Atmosphere

16 pages, 3527 KiB

Open AccessArticle

Comparative Study οf the Frequencies οf Atmospheric Circulation Types at Different Geopotential Levels and Their Relationship with Precipitation in Southern Romania

by Konstantia Tolika, Christina Anagnostopoulou, Myriam Traboulsi, Liliana Zaharia, Dana Maria (Oprea) Constantin, Ioannis Tegoulias and Panagiotis Maheras

Atmosphere 2024, 15(9), 1027; https://doi.org/10.3390/atmos15091027 (registering DOI) - 24 Aug 2024

Abstract

The primary aim of this study is to examine the characteristics of atmospheric circulation patterns at various geopotential levels and their relationship with precipitation in southern Romania during the period from 1961 to 2020. Daily geopotential heights (1000 hPa, 850 hPa, 700 hPa [...] Read more.

The primary aim of this study is to examine the characteristics of atmospheric circulation patterns at various geopotential levels and their relationship with precipitation in southern Romania during the period from 1961 to 2020. Daily geopotential heights (1000 hPa, 850 hPa, 700 hPa and 500 hPa) were utilized in an automatic updated atmospheric circulation scheme for the creation of daily calendars of 12 circulation types (5 anticyclonic and 7 cyclonic) as well as daily time series derived from five stations over the domain of interest. To assess the influence of the atmospheric circulation on precipitation, correlations and time trends were explored between the rainfall totals and the different circulation types. The findings reveal a rising trend in anticyclonic circulation types across the region, while cyclonic types exhibit a consisted decrease. Precipitation and number of rain days percentages associated with specific cyclonic types depend on the geopotential levels, while annual and seasonal precipitation linked to cyclonic types decreases progressively from higher to lower levels. The strongest correlations in circulation type frequencies are observed between adjacent circulation types. Taylor diagram analysis indicates that the relationships between circulation types and precipitation vary both seasonally and across different atmospheric levels. Notably, the two rainiest circulation types are more accurately simulated at higher atmospheric levels (700 hPa and 500 hPa). Full article

(This article belongs to the Special Issue Advances in Rainfall-Induced Hazard Research)

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17 pages, 4782 KiB

Open AccessArticle

Long-Term Validation of Aeolus Level-2B Winds in the Brazilian Amazon

by Alexandre Calzavara Yoshida, Patricia Cristina Venturini, Fábio Juliano da Silva Lopes and Eduardo Landulfo

Atmosphere 2024, 15(9), 1026; https://doi.org/10.3390/atmos15091026 (registering DOI) - 24 Aug 2024

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Abstract

The Atmospheric Dynamics Mission ADM-Aeolus was successfully launched in August 2018 by the European Space Agency (ESA). The Aeolus mission carried a single instrument, the first-ever Doppler wind lidar (DWL) in space, called Atmospheric LAser Doppler INstrument (ALADIN). Aeolus circled the Earth, providing [...] Read more.

The Atmospheric Dynamics Mission ADM-Aeolus was successfully launched in August 2018 by the European Space Agency (ESA). The Aeolus mission carried a single instrument, the first-ever Doppler wind lidar (DWL) in space, called Atmospheric LAser Doppler INstrument (ALADIN). Aeolus circled the Earth, providing vertical profiles of horizontal line-of-sight (HLOS) winds on a global scale. The Aeolus satellite’s measurements filled critical gaps in existing wind observations, particularly in remote regions such as the Brazilian Amazon. This area, characterized by dense rainforests and rich biodiversity, is essential for global climate dynamics. The weather patterns of the Amazon are influenced by atmospheric circulation driven by Hadley cells and the Intertropical Convergence Zone (ITCZ), which are crucial for the distribution of moisture and heat from the equator to the subtropics. The data provided by Aeolus can significantly enhance our understanding of these complex atmospheric processes. In this long-term validation study, we used radiosonde data collected from three stations in the Brazilian Amazon (Cruzeiro do Sul, Porto Velho, and Rio Branco) as a reference to assess the accuracy of the Level 2B (L2B) Rayleigh-clear and Mie-cloudy wind products. Statistical validation was conducted by comparing Aeolus L2B wind products and radiosonde data covering the period from October 2018 to March 2023 for Cruzeiro do Sul and Porto Velho, and from October 2018 to December 2022 for Rio Branco. Considering all available collocated winds, including all stations, a Pearson’s coefficient

