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
Compared with physical models,WRF-Solar,as an excellent numerical forecasting model,includes abundant novel cloud physical and dynamical processes,which enablesenable the high-frequency output of radiation components ...Compared with physical models,WRF-Solar,as an excellent numerical forecasting model,includes abundant novel cloud physical and dynamical processes,which enablesenable the high-frequency output of radiation components which are urgently needed by the solar energy industry.However,the popularisation of WRF-Solar in a wide range of applications,such as the estimation of diffuse radiation,suffers from unpredictable influences of cloud and aerosol optical property parameters.This study assessed the accuracy of the improved numerical weather prediction(WRF-Solar)model in simulating global and diffuse radiation.Aerosol optical properties at 550 nm,which were provided by a moderate resolution imaging spectroradiometer,were used as input to analyse the differences in accuracies obtained by the model with/without aerosol input.The sensitivity of WRF-Solar to aerosol and cloud optical properties and solar zenith angle(SZA)was analysed.The results show the superiority of WRF-Solar to WRF-Dudhia in terms of their root mean square error(RMSE)and mean absolute error(MAE).The coefficients of determination between WRF-Solar and WRF-Dudhia revealed no statistically significant difference,with values greater than 0.9 for the parent and nested domains.In addition,the relative RMSE(RRMSE%)reached 46.60%.The experiment on WRF-Solar and WRF-Dudhia revealed a negative bias for global radiation,but WRF-Solar attained a slightly lower RMSE and higher correlation coefficient than WRF-Dudhia.The WRF-Solar-simulated results on diffuse radiation under clear sky conditions were slightly poorer,with RMSE,RRMSE,mean percentage error and MAE of 181.93 W m^(−2),170.52%,93.04%and 138 W m^(−2),respectively.Based on Himawari-8 cloud data,statistical results on cloud optical thickness(COT)for cloudy days revealed that WRF-Solar overestimated diffuse radiation at COTs greater than 20.Moreover,when the aerosol optical depth was greater than or equal to 0.8,WRF-Solar also overestimated the diffuse radiation,with a mean difference of 58.57 W m^(−2).The errors of WRF-Solar simulations in global and diffuse radiation exhibited a significant dependence on the SZA.The dispersion degree of deviation increased gradually with the decrease in the SZA.Thus,WRF-Solar serves as an improved numerical tool that can provide high temporal and high-spatial-resolution solar radiation data for the prediction of photovoltaic power.Studies should explore the improvement of cumulus parameterisation schemes to enhance the accuracy of solar radiation component estimation and prediction under cloudy conditions.展开更多
The Advanced Geosynchronous Radiation Imager(AGRI)is a mission-critical instrument for the Fengyun series of satellites.AGRI acquires full-disk images every 15 min and views East Asia every 5 min through 14 spectral b...The Advanced Geosynchronous Radiation Imager(AGRI)is a mission-critical instrument for the Fengyun series of satellites.AGRI acquires full-disk images every 15 min and views East Asia every 5 min through 14 spectral bands,enabling the detection of highly variable aerosol optical depth(AOD).Quantitative retrieval of AOD has hitherto been challenging,especially over land.In this study,an AOD retrieval algorithm is proposed that combines deep learning and transfer learning.The algorithm uses core concepts from both the Dark Target(DT)and Deep Blue(DB)algorithms to select features for the machinelearning(ML)algorithm,allowing for AOD retrieval at 550 nm over both dark and bright surfaces.The algorithm consists of two steps:①A baseline deep neural network(DNN)with skip connections is developed using 10 min Advanced Himawari Imager(AHI)AODs as the target variable,and②sunphotometer AODs from 89 ground-based stations are used to fine-tune the DNN parameters.Out-of-station validation shows that the retrieved AOD attains high accuracy,characterized by a coefficient of determination(R2)of 0.70,a mean bias error(MBE)of 0.03,and a percentage of data within the expected error(EE)of 70.7%.A sensitivity study reveals that the top-of-atmosphere reflectance at 650 and 470 nm,as well as the surface reflectance at 650 nm,are the two largest sources of uncertainty impacting the retrieval.In a case study of monitoring an extreme aerosol event,the AGRI AOD is found to be able to capture the detailed temporal evolution of the event.This work demonstrates the superiority of the transfer-learning technique in satellite AOD retrievals and the applicability of the retrieved AGRI AOD in monitoring extreme pollution events.展开更多
本文利用2005~2020年北京地区观测得到的辐射资料,揭示近十多年来北京地区紫外辐射的变化规律,同时对影响紫外辐射长期变化的主要因子进行了分析。结果表明,紫外辐射呈现出明显的日、季节变化特征。日变化呈现出单峰的变化规律,在正午...本文利用2005~2020年北京地区观测得到的辐射资料,揭示近十多年来北京地区紫外辐射的变化规律,同时对影响紫外辐射长期变化的主要因子进行了分析。结果表明,紫外辐射呈现出明显的日、季节变化特征。日变化呈现出单峰的变化规律,在正午时出现一天中的极大值,而早晚则是低值时段,极大值和极小值分别出现在中午12时(北京时,下同;16.26 W m^(−2))和上午08时(5.64 W m^(−2))。紫外辐射从春季开始逐渐增强,到夏季出现一年中的极大值,随后开始下降,直到冬季出现一年中的极小值,月均极大值和极小值分别出现在6月(12.17 W m^(−2))和12月(5.4 W m^(−2))。紫外辐射年均值为9.74 W m^(−2)。紫外辐射与晴空指数呈现正相关,与气溶胶光学厚度和大气细颗粒物PM_(2.5)呈现负相关。展开更多
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.
