ADVANCES IN METEOROLOGY 是一份经过同行评审的开放获取期刊，发表原始研究文章以及气象学和气候学所有领域的评论文章。涵盖的主题包括但不限于预报技术和应用、气象建模、数据分析、大气化学和物理学、气候变化、卫星气象学、海洋气象学和森林气象学。

Advances in Meteorology is a peer-reviewed open access journal that publishes original research articles and review articles in all areas of meteorology and climatology. The topics covered include, but are not limited to, forecasting techniques and applications, meteorological modeling, data analysis, atmospheric chemistry and physics, climate change, satellite meteorology, ocean meteorology, and forest meteorology.

《Advances in Meteorology》是一份经过同行评审的开放获取期刊，专注于发表气象学和气候学各领域的原创研究文章和评论文章。期刊涵盖的主题包括但不限于预报技术与应用、气象建模、数据分析、大气化学与物理、气候变化、卫星气象学、海洋气象学和森林气象学。

Advances in Meteorology

China is now the world’s largest user of coal and also has the highest greenhouse gas emissions associated with the mining and use of coal. Under today’s enormous pressures of the growing shortage of conventional energy sources and the need for emission reductions, the search for clean energy is the most effective strategy to address the energy crisis and global warming. This study utilized satellite remote sensing technology, geographic information system (GIS) technology, and simulated wave data for the South China Sea. The characteristic features of the wave energy were obtained by analysis through the wave resource assessment formula and the results were stored in a GIS database. Software for the evaluation of wave energy in the South China Sea was written. The results should provide accurate, efficient references for wave energy researchers and decision-makers. Based on a 24-year WW3 model simulation wave data and GIS technology, this study presented the characteristic of the wave energy in the SCS; results demonstrated that the SCS has the feasibility and viability for wave energy farming.

Gang Lin

Long Tan Shao

Chong Wei Zheng

Xiao Bin Chen

Li Feng Zeng

Zhi Hong Liu

Rong Bo Li

Wei Lai Shi

Assessment of Wave Energy in the South China Sea Based on GIS Technology

基于GIS技术的南海波浪能评估

Increasing heat will be a significant problem for Central European cities in the future. Shading devices are discussed as a method to mitigate heat stress on citizens. To analyze the physical processes, which are characteristic of shading in terms of urban human-biometeorology, experimental investigations on the thermal effects of shading by a building and shading by tree canopies were conducted in Freiburg (Southwest Germany) during typical Central European summer weather. Urban human-biometeorology stands for the variables air temperature<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mi>a</mml:mi></mml:msub></mml:mrow></mml:math>, mean radiant temperature<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mrow><mml:mtext>mrt</mml:mtext></mml:mrow></mml:msub></mml:mrow></mml:math>, and physiologically equivalent temperature PET, that is the human-biometeorological concept to assess the thermal environment which was applied. The measuring setup consists of specific human-biometeorological stations, which enable the direct or indirect determination of<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mi>a</mml:mi></mml:msub></mml:mrow></mml:math>,<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mrow><mml:mtext>mrt</mml:mtext></mml:mrow></mml:msub></mml:mrow></mml:math>, and PET. With respect to both shading devices, the<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M5"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mi>a</mml:mi></mml:msub></mml:mrow></mml:math>reduction did not exceed 2°C, while PET as a measure for human heat stress was lowered by two thermal sensation steps according to the ASHRAE scale. As<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M6"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mrow><mml:mtext>mrt</mml:mtext></mml:mrow></mml:msub></mml:mrow></mml:math>has the role of a key variable for outdoor thermal comfort during Central European summer weather, all radiant flux densities relevant to the determination of<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M7"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mrow><mml:mtext>mrt</mml:mtext></mml:mrow></mml:msub></mml:mrow></mml:math>were directly measured and analyzed in detail. The results show the crucial significance of the horizontal radiant flux densities for<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M8"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mrow><mml:mtext>mrt</mml:mtext></mml:mrow></mml:msub></mml:mrow></mml:math>and consequently PET.

