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Similar to usual lower records, Bottom- k -lists (Kozan and Tanil, İstatistik J Turk Stat Assoc 13:73–79, 2013) have a wide range of practical applications in meteorology, hydrology, sports, etc. Also, exceedance statistics can be viewed as a close relative to tolerance limits—an important field of statistical science. In this study, an idea of combining these two important subjects together is studied and an exceedance statistic is defined based on bottom- k -lists in an independent and identically distributed (iid) continuous random sequence. Probability mass function (pmf) of a selected exceedance statistic is obtained. Also, an illustrative application of the exceedance statistic is given. [ABSTRACT FROM AUTHOR]

Although Augmented Reality (AR) has been extensively studied in supporting Immersive Analytics (IA), there are still many challenges in visualising and interacting with big and complex datasets. To deal with these datasets, most AR applications utilise NoSQL databases for storing and querying data, especially for managing large volumes of unstructured or semi-structured data. However, NoSQL databases have limitations in their reasoning and inference capabilities, which can result in insufficient support for certain types of queries. To fill this gap, we aim to explore and evaluate whether an intelligent approach based on ontology and linked data can facilitate visual analytics tasks with big datasets on AR interface. We designed and implemented a prototype of this method for meteorological data analytics. An experiment was conducted to evaluate the use of a semantic database with linked data compared to a conventional approach in an AR-based immersive analytics system. The results significantly highlight the performance of semantic approach in helping the users analysing meteorological datasets and their subjective appreciation in working with the AR interface, which is enhanced with ontology and linked data. [ABSTRACT FROM AUTHOR]

The Precipitating Change Project was a 5-year development, implementation, and research study of an innovative 4-week middle school curricular unit in computational weather forecasting that integrates students' learning and use of meteorology and computational thinking (CT) concepts and practices. The project produced a list of CT skills and definitions that students use to predict the weather, CT assessment instruments, and a CT classroom observation protocol. Data was collected from 306 eighth grade (ages 13–14) students in rural indigenous communities in the Artic and urban and suburban Northeast communities in the USA. The project met its goal of producing an intentional instructional sequence that integrates disciplinary science and CT practices to increase students' science knowledge and their ability to use CT skills and processes. The results indicate that teachers were able to use the curriculum to embed CT practices into the classroom. Students, in turn, had the opportunity to practice using these skills in class discussion as evidenced by the classroom observation data, and students' science knowledge of CT content and practices significantly increased as evidenced by their performance on the weather content and CT skills pre- and post-assessments. While statistically significant gains in science knowledge and CT skills and practices were evident in all settings (urban, suburban, and rural indigenous communities), there were noticeable differences in gains in students' CT skills and practices between the three settings and additional research is needed in a diversity of settings to understand this difference. [ABSTRACT FROM AUTHOR]

This study investigates the spatial patterns and variabilities of Seasonal Rainfall Onset Day (OD), Cessation Day (CD), Seasonal Length (SL), and Number of Rainy Days (RD) in Rwanda for the long rain season (LR) of March–April-May (MAM) and the short rain season (SR) of September–October-November–December (SOND). Data used, provided by the Rwanda Meteorology Agency, consisted of a time series of gridded rainfall and temperature from 1983 to 2021. The northern, western, and southwestern regions experience earlier OD than the remaining parts of the country, [mid-February, early March] for LR and [early September, mid-October] for SR. The entire eastern region experiences later OD ([mid-March, end March]) during LR. During SR, the central east and the southeastern regions experience later OD ([mid-October, end November]). During LR and SR, the mean SL and mean RD are highest in the northwestern and southwestern regions and lowest in the central-eastern and southeastern regions. In those regions, the mean SL and mean RD are higher during SR ([81, 116], [49, 74] days) than during LR ([77, 99], [46, 68] days). In the remaining parts of the country, they are lower during SR ([46, 81], [24, 49] days) than during LR ([55, 77], [24, 46] days). The temporal variability (coefficient of variation) is relatively high in different places. During LR, for OD ([21.5, 34] %) over the northwest, central plateau, and eastern regions, for SL ([22.5, 35] %) over the northern and eastern regions, and for RD ([24.5, 32] over the eastern region. During SR, for SL ([23, 31] %) over the southcentral and the central plateau regions, and for RD ([25.5, 38] %) over the northern, western, southern, and central plateau regions. The seasonal length and the number of rainy days are strongly dependent to rainfall intensity, but more dependent in short rains seasons. An investigation done El Nino and the Indian Ocean Dipole indicates that they may have an influence on the studied rainfall characteristics in Rwanda. Results from this study are important, as the country's economy remains dependent on rain-fed agriculture. They will help farmers, policy and decision-making for appropriate adaptation and mitigation strategies and policies. [ABSTRACT FROM AUTHOR]

The next-generation atmospheric dynamics model for numerical weather prediction and climate studies is developed at INM RAS and Hydrometcentre of Russia. The model uses a cubed-sphere grid with quasi-uniform resolution over the sphere. Given the multiple areas of probable model applications, we decided to enhance its flexibility as compared to the previous models. Actually, our model is a set of basic building blocks (differential operators, time-integration schemes, parallel exchanges, etc.) that can be used to construct a wide class of atmospheric equations solvers. Currently, we have developed a non-hydrostatic solver for high-resolution global weather forecasting and a solver using hydrostatic approximation in vertical for climate simulations. Both solvers were tested using generally accepted idealized problems and showed promising results. In this article, we motivate the basic concepts of our model and describe its software and numerical design. Parallel efficiency is examined. The model scales well at least up to 6912 cores of Roshydromet Cray XC-40 system. [ABSTRACT FROM AUTHOR]

Climate change has appeared as a major issue in recent years, and its impacts are seen multi-dimensionally. The local people are the key eyewitnesses of climate change, although the discourse is disciplinary, geographic, and gender biased. In this context, this paper documents the perceptions and narratives of the Tamang, an Indigenous people, who live on the outskirts of the Kathmandu Valley. This is an ethnographic study and applied quantitative and qualitative data. The data of the study were gathered using triangulation methods, i.e., household questionnaire survey (HQS), key informant interview (KII), and focus group discussion (FGD). A total of 94 HQS, nine KII, and three FGD were carried out in 2018 in three sample sites in the outskirts of the valley. The station-based observed climatic data from 1969 to 2022 were collected from the Department of Hydrology and Meteorology. The observed data shows increasing annual rainfall and temperature in Kathmandu; however, the rate of temperature increase is much larger. Seasonal precipitation shows decreasing rainfall in post-monsoon, which enhances the winter drought. The Tamang are the key eyewitness of the changes in climate and this knowledge is inbuilt with their memories which are closely bound to the place. Hence, the life history of elderly people can be an appropriate way of understanding the micro-climatic changes in the local context, which largely failed or ignored to document in scientific or macro-level assessments. [ABSTRACT FROM AUTHOR]

