To improve the forecast accuracy of heavy precipitation, re-forecasts are conducted for the Henan 21.7 rainstorm. The Intermediate Complexity Atmospheric Research Model (ICAR) and the Weather Research and Forecasting Model (WRF) with a 1 km horizontal grid spacing are used for the re-forecasts. The results indicate that heavy precipitation forecasted by ICAR primarily accumulates on the windward slopes of the mountains. In contrast, some severe precipitation forecasted by WRF is beyond the mountains. The main difference between ICAR and WRF is that ICAR excludes the “impacts of physical processes on winds and the nonlinear interactions between the small resolvable-scale disturbances” (briefed as the “physical–dynamical interactions”). Thus, heavy precipitation beyond the mountains is attributed to the “physical–dynamical interactions”. Furthermore, severe precipitation on the windward slopes of the mountains typically aligns with the observations, whereas heavy rainfall beyond the mountains seldom matches the observations. Therefore, severe precipitation on the windward slopes of (beyond) the mountains is more (less) predictable. Based on these findings and theoretical thinking about the predictability of severe precipitation, a scheme of using the ICAR’s prediction to adjust the WRF’s prediction is proposed, thereby improving the forecast accuracy of heavy rainfall.
{"title":"The Application of an Intermediate Complexity Atmospheric Research Model in the Forecasting of the Henan 21.7 Rainstorm","authors":"Xingbao Wang, Qun Xu, Xiajun Deng, Hongjie Zhang, Qianhong Tang, Tingting Zhou, Fengcai Qi, Wenwu Peng","doi":"10.3390/atmos15080959","DOIUrl":"https://doi.org/10.3390/atmos15080959","url":null,"abstract":"To improve the forecast accuracy of heavy precipitation, re-forecasts are conducted for the Henan 21.7 rainstorm. The Intermediate Complexity Atmospheric Research Model (ICAR) and the Weather Research and Forecasting Model (WRF) with a 1 km horizontal grid spacing are used for the re-forecasts. The results indicate that heavy precipitation forecasted by ICAR primarily accumulates on the windward slopes of the mountains. In contrast, some severe precipitation forecasted by WRF is beyond the mountains. The main difference between ICAR and WRF is that ICAR excludes the “impacts of physical processes on winds and the nonlinear interactions between the small resolvable-scale disturbances” (briefed as the “physical–dynamical interactions”). Thus, heavy precipitation beyond the mountains is attributed to the “physical–dynamical interactions”. Furthermore, severe precipitation on the windward slopes of the mountains typically aligns with the observations, whereas heavy rainfall beyond the mountains seldom matches the observations. Therefore, severe precipitation on the windward slopes of (beyond) the mountains is more (less) predictable. Based on these findings and theoretical thinking about the predictability of severe precipitation, a scheme of using the ICAR’s prediction to adjust the WRF’s prediction is proposed, thereby improving the forecast accuracy of heavy rainfall.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"10 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141944020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Homogenization of the time series of observed climatic data aims to remove non-climatic biases caused by technical changes during the history of the climate observations. The spatial redundancy of climate information helps to recognize station-specific inhomogeneities with statistical methods, but the correct detection and removal of inhomogeneity biases is generally not easy for the combined effects of individual inhomogeneities. In a homogenization procedure, several time series of a given climatic variable observed in one climatic region are usually homogenized together via a large number of spatial comparisons between them. Such procedures are called relative homogenization. A relative homogenization procedure may include one or more homogenization cycles where a cycle includes the steps of time series comparison, inhomogeneity detection and corrections for inhomogeneities, and they may include other steps like the filtering of outlier values or spatial interpolations for infilling data gaps. Relative homogenization methods differ according to the number and content of the individual homogenization cycles, the procedure for the time series comparisons, the statistical inhomogeneity detection method, the way of the inhomogeneity bias removal, among other specifics. Efficient homogenization needs the use of tested statistical methods to be included in partly or fully automated homogenization procedures. Due to the large number and high variety of homogenization experiments fulfilled in the Spanish MULTITEST project (2015–2017), its method comparison test results are still the most informative about the efficiencies of homogenization methods in use. This study presents a brief review of the advances in relative homogenization, recalls some key results of the MULTITEST project, and analyzes some theoretical aspects of successful homogenization.
