In order to obtain more accurate precipitation data and better simulate the precipitation on the Tibetan Plateau,the simulation capability of 14 Coupled Model Intercomparison Project Phase 6(CMIP6)models of historical...In order to obtain more accurate precipitation data and better simulate the precipitation on the Tibetan Plateau,the simulation capability of 14 Coupled Model Intercomparison Project Phase 6(CMIP6)models of historical precipitation(1982-2014)on the Qinghai-Tibetan Plateau was evaluated in this study.Results indicate that all models exhibit an overestimation of precipitation through the analysis of the Taylor index,temporal and spatial statistical parameters.To correct the overestimation,a fusion correction method combining the Backpropagation Neural Network Correction(BP)and Quantum Mapping(QM)correction,named BQ method,was proposed.With this method,the historical precipitation of each model was corrected in space and time,respectively.The correction results were then analyzed in time,space,and analysis of variance(ANOVA)with those corrected by the BP and QM methods,respectively.Finally,the fusion correction method results for each model were compared with the Climatic Research Unit(CRU)data for significance analysis to obtain the trends of precipitation increase and decrease for each model.The results show that the IPSL-CM6A-LR model is relatively good in simulating historical precipitation on the Qinghai-Tibetan Plateau(R=0.7,RSME=0.15)among the uncorrected data.In terms of time,the total precipitation corrected by the fusion method has the same interannual trend and the closest precipitation values to the CRU data;In terms of space,the annual average precipitation corrected by the fusion method has the smallest difference with the CRU data,and the total historical annual average precipitation is not significantly different from the CRU data,which is better than BP and QM.Therefore,the correction effect of the fusion method on the historical precipitation of each model is better than that of the QM and BP methods.The precipitation in the central and northeastern parts of the plateau shows a significant increasing trend.The correlation coefficients between monthly precipitation and site-detected precipitation for all models after BQ correction exceed 0.8.展开更多
The visible-light imaging system used in military equipment is often subjected to severe weather conditions, such as fog, haze, and smoke, under complex lighting conditions at night that significantly degrade the acqu...The visible-light imaging system used in military equipment is often subjected to severe weather conditions, such as fog, haze, and smoke, under complex lighting conditions at night that significantly degrade the acquired images. Currently available image defogging methods are mostly suitable for environments with natural light in the daytime, but the clarity of images captured under complex lighting conditions and spatial changes in the presence of fog at night is not satisfactory. This study proposes an algorithm to remove night fog from single images based on an analysis of the statistical characteristics of images in scenes involving night fog. Color channel transfer is designed to compensate for the high attenuation channel of foggy images acquired at night. The distribution of transmittance is estimated by the deep convolutional network DehazeNet, and the spatial variation of atmospheric light is estimated in a point-by-point manner according to the maximum reflection prior to recover the clear image. The results of experiments show that the proposed method can compensate for the high attenuation channel of foggy images at night, remove the effect of glow from a multi-color and non-uniform ambient source of light, and improve the adaptability and visual effect of the removal of night fog from images compared with the conventional method.展开更多
To meet the demands for seamless medium-and short-range weather forecasting during the Beijing Winter Olympics(2022),the Winter Olympics research team at the Earth System Modeling and Prediction Centre(CEMC)of the Chi...To meet the demands for seamless medium-and short-range weather forecasting during the Beijing Winter Olympics(2022),the Winter Olympics research team at the Earth System Modeling and Prediction Centre(CEMC)of the China Meteorological Administration(CMA)developed an integrated global and regional numerical weather prediction(NWP)model system.In support of the Winter Olympics,the system focuses on key short-and medium-range deterministic and ensemble forecast technologies for complex terrain.By introducing a three-dimensional reference atmosphere and a predictor-corrector iterative algorithm into the regional model's dynamical framework,the team enhanced the spatial accuracy and temporal integration stability of the high-resolution regional model.The team also developed data assimilation techniques for dense surface automatic weather stations and high spatiotemporal resolution imagery from China's Fengyun satellites,improving the monitoring and application capability of unconventional observations for the Winter Olympics.Furthermore,they established a3 km high-resolution regional ensemble prediction system by advancing multiscale hybrid initial perturbation techniques and stochastic perturbation methods for physical processes with spatiotemporal correlations,suitable for complex terrain.To enhance deterministic and probabilistic forecasts at grid and station scales over complex terrain,the team studied bias correction techniques across different resolutions and developed methods for rapidly and effectively extracting key forecast information from large volumes of model output.In particular,machine learning-based approaches were employed to process and fuse massive forecast products containing probabilistic information.These efforts led to the development of a seamless Winter Olympics meteorological forecasting system covering a lead time of 0–15 days and the entire competition zone,featuring forecast updates every hour within 24 h,every 3 h within 24–72 h,and every 12 h within 72–360 h.These products were applied comprehensively in real-time operations during the winter training,test events,and the Olympic and Paralympic Games,representing the highest level of China's independently developed NWP systems in meteorological support for major events.The integrated technological achievements have since been incorporated into the national operational NWP system,and they continue to play a vital role in daily forecasting services,disaster prevention and mitigation,and support for major events.展开更多
