Post-processing correction is an effective way to improve the model forecasting result. Especially, the machine learning methods have played increasingly important roles in recent years. Taking the meteorological obse...Post-processing correction is an effective way to improve the model forecasting result. Especially, the machine learning methods have played increasingly important roles in recent years. Taking the meteorological observational data in a period of two years as the reference, the maximum and minimum temperature predictions of Shenyang station from the European Center for Medium-Range Weather Forecasts (ECMWF) and national intelligent grid forecasts are objectively corrected by using wavelet analysis, sliding training and other technologies. The evaluation results show that the sliding training time window of the maximum temperature is smaller than that of the minimum temperature, and their difference is the largest in August, with a difference of 2.6 days. The objective correction product of maximum temperature shows a good performance in spring, while that of minimum temperature performs well throughout the whole year, with an accuracy improvement of 97% to 186%. The correction effect in the central plains is better than in the regions with complex terrain. As for the national intelligent grid forecasts, the objective correction products have shown positive skills in predicting the maximum temperatures in spring (the skill-score reaches 0.59) and in predicting the minimum temperature at most times of the year (the skill-score reaches 0.68).展开更多
[Objective] The aim was to study the refined forecast method of daily highest temperature in Wugang City from July to September. IM[ethod] By dint of ECMWF mode product and T231 in 2009 and 2010 and daily maximum temp...[Objective] The aim was to study the refined forecast method of daily highest temperature in Wugang City from July to September. IM[ethod] By dint of ECMWF mode product and T231 in 2009 and 2010 and daily maximum temperature in the station in corresponding period, multi-factors similar forecast method to select forecast sample, multivariate regression multi-mode integration MOS method, after dynamic corrected mode error and regression error, dynamic forecast equation was concluded to formulate the daily maximum temperature forecast in 24 -120 h in Wugang City from July to September. [ Result] Through selection, error correction, the daily maximum temperature equation in Wugang City from July to September was concluded. Through multiple random sampling, F test was made to pass test with significant test of 0.1. [ Conclusionl The method integrated domestic and foreign forecast mode, made full use of useful information of many modes, absorbed each others advantages, con- sidered local regional environment, lessen mode and regression error, and improved forecast accuracy.展开更多
Change related to climate in Macao was studied on the basis of daily temperature observations over the period 1901-2007. The result shows that annual mean surface air temperature in Macao as a whole rose with a warmin...Change related to climate in Macao was studied on the basis of daily temperature observations over the period 1901-2007. The result shows that annual mean surface air temperature in Macao as a whole rose with a warming rate of about 0.066℃ per 10 years in the recent 107 years. The most evident warming occurred in spring and winter. The interdecadal variations of the seasonal mean temperature in summer and winter appeared as a series of waves with a time scale of about 30 years and 60 years, respectively. The annual mean minimum temperature increased about twice as fast as the annual mean maximum temperature, resulting in a broad decline in the annual mean diurnal range. The interdecadal variations of annual mean maximum temperature are obviously different from those of annual mean minimum temperature. It appears that the increase in the annual mean maximum temperature in the recent 20 years may be part of slow climate fluctuations with a periodicity of about 60 years, whereas that in the annual mean minimum temperature appears to be the continuation of a long-term warming trend.展开更多
The contributions of urban surface expansion to regional warming over subregions of Shanghai and Shanghai as a whole using different methods to calculate the daily mean surface temperature(SAT),including the averages ...The contributions of urban surface expansion to regional warming over subregions of Shanghai and Shanghai as a whole using different methods to calculate the daily mean surface temperature(SAT),including the averages of four daily time-records(0000,0600,1200,and 1800 UTC;T4),eight daily time-records(0000,0300,0600,0900,1200,1500,1800,and 2100 UTC;T8),and the averages of the SAT maximum(Tmax)and minimum(Tmin),Txn,were compared based on simulated results using nested numerical intergrations with the Weather Research and Forecasting regional climate model,where only the satellite-retrieved urban surface distributions differed between two numerical experiments.The contributions from urban-related warming expressed similar intensities when using T8 and Txn,while the smallest values occurred when using T4 over different subregions of Shanghai(with the exception of areas that were defined as urban for both time periods(U2U))and Shanghai as a whole.Similar values for the changing trends could be detected over different subregions when no urban surface expansion(EX1)was detected for both T4 and Txn.The corresponding values increased under urban surface expansion(EX2)and varied over different subregions,revealing much stronger intensities over urban-surface expansion areas;the weakest intensities occurred over U2U areas.The increasing trends for EX2 and relative contributions when using T4 were smaller than those when using Txn,with the exception of those over U2U areas,which could be explained by the changing trends in Tmax and Tmin due to urban surface expansion,especially during intense urban expansion periods.展开更多
文摘Post-processing correction is an effective way to improve the model forecasting result. Especially, the machine learning methods have played increasingly important roles in recent years. Taking the meteorological observational data in a period of two years as the reference, the maximum and minimum temperature predictions of Shenyang station from the European Center for Medium-Range Weather Forecasts (ECMWF) and national intelligent grid forecasts are objectively corrected by using wavelet analysis, sliding training and other technologies. The evaluation results show that the sliding training time window of the maximum temperature is smaller than that of the minimum temperature, and their difference is the largest in August, with a difference of 2.6 days. The objective correction product of maximum temperature shows a good performance in spring, while that of minimum temperature performs well throughout the whole year, with an accuracy improvement of 97% to 186%. The correction effect in the central plains is better than in the regions with complex terrain. As for the national intelligent grid forecasts, the objective correction products have shown positive skills in predicting the maximum temperatures in spring (the skill-score reaches 0.59) and in predicting the minimum temperature at most times of the year (the skill-score reaches 0.68).