{"title":"Relative Homogenization of Climatic Time Series","authors":"Peter Domonkos","doi":"10.3390/atmos15080957","DOIUrl":"https://doi.org/10.3390/atmos15080957","url":null,"abstract":"Homogenization of the time series of observed climatic data aims to remove non-climatic biases caused by technical changes during the history of the climate observations. The spatial redundancy of climate information helps to recognize station-specific inhomogeneities with statistical methods, but the correct detection and removal of inhomogeneity biases is generally not easy for the combined effects of individual inhomogeneities. In a homogenization procedure, several time series of a given climatic variable observed in one climatic region are usually homogenized together via a large number of spatial comparisons between them. Such procedures are called relative homogenization. A relative homogenization procedure may include one or more homogenization cycles where a cycle includes the steps of time series comparison, inhomogeneity detection and corrections for inhomogeneities, and they may include other steps like the filtering of outlier values or spatial interpolations for infilling data gaps. Relative homogenization methods differ according to the number and content of the individual homogenization cycles, the procedure for the time series comparisons, the statistical inhomogeneity detection method, the way of the inhomogeneity bias removal, among other specifics. Efficient homogenization needs the use of tested statistical methods to be included in partly or fully automated homogenization procedures. Due to the large number and high variety of homogenization experiments fulfilled in the Spanish MULTITEST project (2015–2017), its method comparison test results are still the most informative about the efficiencies of homogenization methods in use. This study presents a brief review of the advances in relative homogenization, recalls some key results of the MULTITEST project, and analyzes some theoretical aspects of successful homogenization.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"36 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141944027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Diesel pallet trucks, a type of heavy-duty diesel trucks (HDDTs), have historically been a vital component in logistics and transport due to their high payload capacity. However, they also present significant challenges, particularly in terms of emissions which contribute substantially to urban air pollution. Traditional HDDTs emission measurement methods, such as engine bench tests and those used in laboratory settings, often fail to capture real-world emission behaviors accurately. This study specifically examines the real-world emission characteristics of diesel pallet trucks exceeding 30 t under varying loads (unloaded, half loaded, and fully loaded) and different road conditions (urban, suburban, and high-speed). Considering that data quality is the key to the accuracy of the scheme, this research utilized a portable emission measurement system (PEMS) to capture real-time emissions data of carbon dioxide (CO2), carbon monoxide (CO), nitrogen oxides (NOX), and total hydrocarbons (THC). Key findings demonstrate a direct correlation between vehicle load and emission factors, with the emission factors for CO2, CO, and NOX increasing by 39.5%, 105.4%, and 22.7%, respectively, from unloaded to fully loaded states under comprehensive operating conditions. Regression analyses further provide an emission factor prediction model for HDDPTs, underscoring the continuous relationship between speed, load, and emission rates. These findings provide a scientific basis for pollution control strategies for diesel trucks.
