Short-term local weather forecast using dense weather station by deep neural network
Short-term local weather forecast using dense weather station by deep neural network
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DOI：
10.1109/bigdata.2018.8622195
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发表时间：
2018-12
期刊：
影响因子：
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通讯作者：
Kazuo Yonekura;H. Hattori;Taiji Suzuki
中科院分区：
文献类型：
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作者：
Kazuo Yonekura;H. Hattori;Taiji Suzuki
本文提出了一个非常短期的，即，1小时以内，采用当地天气预报方法。一般来说，由于缺乏地面气象资料和计算资源的限制，3小时内的短期天气预报是困难的。然而，这种短期预测在运输、零售业、农业和能源管理以及我们的日常生活等几种工业情况下越来越受欢迎。为了满足这一巨大需求，开始提供基于超短期天气预报的服务。这种预测的数据来源是私人公司拥有的地面传感器网络，除了国有的地面传感器。私营公司的一些地面气象传感器比国有传感器分布得更密集。我们称这些地面气象传感器网络为密集气象站。其中，例如POTEKA传感器大约每隔2至3公里设置一个，通过移动的网络每分钟提供一次观测数据。这些密集的传感器将其分布在世界各地。然而，用于这种设备的数据挖掘技术尚未得到很好的开发。在本文中，我们提出了一种专门针对密集气象站设备的短期天气预报开发的深度学习架构。我们的建议包括两个折叠：点预测模型和张量预测模型。点预报模型对准确预报密集气象站的位置是有用的。张量预测模型插值点预测模型的预测，以覆盖感兴趣区域周围的整个位置范围。它表明，我们的模型优于现有的国家的最先进的方法，如XGBoost和支持向量机使用大量的真实的观测数据。
This paper proposes a very-short-term, i.e., less than 1-hour, local weather forecast method. In general, a short-term weather forecast within 3 hours is difficult due to lack of surface weather data and limitations of computation resources. However, such a short-term prediction is getting more and more anticipated in several industrial situations such as transportation, retailing business, agriculture, and energy management as well as our daily life. To keep up with this huge demands, services based on very-short-term weather forecast began to be provided. Data sources for such kind of forecast are private company-owned surface sensor networks in addition to nation-owned surface sensors. Some surface weather sensors of private companies are more densely distributed than nation-owned sensors. We call these surface weather sensor network as dense weather stations. Among them, for example, POTEKA sensors are located in roughly every 2 to 3 km and provide observed data every minute through mobile network. Those dense sensors are spreading its locations over the world. However, a data mining technique for such a device has not been well developed. In this paper, we propose a deep learning architecture specifically developed for the short-term weather forecasting based on the dense weather station device. Our proposal consists of two folds: point prediction model and tensor prediction model. The point prediction model is useful for forecasting exactly on the location of the dense weather station. The tensor prediction model interpolate the prediction of the point prediction model to cover whole range of locations around the interested area. It is shown that our model outperforms the existing state-of-the-art methods such as XGBoost and support vector machines using a large real observed data.