Sea fog is a critical meteorological phenomenon that affects production safety and operational efficiency at Ningbo–Zhoushan Port.To improve fine-grained sea fog forecasting in the port area,this study constructs a d...Sea fog is a critical meteorological phenomenon that affects production safety and operational efficiency at Ningbo–Zhoushan Port.To improve fine-grained sea fog forecasting in the port area,this study constructs a dynamic graph by leveraging the strengths of graph neural networks in processing irregularly distributed meteorological station data.The graph is designed based on station locations and wind direction/speed,incorporating the advection term in the atmospheric motion equation.On this basis,a novel forecasting framework is developed that integrates graph attention networks,long short-term memory,and multistep classification output,to train a sea fog forecasting model.The proposed model provides hourly rolling 24-h sea fog forecasts for 72 stations across the port area.Operational validation in 2023 demonstrates its robust performance in forecasting fog occurrence.For 24-h fog occurrence forecasts,the model achieves a threat score(TS)of 0.369(F1=0.539),with an average TS of 0.190 for fixed-time forecasts.The forecasting performance exhibits a typical temporal decay pattern,reaching its highest TS(0.441)at the 1-h lead time.Furthermore,the model demonstrates superior skill in predicting localized patchy fog and fog evolution,outperforming conventional machine learning approaches such as random forest and Gaussian naive Bayes.展开更多
This study explored the observation strategy and effectiveness of synoptic-scale adaptive observations for improving sea fog prediction in coastal regions around the Bohai Sea based on a poorly predicted fog event wit...This study explored the observation strategy and effectiveness of synoptic-scale adaptive observations for improving sea fog prediction in coastal regions around the Bohai Sea based on a poorly predicted fog event with cold-front synoptic pattern(CFSP).An ensemble Kalman filter data assimilation system for the Weather Research and Forecasting model was adopted with ensemble sensitivity analysis(ESA).By comparing observation impacts(estimated from a 40-member ensemble with ESA)among different meteorological observation variables and pressure levels,the temperature at 850 hPa and surface layer(850 hPa-and-surface temperature)was selected as the target observation type.Additionally,the area with large observation impacts for this observation type was predicted in the transition region of the surface low–high system.This area developed southward with the low and moved eastward with the low–high system,which could be explained by the main features of CFSP.Moreover,both experiments assimilating synthetic and real observations showed that assimilating 850 hPa-and-surface temperature observations generally yielded better fog coverage forecasts in areas with greater observation impacts than areas with smaller impacts.However,the effectiveness of adaptive observations was reduced when real observations rather than synthetic observations were assimilated,which is possibly due to factors such as observation and model errors.The main conclusions above were verified by another typical fog event with CFSP characteristics.Results of this study highlight the importance of improved initial conditions in the transition region of the low–high system for improving fog prediction and provide scientific guidance for implementing an observation network for fog forecasting over the Bohai Sea.展开更多
By analyzing the NCEP 1°×1° reanalysis (2004–2008), a number of predictors (factors of variables) are established with the output from the GRAPES model and with reference to the sea fog data from obser...By analyzing the NCEP 1°×1° reanalysis (2004–2008), a number of predictors (factors of variables) are established with the output from the GRAPES model and with reference to the sea fog data from observational stations (2004–2008) and field observations (2006–2008). Based on the criteria and conditions for sea fog appearance at the stations of Zhanjiang, Zhuhai and Shantou, a Model Output Statistics (MOS) scheme for distinguishing and forecasting 24-h sea fog is established and put into use for three representative coastal areas of Guangdong. As shown in an assessment of the forecasts for Zhanjiang and Shantou (March of 2008) and Zhuhai (April of 2008), the scheme was quite capable of forecasting sea fog on the coast of the province, with the accuracy ranging from 84% to 90%, the threat score from 0.40 to 0.50 and the Heidke skill from 0.52 to 0.56.展开更多
