driven by increased sea surface temperatures and cloud-top entrainment as the air is advected towards the Equator. In the LES model, aerosol indirect effects, including impacts on drizzle production, have a small influence on the modeled cloud evolution and SCT, even when aerosol concentrations are lowered to background concentrations. In contrast, local semi-direct effects, i.e., aerosol absorption of solar radiation in the MBL, cause a reduction in cloud cover that can lead to a speed-up of the SCT, in particular during the daytime and during broken cloud conditions, especially in highly polluted situations. The largest impact on the radiative budget comes from aerosol impacts on cloud albedo: the plume with absorbing aerosols produces a total average 3 d of simulations. We find that the moisture accompanying the aerosol plume produces an additional cooling effect that is about as large as the total aerosol radiative effect. Overall, there is still a large uncertainty associated with the radiative and cloud evolution effects of biomass burning aerosols. A comparison between different models in a common framework, combined with constraints from in situ observations, could help to reduce the uncertainty.

Contrails and Their Dependence on Meteorological Situations

Contrails created by aircraft are a very hot topic today because they contribute to the warming of the atmosphere. Air traffic density is very high, and current forecasts predict a further significant increase. Increased air traffic volume is associated with an increased occurrence of contrails and induced cirrus clouds. The scientific level of contrails and their impact on the Earth’s climate is surprisingly low. The scientific studies published so far are mainly based on global models, in situ measurements, and satellite observations of contrails. The research is based on observations of contrails in flight paths in the vicinity of Děčín and Prague, and the collection of flight and meteorological data. It focused on the influence of the meteorological situation on the formation of persistent contrails. The collected data on contrails and meteorological variables were statistically processed using machine learning methods for classification models. Several models were developed to predict and simulate the properties of contrails as a function of given air traffic and meteorological conditions. The Random Forests model produced the best results. Dependencies between meteorological conditions, formation, and contrail lifetime were found. The aim of the study was to identify the possibility of using available meteorological data to predict persistent contrails.

A mixture of mobility and meteorological data provides a high correlation with COVID-19 growth in an infection-naive population: a study for Spanish provinces.

We use Spanish data from August 2020 to March 2021 as a natural experiment to analyze how a standardized measure of COVID-19 growth correlates with asymmetric meteorological and mobility situations in 48 Spanish provinces. The period of time is selected prior to vaccination so that the level of susceptibility was high, and during geographically asymmetric implementation of non-pharmacological interventions. We develop reliable aggregated mobility data from different public sources and also compute the average meteorological time series of temperature, dew point, and UV radiance in each Spanish province from satellite data. We perform a dimensionality reduction of the data using principal component analysis and investigate univariate and multivariate correlations of mobility and meteorological data with COVID-19 growth. We find significant, but generally weak, univariate correlations for weekday aggregated mobility in some, but not all, provinces. On the other hand, principal component analysis shows that the different mobility time series can be properly reduced to three time series. A multivariate time-lagged canonical correlation analysis of the COVID-19 growth rate with these three time series reveals a highly significant correlation, with a median R-squared of 0.65. The univariate correlation between meteorological data and COVID-19 growth is generally not significant, but adding its two main principal components to the mobility multivariate analysis increases correlations significantly, reaching correlation coefficients between 0.6 and 0.98 in all provinces with a median R-squared of 0.85. This result is robust to different approaches in the reduction of dimensionality of the data series. Our results suggest an important effect of mobility on COVID-19 cases growth rate. This effect is generally not observed for meteorological variables, although in some Spanish provinces it can become relevant. The correlation between mobility and growth rate is maximal at a time delay of 2-3 weeks, which agrees well with the expected 5?10 day delays between infection, development of symptoms, and the detection/report of the case.

Extreme precipitation event at the Ross Ice Shelf during the 1911–1912 South Pole run

AbstractIn March 1912, Captain Robert Falcon Scott and his companions perished on their return journey from the South Pole. The Final Blizzard delivered a terminal blow. However, it was only a part of this story of endurance and tragedy. In December 1911, en route to the South Pole, the men had been tent-bound for 4 days due to an exceptionally warm, wet blizzard. This article compares the meteorological situation that the polar party encountered in December 1911 to a similar situation in the modern time and suggests a possible climatology behind the 1911 event.