This study analyses surface wind speed trends over the north Indian region covering Delhi–National Capital Region (NCR) and adjoining areas (lying within latitude 25°–30°N and longitude 75°–80°E) for the recent 40-year period (1981–2020). The analysis reveals an annual stilling of 9.83 × 10−3 m/s/year for the study period. The seasonal analysis indicates the highest stilling in the summer by 14.57 × 10 - 3 m/s/year in absolute terms. The daytime and night-time wind speed variation analysis revealed a significant difference between daytime and night-time wind speeds over the region. However, declining trends for daytime and night-time wind speeds could not be differentiated statistically, i.e., daytime and night-time speeds had been declining at an almost equal rate over the study period in the study region. Further, the dust concentration analysis revealed a significant rise in dust concentration of 0.72 µg/m3/year; the highest trend has been observed for the winter season. The increase in dust concentration and the stilling together make it a significant concern from a health perspective. The stilling may have further implications on the hydrological cycle, wind energy reliance, and other concerns, which affect the climate at the micro-scale. Rapid urbanisation seems to be the most prominent factor for stilling due to an increase in surface roughness, pointing towards a need for attribute analysis in future. The study further identifies challenges in meteorological studies, which include inherent cyclicity in the meteorological variables (such as wind speed and temperature), parameterisation (choice of the independent variable), the need for sophistication in data retrieval processes, including validation (training and testing) and a lack of adequate understanding about atmospheric phenomena for the region under study. These challenges must be systematically addressed in future research to achieve better and more consistent inferences from meteorological analyses. Research Highlights: An annual surface wind speed decline of 9.83 × 10−3 m/s/year has been observed over Delhi-NCR and adjoining areas since 1981. The declining effect is most pronounced in the summer season, amounting to 14.57 ×10−3 m/s/year. Dust concentration has been on continuous rise at the rate of about 0.72 µg/m3/year since 1981. The co-occurrence of dust concentration rise and wind speed decline may be a significant cause of deterioration of air quality in the region. The study envisages the need for systematic and holistic urban and built environment plan-ning. [ABSTRACT FROM AUTHOR]

The West African Monsoon (WAM) system plays a crucial role in the West African climate system because it transports moisture from the Atlantic Ocean into the subcontinent in summer. This study evaluates the capability of the Model for Prediction Across Scales-Atmosphere (MPAS-A) to simulate the characteristic reproduce the WAM system and the associated rainfall-producing features. The MPAS model was used to perform a 30-year global climate simulation (1981–2010) at a regular grid (uniform resolution of 60 km). The simulation was initialized with the Climate Forecast System Reanalysis (CSFR) dataset. The results showed that MPAS simulate well the rainfall pattern over West Africa and reproduces the different phases of the monsoon dynamics system (i.e., the northward progression, the peak period, and the southward retreat). The model also reasonably replicates the pattern of the zonal components of wind and the vertical velocity. However, MPAS underestimates the orographic rainfall over the Guinea Coast, Jos Plateau, and Mount Cameroon. It also underestimates the vertical velocity and zonal wind magnitudes over the region. In addition, the model features a weaker temperature gradient than in the reanalysis. Understanding and correcting the sources of these model biases will enhance the suitability of MPAS for weather and seasonal forecasts over West Africa. Research highlights: In this study, we investigated the ability of the Model for Prediction Across Scales (MPAS) to simulate the West African monsoon rainfall, the associated atmospheric features controlling the variability of the seasonal and annual cycle, and the thermal wind conditions. First, the model is initialized using the CSFR reanalysis. Then, we run the model simulation for 30 years, from 1981 to 2010, using a regular grid (uniform resolution of 60 km). We showed that the model realistically simulated the spatial pattern of rainfall over West Africa. However, it has some problems when it comes to capturing the monsoon features. [ABSTRACT FROM AUTHOR]

In this paper, the influence of emerging low level jet (LLJ) core zone on the Indian summer monsoon rainfall (ISMR) examined during the period 1981–2020 using ERA5 reanalysis, Indian Monsoon Data Assimilation and Analysis (IMDAA) along with Indian Meteorology Department (IMD) rainfall. The analysis spans the entirety of India, encompassing various regions, including Central India (68°–87° E, 15° N–26° N), Northeast India (83°–98.5° E, 21°–30° N), Northwest India (69°–84° E, 22°–37° N), and the Southern Peninsula (74°–85° E, 7°–19° N). Our analysis found a significant correlation between the magnitude of LLJ core zone and ISMR across these regions, except for North-East India, in both data sets (0.5–0.8) at a 0.01 significance level. ERA5 and IMDAA, both data sets, reflect the spatial climatological aspects of LLJ at seasonal and sub-seasonal scales. However, compared to the ERA5 data, the amplitude of LLJ wind is lower in the IMDAA data over the evolving core zone. This study provides pivotal insights into the dynamics of the core LLJ zone and its relationship with ISMR, including its associations with varying Indian monsoon patterns and teleconnections with the El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) phases. This study hold significant implications for various sectors reliant on accurate monsoon predictions and weather forecasting. By elucidating the evolving dynamics of the core zone of the LLJ and its connection with ISMR, the research provides a valuable framework for enhancing the precision of monsoon forecasting models. [ABSTRACT FROM AUTHOR]

Adıyaman, a city recently affected by an earthquake, is facing significant air pollution challenges due to both anthropogenic activities and natural events. The sources of air pollution have been investigated using meteorological variables. Elevated southerly winds, especially prominent in spring and autumn, significantly contribute to dust transport, leading to a decline in local air quality as detected by the HYSPLIT model. Furthermore, using Suomi-NPP Thermal Anomaly satellite product, it is detected and analyzed for crop burning activities. Agricultural practices, including stubble burning, contribute to the exacerbation of PM10 pollution during the summer months, particularly when coupled with winds from all directions except the north. In fall and winter months, heating is identified as the primary cause of pollution. The city center located north of the station is the dominant source of pollution throughout all seasons. The study established the connection between air pollutants and meteorological variables. Furthermore, the Spearman correlation coefficients reveal associations between PM10 and SO2, indicating moderate positive correlations under pressure conditions (r = 0.35, 0.52). Conversely, a negative correlation is observed with windspeed (r = -0.35, -0.50), and temperature also exhibits a negative correlation (r = -0.39, -0.54). During atmospheric conditions with high pressure, PM10 and SO2 concentrations are respectively 41.2% and 117.2% higher. Furthermore, pollutant concentration levels are 29.2% and 53.3% higher on days with low winds. Last, practical strategies for mitigating air pollution have been thoroughly discussed and proposed. It is imperative that decision-makers engaged in city planning and renovation give careful consideration to the profound impact of air pollution on both public health and the environment, particularly in the aftermath of a recent major earthquake. [ABSTRACT FROM AUTHOR]

