提出一种高精度的ZWD模型(tianjin_zwd,TZ)。TZ基于2016-2018年逐小时气压分层的ERA5,欧洲中尺度气象预报中心第五代再分析产品数据,采用BP神经网络建立。然后,根据2019年的ERA5产品导出的ZWD对TZ模型进行了验证。结果表明:相比GPT3模型...提出一种高精度的ZWD模型(tianjin_zwd,TZ)。TZ基于2016-2018年逐小时气压分层的ERA5,欧洲中尺度气象预报中心第五代再分析产品数据,采用BP神经网络建立。然后,根据2019年的ERA5产品导出的ZWD对TZ模型进行了验证。结果表明:相比GPT3模型,TZ模型可提供更贴近真值的ZWD估值;并且,其RMSE由5.0 cm (GPT3)降至4.5 cm,表明10%的精度提升。上述结果表明TZ模型实现了更优的预测性能,该模型的构建策略可为全国其他地区的ZWD建模提供借鉴。展开更多
In micro-blogging contexts such as Twitter,the number of content producers can easily reach tens of thousands,and many users can participate in discussion of any given topic.While many users can introduce diversity,as...In micro-blogging contexts such as Twitter,the number of content producers can easily reach tens of thousands,and many users can participate in discussion of any given topic.While many users can introduce diversity,as not all users are equally influential,it makes it challenging to identify the true influencers,who are generally rated as being interesting and authoritative on a given topic.In this study,the influence of users is measured by performing random walks of the multi-relational data in micro-blogging:retweet,reply,reintroduce,and read.Due to the uncertainty of the reintroduce and read operations,a new method is proposed to determine the transition probabilities of uncertain relational networks.Moreover,we propose a method for performing the combined random walks for the multi-relational influence network,considering both the transition probabilities for intra-and inter-networking.Experiments were conducted on a real Twitter dataset containing about 260 000 users and 2.7million tweets,and the results show that our method is more effective than TwitterRank and other methods used to discover influencers.展开更多
利用全国40个地面台站的观测资料对ERA5及ERA5-Land两种不同空间分辨率的再分析资料开展了地面风速误差评估研究,结果表明:ERA5和ERA5-Land资料多年平均风速偏差的平均值分别为0.08 m s^(−1)、-0.06 m s^(−1),偏差的最大值分别为0.46 m ...利用全国40个地面台站的观测资料对ERA5及ERA5-Land两种不同空间分辨率的再分析资料开展了地面风速误差评估研究,结果表明:ERA5和ERA5-Land资料多年平均风速偏差的平均值分别为0.08 m s^(−1)、-0.06 m s^(−1),偏差的最大值分别为0.46 m s^(−1)、-0.19 m s^(−1),相对偏差的平均值为4.4%、-2.0%,相对偏差的最大值分别为33.0%、-10.1%;月平均风速线性拟合方程的斜率分别为0.93、0.97,截距分别为0.29 m s^(−1)、0.02 m s^(−1),相关系数分别为0.98、0.99;月平均风速均方根误差的平均值分别为0.17 m s^(−1)、0.14 m s^(−1),均方根误差的最大值分别为0.49 m s^(−1)、0.22 m s^(−1),相对均方根误差的平均值为7.4%、5.7%,相对均方根误差的最大值分别为35.2%、13.3%。ERA5-Land高分辨率资料地面风速误差相对较低,有利于提高风能资源评估的准确性。展开更多
文摘提出一种高精度的ZWD模型(tianjin_zwd,TZ)。TZ基于2016-2018年逐小时气压分层的ERA5,欧洲中尺度气象预报中心第五代再分析产品数据,采用BP神经网络建立。然后,根据2019年的ERA5产品导出的ZWD对TZ模型进行了验证。结果表明:相比GPT3模型,TZ模型可提供更贴近真值的ZWD估值;并且,其RMSE由5.0 cm (GPT3)降至4.5 cm,表明10%的精度提升。上述结果表明TZ模型实现了更优的预测性能,该模型的构建策略可为全国其他地区的ZWD建模提供借鉴。
基金supported by National Natural Science Foundation of China under Grants No. 60933005, No. 91124002under Grants No. 012505, No. 2011AA010702, No. 2012AA01A401, No. 2012AA01A402 (863 program)+1 种基金under Grant No.2011A010 (242)NSTM under Grants No.2012BAH38B04, No.2012BAH38B06
文摘In micro-blogging contexts such as Twitter,the number of content producers can easily reach tens of thousands,and many users can participate in discussion of any given topic.While many users can introduce diversity,as not all users are equally influential,it makes it challenging to identify the true influencers,who are generally rated as being interesting and authoritative on a given topic.In this study,the influence of users is measured by performing random walks of the multi-relational data in micro-blogging:retweet,reply,reintroduce,and read.Due to the uncertainty of the reintroduce and read operations,a new method is proposed to determine the transition probabilities of uncertain relational networks.Moreover,we propose a method for performing the combined random walks for the multi-relational influence network,considering both the transition probabilities for intra-and inter-networking.Experiments were conducted on a real Twitter dataset containing about 260 000 users and 2.7million tweets,and the results show that our method is more effective than TwitterRank and other methods used to discover influencers.
文摘利用全国40个地面台站的观测资料对ERA5及ERA5-Land两种不同空间分辨率的再分析资料开展了地面风速误差评估研究,结果表明:ERA5和ERA5-Land资料多年平均风速偏差的平均值分别为0.08 m s^(−1)、-0.06 m s^(−1),偏差的最大值分别为0.46 m s^(−1)、-0.19 m s^(−1),相对偏差的平均值为4.4%、-2.0%,相对偏差的最大值分别为33.0%、-10.1%;月平均风速线性拟合方程的斜率分别为0.93、0.97,截距分别为0.29 m s^(−1)、0.02 m s^(−1),相关系数分别为0.98、0.99;月平均风速均方根误差的平均值分别为0.17 m s^(−1)、0.14 m s^(−1),均方根误差的最大值分别为0.49 m s^(−1)、0.22 m s^(−1),相对均方根误差的平均值为7.4%、5.7%,相对均方根误差的最大值分别为35.2%、13.3%。ERA5-Land高分辨率资料地面风速误差相对较低,有利于提高风能资源评估的准确性。