{"title":"Real-World Emission Characteristics of Diesel Pallet Trucks under Varying Loads: Using the Example of China","authors":"Ye Zhang, Yating Song, Tianshi Feng","doi":"10.3390/atmos15080956","DOIUrl":"https://doi.org/10.3390/atmos15080956","url":null,"abstract":"Diesel pallet trucks, a type of heavy-duty diesel trucks (HDDTs), have historically been a vital component in logistics and transport due to their high payload capacity. However, they also present significant challenges, particularly in terms of emissions which contribute substantially to urban air pollution. Traditional HDDTs emission measurement methods, such as engine bench tests and those used in laboratory settings, often fail to capture real-world emission behaviors accurately. This study specifically examines the real-world emission characteristics of diesel pallet trucks exceeding 30 t under varying loads (unloaded, half loaded, and fully loaded) and different road conditions (urban, suburban, and high-speed). Considering that data quality is the key to the accuracy of the scheme, this research utilized a portable emission measurement system (PEMS) to capture real-time emissions data of carbon dioxide (CO2), carbon monoxide (CO), nitrogen oxides (NOX), and total hydrocarbons (THC). Key findings demonstrate a direct correlation between vehicle load and emission factors, with the emission factors for CO2, CO, and NOX increasing by 39.5%, 105.4%, and 22.7%, respectively, from unloaded to fully loaded states under comprehensive operating conditions. Regression analyses further provide an emission factor prediction model for HDDPTs, underscoring the continuous relationship between speed, load, and emission rates. These findings provide a scientific basis for pollution control strategies for diesel trucks.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"77 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141944026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Peizhi Wang, Qingsong Wang, Yuhuan Jia, Jingjin Ma, Chunying Wang, Liping Qiao, Qingyan Fu, Abdelwahid Mellouki, Hui Chen, Li Li
Many cities in China are facing the dual challenge of PM2.5 and PM10 pollution. There is an urgent need to develop a cost-effective method that can apportion both with high-time resolution. A novel and practical apportionment method is presented in this study. It combines the measurement of particle mass size distribution (PMSD) with an optical particle counter (OPC) and the algorithm of normalized non-negative matrix factorization (N-NMF). Applied in the city center of Baoding, Hebei, this method separates four distinct pollution factors. Their sizes (ordered from the smallest to largest) range from 0.16 μm to 0.6 μm, 0.16 μm to 1.0 μm, 0.5 μm to 17.0 μm, and 2.0 μm to 20.0 μm, respectively. They correspondingly contribute to PM2.5 (PM10) with portions of 26% (17%), 37% (26%), 33% (41%), and 4% (16%), respectively, on average. The smaller three factors are identified as combustion, secondary, and industrial aerosols because of their high correlation with carbonaceous aerosols, nitrate aerosols, and trace elements of Fe/Mn/Ca in PM2.5, respectively. The largest-sized factor is linked to dust aerosols. The primary origin regions, oxidation degrees, and formation mechanisms of each source are further discussed. This provides a scientific basis for the comprehensive management of PM2.5 and PM10 pollution.
{"title":"A Novel Apportionment Method Utilizing Particle Mass Size Distribution across Multiple Particle Size Ranges","authors":"Peizhi Wang, Qingsong Wang, Yuhuan Jia, Jingjin Ma, Chunying Wang, Liping Qiao, Qingyan Fu, Abdelwahid Mellouki, Hui Chen, Li Li","doi":"10.3390/atmos15080955","DOIUrl":"https://doi.org/10.3390/atmos15080955","url":null,"abstract":"Many cities in China are facing the dual challenge of PM2.5 and PM10 pollution. There is an urgent need to develop a cost-effective method that can apportion both with high-time resolution. A novel and practical apportionment method is presented in this study. It combines the measurement of particle mass size distribution (PMSD) with an optical particle counter (OPC) and the algorithm of normalized non-negative matrix factorization (N-NMF). Applied in the city center of Baoding, Hebei, this method separates four distinct pollution factors. Their sizes (ordered from the smallest to largest) range from 0.16 μm to 0.6 μm, 0.16 μm to 1.0 μm, 0.5 μm to 17.0 μm, and 2.0 μm to 20.0 μm, respectively. They correspondingly contribute to PM2.5 (PM10) with portions of 26% (17%), 37% (26%), 33% (41%), and 4% (16%), respectively, on average. The smaller three factors are identified as combustion, secondary, and industrial aerosols because of their high correlation with carbonaceous aerosols, nitrate aerosols, and trace elements of Fe/Mn/Ca in PM2.5, respectively. The largest-sized factor is linked to dust aerosols. The primary origin regions, oxidation degrees, and formation mechanisms of each source are further discussed. This provides a scientific basis for the comprehensive management of PM2.5 and PM10 pollution.