基于卫星散射仪星座的近海风精准预报
Skillful forecasting of offshore winds from satellite scatterometer constellations
- Delft University of Technology（代尔夫特理工大学）
- Bern University of Applied Sciences（伯尔尼应用科技大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出首个基于卫星的近海风临近预报框架WindCastNet，采用部分卷积LSTM网络，在北海评估中较HARMONIE MEPS模型及持续性方法提升了近海风预报精度，为海洋天气预报提供新途径。
AI中文摘要：
日内近海风的精准预报对电力系统运行及日益增长的近海风能并网愈发重要。业务预报主要依赖数值天气预报（NWP），但NWP未针对数分钟至数小时的预见期优化，该时段初始条件精度主导预报技能。尽管卫星散射仪观测常被同化进NWP，但此前从未被直接用于预报。本文提出WindCastNet，首个基于卫星的近海风速与风向临近预报框架，开创了从时空不规则卫星观测中学习的日内预报新范式。WindCastNet基于卫星散射仪星座获取的观测，预测近海风场；其采用部分卷积长短期记忆网络，可利用欧洲、中国、印度散射仪的微波雷达观测，即便这些观测存在空间覆盖不规则、采样异步、重访时间可变的问题。该网络对空间观测掩码与观测间隔进行编码，同时通过连续时间表示实现任意预见期的预报。在北海的评估显示，与HARMONIE MEPS模型相比，WindCastNet在1小时预见期时均方根误差降低23%，2小时预见期时降低7%，且在预报前三小时内较持续性方法表现提升9%-15%。在强风条件及空间非均匀流场下，预报技能有所下降。这些结果表明，卫星散射仪星座可提供独立且具竞争力的短期近海风预报源，为可再生能源预报及更广泛的海洋天气应用（包括热带气旋临近预报）开辟新机遇。
英文摘要：
Accurate intraday forecasts of offshore wind are becoming increasingly important for power system operation and the integration of growing shares of offshore wind energy. Operational forecasts rely predominantly on numerical weather prediction (NWP), which is not optimized for lead times of minutes to hours, where initial-condition accuracy dominates forecast skill. Although satellite scatterometer observations are routinely assimilated into NWP, they have not previously been used directly for forecasting. Here we present WindCastNet, the first satellite-based nowcasting framework for offshore wind speed and direction, introducing a new paradigm for intraday forecasting that learns from spatiotemporally irregular satellite observations. WindCastNet predicts offshore wind fields from observations acquired by satellite scatterometer constellations. WindCastNet employs a partial convolutional long short-term memory network that exploits microwave radar observations from the European, Chinese, and Indian scatterometers despite their irregular spatial coverage, asynchronous sampling, and variable revisit times. Spatial observation masks and inter-observation intervals are encoded, while a continuous temporal representation enables forecasts at arbitrary lead times. Evaluated over the North Sea, WindCastNet reduces the root-mean-square error by 23% and 7% relative to the HARMONIE MEPS model at lead times of 1 and 2 h, respectively, and outperforms persistence by 9-15% during the first three forecast hours. Forecast skill decreases under strong-wind conditions and spatially non-uniform flow. These results demonstrate that satellite scatterometer constellations can provide an independent and competitive source of short-term offshore wind forecasts, opening new opportunities for renewable energy forecasting but also broader marine weather applications, including tropical cyclone nowcasting.
