Atmosphere

22 pages, 14368 KiB

Open AccessArticle

Global Ionospheric TEC Map Prediction Based on Multichannel ED-PredRNN

by Haijun Liu, Yan Ma, Huijun Le, Liangchao Li, Rui Zhou, Jian Xiao, Weifeng Shan, Zhongxiu Wu and Yalan Li

Atmosphere 2025, 16(4), 422; https://doi.org/10.3390/atmos16040422 (registering DOI) - 4 Apr 2025

Abstract

High-precision total electron content (TEC) prediction can improve the accuracy of the Global Navigation Satellite System (GNSS)-based applications. The existing deep learning models for TEC prediction mainly include long short-term memory (LSTM), convolutional long short-term memory (ConvLSTM), and their variants, which contain only [...] Read more.

High-precision total electron content (TEC) prediction can improve the accuracy of the Global Navigation Satellite System (GNSS)-based applications. The existing deep learning models for TEC prediction mainly include long short-term memory (LSTM), convolutional long short-term memory (ConvLSTM), and their variants, which contain only one temporal memory. These models may result in fuzzy prediction results due to neglecting spatial memory, as spatial memory is crucial for capturing the correlations of TEC within the TEC neighborhood. In this paper, we draw inspiration from the predictive recurrent neural network (PredRNN), which has dual memory states to construct a TEC prediction model named Multichannel ED-PredRNN. The highlights of our work include the following: (1) for the first time, a dual memory mechanism was utilized in TEC prediction, which can more fully capture the temporal and spatial features; (2) we modified the n vs. n structure of original PredRNN to an encoder–decoder structure, so as to handle the problem of unequal input and output lengths in TEC prediction; and (3) we expanded the feature channels by extending the Kp, Dst, and F10.7 to the same spatiotemporal resolution as global TEC maps, overlaying them together to form multichannel features, so as to fully utilize the influence of solar and geomagnetic activities on TEC. The proposed Multichannel ED-PredRNN was compared with COPG, ConvLSTM, and convolutional gated recurrent unit (ConvGRU) from multiple perspectives on a data set of 6 years, including comparisons at different solar activities, time periods, latitude regions, single stations, and geomagnetic storm periods. The results show that in almost all cases, the proposed Multichannel ED-PredRNN outperforms the three comparative models, indicating that it can more fully utilize temporal and spatial features to improve the accuracy of TEC prediction. Full article

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

► Show Figures

Figure 1

12 pages, 2162 KiB

Open AccessArticle

A Traceable Calibration for Gaseous Elemental Mercury Measurements in Air and Water

by Teodor D. Andron, Warren T. Corns, Matthew A. Dexter, Igor Živković, Jože Kotnik and Milena Horvat

Atmosphere 2025, 16(4), 421; https://doi.org/10.3390/atmos16040421 (registering DOI) - 4 Apr 2025

Abstract

Calibration is crucial in quantitative analysis, ensuring the traceability of standards for an accurate comparison of results. In mercury determinations, a gas calibrator unit containing liquid mercury is used for calibration by injecting headspace volumes via syringe. The Dumarey equation has been used [...] Read more.

Calibration is crucial in quantitative analysis, ensuring the traceability of standards for an accurate comparison of results. In mercury determinations, a gas calibrator unit containing liquid mercury is used for calibration by injecting headspace volumes via syringe. The Dumarey equation has been used for over 35 years to calculate mercury headspace concentration, aligning closely with saturated vapor pressure equations. However, the 2006 Huber equation yields different values, creating discrepancies. This paper compares calibrations using the Dumarey equations against NIST 3133 certified reference material, with detection by a cold vapor atomic fluorescence spectrophotometer (CV-AFS). The gas standard was injected directly, while Hg^II in NIST 3133 was reduced to Hg⁰ and captured on gold traps. Across 10–24 °C, the Hg⁰ concentration was determined, with uncertainties ranging from 2.9% to 8.4% for a coverage factor of two. No significant differences were found between calibrations using NIST 3133 and the Dumarey equation. These findings provide crucial insights into the traceability and accuracy of mercury calibration methods, ensuring the reliability of measurements used for environmental monitoring and regulatory compliance. Full article

(This article belongs to the Special Issue Mercury in the Atmosphere: Measurements, Intercompartment Exchange, and Modeling)

► Show Figures

Figure 1

15 pages, 9347 KiB

Open AccessArticle

Fine-Scale Identification of Agricultural Flooding Disaster Areas Based on Sentinel-1/2: A Case Study of Shengzhou, Zhejiang Province, China

by Jiayun Li, Jiaqi Gao, Haiyan Chen, Xiaoling Shen, Xiaochen Zhu and Yinhu Qiao

Atmosphere 2025, 16(4), 420; https://doi.org/10.3390/atmos16040420 (registering DOI) - 4 Apr 2025

Abstract

Flood disasters are one of the major natural hazards threatening agricultural production. To reduce agricultural disaster losses, accurately identifying agricultural flood-affected areas is crucial. Taking Shengzhou City as a case study, we proposed a refined method for identifying agricultural flood-affected areas by integrating [...] Read more.