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"10 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141944031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The fundamentals of the design and operation of self-priming pumps, as indispensable equipment in industry, have been the focus of research in the field of fluid mechanics. This paper begins with a comprehensive background on self-priming pumps and gas-liquid two-phase flow, and it outlines recent advances in the field. Self-priming pumps within the gas-liquid two-phase flow state and the spatial and temporal evolution of the transient characteristics of self-priming pumps determine the self-priming pump self-absorption performance. Through mastery of the self-absorption mechanism, high-performance self-absorption pump products can be formed to provide theoretical support for the development of products. In current research, numerical simulation has become an important tool for analyzing and predicting the behavior of gas-liquid two-phase flow in self-priming pumps. This paper reviews existing numerical models of gas-liquid two-phase flow and categorizes them. Reviewing these models not only provides us with a comprehensive understanding of the existing research but also offers possible directions for future research. The complexity of gas–liquid interactions and their impact on pump performance is analyzed. Through these detailed discussions, we are able to identify the challenges in the simulation process and summarize what has been achieved. In order to further improve the accuracy and reliability of simulations, this paper introduces the latest simulation techniques and research methodologies, which provide new perspectives for a deeper understanding of gas-liquid two-phase flow. In addition, this paper investigates a variety of factors which affect the operating efficiency of self-priming pumps, including the design parameters, fluid properties, and operating conditions. Comprehensive consideration of these factors is crucial for optimizing pump performance. Finally, this paper summarizes the current research results and identifies the main findings and deficiencies. Based on this, the need to improve the accuracy of numerical simulations and to study the design parameters in depth to improve pump performance is emphasized.
{"title":"Progress on Numerical Simulation of Gas-Liquid Two-Phase Flow in Self-Priming Pump","authors":"Heng Qian, Hongbo Zhao, Chun Xiang, Zhenhua Duan, Sanxia Zhang, Peijian Zhou","doi":"10.3390/atmos15080953","DOIUrl":"https://doi.org/10.3390/atmos15080953","url":null,"abstract":"The fundamentals of the design and operation of self-priming pumps, as indispensable equipment in industry, have been the focus of research in the field of fluid mechanics. This paper begins with a comprehensive background on self-priming pumps and gas-liquid two-phase flow, and it outlines recent advances in the field. Self-priming pumps within the gas-liquid two-phase flow state and the spatial and temporal evolution of the transient characteristics of self-priming pumps determine the self-priming pump self-absorption performance. Through mastery of the self-absorption mechanism, high-performance self-absorption pump products can be formed to provide theoretical support for the development of products. In current research, numerical simulation has become an important tool for analyzing and predicting the behavior of gas-liquid two-phase flow in self-priming pumps. This paper reviews existing numerical models of gas-liquid two-phase flow and categorizes them. Reviewing these models not only provides us with a comprehensive understanding of the existing research but also offers possible directions for future research. The complexity of gas–liquid interactions and their impact on pump performance is analyzed. Through these detailed discussions, we are able to identify the challenges in the simulation process and summarize what has been achieved. In order to further improve the accuracy and reliability of simulations, this paper introduces the latest simulation techniques and research methodologies, which provide new perspectives for a deeper understanding of gas-liquid two-phase flow. In addition, this paper investigates a variety of factors which affect the operating efficiency of self-priming pumps, including the design parameters, fluid properties, and operating conditions. Comprehensive consideration of these factors is crucial for optimizing pump performance. Finally, this paper summarizes the current research results and identifies the main findings and deficiencies. Based on this, the need to improve the accuracy of numerical simulations and to study the design parameters in depth to improve pump performance is emphasized.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"25 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141944029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
During wintertime temperature inversion episodes, ozone in the Uinta Basin sometimes exceeds the standard of 70 ppb set by the US Environmental Protection Agency. Since ozone formation depends on sunlight, and less sunlight is available during winter, wintertime ozone can only form if snow cover and albedo are high. Researchers have encountered difficulties replicating high albedo values in 3-D weather and photochemical transport model simulations for winter episodes. In this study, a process to assimilate MODIS satellite data into WRF and CAMx models was developed, streamlined, and tested to demonstrate the impacts of data assimilation on the models’ performance. Improvements to the WRF simulation of surface albedo and snow cover were substantial. However, the impact of MODIS data assimilation on WRF performance for other meteorological quantities was minimal, and it had little impact on ozone concentrations in the CAMx photochemical transport model. The contrast between the data assimilation and reference cases was greater for a period with no new snow since albedo appears to decrease too rapidly in default WRF and CAMx configurations. Overall, the improvement from MODIS data assimilation had an observed enhancement in the spatial distribution and temporal evolution of surface characteristics on meteorological quantities and ozone production.
