罕见鸟类预报:使用北极海鸟的长期数据集和数值天气预报模型的综合方法
Masayuki Senzaki1, Kenta Tamura1, Yoshiaki Watanabe2
1Faculty of Environmental Earth Science Hokkaido University Sapporo Hokkaido Japan.
Ecology and evolution
|June 27, 2024
概括
通过新的预测系统,现在可以预测罕见的北极海鸟观测. 这些工具使用天气模式和当地因素来估计罗斯海的每日观测概率.
科学领域:
- 鸟类学 鸟类学是一门学科.
- 机器学习 机器学习
- 气象学 天气学
背景情况:
- 野生动物观测,特别是像罗斯 (Rhodostethia rosea) 这样的罕见物种,为观察者带来了重大的预测挑战.
- 通常需要专家知识来预测观测,这限制了一般野生动物爱好者的可访问性.
研究的目的:
- 开发用户友好的预测系统,用于预测日本北部罕见的罗斯海的每日观测概率.
- 为一个罕见的北极海鸟提供一个提高野生动物观测成功率和成本效益的工具.
主要方法:
- 利用16个冬季的数据集应用自组织地图 (方法A) 来将海观测与俄霍茨克海上的气象压力模式相关联.
- 开发了一个回归模型 (方法B),将海观测与当地环境因素 (包括风力和季节) 联系起来.
- 将这两种方法与全球数值天气预测模型集成在一起,以预测1-5天前的观测概率.
主要成果:
- 方法A根据16种天气模式预测的观测概率从0%到85.7%.
- 方法B预测的概率从0%到95.5%,以北风的强度和季节性进展为关键因素.
- 这两种系统,当与数值天气预测模型一起使用时,准确地预测了从11月到2月的Ross's Gull观测概率.
结论:
- 开发的预测系统对于预测罗斯海观测是有效的,通过为期2年的后续研究得到验证.
- 当两种预测方法都表明观测概率更高时,这些系统特别有效.
- 这些工具促进了成本效益高的野生动物观测,并改善了罕见物种观察者的整体体验.
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