Adaptive dynamic safety domain for different sailing conditions

The problem of navigation safety is considered, in particular, the problem of developing an algorithm for an adaptive dynamic safety domain for various navigation conditions is investigated. The risk assessment of a ship approach situation is based on the safety domain as the minimum area around the ship into which an oncoming ship should not enter. Navigation conditions are considered in accordance with the rules of COLREG‑72 for navigation of vessels with limited visibility and being in sight of each other. To solve this problem, the navigation areas are classified. For further analysis, a table for the conditions and areas of navigation to identify the shape of the domains is created. The hydro meteorological situation, which takes into account the navigation conditions, and as a consequence, the need to increase the size of the safety domain, is considered. Changes in the parameters and shape of ship safety domains for different navigation conditions have been identified. For each district and navigation conditions, an original configuration of the security domain is proposed. The dynamic changes of the domain during movement in traffic separation systems and channels are separately considered. Using an example, a situation when the bow of the domain crosses the dividing line, which can lead to the appearance of false dangerous targets, is simulated. To avoid such situations, a dynamic domain with a changing shape in the form of an ellipse for swimming in a traffic separation system and channels, taking into account sailing conditions with limited visibility and in sight of each other is proposed. For the calculation, an algorithm to determine the shape of the domain under different visibility conditions and navigation areas has been compiled. Calculations for the case when the vessel enters a turn in traffic separation systems and narrow spaces have been performed. The deformation of the domain during rotation is shown.

Satellite derived mixing ratio profile, a resourceful indicator of heat waves: INSAT-3DR sounder based analysis

India, being a tropical country, frequently experiences a natural hazard called heat waves especially during the hot summer season. During the summer months of 2022, India experienced moderate to severe heat waves spanning March to the third week of May. This adverse weather situation severely affects the northwest and the central Indian regions, causing loss of human lives, crop damage, and a scarcity of water. The present study is the first attempt to assess the meteorological situations favoring heat waves using Indian National Satellite (INSAT)-3DR sounder data. Such studies using satellite based sounder data have seldom happened. The study is conducted during March-May 2022 using the mixing ratio data derived from the INSAT-3DR sounder at 500 hPa, 620 hPa, 700 hPa, 850 hPa, and 950 hPa pressure levels of the atmosphere. From the present study, it is observed that the INSAT-3DR derived mixing ratio profile gives clear signatures of an impending heat wave condition. The weather signal can be observed as an apparent dryness representing the low mixing ratio values (<6 g/kg) percolating from upper layers (600 hPa) to lower levels (850 hPa). These percolations (seeping) happen prior to the surface layers actually show the effects of the heat wave. The study verifies the prior studies of heat wave strike over northwest frontiers of Indian land mass and its further expansion to eastern and southern regions. During strong heat wave conditions surface temperatures reaching maximum of 46 ͦ C at northwest regions, 41 ͦ C at eastern regions and 40 ͦ C at southern regions. The corresponding mixing ratio values at 950 hPa diminishes to 2–4 g/kg for northwest regions and 4–6 g/kg at eastern and southern regions. The study also gives hints to locate signals of heat waves regarding their origin, possible expanding directions, and geographical extent from the satellite-derived mixing ratio. The observations from the analysis give scope for further enhancing the study in the future to devise efficient forecasting methodologies to predict heat wave conditions using satellite derived mixing ratio profiles.

Detecting drought-prone regions through drought indices

Abstract Climate change has led to heightened variability in global rainfall patterns, resulting in greater unpredictability and inconsistency, and it has led to the origin of meteorological drought situation. This has amplified the frequency of droughts or drought-like conditions worldwide. India, being primarily agrarian, faces significant challenges due to drought, affecting various regions intermittently. Given the urgency of addressing recurring drought issues, it is crucial to determine specific ‘drought-prone’ areas through the analysis of historical and current meteorological data. It is still a challenge to quantitatively understand where and to what extent the impact of rainfall patterns could lead the drought. Whether any region likely comes under drought-prone area or not? Can we help policy makers to apply their knowledge effectively? It will help to undertake the long-term mitigation measures for drought assessment and management which encompasses early warning, monitoring, and relief toward the good health of the society. The present study is a further step in the same direction in which Akola district in Maharashtra, India has been assessed for drought-prone declaration using two drought measuring indices; seasonality index (SI) and aridity index (AI). For this, the measured meteorological data precipitation, and potential evapotranspiration, from 1952 to 2002 is made available from India Water Portal.

