An Ensemble Kalman Filter and Smoother for Satellite Data Assimilation
An Ensemble Kalman Filter and Smoother for Satellite Data Assimilation
复制标题
用于卫星数据同化的集成卡尔曼滤波器和平滑器
DOI：
10.1198/jasa.2010.ap07636
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发表时间：
2010
影响因子：
3.7
通讯作者：
D. Beletsky
中科院分区：
文献类型：
--
作者：
Jonathan R. Stroud;M. Stein;B. Lesht;D. Schwab;D. Beletsky
本文提出了一种将卫星图像与对流扩散模型相结合的方法，用于环境过程的插值和预测。我们提出了一个动态的状态空间模型和一个集合卡尔曼滤波和平滑算法在线和回溯状态估计。我们的方法解决了卫星数据中固有的高维性，测量偏差和非线性。我们应用该方法的SeaWiFS卫星图像序列在密歇根湖从1998年3月，当一个大的沉积物羽流观察到的图像后，一个大的风暴事件。使用我们的方法，我们结合联合收割机的图像与泥沙输运模型，以产生地图的泥沙浓度和不确定性在空间和时间。我们表明，我们的方法提高了20%-30%，相对于标准方法的样本外RMSE。这篇文章在网上有补充材料。
This paper proposes a methodology for combining satellite images with advection-diffusion models for interpolation and prediction of environmental processes. We propose a dynamic state-space model and an ensemble Kalman filter and smoothing algorithm for on-line and retrospective state estimation. Our approach addresses the high dimensionality, measurement bias, and nonlinearities inherent in satellite data. We apply the method to a sequence of SeaWiFS satellite images in Lake Michigan from March 1998, when a large sediment plume was observed in the images following a major storm event. Using our approach, we combine the images with a sediment transport model to produce maps of sediment concentrations and uncertainties over space and time. We show that our approach improves out-of-sample RMSE by 20%–30% relative to standard approaches. This article has supplementary material online.