(r)

of 0.73 was observed in Rayleigh-clear and 0.85 in Mie-cloudy wind products, revealing a strong correlation between Aeolus and radiosonde winds, suggesting that Aeolus wind products are reliable for capturing wind profiles in the studied region. The observed biases were −0.14 m/s for Rayleigh-clear and −0.40 m/s for Mie-cloudy, fulfilling the mission requirement of having absolute biases below 0.7 m/s. However, when analyzed annually, in 2022, the bias for Rayleigh-clear was −0.95 m/s, which did not meet the mission requirements. Full article

(This article belongs to the Special Issue Satellite Remote Sensing Applied in Atmosphere (2nd Edition))

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21 pages, 6022 KiB

Open AccessArticle

River Flashiness in Great Britain: A Spatio-Temporal Analysis

by Benjamin Olin and Lindsay Beevers

Atmosphere 2024, 15(9), 1025; https://doi.org/10.3390/atmos15091025 (registering DOI) - 24 Aug 2024

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Abstract

Flashiness refers to the rapidity and frequency of fluctuations in river flow. It can provide insights into flooding, by capturing dramatic increases in river flow, as well as contaminant transport, relating to concentrations of diffuse pollution. Despite a very well gauged river system, [...] Read more.

Flashiness refers to the rapidity and frequency of fluctuations in river flow. It can provide insights into flooding, by capturing dramatic increases in river flow, as well as contaminant transport, relating to concentrations of diffuse pollution. Despite a very well gauged river system, there is limited research in Great Britain targeting this component of river flow. This study addresses that gap in knowledge, with a detailed spatio-temporal analysis of river flashiness in Great Britain. Using 513 gauging stations, with historical records of at least 30 years, the average Richards–Baker flashiness index (

\bar{R B I}

) was calculated for 1990–2020, showing an overall west- (0.6–0.8) to east-coast (0.1–0.2) gradient, being higher in the west (with the exception of some gauges in the south-east). Employing random forest models, the main predictor for flashiness was found to be soil composition, with some additional region-specific predictors. These include flood attenuation by reservoirs and catchment areas, affecting flashiness in the north and west of Great Britain. Additionally, using a subset of 208 gauging stations with data recorded from 1970 to 2020, a temporal analysis examined significant breakpoints and/or trends in yearly flashiness, using the Pettitt test and Mann–Kendall trend test, respectively. Increases in flashiness were found mainly in the north-east and south-west of Great Britain, with implications in flooding and river health. On a seasonal scale, and using a monthly

\bar{R B I}

, the timing of flashy events was found to oscillate between autumn and spring over the 50 years, gravitating around winter. Full article

(This article belongs to the Special Issue Impacts of Climate Change on Basin Hydrology)

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17 pages, 8856 KiB

Open AccessArticle

Evaluation of Seasonal Prediction of Extreme Wind Resource Potential over China Based on a Dynamic Prediction System SIDRI-ESS V1.0

by Zixiang Yan, Jinxiao Li, Wen Zhou, Zouxing Lin, Yuxin Zang and Siyuan Li

Atmosphere 2024, 15(9), 1024; https://doi.org/10.3390/atmos15091024 - 23 Aug 2024

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Abstract

Wind resources play a pivotal role in building sustainable energy systems, crucial for mitigating and adapting to climate change. With the increasing frequency of extreme events under global warming, effective prediction of extreme wind resource potential can improve the safety of wind farms [...] Read more.

