Atmosphere

22 pages, 7270 KiB

Open AccessArticle

Microclimate Multivariate Analysis of Two Industrial Areas

by Angela Maria de Arruda, António Lopes and Érico Masiero

Atmosphere 2023, 14(8), 1321; https://doi.org/10.3390/atmos14081321 - 21 Aug 2023

Viewed by 1048

Most of the existing studies on the increase in air temperature (AT) in industrial neighborhoods (UIs) approach the subject from the analysis of the land surface temperature (LST). Therefore, the objective of this study was to analyze, in addition to LST, the variables [...] Read more.

Most of the existing studies on the increase in air temperature (AT) in industrial neighborhoods (UIs) approach the subject from the analysis of the land surface temperature (LST). Therefore, the objective of this study was to analyze, in addition to LST, the variables of air temperature, relative and specific humidity, wind speed and direction, sky view factor and the albedo of the material surfaces, and to verify which of them has a greater impact on the urban microclimate of the UIs of two cities, Sintra/PT and Uberlândia/BR. To develop this analysis, representative sections of industrial urban areas in the previously mentioned cities were selected and computational simulations were carried out with the ENVI-met software to obtain results related to the studied variables. The results of the simulations, analyzed using multivariate analysis, showed that even though the Udia UI has materials with lower albedo (−45%), lower percentages of vegetation (−20%) and lower WS (−40%) than the Sin UI, the AT inside it may be lower than in the unshaded surroundings around 1.3 °C. For Sin UI, a difference in WS of −1.9 m/s, compared to the control points, caused a peak of +1.5 °C in the industrial environment at 13 h, contrary to what happened in Udia UI. Full article

(This article belongs to the Special Issue New Methods for Urban Heat Assessment and Prediction: From IoT to Remote Sensing and AI)

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23 pages, 15454 KiB

Open AccessArticle

Diurnal Characteristics of Heavy Precipitation Events under Different Synoptic Circulation Patterns in the Middle and Lower Reaches of the Yangtze River in Summer

by Haixia Qi, Chunze Lin, Tao Peng, Xiefei Zhi, Chunguang Cui, Wen Chen, Zhiyuan Yin, Tieyuan Shen and Yiheng Xiang

Atmosphere 2023, 14(8), 1320; https://doi.org/10.3390/atmos14081320 - 21 Aug 2023

Viewed by 836

Abstract

Aiming at the rainstorm days (≥50 mm/d) in the middle and lower reaches of the Yangtze River during 2010–2020, the obliquely rotated principal component in T-mode (PCT) method is used to classify the daily mean 850 hPa geopotential height, including Type 1 (vortex/shear [...] Read more.

Aiming at the rainstorm days (≥50 mm/d) in the middle and lower reaches of the Yangtze River during 2010–2020, the obliquely rotated principal component in T-mode (PCT) method is used to classify the daily mean 850 hPa geopotential height, including Type 1 (vortex/shear line), Type 2 (frontal surface), Type 3 (warm shear line), Type 4 (warm inverse trough line), Type 5 (typhoon-westerly trough), and Type 6 (easterly wave). We studied the weather system configurations of different synoptic circulation patterns, their long-term trends, and their impacts on diurnal variations of heavy precipitation and drew the following conclusions: Type 1, Type 2, or Type 3 shows balanced double-peak frequencies of the start time of heavy precipitation during 06:00–08:00 BT and around 17:00 BT, respectively. For Type 1, dynamical lifting and thermal lifting play balanced roles, while for Type 2 and Type 3, dynamical lifting plays a key role. The number of rainstorm stations for Type 1 shows a slight increasing trend, while that for Type 2 or Type 3 shows a significant increasing trend. Type 4, Type 5, or Type 6 show a significant single peak frequency of the start time during 15:00–16:00. Type 5 and Type 6 are mainly affected by dynamical lifting along with favorable cape values, which can trigger rainstorms. The number of rainstorm stations for Type 4 or Type 6 shows a decreasing trend (that for Type 4 is more significant), while that for Type 5 shows a slightly increasing trend. Full article

(This article belongs to the Section Meteorology)

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20 pages, 1703 KiB

Open AccessReview

Kalman Filter and Its Application in Data Assimilation

by Bowen Wang, Zhibin Sun, Xinyue Jiang, Jun Zeng and Runqing Liu

Atmosphere 2023, 14(8), 1319; https://doi.org/10.3390/atmos14081319 - 21 Aug 2023

Cited by 1 | Viewed by 2177

Abstract

In 1960, R.E. Kalman published his famous paper describing a recursive solution, the Kalman filter, to the discrete-data linear filtering problem. In the following decades, thanks to the continuous progress of numerical computing, as well as the increasing demand for weather prediction, target [...] Read more.

In 1960, R.E. Kalman published his famous paper describing a recursive solution, the Kalman filter, to the discrete-data linear filtering problem. In the following decades, thanks to the continuous progress of numerical computing, as well as the increasing demand for weather prediction, target tracking, and many other problems, the Kalman filter has gradually become one of the most important tools in science and engineering. With the continuous improvement of its theory, the Kalman filter and its derivative algorithms have become one of the core algorithms in optimal estimation. This paper attempts to systematically collect and sort out the basic principles of the Kalman filter and some of its important derivative algorithms (mainly including the Extended Kalman filter (EKF), the Unscented Kalman filter (UKF), the Ensemble Kalman filter (EnKF)), as well as the scope of their application, and also to compare their advantages and limitations. In addition, because there are a large number of applications based on the Kalman filter in data assimilation, this paper also provides examples and classifies the applications of both the Kalman filter and its derivative algorithms in the field of data assimilation. Full article

(This article belongs to the Special Issue Data Assimilation Development: Theory, Algorithm, and Applications in Meteorology)

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17 pages, 7256 KiB

Open AccessArticle

The Spatiotemporal Characteristics of Extreme High Temperatures and Urban Vulnerability in Nanchong, China

by Zhaoqi Yin, Weipeng Li, Zhongsheng Chen, Panheng Shui, Xueqi Li and Chanrong Qin

Atmosphere 2023, 14(8), 1318; https://doi.org/10.3390/atmos14081318 - 21 Aug 2023

Cited by 1 | Viewed by 870

Abstract

It is necessary to alleviate the high temperatures and heat wave disasters in cities in southwest China that are beginning to occur because of global warming. During this study, the spatial and temporal characteristics of heat waves in Nanchong from 1961 to 2022 [...] Read more.

