Scholarlink

<p>This study evaluated the effectiveness of the Multi-Source Ensemble Rainfall Analysis (MERA) product in capturing precipitation patterns exclusively during the southwest monsoon season in India. MERA combines data from multiple satellite sources, including INSAT-3D, GPM-IMERG, and GSMaP, to generate high-resolution (4&#xa0;km hourly) rainfall estimates. The methodology involves the bias correction of individual satellite products, weighted blending, and ensemble generation. The performance of MERA was assessed through spatial, temporal, and statistical comparisons with gauge observations and other satellite products for the 2020 monsoon season. The results showed that MERA captures spatial rainfall patterns well, especially in regions with complex topographies. It demonstrates improved accuracy in detecting light to moderate rainfall compared to individual satellite products. However, the performance declines for high-intensity rainfall events. Statistical evaluation shows that MERA exhibits lower errors and stronger correlation with gauge observations compared to other products across most regions. These findings underscore the potential of MERA for improving monsoon rainfall monitoring and prediction in India, while also highlighting the need for continued refinement. Hence, suggesting that MERA could be used to validate high-resolution operational numerical weather prediction (NWP) models, further emphasizing its potential applications in meteorological research and forecasting.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

How well do multi-source ensemble rainfall products capture precipitation during monsoons in India?

  • 期刊论文

  • 发表日期：

    2025年11月3日

查看全文
  • K. Amarjyothi,
  • D. Preveen Kumar,
  • M. Venkatarami Reddy,
  • Ashis. K. Mitra,
  • M. N. Raghavendra Sreevathsa,
  • M. Neha Rajput,
  • V. S. Prasad

摘要

This study evaluated the effectiveness of the Multi-Source Ensemble Rainfall Analysis (MERA) product in capturing precipitation patterns exclusively during the southwest monsoon season in India. MERA combines data from multiple satellite sources, including INSAT-3D, GPM-IMERG, and GSMaP, to generate high-resolution (4 km hourly) rainfall estimates. The methodology involves the bias correction of individual satellite products, weighted blending, and ensemble generation. The performance of MERA was assessed through spatial, temporal, and statistical comparisons with gauge observations and other satellite products for the 2020 monsoon season. The results showed that MERA captures spatial rainfall patterns well, especially in regions with complex topographies. It demonstrates improved accuracy in detecting light to moderate rainfall compared to individual satellite products. However, the performance declines for high-intensity rainfall events. Statistical evaluation shows that MERA exhibits lower errors and stronger correlation with gauge observations compared to other products across most regions. These findings underscore the potential of MERA for improving monsoon rainfall monitoring and prediction in India, while also highlighting the need for continued refinement. Hence, suggesting that MERA could be used to validate high-resolution operational numerical weather prediction (NWP) models, further emphasizing its potential applications in meteorological research and forecasting.