{"title":"Assimilating Satellite-Derived Snow Cover and Albedo Data to Improve 3-D Weather and Photochemical Models","authors":"Colleen Jones, Huy Tran, Trang Tran, Seth Lyman","doi":"10.3390/atmos15080954","DOIUrl":"https://doi.org/10.3390/atmos15080954","url":null,"abstract":"During wintertime temperature inversion episodes, ozone in the Uinta Basin sometimes exceeds the standard of 70 ppb set by the US Environmental Protection Agency. Since ozone formation depends on sunlight, and less sunlight is available during winter, wintertime ozone can only form if snow cover and albedo are high. Researchers have encountered difficulties replicating high albedo values in 3-D weather and photochemical transport model simulations for winter episodes. In this study, a process to assimilate MODIS satellite data into WRF and CAMx models was developed, streamlined, and tested to demonstrate the impacts of data assimilation on the models’ performance. Improvements to the WRF simulation of surface albedo and snow cover were substantial. However, the impact of MODIS data assimilation on WRF performance for other meteorological quantities was minimal, and it had little impact on ozone concentrations in the CAMx photochemical transport model. The contrast between the data assimilation and reference cases was greater for a period with no new snow since albedo appears to decrease too rapidly in default WRF and CAMx configurations. Overall, the improvement from MODIS data assimilation had an observed enhancement in the spatial distribution and temporal evolution of surface characteristics on meteorological quantities and ozone production.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"3 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141944030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiaoting Zhou, Zhiqiang Liu, Lang Wu, Yangqing Wang
CO2 emissions prediction plays a key role in atmospheric environment management and regional sustainable development. Taking the Four Provinces of Mountains and Rivers (Henan, Hebei, Shandong, and Shanxi) in China as an example, the Autoregressive Integrated Moving Average Model (ARIMA) and random forest importance analysis were used to calculate the future trend of the CO2 emission–influencing factors and obtain the main influencing factors. Based on the above, BP neural network (BPNN), support vector machine (SVR), and random forest (RF) models were used to predict the future apparent CO2 emissions of the four provinces. The results show that, in general, population, coal consumption, and per capita GDP are the main factors influencing CO2 emissions. The RF model has the best prediction performance; for instance, RMSE (81.86), R2 (0.905), and MAE (64.69). The prediction results show that the total apparent CO2 emissions of the Four Provinces of Mountains and Rivers will peak in 2028 (with a peak of about 4500 Mt). The apparent CO2 emissions of Henan, Hebei, and Shandong Province peaked in 2011 (with a peak of about 654 Mt), 2013 (with a peak of about 657 Mt), and 2020 (with a peak of about 1273 Mt), respectively. Shanxi is forecast to reach its peak (with a peak of about 2486 Mt) in 2029. The apparent CO2 emissions of all provinces showed an obvious downward trend after reaching their peak. Henan, Hebei Shandong, and Shanxi showed a significant downward trend in 2018, 2023, and 2032, respectively.