It is necessary to alleviate the high temperatures and heat wave disasters in cities in southwest China that are beginning to occur because of global warming. During this study, the spatial and temporal characteristics of heat waves in Nanchong from 1961 to 2022 are analyzed by using the signal smooth method and mutation test. Based on the meteorological data and socioeconomic statistics, the entropy value method is used to obtain the indicator weights to construct a heat wave social vulnerability evaluation index system and conduct vulnerability assessments and classifications. The results show that: ① The heat wave indicators in Nanchong show an increasing trend, although there is a low period of heat waves from 1980 to 1995. Additionally, there are significant mutations in the number of days, frequency, and intensity of high-temperature heat waves from 2009 to 2011, which may be caused by the abnormal high-pressure belt in the mid-latitude. ② The distribution of exposure, sensitivity, and adaptability in Nanchong City, under high temperatures, is uneven in space. Generally, the indicators in the north are lower than those in the south. ③ The high-vulnerability counties are mainly distributed in the east and west of Nanchong, the proportion of the medium social vulnerability index areas are more than a half, while the dominant factor in the distribution pattern is natural factors. ④ The Western Pacific Subtropical High (WPSH) anomaly directly led to the extremely high temperature in Nanchong in the summer of 2022, and the urbanization process index shows a significant positive correlation with the trend of high temperatures and heat waves in Nanchong. Full article

(This article belongs to the Section Climatology)

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8 pages, 4809 KiB

Open AccessCommunication

Thermospheric Density Response to the QBO Signal

by Bo Li, Ruifei Cui and Libin Weng

Atmosphere 2023, 14(8), 1317; https://doi.org/10.3390/atmos14081317 - 21 Aug 2023

Cited by 1 | Viewed by 719

Abstract

In this study, we focused on the periodic variations of global average thermospheric density, derived from orbital decay measurements of about 5000 space objects from 1967 to 2013, by using the wavelet power spectrum method. The results demonstrated that the thermospheric density showed [...] Read more.

In this study, we focused on the periodic variations of global average thermospheric density, derived from orbital decay measurements of about 5000 space objects from 1967 to 2013, by using the wavelet power spectrum method. The results demonstrated that the thermospheric density showed an ~11-year period, with semiannual and annual variations, while the seasonal variation was usually more significant under high solar activity conditions. Importantly, we investigated the possible link between the thermospheric density and the QBO, with the aid of the Global Average Mass Density Model (GAMDM) and the different density residuals method. The difference between the measured density and the GAMDM empirical model seemingly had QBO signal, but the ratio of them revealed that the QBO signal could not detect in the thermospheric density. Comprehensively, we found that the stratospheric QBO cannot impact on the thermosphere, and more data and numerical modeling are needed for further validation. Full article

(This article belongs to the Special Issue Observations and Analysis of Upper Atmosphere)

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13 pages, 973 KiB

Open AccessArticle

Exact Intermittent Solutions in a Turbulence Multi-Branch Shell Model

by Ben Ajzner and Alexandros Alexakis

Atmosphere 2023, 14(8), 1316; https://doi.org/10.3390/atmos14081316 - 20 Aug 2023

Viewed by 832

Abstract

Reproducing complex phenomena with simple models marks our understanding of the phenomena themselves, and this is what Jack Herring’s work demonstrated multiple times. In that spirit, this work studies a turbulence shell model consisting of a hierarchy of structures of different scales [...] Read more.

Reproducing complex phenomena with simple models marks our understanding of the phenomena themselves, and this is what Jack Herring’s work demonstrated multiple times. In that spirit, this work studies a turbulence shell model consisting of a hierarchy of structures of different scales

ℓ_{n}

such that each structure transfers its energy to two substructures of scale

ℓ_{n + 1} = ℓ_{n} / λ

. For this model, we construct exact inertial range solutions that display intermittency, i.e., absence of self-similarity. Using a large ensemble of these solutions, we investigate how the probability distributions of the velocity modes change with scale. It is demonstrated that, while velocity amplitudes are not scale-invariant, their ratios are. Furthermore, using large deviation theory, we show how the probability distributions of the velocity modes can be re-scaled to collapse in a scale-independent form. Finally, we discuss the implications the present results have for real turbulent flows. Full article

(This article belongs to the Special Issue Turbulence from Earth to Planets, Stars and Galaxies—Commemorative Issue Dedicated to the Memory of Jackson Rea Herring)

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14 pages, 11000 KiB

Open AccessArticle

Responses to the Preparation of the 2021 M7.4 Madoi Earthquake in the Lithosphere–Atmosphere–Ionosphere System

by Yali Wang, Weiyu Ma, Binbin Zhao, Chong Yue, Peiyu Zhu, Chen Yu and Li Yao

Atmosphere 2023, 14(8), 1315; https://doi.org/10.3390/atmos14081315 - 20 Aug 2023

Viewed by 1208

Abstract

The purpose of this work is to investigate the responses of multiple parameters to the Madoi earthquake preparation. A new method is employed to extract anomalies in a geomagnetic field. The results show that there were abnormal changes in the lithosphere, atmosphere, and [...] Read more.

The purpose of this work is to investigate the responses of multiple parameters to the Madoi earthquake preparation. A new method is employed to extract anomalies in a geomagnetic field. The results show that there were abnormal changes in the lithosphere, atmosphere, and ionosphere near the epicenter before the earthquake. Despite the differences in spatial and temporal resolutions, the increase in geomagnetic residuals in the lithosphere exhibits similar temporal characteristics to the enhancement of thermal infrared radiation in the atmosphere. Two high–value regions are present in the ground–based geomagnetic high residuals and the ionospheric disturbances. The northern one is around the epicenter of the Madoi earthquake. Near the southern one, an M6.4 Yangbi earthquake occurred four hours before the Madoi earthquake. In this study, we have observed almost all of the physical phenomena that can occur during the preparation of an earthquake, as predicted using the electrostatic channel model. It can be inferred that the electrostatic channel is a possible mechanism for coupling between the lithosphere, atmosphere, and ionosphere during the Madoi earthquake. Full article

(This article belongs to the Special Issue Electromagnetic Observations and Their Applications in Earthquake Research)

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13 pages, 222 KiB

Open AccessEditorial

Fog Decision Support Systems: A Review of the Current Perspectives

by Driss Bari, Thierry Bergot and Robert Tardif

Atmosphere 2023, 14(8), 1314; https://doi.org/10.3390/atmos14081314 - 20 Aug 2023

Cited by 3 | Viewed by 1544

Abstract

Accurate and timely fog forecasts are needed to support decision making for various activities which are critically affected by low visibility conditions [...] Full article

(This article belongs to the Special Issue Decision Support System for Fog)

16 pages, 5381 KiB

Open AccessArticle

Analysis of the Temporal and Spatial Characteristics of PWV and Rainfall with the Typhoon Movement: A Case Study of ‘Meihua’ in 2022

by Zhikun Li, Jin Wang, Changhao Wei and Jiaye Yu

Atmosphere 2023, 14(8), 1313; https://doi.org/10.3390/atmos14081313 - 20 Aug 2023

Viewed by 860

Abstract

The serious and frequent typhoon activities can easily cause extreme precipitation weather in the eastern coastal area of China, which is affected by land and sea differences. To explore the temporal and spatial characteristics of Precipitable Water Vapor (PWV) and rainfall during the [...] Read more.

