This paper discusses the temporal and spatial distribution characteristics of cloud-to-ground (CG) lightning activity over the Qinghai-Tibetan Plateau (QTP) from 2009 to 2018 and their dependence on meteorological factors. It is found that (1) the number of CG flashes fluctuates, reaches a maximum in 2014, and then gradually decreases. The main active period of CG lightning is from June to September each year, after which it decreases rapidly. CG lightning is mainly distributed in the valley areas at around 4800 m above sea level at Lhasa, Nagqu, and Chamdo, and there are differences in the characteristics of CG activity in these three areas. The peak of daily CG lightning occurs at 1000 UTC, and the lowest value is at 0400 UTC. The distribution of CG lightning in all seasons has obvious differences in peak time and the proportion of positive CG (+CG) lightning, with the ratio of +CG lightning to total CG lightning flashes in spring and autumn exceeding 50%. (2) The ratio of +CG lightning to total CG lightning flashes over the QTP is influenced by a combination of thermodynamic and microphysical factors. Over the QTP, greater vertical wind shear leads to the movement of upper positive charges and promotes the occurrence of +CG lightning. Also, the higher total column liquid water content implies higher cloud water content in the warm-cloud region, and the higher cloud-base height implies a thicker warm-cloud region, which is not conducive to the occurrence of +CG lightning. (3) During high-value years (in this study, 2010, 2012, 2014, and 2016), the midlatitude (30°N–60°N) high pressure is strong and the plateau is situated at the intersection of the East Asian and South Asian monsoons and the cold air from the northwest, which strengthens the water vapor convergence and increases the frequency of thunderstorms. When the plateau is under the control of the southerly monsoon from June to September every year, its atmosphere is full of water vapor and lightning activity is accordingly high, with the proportion of +CG lightning being about 10%. Meanwhile, in the remaining months, when controlled by the westerly wind belt, the plateau’s water vapor condition is poor, the level of lightning activity weakens, and the proportion of +CG lightning gradually increases to more than 50%.
{"title":"The Characteristics of Thunderstorms and Their Lightning Activity on the Qinghai-Tibetan Plateau","authors":"Lei Hui, Yunjun Zhou, Zhi-teng Yan","doi":"10.1155/2022/9102145","DOIUrl":"https://doi.org/10.1155/2022/9102145","url":null,"abstract":"This paper discusses the temporal and spatial distribution characteristics of cloud-to-ground (CG) lightning activity over the Qinghai-Tibetan Plateau (QTP) from 2009 to 2018 and their dependence on meteorological factors. It is found that (1) the number of CG flashes fluctuates, reaches a maximum in 2014, and then gradually decreases. The main active period of CG lightning is from June to September each year, after which it decreases rapidly. CG lightning is mainly distributed in the valley areas at around 4800 m above sea level at Lhasa, Nagqu, and Chamdo, and there are differences in the characteristics of CG activity in these three areas. The peak of daily CG lightning occurs at 1000 UTC, and the lowest value is at 0400 UTC. The distribution of CG lightning in all seasons has obvious differences in peak time and the proportion of positive CG (+CG) lightning, with the ratio of +CG lightning to total CG lightning flashes in spring and autumn exceeding 50%. (2) The ratio of +CG lightning to total CG lightning flashes over the QTP is influenced by a combination of thermodynamic and microphysical factors. Over the QTP, greater vertical wind shear leads to the movement of upper positive charges and promotes the occurrence of +CG lightning. Also, the higher total column liquid water content implies higher cloud water content in the warm-cloud region, and the higher cloud-base height implies a thicker warm-cloud region, which is not conducive to the occurrence of +CG lightning. (3) During high-value years (in this study, 2010, 2012, 2014, and 2016), the midlatitude (30°N–60°N) high pressure is strong and the plateau is situated at the intersection of the East Asian and South Asian monsoons and the cold air from the northwest, which strengthens the water vapor convergence and increases the frequency of thunderstorms. When the plateau is under the control of the southerly monsoon from June to September every year, its atmosphere is full of water vapor and lightning activity is accordingly high, with the proportion of +CG lightning being about 10%. Meanwhile, in the remaining months, when controlled by the westerly wind belt, the plateau’s water vapor condition is poor, the level of lightning activity weakens, and the proportion of +CG lightning gradually increases to more than 50%.","PeriodicalId":7353,"journal":{"name":"Advances in Meteorology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45978732","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 ensemble-based Kalman filter requires at least a considerable ensemble (e.g., 10,000 members) to identify relevant error covariance at great distances for multidimensional geophysical systems. However, increasing numerous ensemble sizes will enlarge sampling errors. This study proposes a modified Cholesky decomposition based on the covariance localization (CL) scheme, namely a covariance localization scheme with modified Cholesky decomposition (CL-MC). Our main idea utilizes a modified Cholesky (MC) decomposition technique for estimating the background error covariance matrix; meanwhile, we employ the tunable singular value decomposition method on the background error covariance to improve the ensemble increment and avoid the imbalance of the system. To verify if the proposed method can effectively mitigate the sampling errors, numerical experiments are conducted on the Lorenz-96 model and large-scale model (SPEEDY model). The results show that the CL-MC method outperforms the CL method for different data assimilation parameters (ensemble sizes and localizations). Furthermore, by performing one year of assimilation experiments on the SPEEDY model, it is found that the 1-day forecast RMSEs obtained by the CL are approximately equal to the 5-day forecast RMSEs of CL-MC. So, the CL-MC method has potential advantages for long-term forecasting. Maybe the proposed CL-MC method achieves good prospects for widespread application in atmospheric general circulation models.
