[ Objective] The research aimed to study weather typing and dissipation forecast of the fog in Haizhou Bay. [ Method] Based on the me- teorological observation data of three representative stations in Lianyungang, we ...[ Objective] The research aimed to study weather typing and dissipation forecast of the fog in Haizhou Bay. [ Method] Based on the me- teorological observation data of three representative stations in Lianyungang, we analyzed weather situation before fog occurrence as well as the meteorological elements of coastal fog in Haizhou Bay, and established dissipation rating forecast equation of the fog. [ Result] From the surface weather chart, the fog in Haizhou Bay was divided into four types： low-pressure inverted trough type, prefrontal warm-zone type, high-pressure rear type and high-pressure bottom type. FOg formation was closely related to stratification stability, temperature, relative humidity, wind direction and wind velocity. By using multiple linear regression method, dissipation rating prediction equation of the fog was established. Via test, prediction was correct basically, and it reached 77% that forecast rating error was below level 0.5.[Conclusion] The research could provide favorable reference for forecast and warninq of the fo_q in Haizhou Bay.展开更多
利用2015年大连地区7个主要气象站的地面气温、降水、风向风速和相对湿度观测资料,针对东北区域中尺度数值模式(Weather Research and Forecast,WRF)产品中常规天气要素进行检验分析,了解掌握WRF模式对不同天气要素的预报能力,以期为天...利用2015年大连地区7个主要气象站的地面气温、降水、风向风速和相对湿度观测资料,针对东北区域中尺度数值模式(Weather Research and Forecast,WRF)产品中常规天气要素进行检验分析,了解掌握WRF模式对不同天气要素的预报能力,以期为天气预报业务中WRF模式产品的订正提供参考。结果表明:WRF模式产品的气温预报准确率整体上08时起报的比20时起报的稍好,最低气温预报效果比最高气温稍好,且WRF模式对升温和降温的趋势预报较好,具有一定参考性。WRF模式产品的降水预报准确率相对较高; WRF模式对风向的预报准确率可以达到50%左右,而风速的预报准确率可以达到60%—70%;大雾天气的预报,可以相应参考WRF模式的相对湿度。展开更多
In August 2018, the Institute of Urban Meteorology(IUM) in Beijing co-organized with Sinovation Ventures a Weather Forecasting Contest(WFC)—one of the AI(artificial intelligence) Challenger Global Contests. The WFC a...In August 2018, the Institute of Urban Meteorology(IUM) in Beijing co-organized with Sinovation Ventures a Weather Forecasting Contest(WFC)—one of the AI(artificial intelligence) Challenger Global Contests. The WFC aims to take advantage of the AI techniques to improve the quality of weather forecast. Across the world, more than1000 teams enrolled in the WFC and about 250 teams completed real-time weather forecasts, among which top 5 teams were awarded in the final contest. The contest results show that the AI-based ensemble models exhibited improved skill for forecasts of surface air temperature and relative humidity at 2-m and wind speed at 10-m height.Compared to the IUM operational analog ensemble weather model forecast, the most notable improvements of 24.2%and 17.0% in forecast accuracy for surface 2-m air temperature are achieved by two teams using the AI techniques of time series model, gradient boosting tree, depth probability prediction, and so on. Meanwhile, it is found that reasonable data processing techniques and model composite structure are also important for obtaining better forecasts.展开更多
基金Supported by Youth Science Research Fund in Jiangsu Meteorological Bureau,China(Q201007)Special Item of Forecaster in Jiangsu Province,China(201207)
文摘[ Objective] The research aimed to study weather typing and dissipation forecast of the fog in Haizhou Bay. [ Method] Based on the me- teorological observation data of three representative stations in Lianyungang, we analyzed weather situation before fog occurrence as well as the meteorological elements of coastal fog in Haizhou Bay, and established dissipation rating forecast equation of the fog. [ Result] From the surface weather chart, the fog in Haizhou Bay was divided into four types： low-pressure inverted trough type, prefrontal warm-zone type, high-pressure rear type and high-pressure bottom type. FOg formation was closely related to stratification stability, temperature, relative humidity, wind direction and wind velocity. By using multiple linear regression method, dissipation rating prediction equation of the fog was established. Via test, prediction was correct basically, and it reached 77% that forecast rating error was below level 0.5.[Conclusion] The research could provide favorable reference for forecast and warninq of the fo_q in Haizhou Bay.
文摘利用2015年大连地区7个主要气象站的地面气温、降水、风向风速和相对湿度观测资料,针对东北区域中尺度数值模式(Weather Research and Forecast,WRF)产品中常规天气要素进行检验分析,了解掌握WRF模式对不同天气要素的预报能力,以期为天气预报业务中WRF模式产品的订正提供参考。结果表明:WRF模式产品的气温预报准确率整体上08时起报的比20时起报的稍好,最低气温预报效果比最高气温稍好,且WRF模式对升温和降温的趋势预报较好,具有一定参考性。WRF模式产品的降水预报准确率相对较高; WRF模式对风向的预报准确率可以达到50%左右,而风速的预报准确率可以达到60%—70%;大雾天气的预报,可以相应参考WRF模式的相对湿度。
基金Supported by the National Key Research and Development Program of China(2018YFC1506801)National Natural Science Foundation of China(41505117)Special Funds for Basic Research and Operation in Government Level Research Institutes of Public Welfare Nature(IUMKY201904)
文摘In August 2018, the Institute of Urban Meteorology(IUM) in Beijing co-organized with Sinovation Ventures a Weather Forecasting Contest(WFC)—one of the AI(artificial intelligence) Challenger Global Contests. The WFC aims to take advantage of the AI techniques to improve the quality of weather forecast. Across the world, more than1000 teams enrolled in the WFC and about 250 teams completed real-time weather forecasts, among which top 5 teams were awarded in the final contest. The contest results show that the AI-based ensemble models exhibited improved skill for forecasts of surface air temperature and relative humidity at 2-m and wind speed at 10-m height.Compared to the IUM operational analog ensemble weather model forecast, the most notable improvements of 24.2%and 17.0% in forecast accuracy for surface 2-m air temperature are achieved by two teams using the AI techniques of time series model, gradient boosting tree, depth probability prediction, and so on. Meanwhile, it is found that reasonable data processing techniques and model composite structure are also important for obtaining better forecasts.