The serious and frequent typhoon activities can easily cause extreme precipitation weather in the eastern coastal area of China, which is affected by land and sea differences. To explore the temporal and spatial characteristics of Precipitable Water Vapor (PWV) and rainfall during the typhoon period, the data of the conspicuous case named ‘Meihua’ in 2022 is adopted in analysis. In this paper, firstly, the accuracy of the PWV retrieved by ERA5 was evaluated, which met the experimental analysis requirements, compared with the conference value of the Radiosonde (RS). Secondly, the correlation between PWV, rainfall and the typhoon path were analyzed qualitatively and quantitatively, using 16 meteorological stations in the typhoon path. The results indicated that PWV reached its peak value 2–6 h than rainfall, which was an important reference for rainfall forecasting. Then, the ‘Pearson correlation coefficient’ method was used for the quantitative evaluation of the correlation between PWV and the distance of the ‘weather station-typhoon’. The results showed that PWV had an obvious upward trend, with a decrease in the distance between the ‘weather station-typhoon’. The variation in PWV is intense at a reduced distance, and can reach its peak 16 h before the arrival of the typhoon. A strong negative correlation was demonstrated, with an average value of −0.73 for the Pearson correlation coefficient. Analyzing the temporal and spatial changes of the typhoon track, PWV and rainfall, the results show that before the typhoon passes through the region, both the PWV and rainfall certainly reach their maximum. The variation trends of PWV and rainfall in the period of the typhoon are significantly consistent. The center of PWV and rainfall is mainly located on the northwest side of the typhoon center, which showed obvious asymmetry. Full article

(This article belongs to the Section Meteorology)

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3 pages, 190 KiB

Open AccessEditorial

Shipping Emissions and Air Pollution: Latest Methodological Developments and Applications

by Yuanqing Zhu and Long Liu

Atmosphere 2023, 14(8), 1312; https://doi.org/10.3390/atmos14081312 - 20 Aug 2023

Viewed by 1013

Abstract

Shipping, which accounts for over 80% of international trade transportation, is the most cost-effective and efficient mode of transportation [...] Full article

(This article belongs to the Special Issue Shipping Emissions and Air Pollution)

25 pages, 3711 KiB

Open AccessReview

Advances of Phase-Field Model in the Numerical Simulation of Multiphase Flows: A Review

by Jingfa Li, Dukui Zheng and Wei Zhang

Atmosphere 2023, 14(8), 1311; https://doi.org/10.3390/atmos14081311 - 19 Aug 2023

Cited by 1 | Viewed by 1881

Abstract

The phase-field model (PFM) is gaining increasing attention in the application of multiphase flows due to its advantages, in which the phase interface is treated as a narrow layer and phase parameters change smoothly and continually at this thin layer. Thus, the construction [...] Read more.

The phase-field model (PFM) is gaining increasing attention in the application of multiphase flows due to its advantages, in which the phase interface is treated as a narrow layer and phase parameters change smoothly and continually at this thin layer. Thus, the construction or tracking of the phase interface can be avoided, and the bulk phase and phase interface can be simulated integrally. PFM provides a useful alternative that does not suffer from problems with either the mass conservation or the accurate computation of surface tension. In this paper, the state of the art of PFM in the numerical modeling and simulation of multiphase flows is comprehensively reviewed. Starting with a brief description of historical developments in the PFM, we continue to take a tour into the basic concepts, fundamental theory, and mathematical models. Then, the commonly used numerical schemes and algorithms for solving the governing systems of PFM in the application of multiphase flows are presented. The various applications and representative results, especially in non-match density scenarios of multiphase flows, are reviewed. The primary challenges and research focus of PFM are analyzed and summarized as well. This review is expected to provide a valuable reference for PFM in the application of multiphase flows. Full article

(This article belongs to the Special Issue Recent Developments in Carbon Emissions Reduction Approaches)

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16 pages, 7488 KiB

Open AccessArticle

Characteristics of Advection Fog at Qingdao Liuting International Airport

by Zhiwei Zhang, Yunying Li, Laurent Li, Chao Zhang and Guorong Sun

Atmosphere 2023, 14(8), 1310; https://doi.org/10.3390/atmos14081310 - 19 Aug 2023

Viewed by 866

Abstract

The advection fog characteristics at Qingdao Liuting International Airport during 2000–2022 are studied based on surface observation, sounding and reanalysis data. Surface observation data show that there were two types of fog: evaporation fog (EF) dominated by northwesterly wind in winter and cooling [...] Read more.

The advection fog characteristics at Qingdao Liuting International Airport during 2000–2022 are studied based on surface observation, sounding and reanalysis data. Surface observation data show that there were two types of fog: evaporation fog (EF) dominated by northwesterly wind in winter and cooling fog (CF) dominated by southeasterly wind in spring and summer. CF is thicker than EF due to different planetary boundary layer (PBL) structures. For EF, the middle and low troposphere are affected by dry and cold air, while CF is affected by warm and moist air below 850 hPa. When EF formed, downdrafts and a positive vertical gradient of the pseudo-equivalent potential temperature indicate stable PBL, surface heat flux is upward from sea to atmosphere and surface wind diverges near the air–sea interface. When CF formed, these characteristics are reversed. Fog is significantly affected by sea–land–atmosphere interactions. The moisture source is mainly from surface fluxes released by the Yellow Sea in the case of EF, while it is from moist air at low latitudes and local land transpiration in the case of CF. The difference in temperature between the sea surface and surface air changes from the range of 0–8 K for EF but from −4–0 K for CF. Full article

(This article belongs to the Section Meteorology)

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15 pages, 5153 KiB

Open AccessArticle

Short-Term Rainfall Forecasting by Combining BP-NN Algorithm and GNSS Technique for Landslide-Prone Areas

by Zufeng Li, Yongjie Ma, Jing Liu, Yang Liu, Wei Ren and Qingzhi Zhao

Atmosphere 2023, 14(8), 1309; https://doi.org/10.3390/atmos14081309 - 18 Aug 2023

Cited by 1 | Viewed by 1027

Abstract

Extreme rainfall is the main contributing factor to landslides. Therefore, it is of great significance to monitor and forecast short-term rainfall in landslide-prone areas. However, the spatial scale of landslide-prone areas is small, and traditional numerical forecast models have difficulty in accurately forecasting [...] Read more.

Extreme rainfall is the main contributing factor to landslides. Therefore, it is of great significance to monitor and forecast short-term rainfall in landslide-prone areas. However, the spatial scale of landslide-prone areas is small, and traditional numerical forecast models have difficulty in accurately forecasting rainfall on this scale. To solve the above problem, this study proposes a short-term rainfall forecasting method for landslide-prone areas by combining the back-propagation neural network (BP-NN) algorithm and global navigation satellite system (GNSS) observations to achieve accurate short-term rainfall forecasting in landslide-prone areas. Firstly, a high-precision atmospheric weighted-average temperature (Tm) model is established using radiosonde data to obtain high-precision precipitable water vapor (PWV) estimates. Secondly, the BP-NN algorithm is introduced, and the GNSS-derived PWV, temperature and pressure from a meteorological station, and rainfall for the previous and next hour are used as input parameters to establish a BP-NN-based rainfall forecast model. As an illustrative case, experiments are conducted in a landslide-prone area in Yunnan Province using data from 15 GNSS stations and the corresponding meteorological station. Statistical results show that the established regional Tm model has high accuracy, with an average root mean square (RMS) and bias of 3 K and 0.15 K, respectively. In addition, the short-term rainfall forecast model based on the BP algorithm achieves a true detection rate of up to 93.70% and a false forecast rate of as low as 38.30%, which is significant for short-term rainfall forecasting in landslide-prone areas. Full article

(This article belongs to the Special Issue GNSS Remote Sensing in Atmosphere and Environment)

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17 pages, 1513 KiB

Open AccessArticle

Wind Speed Modeling for Wind Farms Based on Deterministic Broad Learning System

by Lin Wang and Anke Xue

Atmosphere 2023, 14(8), 1308; https://doi.org/10.3390/atmos14081308 - 18 Aug 2023

Viewed by 749

Abstract

As the penetration rate of wind power in the grid continues to increase, wind speed forecasting plays a crucial role in wind power generation systems. Wind speed prediction helps optimize the operation and management of wind power generation, enhancing efficiency and reliability. However, [...] Read more.