Hyunjung Lee

Jutta Holst

Helmut Mayer

Modification of Human-Biometeorologically Significant Radiant Flux Densities by Shading as Local Method to Mitigate Heat Stress in Summer within Urban Street Canyons

城市街道峡谷中夏季热应力缓解的局部方法：遮阳对人类生物气象学显著辐射通量密度的影响

The effects of agricultural development on observed changes in near-surface atmospheric temperature and moisture from 1960 to 2014 over Northeast China are evaluated using data from 109 meteorological stations. Cultivated land fraction (CF) within a 3 km radius of the meteorological station is used as a quantitative indicator of agricultural intensity. Stations with large CFs experience a less significant increase in air temperature, especially in daily maximum temperature (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant="normal">m</mml:mi><mml:mi mathvariant="normal">a</mml:mi><mml:mi mathvariant="normal">x</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math>) and a more rapid increase in vapor pressure (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:msub><mml:mrow><mml:mi>e</mml:mi></mml:mrow><mml:mrow><mml:mi>a</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math>) and relative humidity (RH) than stations with small CFs, especially during the main growing season (from May to September). Compared with the reference station group with<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mi mathvariant="normal">C</mml:mi><mml:mi mathvariant="normal">F</mml:mi><mml:mo>&lt;</mml:mo><mml:mn fontstyle="italic">0.2</mml:mn></mml:math>, cooling effects during May to September in terms of daily mean, maximum, and minimum temperature by −0.067°C, −0.081°C, and −0.069°C per decade and wetting effects of May to September regarding<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mml:mrow><mml:msub><mml:mrow><mml:mi>e</mml:mi></mml:mrow><mml:mrow><mml:mi>a</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math>by 0.075 hPa and RH by 0.56% per decade exist for the station group with<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M5"><mml:mi mathvariant="normal">C</mml:mi><mml:mi mathvariant="normal">F</mml:mi><mml:mo>&gt;</mml:mo><mml:mn mathvariant="normal">0.5</mml:mn></mml:math>. The cooling and wetting effects can be attributed to the agricultural development and thus should be considered when analyzing the near-surface atmospheric temperature and moisture records in Northeast China.

Nana Zhao

Songjun Han

Di Xu

Jiandong Wang

Hongjing Yu

Cooling and Wetting Effects of Agricultural Development on Near-Surface Atmosphere over Northeast China

农业开发对东北地区近地表大气冷却和湿润效应

The objective of this study was to evaluate the historical trends and variability of rainfall and temperature and to estimate the extent of change across various agroecological zones (AEZs) in the West Arsi Zone of south‐central Ethiopia from 1983 to 2019. Data on daily temperature and rainfall were collected from the Ethiopian Meteorological Institute. The study utilized the Mann–Kendall test, Sen’s slope estimator, and descriptive statistical methods to analyze data and identify trends. The analysis revealed a significant increase in mean annual temperature ( p &lt; 0.001) with 0.045°C per annum. The lowland and midland experienced significant increases in mean annual temperature ( p &lt; 0.001) by 0.063°C and 0.094°C per year, respectively, while insignificant increase was found in the highland AEZ. Analysis of rainfall revealed high interannual and seasonal variability, with the highest rainfall variability observed in the lowland. The analysis also revealed the study area experienced a significant decrease in annual average rainfall ( p &lt; 0.01) by 3.704 mm and Belg rainfall, short rainy season from mid‐February to May ( p &lt; 0.05) by 22.46 mm per year. The annual number of rainy days (RDs &gt;1 mm) shows a significant decreasing trend ( p &lt; 0.001). The analysis further revealed decreasing trends of rainfall in all AEZs where the midland experienced a significant decrease in annual rainfall ( p &lt; 0.01) by 8.72 mm and Belg rainfall ( p &lt; 0.05) by 4.734 mm. In the Kiremt rainfall pattern, there was a decreasing wet spell and an increasing dry spell, and the annual rainfall coefficient of variation (CV) was 17.22% in the highland, 26% in the midland, and 32.36% in the lowland. Overall, the detected alterations in rainfall patterns, coupled with their considerable variability, are expected to negatively affect rain‐fed agriculture and the environment. The results of this study are anticipated to offer crucial insights into the spatiotemporal variability and climate trends, aiding decision‐makers and development planners in crafting tailored strategies to enhance climate resilience.

Abebe Engda

Fantaw Yimer

Muluken Mekuyie

Menfese Tadesse

Spatiotemporal Analysis of Climate Variability and Trends in West Arsi Zone, South‐Central Ethiopia

