By using the routine weather data and the numerical value forecast products,applying the weather analysis and diagnostic analysis methods,the cold wave weather which happened in the south area of Dalian was analyzed o...By using the routine weather data and the numerical value forecast products,applying the weather analysis and diagnostic analysis methods,the cold wave weather which happened in the south area of Dalian was analyzed on December 29-30,2009.The results showed that based on the temperature rise in prior period,the strong cold air accumulated in Mongolia and passed Ulan Bator,Erenhot to invade Dalian area.In the cold wave process,the circulation situations in the middle and high latitudes were the 'one ridge and one trough' pattern in Asia.The dynamic mechanisms were the rotary low-pressure trough in high altitude and the strong frontal zone,and the flow field which induced the cold wave to break out was the 'low trough rotation pattern'.After the cold air broke out,Dalian area was controlled by the strong cold advection.The cold high-pressure on the ground entered into the key zone and reached the intensity of cold wave.However,the circulation of cold wave occurrence was southerly,and the shifts of cold air and influence system were quicker.Therefore,the cold wave appeared in Dalian's south areas which included Lvshun,Dalian and Jinzhou.On this basis,the key point of cold wave weather forecast in Dalian area was summarized.展开更多
[Objective] The reason for the unsuccessful forecast of a heavy rainfall event in Yingkou was analyzed. [Method] Based on the precipitation data observed by automatic weather stations and MICAPS data, a heavy rainfall...[Objective] The reason for the unsuccessful forecast of a heavy rainfall event in Yingkou was analyzed. [Method] Based on the precipitation data observed by automatic weather stations and MICAPS data, a heavy rainfall Event was studied in Yingkou from 19 July to 21 July in 2010. Then the analysis of an unsuccessful forecasting for the heavy rainfall on 21 July was illustrated by CINRAD-SA data, satellite data and numerical forecast products. [Result] The main reason for the unsuccessful forecast was that the duration of the rainfall was long and inconsecutive. The distribution was uneven. Strong precipitation on 21st was different from the one in previous two durations. It was regional short term strong precipitation. And the forecast difficulty was large; the numerical forecast was unstable and erroneous;strong precipitation occurred in the night on 20th, which was shortly before the strong precipitation in the evening of 21st. This would easily confuse the reporter. Besides, the short term stillness of radar and cloud during this time would form certain disturbance. The focus of rainstorm forecast should based on the numerical forecast instead of element forecast;insisting on situation analysis and taking element judgment as auxiliary;as for strong precipitation forecast, there was large error in numerical forecast and can not be relied. Reporter should report the correct one based on experience. [Conclusion] The study provided reference for the forecast of rainstorm.展开更多
Based on the data of sandstorm at 11 stations in Ulanqab City from 1990 to 2021,the spatial and temporal variation characteristics of sand-storm weather were analyzed firstly,and then the conceptual models of cold fro...Based on the data of sandstorm at 11 stations in Ulanqab City from 1990 to 2021,the spatial and temporal variation characteristics of sand-storm weather were analyzed firstly,and then the conceptual models of cold front and Mongolian cyclone sandstorm were obtained by analyzing sandstorm cases.Finally,the forecast points of the two types of sandstorm weather were given to provide some scientific basis and reference for the prediction of local sandstorm weather in the future.展开更多
Time series data is a kind of data accumulated over time,which can describe the change of phenomenon.This kind of data reflects the degree of change of a certain thing or phenomenon.The existing technologies such as L...Time series data is a kind of data accumulated over time,which can describe the change of phenomenon.This kind of data reflects the degree of change of a certain thing or phenomenon.The existing technologies such as LSTM and ARIMA are better than convolutional neural network in time series prediction,but they are not enough to mine the periodicity of data.In this article,we perform periodic analysis on two types of time series data,select time metrics with high periodic characteristics,and propose a multi-scale prediction model based on the attention mechanism for the periodic trend of the data.A loss calculation method for traffic time series characteristics is proposed as well.Multiple experiments have been conducted on actual data sets.The experiments show that the method proposed in this paper has better performance than commonly used traffic prediction methods(ARIMA,LSTM,etc.)and 3%-5%increase on MAPE.展开更多