文摘In order to obtain more accurate precipitation data and better simulate the precipitation on the Tibetan Plateau,the simulation capability of 14 Coupled Model Intercomparison Project Phase 6(CMIP6)models of historical precipitation(1982-2014)on the Qinghai-Tibetan Plateau was evaluated in this study.Results indicate that all models exhibit an overestimation of precipitation through the analysis of the Taylor index,temporal and spatial statistical parameters.To correct the overestimation,a fusion correction method combining the Backpropagation Neural Network Correction(BP)and Quantum Mapping(QM)correction,named BQ method,was proposed.With this method,the historical precipitation of each model was corrected in space and time,respectively.The correction results were then analyzed in time,space,and analysis of variance(ANOVA)with those corrected by the BP and QM methods,respectively.Finally,the fusion correction method results for each model were compared with the Climatic Research Unit(CRU)data for significance analysis to obtain the trends of precipitation increase and decrease for each model.The results show that the IPSL-CM6A-LR model is relatively good in simulating historical precipitation on the Qinghai-Tibetan Plateau(R=0.7,RSME=0.15)among the uncorrected data.In terms of time,the total precipitation corrected by the fusion method has the same interannual trend and the closest precipitation values to the CRU data;In terms of space,the annual average precipitation corrected by the fusion method has the smallest difference with the CRU data,and the total historical annual average precipitation is not significantly different from the CRU data,which is better than BP and QM.Therefore,the correction effect of the fusion method on the historical precipitation of each model is better than that of the QM and BP methods.The precipitation in the central and northeastern parts of the plateau shows a significant increasing trend.The correlation coefficients between monthly precipitation and site-detected precipitation for all models after BQ correction exceed 0.8.
基金supported by a grant from the Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology (Grant No. GZZKFJJ2020004)the National Natural Science Foundation of China (Grant Nos. 61875013 and 61827814)the Natural Science Foundation of Beijing Municipality (Grant No. Z190018)。
文摘The visible-light imaging system used in military equipment is often subjected to severe weather conditions, such as fog, haze, and smoke, under complex lighting conditions at night that significantly degrade the acquired images. Currently available image defogging methods are mostly suitable for environments with natural light in the daytime, but the clarity of images captured under complex lighting conditions and spatial changes in the presence of fog at night is not satisfactory. This study proposes an algorithm to remove night fog from single images based on an analysis of the statistical characteristics of images in scenes involving night fog. Color channel transfer is designed to compensate for the high attenuation channel of foggy images acquired at night. The distribution of transmittance is estimated by the deep convolutional network DehazeNet, and the spatial variation of atmospheric light is estimated in a point-by-point manner according to the maximum reflection prior to recover the clear image. The results of experiments show that the proposed method can compensate for the high attenuation channel of foggy images at night, remove the effect of glow from a multi-color and non-uniform ambient source of light, and improve the adaptability and visual effect of the removal of night fog from images compared with the conventional method.
基金supported by the National Natural Science Foundation of China(NSFC)Major Program(Grant No.42090032)NSFC Projects(Grant Nos.42475169,42175012)the Science and Technology Winter Olympics Special Subject(Grant No.2018YFF0300103)。
文摘To meet the demands for seamless medium-and short-range weather forecasting during the Beijing Winter Olympics(2022),the Winter Olympics research team at the Earth System Modeling and Prediction Centre(CEMC)of the China Meteorological Administration(CMA)developed an integrated global and regional numerical weather prediction(NWP)model system.In support of the Winter Olympics,the system focuses on key short-and medium-range deterministic and ensemble forecast technologies for complex terrain.By introducing a three-dimensional reference atmosphere and a predictor-corrector iterative algorithm into the regional model's dynamical framework,the team enhanced the spatial accuracy and temporal integration stability of the high-resolution regional model.The team also developed data assimilation techniques for dense surface automatic weather stations and high spatiotemporal resolution imagery from China's Fengyun satellites,improving the monitoring and application capability of unconventional observations for the Winter Olympics.Furthermore,they established a3 km high-resolution regional ensemble prediction system by advancing multiscale hybrid initial perturbation techniques and stochastic perturbation methods for physical processes with spatiotemporal correlations,suitable for complex terrain.To enhance deterministic and probabilistic forecasts at grid and station scales over complex terrain,the team studied bias correction techniques across different resolutions and developed methods for rapidly and effectively extracting key forecast information from large volumes of model output.In particular,machine learning-based approaches were employed to process and fuse massive forecast products containing probabilistic information.These efforts led to the development of a seamless Winter Olympics meteorological forecasting system covering a lead time of 0–15 days and the entire competition zone,featuring forecast updates every hour within 24 h,every 3 h within 24–72 h,and every 12 h within 72–360 h.These products were applied comprehensively in real-time operations during the winter training,test events,and the Olympic and Paralympic Games,representing the highest level of China's independently developed NWP systems in meteorological support for major events.The integrated technological achievements have since been incorporated into the national operational NWP system,and they continue to play a vital role in daily forecasting services,disaster prevention and mitigation,and support for major events.
