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Verification of 24-h Quantitative Precipitation Forecasts over the Pacific Northwest from a High-Resolution Ensemble Kalman Filter System

机译:从高分辨率集合卡尔曼滤波系统验证太平洋西北地区的24小时定量降水预测

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Environment and Climate Change Canada (ECCC) has recently developed an experimental highresolution EnKF (HREnKF) regional ensemble prediction system, which it tested over the Pacific Northwest of North America for the first half of February 2011. The HREnKF has 2.5-km horizontal grid spacing and assimilates surface and upper-air observations every hour. To determine the benefits of the HREnKF over less expensive alternatives, its 24-h quantitative precipitation forecasts are compared with those from a lower-resolution (15 km) regional ensemble Kalman filter (REnKF) system and to ensembles directly downscaled from the REnKF using the same grid as the HREnKF but with no additional data assimilation (DS). The forecasts are verified against rain gauge observations and gridded precipitation analyses, the latter of which are characterized by uncertainties of comparable magnitude to the model forecast errors. Nonetheless, both deterministic and probabilistic verification indicates robust improvements in forecast skill owing to the finer grids of the HREnKF and DS. The HREnKF exhibits a further improvement in performance over the DS in the first few forecast hours, suggesting a modest positive impact of data assimilation. However, this improvement is not statistically significant and may be attributable to other factors.
机译:加拿大环境与气候变化(ECCC)最近开发了一个实验高度恩克夫(Hronkf)区域集合预测系统,它于2011年2月上半年对北美太平洋西北部进行了测试.Hronkf水平栅格间距有2.5公里并每小时吸收表面和上空气观察。为了确定Hrenkf在更昂贵的替代方案中的益处,将其24小时定量降水预测与来自较低分辨率(15公里)区域合奏卡尔曼滤波器(Renkf)系统的定量预测进行了比较,并且使用了从Renkf直接折叠的集合与HRONKF相同的网格,但没有额外的数据同化(DS)。预测是针对雨量仪观测和网格降水分析的验证,其中后者的特征在于与模型预测误差相当数量的不确定性。尽管如此,既确定性和概率验证都表明由于HRONKF和DS的更精细的网格,对预测技能的强大改进。 Hronkf在前几个预测时间内对DS进行了进一步的性能,这表明数据同化的正常影响。然而,这种改进没有统计学意义,并且可能归因于其他因素。

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