Flood disasters are one of the major natural hazards threatening agricultural production. To reduce agricultural disaster losses, accurately identifying agricultural flood-affected areas is crucial. Taking Shengzhou City as a case study, we proposed a refined method for identifying agricultural flood-affected areas by integrating microwave and optical remote sensing data with deep learning techniques, GIS, and the pixel-based direct differencing method. Complementary advantages of microwave and optical remote sensing data can effectively solve the problem of difficulty in accurately detecting floods due to thick clouds before and after flood disasters. Deep learning technology can effectively identify farmland areas, and the pixel direct difference method can accurately analyze agricultural flood disasters. Analyzing three typical rainfall events along with the topographical and geomorphological characteristics of Shengzhou City, the results indicate that agricultural flood disaster areas exhibit significant spatial heterogeneity. The primary influencing factors include rainfall intensity, topography, and drainage infrastructure. The northern, eastern, and southwestern regions of Shengzhou City, particularly the peripheral areas adjacent to mountainous and hilly terrains, contain most of the flood-affected farmland. These areas, characterized by low-lying topography, are highly susceptible to flood disasters. Therefore, optimizing the drainage systems of farmland in low-lying areas near mountainous and hilly regions of Shengzhou City is essential to enhance flood resilience. Full article

(This article belongs to the Section Meteorology)

► Show Figures

Figure 1

22 pages, 40986 KiB

Open AccessArticle

Modeling Short-Term Drought for SPEI in Mainland China Using the XGBoost Model

by Fanchao Zeng, Qing Gao, Lifeng Wu, Zhilong Rao, Zihan Wang, Xinjian Zhang, Fuqi Yao and Jinwei Sun

Atmosphere 2025, 16(4), 419; https://doi.org/10.3390/atmos16040419 (registering DOI) - 4 Apr 2025

Abstract

Accurate drought prediction is crucial for optimizing water resource allocation, safeguarding agricultural productivity, and maintaining ecosystem stability. This study develops a methodological framework for short-term drought forecasting using SPEI time series (1979–2020) and evaluates three predictive models: (1) a baseline XGBoost model (XGBoost1), [...] Read more.

Accurate drought prediction is crucial for optimizing water resource allocation, safeguarding agricultural productivity, and maintaining ecosystem stability. This study develops a methodological framework for short-term drought forecasting using SPEI time series (1979–2020) and evaluates three predictive models: (1) a baseline XGBoost model (XGBoost1), (2) a feature-optimized XGBoost variant incorporating Pearson correlation analysis (XGBoost2), and (3) an enhanced CPSO-XGBoost model integrating hybrid particle swarm optimization with dual mechanisms of binary feature selection and parameter tuning. Key findings reveal spatiotemporal prediction patterns: temporal-scale dependencies show all models exhibit limited capability at SPEI-1 (R²: 0.32–0.41, RMSE: 0.68–0.79) but achieve progressive accuracy improvement, peaking at SPEI-12 where CPSO-XGBoost attains optimal performance (R²: 0.85–0.90, RMSE: 0.33–0.43) with 18.7–23.4% error reduction versus baselines. Regionally, humid zones (South China/Central-Southern) demonstrate peak accuracy at SPEI-12 (R² ≈ 0.90, RMSE < 0.35), while arid regions (Northwest Desert/Qinghai-Tibet Plateau) show dramatic improvement from SPEI-1 (R² < 0.35, RMSE > 1.0) to SPEI-12 (R² > 0.85, RMSE reduction > 52%). Multivariate probability density analysis confirms the model’s robustness through enhanced capture of nonlinear atmospheric-land interactions and reduced parameterization uncertainties via swarm intelligence optimization. The CPSO-XGBoost’s superiority stems from synergistic optimization: binary particle swarm feature selection enhances input relevance while adaptive parameter tuning improves computational efficiency, collectively addressing climate variability challenges across diverse terrains. These findings establish an advanced computational framework for drought early warning systems, providing critical support for climate-resilient water management and agricultural risk mitigation through spatiotemporally adaptive predictions. Full article

(This article belongs to the Special Issue Advances in Methods for the Investigation of the Atmospheric Water Cycle)

► Show Figures

Figure 1

25 pages, 6362 KiB

Open AccessArticle

Assessing Climate Change Impacts on Cropland and Greenhouse Gas Emissions Using Remote Sensing and Machine Learning

by Nehir Uyar and Azize Uyar

Atmosphere 2025, 16(4), 418; https://doi.org/10.3390/atmos16040418 (registering DOI) - 3 Apr 2025

Abstract

This study investigated the impact of grassland and cropland expansion on carbon (C) and nitrous oxide (N₂O) emissions using remote sensing data and machine learning models. The research focused on agricultural land-use changes in South Sumatra from 1992 to 2018, utilizing [...] Read more.