In the South China Sea, sea fog brings severe disasters every year, but forecasters have yet to implement an effective seafog forecast. To address this issue, we test a liquid-water-content-only(LWC-only) operational ...In the South China Sea, sea fog brings severe disasters every year, but forecasters have yet to implement an effective seafog forecast. To address this issue, we test a liquid-water-content-only(LWC-only) operational sea-fog prediction method based on a regional mesoscale numerical model with a horizontal resolution of about 3 km, the Global and Regional Assimilation and Prediction System(GRAPES), hereafter GRAPES-3 km. GRAPES-3 km models the LWC over the sea, from which we infer the visibility that is then used to identify fog. We test the GRAPES-3 km here against measurements in 2016 and 2017 from coastal-station observations, as well as from buoy data, data from the Integrated Observation Platform for Marine Meteorology, and retrieved fog and cloud patterns from Himawari-8 satellite data. For two cases that we examine in detail, the forecast region of sea fog overlaps well with the multi-observational data within 72 h. Considering forecasting for0–24 h, GRAPES-3 km has a 2-year-average equitable threat score(ETS) of 0.20 and a Heidke skill score(HSS) of 0.335,which is about 5.6%(ETS) and 6.4%(HSS) better than our previous method(GRAPES-MOS). Moreover, the stations near the particularly foggy region around the Leizhou Peninsula have relatively high forecast scores compared to other sea areas.Overall, the results show that GRAPES-3 km can roughly predict the formation, evolution, and dissipation of sea fog on the southern China coast.展开更多
基于WRF(Weather Research and Forecasting)模式及其杂合三维变分(Hybird-3DVAR)同化模块,对2006年3月发生的一次大范围黄海海雾进行了集合预报尝试。详细分析了其预报效果,并与决定性预报结果作了比较。研究揭示:集合预报50%概率雾区...基于WRF(Weather Research and Forecasting)模式及其杂合三维变分(Hybird-3DVAR)同化模块,对2006年3月发生的一次大范围黄海海雾进行了集合预报尝试。详细分析了其预报效果,并与决定性预报结果作了比较。研究揭示:集合预报50%概率雾区预报的公正预兆得分(Equitable threat score,ETS)优于决定性预报大约29%;集合预报中加入海温扰动非常必要,对浓雾预报改善作用明显,ETS提高至少10%;在集合预报中混用YSU与MYNN边界层方案的做法,可以降低只使用其中之一可能导致的预报误差。研究表明,借助Hybrid-3DVAR开展黄海海雾的集合预报技术上可行,集合预报将成为黄海海雾数值预报的一种有希望的途径。展开更多
2009年4月9—12日黄海海域发生了一次受高压系统影响的海雾过程。利用卫星观测与探空数据、WRF模式(Weather Research and Forecasting Model)对此次海雾过程及相伴的大气波导进行了观测分析与数值模拟。海雾与波导发展可分为3个阶段:(1...2009年4月9—12日黄海海域发生了一次受高压系统影响的海雾过程。利用卫星观测与探空数据、WRF模式(Weather Research and Forecasting Model)对此次海雾过程及相伴的大气波导进行了观测分析与数值模拟。海雾与波导发展可分为3个阶段:(1)大气波导先于海雾存在于黄海海面;受高压下沉影响,黄海上空存在逆温层和较强的湿度梯度,表现为较强的贴海表面波导和非贴海表面波导。(2)海雾始于高压西部,并随高压系统逐渐东移减弱,向黄海北部扩展;辐射冷却虽然使雾顶附近逆温增强,但海雾的机械湍流使其顶部湿度梯度减小,雾顶附近对应弱悬空波导或波导消失。(3)高压系统影响使干空气下沉到雾区导致黄海海雾消散;雾顶附近逆温仍存在,同时湿度梯度增大,黄海上空逐渐变为非贴海表面波导。本研究结果表明:高压系统不仅极易为波导的发生提供有利条件,而且有利于海雾的生成,在海雾演变过程中主要是雾顶水汽梯度的变化导致了波导类型及强度的变化。展开更多
为推进海洋气象精细化网格预报建设,国家气象中心从2013年开始研发海雾客观预报方法,对基于配料法建立的海雾客观预报方法不断进行改进,并研发了基于决策树方法的海雾预报方法和海雾预报指数预报产品,客观方法对我国北部海区主要海雾过...为推进海洋气象精细化网格预报建设,国家气象中心从2013年开始研发海雾客观预报方法,对基于配料法建立的海雾客观预报方法不断进行改进,并研发了基于决策树方法的海雾预报方法和海雾预报指数预报产品,客观方法对我国北部海区主要海雾过程预报效果较好,对2018年我国北部海区有无雾的24 h预报TS评分平均为0.25左右。将海雾客观预报产品集成应用于海洋气象智能网格预报背景场的生成规则中,生成天气现象网格预报结果,为主观预报订正提供了有力的支撑。基于精细化网格预报生成的海雾主观预报结果对2018年黄海中部和南部大雾预报的24 h TS评分接近0.25,对北部湾大雾预报的24 h TS评分在0.15左右。展开更多
基金Supported by the Ningbo Public Welfare Science and Technology Project(2022S181)Key Project of Zhejiang Meteorological Sci-Tech Program(2024ZD26)+2 种基金General Project of Ningbo Meteorological Sci-Tech Program(NBQX2024005B)“Jianbing”and“Lingyan”Key Research and Development Program of Zhejiang Province(2025C02258)Academician Workstation of Ningbo Meteorological Observatory。