Developing reliable weather prediction systems is still a challenging task due to the complexity of the Earth System and the chaotic behavior of its compo-nents.Small errors introduced by observations,their assimilati...Developing reliable weather prediction systems is still a challenging task due to the complexity of the Earth System and the chaotic behavior of its compo-nents.Small errors introduced by observations,their assimilation and the forecast model configuration escalate chaotically,leading to a significant loss in forecast skill with time.Traditionally,rainfall forecasts have been generated at grid-based spatial resolutions,providing valuable information on regional precipitation patterns.However,Weather varies markedly within a grid box and forecasts for specific sites have occasionally failed inevitably.The grid-based forecasts may not always meet the needs of decision-makers at specific points of interest.The major challenge is dealing with variations in sub-grid variability,that is to say,the variation seen amongst rainfall point values within a given model grid box,more especially in convective situations.While ensemble forecasts have shown promise in capturing the uncertainty inherent in average rainfall predictions of much larger grid boxes,their utility at point locations has not been extensively explored.Most evaluation studies focus on grid-based verification metrics,which may not accurately reflect forecast per-formance at individual points of interest.EcPoint,a post-processing approach developed at the European Centre for Medium Range Forecasts(ECMWF),is tailored to forecast rainfall at point locations.In this study,we evaluate the performance of the EcPoint post-processing method over the south China re-gion.The analysis focuses on the reliability,accuracy and discrimination skill of this post-processing method over the three provinces in south China(An-hui,Zhejiang and Jiangsu).We examine performance versus lead time,sea-sons,and altitude.Through verifications,the study highlighted the added value of the post-processing method over Raw ensemble forecasts.One year of verification demonstrates that,between the Raw ensemble and post-pro-cessed EcPoint forecasts,EcPoint is the more reliable and skillful system,add-ing significant value to most rainfall events occurring during the day and the seasonal associated events,as well as the topography-associated rainfall events.To complement the one-year verification analysis,a case study was conducted on an extremely heavy rainfall event observed on June 2,2022 at 12 UTC.The analysis demonstrated EcPoint’s ability to provide more localized and refined forecasts whereas Raw didn’t provide any possibility of rainfall,particularly at short lead times.At longer lead times,EcPoint ensembles maintained rela-tively low probabilities,but offered improved performance in capturing rain-fall variability,while Raw ensemble exhibited broader but less precise rainfall predictions with a tendency of over warning of some areas.Future work can extend the evaluation to more diverse climatic and topographic regions of China to enhance the general applicability of the method.Although based solely on the global ECMWF-IFS model,EcPoint performs well over the small domain of south China(three provinces).Besides verifying EcPoint,the study confirms that the post-processing method can significantly improve the fore-cast performance.展开更多
文摘By using the routine weather data and the numerical value forecast products,applying the weather analysis and diagnostic analysis methods,the cold wave weather which happened in the south area of Dalian was analyzed on December 29-30,2009.The results showed that based on the temperature rise in prior period,the strong cold air accumulated in Mongolia and passed Ulan Bator,Erenhot to invade Dalian area.In the cold wave process,the circulation situations in the middle and high latitudes were the 'one ridge and one trough' pattern in Asia.The dynamic mechanisms were the rotary low-pressure trough in high altitude and the strong frontal zone,and the flow field which induced the cold wave to break out was the 'low trough rotation pattern'.After the cold air broke out,Dalian area was controlled by the strong cold advection.The cold high-pressure on the ground entered into the key zone and reached the intensity of cold wave.However,the circulation of cold wave occurrence was southerly,and the shifts of cold air and influence system were quicker.Therefore,the cold wave appeared in Dalian's south areas which included Lvshun,Dalian and Jinzhou.On this basis,the key point of cold wave weather forecast in Dalian area was summarized.
文摘[Objective] The reason for the unsuccessful forecast of a heavy rainfall event in Yingkou was analyzed. [Method] Based on the precipitation data observed by automatic weather stations and MICAPS data, a heavy rainfall Event was studied in Yingkou from 19 July to 21 July in 2010. Then the analysis of an unsuccessful forecasting for the heavy rainfall on 21 July was illustrated by CINRAD-SA data, satellite data and numerical forecast products. [Result] The main reason for the unsuccessful forecast was that the duration of the rainfall was long and inconsecutive. The distribution was uneven. Strong precipitation on 21st was different from the one in previous two durations. It was regional short term strong precipitation. And the forecast difficulty was large; the numerical forecast was unstable and erroneous;strong precipitation occurred in the night on 20th, which was shortly before the strong precipitation in the evening of 21st. This would easily confuse the reporter. Besides, the short term stillness of radar and cloud during this time would form certain disturbance. The focus of rainstorm forecast should based on the numerical forecast instead of element forecast;insisting on situation analysis and taking element judgment as auxiliary;as for strong precipitation forecast, there was large error in numerical forecast and can not be relied. Reporter should report the correct one based on experience. [Conclusion] The study provided reference for the forecast of rainstorm.
