Predicting heavy rainfall events (HREs) with lead time poses a significant challenge for meteorological agencies, especially in mountainous regions like Assam. In this study, we simulated a real-time HRE that occurred between June 13 and 17, 2023, resulting in severe flooding in Assam. To enhance rainfall prediction, we integrated output from the weather research and forecasting (WRF) model into a deep learning (DL) model. When comparing the district-level performance of WRF and DL models, it becomes evident that the DL model excels in capturing HREs with a significant accuracy of 54.4%, outperforming WRF’s accuracy of only 22.8%. The proposed model demonstrates a mean absolute error (MAE) of under 30 mm, outperforming WRF’s more than 50-mm MAE for days 2–4, as compared with the India Meteorological Department (IMD). Remarkably, the DL model accurately represents rainfall intensity and magnitude in the western and southern parts of Assam. This study is the first of its kind to focus on a district-scale analysis in Assam.

中文翻译：

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利用深度学习最小化阿萨姆邦实时强降雨事件的预测误差

提前预测强降雨事件 (HRE) 对气象机构来说是一项重大挑战，特别是在阿萨姆邦等山区。在这项研究中，我们模拟了 2023 年 6 月 13 日至 17 日期间发生的实时 HRE，导致阿萨姆邦发生严重洪水。为了增强降雨预测，我们将天气研究和预报 (WRF) 模型的输出集成到深度学习 (DL) 模型中。在比较 WRF 和 DL 模型的区级性能时，很明显，DL 模型在捕获 HRE 方面表现出色，准确率高达 54.4%，优于 WRF 仅 22.8% 的准确率。与印度气象部门 (IMD) 相比，拟议模型的平均绝对误差 (MAE) 低于 30 毫米，优于 WRF 在第 2-4 天超过 50 毫米的 MAE。值得注意的是，DL 模型准确地反映了阿萨姆邦西部和南部地区的降雨强度和强度。这项研究是首次针对阿萨姆邦地区规模的分析。