The present study focuses on determining the post COVID 19 status of air pollution and temporal variability of pollutants over the Okhla industrial area, New Delhi. The 24 hourly concentration datasets of PM10, PM2.5, NO2 and SO2, for the period of November 2021 to November 2022 were collected from continuous ambient air quality monitoring station of study area. During the study period, the concentration of PM10 and PM2.5 was observed to be varying from 9.73 to 640.14 µg/m3 and 6.18 to 448.06 µg/m3, respectively. The average values of PM10 and PM2.5 were recorded as 236.57 and 107.33 µg/m3, respectively; which were much higher than the standard values of Central Pollution Control Board. In case of gaseous pollutants, the mean value of SO2 was recorded as 9.31 µg/m3 which was much lesser as compared to the set standards. Contrary, the values for NO2 varied from 14.75 to 219.36 µg/m3 and these values were on the higher side when compared with standards. The Bivariate plots were also drawn to examine the role of wind speed and wind direction in the study area. The results revealed that at low wind speed, high concentrations were prevailing and potential sources of pollutant are in south east direction. Overall, the results of this study highlighted that situation is alarming after COVID 19, and need immediate intervention for the abatement of air pollution in studied region. [ABSTRACT FROM AUTHOR]

Oceanography, or Oceanology, is a very broad name covering all the traditional sciences (physics, chemistry, biology etc.) which can be studied in the ocean context. Hence the distinction must be made using the specific names of Physical, Chemical or Biological Oceanography. This particular review is devoted to the history of Physical Oceanography, a discipline which remained at the empirical stage until roughly the middle of last century, with a slow development of the rigorous, mathematical approach and analysis required to fully understand the processes and dynamics at all space/time scales. This review wants to introduce the uninformed reader to this relatively new science, providing a concise but hopefully comprehensive outline of the milestones of its history, from the adventurous beginnings of the past centuries, to the successive, very recent theoretical and observational advances. In this progress, the scientific "giants" who made possible these advances are also presented. Their choice constitutes not only the author's personal opinion but that of the oceanographic community at large which recognizes their crucial impact in making physical oceanography the quantitative, exact science of today. It is the author's privilege to have personally known most of them, not only as teachers but as mentors and friends. [ABSTRACT FROM AUTHOR]

Drought, a major phenomenon impacting water resources, viability, sustainability, and the economy, has been one of the most important hydrological concerns. In the literature, it has been classified into four groups that are meteorological, agricultural, hydrological, and socio-economic. Meteorological drought expresses the precipitation deficits when they are significantly below those recorded normal times. In this study, using the Standard Precipitation Index (SPI) and mean monthly precipitation records of 17 stations which have been obtained from the General Directorate of Meteorology of Türkiye, a drought monitoring analysis has been conducted for Kızılırmak Basin, which is the second largest water basin of the country and water source of many provinces allowing for the time scales of 1, 3, 6, 9, 12, and 24 months considering the cases of "dry" (SPI ≤ − 1.5) and "wet" (SPI ≥ 1.5). To detect the possible trends in two categories of Severe and Extreme Drought, SED (SPI ≤ − 1.5), and Severe and Extreme Wet, SEW (SPI ≥ 1.5) of all time scales, a developed form of innovative trend analysis (ITA) is performed by adding two vertical lines. In addition, a traditional the Mann–Kendall test is applied to the SPI series. The findings indicate that dry occurrences tend to outnumber wet occurrences across various time scales. Analysis reveals that a significant majority of results across all time scales exhibit a consistent trend (89%), with a notable increase in the SEW category (62.74%) and a decrease in the SED category (60.78%). Mann–Kendall analysis demonstrates that 67% of the observed trends show a decrease, while 33% show an increase across all time scales. [ABSTRACT FROM AUTHOR]

The Adrar site is among sites of the highest solar radiation potential in the world. This region is characterized by high ambient temperature in the summer, which in some days is exceed 45 °C, where the high ambient temperature is affects the solar cells.The aim of this paper is to assessments the PV cells types mono crystalline, poly crystalline, micro crystalline amorphous and tripple junction for meteorological variables of Adrar environment.These models were tested by using input data from meteorological station of (URERMS-Adrar),and these inputs are used in software for PV system (PVsyst) to identify the characteristics of these types of PV module. The comparison between PV cells type mono-crystalline and poly-crystalline of same maximum power (250 W) it show that the cells type poly crystalline it more affected by the temperature of than mono crystalline. Finally, have reached that the assessments the performance of PV module is linked to weather (clear sky, overcast sky, shading, cold and hot). [ABSTRACT FROM AUTHOR]

Hemorrhagic fever with renal syndrome (HFRS) poses a major threat in Shandong. This study aimed to investigate the long- and short-term asymmetric effects of meteorological factors on HFRS and establish an early forecasting system using autoregressive distributed lag (ARDL) and nonlinear ARDL (NARDL) models. Between 2004 and 2019, HFRS exhibited a declining trend (average annual percentage change = − 9.568%, 95% CI − 16.165 to − 2.451%) with a bimodal seasonality. A long-term asymmetric influence of aggregate precipitation (AP) (Wald long-run asymmetry [WLR] = − 2.697, P = 0.008) and aggregate sunshine hours (ASH) (WLR = 2.561, P = 0.011) on HFRS was observed. Additionally, a short-term asymmetric impact of AP (Wald short-run symmetry [WSR] = − 2.419, P = 0.017), ASH (WSR = 2.075, P = 0.04), mean wind velocity (MWV) (WSR = − 4.594, P < 0.001), and mean relative humidity (MRH) (WSR = − 2.515, P = 0.013) on HFRS was identified. Also, HFRS demonstrated notable variations in response to positive and negative changes in ∆MRH(−), ∆AP(+), ∆MWV(+), and ∆ASH(−) at 0–2 month delays over the short term. In terms of forecasting, the NARDL model demonstrated lower error rates compared to ARDL. Meteorological parameters have substantial long- and short-term asymmetric and/or symmetric impacts on HFRS. Merging NARDL model with meteorological factors can enhance early warning systems and support proactive measures to mitigate the disease's impact. [ABSTRACT FROM AUTHOR]

In this study, we evaluated extreme climate indicators for precipitation and temperature in the Brazilian Legal Amazon (BLA) from 1961 to 2021. Data from 38 National Institute of Meteorology (INMET) weather stations with a record failure rate less than 12% were used, and RClimDex software and the Mann–Kendall test, Pettitt test, standard normal homogeneity test (SNHT), and Buishand test were employed to analyse the data. The results showed increased extreme rainfall events, including the annual total rainfall divided by wet days (SDII). More frequent discontinuities were observed in the 1980s (SNHT) and 1990s (Pettitt and Buishand tests). The extreme temperature climate indicators also significantly increased, particularly in the 1990s. These increases are likely linked to changes in local climate conditions due to agricultural expansion, intensified industrial activities, and regional urbanization. This study underscores the need for proactive measures to curb illegal deforestation and reduce CO2 emissions and further research to better understand and mitigate the adverse impacts of these changes on the BLA environment and local communities. [ABSTRACT FROM AUTHOR]