{"title":"Study on CO2 Emission Forecast of “Four Provinces of Mountains and Rivers” Based on Time-SeriesMachine Learning","authors":"Xiaoting Zhou, Zhiqiang Liu, Lang Wu, Yangqing Wang","doi":"10.3390/atmos15080949","DOIUrl":"https://doi.org/10.3390/atmos15080949","url":null,"abstract":"CO2 emissions prediction plays a key role in atmospheric environment management and regional sustainable development. Taking the Four Provinces of Mountains and Rivers (Henan, Hebei, Shandong, and Shanxi) in China as an example, the Autoregressive Integrated Moving Average Model (ARIMA) and random forest importance analysis were used to calculate the future trend of the CO2 emission–influencing factors and obtain the main influencing factors. Based on the above, BP neural network (BPNN), support vector machine (SVR), and random forest (RF) models were used to predict the future apparent CO2 emissions of the four provinces. The results show that, in general, population, coal consumption, and per capita GDP are the main factors influencing CO2 emissions. The RF model has the best prediction performance; for instance, RMSE (81.86), R2 (0.905), and MAE (64.69). The prediction results show that the total apparent CO2 emissions of the Four Provinces of Mountains and Rivers will peak in 2028 (with a peak of about 4500 Mt). The apparent CO2 emissions of Henan, Hebei, and Shandong Province peaked in 2011 (with a peak of about 654 Mt), 2013 (with a peak of about 657 Mt), and 2020 (with a peak of about 1273 Mt), respectively. Shanxi is forecast to reach its peak (with a peak of about 2486 Mt) in 2029. The apparent CO2 emissions of all provinces showed an obvious downward trend after reaching their peak. Henan, Hebei Shandong, and Shanxi showed a significant downward trend in 2018, 2023, and 2032, respectively.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"30 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141944034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sarah Balkissoon, Y. Charles Li, Anthony R. Lupo, Samuel Walsh, Lukas McGuire
Dimensional analysis shows that the relation between wind speed and maximum or mean water wave height takes the form H=cU02g, where H is the maximum or mean water wave height caused by wind of speed U0, g is the gravitational acceleration, and c is a dimensionless constant. This relation is important in predicting the maximum or mean water wave height caused by a tropical cyclone. Firstly, the mathematical and theoretical justification for determining c is presented. Verification is conducted using four tropical cyclones as case studies for determining c using significant wave heights rather than the overall maximum and mean. The observed values of c are analyzed statistically. On the days when the fixed buoy captured the highest wind speeds, the frequency distributions of the data for c are close to a bell shape with very small standard deviations in comparison with the mean values; thus, the mean values provide good predictions for c. In view of the fact that tropical cyclone waves are turbulent and the background waves caused by many other factors such as lunar tidal effect cannot be ignored, the obtained results for c are quite satisfactory. This method provides a direct approach in the prediction of the wave height or the wind speeds given the c value and can serve an interpolation methodology to increase the temporal resolution of the data.
尺寸分析表明，风速与最大或平均水波高度之间的关系形式为 H=cU02g，其中 H 为风速 U0 引起的最大或平均水波高度，g 为重力加速度，c 为无量纲常数。这一关系对于预测热带气旋引起的最大或平均水波高度非常重要。首先，介绍了确定 c 的数学和理论依据。以四个热带气旋为案例进行验证，使用显著波高而不是总体最大值和平均值来确定 c。对观测到的 c 值进行了统计分析。在固定浮标捕捉到最高风速的日子里，c 的数据频率分布接近钟形，与平均值相比，标准偏差非常小；因此，平均值为 c 提供了良好的预测。这种方法提供了在给定 c 值的情况下预测波高或风速的直接方法，并可作为一种插值方法来提高数据的时间分辨率。
{"title":"On the Relation between Wind Speed and Maximum or Mean Water Wave Height","authors":"Sarah Balkissoon, Y. Charles Li, Anthony R. Lupo, Samuel Walsh, Lukas McGuire","doi":"10.3390/atmos15080948","DOIUrl":"https://doi.org/10.3390/atmos15080948","url":null,"abstract":"Dimensional analysis shows that the relation between wind speed and maximum or mean water wave height takes the form H=cU02g, where H is the maximum or mean water wave height caused by wind of speed U0, g is the gravitational acceleration, and c is a dimensionless constant. This relation is important in predicting the maximum or mean water wave height caused by a tropical cyclone. Firstly, the mathematical and theoretical justification for determining c is presented. Verification is conducted using four tropical cyclones as case studies for determining c using significant wave heights rather than the overall maximum and mean. The observed values of c are analyzed statistically. On the days when the fixed buoy captured the highest wind speeds, the frequency distributions of the data for c are close to a bell shape with very small standard deviations in comparison with the mean values; thus, the mean values provide good predictions for c. In view of the fact that tropical cyclone waves are turbulent and the background waves caused by many other factors such as lunar tidal effect cannot be ignored, the obtained results for c are quite satisfactory. This method provides a direct approach in the prediction of the wave height or the wind speeds given the c value and can serve an interpolation methodology to increase the temporal resolution of the data.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"76 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141944032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Francesco D’Amico, Ivano Ammoscato, Daniel Gullì, Elenio Avolio, Teresa Lo Feudo, Mariafrancesca De Pino, Paolo Cristofanelli, Luana Malacaria, Domenico Parise, Salvatore Sinopoli, Giorgia De Benedetto, Claudia Roberta Calidonna