西阿尔西地区气候变异与趋势的时空分析，埃塞俄比亚中南部

Cloud coverage poses a prevalent challenge in optical remote sensing image processing, significantly hindering the visibility and interpretability of ground‐level information. The application of deep learning technology in remote sensing image cloud detection has witnessed a remarkable advancement, rendering it an increasingly pervasive and potent solution to the challenge of cloud coverage. To thoroughly investigate the effectiveness of diverse deep learning architectures in cloud detection tasks, this research selected five seminal models: AlexNet, VGG16, GoogLeNet, ResNet34, and the cutting‐edge Swin Transformer, and performed a comprehensive comparative analysis of their application in identifying clouds within medium‐resolution Landsat 8 satellite imagery. During the training phase, each model distinctly demonstrated unique strengths in enhancing accuracy, refining loss functions, optimizing resource utilization, and accelerating training processes. In the test phase, the models’ performance was rigorously evaluated using overall accuracy (OA) and other quantitative metrics, providing solid empirical evidence for the variations in their performance. Furthermore, an insightful analysis of the visualization outcomes and a meticulous comparison of the nuanced differences between model predictions and actual cloud images underscored the individual models’ exceptional abilities in capturing intricate cloud details and executing precise edge processing. To alleviate the cumbersome process of pixel‐level labeling, this study adopts the effective block‐level labeling approach. This approach drastically streamlines annotation processes, ultimately translating into substantial cost savings and a marked reduction in time required for data preparation. The experimental results demonstrate that the Swin Transformer network performs particularly well in the cloud detection task of Landsat 8 images, achieving an OA of 90.73% under block‐level annotation, significantly higher than other comparison models, showcasing excellent cloud detection capabilities. Furthermore, the study also found that the size of input image blocks has a significant impact on detection results. Under the same network architecture, using 32 × 32 image blocks for cloud detection improves the OA by at least 10 percentage points compared to using 64 × 64 image blocks. This finding provides important insights for optimizing cloud detection algorithms. In summary, this study not only verifies the superiority of Swin Transformer in cloud detection in remote sensing images but also reveals the impact of image block size on detection performance, providing new ideas and directions for future advancements in remote sensing image processing and cloud detection technology.

Miao Hui

Huiying Xie

Yufeng Chi

Deep Learning Network Comparison and Validation in Landsat Imagery Cloud Detection

深度学习网络在Landsat影像云检测中的比较与验证

The investigation into changing rainfall patterns in the Indonesian Maritime Continent (IMC) involved testing for trends, step changes, and variance nonstationarity using the Mann–Kendall, Pettitt, and White tests, respectively. The analysis covered data from 106 meteorological stations over the period from 1981 to 2021, with all tests conducted at annual time scales to understand the evolving precipitation dynamics in the region. According to the findings of this study, the average annual rainfall in IMC is nonstationary. Rainfall has increased dramatically by 12.72 mm with a significant shift point in 1994. However, this characteristic is likely to vary if the analysis period is extended, reduced, or conducted in different time frames. The spatial analysis indicates that 35.8% of meteorological stations observed a notable increase in rainfall, while 28.3% experienced a significant shift, and 16% displayed considerable variation. Consequently, the study only partially identified the nonstationary nature of rainfall in the IMC. Moreover, the research highlights a substantial rise in rainfall in the central to eastern IMC region, whereas the central to western region predominantly shows a decrease. This nonstationary test helps avoid errors in climatic conditions and analytical methods, presenting recommendations for hydrological projects. Consequently, employing this nonstationary test helps prevent inaccuracies in understanding climatic conditions and analytical techniques, thereby offering valuable suggestions for hydrological projects.

Gian Gardian Sudarman

Tri Wahyu Hadi

Nining Sari Ningsih

Ardhasena Sopaheluwakan

Muhammad Ridho Syahputra

Fiolenta Marpaung

Nonstationary Changes in Annual Rainfall over Indonesia’s Maritime Continent

印度尼西亚海洋大陆年降雨量的非平稳变化

Land surface temperature (LST) is a critical indicator variable in climate science. In this study, the variation of LST on the island of New Guinea during 2000 to 2019 was investigated using a cubic spline model and a multivariate regression model. The data were obtained from the National Aeronautics and Space Administration moderate resolution imaging spectroradiometer database. This study focused on 90 subregions with 105-pixels of latitude 90 kilometer apart. These subregions were categorized into 10 super-regions. The results showed that the mean change in LST for all 90 subregions was +0.086°C per decade with a confidence interval of (0.028, 0.144) o C. There were five super-regions with a significant mean LST change. LST increased significantly in the central-north, central-south of the island (super-regions B1, C1, and C2 with 0.117°C, 0.162°C, and 0.185°C, respectively) and the southern part of Papua New Guinea (super-region E2 with 0.217°C), whereas it decreased in the middle part of the Indonesian territories (A2 with −0.122°C). The results also showed that LST variation occurs at the subregional level. Climate change mitigation methods are critical for reducing temperature rise and limiting any negative effects on the region.

