In China, decision-making meteorological services provide meteorological information for the production organization, disaster prevention and mitigation by the CPC committee, government, military leaders and decision-...In China, decision-making meteorological services provide meteorological information for the production organization, disaster prevention and mitigation by the CPC committee, government, military leaders and decision-making departments at all levels, as well as scientific decision-making in the areas of rational development and utilization of climate resources and environmental protection. In order to understand the user’s satisfaction with the decision-making meteorological service, the Shaanxi Provincial Meteorological Bureau conducted a statistical survey, and the results showed that: 1) In 2017, the satisfaction level of provincial-level decision-making meteorological services in Shaanxi, China was 92.77%. Among them, the satisfaction index of “Ministry Department Service Personnel Professional Image and Service Awareness” was 94.12%, and the “Weather Forecast Warning Accuracy” satisfaction index was 90.18%. 2) Decision-making users have become an important channel for obtaining meteorological information through meteorological websites, televisions, mobile phone text messages, APP mobile applications, broadcasting, and Meteorological Information Express. Rainstorms, floods, high temperature heat, cold winds, hail, precipitation, and lightning are still the main concerns of decision-makers. 3) The focus on haze and UV intensity is 15% and 8% higher than that of 2016. The next 1 - 3 days weather forecast and 0 - 6 hours short-term forecast are still the most valuable forecast products for decision-making users. Compared with 2016, the next 1 - 3 days weather forecast, future 0 - 6 hours forecast, traffic meteorology, precipitation probability, and air quality forecast increase by 1% to 14% in the year of 2017.展开更多
Deep learning-based methods have become alternatives to traditional numerical weather prediction systems,offering faster computation and the ability to utilize large historical datasets.However,the application of deep...Deep learning-based methods have become alternatives to traditional numerical weather prediction systems,offering faster computation and the ability to utilize large historical datasets.However,the application of deep learning to medium-range regional weather forecasting with limited data remains a significant challenge.In this work,three key solutions are proposed:(1)motivated by the need to improve model performance in data-scarce regional forecasting scenarios,the authors innovatively apply semantic segmentation models,to better capture spatiotemporal features and improve prediction accuracy;(2)recognizing the challenge of overfitting and the inability of traditional noise-based data augmentation methods to effectively enhance model robustness,a novel learnable Gaussian noise mechanism is introduced that allows the model to adaptively optimize perturbations for different locations,ensuring more effective learning;and(3)to address the issue of error accumulation in autoregressive prediction,as well as the challenge of learning difficulty and the lack of intermediate data utilization in one-shot prediction,the authors propose a cascade prediction approach that effectively resolves these problems while significantly improving model forecasting performance.The method achieves a competitive result in The East China Regional AI Medium Range Weather Forecasting Competition.Ablation experiments further validate the effectiveness of each component,highlighting their contributions to enhancing prediction performance.展开更多
A previous multiple-AGCM study suggested that Indian Ocean Warming （IOW） tends to warm and weaken the southern polar vortex.Such an impact is robust because of a qualitative consistency among the five AGCMs used.How...A previous multiple-AGCM study suggested that Indian Ocean Warming （IOW） tends to warm and weaken the southern polar vortex.Such an impact is robust because of a qualitative consistency among the five AGCMs used.However,a significant difference exists in the modeled strengths,particularly in the stratosphere,with those in three of the AGCMs （CCM3,CAM3,and GFS） being four to five times as strong as those in the two other models （GFDL AM2,ECHAM5）.As to which case reflects reality is an important issue not only for quantifying the role of tropical ocean warming in the recent modest recovery of the ozone hole over the Antarctic,but also for projecting its future trend.This issue is addressed in the present study through comparing the models＇ climatological mean states and intrinsic variability,particularly those influencing tropospheric signals to propagate upward and reach the stratosphere.The results suggest that differences in intrinsic variability of model atmospheres provide implications for the difference.Based on a comparison with observations,it is speculated that the impact in the real world may be closer to the modest one simulated by GFDL AM2 and ECHAM5,rather than the strong one simulated by the three other models （CCM3,CAM3 and GFS）.In particular,IOW during the past 50 years may have dynamically induced a 1.0℃ warming in the polar lower stratosphere （～100 hPa）,which canceled a fraction of radiative cooling due to ozone depletion.展开更多
UV radiation plays an important role in climate change and photochemical reactions,and in Ecosystem Research.In this study,the authors presented study results of China’s National Basic Research Program Study on the c...UV radiation plays an important role in climate change and photochemical reactions,and in Ecosystem Research.In this study,the authors presented study results of China’s National Basic Research Program Study on the climatic characteristics and reconstruction method of UV radiation in China.The spatiotemporal variation of UV radiation in China has been discussed,and then an effcient modeling method has been established to obtain history UV radiation data to analyse the variation trends of UV radiation in China.Finally,the influence of aerosol,cloud,ozone,and water vapor on UV radiation has been discussed.展开更多