{"title":"Modifying Covariance Localization to Mitigate Sampling Errors from the Ensemble Data Assimilation","authors":"Mingheng Chang, H. Zuo, Jikai Duan","doi":"10.1155/2022/6101721","DOIUrl":"https://doi.org/10.1155/2022/6101721","url":null,"abstract":"The ensemble-based Kalman filter requires at least a considerable ensemble (e.g., 10,000 members) to identify relevant error covariance at great distances for multidimensional geophysical systems. However, increasing numerous ensemble sizes will enlarge sampling errors. This study proposes a modified Cholesky decomposition based on the covariance localization (CL) scheme, namely a covariance localization scheme with modified Cholesky decomposition (CL-MC). Our main idea utilizes a modified Cholesky (MC) decomposition technique for estimating the background error covariance matrix; meanwhile, we employ the tunable singular value decomposition method on the background error covariance to improve the ensemble increment and avoid the imbalance of the system. To verify if the proposed method can effectively mitigate the sampling errors, numerical experiments are conducted on the Lorenz-96 model and large-scale model (SPEEDY model). The results show that the CL-MC method outperforms the CL method for different data assimilation parameters (ensemble sizes and localizations). Furthermore, by performing one year of assimilation experiments on the SPEEDY model, it is found that the 1-day forecast RMSEs obtained by the CL are approximately equal to the 5-day forecast RMSEs of CL-MC. So, the CL-MC method has potential advantages for long-term forecasting. Maybe the proposed CL-MC method achieves good prospects for widespread application in atmospheric general circulation models.","PeriodicalId":7353,"journal":{"name":"Advances in Meteorology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2022-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43591217","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}
Deying Wang, Jizhi Wang, Yuanqin Yang, Liangke Liu, Wenxing Jia, J. Zhong, Yaqiang Wang
Today, among the existing numerical weather prediction models, those detailing target classifications have been sufficiently explored; however, there are still many weather forecasting goals and needs, and research from theoretical to practical methods still needs additional study. For example, it is important to know as early as possible (months to years in advance) the forecast during a “specific large public event,” such as the hourly weather forecast for the Olympic Games. This study elaborates on the theory and methods for such ultra-early prediction of severe transient weather processes in the atmosphere. The main results of this study include (1) establishing the academic concept to capture precursor signals in modern meteorology and provide definitions; (2) establishing methods for capturing precursory signal quantification of unfavorable weather and proposing quantitative measurable thresholds; and (3) proposing the “ultra-early prediction” target task. A typical case is discussed: the meteorological conditions of the Beijing Winter Olympics, which serves as an example of social demand for weather forecasting of “special large-scale public activities,” as the case results show that the real-time observations during the Beijing Winter Olympics are consistent with the forecast and followed the precursor signal developed using the theoretical and methodological approaches in this study. The numerical quantization indicators for precursor signals include: (1) for a decrease in the height of the mixed layer hidden in the diurnal change; the precursor signal threshold is defined as a drop of more than 100 m for 3 consecutive days; (2) the signal of the δΘe displayed as a change by “negative ⟶ positive” of more than seven days in a continuous period. (3) the supersaturation (S) with thresholds reaching 6–7%, as well as the threshold <0.5 × 10−3 for saturated condensation flux signals (ξp); and (4) the hourly resolution transport index of PLAM (parameter linking air-pollution to meteorological condition) PLAM ⟶ obj remaining continuous for 48 h, with its threshold reaching more than 100.