This study investigated the impact of grassland and cropland expansion on carbon (C) and nitrous oxide (N₂O) emissions using remote sensing data and machine learning models. The research focused on agricultural land-use changes in South Sumatra from 1992 to 2018, utilizing Landsat satellite imagery and Google Earth Engine (GEE) for spatial and temporal analysis. Machine learning algorithms, including gradient boosting trees (GBT), random forest (RF), support vector machines (SVM), and classification and regression trees (CART), were employed to estimate greenhouse gas emissions based on multiple environmental parameters. These parameters include enhanced vegetation index (EVI), land surface temperature (LST), normalized difference vegetation index (NDVI), albedo, elevation, humidity, population density, precipitation, soil moisture, and wind speed. The results revealed a strong correlation between agricultural expansion and increased C and N₂O emissions, with RF and GBT models demonstrating superior predictive accuracy. Specifically, GBT and RF achieved the highest R² value (0.71, 0.59) and the lowest error metrics in modeling emissions, whereas SVM performed poorly across all cases. The study highlights the effectiveness of machine learning in quantifying emission dynamics and underscores the necessity of sustainable land management strategies to mitigate greenhouse gas emissions. By integrating remote sensing and data-driven methodologies, this research contributes to climate change mitigation policies and precision agriculture strategies aimed at balancing food security and environmental sustainability. Full article

(This article belongs to the Special Issue Observation of Climate Change and Cropland with Satellite Data)

► Show Figures

Figure 1

24 pages, 3083 KiB

Open AccessArticle

Modelling of Nanoparticle Number Emissions from Road Transport—An Urban Scale Emission Inventory

by Said Munir, Haibo Chen and Richard Crowther

Atmosphere 2025, 16(4), 417; https://doi.org/10.3390/atmos16040417 (registering DOI) - 3 Apr 2025

Abstract

Atmospheric nanoparticles, due to their tiny size up to 100 nanometres in diameter, have negligible mass and are better characterised by their particle number concentration. Atmospheric nanoparticle numbers are not regulated due to insufficient data availability, which emphasises the importance of this research. [...] Read more.

Atmospheric nanoparticles, due to their tiny size up to 100 nanometres in diameter, have negligible mass and are better characterised by their particle number concentration. Atmospheric nanoparticle numbers are not regulated due to insufficient data availability, which emphasises the importance of this research. In this paper, nanoparticle number emissions are estimated using nanoparticle number emission factors (NPNEF) and road traffic characteristics. Traffic flow and fleet composition were estimated using the Leeds Transport Model, which showed that the road traffic in Leeds consisted of 41% petrol cars, 43% diesel cars, 9% LGV, 2% HGV, and 4.5% buses and coaches. Two approaches were used for emission estimation: (a) a detailed model, which required detailed information on traffic flow and fleet composition and NPNEFs of various vehicle types; and (b) a simple model, which used total traffic flow and a single NPNEF of mixed fleet. The estimations of both models demonstrated a strong correlation with each other using the values of R, RMSE, FAC2, and MB, which were 1, 2.77 × 10¹⁷, 0.95, and −1.92 × 10¹⁷, respectively. Eastern and southern parts of the city experienced higher levels of emissions. Future work will include fine-tuning the road traffic emission inventory and quantifying other emission sources. Full article

(This article belongs to the Special Issue Modeling and Monitoring of Air Quality: From Data to Predictions)

► Show Figures

Figure 1

15 pages, 1819 KiB

Open AccessArticle

Urban Microclimates in Action! High-Resolution Temperature and Humidity Differences Across Diverse Urban Terrain

by Steven R. Schultze, Jade Martin, Katie West, Laken Swinea and Benjamin J. Linzmeier

Atmosphere 2025, 16(4), 416; https://doi.org/10.3390/atmos16040416 - 3 Apr 2025

Abstract

With more than half of the world already living in urban spaces—a number set to increase in the coming decades—the need is clear to understand urban microclimates and extremes. This study placed twenty MX2302a HOBOmobile weather microsensors placed in aerated housings across the [...] Read more.

