针对天气雷达网中的不同波段雷达反射率因子整体偏差较大,太阳法强度标定仅能测试接收通道一致性,无法测量发射双通道引入误差等问题,该文利用无人机高精度RTK或激光测距功能,实时获取金属球位置,设计了“粗调、细调、凝视”的雷达目标...针对天气雷达网中的不同波段雷达反射率因子整体偏差较大,太阳法强度标定仅能测试接收通道一致性,无法测量发射双通道引入误差等问题,该文利用无人机高精度RTK或激光测距功能,实时获取金属球位置,设计了“粗调、细调、凝视”的雷达目标锁定方法,确定目标的最佳观测位置,通过计算金属球反射率因子实测值与理论值的偏差,实现了双偏振天气雷达全链路真值标定。结合襄阳S波段双偏振多普勒天气雷达在2025年4月25日开展了3次金属球标定试验,验证标定技术的可行性。试验结果表明,金属球反射率因子平均偏差ΔZ约0.27 d B,满足ΔZ≤0.5 dB的技术指标要求,雷达系统探测精度总体良好。使用无人机悬吊金属球标定反射率因子可以作为双偏振天气雷达全链路测试的重要手段,也为在线监测雷达健康状态提供了定期交叉检验依据。展开更多
多源降水融合技术是精准估算降水时空分布的重要手段,但常规融合方法难以充分考虑降水的空间局部相关性和时间依赖性以再现降水的空间分布格局。该研究选择3套卫星降水产品(IMERG,CMORPH和GSMaP)和站点观测数据,构建三维卷积神经网络(th...多源降水融合技术是精准估算降水时空分布的重要手段,但常规融合方法难以充分考虑降水的空间局部相关性和时间依赖性以再现降水的空间分布格局。该研究选择3套卫星降水产品(IMERG,CMORPH和GSMaP)和站点观测数据,构建三维卷积神经网络(three-dimensional convolutional neural network,3DCNN)和卷积长短期记忆神经网络(convolution long short term memory neural network,ConvLSTM)集成的时空深度学习融合模型(3DCNN-ConvLSTM),通过深度挖掘降水的时空变化特征,实现降水数据的精确估计。结果表明,在日尺度上,3DCNN-ConvLSTM融合降水的性能显著优于原始卫星降水产品,融合后的相关系数和克林-古普塔效率系数分别提高至0.679和0.64,均方根误差较融合前降低11.7%~24.4%,平均绝对误差降幅为9.3%~20.7%,且针对不同强度降水事件的捕捉精度更高;在月尺度上,各月降水性能得到不同程度的改善,其中高降水月份提升更显著;在空间尺度上,融合模型校正了原始降水产品在空间上的高估现象,在不同地形上表现出最高相关性及最小误差。与其他融合模型相比,3DCNN-ConvLSTM在提升降水数据精度方面表现更出色。总之,考虑了降水时空相关性的多源降水融合模型,能够有效提升闽浙赣地区降水数据质量,在多源降水融合领域有一定应用价值。展开更多
Here we report on simultaneous lidar observations of sporadic Ni(Nis)layers and sporadic Na(Nas)layers in the atmosphere over Yanqing,Beijing(40.42°N,116.02°E)from April 2019 to October 2022.During 343 night...Here we report on simultaneous lidar observations of sporadic Ni(Nis)layers and sporadic Na(Nas)layers in the atmosphere over Yanqing,Beijing(40.42°N,116.02°E)from April 2019 to October 2022.During 343 nights of observation,68 Nis and 56 Nas were observed.The seasonal variation of Nis and Nas was also obtained,with the highest occurrence of Nis being in July(43%)and that of Nas being in June(61%).We found that the seasonal variation of Nis is similar to that of Nas and that both occur more frequently in summer than in winter.In addition,we found 23 events in which Nis and Nas occur simultaneously.The average peak altitude of Nas is approximately 1 km higher than that of Nis,and the peak density ratio of Nas to Nis is approximately 5,which is half the density ratio of the two main layers.Additionally,the strength factor for Nas is smaller than that for Nis.Through data analysis of sporadic E layers(Es),we found that Nis and Nas has a significant correlation with Es.The neutralization rates of Ni^(+)/Na^(+)were calculated according to the dissociative recombination reaction of Ni^(+)/Na^(+)and the WACCM-Ni(Whole Atmosphere Community Climate Model of Ni).The production rates of Ni and Na were estimated to be approximately 1:4.4,which is consistent with the density ratio of Nis to Nas.The results showed that the neutralization reaction of Ni+,Na+,and electrons in Es is the main reason for the formation of the Nis layer and the Nas layer.展开更多