This study addresses into the critical issue of drought as a natural disaster, especially in regions characterized by arid and semi-arid climates like Turkey. The primary aim of this study is to investigate the historical occurrences of meteorological drought events in Turkey, focusing on their past frequency, intensity, and spatial distribution. The study employs the Standardized Precipitation Evapotranspiration Index (SPEI) method and utilizes 50 years of monthly temperature and precipitation data collected from 222 meteorological stations across the country. Drought severity is assessed using the run theory method, and trends in drought patterns are analyzed through the Mann–Kendall trend test. Additionally, the text explores the connection between elevation and the geographical distribution of drought events. The study's findings reveal a noticeable increase in the occurrence of drought periods over time. Among the selected periods, the most widespread drought event was observed in the year 2001. The Bozcaada meteorology station exhibited the highest frequency of drought with a value of 223, while the Ispir meteorology station recorded the lowest frequency with a value of 151. Over the course of the 50-year analysis, no significant correlation was found between drought and elevation, although a gradual increase was noted in the last 10 years. The results also indicate a gradual north-to-south increase in drought intensity in Turkey. The study identifies four distinct drought hotspots in the country: the Western Anatolia Region, Central and Southern Anatolia Region, Southeastern Anatolia Region, and Eastern Anatolia Region. [ABSTRACT FROM AUTHOR]

Tropospheric ozone is an air pollutant at the ground level and a greenhouse gas which significantly contributes to the global warming. Strong anthropogenic emissions in and around urban environments enhance surface ozone pollution impacting the human health and vegetation adversely. However, observations are often scarce and the factors driving ozone variability remain uncertain in the developing regions of the world. In this regard, here, we conducted machine learning (ML) simulations of ozone variability and comprehensively examined the governing factors over a major urban environment (Ahmedabad) in western India. Ozone precursors (NO2, NO, CO, C5H8 and CH2O) from the CAMS (Copernicus Atmosphere Monitoring Service) reanalysis and meteorological parameters from the ERA5 (European Centre for Medium-Range Weather Forecast’s (ECMWF) fifth-generation reanalysis) were included as features in the ML models. Automated ML (AutoML) fitted the deep learning model optimally and simulated the daily ozone with root mean square error (RMSE) of ~2 ppbv reproducing 84–88% of variability. The model performance achieved here is comparable to widely used ML models (RF—Random Forest and XGBoost—eXtreme Gradient Boosting). Explainability of the models is discussed through different schemes of feature importance, including SAGE (Shapley Additive Global importancE) and permutation importance. The leading features are found to be different from different feature importance schemes. We show that urban ozone could be simulated well (RMSE = 2.5 ppbv and R2 = 0.78) by considering first four leading features, from different schemes, which are consistent with ozone photochemistry. Our study underscores the need to conduct science-informed analysis of feature importance from multiple schemes to infer the roles of input variables in ozone variability. AutoML-based studies, exploiting potentials of long-term observations, can strongly complement the conventional chemistry-transport modelling and can also help in accurate simulation and forecast of urban ozone. [ABSTRACT FROM AUTHOR]

Weather observations and forecasts are crucial for aviation safety and efficiency, particularly in the presence of hazards such as fog. Hence, climatological studies of this phenomenon are standard information for the aviation industry. Aeronautical stations combined with synoptic stations have to provide local and current weather conditions in two different types of reports by using the same instrumentation: SYNOP for synoptic purposes and METAR for aeronautical purposes. When climatological studies have to be carried out, the question of which type of report is more appropriate for the task arises. In this work, a comparison between these two types of hourly meteorological observation reports is made, with the aim of identifying the conditions under which differences can have a significant impact on the final results. For this purpose, SYNOP and METAR reports for the period 2000–2019 from 13 Argentine airports were selected from different databases. Results show that level of agreement depends strongly on the variable of interest. A key difference is found in "present weather" mainly due to different reporting rules. In this context, six different selection criteria using visibility, dew point depression, and present weather are used to evaluate the impact of the differences found in the fog and mist climatology. In conclusion, SYNOP data could be used instead of METAR in statistical studies if the need arises, but with the caveat that the frequency of fog occurrence should not be interpreted too strictly. [ABSTRACT FROM AUTHOR]

Winter tourism is an important economic factor in the European Alps, which could be exposed to severely changing meteorological conditions due to climate change in the future. The extent to which meteorology influences winter tourism figures has so far been analyzed mainly based on monthly or seasonal data and in relation to skier numbers. Therefore, we record for the first time daily visitor numbers at five Bavarian winter tourism destinations based on 1518 webcam images using object detection and link them to meteorological and time-related variables. Our results show that parameters such as temperature, cloud cover or sunshine duration, precipitation, snow depth, wind speed, and relative humidity play a role especially at locations that include other forms of winter tourism in addition to skiing. In the ski resorts studied, on the other hand, skiing is mostly independent of current weather conditions, which can be attributed mainly to artificial snowmaking. Moreover, at the webcam sites studied, weekends and vacation periods had an equal or even stronger influence on daily visitor numbers than the current weather conditions. The extent to which weather impacts the (future) visitor numbers of a winter tourism destination must therefore be investigated individually and with the inclusion of non-meteorological variables influencing human behavior. [ABSTRACT FROM AUTHOR]

Scientific objectives, instruments, and measurement program of the scientific instrumentation of the Kazachok stationary landing platform of the State Corporation Roscosmos and the European Space Agency (ESA) ExoMars-2022 project are presented. The scientific objectives of research on the landing platform included the long-term climate monitoring, the studies of the atmospheric composition, the mechanisms for dust lifting and related electrical phenomena, atmosphere–surface interactions, the subsurface water abundance, monitoring the radiation situation, and the study of Mars internal structure. To address these problems, 11 Russian and two European instruments with a total mass of 45 kg were built, tested and integrated into the spacecraft. These include a television camera system, meteorological complexes, a suite for studying dust and related electrical phenomena, optical spectrometers and an analytical complex for studying the atmospheric composition, a microwave radiometer, the neutron and gamma spectrometers for surface research, a seismometer, magnetometers and a Mars proper motion experiment to study its internal structure. Although the ExoMars-2022 project has been discontinued, the scientific objectives of the landing platform have not lost their relevance, and the technical solutions and developments implemented in scientific equipment are of interest and promising for further Mars exploration. [ABSTRACT FROM AUTHOR]