文摘Sea fog is a critical meteorological phenomenon that affects production safety and operational efficiency at Ningbo–Zhoushan Port.To improve fine-grained sea fog forecasting in the port area,this study constructs a dynamic graph by leveraging the strengths of graph neural networks in processing irregularly distributed meteorological station data.The graph is designed based on station locations and wind direction/speed,incorporating the advection term in the atmospheric motion equation.On this basis,a novel forecasting framework is developed that integrates graph attention networks,long short-term memory,and multistep classification output,to train a sea fog forecasting model.The proposed model provides hourly rolling 24-h sea fog forecasts for 72 stations across the port area.Operational validation in 2023 demonstrates its robust performance in forecasting fog occurrence.For 24-h fog occurrence forecasts,the model achieves a threat score(TS)of 0.369(F1=0.539),with an average TS of 0.190 for fixed-time forecasts.The forecasting performance exhibits a typical temporal decay pattern,reaching its highest TS(0.441)at the 1-h lead time.Furthermore,the model demonstrates superior skill in predicting localized patchy fog and fog evolution,outperforming conventional machine learning approaches such as random forest and Gaussian naive Bayes.
基金supported by the National Natural Science Foundation of China(Grant No.41705081)the Shandong Natural Science Foundation Project(Grant No.ZR2019ZD12)the Laoshan Laboratory(Grant No.LSKJ202202203).
文摘This study explored the observation strategy and effectiveness of synoptic-scale adaptive observations for improving sea fog prediction in coastal regions around the Bohai Sea based on a poorly predicted fog event with cold-front synoptic pattern(CFSP).An ensemble Kalman filter data assimilation system for the Weather Research and Forecasting model was adopted with ensemble sensitivity analysis(ESA).By comparing observation impacts(estimated from a 40-member ensemble with ESA)among different meteorological observation variables and pressure levels,the temperature at 850 hPa and surface layer(850 hPa-and-surface temperature)was selected as the target observation type.Additionally,the area with large observation impacts for this observation type was predicted in the transition region of the surface low–high system.This area developed southward with the low and moved eastward with the low–high system,which could be explained by the main features of CFSP.Moreover,both experiments assimilating synthetic and real observations showed that assimilating 850 hPa-and-surface temperature observations generally yielded better fog coverage forecasts in areas with greater observation impacts than areas with smaller impacts.However,the effectiveness of adaptive observations was reduced when real observations rather than synthetic observations were assimilated,which is possibly due to factors such as observation and model errors.The main conclusions above were verified by another typical fog event with CFSP characteristics.Results of this study highlight the importance of improved initial conditions in the transition region of the low–high system for improving fog prediction and provide scientific guidance for implementing an observation network for fog forecasting over the Bohai Sea.
基金Natural Science Foundation of China (40675013)Research on Techniques of Specialized Forecast of Sea Fog and Visibility at the Pearl River Mouth+2 种基金project of Science and Technology Program of Guangdong Province (2006B37202005)Research on System of Monitoring Sea Fog for the Pearl River Mouthproject of Meteorological Science of Guangdong Meteorological Bureau
文摘By analyzing the NCEP 1°×1° reanalysis (2004–2008), a number of predictors (factors of variables) are established with the output from the GRAPES model and with reference to the sea fog data from observational stations (2004–2008) and field observations (2006–2008). Based on the criteria and conditions for sea fog appearance at the stations of Zhanjiang, Zhuhai and Shantou, a Model Output Statistics (MOS) scheme for distinguishing and forecasting 24-h sea fog is established and put into use for three representative coastal areas of Guangdong. As shown in an assessment of the forecasts for Zhanjiang and Shantou (March of 2008) and Zhuhai (April of 2008), the scheme was quite capable of forecasting sea fog on the coast of the province, with the accuracy ranging from 84% to 90%, the threat score from 0.40 to 0.50 and the Heidke skill from 0.52 to 0.56.
