Data-driven model of the local wind field over two small lakes in Jyvaskyla, Finland
Data-driven model of the local wind field over two small lakes in Jyvaskyla, Finland
复制标题
芬兰于韦斯屈莱两个小湖当地风场的数据驱动模型
DOI：
10.1007/s00703-021-00857-3
复制
发表时间：
2022
影响因子：
2
通讯作者：
H. Suito,
中科院分区：
文献类型：
--
作者：
T. Shuku;J. Ropponen;J. Juntunen;H. Suito,
本研究提出了一个数据驱动的模式，当地的风场在两个小湖在于韦斯屈莱，芬兰。2015年和2016年夏季安装的五个临时监测站观测了两个湖泊周围的风速/风向。此外，位于湖泊以北15公里处的官方气象站是永久可用的。我们的目标是开发一个模型，可以评估风速和风向的两个湖泊使用的数据，只有从永久站。统计分析表明：（1）局地风速与海拔高度相关，其周期性变化与官方观测资料一致;（2）局地风向场具有空间均匀性，与官方观测资料有较强的相关性。在此基础上，建立了基于数字高程模型（DEM）和官方站数据的风速风向空间分布回归模型。我们比较了预测的风速/方向的建议模式与相应的观测数据和数值结果的模式验证。我们发现，该模式可以有效地模拟非均匀的本地风场，并考虑估计的不确定性。
This study presents a data-driven model of the local wind field over two small lakes in Jyväskylä, Finland. Five temporary monitoring stations installed during the summers of 2015 and 2016 observed wind speed/direction around the two lakes. In addition, an official meteorological station located 15 km north of the lakes is permanently available. Our goal was to develop a model that could evaluate wind speed and direction over the two lakes using only data from the permanent station. Statistical analysis for the spatio-temporal wind data revealed that (1) local wind speed is correlated with the elevation and its cyclic pattern is identical to that of the official-station data, and (2) the local wind direction field is spatially homogeneous and is strongly correlated with the official-station data. Based on these results, we built two regression models for estimating spatial distribution of local wind speed and directions based on the digital elevation model (DEM) and official-station data. We compared the predicted wind speeds/directions by the proposed model with the corresponding observation data and a numerical result for model validation. We found that the proposed model could effectively simulate heterogeneous local wind fields and considers uncertainty of estimates.