Northeast Asia experienced unprecedented abrupt warming in the 1990s since the last century. Based on a robust time series and rank frequency evaluation, the Max Planck Institute for Meteorology Grand Ensembles of CMIP5 (MPI-GE5), CMIP6 (MPI-GE6), EC-Earth3 and IPSL-CM6A-LR were identified as the models that best simulate the external forcing and internal variability in observations and represent observations most adequately. The negative-to-positive phase transition of the Atlantic multidecadal variability (AMV), combined with the external forcing, can explain 88% [60%−111%] of the 1990s warming. With prescribed anthropogenic emissions in the near future, a phase shift in the AMV to +2 (-2) standard deviation will amplify (weaken) the warming over Northeast Asia by 37% [29%−49%] (19% [15%−25%]). This highlights the importance of natural climate variability in Northeast Asia's government decision-making and risk management, and emphasizes that only climate models with an adequate representation of forced warming can quantify these contributions correctly. [ABSTRACT FROM AUTHOR]

The present study assesses trends in rainfall in Madhya Pradesh, Central India, over the long (1871–2016) as well as short (1987–2016) temporal scales. The region is home to disadvantageous groups of the population including vulnerable tribals who depend on rainfall-based agriculture and forest products. Therefore, any disruption in rainfall trends due to climate change needs to be documented for better planning. This study focuses on two meteorological subdivisions, East and West Madhya Pradesh, which receive a major portion of rainfall through the Bay of Bengal branch and the Arabian Sea branch of the Indian Summer Monsoon. The study is based on the rainfall series provided by the Indian Institute of Tropical Meteorology, Pune, India. This study used the Mann–Kendall test to detect a trend and Sen's Slope to estimate the magnitude of change in rainfall. Statistically significant negative trends were detected in total annual (− 1.125 mm y − 1) and monsoon (− 1.242 mm y − 1) rainfall over long-term observation in East Madhya Pradesh only. However, during the last 30 years, no trends have been recorded in East Madhya Pradesh. In West Madhya Pradesh, rainfall has not recorded any trend for long- as well as short-term observation periods. The non-availability of a trend in rainfall during May and October at both meteorological subdivisions shows that there is no shift in the duration of the monsoon season over long as well as short temporal observations. The decreasing rainfall trends in Eastern Madhya Pradesh over the long-term observation seem to be caused by changing patterns of tropical cyclone frequency in the Bay of Bengal. [ABSTRACT FROM AUTHOR]

This paper is an effort of geo-statistical analysis of rainfall variability and trend detection in the eastern Hindu Kush region located in the north-west of Pakistan. The eastern section of the HK region lies in the western part of Pakistan. Exploring rainfall variability and quantifying its trend and magnitude is one of the key indicators among all climatic parameters. In the study area, Pakistan Meteorology Department (PMD) has established seven meteorological stations: Drosh, Chitral, Dir, Timergara, Saidu Sharif, Malam Jabba, and Kalam. Daily, mean monthly, and mean annual rainfall time series data for all the met stations were geo-statistically analyzed in the GIS environment for detecting monthly and annual variability in rainfall, variability, and trend detection. Mann–Kendall (MK) and Theil-Sen's slope (TSS) statistical tests were applied to rainfall data. Initially, the MK test was applied for detection of trends and TSS test was used to quantify the change in magnitude. The results indicate that the rainfall variability in intensity and trend pattern detection. The analysis confirms that an extremely significant rainfall trend in the case of mean annual rainfall was predicted at Dir and Malam Jabba meteorological stations. Opposite to this, at Kalam and Chitral stations, a less significant rainfall trend was noted. In a similar context, no prominent rainfall trend has been found at Drosh, Timergara, and Saidu Sharif meteorological stations. Likewise, using TSS, an extremely negative variation in the magnitude of rainfall was verified at Kalam and Malam Jabba. However, a noteworthy positive change in rainfall magnitude has been noted at Dir and Saidu Sharif meteorological stations. The findings of this research have the potential to assist the decision and policy makers and academicians to think truly and conduct more scientific research studies to mitigate climate change. [ABSTRACT FROM AUTHOR]

We present the most significant results of Russian scientists in the field of atmospheric ozone research for 2019–2022 and examine observations of tropospheric ozone, its distribution and variability on the territory of the Russian Federation, its relation with atmospheric parameters, modeling of formation processes, and its impact on public health. The state of stratospheric ozone over Russia, modeling of processes in the ozonosphere, and methods and instruments being developed are also analyzed. The review is a part of Russia's national report on meteorology and atmospheric sciences, which was prepared for the International Association of Meteorology and Atmospheric Sciences (IAMAS). It has been reviewed and approved at the 28th General Assembly of the International Union of Geodesy and Geophysics (IUGG). [ABSTRACT FROM AUTHOR]

This review contains the most significant results of Russian studies in the field of atmospheric electricity in 2019–2022. It is part of the Russian National Report on Meteorology and Atmospheric Sciences to the International Association of Meteorology and Atmospheric Sciences (IAMAS). The report was presented and approved at the XXVIII General Assembly of the International Union of Geodesy and Geophysics ((IUGG). The review is followed by a list of the main published works on the studies of atmospheric electricity of Russian scientists in 2019–2022. [ABSTRACT FROM AUTHOR]

A brief overview of the work of Russian scientists in the field of atmospheric chemistry in 20190–2022 is presented, including work on the chemistry of the troposphere, the chemistry of the ozone layer, the study of heterophase processes, and the chemical aspects of climate and its change. The review was prepared in the Commission on Atmospheric Chemistry of the Section of Meteorology and Atmospheric Sciences of the National Geophysical Committee. [ABSTRACT FROM AUTHOR]

A review of studies on the atmospheres of planets in the solar system performed by Russian scientists in 2019–2022 is presented. This review was prepared in the Commission on Planetary Atmospheres of the National Geophysical Committee for the National Report on Meteorology and Atmospheric Science at the 28th General Assembly of the International Union of Geodesy and Geophysics (IUGG 2023) in Berlin. [ABSTRACT FROM AUTHOR]

This review outlines the most significant results of research in dynamic meteorology performed by Russian scientists in 2019–2022. It is part of the Russian National Report on Meteorology and Atmospheric Sciences submitted to the International Association of Meteorology and Atmospheric Sciences (IAMAS). The review is supplemented by a list of main publications of Russian scientists on dynamic meteorology in 2019–2022. [ABSTRACT FROM AUTHOR]

A review of the results of Russian studies of the middle atmosphere in 2019–2022, prepared by the Commission on the Middle Atmosphere of the Meteorology and Atmospheric Sciences Section (MASS) of the Russian National Geophysical Committee for the National Report on Meteorology and Atmospheric Sciences to the 28th General Assembly of the International Union of Geodesy and Geophysics (Germany, 2023), is presented. [ABSTRACT FROM AUTHOR]

This publication is an overview of the results of Russian polar research in 2019–2022, prepared in the Commission on Polar Meteorology of the National Geophysical Committee for the National Report on Meteorology and Atmospheric Sciences for the 28th General Assembly of the International Union of Geodesy and Geophysics (Berlin, Germany, July 11–20, 2023). [ABSTRACT FROM AUTHOR]

