Cloud radiative kernels were built by BCC_RAD(Beijing Climate Center radiative transfer model)radiative transfer code.Then,short-term cloud feedback and its mechanisms in East Asia(0.5°S−60.5°N,69.5°−15...Cloud radiative kernels were built by BCC_RAD(Beijing Climate Center radiative transfer model)radiative transfer code.Then,short-term cloud feedback and its mechanisms in East Asia(0.5°S−60.5°N,69.5°−150.5°E)were analyzed quantitatively using the kernels combined with MODIS satellite data from July 2002 to June 2018.According to the surface and monsoon types,four subregions in East Asia-the Tibetan Plateau,northwest,temperate monsoon(TM),and subtropical monsoon(SM)—were selected.The average longwave,shortwave,and net cloud feedbacks in East Asia are−0.68±1.20,1.34±1.08,and 0.66±0.40 W m^−2 K^−1(±2σ),respectively,among which the net feedback is dominated by the positive shortwave feedback.Positive feedback in SM is the strongest of all subregions,mainly due to the contributions of nimbostratus and stratus.In East Asia,short-term feedback in spring is primarily caused by marine stratus in SM,in summer is primarily driven by deep convective cloud in TM,in autumn is mainly caused by land nimbostratus in SM,and in winter is mainly driven by land stratus in SM.Cloud feedback in East Asia is chiefly driven by decreases in mid-level and low cloud fraction owing to the changes in relative humidity,and a decrease in low cloud optical thickness due to the changes in cloud water content.展开更多
为提高短期电力负荷预测精度,本文提出了一种基于快照反馈机制改进的变分模态分解技术VMDSF(variational mode decomposition with a snapshot of feedback)和带有循环滑窗策略优化的长短时记忆网络CSLSTM(long short-term memory with ...为提高短期电力负荷预测精度,本文提出了一种基于快照反馈机制改进的变分模态分解技术VMDSF(variational mode decomposition with a snapshot of feedback)和带有循环滑窗策略优化的长短时记忆网络CSLSTM(long short-term memory with circular sliding window)的组合预测方法 VMDSF-CSLSTM。为降低原始序列的不稳定性及复杂性,本文首先使用VMDSF将原始电力负荷序列分解成多个子序列。然后结合网格搜索法对CSLSTM进行最优参数寻找,得到含有最优模型参数的电力负荷短期预测模型。最后,使用2013年澳大利亚4个区域的电力负荷数据集,对本文方法进行算例测试,测试结果表明了本组合模型的有效性。展开更多
基金supported by the National Key R&D Program of China(Grant No.2017YFA0603502)the National Natural Science Foundation of China(Grant Nos.91644211 and 41575002).
文摘Cloud radiative kernels were built by BCC_RAD(Beijing Climate Center radiative transfer model)radiative transfer code.Then,short-term cloud feedback and its mechanisms in East Asia(0.5°S−60.5°N,69.5°−150.5°E)were analyzed quantitatively using the kernels combined with MODIS satellite data from July 2002 to June 2018.According to the surface and monsoon types,four subregions in East Asia-the Tibetan Plateau,northwest,temperate monsoon(TM),and subtropical monsoon(SM)—were selected.The average longwave,shortwave,and net cloud feedbacks in East Asia are−0.68±1.20,1.34±1.08,and 0.66±0.40 W m^−2 K^−1(±2σ),respectively,among which the net feedback is dominated by the positive shortwave feedback.Positive feedback in SM is the strongest of all subregions,mainly due to the contributions of nimbostratus and stratus.In East Asia,short-term feedback in spring is primarily caused by marine stratus in SM,in summer is primarily driven by deep convective cloud in TM,in autumn is mainly caused by land nimbostratus in SM,and in winter is mainly driven by land stratus in SM.Cloud feedback in East Asia is chiefly driven by decreases in mid-level and low cloud fraction owing to the changes in relative humidity,and a decrease in low cloud optical thickness due to the changes in cloud water content.
文摘为提高短期电力负荷预测精度,本文提出了一种基于快照反馈机制改进的变分模态分解技术VMDSF(variational mode decomposition with a snapshot of feedback)和带有循环滑窗策略优化的长短时记忆网络CSLSTM(long short-term memory with circular sliding window)的组合预测方法 VMDSF-CSLSTM。为降低原始序列的不稳定性及复杂性,本文首先使用VMDSF将原始电力负荷序列分解成多个子序列。然后结合网格搜索法对CSLSTM进行最优参数寻找,得到含有最优模型参数的电力负荷短期预测模型。最后,使用2013年澳大利亚4个区域的电力负荷数据集,对本文方法进行算例测试,测试结果表明了本组合模型的有效性。
