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An Application of the Multi-Physics Ensemble Kalman Filter to Typhoon Forecast

机译：多物理集合卡尔曼滤波在台风预报中的应用

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This study examines the roles of the multi-physics approach in accounting for model errors for typhoon forecasts with the local ensemble transform Kalman filter (LETKF). Experiments with forecasts of Typhoon Conson (2010) using the weather research and forecasting (WRF) model show that use of the WRF's multiple physical parameterization schemes to represent the model uncertainties can help the LETKF provide better forecasts of Typhoon Conson in terms of the forecast errors, the ensemble spread, the root mean square errors, the cross-correlation between mass and wind field as well as the coherent structure of the ensemble spread along the storm center. Sensitivity experiments with the WRF model show that the optimum number of the multiphysics ensemble is roughly equal to the number of combinations of different physics schemes assigned in the multi-physics ensemble. Additional idealized experiments with the Lorenz 40-variable model to isolate the dual roles of the multi-physics ensemble in correcting model errors and expanding the local ensemble space show that the multi-physics approach appears to be more essential in augmenting the local rank representation of the LETKF algorithm rather than directly accounting for model errors during the early cycles. The results in this study suggest that the multi-physics approach is a good option for short-range forecast applications with full physics models in which the spinup of the ensemble Kalman filter may take too long for the ensemble spread to capture efficiently model errors and cross-correlations among model variables.
机译：这项研究研究了多物理场方法在利用本地整体变换卡尔曼滤波器（LETKF）解释台风预报的模型误差中的作用。使用天气研究和预报（WRF）模型对台风康森（2010）进行预报的实验表明，使用WRF的多种物理参数化方案表示模型的不确定性可以帮助LETKF在预报误差方面提供更好的台风康森预报，集合扩展，均方根误差，质量与风场之间的互相关以及集合沿着风暴中心的相干结构。 WRF模型的敏感性实验表明，多物理场合奏的最佳数量大致等于在多物理场合奏中分配的不同物理方案的组合数。使用Lorenz 40变量模型进行的其他理想化实验可以隔离多物理场合奏在校正模型误差和扩展局部合奏空间中的双重作用，这表明多物理场方法对于增强C的局部秩表示似乎更为重要。 LETKF算法，而不是直接考虑早期周期中的模型错误。这项研究的结果表明，多物理方法对于具有完整物理模型的短距离预测应用是一个不错的选择，在该应用中，集合卡尔曼滤波器的自旋可能花费太长时间，以至于集合扩展无法有效捕获模型误差并交叉模型变量之间的相关性。

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