文摘[Objective] The aim was to study the refined forecast method of daily highest temperature in Wugang City from July to September. IM[ethod] By dint of ECMWF mode product and T231 in 2009 and 2010 and daily maximum temperature in the station in corresponding period, multi-factors similar forecast method to select forecast sample, multivariate regression multi-mode integration MOS method, after dynamic corrected mode error and regression error, dynamic forecast equation was concluded to formulate the daily maximum temperature forecast in 24 -120 h in Wugang City from July to September. [ Result] Through selection, error correction, the daily maximum temperature equation in Wugang City from July to September was concluded. Through multiple random sampling, F test was made to pass test with significant test of 0.1. [ Conclusionl The method integrated domestic and foreign forecast mode, made full use of useful information of many modes, absorbed each others advantages, con- sidered local regional environment, lessen mode and regression error, and improved forecast accuracy.
文摘Change related to climate in Macao was studied on the basis of daily temperature observations over the period 1901-2007. The result shows that annual mean surface air temperature in Macao as a whole rose with a warming rate of about 0.066℃ per 10 years in the recent 107 years. The most evident warming occurred in spring and winter. The interdecadal variations of the seasonal mean temperature in summer and winter appeared as a series of waves with a time scale of about 30 years and 60 years, respectively. The annual mean minimum temperature increased about twice as fast as the annual mean maximum temperature, resulting in a broad decline in the annual mean diurnal range. The interdecadal variations of annual mean maximum temperature are obviously different from those of annual mean minimum temperature. It appears that the increase in the annual mean maximum temperature in the recent 20 years may be part of slow climate fluctuations with a periodicity of about 60 years, whereas that in the annual mean minimum temperature appears to be the continuation of a long-term warming trend.
基金This work was supported by the National Natural Science Foundation of China [grant numbers 41775087 and41675149]the National Key R&D Program of China [grant number 2016YFA0600403]+2 种基金the Chinese Academy of Sciences Strategic Priority Program [grant number XDA05090206]the National Key Basic Research Program on Global Change [grant number 2011CB952003]the Jiangsu Collaborative Innovation Center for Climatic Change
文摘The contributions of urban surface expansion to regional warming over subregions of Shanghai and Shanghai as a whole using different methods to calculate the daily mean surface temperature(SAT),including the averages of four daily time-records(0000,0600,1200,and 1800 UTC;T4),eight daily time-records(0000,0300,0600,0900,1200,1500,1800,and 2100 UTC;T8),and the averages of the SAT maximum(Tmax)and minimum(Tmin),Txn,were compared based on simulated results using nested numerical intergrations with the Weather Research and Forecasting regional climate model,where only the satellite-retrieved urban surface distributions differed between two numerical experiments.The contributions from urban-related warming expressed similar intensities when using T8 and Txn,while the smallest values occurred when using T4 over different subregions of Shanghai(with the exception of areas that were defined as urban for both time periods(U2U))and Shanghai as a whole.Similar values for the changing trends could be detected over different subregions when no urban surface expansion(EX1)was detected for both T4 and Txn.The corresponding values increased under urban surface expansion(EX2)and varied over different subregions,revealing much stronger intensities over urban-surface expansion areas;the weakest intensities occurred over U2U areas.The increasing trends for EX2 and relative contributions when using T4 were smaller than those when using Txn,with the exception of those over U2U areas,which could be explained by the changing trends in Tmax and Tmin due to urban surface expansion,especially during intense urban expansion periods.