With more than half of the world already living in urban spaces—a number set to increase in the coming decades—the need is clear to understand urban microclimates and extremes. This study placed twenty MX2302a HOBOmobile weather microsensors placed in aerated housings across the ~4 km² of the campus of the University of South Alabama from September to November 2022 and recorded temperature, relative humidity, and dewpoint every minute during the study period. These sensors were placed in situ, which allowed for the diversity in land cover, canopy cover, and aspect—large microclimatic drivers—to be captured. Sensors were compared to a campus mesonet station, part of the South Alabama Mesonet, a member of the National Mesonet Program. During the study period, temperatures were found to vary as much as 13 °C at the same minute across campus, with substantial changes in humidities and dewpoints also found. For example, the campus mesonet may have read 32 °C, yet the sensors could read as low as 29 °C and as high as 42 °C at the same moment. This study shows that the world is far more complex than what is seen at the mesoscale under idealized conditions, and the implications for society are considered. Full article

(This article belongs to the Section Climatology)

► Show Figures

Figure 1

13 pages, 485 KiB

Open AccessArticle

Long-Term Trends in PM₁₀, PM_2.5, and Trace Elements in Ambient Air: Environmental and Health Risks from 2020 to 2024

by Heba M. Adly and Saleh A. K. Saleh

Atmosphere 2025, 16(4), 415; https://doi.org/10.3390/atmos16040415 - 3 Apr 2025

Abstract

This study aimed to assess the long-term trends in PM₁₀, PM_2.5, and hazardous trace elements in Makkah from 2020 to 2024, evaluating seasonal variations, health risks, and potential mitigation strategies. The results indicated that the PM₁₀ concentrations ranged [...] Read more.

This study aimed to assess the long-term trends in PM₁₀, PM_2.5, and hazardous trace elements in Makkah from 2020 to 2024, evaluating seasonal variations, health risks, and potential mitigation strategies. The results indicated that the PM₁₀ concentrations ranged from a minimum of 127.7 ± 14.2 µg/m³ (2020) to a maximum of 138.3 ± 15.7 µg/m³ (2024), while PM_2.5 levels varied between 100.7 ± 18.7 µg/m³ and 109.8 ± 21.3 µg/m³. A seasonal analysis showed the highest PM₁₀ and PM_2.5 levels during winter (147.8 ± 16.4 µg/m³ and 119.5 ± 21.7 µg/m³ in 2024, respectively), coinciding with lower wind speeds and reduced dispersion. Among the nine trace elements analyzed, Cr VI exhibited the highest increase from 0.008 ± 0.001 µg/m³ (2020) to 0.012 ± 0.001 µg/m³ (2024), while Cd and Ni also rose significantly. The excess cancer risk (ECR) associated with these pollutants exceeded the recommended threshold, with a strong correlation between PM₁₀ and ECR (r = 0.85–0.93, p < 0.01). These findings highlight the need for enhanced air quality monitoring and sustainable urban planning. Future research should focus on identifying the dominant pollution sources and assessing the long-term health impacts to support evidence-based air quality management in Makkah. Full article

(This article belongs to the Special Issue Global and Regional Perspectives on Particulate Matter and Air Quality: Environmental and Health Impacts, Challenges, Policies, and Solutions)

► Show Figures

Figure 1

22 pages, 10633 KiB

Open AccessArticle

Spatiotemporal Rainfall Variability and Trends Analysis over the Enkangala Escarpment of South Africa (1972–2022)

by Hadisu Bello Abubakar, Mary C. Scholes and Francois A. Engelbrecht

Atmosphere 2025, 16(4), 414; https://doi.org/10.3390/atmos16040414 - 3 Apr 2025

Abstract

This study explores rainfall variability and trends in the Enkangala Escarpment of South Africa using station data from 1972 to 2022 (51 years). The coefficient of variation (CV) is indicative of pronounced inter-annual variability in seasonal rainfall totals across the region. The trend-free [...] Read more.

This study explores rainfall variability and trends in the Enkangala Escarpment of South Africa using station data from 1972 to 2022 (51 years). The coefficient of variation (CV) is indicative of pronounced inter-annual variability in seasonal rainfall totals across the region. The trend-free pre-whitening Mann–Kendall (TFPWMK) test and innovative trend analysis (ITA) were used to determine the presence of monotonic trends in the station records, despite the pronounced inter-annual variability in the time series. Sen’s slope estimator was used to quantify the magnitude of the trends. For a given season, the ITA test, in general, allocates local statistical significance to the time series for more stations compared to the TFPWMK test. For winter, spring and summer, there is spatial coherency of decreasing rainfall trends across the Enkangala Escarpment. These trends also exhibit local significance for spring at most stations, and are indicative of less favorable growing conditions for crops during this season. Reduced spring rainfall is likely to also translate to later planting dates (a shorter growing season) and a longer burning season. Trends for autumn are generally weak and lack in local statistical significance or spatial coherency. Full article

(This article belongs to the Section Climatology)

► Show Figures

Figure 1

22 pages, 3729 KiB

Open AccessArticle

Differential Impacts on Human Physiological Responses on Heatwave and Non-Heatwave Days: A Comparative Study Using Wearable Devices in Beijing

by Tong Sheng, Qi Liu, Yumeng Kou, Guole Shang and Miaomiao Xie

Atmosphere 2025, 16(4), 413; https://doi.org/10.3390/atmos16040413 - 1 Apr 2025

Abstract

As global warming intensifies heatwave events, their impact on human health is becoming increasingly significant. To further understand the dynamic response of humans to heatwaves, this study selected samples from different age groups under various thermal conditions in Beijing, China. Physiological parameters, including [...] Read more.