Substantiation of source data on the parametric algorithms for the classification of weather hazards

The meteorological situation is one of the decisive factors determining the safety and frequency of civil aviation flights. Weather hazards (WH), associated with cumulonimbus clouds, such as a heavy shower, thunderstorm, hail, combined with high atmosphere turbulence, quite often lead to aviation events and even accidents. Currently, a domestic weather radar system of the near airfield zone (WR) “Monocle” has been developed and successfully operated. The criteria for the classification of meteorological phenomena (MP), used in the WR, have been developed individually for each phenomenon and have some heuristic character. These criteria are cumbersome and complicate the process of automating the WH classification. In this case, there is a natural desire to generalize the criteria and optimize them in accordance with the theory of distinguishing statistical hypotheses. This article discusses the application of the Bayesian approach to the WH classification. The statistical Bayesian decision theory assumes decision-making in terms of the probability theory when all significant probabilistic values, so-called sufficient statistics, are known. In order to obtain statistical descriptions of the probability distributions of reflectivity and the eddy dissipation rate (EDR), an analysis of radar signals, reflected from such MP as a rain shower, thunderstorm, hail was carried out. The article provides brief descriptions of the methods of conducting experiments to form statistical database and its analysis. Based on the above methods, the statistical parameter H(EDRmax) analysis for a rain shower, the amplitude distribution of reflectivity parameters and the EDR (Zmax, EDRmax) for thunderstorms and hail was carried out, which showed the low distinguishing ability of each individual parameter when solving the problem to classify MP within the assigned alphabet. The obvious solution is dictated by the theory of recognition. To increase the classification confidence, it is essential to share information parameters, for example, in the form of multidimensional distribution densities of the probabilities of random parameters. The article presents a parametric description of the MP “rain shower-thunderstorm-hail” classification features. An analysis to evaluate the probabilistic characteristics of the WH classification for the adopted empirical classification criteria in the WR shows that the adopted criteria are far from optimal in terms of the probabilities of the correct classification, especially in the rain shower case. It is obvious that a problem solution of the assigned classification confidence is associated with the optimization of the feature space and classification criteria. Based on the data obtained, it is necessary to build an algorithm to classify the WH “rain shower-thunderstorm-hail”.

Analysis of air temperature variation with atmospheric pollutants and meteorological parameters using SPSS

Temperature variation in the atmosphere is closely related to climatic change, environmental pollution and human health. It has become the prime concern of most countries due to the anthropogenic activities and adverse meteorological situations. This temperature variation depends on various factors like the meteorological parameters as well as the air pollutants. The analysis of temperature variation with these factors are done using Geographically Weighted Regression Modelling (GWR) in Statistical Package for Social Sciences (SPSS). This idea is used to study the chronographic change of relationship between a dependent attribute and one or more independent attribute from the output obtained from GWR equation from SPSS Software. In this study the variation of air temperature is analyzed with pollutants and meteorological parameters. The study areas chosen are Vytila, MG Road, Eloor, Methanam, Kalamasserry. The concentration of NO2, SO2, Respirable suspended particulate matter (RSPM) and the meteorological parameters like Relative Humidity (RH), precipitation and atmospheric temperature over 2010-2020 were collected. A numerical model is developed using the above data to study the variation of temperature in the selected areas.

Study of the Effect of Different Atmospheric Conditions on the Temporal Evolution of the Mixing Layer over Madrid during the Year 2020 by Means of Two Different Methods: Ceilometer Signals and the ECMWF-IFS Meteorological Model