{"title":"Study on the Precursor Signal Capturing of Unfavorable Weather: Months/Years in Advance to Ultra-Early Forecast for Hourly Transient Weather Changes during the Beijing Winter Olympics","authors":"Deying Wang, Jizhi Wang, Yuanqin Yang, Liangke Liu, Wenxing Jia, J. Zhong, Yaqiang Wang","doi":"10.1155/2022/1409229","DOIUrl":"https://doi.org/10.1155/2022/1409229","url":null,"abstract":"Today, among the existing numerical weather prediction models, those detailing target classifications have been sufficiently explored; however, there are still many weather forecasting goals and needs, and research from theoretical to practical methods still needs additional study. For example, it is important to know as early as possible (months to years in advance) the forecast during a “specific large public event,” such as the hourly weather forecast for the Olympic Games. This study elaborates on the theory and methods for such ultra-early prediction of severe transient weather processes in the atmosphere. The main results of this study include (1) establishing the academic concept to capture precursor signals in modern meteorology and provide definitions; (2) establishing methods for capturing precursory signal quantification of unfavorable weather and proposing quantitative measurable thresholds; and (3) proposing the “ultra-early prediction” target task. A typical case is discussed: the meteorological conditions of the Beijing Winter Olympics, which serves as an example of social demand for weather forecasting of “special large-scale public activities,” as the case results show that the real-time observations during the Beijing Winter Olympics are consistent with the forecast and followed the precursor signal developed using the theoretical and methodological approaches in this study. The numerical quantization indicators for precursor signals include: (1) for a decrease in the height of the mixed layer hidden in the diurnal change; the precursor signal threshold is defined as a drop of more than 100 m for 3 consecutive days; (2) the signal of the δΘe displayed as a change by “negative ⟶ positive” of more than seven days in a continuous period. (3) the supersaturation (S) with thresholds reaching 6–7%, as well as the threshold <0.5 × 10−3 for saturated condensation flux signals (ξp); and (4) the hourly resolution transport index of PLAM (parameter linking air-pollution to meteorological condition) PLAM ⟶ obj remaining continuous for 48 h, with its threshold reaching more than 100.","PeriodicalId":7353,"journal":{"name":"Advances in Meteorology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45694534","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}
Forecasting applications based on hourly meteorological predictions for weather variables are nowadays used in energy market operations, planning of gas and power supply, and renewable energy, among others. Available meteorological and climatological data, as well as critical thresholds of rainfall, may also have a key role in the hazard classification, related to slope instabilities of pipelines and critical infrastructures along routes. The present study concerns the performance of a weather forecast model in the framework of an early warning system (EWS) application, which supports the integrity management of oil and gas pipelines. This EWS has been applied on to a specific area: the Val d’Agri basin in the Basilicata region of Southern Italy, which is extensively affected by several landslides and floods. The hourly precipitation forecasts are provided by a dedicated meteorological model, the KALM-HD, using two different horizontal resolutions, 1.25 and 5 km, to analyze possible influences of the mesh grid size as well. On this area, several weather stations were specifically deployed to obtain observed data in a region where hydrogeological hazards are relevant for asset management. A comparison among observations and the KALM-HD scaled forecasts on six of these weather stations is presented to assess the model performance. Besides, precipitation, temperature, and wind speed are evaluated as well. The forecasting analysis is performed considering two years of data both on an overall and seasonal basis. Results show that the KALM-HD performs well with the 1.25 km grid, particularly on temperature and wind speed variables. Since weather stations can be gathered in two main sets depending on their positions, differences arise in the forecast quality of these two groups, related to orography and thermal effects, whose detection is difficult in the typical narrow valleys characterizing the area of study. This issue prevalently influences temperatures and local winds, which, these latter, are generally underestimated, while precipitation is mainly driven by synoptic circulation and its interaction with mesoscale meteorological features.
以每小时气象预报天气变量为基础的预报应用，如今已应用于能源市场运作、天然气和电力供应规划以及可再生能源等方面。现有的气象和气候数据以及降雨的临界阈值也可能在与管道和沿线关键基础设施的斜坡不稳定有关的危险分类中发挥关键作用。本研究关注早期预警系统(EWS)应用框架中天气预报模型的性能，该模型支持石油和天然气管道的完整性管理。该EWS已应用于一个特定地区:意大利南部巴西利卡塔地区的Val d 'Agri盆地，该地区受到多次山体滑坡和洪水的广泛影响。每小时的降水预报是由一个专门的气象模式KALM-HD提供的，使用两种不同的水平分辨率，1.25和5公里，以分析网格尺寸的可能影响。在这一地区，专门部署了几个气象站，以便在水文地质灾害与资产管理有关的地区获取观测数据。将其中6个气象站的观测结果与KALM-HD标度预报结果进行比较，以评估模式的性能。此外，还评估了降水、温度和风速。预测分析是根据两年的总体和季节性数据进行的。结果表明，KALM-HD在1.25 km网格上表现良好，特别是在温度和风速变量上。由于气象站可以根据其位置分为两组，因此这两组气象站的预报质量存在差异，这与地形和热效应有关，在研究地区典型的狭窄山谷中很难检测到。这一问题普遍影响温度和局地风，而后者通常被低估，而降水主要受天气环流及其与中尺度气象特征的相互作用驱动。