As global warming intensifies heatwave events, their impact on human health is becoming increasingly significant. To further understand the dynamic response of humans to heatwaves, this study selected samples from different age groups under various thermal conditions in Beijing, China. Physiological parameters, including blood pressure, blood glucose, heart rate, blood oxygen, and body temperature, were monitored during both heatwave and non-heatwave periods using wearable devices. The results show that during heatwaves, the average blood pressure increased by about 10%, blood glucose levels rose by about 4%, heart rates increased by about 12%, and blood oxygen levels decreased by an average of about 7%. These effects were particularly pronounced in people aged 50 and above, with heart rate and blood oxygen saturation showing significant age-related differences. The study indicates that heat waves have a more substantial impact on the elderly population, with age being a key factor in determining physiological responses to extreme heat. Therefore, it is necessary to develop age-specific health management strategies to address vulnerability under high-temperature conditions. Full article

(This article belongs to the Special Issue Climate Change and Extreme Weather Disaster Risks)

► Show Figures

Figure 1

20 pages, 2702 KiB

Open AccessArticle

Imbalance Term in the TKE Budget over Waves

by Linta Vonta, Denis Bourras, Saïd Benjeddou, Christopher Luneau, Julien Touboul, Philippe Fraunié, Alexei Sentchev and Antoine Villefer

Atmosphere 2025, 16(4), 412; https://doi.org/10.3390/atmos16040412 - 31 Mar 2025

Abstract

In an attempt to reconciliate air-sea momentum flux estimates derived from open sea observations, from large eddy simulation output fields, and from wind-wave tank measurements, a series of dedicated experiments were conducted in the wind-wave tank of the Large Air-Sea Facility of Marseille, [...] Read more.

In an attempt to reconciliate air-sea momentum flux estimates derived from open sea observations, from large eddy simulation output fields, and from wind-wave tank measurements, a series of dedicated experiments were conducted in the wind-wave tank of the Large Air-Sea Facility of Marseille, France. The turbulent friction velocity, upon which the momentum flux depends, was estimated from wind measurements by applying four classical methods including the eddy-covariance method and the inertial-dissipation method. The collected data were used to investigate some characteristics of the wave-influenced boundary layer that were predicted by previous simulations, and to quantify a wave-dependent term of the turbulent kinetic energy equation, the so-called imbalance term

ϕ_{imb}

. Our results show that the turbulent stress decreases toward lower heights where the effect of waves is large, as in the simulations, and that

ϕ_{imb}

is in the range 0.3 to 0.7, which is comparable to the value found with open sea data (0.4). These preliminary results have to be confirmed with wave-following probes, because the estimated eddy-covariance flux slightly varied with height, thus it could not be strictly considered to be equal to a constant total flux. Full article

(This article belongs to the Special Issue Recent Advances in Air-Sea Interactions, Climate Variability, and Predictability (2nd Edition))

► Show Figures

Figure 1

15 pages, 4242 KiB

Open AccessArticle

The Correlation Between Surface Temperature and Surface PM_2.5 in Nanchang Region, China

by Weihong Wang, Gong Zhang, Yong Luo, Xuan Liang, Linqi Liu, Kunshui Luo and Yuexin Xiao

Atmosphere 2025, 16(4), 411; https://doi.org/10.3390/atmos16040411 - 31 Mar 2025

Abstract

PM_2.5 plays a significant role in urban climate, especially as urban development accelerates. In this study, surface PM_2.5, skin temperature, surface air temperature, net longwave radiation, net shortwave radiation, sensible heat flux, and latent heat flux were directly analyzed in [...] Read more.

PM_2.5 plays a significant role in urban climate, especially as urban development accelerates. In this study, surface PM_2.5, skin temperature, surface air temperature, net longwave radiation, net shortwave radiation, sensible heat flux, and latent heat flux were directly analyzed in Nanchang from 2020 to 2022. The results indicate that PM_2.5 in Nanchang is highest during winter and lowest in summer. On an annual scale, surface PM_2.5 reduces skin and surface air temperatures at a rate of 0.75 °C/(μg m⁻³) by decreasing net solar radiation and increasing net longwave radiation at night. Conversely, it increases air temperature by absorbing radiation, leading to a surface inversion. Furthermore, surface PM_2.5 influences surface air and skin temperatures by modulating the latent heat fluxes. Full article

(This article belongs to the Section Air Quality)

► Show Figures

Figure 1

20 pages, 3187 KiB

Open AccessArticle

Assessing the Impact of Ambient Noise on Outdoor Thermal Comfort on University Campuses: A Pilot Study in China’s Cold Region

by Shaobo Ning, Wenqiang Jing, Zhemin Ge and Zeming Qin

Atmosphere 2025, 16(4), 410; https://doi.org/10.3390/atmos16040410 - 31 Mar 2025

Abstract

This study investigates the impact of different noise levels on thermal comfort in outdoor environments. The research was conducted in two university squares in Xi’an, China, exhibiting distinct noise exposures, with twenty volunteers participating in the study. These individuals provided subjective evaluations of [...] Read more.