As the penetration rate of wind power in the grid continues to increase, wind speed forecasting plays a crucial role in wind power generation systems. Wind speed prediction helps optimize the operation and management of wind power generation, enhancing efficiency and reliability. However, wind speed is a nonlinear and nonstationary system, and traditional statistical methods and classical intelligent algorithms struggle to cope with dynamically updating operating conditions based on sampled data. Therefore, from the perspective of optimizing intelligent algorithms, a wind speed prediction model for wind farms was researched. In this study, we propose the Deterministic Broad Learning System (DBLS) algorithm for wind farm wind speed prediction. It effectively addresses the issues of data saturation and local minima that often occur in continuous-time system modeling. To adapt to the continuous updating of sample data, we improve the sample input of the Broad Learning System (BLS) by using a fixed-width input. When new samples are added, an equivalent number of old samples is removed to maintain the same input width, ensuring the feature capture capability of the model. Additionally, we construct a dataset of wind speed samples from 10 wind farms in Gansu Province, China. Based on this dataset, we conducted comparative experiments between the DBLS and other algorithms such as Random Forest (RF), Support Vector Regression (SVR), Extreme Learning Machines (ELM), and BLS. The comparison analysis of different algorithms was conducted using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Among them, the DBLS algorithm exhibited the best performance. The RMSE of the DBLS ranged from 0.762 m/s to 0.776 m/s, and the MAPE of the DBLS ranged from 0.138 to 0.149. Full article

(This article belongs to the Special Issue Atmospheric Data Prediction Using Statistical, and Machine Learning Approaches of Artificial Intelligence)

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16 pages, 5620 KiB

Open AccessArticle

Modeling of a Rotary Adsorber for Continuous Capture of Indoor Carbon Dioxide

by Lumeng Liu, Ning Wan, Wenmao Zeng, Jiachen Shi, Meng Liu and Huan Liu

Atmosphere 2023, 14(8), 1307; https://doi.org/10.3390/atmos14081307 - 18 Aug 2023

Viewed by 946

Abstract

Removing indoor CO₂ as a pollutant via solid sorbents is a promising solution to maintaining acceptable indoor air quality while minimizing the energy consumption of ventilation. Compared to fixed-bed and fluidized-bed configurations, which require at least two beds to allow for continuous [...] Read more.

Removing indoor CO₂ as a pollutant via solid sorbents is a promising solution to maintaining acceptable indoor air quality while minimizing the energy consumption of ventilation. Compared to fixed-bed and fluidized-bed configurations, which require at least two beds to allow for continuous operation, a rotary adsorber is more compact and suitable to be integrated into the ventilation systems of buildings. In the present study, a regenerative rotary adsorber based on temperature swing adsorption was modeled to investigate continuous CO₂ capture in an indoor environment. The governing equations of heat and mass transfer processes associated with the capture were established and coded in ANSYS Fluent software. The spatiotemporal variations of CO₂ concentration and temperature in gas and solid phases within the rotary adsorber were obtained. The key findings are: (1) adjusting the speed mainly affects circumferential concentration and temperature distribution, but has little impact on axial concentration and temperature; (2) Increasing desorption inlet flow rate has little impact on adsorption outlet concentration, but significantly decreases desorption outlet concentration; (3) Raising desorption inlet temperature can increase both adsorption and desorption outlet average concentrations; (4) Reducing the volume proportion of the desorption sector will slightly increase adsorption outlet concentration and slightly decrease desorption outlet concentration, but barely affects average adsorption and desorption outlet temperatures. Full article

(This article belongs to the Section Air Pollution Control)

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22 pages, 8664 KiB

Open AccessArticle

Differences in Outdoor Thermal Comfort between Local and Non-Local Tourists in Winter in Tourist Attractions in a City in a Severely Cold Region

by Zheming Liu, Weiqing Xu, Chenxin Hu, Caiyi Zhao, Tong Yang, Tianyu Xi and Qiaochu Wang

Atmosphere 2023, 14(8), 1306; https://doi.org/10.3390/atmos14081306 - 17 Aug 2023

Viewed by 1062

Abstract

The unique climate and the landscape of severely cold regions in winter attract many tourists. The outdoor thermal environment affects the space use and the tourist experience, becoming one of the key factors in the design of tourist attractions. The outdoor thermal comfort [...] Read more.

The unique climate and the landscape of severely cold regions in winter attract many tourists. The outdoor thermal environment affects the space use and the tourist experience, becoming one of the key factors in the design of tourist attractions. The outdoor thermal comfort of tourists from different regions should be considered, but it has been poorly studied in winter in severely cold regions. This paper explores the differences in outdoor thermal comfort in winter between local and non-local tourists through the field measurement of the thermal environment and a questionnaire survey of thermal comfort at tourist attractions in Harbin, China. The results show that the proportion of local tourists who expect the air temperature and solar radiation to rise in winter is higher than that of non-local tourists. The thermal sensation vote of local tourists is generally higher than that of non-local tourists. When the Physiologically Equivalent Temperature (PET) < −6 °C, the thermal satisfaction of non-local tourists is higher than that of local tourists. When the PET value is −10 °C, the thermal comfort of non-local tourists is the highest. The thermal comfort decreases with the rise or fall of the PET value. When −28 °C < PET < −7 °C, the thermal comfort of non-local tourists is generally higher than that of local tourists. This paper provides a reference and evaluation basis for urban tourist attractions’ outdoor thermal environment design in severely cold regions. Full article

(This article belongs to the Section Biometeorology)

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20 pages, 6374 KiB

Open AccessArticle

Spatiotemporal Analysis of Urban Carbon Metabolism and Its Response to Land Use Change: A Case Study of Beijing, China

by Yingjie Hu, Jin Sun and Ji Zheng

Atmosphere 2023, 14(8), 1305; https://doi.org/10.3390/atmos14081305 - 17 Aug 2023

Viewed by 1019

Abstract

Analyzing the spatial pattern of urban carbon metabolism could provide insights into spatial adjustments to mitigate the greenhouse effect. Using CASA and empirical coefficients, we quantitatively analyzed and mapped the spatial pattern of the urban carbon metabolism of Beijing and its response to [...] Read more.

