scholarly journals A fast vibration source tracking algorithm using pixel-level digital filters

2018 ◽  
Vol 84 (857) ◽  
pp. 17-00469-17-00469
Author(s):  
Kohei SHIMASAKI ◽  
Mingjun JIANG ◽  
Takeshi TAKAKI ◽  
Idaku ISHII
Keyword(s):  
Digital Filters ◽  
Tracking Algorithm ◽  
Source Tracking ◽  
Vibration Source ◽  
Fast Vibration

Real-Time Vibration Source Tracking Using High-Speed Vision

2017 ◽  
Vol 17 (5) ◽  
pp. 1513-1527 ◽  
Author(s):  
Mingjun Jiang ◽  
Qingyi Gu ◽  
Tadayoshi Aoyama ◽  
Takes Takaki ◽  
Idaku Ishii
Keyword(s):  
Real Time ◽  
High Speed ◽  
Source Tracking ◽  
Vibration Source

Python-Based Supercell Tracking for Coarse Temporal and Spatial Resolution Numerical Model Simulations

2021 ◽  
Author(s):  
Matthew E. Gropp ◽  
Casey E. Davenport
Keyword(s):  
Open Source ◽  
Spatial Resolution ◽  
Climate Model ◽  
Regional Climate ◽  
Three Dimensional ◽  
Model Verification ◽  
Tracking Algorithm ◽  
Data Availability ◽  
Source Tracking ◽  
Temporal And Spatial

AbstractDeep convective thunderstorm tracking methodologies and software have become useful and necessary tools across many applications, from nowcasting to model verification. Despite many available options, many of these pre-existing methods lack a customizable, fast, and flexible methodology that can track supercell thunderstorms within convective-allowing climate datasets with coarse temporal and spatial resolution. This project serves as one option to solve this issue via an all-in-one tracking methodology, built upon several open-source Python libraries, and designed to work with various temporal resolutions, including hourly. Unique to this approach is accounting for varying data availability of different model variables, while still sufficiently and accurately tracking specific convective features; in this case, supercells were the focus. To help distinguish supercells from ordinary cells, updraft helicity and other three-dimensional atmospheric data were incorporated into the tracking algorithm to confirm its supercellular status. Deviant motion from the mean wind was also used identify supercells. The tracking algorithm was tested and performed on a dynamically-downscaled regional climate model dataset with 4 km horizontal grid spacing. Each supercell was tracked for its entire lifetime over the course of 26 years of model output, resulting in a supercell climatology over the central United States. Due to the tracking configuration and dataset used, the tracking performs most consistently for long-lived and strong supercells compared to weak and short-lived supercells. This tracking methodology allows for customizable open-source tracking of supercells in any downscaled convective-allowing dataset, even with coarse temporal resolution.

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