This study investigates the impact of different noise levels on thermal comfort in outdoor environments. The research was conducted in two university squares in Xi’an, China, exhibiting distinct noise exposures, with twenty volunteers participating in the study. These individuals provided subjective evaluations of thermal comfort through questionnaires while situated in environments with disparate acoustic conditions in conjunction with the documentation of prevailing meteorological circumstances. The analysis yielded three salient findings. Initially, a marked elevation in perceived warmth was noted in environments experiencing higher noise levels, with 35.29% of subjects in the high-noise plaza (HP) reporting feeling warm (TSV = 2), which was 11.76 percentage points higher than in the low-noise plaza (LP). This included a 5.88 percentage point uptick in the frequency of “hot” (TSV = 3) thermal sensations reported in the HP. Furthermore, an intensification of thermal discomfort was observed in noisier settings, with the thermal comfort vote (TCV) in HP encompassing a spectrum from very uncomfortable to neutral and a predominant 90% of TCVs indicating discomfort, 35.29% of which were deemed very uncomfortable. Lastly, the findings suggest that high-decibel noise exposure notably amplifies the perception of heat within a specific high-temperature bandwidth. Beyond this delineated thermal threshold, the influence of noise on thermal sensation substantially diminishes. Full article

(This article belongs to the Special Issue Future-Proofing Buildings for Enhanced Indoor Air Quality and Thermal Comfort: An Opportunity for Resilient Tomorrow)

► Show Figures

Figure 1

14 pages, 7141 KiB

Open AccessArticle

Atmospheric Circulation Conditions During Spring Frosts in Southeastern Poland (1981–2023)

by Piotr Piotrowski and Krzysztof Bartoszek

Atmosphere 2025, 16(4), 409; https://doi.org/10.3390/atmos16040409 - 31 Mar 2025

Abstract

Spring frosts were examined using 1981–2023 meteorological data from five weather stations located in areas with a significant share of agricultural crops, especially orchards. Days with frost were determined from air temperature data at 5 and 200 cm above ground level from the [...] Read more.

Spring frosts were examined using 1981–2023 meteorological data from five weather stations located in areas with a significant share of agricultural crops, especially orchards. Days with frost were determined from air temperature data at 5 and 200 cm above ground level from the onset of the growing season. Atmospheric circulation conditions during frosts were analyzed using the HYSPLIT model (backward trajectory clusters) and empirical orthogonal functions (EOFs). EOFs were used to identify pressure distributions at the 850 hPa level that favor the occurrence of spring frosts. In the first half of spring, frosts were mainly associated with high-pressure systems centered over Poland and the advection of air masses from the northwest, west, and east. Meanwhile in the second part of spring, they occurred during the advection of Arctic air masses from the north and northeast. Changes in the pressure distribution and backward trajectories in the first and second parts of spring indicate a change in the thermal properties of air masses over eastern Europe with increasing day length and angle of sunlight incidence. Furthermore, a decrease in the frequency of spring frosts has been observed in the study area since the 1990s. Full article

(This article belongs to the Section Climatology)

► Show Figures

Figure 1

19 pages, 6913 KiB

Open AccessArticle

Regionalization of the Hargreaves-Samani Coefficients to Estimate Reference Evapotranspiration in High-Altitude Areas

by Apolinario Lujano, Miguel Sanchez-Delgado, Nestor Montalvo-Arquiñigo, Absalon Vasquez-Villanueva, Abel Mejia-Marcacuzco and Efrain Lujano

Atmosphere 2025, 16(4), 408; https://doi.org/10.3390/atmos16040408 - 31 Mar 2025

Abstract

The Penman-Monteith (PM) equation is considered the most accurate method for estimating reference evapotranspiration (ETo); however, its application requires a large amount of data that is not always available. This study aimed to regionalize the coefficients of the Hargreaves-Samani (HS) equation to estimate [...] Read more.

The Penman-Monteith (PM) equation is considered the most accurate method for estimating reference evapotranspiration (ETo); however, its application requires a large amount of data that is not always available. This study aimed to regionalize the coefficients of the Hargreaves-Samani (HS) equation to estimate ETo in high-altitude areas, specifically the Peruvian Altiplano (PA). The methodology included (1) evaluation of the original HS equation, (2) calibration and validation of the empirical coefficient (

C H

) and empirical exponent (

E H

) at each weather station, and (3) regionalization of the calibrated coefficients using a multiple linear regression approach. The results showed that the original HS equation had NSE values ranging from −0.57 to 0.87, PBIAS from −18.60% to 12.70%, MAE from 0.16 to 0.65 mm/d, and RMSE from 0.20 to 0.67 mm/d. After calibrating