Results of Russian studies on cloud physics, precipitation, and weather modification in 2019–2022 are presented based on a survey prepared for the Russian National Report on Meteorology and Atmospheric Sciences to the 28th General Assembly of the International Union of Geodesy and Geophysics. Results concerning general issues of the observation and modeling of clouds and precipitation, including convective clouds; issues of studying the microphysical and optical characteristics of clouds; and the weather modification are discussed. [ABSTRACT FROM AUTHOR]

The results of Russian studies of climate and its changes (published in 2019–2022) are presented based on a review prepared for the National Report on Meteorology and Atmospheric Sciences for the 28th General Assembly of the International Union of Geodesy and Geophysics (Berlin, Germany, July 11–20, 2023). [ABSTRACT FROM AUTHOR]

The aim of the study was to characterise bioclimatic conditions in Poland in the times of progressing warming. This type of research permits the verification whether the progressing climate warming also translates into a change in bioclimatic conditions. This study was based on data obtained for the period 1966–2021 from 37 synoptic stations located in Poland. All the data series were uniform and obtained from the data base of the Institute of Meteorology and Water Management—National Research Institute (IMGW-PIB). The study revealed high variability of bioclimatic conditions in Poland both in spatial and in temporal terms. The lowest mean annual PET values were recorded in the north and north-east of the country and the highest in the south-west of Poland. The study revealed changes in the frequency of occurrence of days with cold and heat stress, as well as days with no thermal stress. The most intensive changes were determined for days with cold stress. A decrease in the number of days in this category translated into an increase in the number of days with no thermal stress and days with heat stress. [ABSTRACT FROM AUTHOR]

Size of tropical cyclone (TC) is often asymmetric in nature. Yet, there is a lack of systematic, clean, and intuitive definition/expression to specify the asymmetry of TC size. Here, we introduce a novel index, TC size asymmetry index (SAI), which specifies both the degree and pattern of the asymmetry synthetically. In particular, the symbolic form of SAI is vividly designated for identifying the latter. The SAI proposes 1 quasi-symmetric pattern and 28 asymmetric patterns in total. The 41-year (1979–2019) global climatology of SAI shows that the distribution of the degree of TC size asymmetry is trimodal. Elementarily, the degree and pattern of asymmetry are found to be TC intensity, TC movement, time, and space dependent. The introduction of SAI provides an insight into the subject and lays an important foundation for future applications and research. Furthermore, besides meteorology, it could inspire other fields to index the geometric asymmetries of other kinds. [ABSTRACT FROM AUTHOR]

The PM2.5 (particulate matter with a diameter of fewer than 2.5 µm) has become a global topic in environmental science. The neural network that based on the non-linear regression algorithm, e.g., deep learning, is now believed to be one of the most facile and advanced approaches in PM2.5 concentration prediction. In this study, we proposed a PM2.5 predictor using deep learning as infrastructure and meteorological data as input, for predicting the next hour PM2.5 concentration in Beijing Aotizhongxin monitor point. We efficiently use the parameter's spatiotemporal correlation by concatenating the dataset with time series. The predicted PM2.5 concentration was based on meteorology changes over a period. Therefore, the accuracy would increase with the period growing. By extracting the intrinsic features between meteorological and PM2.5 concentration, a fast and accurate prediction was carried out. The R square score reached maximum of 0.98 and remained an average of 0.9295 in the whole test. The average bias of the model is 9 μg on the validation set and 1 μg on the training set. Moreover, the differences between the predictions and expectations can be further regarded as the estimation for the emission change. Such results can provide scientific advice to supervisory and policy workers. [ABSTRACT FROM AUTHOR]

Real-time black carbon (BC) and fine particulate matter (PM) were collected from January to December 2017 with a portable Aethalometer and air sampler in the urban environment of Jamshedpur, India. In the present study, the diurnal and seasonal variations of BC and meteorological variables were analysed. The diurnal variation of BC ranged from 2.1 to 15.5 gm-3, with the lowest concentration during the monsoon season and the highest during the winter season, because in monsoon most of the BC and PM are settled down. The annual mean BC mass concentration was observed at 6.22 ± 3.95 µgm−3. While, fine PM2.5 varied from 41.6 to 260.3 µgm-3, with an annual mean of 97.49 ± 63.52 µgm−3. During monsoon, the BC mass concentration shows a value of ˂ 3 µgm−3. Additionally, the percentage contribution of BC in PM2.5 was determined to be around 5.06% (winter), 6.32% (summer), 5.20% (monsoon), and 7.21% (post-monsoon). The change in BC concerning different meteorological parameters was systematically studied, in which an exciting inverse relationship was noticed between BC concentration and temperature. The correlation between BC and wind speed was also established as a negative connection during study periods. It also observed a negative correlation with precipitation. Finally, the air back trajectory was analysed using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT), which revealed that the essential campaign of aerosol-bounded air parcels was mostly coming from the western part of India, with some marine air masses also joining from the Bay of Bengal during summer and post-monsoon. Overall study shows that highest BC and PM2.5 was observed in the winter season because of mixed layer height (MLH). [ABSTRACT FROM AUTHOR]

The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) was a yearlong expedition supported by the icebreaker R/V Polarstern, following the Transpolar Drift from October 2019 to October 2020. The campaign documented an annual cycle of physical, biological, and chemical processes impacting the atmosphere-ice-ocean system. Of central importance were measurements of the thermodynamic and dynamic evolution of the sea ice. A multi-agency international team led by the University of Colorado/CIRES and NOAA-PSL observed meteorology and surface-atmosphere energy exchanges, including radiation; turbulent momentum flux; turbulent latent and sensible heat flux; and snow conductive flux. There were four stations on the ice, a 10 m micrometeorological tower paired with a 23/30 m mast and radiation station and three autonomous Atmospheric Surface Flux Stations. Collectively, the four stations acquired ~928 days of data. This manuscript documents the acquisition and post-processing of those measurements and provides a guide for researchers to access and use the data products. [ABSTRACT FROM AUTHOR]

In this study, the relationships between meteorological parameters (relative humidity, wind speed, temperature, planetary boundary layer, and rainfall) and air pollutants (particulate matter and gaseous pollutants) have been evaluated during a 3-year period from 2019 to 2021. Diffusion and dispersion of air contaminants were significantly influenced by meteorology over the capital city. The results of correlation matrix and principal component analysis (PCA) suggest a season’s specific influence of meteorological parameters on atmospheric pollutants’ concentration. Temperature has the strongest negative impact on pollutants’ concentration, and all the other studied meteorological parameters negatively (reduced) as well as positively (increased) impacted the air pollutants’ concentration. A two-way process was involved during the interaction of pollutants with relative humidity and wind speed. Due to enhanced moisture-holding capacity during non-monsoon summers, particles get larger and settle down on the ground via dry deposition processes. Winter’s decreased moisture-holding capacity causes water vapour coupled with air contaminants to remain suspended and further deteriorate the quality of the air. High wind speed helps in the dispersion and dilution but a high wind speed associated with dust particles may increase the pollutants’ level downwind side. The PM2.5/PM10 variation revealed that the accumulation effect of relative humidity on PM2.5 was more intense than PM10. Daily average location-specific rainfall data revealed that moderate to high rainfall has a potential wet scavenging impact on both particulate matters and gaseous pollutants. [ABSTRACT FROM AUTHOR]

