Kalman Filtering-Based Probabilistic Nowcasting of Object-Oriented Tracked Convective Storms
Kalman Filtering-Based Probabilistic Nowcasting of Object-Oriented Tracked Convective Storms
复制标题
基于卡尔曼滤波的面向对象跟踪对流风暴概率临近预报
DOI：
10.1175/jtech-d-14-00184.1
复制
发表时间：
2015
影响因子：
2.2
通讯作者：
D. Moisseev
中科院分区：
文献类型：
--
作者：
P. Rossi;V. Chandrasekar;V. Hasu;D. Moisseev
基于天气雷达的面向对象对流风暴跟踪是分析和临近预报对流风暴的标准方法。然而，大多数当前的风暴跟踪算法只提供了一个确定性的方式与有限的能力，以估计相关的不确定性nowcast.This本文提出了一种方法的概率临近预报的对流风暴，解决了临近预报的不确定性问题。该方法首先利用一个二维雷达风暴识别和跟踪算法，结合卡尔曼滤波的噪声测量风暴质心与连续白色噪声加速模型。为了验证该方法，使用芬兰的复合天气雷达资料，每5 min计算一次20-60 min的临近预报，其中包括22000个跟踪风暴。我...
AbstractThe weather radar–based object-oriented convective storm tracking is a standard approach for analyzing and nowcasting convective storms. However, the majority of current storm-tracking algorithms provide nowcasts only in a deterministic fashion with limited ability to estimate the related uncertainties.This paper proposes a method for probabilistic nowcasting of convective storms that addresses the issue of uncertainty of nowcasts. The approach first utilizes a two-dimensional radar-based storm identification and tracking algorithm in conjunction with the Kalman filtering of noisy measurements of storm centroid with the continuous white noise acceleration model. The resulting smoothed estimates of storm centroid and velocity components and their error covariance values are then applied to nowcast the probability of storm occurrence.To verify the approach, 20–60-min nowcasts were computed every 5 min using composite weather radar data in Finland including approximately 22 000 tracked storms. The ve...