Wind resources play a pivotal role in building sustainable energy systems, crucial for mitigating and adapting to climate change. With the increasing frequency of extreme events under global warming, effective prediction of extreme wind resource potential can improve the safety of wind farms and other infrastructure, while optimizing resource allocation and emergency response plans. In this study, we evaluate the seasonal prediction skill for summer extreme wind events over China using a 20-year hindcast dataset generated by a dynamical seamless prediction system designed by Shanghai Investigation, Design and Research Institute Co., Ltd. (Shanghai, China) (SIDRI−ESS V1.0). Firstly, the hindcast effectively simulates the spatial distribution of summer extreme wind speed thresholds, even though it tends to overestimate the thresholds in most regions. Secondly, high prediction skills, measured by temporal correlation coefficient (TCC) and normalized root mean square error (nRMSE), are observed in northeast China, central east China, southeast China, and the Tibetan Plateau (TCC is about 0.5 and the nRMSE is below 0.9 in these regions). The highest skills emerge in southeast China with a maximum TCC greater than 0.7, and effective prediction skill can extend up to a 5-month lead time. Ensemble prediction significantly enhances predictive skill and reduces uncertainty, with 24 ensemble members being sufficient to saturate TCC and 12–16 members for nRMSE in most key regions and lead times. Furthermore, we show that the prediction skill for extreme wind counts is strongly related to the prediction skill for summer mean wind speeds, particularly in southeast China. Overall, SIDRI−ESS V1.0 shows promising performance in predicting extreme winds and has great potential to provide services to the wind industry. It can effectively help to optimize wind farm operating strategies and improve power generation efficiency. However, further improvements are needed, particularly in areas where prediction skills for extreme winds are influenced by smaller-scale weather phenomena and areas with complex underlying surfaces and climate characteristics. Full article

(This article belongs to the Special Issue Prediction and Modeling of Extreme Weather Events)

21 pages, 8218 KiB

Open AccessArticle

Qualitative and Quantitative Analyses of Automotive Exhaust Plumes for Remote Emission Sensing Application Using Gas Schlieren Imaging Sensor System

by Hafiz Hashim Imtiaz, Paul Schaffer, Yingjie Liu, Paul Hesse, Alexander Bergmann and Martin Kupper

Atmosphere 2024, 15(9), 1023; https://doi.org/10.3390/atmos15091023 - 23 Aug 2024

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Abstract

Remote emission sensing (RES) is a state-of-the-art technique for monitoring thousands of vehicles on the road every day to detect high emitters. Modern commercial RES systems use absorption spectroscopy to measure the ratio of pollutants to CO₂ from vehicle exhaust gases. In [...] Read more.

Remote emission sensing (RES) is a state-of-the-art technique for monitoring thousands of vehicles on the road every day to detect high emitters. Modern commercial RES systems use absorption spectroscopy to measure the ratio of pollutants to CO₂ from vehicle exhaust gases. In this work, we present an approach to enable direct concentration measurements by spectroscopic techniques in RES through measurement of the absorption path length. Our gas schlieren imaging sensor (GSIS) system operates on the principle of background-oriented schlieren (BOS) imaging in combination with advanced image processing and deep learning techniques to calculate detected exhaust plume sizes. We performed a qualitative as well as a quantitative analysis of vehicle exhaust and plume dimensions with the GSIS system. We present the system details and results from the GSIS system in the lab in comparison to a BOS model based on flow simulations, the results from characterization measurements in the lab with defined gas mixtures and temperatures, and the results from measurements on the road from different vehicles. Full article

(This article belongs to the Special Issue Transport Emissions and Their Environmental Impacts)

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20 pages, 12936 KiB

Open AccessArticle

Dynamic Changes and Influencing Factors Analysis of Groundwater Icings in the Permafrost Region in Central Sakha (Yakutia) Republic under Modern Climatic Conditions

by Miao Yu, Nadezhda Pavlova, Jing Zhao and Changlei Dai

Atmosphere 2024, 15(9), 1022; https://doi.org/10.3390/atmos15091022 - 23 Aug 2024

Viewed by 156

Abstract

In central Sakha (Yakutia) Republic, groundwater icings, primarily formed by intrapermafrost water, are less prone to contamination and serve as a stable freshwater resource. The periodic growth of icings threatens infrastructure such as roads, railways, and bridges in permafrost areas. Therefore, research in [...] Read more.

