...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Meteorological applications >Prediction of Indian summer monsoon rainfall: a weighted multi-model ensemble to enhance probabilistic forecast skills
【24h】

Prediction of Indian summer monsoon rainfall: a weighted multi-model ensemble to enhance probabilistic forecast skills

机译：印度夏季季风降水的预测：加权多模式合奏以增强概率预报技能

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

India gets the maximum amount of rainfall during the months of June to September (JJAS) which is known as the summer monsoon season. The erratic nature of Indian summer monsoon rainfall (ISMR), in terms of both rainfall amount and distribution, is highly responsible for the interannual variability in agricultural production as well as occurrence of floods and droughts. Accurate seasonal predictions of ISMR are required for appropriate hydrological planning and disaster management systems. Studies have revealed that probabilistic prediction, based on the products of General Circulation Models (GCMs), can be generated in a parametric as well as non-parametric manner. The present paper discusses the enhancement of probabilistic prediction by improving the potential predictable signal obtained from these GCMs. A Singular-Value-Decomposition based multiple linear regression method (SVD-MLR) has been applied to improve the signal and a simple average of all GCMs (EM) has been used as the benchmark to examine the skill of the SVD-MLR method. The potential of the proposed method has been assessed through Brier Skill Score (BSS) and Rank Probability Skill Score (RPSS). A rigorous analysis has finally revealed that SVD-MLR method has better skill than EM in predicting the typical nature of observed monsoon rainfall in extreme years.
机译：印度在6月至9月（JJAS）的降雨量最多，这被称为夏季季风季节。就降雨数量和分布而言，印度夏季风降雨（ISMR）的不稳定性是造成农业生产年际变化以及发生洪水和干旱的主要原因。适当的水文计划和灾害管理系统需要ISMR的准确季节预测。研究表明，基于通用循环模型（GCM）乘积的概率预测可以以参数方式也可以以非参数方式生成。本文讨论了通过改善从这些GCM获得的潜在可预测信号来增强概率预测。已应用基于奇异值分解的多元线性回归方法（SVD-MLR）来改善信号，并且已将所有GCM（EM）的简单平均值用作基准，以检查SVD-MLR方法的技能。已通过Brier技能评分（BSS）和等级概率技能评分（RPSS）评估了该方法的潜力。严格的分析最终表明，SVD-MLR方法在预测极端年份观测到的季风降雨的典型性质方面比EM具有更好的技巧。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号