Due to its high short-term global warming potential (GWP) compared to carbon dioxide, methane (CH4) is a considerable agent of climate change. This research is aimed at analyzing data on methane gathered at the GAW (Global Atmosphere Watch) station of Lamezia Terme (Calabria, Southern Italy) spanning seven years of continuous measurements (2016–2022) and integrating the results with key meteorological data. Compared to previous studies on detected methane mole fractions at the same station, daily-to-yearly patterns have become more prominent thanks to the analysis of a much larger dataset. Overall, the yearly increase of methane at the Lamezia Terme station is in general agreement with global measurements by NOAA, though local peaks are present, and an increase linked to COVID-19 is identified. Seasonal changes and trends have proved to be fully cyclic, with the daily cycles being largely driven by local wind circulation patterns and synoptic features. Outbreak events have been statistically evaluated depending on their weekday of occurrence to test possible correlations with anthropogenic activities. A cross analysis between methane peaks and specific wind directions has also proved that local sources may be deemed responsible for the highest mole fractions.
{"title":"Integrated Analysis of Methane Cycles and Trends at the WMO/GAW Station of Lamezia Terme (Calabria, Southern Italy)","authors":"Francesco D’Amico, Ivano Ammoscato, Daniel Gullì, Elenio Avolio, Teresa Lo Feudo, Mariafrancesca De Pino, Paolo Cristofanelli, Luana Malacaria, Domenico Parise, Salvatore Sinopoli, Giorgia De Benedetto, Claudia Roberta Calidonna","doi":"10.3390/atmos15080946","DOIUrl":"https://doi.org/10.3390/atmos15080946","url":null,"abstract":"Due to its high short-term global warming potential (GWP) compared to carbon dioxide, methane (CH4) is a considerable agent of climate change. This research is aimed at analyzing data on methane gathered at the GAW (Global Atmosphere Watch) station of Lamezia Terme (Calabria, Southern Italy) spanning seven years of continuous measurements (2016–2022) and integrating the results with key meteorological data. Compared to previous studies on detected methane mole fractions at the same station, daily-to-yearly patterns have become more prominent thanks to the analysis of a much larger dataset. Overall, the yearly increase of methane at the Lamezia Terme station is in general agreement with global measurements by NOAA, though local peaks are present, and an increase linked to COVID-19 is identified. Seasonal changes and trends have proved to be fully cyclic, with the daily cycles being largely driven by local wind circulation patterns and synoptic features. Outbreak events have been statistically evaluated depending on their weekday of occurrence to test possible correlations with anthropogenic activities. A cross analysis between methane peaks and specific wind directions has also proved that local sources may be deemed responsible for the highest mole fractions.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"198 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141944033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The carbon source/sink nature and the water balance of a drip-irrigated and mulched watermelon cultivated under a semi-arid climate were investigated. Biodegradable films, plants and some fruits were left on the soil as green manure. The study spanned from watermelon planting to the subsequent crop (June–November 2023). The eddy covariance technique was employed to monitor water vapor (H2O) and carbon dioxide (CO2) fluxes, which were partitioned into transpiration, evaporation, photosynthesis and respiration, respectively, using the flux variance similarity method.This method utilizesthe Monin–Obukhov similarity theory to separate stomatal (photosynthesis and transpiration) from non-stomatal (respiration and evaporation) processes. The results indicate that mulching films contribute to carbon sequestration in the soil (+19.3 g C m−2). However, the mulched watermelon crop presented in this study functions as a net carbon source, with a net biome exchange, representing the net rate of C accumulation in or loss from ecosystems, equal to +230 g C m−2. This is primarily due to the substantial amount of carbon exported through marketable fruits. Fixed water scheduling led to water waste through deep percolation (approximately 1/6 of the water supplied), which also contributed to the loss of organic carbon via leaching (−4.3 g C m−2). These findings recommend further research to enhance the sustainability of this crop in terms of both water and carbon balances.