Analyzing the spatial pattern of urban carbon metabolism could provide insights into spatial adjustments to mitigate the greenhouse effect. Using CASA and empirical coefficients, we quantitatively analyzed and mapped the spatial pattern of the urban carbon metabolism of Beijing and its response to land use change from 2000 to 2020. The results showed that the carbon emission rate of Beijing increased in the first decade and decreased in the next, while the carbon sequestration rate kept rising over the past two decades. The net carbon emission rate of Beijing averaged 1284.52 × 10⁷ kg C yr⁻¹, indicating that the city functioned as a net carbon source throughout the study period. The most harmful carbon transitions were always sourced from the southeastern suburban area, where the natural components were converted to artificial components, while beneficial carbon transitions were in the urban central area, where the artificial component with a higher carbon emission density was converted to the other types of artificial components with relatively a lower carbon emission density, and the northwestern mountainous areas, where land use types transferred out of and into the forest or grass. The spatiotemporal change in urban carbon metabolism was highly correlated with the land use transition, and the land use change from cultivated land to industrial land accounted for 34.87% of the harmful carbon transitions. These results of key carbon flows and hotspots provide insights for policymaking in the effective management of reducing carbon emissions and enhancing carbon sequestration. Full article

(This article belongs to the Special Issue Carbon Emission and Carbon Neutrality in China)

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21 pages, 6439 KiB

Open AccessArticle

Assessing the Impacts of COVID-19 on SO₂, NO₂, and CO Trends in Durban Using TROPOMI, AIRS, OMI, and MERRA-2 Data

by Boitumelo Mokgoja, Paidamwoyo Mhangara and Lerato Shikwambana

Atmosphere 2023, 14(8), 1304; https://doi.org/10.3390/atmos14081304 - 17 Aug 2023

Viewed by 1204

Abstract

This research report investigated the impacts of the COVID-19 lockdown restrictions on CO, SO₂, and NO₂ trends in Durban from 2019 to 2021. The COVID-19 lockdown restrictions proved to decrease greenhouse gas (GHG) emissions globally; however, the decrease in GHG [...] Read more.

This research report investigated the impacts of the COVID-19 lockdown restrictions on CO, SO₂, and NO₂ trends in Durban from 2019 to 2021. The COVID-19 lockdown restrictions proved to decrease greenhouse gas (GHG) emissions globally; however, the decrease in GHG emissions was for a short period only. Space-borne technology has been used by researchers to understand the spatial and temporal trends of GHGs. This study used Sentinel-5P to map the spatial distribution of CO, SO₂, and NO₂. Use was also made of the Atmospheric Infrared Sounder (AIRS), Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), and the Ozone Monitoring Instrument (OMI) to understand the temporal trends of CO, SO₂, and NO₂, respectively. To validate the results of this study, we used the Sequential Mann–Kendall (SQMK) test. This study indicated that there were no significant changes in all the investigated gases. Therefore, this study failed to reject the null hypothesis of the SQMK test that there was no significant trend for all investigated gasses. Increasing trends were observed for CO, SO₂, and NO₂ trends during winter months throughout the study period, whereas a decreasing trend was observed in all investigated gases during the spring months. This shows that meteorological factors play a significant role in the accumulation of air pollutants in the atmosphere. Most importantly, this study has noted that there was an inverse relationship between the trends of all investigated gases and the COVID-19 lockdown restrictions. Full article

(This article belongs to the Special Issue Sources, Spatio-Temporal Distribution and Health Effects of Atmospheric Compositions)

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20 pages, 2393 KiB

Open AccessArticle

Revisiting Climate-Related Agricultural Losses across South America and Their Future Perspectives

by Célia M. Gouveia, Flávio Justino, Carlos Gurjao, Lormido Zita and Catarina Alonso

Atmosphere 2023, 14(8), 1303; https://doi.org/10.3390/atmos14081303 - 17 Aug 2023

Viewed by 1511

Abstract

Climate plays a major role in the spatiotemporal distribution of most agricultural systems, and the economic losses related to climate and weather extremes have escalated significantly in the last decades. South America is one of the most productive agricultural areas of the globe. [...] Read more.

Climate plays a major role in the spatiotemporal distribution of most agricultural systems, and the economic losses related to climate and weather extremes have escalated significantly in the last decades. South America is one of the most productive agricultural areas of the globe. In recent years, remote sensing data and geographic information systems have been used to improve geo-environmental hazard assessment. However, food security is still highly dependent on small farmer practices that are frequently the most vulnerable to climate extremes. This work reviews climate and weather extremes’ impacts on crop production for South American countries, focusing on the projected ones considering different climate scenarios and countries. A positive trend in the productivity of maize, mainly related to agricultural improvements, was recently observed in Colombia, Ecuador, and Uruguay by up to 200%, as well as in the case of soybean in Bolivia and Uruguay by about 125%. Despite the generalized adverse impacts of climate extremes, results from agrometeorological models generally indicate an increase in crop production in southern regions of Chile (and highlands) and Brazil mainly related to increased temperature. Positive impacts in response to CO₂ fertilization are also foreseen in Peru and Brazil (southeast, south, and Minas Gerais); in particular, in Brazil, increases in productivity can be raised by about 40%. The use of double-cropping systems, although with very good results in recent years, may also be at risk in a few decades, mainly due to forecasted precipitation decrease, delay in rainy season onset, and temperature increase. The development of timely early warning systems is imperative to produce technically accurate alerts and the interpretation of the risk assessment based on the link between producers and consumers. Promoting climate index insurance is crucial to build resilient food production, but its implementation should rely on regional or international support systems. Moreover, the implementation of adaptation and mitigation also requires climate-resilient technologies that involve an interdisciplinary approach. Full article

(This article belongs to the Section Meteorology)

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20 pages, 5618 KiB

Open AccessArticle

Intriguing Aspects of Polar-to-Tropical Mesospheric Teleconnections during the 2018 SSW: A Meteor Radar Network Study

by Sunkara Eswaraiah, Kyong-Hwan Seo, Kondapalli Niranjan Kumar, Andrey V. Koval, Madineni Venkat Ratnam, Chalachew Kindie Mengist, Gasti Venkata Chalapathi, Huixin Liu, Young-Sil Kwak, Eugeny Merzlyakov, Christoph Jacobi, Yong-Ha Kim, Sarangam Vijaya Bhaskara Rao and Nicholas J. Mitchell

Atmosphere 2023, 14(8), 1302; https://doi.org/10.3390/atmos14081302 - 17 Aug 2023

Viewed by 992

Abstract

Using a network of meteor radar observations, observational evidence of polar-to-tropical mesospheric coupling during the 2018 major sudden stratosphere warming (SSW) event in the northern hemisphere is presented. In the tropical lower mesosphere, a maximum zonal wind reversal (−24 m/s) is noted and [...] Read more.

Using a network of meteor radar observations, observational evidence of polar-to-tropical mesospheric coupling during the 2018 major sudden stratosphere warming (SSW) event in the northern hemisphere is presented. In the tropical lower mesosphere, a maximum zonal wind reversal (−24 m/s) is noted and compared with that identified in the extra-tropical regions. Moreover, a time delay in the wind reversal between the tropical/polar stations and the mid-latitudes is detected. A wide spectrum of waves with periods of 2 to 16 days and 30–60 days were observed. The wind reversal in the mesosphere is due to the propagation of dominant intra-seasonal oscillations (ISOs) of 30–60 days and the presence and superposition of 8-day period planetary waves (PWs). The ISO phase propagation is observed from high to low latitudes (60° N to 20° N) in contrast to the 8-day PW phase propagation, indicating the change in the meridional propagation of winds during SSW, hence the change in the meridional circulation. The superposition of dominant ISOs and weak 8-day PWs could be responsible for the delay of the wind reversal in the tropical mesosphere. Therefore, this study has strong implications for understanding the reversed (polar to tropical) mesospheric meridional circulation by considering the ISOs during SSW. Full article

(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)

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19 pages, 9635 KiB

Open AccessArticle

Spatial and Temporal Variability of Extreme Precipitation Events in the Southeastern United States

by Mohammad Siddiqur Rahman, Jason C. Senkbeil and David J. Keellings

Atmosphere 2023, 14(8), 1301; https://doi.org/10.3390/atmos14081301 - 17 Aug 2023

Cited by 1 | Viewed by 1676

Abstract

Much of the Southeastern United States (SeUS) has experienced an increasing number of extreme precipitation events in recent decades. Characterizing these extreme precipitation events is critical for assessing risk from future hydroclimatic extremes and potential flash flooding. A threshold of one inch per [...] Read more.

