Paul Joe

3D regularized velocity from 3D Doppler radial velocity

The availability of sequences of 3D Doppler radial velocity datasets provides sufficient information to estimate the 3D velocity of Doppler storms. We present a regularization framework for computing the 3D velocity field of storms from the underlying 3D radial velocities via an intermediate least squares computation. We obtain very realistic Doppler velocities, which can be used to estimate and predict the motion of Doppler storms. Such information is fundamental in the tracking of Doppler storms over time.

Tracking severe weather storms in Doppler radar images

We describe an automatic storm‐tracking system to help with the forecasting of severe storms. In this article, we present the concepts of fuzzy point, fuzzy vector, fuzzy length of a fuzzy vector, and fuzzy angle between two nonzero fuzzy vectors, that are used in our tracking algorithm. These concepts are used to overcome some of the limitations of our previous work, where fixed center‐of‐mass storm centers did not provide smooth tracks over time, while at the same time, their detection was very threshold sensitive. Our algorithm uses region splitting with dynamic thresholding to determine storm masses in Doppler radar intensity images. We represent the center of a hypothesized storm using a fuzzy point. These fuzzy storm centers are then tracked over time using an incremental relaxation algorithm. We have developed a visualization program using the X11 Athena toolkit for our storm visualization tool. The algorithm was tested on seven real radar image sequences obtained from the Atmospheric Environment Service radar station at King City, Ontario, Canada. We can obtain storm tracks that are long and smooth and which closely match an expert meteorologists perception.

3D Velocity from 3D Doppler Radial Velocity

We present local least squares and regularization frameworks for computing 3D velocity (3D optical flow) from 3D radial velocity measured by a Doppler radar. We demonstrate the performance of our algorithms quantitatively on synthetic radial velocity data and qualitatively on real radial velocity data, obtained from the Doppler radar at Kurnell Radar station, Botany Bay, New South Wales, Australia. Radial velocity can be used to predict the future positions of storms in sequences of Doppler radar datasets.© 2005 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 15, 189–198, 2005

Tracking fuzzy storm centers in Doppler radar images

We describe an automatic storm tracking system to help with the forecasting of severe storms. The concepts fuzzy point, fuzzy vector, fuzzy length of a fuzzy vector, and the fuzzy angle between two non-zero fuzzy vectors are first examined. We use a region splitting algorithm with dynamic thresholding to determine storm masses in Doppler radar intensity images. We represent the center of an hypothesized storm using a fuzzy point. These fuzzy storm centers are tracked over time using an incremental relaxation algorithm. The algorithms are tested on actual radar images obtained from the Atmospheric Environment Service radar station at King City, Ontario, Canada. The algorithms are capable of producing storm tracks which closely match human perception.

FROST-2014: The Sochi Winter Olympics International Project

CapsuleSix nowcasting systems, nine deterministic mesoscale numerical weather prediction models, and six ensemble prediction systems took part in the FROST-2014 project.

Skeleton-Based Tornado Hook Echo Detection

We propose and evaluate a method to identify tornadoes automatically in Doppler radar imagery by detecting hook echoes, which are important signatures of tornadoes, in Doppler radar precipitation density data. Our method uses a skeleton to represent 2D storm shapes. To characterize hook echoes, we propose four shape features of skeletons: curvature, curve orientation, thickness variation, boundary proximity, and two shape properties of tornadoes: southwest localization and the ratio of storm size to model hook echo size. To evaluate the hook echo detection algorithm, the hook echoes detected in several radar datasets by the algorithm are compared to those proposed by an expert. The effectiveness of the algorithm is quantified using a critical success index (CSI) analysis.

Tracking Severe Storms Using a Pseudo Storm Concept

Tracking storms in radar images can be conceived of as a problem of tracking deformable objects. Our current relaxation labelling-based tracking algorithm that represents these deformable objects as “fuzzy” points can track objects that undergo shape deformations. One other type of deformation is the splitting of an object into multiple objects or the merging of multiple objects into one object from one image to the next. With our current algorithm, tracks are interrupted when such events happen in image sequences. We remove this deficiency of the current algorithm by adding the concept of a Pseudo Storm to its representational repertoire. With only minor modifications to the current algorithm, the new algorithm can track deformable objects that undergo both merging and splitting events. The new pseudo storm tracking algorithm outperforms our previous storm tracking algorithm for Great Lakes Doppler precipitation datasets.