Atmospheric aerosols are one of the main factors that contribute to poor air quality. These aerosols are mostly concentrated within the atmospheric boundary layer (ABL) and mixing layer (ML). The ABL extends from ground level to the lowest level of the troposphere directly affected by surface temperature, solar irradiance, the orography and its proximity to coastal areas, causing turbulence in a daily cycle. This turbulence controls the vertical mixing of aerosols and pollutants and their dispersion in the ML. Therefore, proper characterization of these layers is of crucial importance in numerical weather forecasting and climate models; however, their estimation nowadays presents some spatial and temporal limitations. In order to deal with these limitations and to assess the influence of different meteorological conditions on the temporal evolution of the aforementioned layers, the evolution of the ML over Madrid (Spain) has been studied for the year 2020 by means of ceilometer profiles fed into the STRATfinder algorithm. This algorithm is able to give reliable estimates of the height of the ABL (ABLH) and ML (MLH). The results are compared with the ECMWF-IFS model predictions, which is able to compute the MLH under any meteorological condition. Then, the influence of the meteorology in the estimation of MLHs was established by classifying data based on the season and six different prevalent synoptic meteorological situations defined using ground-level pressure fields, as well as by splitting the days into four periods (morning, daytime, evening and nighttime). Our results show that both datasets, the STRATfinder values and the ECMWF-IFS model computations, are very sensitive to the meteorological conditions that play a main role in the MLH temporal evolution. For instance, high solar irradiance and ground radiation cause high turbulence and convection that lead to a well-developed ML. In cases in which the ML is well developed, both methods show similar results, and there are therefore better correlations between them. On the contrary, the results presented here show that the presence of high relative humidity and low temperatures hamper the growth of the ML, causing different errors in both MLH estimations and poor correlations between them. Furthermore, the ECMWF-IFS model has shown a sharp decrease, identified as an artificial behavior from 16:00 UTC, because of the influence of low solar zenith angles and the temporal interpolation. The STRATfinder algorithm also shows a sharp decrease just before the sunset because of the way the algorithm distinguishes between the ML and the residual layer. Thus, this study concludes that the MLH temporal evolution still needs to be characterized using complementary tools, since the methods presented here are strongly affected by the meteorological conditions and do not show enough reliability to work individually. However, ceilometer measurements offer great potential as a correction tool for ABL heights derived from models involved in air pollution dispersion assessments.

Behavior of 222Rn, 220Rn and their progenies along a daily cycle for different meteorological situations: Implications on atmospheric aerosol residence times and Rn daughters' equilibrium factors

The correct assessment of the radiological hazard from radon and daughters, external and internal doses, residence times and equilibrium factors, implies the need to properly determine 222Rn (radon), 220Rn (thoron) and their respective short-lived progenies (214Pb and 214Bi, and 212Pb and 212Bi, respectively), where the precise measurements of both progenies are quite complex due to their very short half-lives. In addition, it is important to study the temporal behavior of all these radionuclides along daily cycles. Therefore, the aim of this study was to analyze the temporal evolution of radon, thoron and their progenies, and of their activity ratios along daily cycles for two different meteorological situations (synoptic and mesoscale processes). Radon and thoron were measured using a radon monitoring system, while their respective progenies were collected onto atmospheric filters using an ASS-500 sampler, and then measured by gamma-ray spectrometry. Furthermore, the different relationships between the concentrations of radionuclides and the different meteorological variables of interest (temperature, ABL height, and speed and direction of the wind) were found. Finally, the atmospheric aerosol residence times and Rn daughters’ equilibrium factors were estimated for each sampling carried out along the two daily cycles, finding results consistent with previous studies.

Formation of an unprecedented yellow snow episode in Xinjiang on December 1, 2018

On 1 December 2018, a heavy yellow snow fell in Urumqi (87°37′E, 43°47′N) — the largest city of northwest China's Xinjiang province, which was the first case that the yellow snow has been observed in winter. The air parcel trajectories obtained from Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model and the dust surface mass concentration from Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) were adopted to identify the potential sources and transport paths of pollutants responsible for this yellow snow episode. The meteorological situation and the European Center for Medium-Range Weather Forecasts (ECMWF) forecast products have been utilized to analyze the supportive meteorological conditions. The results showed that the heavy snow in Urumqi was contaminated by the yellow dust originated in Karamay of Xinjiang province. The strong surface winds in Karamay lifted large amounts of dust into the atmosphere. Then the airborne dusts were transported to Urumqi rapidly by strong low-level winds, where precipitation in connection with the upper trough and the cold front lead to the yellow snow episode. This study can provide important scientific significance for predicting this kind of event (yellow snow).