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首页> 外文期刊>Monthly Weather Review >Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. Part Ⅱ: Imperfect Model Experiments
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Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. Part Ⅱ: Imperfect Model Experiments

机译：中尺度和区域尺度数据同化的集成卡尔曼滤波器测试。第二部分：不完美的模型实验

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In Part Ⅰ of this two-part work, the feasibility of using an ensemble Kalman filter (EnKF) for mesoscale and regional-scale data assimilation through various observing system simulation experiments was demonstrated assuming a perfect forecast model for a winter snowstorm event that occurred on 24-26 January 2000. The current study seeks to explore the performance of the EnKF for the same event in the presence of significant model errors due to physical parameterizations by assimilating synthetic sounding and surface observations with typical temporal and spatial resolutions. The EnKF performance with imperfect models is also examined for a warm-season mesoscale convective vortex (MCV) event that occurred on 10-13 June 2003. The significance of model error in both warm- and cold-season events is demonstrated when the use of different cumulus para'meterization schemes within different ensembles results in significantly different forecasts in terms of both ensemble mean and spread. Nevertheless, the EnKF performed reasonably well in most experiments with the imperfect model assumption (though its performance can sometimes be significantly degraded). As in Part Ⅰ, where the perfect model assumption was utilized, most analysis error reduction comes from larger scales. Results show that using a combination of different physical parameterization schemes in the ensemble forecast can significantly improve filter performance. A multischeme ensemble has the potential to provide better background error covariance estimation and a smaller ensemble bias. There are noticeable differences in the performance of the EnKF for different flow regimes. [In the imperfect scenarios considered, the improvement over the reference ensembles (pure ensemble forecasts without data assimilation) after 24 h of assimilation for the winter snowstorm event ranges from 36% to 67%. This is higher than the 26%-45% improvement noted after 36 h of assimilation for the warm-season MCV event. Scale- and flow-dependent error growth dynamics and predictability are possible causes for the differences in improvement. Compared to the power spectrum analyses for the snowstorm, it is found that forecast errors and ensemble spreads in the warm-season MCV event have relatively smaller power at larger scales and an overall smaller growth rate.
机译：在这一由两部分组成的工作的第一部分中，通过假设各种天气观测系统模拟的理想预测模型，证明了使用集成卡尔曼滤波（EnKF）通过各种观测系统模拟实验对中尺度和区域尺度数据进行同化的可行性。 2000年1月24日至26日。当前的研究试图通过将合成的测深和地面观测与典型的时空分辨率同化，来探讨由于物理参数设置而导致存在重大模型误差的EnKF的性能。还检查了具有不完美模型的EnKF性能，以分析发生在2003年6月10日至13日的暖季中尺度对流涡旋（MCV）事件。在不同的合奏中，不同的累积参数化方案在整体均值和散布方面导致明显不同的预测。尽管如此，在不完善的模型假设下，EnKF在大多数实验中仍表现良好（尽管有时其性能有时会大大降低）。像在第一部分中那样，使用了理想的模型假设，大多数分析误差的减少来自更大的尺度。结果表明，在集合预报中结合使用不同的物理参数化方案可以显着提高过滤器性能。多方案合奏有可能提供更好的背景误差协方差估计和较小的合奏偏差。 EnKF在不同流动方式下的性能存在明显差异。 [在考虑的不完美场景中，冬季暴风雪事件同化24小时后，参考集合的改进（无数据同化的纯集合预报）范围为36％至67％。这比暖季MCV事件同化36小时后注意到的26％-45％的改善要高。与比例和流量相关的误差增长动态和可预测性可能是导致改进差异的原因。与针对暴风雪的功率谱分析相比，发现在暖季MCV事件中的预测误差和集合散布在较大规模上具有相对较小的功率，总体上具有较小的增长率。

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