短期到中期范围温度预测的概率后处理:对印度热浪预测的影响
Sakila Saminathan1, Subhasis Mitra2
1Department of Civil Engineering, Indian Institute of Technology Palakkad, Near Gramalakshmi Mudralayam, Malampuzha Road, Kanjikode, Palakkad, 678623, Kerala, India. sakilasaminathan@gmail.com.
Environmental monitoring and assessment
|February 20, 2024
概括
概率后处理技术显著改善了印度各地的空气温度预测,优于传统方法. 非均高斯回归 (NGR) 显示出最佳性能,增强了早期预警系统的热浪预测技能.
科学领域:
- 气象学和气候科学 气象学和气候科学
- 大气科学 大气科学
- 环境科学 环境科学
背景情况:
- 准确的空气温度预测对于管理热中风等热灾难至关重要.
- 数字天气预报 (NWP) 模型通常具有需要后处理的偏差.
- 印度对用于温度预测的概率后处理技术 (PPT) 的研究有限.
研究的目的:
- 评估非均高斯回归 (NGR) 和贝叶斯模型平均值 (BMA) 以改善印度的NWP温度预测.
- 评估PPT对印度各地热浪预测技能的影响.
- 为印度温度预测确定最有效的PPT.
主要方法:
- 利用ECMWF和GEFS NWP模型中的每日温度预测.
- 应用概率后处理技术:非同质高斯回归 (NGR) 和贝叶斯模型平均值 (BMA).
- 评估了包括喜马拉雅地区在内的印度次大陆的预报表现,并评估了热浪预测技能.
主要成果:
- 概率PPT在印度各地的温度预测中显著优于传统方法.
- 与印度地区的BMA和其他PPT相比,NGR表现优越.
- 虽然概率技术改善了喜马拉雅山脉等技能较低的地区的预测,但它们并没有实现熟练的预测.
- 使用NGR的加工后最高温度 (Tmax) 显著提高了脆弱地区热浪预测的准确性.
结论:
- 概率后处理,特别是NGR,为印度的温度预测和热浪预测提供了显著的改善.
- 这些发现支持印度开发增强的热浪预警系统.
- 可能需要进一步的研究来提高预测技能在本质上低原始预测准确度的地区,如喜马拉雅山脉.
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