研究了半干旱气候下滴灌和地膜覆盖西瓜的碳源/汇性质和水分平衡。生物降解薄膜、植物和一些水果作为绿肥留在了土壤上。研究时间跨度从西瓜种植到后续作物（2023 年 6 月至 11 月）。该方法利用莫宁-奥布霍夫相似理论将气孔过程（光合作用和蒸腾作用）与非气孔过程（呼吸作用和蒸腾作用）分开。结果表明，地膜覆盖有助于土壤固碳（+19.3 g C m-2）。然而，本研究中的地膜覆盖西瓜作物是一个净碳源，其生物群落净交换量（代表生态系统中碳积累或碳损失的净速率）相当于 +230 g C m-2。这主要是由于大量碳通过可销售的果实输出。固定的水量调度导致深层渗漏造成水资源浪费（约占供水量的 1/6），这也造成了有机碳的沥滤损失（-4.3 克 C m-2）。这些发现建议进一步开展研究，以提高这种作物在水和碳平衡方面的可持续性。
{"title":"Carbon and Water Balances in a Watermelon Crop Mulched with Biodegradable Films in Mediterranean Conditions at Extended Growth Season Scale","authors":"Rossana M. Ferrara, Alessandro Azzolini, Alessandro Ciurlia, Gabriele De Carolis, Marcello Mastrangelo, Valerio Minorenti, Alessandro Montaghi, Mariagrazia Piarulli, Sergio Ruggieri, Carolina Vitti, Nicola Martinelli, Gianfranco Rana","doi":"10.3390/atmos15080945","DOIUrl":"https://doi.org/10.3390/atmos15080945","url":null,"abstract":"The carbon source/sink nature and the water balance of a drip-irrigated and mulched watermelon cultivated under a semi-arid climate were investigated. Biodegradable films, plants and some fruits were left on the soil as green manure. The study spanned from watermelon planting to the subsequent crop (June–November 2023). The eddy covariance technique was employed to monitor water vapor (H2O) and carbon dioxide (CO2) fluxes, which were partitioned into transpiration, evaporation, photosynthesis and respiration, respectively, using the flux variance similarity method.This method utilizesthe Monin–Obukhov similarity theory to separate stomatal (photosynthesis and transpiration) from non-stomatal (respiration and evaporation) processes. The results indicate that mulching films contribute to carbon sequestration in the soil (+19.3 g C m−2). However, the mulched watermelon crop presented in this study functions as a net carbon source, with a net biome exchange, representing the net rate of C accumulation in or loss from ecosystems, equal to +230 g C m−2. This is primarily due to the substantial amount of carbon exported through marketable fruits. Fixed water scheduling led to water waste through deep percolation (approximately 1/6 of the water supplied), which also contributed to the loss of organic carbon via leaching (−4.3 g C m−2). These findings recommend further research to enhance the sustainability of this crop in terms of both water and carbon balances.","PeriodicalId":8580,"journal":{"name":"Atmosphere","volume":"36 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141944117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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