In central Sakha (Yakutia) Republic, groundwater icings, primarily formed by intrapermafrost water, are less prone to contamination and serve as a stable freshwater resource. The periodic growth of icings threatens infrastructure such as roads, railways, and bridges in permafrost areas. Therefore, research in this field has become urgently necessary. This study aims to analyze the impacts of various factors on the scale of icing formation using Landsat satellite data, Gravity Recovery and Climate Experiment (GRACE)/GRACE Follow-On (GRACE-FO) data, Global Land Data Assimilation System (GLDAS) data, and field observation results. The results showed that the surface area of icings in the study area showed an overall increasing trend from 2002 to 2022, with an average growth rate of 0.06 km²/year. Suprapermafrost water and intrapermafrost water are the main sources of icings in the study area. The total Groundwater Storage Anomaly (GWSA) values from October to April showed a strong correlation with the maximum icing areas. Icings fed by suprapermafrost water were influenced by precipitation in early autumn, while those fed by intrapermafrost water were more affected by talik size and distribution. Climate warming contributed to the degradation of the continuous permafrost covering an area of 166 km² to discontinuous permafrost, releasing additional groundwater. This may also be one of the reasons for the observed increasing trend in icing areas. This study can provide valuable insights into water resource management and infrastructure construction in permafrost regions. Full article

(This article belongs to the Special Issue Impacts of Land Use and Climate Change in Urban Area: Big Data and Machine Learning)

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14 pages, 2931 KiB

Open AccessArticle

Influence of Field Sampling Methods on Measuring Volatile Organic Compounds in a Swine Facility Using SUMMA Canisters

by Xin Li, Qinqin Sun, Lei Yu, Xiaoshuai Wang, Li Feng and Kaiying Wang

Atmosphere 2024, 15(9), 1021; https://doi.org/10.3390/atmos15091021 - 23 Aug 2024

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Abstract

Volatile organic compounds (VOCs) play a crucial role in emission control, being one of the most important sources of odor while also serving as significant precursors to secondary organic aerosols and ozone formation. Appropriate sampling methods are essential for accurately assessing the concentration [...] Read more.

Volatile organic compounds (VOCs) play a crucial role in emission control, being one of the most important sources of odor while also serving as significant precursors to secondary organic aerosols and ozone formation. Appropriate sampling methods are essential for accurately assessing the concentration and composition of VOCs within swine barns. In this study, the effects of both passive air sampling and active air sampling on VOCs were evaluated, and the influence of storage time on the VOC stability in sampling canisters for both methods was investigated. SUMMA canisters, which are electropolished and passivated with silanization, offer excellent corrosion protection and resistance to high pressure and temperature and were used in this study. The predominant component categories prevailing within the pig house were found to be oxygenated VOCs (OVOCs) and volatile sulfur compounds (VSCs), with ethanol emerging as the most abundant component of VOCs detected. Notably, the statistical analysis results revealed no significant differences between passive and active sampling regarding the impact of storage time on substance concentration. Changes in canister pressure also did not significantly affect substance stability. The results showed that the C2–C3 compounds remained relatively stable, especially within 3 days, with recoveries above 80% within 20 days. Methyl sulfide, dimethyl disulfide, and ethanol were more stable within the first week, but their recoveries significantly dropped by day 20, with methyl sulfide and dimethyl disulfide at 62.3% and 65.3%, respectively. This study contributes to the development of a foundation for selecting appropriate VOC sampling methods in swine facilities for conducting a rational analysis of VOC samples. Full article