Much of the Southeastern United States (SeUS) has experienced an increasing number of extreme precipitation events in recent decades. Characterizing these extreme precipitation events is critical for assessing risk from future hydroclimatic extremes and potential flash flooding. A threshold of one inch per hour (1IPH) was used to indicate an extreme precipitation event. Non-parametric tests were run to identify trends in 1IPH event frequency and locate time series change points. In the last 20 years, 1IPH events increased by 53 percent in the SeUS, and 21/61 stations recorded significant increasing trends. A change point is identified in 15/61 stations. June, July, and August are generally the peak time for 1IPH events, but Florida, Louisiana, and Mississippi recorded longer peak seasons. For the time between events, 17/61 stations recorded significant decreasing trends, implying that 1IPH events are increasing in frequency. Four teleconnection indices were positively correlated with 1IPH events. The SeUS experiences considerable tropical cyclone-induced extreme precipitation, yet only seven percent of 1IPH events overlapped with tropical cyclones. Therefore, the increasing frequency of 1IPH events is likely the result of a combination of baroclinic frontal zones or regional and mesoscale convective features. Causes for the increasing frequency of 1IPH events require further research. Full article

(This article belongs to the Section Climatology)

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14 pages, 7383 KiB

Open AccessArticle

Multi-Temporal Variabilities of Extreme Precipitation over Central Asia and Associated Planetary-Scale Climate Modes

by Wei Tang, Fang Xiao and Sheng Lai

Atmosphere 2023, 14(8), 1300; https://doi.org/10.3390/atmos14081300 - 17 Aug 2023

Viewed by 725

Abstract

Arid- and semi-arid Central Asia is particularly sensitive to climate change. The changes in extreme precipitation in Central Asia stemming from climate warming are the subject of intense debate within the scientific community. This study employed a Morlet wavelet analysis to examine the [...] Read more.

Arid- and semi-arid Central Asia is particularly sensitive to climate change. The changes in extreme precipitation in Central Asia stemming from climate warming are the subject of intense debate within the scientific community. This study employed a Morlet wavelet analysis to examine the annual occurrence number of extreme precipitation in Central Asia from May to September during the period of 1951–2005. Their modulating planetary-scale climate modes were identified by using linear regression analysis. Two major scales of the temporal variability were derived: 2–3.9 years and 4–6 years. The dominant variability was a 2–3.9-year scale and was associated with the negative phase of the Polar/Eurasia (POL) pattern. The 4–6-year scale provided a secondary contribution and was closely linked to the negative phase of the North Atlantic Oscillation (NAO). These planetary climate modes acted as precursors of extreme precipitation over Central Asia. The negative phase of POL directly contributed to a negative height anomaly over Central Asia, which was intimately related to extreme precipitation. In contrast, the negative NAO phase possibly manifested as a Rossby wave source, which was subsequently exported to Central Asia through a negative–positive–negative Rossby wave train. Full article

(This article belongs to the Special Issue Climate Extremes in China)

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21 pages, 933 KiB

Open AccessArticle

Long Memory Cointegration in the Analysis of Maximum, Minimum and Range Temperatures in Africa: Implications for Climate Change

by OlaOluwa S. Yaya, Oluwaseun A. Adesina, Hammed A. Olayinka, Oluseyi E. Ogunsola and Luis A. Gil-Alana

Atmosphere 2023, 14(8), 1299; https://doi.org/10.3390/atmos14081299 - 16 Aug 2023

Cited by 2 | Viewed by 880

Abstract

This paper deals with the analysis of the temperatures in a group of 36 African countries. By looking at the maximum, minimum and the range (the difference between the maximum and the minimum) and using a long memory model based on fractional integration [...] Read more.

This paper deals with the analysis of the temperatures in a group of 36 African countries. By looking at the maximum, minimum and the range (the difference between the maximum and the minimum) and using a long memory model based on fractional integration and cointegration, we first show that all series display a long memory pattern, with a significant positive time trend in 29 countries for the maximum temperatures and in 33 for the minimum ones. Looking at the range, the estimated value for the order of integration is smaller than the one based on maximum or minimum temperatures in 17 countries. Performing fractional cointegration tests between the maximum and minimum temperatures, our results indicate that the two series cointegrate in the classical sense (i.e., with a short memory equilibrium relationship) in a group of 11 countries, and there is another group of eight countries displaying cointegration in the fractional sense. The remaining 17 countries with no evidence of cointegration are therefore at a very high risk of climate change due to the absence of long-term co-movement in their maximum and minimum temperatures. Findings in this paper are of tremendous interpretations and relevance for the analysis and climate projections in Africa. Full article

(This article belongs to the Special Issue Statistical Approaches in Climatic Parameters Prediction)

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20 pages, 12725 KiB

Open AccessArticle

Study of Methane Emission and Geological Sources in Northeast China Permafrost Area Related to Engineering Construction and Climate Disturbance Based on Ground Monitoring and AIRS

by Zhichao Xu, Yunshan Chen, Wei Shan, Chao Deng, Min Ma, Yuexing Wu, Yu Mao, Xingyu Ding and Jing Ji

Atmosphere 2023, 14(8), 1298; https://doi.org/10.3390/atmos14081298 - 16 Aug 2023

Cited by 1 | Viewed by 1002

Abstract

China’s largest high-latitude permafrost distribution zone is in Northeast China. With the intensification of global warming and engineering construction, the carbon stored in permafrost will gradually thaw and be released in the form of methane gas. However, research on the changes in methane [...] Read more.

China’s largest high-latitude permafrost distribution zone is in Northeast China. With the intensification of global warming and engineering construction, the carbon stored in permafrost will gradually thaw and be released in the form of methane gas. However, research on the changes in methane concentration and emission sources in this area is still unclear. In this paper, the AIRS (Atmospheric Infrared Sounder) data carried by the Aqua satellite were used to analyze the distribution and change trends in the overall methane concentration in the near-surface troposphere in Northeast China from 2003 to 2022. These data, combined with national meteorological and on-site monitoring data, were used to study the methane emission characteristics and sources in the permafrost area in Northeast China. The results show that the methane concentration in the near-surface troposphere of Northeast China is mainly concentrated in the permafrost area of the Da and Xiao Xing’an Mountains. From 2003 to 2022, the methane concentration in the near-surface troposphere of the permafrost area in Northeast China showed a rapid growth trend, with an average linear trend growth rate of 4.787 ppbv/a. In addition, the methane concentration in the near-surface troposphere of the permafrost area shows a significant bimodal seasonal variation pattern. The first peak appears in summer (June–August), with its maximum value appearing in August, and the second peak appears in winter (December–February), with its maximum value appearing in December. Combined with ground surface methane concentration monitoring, it was found that the maximum annual ground surface methane concentration in degraded permafrost areas occurred in spring, causing the maximum average growth rate in methane concentration, also in spring, in the near-surface troposphere of permafrost areas in Northeast China (with an average value of 6.05 ppbv/a). The growth rate of methane concentration in the southern permafrost degradation zone is higher than that in the northern permafrost stable zone. In addition, with the degradation of permafrost, the geological methane stored deep underground (methane hydrate, coal seam, etc., mainly derived from the accumulation of ancient microbial origin) in the frozen layer will become an important source of near-surface troposphere methane in the permafrost degradation area. Due to the influence of high-permeability channels after permafrost degradation, the release rate of methane gas in spring is faster than predicted, and the growth rate of methane concentration in the near-surface troposphere of permafrost areas can be increased by more than twice. These conclusions can provide a data supplement for the study of the carbon cycle in permafrost areas in Northeast China. Full article