C H

and

E H

, performance improved significantly, achieving validation values of NSE ranging from 0.67 to 0.94, PBIAS from −0.55% to 1.37%, MAE from 0.01 to 0.05 mm/d, and RMSE from 0.13 to 0.21 mm/d. Finally, the regionalization of 0.859 and 0.744, respectively. These results indicate that the HS equation, with calibrated and regionalized coefficients, is a viable alternative for estimating ETo in regions with limited meteorological data. Full article

(This article belongs to the Special Issue Observation and Modeling of Evapotranspiration)

► Show Figures

Figure 1

27 pages, 7046 KiB

Open AccessArticle

Analysis of Local Water Humidity Effect Characteristics Based on Meteorological Data: A Case Study of Nanjing

by Kai Liu, Yan Zeng, Xinfa Qiu and Yuheng Zhong

Atmosphere 2025, 16(4), 407; https://doi.org/10.3390/atmos16040407 - 31 Mar 2025

Abstract

In order to explore the variation law and causes of the humidity effect of local water bodies, this paper selects the data of encrypted automatic weather stations (encrypted stations) and national conventional meteorological stations (conventional stations) in Nanjing from 2014 to 2020, and [...] Read more.

In order to explore the variation law and causes of the humidity effect of local water bodies, this paper selects the data of encrypted automatic weather stations (encrypted stations) and national conventional meteorological stations (conventional stations) in Nanjing from 2014 to 2020, and systematically studies the humidity effects and influencing factors of urban water bodies by constructing the humidity effect intensity (E) based on the conventional stations. The results show that the humidity effect of urban water has significant diurnal and monthly variation characteristics, and is extremely sensitive to temperature change, and compared to nighttime, the daytime period is generally more humid. The humidity effect is mostly normal in winter, while the humidification and humidity reduction effects in summer are particularly significant. There are also significant differences in the humidity effect between different typical water stations, which are mainly influenced by the background environment of urban and suburban areas, macro wind field, and local wind field configuration around the water body due to the dense building density in the main urban area, which is characterized by dry humidification, while the suburbs are characterized by humidity. When the water body is located on the side of a large water body (river or lake), the influence of local water–land wind field and macro wind field on the humidity effect is particularly significant, and the water wind will significantly enhance the humidity effect, while the land breeze will weaken the humidity effect. The research results can provide a reference for the urban planning and the design of the surrounding environment of water bodies in Nanjing. Full article

(This article belongs to the Section Meteorology)

► Show Figures

Figure 1

25 pages, 3007 KiB

Open AccessReview

Jump in Tropospheric Methane Concentrations in 2020–2021 and Slowdown in 2022–2024: New Hypotheses on Causation

by Tingzhen Ming, Renaud de Richter, Benjamin S. Felzer and Wei Li

Atmosphere 2025, 16(4), 406; https://doi.org/10.3390/atmos16040406 - 31 Mar 2025

Abstract

Earth’s atmospheric methane (CH₄) concentration has risen more than 162% since pre-industrial levels in the mid-18th century, and about 30% of the rise in global temperatures since the pre-industrial era is due to CH₄ The build-up of methane in the [...] Read more.

Earth’s atmospheric methane (CH₄) concentration has risen more than 162% since pre-industrial levels in the mid-18th century, and about 30% of the rise in global temperatures since the pre-industrial era is due to CH₄ The build-up of methane in the atmosphere in 2020–2022 was the largest since systematic measurements started in 1983, more than double the average yearly growth rate measured over the previous 17 years (15.2 ppb yr⁻¹ vs. 5.71 ppb yr⁻¹, respectively). During 2020, with a growth rate of 14.81 ppb yr⁻¹, the level of atmospheric CH₄ broke the previous record (which was set in 1991), and it was broken again immediately the following year, with an increase of 17.64 ppb yr⁻¹ in 2021. For 2022, the final estimate is 13.25 ppb yr⁻¹, the fourth largest annual growth rate. The most recent explanations for this surge in tropospheric CH₄ include increased emissions from tropical wetlands, more floods, and increased temperatures. For 2020 and part of 2021, a reduction in the oxidative capacity of the atmosphere due to COVID-19 lockdowns was also proposed. Our main hypothesis is that this CH₄ surge in 2020–2021 may also be caused by reduced sulfate emissions, which have been shown to decrease methanotrophy and increase methanogenesis rates in wetlands. Then, for the CH₄ slowdown in 2022–2024, our hypotheses are that the emissions from wetlands remained high, but that there was an even higher increase in the oxidative capacity of the atmosphere due to multiple other parameters that are detailed in this article. This perspective review paper is mainly qualitative; it demonstrates that coupled climate–chemistry models will also need to integrate biochemistry, as the evolution of the atmospheric composition is multifactorial and non-linear. Full article

(This article belongs to the Special Issue Land-Atmosphere Interactions)

► Show Figures

Figure 1

25 pages, 18044 KiB

Open AccessArticle

Atmospheric Energetics of Three Contrasting West African Monsoon Seasons as Simulated by a Regional Climate Model

by Yves Ngueto, René Laprise and Oumarou Nikiéma

Atmosphere 2025, 16(4), 405; https://doi.org/10.3390/atmos16040405 - 31 Mar 2025

Abstract

The West African atmospheric energy budget is assessed for the first time across three contrasting monsoon seasons (dry, wet, and moderate) using the latest version of the Canadian Regional Climate Model (CRCM6/GEM5). The model is driven by ERA5 reanalysis from the European Centre [...] Read more.