Munawar Munawar

Rhysa McNeil

Tofan Agung Eka Prasetya

Rohana Jani

Don McNeil

Nitinun Pongsiri

Modelling Land Surface Temperature Variation in New Guinea Island from 2000 to 2019 Using a Cubic Spline Model

使用三次样条模型模拟2000年至2019年新几内亚岛地表温度变化

The current paper evaluates the weather research and forecasting (WRF) model sensitivity to five different combinations of cumulus, microphysics, radiation, and planetary boundary layer (PBL) schemes over Loess Plateau for the period 2015, in terms of 2 m temperature and precipitation. The WRF configuration consists of a 10 km resolution domain nested in a coarser domain driven by European Center for Medium-Range Weather Forecasts Reanalysis (ERA-Interim) data. The model simulated 2 m temperature and precipitation have been evaluated at daily and monthly scales with gridded observational dataset. The analysis shows that all experiments reproduce well the daily 2 m temperature, with overestimation particularly in the low-temperature range. Precipitation is less well simulated, with underestimation in all range, especially for intense rainfall. Comparing with ERA-Interim, WRF shows no clear benefit in simulating daily 2 m temperature while prominent improvement in simulating daily precipitation. WRF simulations capture the annual cycle of monthly 2 m temperature and precipitation with a warm bias and wet bias for most experiments in summer. Some reasonable configurations are identified. The “best” configuration depends on the criteria.

Siliang Liu

Sensitivity of WRF-Simulated 2 m Temperature and Precipitation to Physics Options over the Loess Plateau

WRF模拟的黄土高原2米气温和降水量对物理选项的敏感性

The rapid development of urbanization makes the phenomenon of urban heat islands even more serious. Predicting the impact of land cover change on urban heat island has become one of the research hotspots. Taking Wuhan, China, as an example, this study simulated the land type change in 2020 through the Cellular Automata-Markov-Chain (CA-Markov) model. The urban heat island in 2020 was simulated and analyzed in conjunction with the Weather Research &amp; Forecasting Model (WRF), and the simulation results of wind velocity and temperature were confirmed using weather station observation data. Based on this, the land cover and urban heat island of Wuhan in 2030 were predicted. The temperature was found to be well-fit by CA-Markov simulated land use data, with an average inaccuracy of about 2.5°C for weather stations. Wind speed had a poor fitting effect; the average error was roughly 2 m/s. The built-up area was the center of the high temperature area both before and after the prediction, the water was the low temperature area, and the peak heat island happened at night. According to the forecast results, there will be more built-up land in 2030, and there will be a greater intensity of heat islands than in 2020.

Wanyi Zuo

Zhigang Ren

Xiaofang Shan

Zeng Zhou

Qinli Deng

Analysis of Urban Heat Island Effect in Wuhan Urban Area Based on Prediction of Urban Underlying Surface Coverage Type Change

基于城市下垫面覆盖类型变化预测的武汉城区热岛效应分析

This study was employed to investigate the temporal variability and trend analysis of areal rainfall in the Muger subwatershed, Upper Blue Nile, Ethiopia. The study was run over the following procedures to handle the main objective: (1) determining the areal rainfall from gauged point rainfall using the Thiessen polygon method, (2) grouping the months in the season according to the study area, (3) evaluating the temporal dynamics of annual and seasonal areal rainfall using the coefficient of variation (CV), standard anomaly index (SAI), and precipitation concentration index (PCI), and (4) analyzing the trend of annual and seasonal areal rainfall using modified Mann–Kendall’s (modifiedmk) test in RStudio. Based on the temporal variability analysis, CV results depict that annual and summer areal rainfall had low variability with values of 13.43% and 13.7%, respectively. Winter and spring areal rainfall shows high variation with a CV value of 50.5% and 36%, respectively. According to the SAI output, around 70% of the considered year was in the normal condition of wetness. On the other hand, the seasonal (winter, spring, and summer) rainfall distribution of the study area shows strong irregularity distribution throughout the considered years as a result of PCI with a value of 57.5%. The trend of the areal rainfall was shown to be both increasing and decreasing. However, the trend was insignificant with a 10% confidence level.

Alene Moshe

Manamno Beza

Temporal Dynamics and Trend Analysis of Areal Rainfall in Muger Subwatershed, Upper Blue Nile, Ethiopia

埃塞俄比亚上蓝尼罗河穆格尔子流域面雨量的时间动态与趋势分析

Abbreviation	Adv. Meteorol.
Journal Impact	1.99
Quartiles(Global)	METEOROLOGY & ATMOSPHERIC SCIENCES(Q3)

ISSN	1687-9309, 1687-9317
h-index	49

Publication Information	Publisher: Hindawi Limited，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2012，Proportion of original research papers: 97.56%，Self Citation Rate:9.50%， Gold OA Rate: 98.73%
Journal Fees	$1550Details
Average review cycle	网友分享经验：>12周，或约稿
Average recruitment ratio	网友分享经验：容易

Advances in Meteorology

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