Intra-hour cloud index forecasting with data assimilation
Intra-hour cloud index forecasting with data assimilation
复制标题
通过数据同化进行小时内云指数预测
DOI：
10.1016/j.solener.2019.03.065
复制
发表时间：
2019
期刊：
影响因子：
6.7
通讯作者：
Morzfeld, Matthias
中科院分区：
文献类型：
--
作者：
Harty, Travis M.;Holmgren, William F.;Lorenzo, Antonio T.;Morzfeld, Matthias
我们引入了一个计算框架来预测云指数（CI）字段长达一个小时的空间域，覆盖一个城市。这种小时内CI预测对于产生公用事业规模太阳能和分布式屋顶太阳能的太阳能预测是重要的。我们的方法结合了一个二维平流模型与云运动矢量（CMVs）来自中尺度数值天气预报（NWP）模型和稀疏光流连续作用，地球同步卫星图像。我们使用集合数据同化联合收割机结合这些来源的云运动信息的基础上，每个数据源的不确定性。我们的技术产生的预测，有类似或更低的均方根误差比参考技术，只使用光流，NWP CMV领域，或持久性。我们描述了如何操作的方法在三个有代表性的案例研究和目前的结果从39阴天。
We introduce a computational framework to forecast cloud index (CI) fields for up to one hour on a spatial domain that covers a city. Such intra-hour CI forecasts are important to produce solar power forecasts of utility scale solar power and distributed rooftop solar. Our method combines a 2D advection model with cloud motion vectors (CMVs) derived from a mesoscale numerical weather prediction (NWP) model and sparse optical flow acting on successive, geostationary satellite images. We use ensemble data assimilation to combine these sources of cloud motion information based on the uncertainty of each data source. Our technique produces forecasts that have similar or lower root mean square error than reference techniques that use only optical flow, NWP CMV fields, or persistence. We describe how the method operates on three representative case studies and present results from 39 cloudy days.