(This article belongs to the Section Air Quality)

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17 pages, 9153 KiB

Open AccessArticle

Climate Change Will Lead to a Significant Reduction in the Global Cultivation of Panicum milliaceum

by Pan Jiang, Junyi Jiang, Cong Yang, Xinchen Gu, Yi Huang and Liang Liu

Atmosphere 2023, 14(8), 1297; https://doi.org/10.3390/atmos14081297 - 16 Aug 2023

Cited by 1 | Viewed by 825

Abstract

Panicum milliaceum is a specialty crop that maintains the economic stability of agriculture in arid and barren regions of the world. Predicting the potential geographic distribution of Panicum milliaceum globally and clarifying the ecological needs of Panicum milliaceum will help to advance the [...] Read more.

Panicum milliaceum is a specialty crop that maintains the economic stability of agriculture in arid and barren regions of the world. Predicting the potential geographic distribution of Panicum milliaceum globally and clarifying the ecological needs of Panicum milliaceum will help to advance the development of agriculture, which is important for the maintenance of human life and health. In this study, based on 5637 global distribution records of Panicum milliaceum, we used the MaxEnt model and ArcGIS software, the Beijing Climate Center Climate System Model (BCC-CSM2-MR) was selected to predict the potential global geographic distribution of Panicum milliaceum in the present and future in combination with the environmental factor variables; we evaluated the significant factors constraining the potential geographic distribution of Panicum milliaceum by combining the contributions of environmental factor variables; and we assessed the accuracy of the MaxEnt model by using AUC values and Kappa statistics. The results showed that the MaxEnt model was highly accurate, the simulation results were credible, and the total suitable area of Panicum milliaceum in the world is 4563.82 × 10⁴ km². The high habitat area of Panicum milliaceum is 484.95 × 10⁴ km², accounting for 10.63% of the total habitat area, and is mainly distributed in the United States, the Russian Federation, France, Ukraine, Australia, Germany, etc. The soil factor (hswd) was the most important environmental factor limiting the potential geographic distribution of Panicum milliaceum, followed by the precipitation factor (Precipitation of the Driest Month, bio14) and temperature factor (Mean Temperature of the Wettest Quarter, bio8). Under four future climate change scenarios, the area of the potential geographic distribution of Panicum milliaceum decreased to different extents at different levels compared to the contemporary period. Therefore, climate change may significantly affect the global distribution pattern of Panicum milliaceum cultivation in the future and thus reshape global Panicum milliaceum production and trade patterns. Full article

(This article belongs to the Special Issue Climate Change and Agriculture: Impacts and Adaptation)

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17 pages, 12824 KiB

Open AccessArticle

Automatic Recognition of Vertical-Line Pulse Train from China Seismo-Electromagnetic Satellite Based on Unsupervised Clustering

by Ying Han, Yalan Li, Jing Yuan, Jianping Huang, Xuhui Shen, Zhong Li, Li Ma, Yanxia Zhang, Xinfang Chen and Yali Wang

Atmosphere 2023, 14(8), 1296; https://doi.org/10.3390/atmos14081296 - 16 Aug 2023

Cited by 1 | Viewed by 909

Abstract

Pulse signals refer to electromagnetic waveforms with short duration and high peak energy in the time domain. Spatial electromagnetic pulse interference signals can be caused by various factors such as lightning, arc discharge, solar disturbances, and electromagnetic disturbances in space. Pulse disturbance signals [...] Read more.

Pulse signals refer to electromagnetic waveforms with short duration and high peak energy in the time domain. Spatial electromagnetic pulse interference signals can be caused by various factors such as lightning, arc discharge, solar disturbances, and electromagnetic disturbances in space. Pulse disturbance signals appear as instantaneous, high-energy vertical-line pulse trains (VLPTs) on the spectrogram. This paper uses computer vision techniques and unsupervised clustering algorithms to process and analyze VLPT on very-low-frequency (VLF) waveform spectrograms collected by the China Seismo-Electromagnetic Satellite (CSES) electric field detector. First, the waveform data are transformed into time–frequency spectrograms with a duration of 8 s using the short-time Fourier transform. Then, the spectrograms are subjected to grayscale transformation, vertical line feature extraction, and binarization preprocessing. In the third step, the preprocessed data are dimensionally reduced and fed into an unsupervised K-means++ clustering model to achieve automatic recognition and labeling of VLPTs. By recognizing and studying VLPT, not only can interference be recognized, but the temporal and spatial locations of these interferences can also be determined. This lays the foundation for identifying VLPT sources and gaining deeper insights into the generation, propagation, and characteristics of electromagnetic radiation. Full article

(This article belongs to the Special Issue Detection of Perturbations Associated with Earthquakes during the LAIC Process Based on the Multi-Source Data)

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20 pages, 13320 KiB

Open AccessArticle

Evolution of Atmospheric Carbon Dioxide and Methane Mole Fractions in the Yangtze River Delta, China

by Kai Jiang, Qianli Ma, Kunpeng Zang, Yi Lin, Yuanyuan Chen, Shuo Liu, Xuemei Qing, Shanshan Qiu, Haoyu Xiong, Haixiang Hong, Jiaxin Li and Shuangxi Fang

Atmosphere 2023, 14(8), 1295; https://doi.org/10.3390/atmos14081295 - 16 Aug 2023

Cited by 1 | Viewed by 921

Abstract

As the most economically developed region in China, the Yangtze River Delta (YRD) region contributed to ~17% of the total anthropogenic CO₂ emissions from China. However, the studies of atmospheric CO₂ and CH₄ in this area are relatively sparse and [...] Read more.

As the most economically developed region in China, the Yangtze River Delta (YRD) region contributed to ~17% of the total anthropogenic CO₂ emissions from China. However, the studies of atmospheric CO₂ and CH₄ in this area are relatively sparse and unsystematic. Here, we analyze the changing characters of those gases in different development periods of China, based on the 11-year atmospheric CO₂ and CH₄ records (from 2010 to 2020) at one of the four Chinese sites participating in the World Meteorological Organization/Global Atmospheric Watch (WMO/GAW) program (Lin’an regional background station), located in the center of YRD region, China. The annual average atmospheric CO₂ and CH₄ mole fractions at LAN have been increasing continuously, with growth rates of 2.57 ± 0.14 ppm yr⁻¹ and 10.3 ± 1.3 ppb yr⁻¹, respectively. Due to the complex influence of regional sources and sinks, the characteristics of atmospheric CO₂ and CH₄ varied in different periods: (i) The diurnal and seasonal variations of both CO₂ and CH₄ in different periods were overall similar, but the amplitudes were different. (ii) The elevated mole fractions in all wind sectors tended to be uniform. (iii) The potential source regions of both gases expanded over time. (iv) The growth rate in recent years (2016–2020) changed significantly less than that in the earlier period (2010–2015). Our results indicated that the CO₂ and CH₄ mole fractions were mainly correlated to the regional economic development, despite the influence of special events such as the G20 Summit and COVID-19 lockdown. Full article

(This article belongs to the Section Air Quality)

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16 pages, 3283 KiB

Open AccessArticle

MSAFormer: A Transformer-Based Model for PM_2.5 Prediction Leveraging Sparse Autoencoding of Multi-Site Meteorological Features in Urban Areas

by Hongqing Wang, Lifu Zhang and Rong Wu

Atmosphere 2023, 14(8), 1294; https://doi.org/10.3390/atmos14081294 - 16 Aug 2023

Viewed by 941

Abstract

The accurate prediction of PM_2.5 concentration, a matter of paramount importance in environmental science and public health, has remained a substantial challenge. Conventional methodologies for predicting PM_2.5 concentration often grapple with capturing complex dynamics and nonlinear relationships inherent in multi-station meteorological [...] Read more.

