当前位置: X-MOL 学术J. Hydrol. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Improving short to medium range GEFS precipitation forecast in India
Journal of Hydrology ( IF 5.9 ) Pub Date : 2021-05-07 , DOI: 10.1016/j.jhydrol.2021.126431
Sakila Saminathan , Hanoi Medina , Subhasis Mitra , Di Tian

This study aims to enhance daily precipitation forecasts over the Indian subcontinent through post-processing the Global Ensemble Forecast System (GEFS) outputs using Analog (AN) and Logistic Regression (LR) techniques. Both raw and post-processed GEFS precipitation forecasts were evaluated against the Indian Meteorological Department observed dataset using probabilistic and deterministic forecast evaluation metrics, namely Brier Skill Score (BSS) and Root Mean Square Error (RMSE), respectively. Results found that the LR and AN post-processing method considerably improved short to medium range (1-7 day) precipitation forecasts over India. A comparison of the techniques with GEFS version 12 (GEFSv12) forecasts across different basins suggests that both methods were able to provide skillful precipitation forecasts in all the river basins, except that the AN method underperformed for the western ghats. The seasonal analysis also showed that the raw and the post-processed forecasts under-performed during the monsoon season while performing comparatively well during the non-monsoon season. The comparison of LR and AN methods showed that LR outperformed the AN method. The forecasts enhanced using both the post-processing techniques were more reliable and skillful than the GEFSv12 precipitation forecast. Comparison of the recent GEFS version with the older version of GEFS showed that the performance of the earlier version was slightly better compared to the latest version, however none of the forecasts can be used for decision making purposes without post-processing.



中文翻译:

改善印度GEFS的中短期降水预报

这项研究旨在通过使用模拟(AN)和Logistic回归(LR)技术对全球整体预报系统(GEFS)的输出进行后处理,以提高印度次大陆的每日降水预报。分别使用概率和确定性预测评估指标,即Brier技能评分(BSS)和均方根误差(RMSE),根据印度气象部门的观测数据集对原始和后处理的GEFS降水预测进行了评估。结果发现,LR和AN后处理方法极大地改善了印度中短期(1-7天)的降水预报。将这些技术与不同流域的GEFS第12版(GEFSv12)预报进行比较后,发现这两种方法都能在所有流域提供熟练的降水预报,除了AN方法在西高止山脉方面表现不佳。季节性分析还显示,原始和后处理的预报在季风季节表现不佳,而在非季风季节则表现相对较好。LR和AN方法的比较表明LR优于AN方法。与GEFSv12降水预报相比,使用这两种后处理技术增强的预报更加可靠和熟练。最新GEFS版本与旧版本GEFS的比较表明,较早版本的性能要比最新版本好一些,但是如果不进行后处理,则任何预测都不能用于决策目的。季节性分析还显示,原始和后处理的预报在季风季节表现不佳,而在非季风季节则表现相对较好。LR和AN方法的比较表明LR优于AN方法。与GEFSv12降水预报相比,使用这两种后处理技术增强的预报更加可靠和熟练。最新GEFS版本与旧版本GEFS的比较表明,较早版本的性能要比最新版本好一些,但是如果不进行后处理,则任何预测都不能用于决策目的。季节性分析还显示,原始和后处理的预报在季风季节表现不佳,而在非季风季节则表现相对较好。LR和AN方法的比较表明LR优于AN方法。与GEFSv12降水预报相比,使用这两种后处理技术增强的预报更加可靠和熟练。最新GEFS版本与旧版本GEFS的比较表明,较早版本的性能要比最新版本好一些,但是如果不进行后处理,则任何预测都不能用于决策目的。LR和AN方法的比较表明LR优于AN方法。与GEFSv12降水预报相比,使用这两种后处理技术增强的预报更加可靠和熟练。最新GEFS版本与旧版本GEFS的比较表明,较早版本的性能要比最新版本好一些,但是如果不进行后处理,则任何预测都不能用于决策目的。LR和AN方法的比较表明LR优于AN方法。与GEFSv12降水预报相比,使用这两种后处理技术增强的预报更加可靠和熟练。最新GEFS版本与旧版本GEFS的比较表明,较早版本的性能要比最新版本好一些,但是如果不进行后处理,则任何预测都不能用于决策目的。

更新日期:2021-05-07
down
wechat
bug