文摘In China, decision-making meteorological services provide meteorological information for the production organization, disaster prevention and mitigation by the CPC committee, government, military leaders and decision-making departments at all levels, as well as scientific decision-making in the areas of rational development and utilization of climate resources and environmental protection. In order to understand the user’s satisfaction with the decision-making meteorological service, the Shaanxi Provincial Meteorological Bureau conducted a statistical survey, and the results showed that: 1) In 2017, the satisfaction level of provincial-level decision-making meteorological services in Shaanxi, China was 92.77%. Among them, the satisfaction index of “Ministry Department Service Personnel Professional Image and Service Awareness” was 94.12%, and the “Weather Forecast Warning Accuracy” satisfaction index was 90.18%. 2) Decision-making users have become an important channel for obtaining meteorological information through meteorological websites, televisions, mobile phone text messages, APP mobile applications, broadcasting, and Meteorological Information Express. Rainstorms, floods, high temperature heat, cold winds, hail, precipitation, and lightning are still the main concerns of decision-makers. 3) The focus on haze and UV intensity is 15% and 8% higher than that of 2016. The next 1 - 3 days weather forecast and 0 - 6 hours short-term forecast are still the most valuable forecast products for decision-making users. Compared with 2016, the next 1 - 3 days weather forecast, future 0 - 6 hours forecast, traffic meteorology, precipitation probability, and air quality forecast increase by 1% to 14% in the year of 2017.
基金supported by the National Natural Science Foundation of China[grant number 62376217]the Young Elite Scientists Sponsorship Program by CAST[grant number 2023QNRC001]the Joint Research Project for Meteorological Capacity Improvement[grant number 24NLTSZ003]。
文摘Deep learning-based methods have become alternatives to traditional numerical weather prediction systems,offering faster computation and the ability to utilize large historical datasets.However,the application of deep learning to medium-range regional weather forecasting with limited data remains a significant challenge.In this work,three key solutions are proposed:(1)motivated by the need to improve model performance in data-scarce regional forecasting scenarios,the authors innovatively apply semantic segmentation models,to better capture spatiotemporal features and improve prediction accuracy;(2)recognizing the challenge of overfitting and the inability of traditional noise-based data augmentation methods to effectively enhance model robustness,a novel learnable Gaussian noise mechanism is introduced that allows the model to adaptively optimize perturbations for different locations,ensuring more effective learning;and(3)to address the issue of error accumulation in autoregressive prediction,as well as the challenge of learning difficulty and the lack of intermediate data utilization in one-shot prediction,the authors propose a cascade prediction approach that effectively resolves these problems while significantly improving model forecasting performance.The method achieves a competitive result in The East China Regional AI Medium Range Weather Forecasting Competition.Ablation experiments further validate the effectiveness of each component,highlighting their contributions to enhancing prediction performance.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA05090406)the National Key Basic Research Program of China (Grant Nos. 2012CB417403 and 2010CB428602)
文摘A previous multiple-AGCM study suggested that Indian Ocean Warming （IOW） tends to warm and weaken the southern polar vortex.Such an impact is robust because of a qualitative consistency among the five AGCMs used.However,a significant difference exists in the modeled strengths,particularly in the stratosphere,with those in three of the AGCMs （CCM3,CAM3,and GFS） being four to five times as strong as those in the two other models （GFDL AM2,ECHAM5）.As to which case reflects reality is an important issue not only for quantifying the role of tropical ocean warming in the recent modest recovery of the ozone hole over the Antarctic,but also for projecting its future trend.This issue is addressed in the present study through comparing the models＇ climatological mean states and intrinsic variability,particularly those influencing tropospheric signals to propagate upward and reach the stratosphere.The results suggest that differences in intrinsic variability of model atmospheres provide implications for the difference.Based on a comparison with observations,it is speculated that the impact in the real world may be closer to the modest one simulated by GFDL AM2 and ECHAM5,rather than the strong one simulated by the three other models （CCM3,CAM3 and GFS）.In particular,IOW during the past 50 years may have dynamically induced a 1.0℃ warming in the polar lower stratosphere （～100 hPa）,which canceled a fraction of radiative cooling due to ozone depletion.
基金supported by the National Basic Research Program of China [grant number 2017YFC0210003]the National Natural Science Foundation of China [grant number 41275165]
文摘UV radiation plays an important role in climate change and photochemical reactions,and in Ecosystem Research.In this study,the authors presented study results of China’s National Basic Research Program Study on the climatic characteristics and reconstruction method of UV radiation in China.The spatiotemporal variation of UV radiation in China has been discussed,and then an effcient modeling method has been established to obtain history UV radiation data to analyse the variation trends of UV radiation in China.Finally,the influence of aerosol,cloud,ozone,and water vapor on UV radiation has been discussed.