(This article belongs to the Collection Livestock Odor Issues and Air Quality)

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18 pages, 13641 KiB

Open AccessArticle

A Deep Forest Algorithm Based on TropOMI Satellite Data to Estimate Near-Ground Ozone Concentration

by Mao Zong, Tianhong Song, Yan Zhang, Yu Feng and Shurui Fan

Atmosphere 2024, 15(9), 1020; https://doi.org/10.3390/atmos15091020 - 23 Aug 2024

Viewed by 195

Abstract

The accurate estimation of near-ground ozone (O₃) concentration is of great significance to human health and the ecological environment. In order to improve the accuracy of estimating ground-level O₃ concentration, this study adopted a deep forest algorithm to construct a [...] Read more.

The accurate estimation of near-ground ozone (O₃) concentration is of great significance to human health and the ecological environment. In order to improve the accuracy of estimating ground-level O₃ concentration, this study adopted a deep forest algorithm to construct a model for estimating near-ground O₃ concentration. It is pointed out whether input data on particulate matter (PM_2.5) and nitrogen dioxide (NO₂) concentrations also affect the estimation accuracy. The model first uses the multi-granularity scanning technique to learn the features of the training set, and then it adopts the cascade forest structure to train the processed data, and at the same time, it adaptively adjusts the number of layers in order to achieve a better performance. Daily near-ground O₃ concentrations in Shijiazhuang were estimated using satellite O₃ column concentrations, ground-based PM_2.5 and NO₂ concentration data, meteorological element data, and elevation data. The deep forest model was compared with six models, namely, random forest, CatBoost, XGBoost, LightGBM, Decision Tree, and GBDT. The R-squared (R²), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) of the proposed deep forest model were 0.9560, 13.2542, and 9.0250, respectively, which had significant advantages over other tree-based regression models. Meanwhile, the model performance was improved by adding NO₂ and PM_2.5 features to the model estimations, indicating the necessity of synergistic observations of NO₂, PM_2.5, and O₃. Finally, the seasonal distribution of O₃ concentrations in the Shijiazhuang area was plotted, with the highest O₃ concentrations in the summer, the lowest in the winter, and the O₃ concentration is in the middle of spring and autumn. Full article

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

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16 pages, 7556 KiB

Open AccessArticle

Warm and Dry Compound Events in Poland

by Joanna Wibig and Joanna Jędruszkiewicz

Atmosphere 2024, 15(9), 1019; https://doi.org/10.3390/atmos15091019 - 23 Aug 2024

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Abstract

The aim of this paper was to characterize the warm and dry compound events (WD days) in Poland during the period of 1966–2023, focusing on assessing the frequency and intensity of such events and their spatial and temporal variability, as well as on [...] Read more.

The aim of this paper was to characterize the warm and dry compound events (WD days) in Poland during the period of 1966–2023, focusing on assessing the frequency and intensity of such events and their spatial and temporal variability, as well as on the driving factors of warm and dry compound events. WD days are those days that have a maximum temperature equal to or higher than the 90th percentile and the precipitation on that day and the 14 preceding days are equal to or less than the 25 percentile. During 1966–2023, the frequency of WD days increased significantly, mainly in April, the summer months, and December. Higher temperatures favored the occurrence of WD days from March to November, but, in winter months, the heat did not favor the occurrence of WD days. The exception was December, when high temperatures in the first part of the analyzed period did not favor the occurrence of a dry day, whereas, in the second part, it did. The strongest influence on the frequency of WD days had the East Atlantic pattern, where air flowed over Poland from the southwest. Warm and humid air flowing from the Atlantic Ocean must overcome the mountain barrier; therefore, it flows to Poland as warm and dry air. From spring to autumn, the WD days were related to an increase in the geopotential height in central Eastern Europe, and, in the winter, they were related with blocking over the Balkans. Full article

(This article belongs to the Section Climatology)

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