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Retrospective forecasts of the upcoming winter season snow accumulation in the Inn headwaters (European Alps)

机译:冬季冬季雪积累的回顾性预测(欧洲阿尔卑斯山)

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This article presents analyses of retrospective seasonal forecasts of snow accumulation. Re-forecasts with 4 months' lead time from two coupled atmosphere-ocean general circulation models (NCEP CFSv2 and MetOffice GloSea5) drive the Alpine Water balance and Runoff Estimation model (AWARE) in order to predict mid-winter snow accumulation in the Inn headwaters. As snowpack is hydrological storage that evolves during the winter season, it is strongly dependent on precipitation totals of the previous months. Climate model (CM) predictions of precipitation totals integrated from November to February (NDJF) compare reasonably well with observations. Even though predictions for precipitation may not be significantly more skilful than for temperature, the predictive skill achieved for precipitation is retained in subsequent water balance simulations when snow water equivalent (SWE) in February is considered. Given the AWARE simulations driven by observed meteorological fields as a benchmark for SWE analyses, the correlation achieved using GloSea5-AWARE SWE predictions is r = 0.57. The tendency of SWE anomalies (i.e. the sign of anomalies) is correctly predicted in 11 of 13 years. For CFSv2-AWARE, the corresponding values are r = 0.28 and 7 of 13 years. The results suggest that some seasonal prediction of hydrological model storage tendencies in parts of Europe is possible.
机译:本文介绍了回顾性季节性雪积累预测。重新预测来自两个耦合大气 - 海洋一般循环模型(NCEP CFSv2和Metoffice Glosea5)推动高山水平衡和径流估计模型(意识)以预测旅馆的中间雪积累。随着Snowpack是水文储存,在冬季演变,它强烈依赖前几个月的降水量。 11月至2月(NDJF)的降水总量预测(NDJF)的气候模型(CM)预测与观察结果相比。尽管对温度的对沉淀的预测不太明显比温度更明显,但是当考虑2月份的降水量(SWE)时,在随后的水平衡模拟中获得了沉淀的预测技能。鉴于观察到的气象领域驱动的血液模拟作为SWE分析的基准,使用GloseA5感知SWE预测实现的相关性是r = 0.57。 SWE异常的趋势(即异常的迹象)于13年的11年内被正确预测。对于CFSv2感知,相应的值是r = 0.28和13岁。结果表明,欧洲部位水文模型储存趋势的一些季节性预测是可能的。

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