The use of techniques based on artificial intelligence and machine learning for the simulation of many processes is becoming increasingly important in environmental sciences, with applications in the study of time series of atmospheric properties, such as pollution levels. The present work aimed to evaluate the efficiency of a model based on Artificial Neural Networks (ANN) in the simulation PM10 from meteorological data observed between 2018 and 2019 in Guaíba, southern Brazil, thus also having an estimate of the influence of atmospheric conditions on local air pollution. For this purpose, meteorological and PM10 data obtained from the stations Parque 35, sustained by Celulose Riograndense (CMPC), and A-801, sustained by the National Institute of Meteorology (INMET), were used. The ANN used for the simulation was of the Multilayer Perceptron type, trained by the backpropagation algorithm with cross-validation. The results obtained indicate that the simulation was satisfactory with a Nash–Sutcliffe index (NSE) of 0.64, a linear correlation coefficient (R) of 0.81, a relative error (Er) of 26% and a root mean square error (RMSE) of 7.40 µg/m3. Thus, even with some difficulty in estimating extreme concentrations, the model was suitable for the largest range observed, of 10 µg/m3 to 50 µg/m3. For this dataset, the model proved to be an useful assessment tool and has the potential to be applied operationally to contribute to the monitoring and control of air quality levels both in the study area and in other regions of Brazil and the world. [ABSTRACT FROM AUTHOR]

Lockdown had a significant influence on both local and global economies, and it will take some time for things to return to normal. However, one major advantage of this lockdown has emerged as an improvement in the air quality of cities around the world. In the current study, the impact of the COVID-19 lockdown on the improved air quality is assessed over the four major cities (Amravati, Rajamahendravaram, Tirupati, and Vizag) of Andhra Pradesh (AP). The study results emphasized a substantial reduction in the levels of PM2.5 in the range (40–60%), PM10(20–40%), NO2(26–74%), SO2(5–26%), CO(6–82%) over the study regions on comparison between before and after lockdown periods. The average Air Quality Index (AQI) majorly declined in Vizag (34%), followed by Amaravati (26%), Tirupati (31%), and slightly in Rajamahendravaram (4%). With the decrease in the AQI, it is observed that there is an increase in southwest monsoon rainfall intensity over the AP state.Further, this study identified the potential sources impacting during lockdown towards study areas with the application of HYSPLIT Back Trajectory Analysis (BTA). The analysis revealed that the primary sources were predominantly from the Bay of Bengal (BOB), within the state, and partially from neighbouring regions Odisha, Tamil Nadu, Chhattisgarh, and Telangana. [ABSTRACT FROM AUTHOR]

This paper introduces a fast urban microscale meteorological model with a horizontal resolution of O(10) m, named URBAN (Urban Rapid & Building-Aware Neighborhood), which is capable of rapid assessment of meteorological fields over key urban areas, including wind speed, air temperature, humidity and thermal comfort index, with the execution time less than 10 minutes consuming 1 CPU core. URBAN uses a fast wind diagnostic method to construct three-dimensional (3-D) wind fields surrounding complex building clusters with their geometry resolved explicitly To enhance the accuracy of wind reconstruction and the continuity of the initial wind field around irregular buildings, we propose a new parameterization method based on stream functions, which can accurately characterize the influences of complex urban building clusters on the three-dimensional wind field The model can provide various results for the meteorological service of large outdoor activities, including conventional meteorological elements (wind, temperature, humidity, radiation, etc.) and the Universal Thermal Comfort Index, which is derived from the relationship between physiological processes and environmental meteorological conditions. In this paper, URBAN is applied to develop an automatic analysis and forecast system of microscale meteorological elements over the central Beijing region in summer during a large outdoor event. By comparing with the half-hourly observations from three auto weather stations (AWSs) in the region, the root-mean-square errors (RMSEs) of the modeled 10-meter-height wind speed, 2-meter-height air temperature and humidity are 0.98 m s−1,1.37 °C and 7.37%, respectively. [ABSTRACT FROM AUTHOR]

This paper explored urban-ozone variability under a minimum local emission condition during the nationwide lockdown, i.e., March 25 to May 31, 2020, focusing on the impacts of meteorology and transported pollutants. We analyzed data from the 27 urban sites across the Indo-Gangetic Plain (IGP). Overall, the concentrations of the primary emission products, viz., NO X (= NO + NO 2 ), CO, and PM 2.5 declined by ∼ 51, ~30, and ~37% respectively. However, the concentration of O 3 , a secondary photochemical product was enhanced by ~ 17% across the IGP compared to the normal time. Interestingly, ~ 30 and ~ 20% enhancements of O 3 concentration were calculated over the upper and central regions of the IGP, respectively but that declined by ∼ 29% over the lower region of the IGP. NO / NO 2 ratio was reduced over the upper (~ 38%) and central (~ 22%) IGP during lockdown period, which could result from the intrusion of the polluted continental air mass. In contrast, relatively clean marine air mass prevailed over the lower IGP and the NO / NO 2 ratio increased by ~ 40%. Also, O 3 forming photochemistry could have been suppressed due to lowered sunlight and temperature during the lockdown period over the lower IGP. [ABSTRACT FROM AUTHOR]

The present study focused on the temporal variability, meteorological influences, potential sources, and long-range transport of atmospheric aerosols over two contrasting environments during 2011–2013. We have chosen Agartala (AGR) city in Northeast India as one of our sites representing the rural-continental environment and Patiala (PTA) as an urban site in Northwest India. The seasonal averaged equivalent black carbon (eBC) concentration in AGR ranges from 1.55 to 38.11 µg/m3 with an average value of 9.87 ± 8.17 µg/m3, whereas, at an urban location, PTA value ranges from 1.30 to 15.57 µg/m3 with an average value of 7.83 ± 3.51 µg/m3. The annual average eBC concentration over AGR was observed to be ~ 3 times higher than PTA. Two diurnal peaks (morning and evening) in eBC have been observed at both sites but were observed to be more prominent at AGR than at PTA. Spectral aerosol optical depth (AOD) has been observed to be in the range from 0.33 ± 0.09 (post-monsoon) to 0.85 ± 0.22 (winter) at AGR and 0.47 ± 0.04 (pre-monsoon) to 0.74 ± 0.09 (post-monsoon) at PTA. The concentration of eBC and its diurnal and seasonal variation indicates the primary sources of eBC as local sources, synoptic meteorology, planetary boundary layer (PBL) dynamics, and distant transportation of aerosols. The wintertime higher values of eBC at AGR than at PTA are linked with the transportation of eBC from the Indo-Gangetic Plain (IGP). Furthermore, it is evident that eBC aerosols are transported from local and regional sources, which is supported by concentration-weighted trajectory (CWT) analysis results. [ABSTRACT FROM AUTHOR]