基金supported jointly by the National Natural Science Foundation of China (Grant Nos. 41675021, 41605006 and 41675019)the Meteorological Sciences Research Project (Grant No. GRMC2017M04)the Innovation Team of Forecasting Technology for Typhoon and Marine Meteorology of the Weather Bureau of Guangdong Province
文摘In the South China Sea, sea fog brings severe disasters every year, but forecasters have yet to implement an effective seafog forecast. To address this issue, we test a liquid-water-content-only(LWC-only) operational sea-fog prediction method based on a regional mesoscale numerical model with a horizontal resolution of about 3 km, the Global and Regional Assimilation and Prediction System(GRAPES), hereafter GRAPES-3 km. GRAPES-3 km models the LWC over the sea, from which we infer the visibility that is then used to identify fog. We test the GRAPES-3 km here against measurements in 2016 and 2017 from coastal-station observations, as well as from buoy data, data from the Integrated Observation Platform for Marine Meteorology, and retrieved fog and cloud patterns from Himawari-8 satellite data. For two cases that we examine in detail, the forecast region of sea fog overlaps well with the multi-observational data within 72 h. Considering forecasting for0–24 h, GRAPES-3 km has a 2-year-average equitable threat score(ETS) of 0.20 and a Heidke skill score(HSS) of 0.335,which is about 5.6%(ETS) and 6.4%(HSS) better than our previous method(GRAPES-MOS). Moreover, the stations near the particularly foggy region around the Leizhou Peninsula have relatively high forecast scores compared to other sea areas.Overall, the results show that GRAPES-3 km can roughly predict the formation, evolution, and dissipation of sea fog on the southern China coast.
文摘基于WRF(Weather Research and Forecasting)模式及其杂合三维变分(Hybird-3DVAR)同化模块,对2006年3月发生的一次大范围黄海海雾进行了集合预报尝试。详细分析了其预报效果,并与决定性预报结果作了比较。研究揭示:集合预报50%概率雾区预报的公正预兆得分(Equitable threat score,ETS)优于决定性预报大约29%;集合预报中加入海温扰动非常必要,对浓雾预报改善作用明显,ETS提高至少10%;在集合预报中混用YSU与MYNN边界层方案的做法,可以降低只使用其中之一可能导致的预报误差。研究表明,借助Hybrid-3DVAR开展黄海海雾的集合预报技术上可行,集合预报将成为黄海海雾数值预报的一种有希望的途径。
文摘2009年4月9—12日黄海海域发生了一次受高压系统影响的海雾过程。利用卫星观测与探空数据、WRF模式(Weather Research and Forecasting Model)对此次海雾过程及相伴的大气波导进行了观测分析与数值模拟。海雾与波导发展可分为3个阶段:(1)大气波导先于海雾存在于黄海海面;受高压下沉影响,黄海上空存在逆温层和较强的湿度梯度,表现为较强的贴海表面波导和非贴海表面波导。(2)海雾始于高压西部,并随高压系统逐渐东移减弱,向黄海北部扩展;辐射冷却虽然使雾顶附近逆温增强,但海雾的机械湍流使其顶部湿度梯度减小,雾顶附近对应弱悬空波导或波导消失。(3)高压系统影响使干空气下沉到雾区导致黄海海雾消散;雾顶附近逆温仍存在,同时湿度梯度增大,黄海上空逐渐变为非贴海表面波导。本研究结果表明:高压系统不仅极易为波导的发生提供有利条件,而且有利于海雾的生成,在海雾演变过程中主要是雾顶水汽梯度的变化导致了波导类型及强度的变化。
文摘为推进海洋气象精细化网格预报建设,国家气象中心从2013年开始研发海雾客观预报方法,对基于配料法建立的海雾客观预报方法不断进行改进,并研发了基于决策树方法的海雾预报方法和海雾预报指数预报产品,客观方法对我国北部海区主要海雾过程预报效果较好,对2018年我国北部海区有无雾的24 h预报TS评分平均为0.25左右。将海雾客观预报产品集成应用于海洋气象智能网格预报背景场的生成规则中,生成天气现象网格预报结果,为主观预报订正提供了有力的支撑。基于精细化网格预报生成的海雾主观预报结果对2018年黄海中部和南部大雾预报的24 h TS评分接近0.25,对北部湾大雾预报的24 h TS评分在0.15左右。