The West African atmospheric energy budget is assessed for the first time across three contrasting monsoon seasons (dry, wet, and moderate) using the latest version of the Canadian Regional Climate Model (CRCM6/GEM5). The model is driven by ERA5 reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF). A formalism appropriate for regional climate energetics is employed to quantify the primary physical processes occurring during the West African Monsoon, with the aim of highlighting those that exhibit significant inter-seasonal variability. The atmospheric energy path shows that the time-mean available enthalpy (A_M) reservoir, reflecting high surface temperatures and a lapse rate characteristic of a dry atmosphere, dominates other energy reservoirs. A_M is converted into the time-mean kinetic energy (K_M) and the time-variability available enthalpy (A_E) reservoirs, which are converted into a time-variability kinetic energy reservoir (K_E) through barotropic and baroclinic processes. A_E is the lowest energy reservoir, confirming smaller temperature variations in the tropics compared to higher latitudes. Kinetic energy reservoirs K_M and K_E have the same order of magnitude, suggesting that mean flow is as important as eddy activities during the season. The atmospheric energy cycle computed for three contrasting rainy seasons shows that time-variability energy reservoirs (A_E and K_E) and main terms acting upon them, are proportional to the rainfall activity, being higher (lower) during rainy (dry) years. It also reveals that, while C_A (conversion from A_M to A_E) and the generation term G_E feed wave’s development, the frictional term D_E counteracts the generation of K_E to dampen the creation of transient eddies. These findings suggest that the atmospheric energetic formalism could be applied on West African seasonal forecasts and future climate simulations to implement adaptation strategies. Full article

(This article belongs to the Section Climatology)

► Show Figures

Figure 1

8 pages, 1371 KiB

Open AccessBrief Report

Underestimation of Diurnal Variations in ERA5 Temperature and Relative Humidity over Tropical Indian Ocean

by Jeongwook Park, Hanna Na and Eunsun Lee

Atmosphere 2025, 16(4), 404; https://doi.org/10.3390/atmos16040404 - 31 Mar 2025

Abstract

Significant upwelling in the equatorial ocean influences complex ocean–atmosphere interactions and contributes to diurnal variations in the lower troposphere. This study compares the temperature and relative humidity data from radiosonde observations over the tropical Indian Ocean with those from the ERA5, highlighting the [...] Read more.

Significant upwelling in the equatorial ocean influences complex ocean–atmosphere interactions and contributes to diurnal variations in the lower troposphere. This study compares the temperature and relative humidity data from radiosonde observations over the tropical Indian Ocean with those from the ERA5, highlighting the underestimation of the diurnal variations in the ERA5. Radiosonde measurements were conducted at 3 h intervals for approximately 24 h, from 31 May to 1 June 2023, to investigate the diurnal variations in the lower troposphere at two fixed locations: (1) 65°E and 8°S in the upwelling region (Station 8) from 28 to 29 May 2023, and (2) 65°E and 4°S outside the upwelling region (Station 4). The radiosonde observations reveal pronounced diurnal variations in temperature and relative humidity between 950 and 650 hPa. The maximum diurnal range (maximum minus minimum) for temperature is observed above 800 hPa, with Station 8 exhibiting 4.7 °C and Station 4 exhibiting 2.7 °C. For relative humidity, Station 8 shows a diurnal range of 84%, while at Station 4, notable variations are observed only below 650 hPa, reaching 76%. However, the ERA5 underestimates the diurnal variations both within and outside the upwelling region. This underestimation is particularly evident between 850 and 750 hPa and is more pronounced within the upwelling region, where the diurnal range is larger. The diurnal ranges calculated from the ERA5 for 2004–2023 suggest that the reanalysis dataset exhibits limitations in capturing diurnal variations, particularly over the upwelling region. This report highlights the need for more in situ observations of the atmospheric variables to better represent diurnal variations in the tropical Indian Ocean. Full article

(This article belongs to the Section Biosphere/Hydrosphere/Land–Atmosphere Interactions)

► Show Figures

Figure 1

Journal Description

Atmosphere

Latest Articles

Journal Menu

Journal Browser

Highly Accessed Articles

Latest Books

E-Mail Alert

News

Topics

Conferences

Special Issues

Topical Collections

Further Information

Guidelines

MDPI Initiatives

Follow MDPI