通过后处理改进户外通用热气候指数的运行预测
Danijela Kuzmanović1, Jana Banko2, Gregor Skok3
1Faculty of Mathematics and Physics, University of Ljubljana, Jadranska Cesta 19, Ljubljana, 1000, Slovenia.
International journal of biometeorology
|March 5, 2024
概括
提高通用热气候指数 (UTCI) 预测准确度对于了解户外热舒适度至关重要. 机器学习方法显著减少了预测错误,提高了可靠性.
科学领域:
- 环境科学 环境科学
- 气象学 天气学
- 人体生理学 人体生理学
背景情况:
- 全球热气候指数 (UTCI) 量化了户外热舒适度,将空气温度,湿度,风,辐射和服装效应整合起来.
- 由于气象预报的不准确性,运营UTCI预报经常会出现重大错误.
- 斯洛文尼亚密集的气象网络,包括持续的太阳辐射测量,为改善UTCI精度提供了潜力.
研究的目的:
- 为了验证斯洛文尼亚UTCI运营预报的准确性.
- 通过使用机器学习技术进行后处理,提高UTCI预测的精度.
- 评估线性回归和神经网络在减少UTCI预测错误方面的有效性.
主要方法:
- 利用了来自斯洛文尼亚42个站的7年的每小时气象数据.
- 验证了第一天的操作UTCI预报.
- 应用线性回归和神经网络模型用于后处理和减少错误.
主要成果:
- 这两种机器学习方法都大大减少了每日平均误差,从大约2.6°C降至接近零.
- 神经网络将每日平均绝对误差从5°C降低到3°C,而线性回归将其降低到3.5°C.
- 机器学习模型,特别是神经网络,显著降低了预测错误的日间变化.
结论:
- 机器学习模型有效地提高了运营UTCI预测的准确性.
- 太阳辐射数据的整合增强了UTCI估计.
- 神经网络在减少UTCI预测错误和它们对一天时间的依赖方面特别有前途.
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