{"title":"Local-Scale Weather Forecasts over a Complex Terrain in an Early Warning Framework: Performance Analysis for the Val d’Agri (Southern Italy) Case Study","authors":"G. Giunta, A. Ceppi, R. Salerno","doi":"10.1155/2022/2179246","DOIUrl":"https://doi.org/10.1155/2022/2179246","url":null,"abstract":"Forecasting applications based on hourly meteorological predictions for weather variables are nowadays used in energy market operations, planning of gas and power supply, and renewable energy, among others. Available meteorological and climatological data, as well as critical thresholds of rainfall, may also have a key role in the hazard classification, related to slope instabilities of pipelines and critical infrastructures along routes. The present study concerns the performance of a weather forecast model in the framework of an early warning system (EWS) application, which supports the integrity management of oil and gas pipelines. This EWS has been applied on to a specific area: the Val d’Agri basin in the Basilicata region of Southern Italy, which is extensively affected by several landslides and floods. The hourly precipitation forecasts are provided by a dedicated meteorological model, the KALM-HD, using two different horizontal resolutions, 1.25 and 5 km, to analyze possible influences of the mesh grid size as well. On this area, several weather stations were specifically deployed to obtain observed data in a region where hydrogeological hazards are relevant for asset management. A comparison among observations and the KALM-HD scaled forecasts on six of these weather stations is presented to assess the model performance. Besides, precipitation, temperature, and wind speed are evaluated as well. The forecasting analysis is performed considering two years of data both on an overall and seasonal basis. Results show that the KALM-HD performs well with the 1.25 km grid, particularly on temperature and wind speed variables. Since weather stations can be gathered in two main sets depending on their positions, differences arise in the forecast quality of these two groups, related to orography and thermal effects, whose detection is difficult in the typical narrow valleys characterizing the area of study. This issue prevalently influences temperatures and local winds, which, these latter, are generally underestimated, while precipitation is mainly driven by synoptic circulation and its interaction with mesoscale meteorological features.","PeriodicalId":7353,"journal":{"name":"Advances in Meteorology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45846166","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}
Hazardous weather, turbulence, wind, and thermals pose a ubiquitous challenge to Unmanned Aircraft Systems (UAS) and electric-Vertical Take-Off and Landing (e-VTOL) aircrafts, and the safe integration of UAS into urban area requires accurate high-granularity wind data especially during landing and takeoff phases. Two models, namely, Open-Source Field Operation and Manipulation (OpenFOAM) software package and Weather Research and Forecasting (WRF) model, are used in the present study to simulate airflow over Downtown Oklahoma City, Oklahoma, United States. Results show that computational fluid dynamics wind simulation driven by the atmospheric simulation significantly improves the simulated wind speed because the accurate modeling of the buildings affects wind patterns. The evaluation of different simulations against six Micronet stations shows that WRF-CFD numerical evaluation is a reliable method to understand the complicated wind flow within built-up areas. The comparison of wind distributions of simulations at different resolutions shows better description of wind variability and gusts generated by the urban flows. Simulations assuming anisotropy and isotropy of turbulence show small differences in the predicted wind speeds over Downtown Oklahoma City given the stable atmospheric stratification showing that turbulent eddy scales at the evaluation locations are within the inertial subrange and confirming that turbulence is locally isotropic.
{"title":"Improving Wind Speed Forecasting for Urban Air Mobility Using Coupled Simulations","authors":"Mounir Chrit, M. Majdi","doi":"10.1155/2022/2629432","DOIUrl":"https://doi.org/10.1155/2022/2629432","url":null,"abstract":"Hazardous weather, turbulence, wind, and thermals pose a ubiquitous challenge to Unmanned Aircraft Systems (UAS) and electric-Vertical Take-Off and Landing (e-VTOL) aircrafts, and the safe integration of UAS into urban area requires accurate high-granularity wind data especially during landing and takeoff phases. Two models, namely, Open-Source Field Operation and Manipulation (OpenFOAM) software package and Weather Research and Forecasting (WRF) model, are used in the present study to simulate airflow over Downtown Oklahoma City, Oklahoma, United States. Results show that computational fluid dynamics wind simulation driven by the atmospheric simulation significantly improves the simulated wind speed because the accurate modeling of the buildings affects wind patterns. The evaluation of different simulations against six Micronet stations shows that WRF-CFD numerical evaluation is a reliable method to understand the complicated wind flow within built-up areas. The comparison of wind distributions of simulations at different resolutions shows better description of wind variability and gusts generated by the urban flows. Simulations assuming anisotropy and isotropy of turbulence show small differences in the predicted wind speeds over Downtown Oklahoma City given the stable atmospheric stratification showing that turbulent eddy scales at the evaluation locations are within the inertial subrange and confirming that turbulence is locally isotropic.","PeriodicalId":7353,"journal":{"name":"Advances in Meteorology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47483624","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}