文摘Based on the data of sandstorm at 11 stations in Ulanqab City from 1990 to 2021,the spatial and temporal variation characteristics of sand-storm weather were analyzed firstly,and then the conceptual models of cold front and Mongolian cyclone sandstorm were obtained by analyzing sandstorm cases.Finally,the forecast points of the two types of sandstorm weather were given to provide some scientific basis and reference for the prediction of local sandstorm weather in the future.
基金This work is supported by the National Natural Science Foundation of China(Grant No.61971057)MoE-CMCC Artifical Intelligence Project(No.MCM20190701).
文摘Time series data is a kind of data accumulated over time,which can describe the change of phenomenon.This kind of data reflects the degree of change of a certain thing or phenomenon.The existing technologies such as LSTM and ARIMA are better than convolutional neural network in time series prediction,but they are not enough to mine the periodicity of data.In this article,we perform periodic analysis on two types of time series data,select time metrics with high periodic characteristics,and propose a multi-scale prediction model based on the attention mechanism for the periodic trend of the data.A loss calculation method for traffic time series characteristics is proposed as well.Multiple experiments have been conducted on actual data sets.The experiments show that the method proposed in this paper has better performance than commonly used traffic prediction methods(ARIMA,LSTM,etc.)and 3%-5%increase on MAPE.
文摘Developing reliable weather prediction systems is still a challenging task due to the complexity of the Earth System and the chaotic behavior of its compo-nents.Small errors introduced by observations,their assimilation and the forecast model configuration escalate chaotically,leading to a significant loss in forecast skill with time.Traditionally,rainfall forecasts have been generated at grid-based spatial resolutions,providing valuable information on regional precipitation patterns.However,Weather varies markedly within a grid box and forecasts for specific sites have occasionally failed inevitably.The grid-based forecasts may not always meet the needs of decision-makers at specific points of interest.The major challenge is dealing with variations in sub-grid variability,that is to say,the variation seen amongst rainfall point values within a given model grid box,more especially in convective situations.While ensemble forecasts have shown promise in capturing the uncertainty inherent in average rainfall predictions of much larger grid boxes,their utility at point locations has not been extensively explored.Most evaluation studies focus on grid-based verification metrics,which may not accurately reflect forecast per-formance at individual points of interest.EcPoint,a post-processing approach developed at the European Centre for Medium Range Forecasts(ECMWF),is tailored to forecast rainfall at point locations.In this study,we evaluate the performance of the EcPoint post-processing method over the south China re-gion.The analysis focuses on the reliability,accuracy and discrimination skill of this post-processing method over the three provinces in south China(An-hui,Zhejiang and Jiangsu).We examine performance versus lead time,sea-sons,and altitude.Through verifications,the study highlighted the added value of the post-processing method over Raw ensemble forecasts.One year of verification demonstrates that,between the Raw ensemble and post-pro-cessed EcPoint forecasts,EcPoint is the more reliable and skillful system,add-ing significant value to most rainfall events occurring during the day and the seasonal associated events,as well as the topography-associated rainfall events.To complement the one-year verification analysis,a case study was conducted on an extremely heavy rainfall event observed on June 2,2022 at 12 UTC.The analysis demonstrated EcPoint’s ability to provide more localized and refined forecasts whereas Raw didn’t provide any possibility of rainfall,particularly at short lead times.At longer lead times,EcPoint ensembles maintained rela-tively low probabilities,but offered improved performance in capturing rain-fall variability,while Raw ensemble exhibited broader but less precise rainfall predictions with a tendency of over warning of some areas.Future work can extend the evaluation to more diverse climatic and topographic regions of China to enhance the general applicability of the method.Although based solely on the global ECMWF-IFS model,EcPoint performs well over the small domain of south China(three provinces).Besides verifying EcPoint,the study confirms that the post-processing method can significantly improve the fore-cast performance.