文摘针对天气雷达网中的不同波段雷达反射率因子整体偏差较大,太阳法强度标定仅能测试接收通道一致性,无法测量发射双通道引入误差等问题,该文利用无人机高精度RTK或激光测距功能,实时获取金属球位置,设计了“粗调、细调、凝视”的雷达目标锁定方法,确定目标的最佳观测位置,通过计算金属球反射率因子实测值与理论值的偏差,实现了双偏振天气雷达全链路真值标定。结合襄阳S波段双偏振多普勒天气雷达在2025年4月25日开展了3次金属球标定试验,验证标定技术的可行性。试验结果表明,金属球反射率因子平均偏差ΔZ约0.27 d B,满足ΔZ≤0.5 dB的技术指标要求,雷达系统探测精度总体良好。使用无人机悬吊金属球标定反射率因子可以作为双偏振天气雷达全链路测试的重要手段,也为在线监测雷达健康状态提供了定期交叉检验依据。
文摘多源降水融合技术是精准估算降水时空分布的重要手段,但常规融合方法难以充分考虑降水的空间局部相关性和时间依赖性以再现降水的空间分布格局。该研究选择3套卫星降水产品(IMERG,CMORPH和GSMaP)和站点观测数据,构建三维卷积神经网络(three-dimensional convolutional neural network,3DCNN)和卷积长短期记忆神经网络(convolution long short term memory neural network,ConvLSTM)集成的时空深度学习融合模型(3DCNN-ConvLSTM),通过深度挖掘降水的时空变化特征,实现降水数据的精确估计。结果表明,在日尺度上,3DCNN-ConvLSTM融合降水的性能显著优于原始卫星降水产品,融合后的相关系数和克林-古普塔效率系数分别提高至0.679和0.64,均方根误差较融合前降低11.7%~24.4%,平均绝对误差降幅为9.3%~20.7%,且针对不同强度降水事件的捕捉精度更高;在月尺度上,各月降水性能得到不同程度的改善,其中高降水月份提升更显著;在空间尺度上,融合模型校正了原始降水产品在空间上的高估现象,在不同地形上表现出最高相关性及最小误差。与其他融合模型相比,3DCNN-ConvLSTM在提升降水数据精度方面表现更出色。总之,考虑了降水时空相关性的多源降水融合模型,能够有效提升闽浙赣地区降水数据质量,在多源降水融合领域有一定应用价值。
基金supported by the Specialized Research Fund for State Key Laboratories,Chinese Meridian Project,the Specialized Research Fund for the State Key Laboratory of Solar Activity and Space Weather,postgraduate Education Reform and Quality Improvement Project of Henan Province(Grant No.YJS2024JD32)Natural Science Foundation Project of Henan Province(Grant No.242300420253)National Natural Science Foundation of China for Young Scientists(Grant No.42504156)funding.
文摘Here we report on simultaneous lidar observations of sporadic Ni(Nis)layers and sporadic Na(Nas)layers in the atmosphere over Yanqing,Beijing(40.42°N,116.02°E)from April 2019 to October 2022.During 343 nights of observation,68 Nis and 56 Nas were observed.The seasonal variation of Nis and Nas was also obtained,with the highest occurrence of Nis being in July(43%)and that of Nas being in June(61%).We found that the seasonal variation of Nis is similar to that of Nas and that both occur more frequently in summer than in winter.In addition,we found 23 events in which Nis and Nas occur simultaneously.The average peak altitude of Nas is approximately 1 km higher than that of Nis,and the peak density ratio of Nas to Nis is approximately 5,which is half the density ratio of the two main layers.Additionally,the strength factor for Nas is smaller than that for Nis.Through data analysis of sporadic E layers(Es),we found that Nis and Nas has a significant correlation with Es.The neutralization rates of Ni^(+)/Na^(+)were calculated according to the dissociative recombination reaction of Ni^(+)/Na^(+)and the WACCM-Ni(Whole Atmosphere Community Climate Model of Ni).The production rates of Ni and Na were estimated to be approximately 1:4.4,which is consistent with the density ratio of Nis to Nas.The results showed that the neutralization reaction of Ni+,Na+,and electrons in Es is the main reason for the formation of the Nis layer and the Nas layer.