Climate reanalysis products have been widely used to overcome the absence of high-quality and long-term observational records for wind energy users. In this study, the applicability of two popular reanalysis datasets (ERA5 and MERRA2) in estimating wind characteristics for four tall tower observatories (TTOs) in South China was assessed. For each TTO, linear and nonlinear downscaling techniques, namely, multiple linear regression (MLR) and an artificial neural network (ANN), respectively, were adopted for the downscaling of the scalar wind speed and the corresponding U/V components. The downscaled wind speed and U/V components were subsequently compared with the TTO observations by correlation coefficient (Pearson’s r), the root mean square error (RMSE), the uncertainty analysis (U95), and the reliability analysis (RE). According to the results, ERA5 had a better applicability (higher Pearson’s r and RE, but lower RMSE and U95) in estimating TTO wind speed than MERRA2 when using both the MLR and ANN downscaling method. The average Pearson’s r, RE, RMSE, and U95 of the downscaled wind from ERA5 by the MLR (ANN) method were 0.66 (0.69), 40.8% (41.8%), 2.20 m/s (2.11 m/s), 0.181 m/s (0.179 m/s), respectively, and 0.60 (0.63), 38.0% (39.7%), 2.32 m/s (2.25 m/s), 0.189 m/s (0.187 m/s), respectively, for MERRA2. The wind components analysis showed that the better performance of ERA5 was attributed to its smaller error in estimating V component than MERRA2. For the wind direction, the two reanalysis datasets did not display distinct differences. Additionally, the misalignment of the wind direction between the reanalysis products and the TTOs was higher for the secondary predominant wind direction (SPWD) than for the predominant wind direction (PWD). Furthermore, we found that the reanalysis U wind had a higher RMSE but a lower RE and Pearson’s r than the V wind, which indicates that the misalignment in the wind direction was mainly associated with the deviation in the U component.
{"title":"Comparison of the Applicability of Two Reanalysis Products in Estimating Tall Tower Wind Based on Multiple Linear Regression and Artificial Neural Network in South China","authors":"Xiangxiang Li, X. Qin, Jun Yang, Weiming Xu","doi":"10.1155/2022/6573202","DOIUrl":"https://doi.org/10.1155/2022/6573202","url":null,"abstract":"Climate reanalysis products have been widely used to overcome the absence of high-quality and long-term observational records for wind energy users. In this study, the applicability of two popular reanalysis datasets (ERA5 and MERRA2) in estimating wind characteristics for four tall tower observatories (TTOs) in South China was assessed. For each TTO, linear and nonlinear downscaling techniques, namely, multiple linear regression (MLR) and an artificial neural network (ANN), respectively, were adopted for the downscaling of the scalar wind speed and the corresponding U/V components. The downscaled wind speed and U/V components were subsequently compared with the TTO observations by correlation coefficient (Pearson’s r), the root mean square error (RMSE), the uncertainty analysis (U95), and the reliability analysis (RE). According to the results, ERA5 had a better applicability (higher Pearson’s r and RE, but lower RMSE and U95) in estimating TTO wind speed than MERRA2 when using both the MLR and ANN downscaling method. The average Pearson’s r, RE, RMSE, and U95 of the downscaled wind from ERA5 by the MLR (ANN) method were 0.66 (0.69), 40.8% (41.8%), 2.20 m/s (2.11 m/s), 0.181 m/s (0.179 m/s), respectively, and 0.60 (0.63), 38.0% (39.7%), 2.32 m/s (2.25 m/s), 0.189 m/s (0.187 m/s), respectively, for MERRA2. The wind components analysis showed that the better performance of ERA5 was attributed to its smaller error in estimating V component than MERRA2. For the wind direction, the two reanalysis datasets did not display distinct differences. Additionally, the misalignment of the wind direction between the reanalysis products and the TTOs was higher for the secondary predominant wind direction (SPWD) than for the predominant wind direction (PWD). Furthermore, we found that the reanalysis U wind had a higher RMSE but a lower RE and Pearson’s r than the V wind, which indicates that the misalignment in the wind direction was mainly associated with the deviation in the U component.","PeriodicalId":7353,"journal":{"name":"Advances in Meteorology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2022-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48169508","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}
Mangroves are an essential plant community in coastal ecosystems. While the importance of mangrove ecosystems is well acknowledged, climate change is expected to have a considerable negative impact on them, especially in terms of temperature, precipitation, sea level rise (SLR), ocean currents, and increasing storminess. Sri Lanka ranks near the bottom of the list of countries researching this problem, even though the scientific community's interest in examining the variation in mangrove health in response to climate change has gained significant attention. Consequently, this study illustrates how the leaf area index, a measure of mangrove health, fluctuates in response to varying precipitation, particularly during droughts in Sri Lanka's Kadolkele mangrove forest. The measurements of the normalized difference vegetation index (NDVI) were used to produce the leaf area index (LAI), which was then combined with the standard precipitation index (SPI) to estimate the health of the mangroves. The climate scenario, RCP8.5, was used to forecast future SPI (2021–2100), and LAI was modeled under the observed (1991–2019) and expected (2021–2100) drought events. The study reveals that the forecasted drought intensities modeled using the RCP8.5 scenario have no significant variations on LAI, even though some severe and extreme drought conditions exist. Nevertheless, the health of the mangrove ecosystem is predicted to deteriorate under drought conditions and rebound when drought intensity decreases. The extreme drought state (-2.05) was identified in 2064; therefore, LAI has showcased its lowest (0.04). LAI and SPI are projected to gradually increase from 2064 to 2100, while high fluctuations are observed from 2021 to 2064. Limited availability of LAI values with required details (measured date, time, and sample locations) and cloud-free Landsat images have affected the study results. This research presents a comprehensive understanding of Kadolkele mangrove forest under future droughts; thus, alarming relevant authorities to develop management plans to safeguard these critical ecosystems.