基金supported by the National Natural Science Foundation of China(41975044,41925007,42371354,42375129,and 41801021)Fundamental Research Funds for National University,China University of Geosciences,Wuhan(CUGDCJJ202201).
文摘Compared with physical models,WRF-Solar,as an excellent numerical forecasting model,includes abundant novel cloud physical and dynamical processes,which enablesenable the high-frequency output of radiation components which are urgently needed by the solar energy industry.However,the popularisation of WRF-Solar in a wide range of applications,such as the estimation of diffuse radiation,suffers from unpredictable influences of cloud and aerosol optical property parameters.This study assessed the accuracy of the improved numerical weather prediction(WRF-Solar)model in simulating global and diffuse radiation.Aerosol optical properties at 550 nm,which were provided by a moderate resolution imaging spectroradiometer,were used as input to analyse the differences in accuracies obtained by the model with/without aerosol input.The sensitivity of WRF-Solar to aerosol and cloud optical properties and solar zenith angle(SZA)was analysed.The results show the superiority of WRF-Solar to WRF-Dudhia in terms of their root mean square error(RMSE)and mean absolute error(MAE).The coefficients of determination between WRF-Solar and WRF-Dudhia revealed no statistically significant difference,with values greater than 0.9 for the parent and nested domains.In addition,the relative RMSE(RRMSE%)reached 46.60%.The experiment on WRF-Solar and WRF-Dudhia revealed a negative bias for global radiation,but WRF-Solar attained a slightly lower RMSE and higher correlation coefficient than WRF-Dudhia.The WRF-Solar-simulated results on diffuse radiation under clear sky conditions were slightly poorer,with RMSE,RRMSE,mean percentage error and MAE of 181.93 W m^(−2),170.52%,93.04%and 138 W m^(−2),respectively.Based on Himawari-8 cloud data,statistical results on cloud optical thickness(COT)for cloudy days revealed that WRF-Solar overestimated diffuse radiation at COTs greater than 20.Moreover,when the aerosol optical depth was greater than or equal to 0.8,WRF-Solar also overestimated the diffuse radiation,with a mean difference of 58.57 W m^(−2).The errors of WRF-Solar simulations in global and diffuse radiation exhibited a significant dependence on the SZA.The dispersion degree of deviation increased gradually with the decrease in the SZA.Thus,WRF-Solar serves as an improved numerical tool that can provide high temporal and high-spatial-resolution solar radiation data for the prediction of photovoltaic power.Studies should explore the improvement of cumulus parameterisation schemes to enhance the accuracy of solar radiation component estimation and prediction under cloudy conditions.
基金supported by the National Natural Science of Foundation of China(41825011,42030608,42105128,and 42075079)the Opening Foundation of Key Laboratory of Atmospheric Sounding,the CMA and the CMA Research Center on Meteorological Observation Engineering Technology(U2021Z03).
文摘The Advanced Geosynchronous Radiation Imager(AGRI)is a mission-critical instrument for the Fengyun series of satellites.AGRI acquires full-disk images every 15 min and views East Asia every 5 min through 14 spectral bands,enabling the detection of highly variable aerosol optical depth(AOD).Quantitative retrieval of AOD has hitherto been challenging,especially over land.In this study,an AOD retrieval algorithm is proposed that combines deep learning and transfer learning.The algorithm uses core concepts from both the Dark Target(DT)and Deep Blue(DB)algorithms to select features for the machinelearning(ML)algorithm,allowing for AOD retrieval at 550 nm over both dark and bright surfaces.The algorithm consists of two steps:①A baseline deep neural network(DNN)with skip connections is developed using 10 min Advanced Himawari Imager(AHI)AODs as the target variable,and②sunphotometer AODs from 89 ground-based stations are used to fine-tune the DNN parameters.Out-of-station validation shows that the retrieved AOD attains high accuracy,characterized by a coefficient of determination(R2)of 0.70,a mean bias error(MBE)of 0.03,and a percentage of data within the expected error(EE)of 70.7%.A sensitivity study reveals that the top-of-atmosphere reflectance at 650 and 470 nm,as well as the surface reflectance at 650 nm,are the two largest sources of uncertainty impacting the retrieval.In a case study of monitoring an extreme aerosol event,the AGRI AOD is found to be able to capture the detailed temporal evolution of the event.This work demonstrates the superiority of the transfer-learning technique in satellite AOD retrievals and the applicability of the retrieved AGRI AOD in monitoring extreme pollution events.
文摘本文利用2005~2020年北京地区观测得到的辐射资料,揭示近十多年来北京地区紫外辐射的变化规律,同时对影响紫外辐射长期变化的主要因子进行了分析。结果表明,紫外辐射呈现出明显的日、季节变化特征。日变化呈现出单峰的变化规律,在正午时出现一天中的极大值,而早晚则是低值时段,极大值和极小值分别出现在中午12时(北京时,下同;16.26 W m^(−2))和上午08时(5.64 W m^(−2))。紫外辐射从春季开始逐渐增强,到夏季出现一年中的极大值,随后开始下降,直到冬季出现一年中的极小值,月均极大值和极小值分别出现在6月(12.17 W m^(−2))和12月(5.4 W m^(−2))。紫外辐射年均值为9.74 W m^(−2)。紫外辐射与晴空指数呈现正相关,与气溶胶光学厚度和大气细颗粒物PM_(2.5)呈现负相关。
