Comparison of Ensemble Kalman Filter-Based Forecasts to Traditional Ensemble and Deterministic Forecasts for a Case Study of Banded Snow
Comparison of Ensemble Kalman Filter-Based Forecasts to Traditional Ensemble and Deterministic Forecasts for a Case Study of Banded Snow
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基于集合卡尔曼滤波器的预测与传统集合和确定性预测的比较（以带状雪为例）
DOI：
10.1175/waf-d-11-00030.1
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发表时间：
2012
影响因子：
2.9
通讯作者：
M. Coniglio
中科院分区：
文献类型：
--
作者：
A. Suarez;H. Reeves;Dustan M. Wheatley;M. Coniglio
摘要将集合卡尔曼滤波（EnKF）技术与其他建模方法进行了比较，并以带状雪为例进行了研究。这些预报包括12公里和3公里网格间隔的确定性预报（D12和D3），12公里30人集合（E12），以及12公里30人集合与基于EnKF的四维数据同化（EKF 12）。在D12和D3中，流动模式对于带状雪并不理想，但它们在正确的位置具有相似的降水累积。提高分辨率并没有改善定量降水预报。E12集合平均在近似正确的位置有一个有利于条带和降水的流型，尽管相关特征的量级和概率很低。六个成员产生了良好的预测的流型和降水结构。EKF 12集合平均具有理想的带状雪流型，平均值产生带状降水，但相关特征偏北约100 km。EKF12是一款...
AbstractThe ensemble Kalman filter (EnKF) technique is compared to other modeling approaches for a case study of banded snow. The forecasts include a 12- and 3-km grid-spaced deterministic forecast (D12 and D3), a 12-km 30-member ensemble (E12), and a 12-km 30-member ensemble with EnKF-based four-dimensional data assimilation (EKF12). In D12 and D3, flow patterns are not ideal for banded snow, but they have similar precipitation accumulations in the correct location. The increased resolution did not improve the quantitative precipitation forecast. The E12 ensemble mean has a flow pattern favorable for banding and precipitation in the approximate correct location, although the magnitudes and probabilities of relevant features are quite low. Six members produced good forecasts of the flow patterns and the precipitation structure. The EKF12 ensemble mean has an ideal flow pattern for banded snow and the mean produces banded precipitation, but relevant features are about 100 km too far north. The EKF12 has a ...