{"title":"Variation of Leaf Area Index (LAI) under Changing Climate: Kadolkele Mangrove Forest, Sri Lanka","authors":"Randika K. Makumbura, Upaka S. Rathnayake","doi":"10.1155/2022/9693303","DOIUrl":"https://doi.org/10.1155/2022/9693303","url":null,"abstract":"Mangroves are an essential plant community in coastal ecosystems. While the importance of mangrove ecosystems is well acknowledged, climate change is expected to have a considerable negative impact on them, especially in terms of temperature, precipitation, sea level rise (SLR), ocean currents, and increasing storminess. Sri Lanka ranks near the bottom of the list of countries researching this problem, even though the scientific community's interest in examining the variation in mangrove health in response to climate change has gained significant attention. Consequently, this study illustrates how the leaf area index, a measure of mangrove health, fluctuates in response to varying precipitation, particularly during droughts in Sri Lanka's Kadolkele mangrove forest. The measurements of the normalized difference vegetation index (NDVI) were used to produce the leaf area index (LAI), which was then combined with the standard precipitation index (SPI) to estimate the health of the mangroves. The climate scenario, RCP8.5, was used to forecast future SPI (2021–2100), and LAI was modeled under the observed (1991–2019) and expected (2021–2100) drought events. The study reveals that the forecasted drought intensities modeled using the RCP8.5 scenario have no significant variations on LAI, even though some severe and extreme drought conditions exist. Nevertheless, the health of the mangrove ecosystem is predicted to deteriorate under drought conditions and rebound when drought intensity decreases. The extreme drought state (-2.05) was identified in 2064; therefore, LAI has showcased its lowest (0.04). LAI and SPI are projected to gradually increase from 2064 to 2100, while high fluctuations are observed from 2021 to 2064. Limited availability of LAI values with required details (measured date, time, and sample locations) and cloud-free Landsat images have affected the study results. This research presents a comprehensive understanding of Kadolkele mangrove forest under future droughts; thus, alarming relevant authorities to develop management plans to safeguard these critical ecosystems.","PeriodicalId":7353,"journal":{"name":"Advances in Meteorology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41849518","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}
Climate change is a major threat to agricultural production and undermines the efforts to achieve sustainable development goals in poor countries such as Ethiopia that have climate-sensitive economies. The objective of this study was to assess characterization of local climate and its impact on productivity faba bean (Vicia faba L.) varieties (Gora and Tumsa) productivity in Welmera watershed area, central Ethiopia. Historical climate (1988–2017) and eight years of crop yield data were obtained from National Meteorological Agency of Ethiopia and Holeta Agricultural Research Center. Trend, variability, correlation, and regression analyses were carried out to characterize the climate of the area and establish association between faba bean productivity and climate change. The area received mean annual rainfall of 970 mm with SD of 145.6 and coefficient of variation (CV %) of 15%. The earliest and latest onset of rainfall were April 1 (92 DOY) and July 5 (187 DOY), whereas, the end date of rainy season was on September 2 (246 DOY) and October 31 (305 DOY), respectively. The average length of the growing period was 119 days, with a CV% of 35.2%. The probability of dry spell less than 7 days was high (>80%) until the last decade of May (151 DOY); however, the probability sharply declined and reached 0% on the first decade of July (192 DOY). Kiremt (long rainy season that occurs from June to September) and belg (short rainy season that falls from February to April/May) rainfall had increasing trends at a rate of 4.7 mm and 2.32 mm/year, respectively. The annual maximum temperature showed increasing trend at a rate of 0.06°C per year and by a factor of 0.34°C, which is not statistically significant. The year 2014 was exceptionally drought year while 1988 was wettest year. Kiremt (JJAS) start of rain and rainy day had strong correlation and negative impact on Gora yield with (r = −0.407 and −0.369), respectively. The findings suggests large variation in rainfall and temperature in the study area which constraints faba bean production. Investment on agricultural sector to enhance farmer’s adaptation capacity is essential to reduce the adverse impacts of climate change and variability on faba bean yield. More research that combines household panel data with long-term climate data is necessary to better understand climate and its impact on faba bean yield.