The generation of acoustic-gravity waves by meteorological heat sources in the troposphere and the propagation of these waves to the heights of the upper atmosphere are studied theoretically. Equations that describe separately the generation and propagation of acoustic and gravity waves by a local heat source are derived. The heat source of the waves is divided into partial sources of gravity and acoustic waves (AWs). The power of these partial sources is estimated and it is shown that the power of the sources differs by about a factor of two, regardless of the shape, size, and frequency of the heat source. It is shown that in the case of heat sources of the waves, the generation of gravity waves cannot occur without the corresponding generation of AWs: these waves are generated only in pairs. The splitting of the problem of waves from a heat source into problems of waves from gravity and acoustic sources is illustrated by the direct modeling of these waves. The application of the obtained results to the problem of parametrization of acoustic-gravity waves in general circulation and climate models is discussed. [ABSTRACT FROM AUTHOR]

A large fraction of the uncertainty around future global warming is due to the cooling effect of aerosol-liquid cloud interactions, and in particular to the elusive sign of liquid water path (LWP) adjustments to aerosol perturbations. To quantify this adjustment, we propose a causal approach that combines physical knowledge in the form of a causal graph with geostationary satellite observations of stratocumulus clouds. This allows us to remove confounding influences from large-scale meteorology and to disentangle counteracting physical processes (cloud-top entrainment enhancement and precipitation suppression due to aerosol perturbations) on different timescales. This results in weak LWP adjustments that are time-dependent (first positive then negative) and meteorological regime-dependent. More importantly, the causal approach reveals that failing to account for covariations of cloud droplet sizes and cloud depth, which are, respectively, a mediator and a confounder of entrainment and precipitation influences, leads to an overly negative aerosol-induced LWP response. This would result in an underestimation of the cooling influence of aerosol-cloud interactions. [ABSTRACT FROM AUTHOR]

Air pollutant concentration is a function of emission rates and meteorology. To accurately evaluate the effect of control measures, the meteorological covariate must be corrected from the observations. This study quantified the impacts of emission abatement and meteorological condition on the interannual variations of SO2, NO2, CO, O3, PM10 and PM2.5 concentrations in 31 major Chinese cities using an optimized machine learning-based meteorological normalization technique. Overall, the annual average concentrations of SO2, NO2, CO, PM10 and PM2.5 were reduced by 86%, 51%, 99%, 86% and 88% from 2015 to 2020, respectively, in the studied cities, attributable to their emission reductions. However, the concentration of O3 was found with no significant decrease with the reduction of precursors. Emission abatement notably improved air quality between 2015 and 2018. Such a decline in emissions tended to progressively slow down since 2018. Overall, the meteorological conditions in 2016–2017 and 2018–2019 were unfavorable for a better air quality, while it became favorable in 2020–2021. Specifically, emission abatement in 2021 further lowered the concentrations of SO2, NO2, CO, and PM2.5, while the emission of PM10 increased. And changes in precursors emissions worsened O3 air quality. To meet the demand of improving air quality, more aggressive abatement measures need to be formulated to synergistically reduce NOx, volatile organic compounds, and coarse particles. [ABSTRACT FROM AUTHOR]

Documentation of the skill of a prediction system and its comparison with those of leading modelling centres are crucial in model development. This facilitates understanding the limitations of the existing prediction system and aids in its improvement. The current study compares the extended range prediction skill of the Indian Institute of Tropical Meteorology (IITM) generated real-time forecast with that of the UK Met Office (UKMO) forecast during the boreal summer monsoon season. It is found that both models suffer from biases in the climatological mean state of the monsoon. IITM forecast possesses a skill comparable to UKMO coupled seasonal forecast as compared to the observation in the first two weeks leads over most of the meteorological subdivisions during the monsoon months of June to September. However, at longer leads, the UKMO model outperforms the IITM model, which could be credited to its enhanced skill in predicting the monsoon intraseasonal oscillations and the better representation of monsoon variability at the intraseasonal time scale. [ABSTRACT FROM AUTHOR]

We examine impacts of the Madden–Julian Oscillation (MJO) on Australian springtime temperatures and extremes, explore the mechanisms behind the teleconnections, and assess their prediction in retrospective forecasts using the Bureau of Meteorology's ACCESS-S1 dynamical forecast system. The MJO incites strong and significant warming across southern Australia in phases 2, 3 and 4 when its active convection propagates over the Indian Ocean and Maritime Continent. The heat signal appears strongest in south-eastern Australia during MJO phases 2 and 3 in the vicinity of a deep anticyclonic anomaly which brings warmer airflow to south-western Australia while promoting shortwave radiative heating in the southeast. This occurs as part of a Rossby wave train that emanates from the Indian Ocean and disperses across the Southern Hemisphere along a great circle route towards South America, in response to MJO convective heating on the equator. Importantly, we show the wave train emerges from the divergent outflow from anomalous MJO convection, rather than from the Rossby waves that exist within the MJO's baroclinic structure. Feedbacks between transient eddies and the low frequency flow to the south of Australia and southeast of South America reinforce the wave train in phases 1–3 but act against it during its demise in phase 4. The MJO is a source of subseasonal predictability of springtime heat and cold events over southern Australia in ACCESS-S1 at lead times of 2–4 weeks, yet there remains room for improvement in the model's depiction of the MJO and its teleconnection to the Southern Hemisphere. [ABSTRACT FROM AUTHOR]

In recent years, the issue of governance of the ecological environment has been a subject of high of social concern, and the challenges in the governance of smog is even more remarkable. China has accumulated a vast real-time monitoring system, which mainly obtains pollution data through national monitoring stations, providing a strong foundation for atmospheric governance. However, there are still several challenges in obtaining data from national monitoring stations, such as high cost and difficulty in comprehensive coverage. In the internet world, big data, cloud computing and other technologies are rapidly developing. It is imperative that new countermeasures counterfeit, falsify, and establish a sound long-term supervision mechanism. It is a popular research issue in academia and a policy difficulty faced by government departments. In terms of statistical models, various deep learning methods that have made major breakthroughs in the field of computer vision are used to try to obtain standardized estimates of concentration based on picture data. In general, based on the collection and arrangement of a large amount of image data over the past three years, 7 types of deep learning models have been constructed, which can achieve fast reading and accurate estimation of PM2.5 concentrations. Based on this model, we have put forward practical policy recommendations with a view to helping the early realization of smart monitoring to reduce concentration. For the standardized PM2.5 data, the minimum estimate error can reach 0.42. On this basis, we also put forward policy recommendations with practical value, with a view to helping the early realization of smart monitoring of pollutant concentrations. [ABSTRACT FROM AUTHOR]