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Exploring the predictability of within-season rainfall statistics of the Bangladesh monsoon using North American Multimodel Ensemble outputs

机译:使用北美多模组集合输出探索孟加拉国季季季季季季季季斯通季度降雨统计的可预测性

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Improvement of rainfall forecasts on seasonal to within-season timescales is crucial for many vulnerable regions and nations, including Bangladesh. For South Asia, seasonal predictability of rainfall can be quite challenging, and Bangladesh has limited predictive skill with respect to total seasonal rainfall due to its weak relationship with ENSO variability. The relationship between total seasonal monsoon rainfall from June through September (JJAS), as simulated by the North American Multimodel Ensemble (NMME), and within-season observed rainfall statistics for Bangladesh were explored, employing 25-year cross-validations at lead times up to 3 months. The model hindcasts of total JJAS rainfall demonstrate only low-to-modest skill at predicting total observed seasonal rainfall, but a more robust predictive relationship is found for the number of observed dry and wet spells within the season, more so than with the number of extreme dry or wet days. A small ensemble of NMME models could be used to provide more valuable information to aid decision-makers than previously thought, with important implications for agricultural decision-making and climate services.
机译:在季节性范围内的降雨预测的降雨预测对于包括孟加拉国在内的许多弱势地区和国家来说至关重要。对于南亚来说,降雨的季节性可预测性可能是非常具有挑战性的,并且由于与ENSO可变性的关系薄弱,孟加拉国对总季节性降雨有限。探讨了6月至9月份的季节性季风降雨量(JJAS)的关系,由北美多模块(JJA)和孟加拉国境内观测到的孟加拉国降雨统计,在领先时期使用了25年的交叉验证到3个月。总JJAS降雨的模型Hindcasts只表现出在预测季节降雨的总季节降雨中的低至适度技能,但在本赛季内观察到的干湿法术的数量,发现了更强大的预测关系,而不是与数量相比极端干燥或潮湿的日子。可以使用NMME模型的一个小型集合来提供比以前认为的决策者更有价值的信息,并对农业决策和气候服务的重要意义。

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