{"title":"Characterization of Local Climate and Its Impact on Faba Bean (Vicia faba L.) Yield in Central Ethiopia","authors":"G. A. Bogale, Mengistu Mengesha, G. Hadgu","doi":"10.1155/2022/8759596","DOIUrl":"https://doi.org/10.1155/2022/8759596","url":null,"abstract":"Climate change is a major threat to agricultural production and undermines the efforts to achieve sustainable development goals in poor countries such as Ethiopia that have climate-sensitive economies. The objective of this study was to assess characterization of local climate and its impact on productivity faba bean (Vicia faba L.) varieties (Gora and Tumsa) productivity in Welmera watershed area, central Ethiopia. Historical climate (1988–2017) and eight years of crop yield data were obtained from National Meteorological Agency of Ethiopia and Holeta Agricultural Research Center. Trend, variability, correlation, and regression analyses were carried out to characterize the climate of the area and establish association between faba bean productivity and climate change. The area received mean annual rainfall of 970 mm with SD of 145.6 and coefficient of variation (CV %) of 15%. The earliest and latest onset of rainfall were April 1 (92 DOY) and July 5 (187 DOY), whereas, the end date of rainy season was on September 2 (246 DOY) and October 31 (305 DOY), respectively. The average length of the growing period was 119 days, with a CV% of 35.2%. The probability of dry spell less than 7 days was high (>80%) until the last decade of May (151 DOY); however, the probability sharply declined and reached 0% on the first decade of July (192 DOY). Kiremt (long rainy season that occurs from June to September) and belg (short rainy season that falls from February to April/May) rainfall had increasing trends at a rate of 4.7 mm and 2.32 mm/year, respectively. The annual maximum temperature showed increasing trend at a rate of 0.06°C per year and by a factor of 0.34°C, which is not statistically significant. The year 2014 was exceptionally drought year while 1988 was wettest year. Kiremt (JJAS) start of rain and rainy day had strong correlation and negative impact on Gora yield with (r = −0.407 and −0.369), respectively. The findings suggests large variation in rainfall and temperature in the study area which constraints faba bean production. Investment on agricultural sector to enhance farmer’s adaptation capacity is essential to reduce the adverse impacts of climate change and variability on faba bean yield. More research that combines household panel data with long-term climate data is necessary to better understand climate and its impact on faba bean yield.","PeriodicalId":7353,"journal":{"name":"Advances in Meteorology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2022-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48401010","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}
Yang Liu, Huizhi Liu, Feng Li, Qun Du, Lujun Xu, Yaohui Li
Water and carbon exchanges between grassland and the atmosphere are important processes for water balance and carbon balance. Based on eddy covariance observations over a semiarid alpine steppe ecosystem in Bange on the Tibetan Plateau during the growing season from 2014 to 2017, the variations in evapotranspiration (ET), net ecosystem exchange (NEE), and their components and the associated driving factors were analyzed. Linear and nonlinear models were applied to investigate the relationships between fluxes and their controlling factors over different timescales. The results show that the average ET for the growing season ranged from 1.1 to 2.4 mm/d with an average of 2.0 mm/d for the four consecutive years. Drought conditions reduced the surface conductance and hence the Priestley–Taylor coefficient. Mean T/ET was low (0.34) due to low vegetation cover. Plant growth increased the T/ET ratio during the growing season, whereas soil water content (SWC) explained most of the variation of ET and E on daily and monthly scales. The Enhanced Vegetation Index (EVI) was the most important controlling factor for temperature. Transpiration increased with SWC in dry conditions. For the growing season in 2014, 2016, and 2017, Bange was a carbon sink, while it was a carbon source in 2015. The largest CO2 flux was higher and the temperature sensitivity coefficient (Q10) was lower for 2015 than for the other three years. SWC affected these photosynthesis and respiration parameters. The ratio of respiration (Re) to gross primary production (GPP) was the highest during the 2015 growing season. Both on daily and monthly scales, Re was positively and linearly correlated with GPP. The most important controlling factor for the CO2 flux was EVI on daily and monthly scales.