The accurate prediction of PM_2.5 concentration, a matter of paramount importance in environmental science and public health, has remained a substantial challenge. Conventional methodologies for predicting PM_2.5 concentration often grapple with capturing complex dynamics and nonlinear relationships inherent in multi-station meteorological data. To address this issue, we have devised a novel deep learning model, named the Meteorological Sparse Autoencoding Transformer (MSAFormer). The MSAFormer leverages the strengths of the Transformer architecture, effectively incorporating a Meteorological Sparse Autoencoding module, a Meteorological Positional Embedding Module, and a PM_2.5 Prediction Transformer Module. The Sparse Autoencoding Module serves to extract salient features from high-dimensional, multi-station meteorological data. Subsequently, the Positional Embedding Module applies a one-dimensional Convolutional Neural Network to flatten the sparse-encoded features, facilitating data processing in the subsequent Transformer module. Finally, the PM_2.5 Prediction Transformer Module utilizes a self-attention mechanism to handle temporal dependencies in the input data, predicting future PM_2.5 concentrations. Experimental results underscore that the MSAFormer model achieves a significant improvement in predicting PM_2.5 concentrations in the Haidian district compared to traditional methods. This research offers a novel predictive tool for the field of environmental science and illustrates the potential of deep learning in the analysis of environmental meteorological data. Full article

(This article belongs to the Special Issue Urban Airflow and Pollutant Dispersion: Monitoring, Modeling, Challenges, and New Perspectives)

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21 pages, 6824 KiB

Open AccessArticle

Rainfall Variability and Teleconnections with Large-Scale Atmospheric Circulation Patterns in West-Central Morocco

by Sara Boughdadi, Yassine Ait Brahim, Abdelhafid El Alaoui El Fels and Mohamed Elmehdi Saidi

Atmosphere 2023, 14(8), 1293; https://doi.org/10.3390/atmos14081293 - 16 Aug 2023

Cited by 2 | Viewed by 1083

Abstract

Morocco is characterized by a semi-arid climate influenced by the Mediterranean, Atlantic, and Saharan environments, resulting in high variability in rainfall and hydrological conditions. Certain regions suffer from insufficient understanding concerning the spatiotemporal patterns of precipitation, along with facing recurrent periods of drought. [...] Read more.

Morocco is characterized by a semi-arid climate influenced by the Mediterranean, Atlantic, and Saharan environments, resulting in high variability in rainfall and hydrological conditions. Certain regions suffer from insufficient understanding concerning the spatiotemporal patterns of precipitation, along with facing recurrent periods of drought. This study aims to characterize the current trends and periodicities of precipitation in west-central Morocco at monthly and annual scales, using data from six rain gauges. The link between monthly precipitation and both the North Atlantic Oscillation (NAO) and the Western Mediterranean Oscillation (WeMO) indices was tested to identify potential teleconnections with large-scale variability modes. The results reveal interannual variability in precipitation and climate indices, while showing decreasing insignificant trends in annual precipitation. On a monthly scale, temporal precipitation patterns are similar to the annual scale. Furthermore, a remarkably robust and significant component with a periodicity of 6–8 years emerges consistently across all monitoring stations. Intriguingly, this band exhibits a more pronounced presence on the plains as opposed to the mountainous stations. Additionally, it is noteworthy that the NAO modulated winter precipitation, whereas the influence of the WeMO extends until March and April. This mode could be linked to the fluctuations of the WeMO from 1985 to 2005 and, subsequently, to NAO variations. Indeed, this is consistent with the strong significant correlations observed between rainfall and the NAO/WeMO. This study serves as a baseline for future research aiming to understand the influence of climate indices on rainfall in the North African region. Full article

(This article belongs to the Section Climatology)

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19 pages, 3883 KiB

Open AccessArticle

Optimal Probability Distribution and Applicable Minimum Time-Scale for Daily Standardized Precipitation Index Time Series in South Korea

by Chaelim Lee, Jiyu Seo, Jeongeun Won and Sangdan Kim

Atmosphere 2023, 14(8), 1292; https://doi.org/10.3390/atmos14081292 - 16 Aug 2023

Cited by 2 | Viewed by 1059

Abstract

The Standardized Precipitation Index (SPI) is a standardized measure of the variability of precipitation and is widely used for drought assessment around the world. In general, the probability distribution used to calculate the SPI in many studies is Gamma. In addition, a monthly [...] Read more.

The Standardized Precipitation Index (SPI) is a standardized measure of the variability of precipitation and is widely used for drought assessment around the world. In general, the probability distribution used to calculate the SPI in many studies is Gamma. In addition, a monthly time-scale is applied to calculate the SPI to assess drought based on atmospheric moisture supply over the medium-to-long term. However, probability distributions other than Gamma are applied in various regions, and the need for a daily time-scale is emerging as concerns about fresh drought increase. There are two main innovations of our work. The first is that we investigate the optimal probability distribution of daily SPIs rather than monthly SPIs, and the second is that we address the issue of determining the minimum time-scale that can be applied when applying a daily time-scale. In this study, we investigate the optimal probability distribution and the minimum-applicable time-scale for calculating the daily SPI using daily precipitation time series observed over 42 years at 56 sites in South Korea. Six candidate probability distributions (Gumbel, Gamma, GEV, Log-logistic, Log-normal, and Weibull) and ten time-scales (5 day, 10 day, 15 day, 21 day, 30 day, 60 day, 90 day, 180 day, 270 day, and 365 day) were applied to calculate the daily SPI. A chi-square test and AIC were applied to investigate the appropriate probability distribution for each time-scale, and the normality of the daily SPI time series derived from each probability distribution were compared. The Weibull distribution was suitable for calculating the daily SPI for short time-scales of 30 days or less, while the GEV distribution was suitable for longer time-scales of 270 days or more. However, overall, Gamma was found to be the best probability distribution. While there were some regional variations, the minimum time-scales that could be applied per season were as follows: 15 days for spring and summer, 21 days for fall, and 30 days for winter. It is shown that the minimum time-scale depends on how many zero values are included in the moving cumulative-precipitation time series, and it is shown that it is appropriate to have less than about 2.5%. Finally, the applicability of the GEV distribution is investigated. Full article

(This article belongs to the Section Meteorology)

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