Intelligent Revision Application of Similar Collection Method in Strong Wind Forecasting in Complex Terrain Areas of Transmission Channels
- 10.2991/978-2-494069-31-2_16How to use a DOI?
- Transmission channels; Micrometeorological device observation; Revision of numerical forecasts; Similar set methods; Strong wind forecasting for complex terrain
With the continuous development of numerical weather prediction technology, the weather forecast mode can provide refined forecasts of meteorological elements such as wind fields and temperatures for the power grid, but there are large errors in the terrain of the power grid transmission channel area. This paper selects in-situ microeoreorological observations of power grid transmission channels during Typhoon Hagupit no. 04 in 2020, and uses the similar set (AnEn) algorithm to re-analyze grid data of European short- and medium-term forecasting centers, and constructs a revised model of wind field forecasting in complex terrain areas of transmission channels, and applies them to strong wind forecasting of transmission channels. The results show that the numerical forecasting mode has a large forecast error in the near-ground area of the complex transmission channel, with a root mean square error of 5.1 m/s. The forecast error in the complex terrain area of western Zhejiang is greater than that in the northern and eastern coastal areas; The similar set method can effectively correct the near-ground wind field, and the forecast error is 4.09 m/s before revision, and the error is reduced to 1 m/s after revision.
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Cite this article
TY - CONF AU - PengYou Lai AU - JingTao Yang AU - LeXi Liu PY - 2022 DA - 2022/12/29 TI - Intelligent Revision Application of Similar Collection Method in Strong Wind Forecasting in Complex Terrain Areas of Transmission Channels BT - Proceedings of the 2022 6th International Seminar on Education, Management and Social Sciences (ISEMSS 2022) PB - Atlantis Press SP - 129 EP - 137 SN - 2352-5398 UR - https://doi.org/10.2991/978-2-494069-31-2_16 DO - 10.2991/978-2-494069-31-2_16 ID - Lai2022 ER -