{"title":"Interannual Variations of Water and Carbon Dioxide Fluxes over a Semiarid Alpine Steppe on the Tibetan Plateau","authors":"Yang Liu, Huizhi Liu, Feng Li, Qun Du, Lujun Xu, Yaohui Li","doi":"10.1155/2022/7368882","DOIUrl":"https://doi.org/10.1155/2022/7368882","url":null,"abstract":"Water and carbon exchanges between grassland and the atmosphere are important processes for water balance and carbon balance. Based on eddy covariance observations over a semiarid alpine steppe ecosystem in Bange on the Tibetan Plateau during the growing season from 2014 to 2017, the variations in evapotranspiration (ET), net ecosystem exchange (NEE), and their components and the associated driving factors were analyzed. Linear and nonlinear models were applied to investigate the relationships between fluxes and their controlling factors over different timescales. The results show that the average ET for the growing season ranged from 1.1 to 2.4 mm/d with an average of 2.0 mm/d for the four consecutive years. Drought conditions reduced the surface conductance and hence the Priestley–Taylor coefficient. Mean T/ET was low (0.34) due to low vegetation cover. Plant growth increased the T/ET ratio during the growing season, whereas soil water content (SWC) explained most of the variation of ET and E on daily and monthly scales. The Enhanced Vegetation Index (EVI) was the most important controlling factor for temperature. Transpiration increased with SWC in dry conditions. For the growing season in 2014, 2016, and 2017, Bange was a carbon sink, while it was a carbon source in 2015. The largest CO2 flux was higher and the temperature sensitivity coefficient (Q10) was lower for 2015 than for the other three years. SWC affected these photosynthesis and respiration parameters. The ratio of respiration (Re) to gross primary production (GPP) was the highest during the 2015 growing season. Both on daily and monthly scales, Re was positively and linearly correlated with GPP. The most important controlling factor for the CO2 flux was EVI on daily and monthly scales.","PeriodicalId":7353,"journal":{"name":"Advances in Meteorology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48485041","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}
M. Alomar, Faidhalrahman Khaleel, Abdulwahab Abdulrazaaq AlSaadi, Mohammed Majeed Hameed, M. Alsaadi, N. Al‐Ansari
Air pollution is one of humanity's most critical environmental issues and is considered contentious in several countries worldwide. As a result, accurate prediction is critical in human health management and government decision-making for environmental management. In this study, three artificial intelligence (AI) approaches, namely group method of data handling neural network (GMDHNN), extreme learning machine (ELM), and gradient boosting regression (GBR) tree, are used to predict the hourly concentration of PM2.5 over a Dorset station located in Canada. The investigation has been performed to quantify the effect of data length on the AI modeling performance. Accordingly, nine different ratios (50/50, 55/45, 60/40, 65/35, 70/30, 75/25, 80/20, 85/15, and 90/10) are employed to split the data into training and testing datasets for assessing the performance of applied models. The results showed that the data division significantly impacted the model's capacity, and the 60/40 ratio was found more suitable for developing predictive models. Furthermore, the results showed that the ELM model provides more precise predictions of PM2.5 concentrations than the other models. Also, a vital feature of the ELM model is its ability to adapt to the potential changes in training and testing data ratio. To summarize, the results reported in this study demonstrated an efficient method for selecting the optimal dataset ratios and the best AI model to predict properly which would be helpful in the design of an accurate model for solving different environmental issues.
{"title":"The Influence of Data Length on the Performance of Artificial Intelligence Models in Predicting Air Pollution","authors":"M. Alomar, Faidhalrahman Khaleel, Abdulwahab Abdulrazaaq AlSaadi, Mohammed Majeed Hameed, M. Alsaadi, N. Al‐Ansari","doi":"10.1155/2022/5346647","DOIUrl":"https://doi.org/10.1155/2022/5346647","url":null,"abstract":"Air pollution is one of humanity's most critical environmental issues and is considered contentious in several countries worldwide. As a result, accurate prediction is critical in human health management and government decision-making for environmental management. In this study, three artificial intelligence (AI) approaches, namely group method of data handling neural network (GMDHNN), extreme learning machine (ELM), and gradient boosting regression (GBR) tree, are used to predict the hourly concentration of PM2.5 over a Dorset station located in Canada. The investigation has been performed to quantify the effect of data length on the AI modeling performance. Accordingly, nine different ratios (50/50, 55/45, 60/40, 65/35, 70/30, 75/25, 80/20, 85/15, and 90/10) are employed to split the data into training and testing datasets for assessing the performance of applied models. The results showed that the data division significantly impacted the model's capacity, and the 60/40 ratio was found more suitable for developing predictive models. Furthermore, the results showed that the ELM model provides more precise predictions of PM2.5 concentrations than the other models. Also, a vital feature of the ELM model is its ability to adapt to the potential changes in training and testing data ratio. To summarize, the results reported in this study demonstrated an efficient method for selecting the optimal dataset ratios and the best AI model to predict properly which would be helpful in the design of an accurate model for solving different environmental issues.","PeriodicalId":7353,"journal":{"name":"Advances in Meteorology","volume":"23 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64781713","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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