An Object-Based Method for Tracking Convective Storms in Convection Allowing Models

The steady-state assumption commonly used in object-based tracking algorithms may be insufficient to determine the right track when a convective storm goes through a complicated evolution. Such an issue is exacerbated by the relatively coarse output frequency of current convection allowing model (CA...

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Autores principales:	Fan Han, Xuguang Wang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	convective storm tracking convection allowing models severe weather Meteorology. Climatology QC851-999
Acceso en línea:	https://doaj.org/article/dbb2bfcf0f614ffcab670501fc5af70d
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spelling	oai:doaj.org-article:dbb2bfcf0f614ffcab670501fc5af70d2021-11-25T16:45:58ZAn Object-Based Method for Tracking Convective Storms in Convection Allowing Models10.3390/atmos121115352073-4433https://doaj.org/article/dbb2bfcf0f614ffcab670501fc5af70d2021-11-01T00:00:00Zhttps://www.mdpi.com/2073-4433/12/11/1535https://doaj.org/toc/2073-4433The steady-state assumption commonly used in object-based tracking algorithms may be insufficient to determine the right track when a convective storm goes through a complicated evolution. Such an issue is exacerbated by the relatively coarse output frequency of current convection allowing model (CAM) forecasts (e.g., hourly), giving rise to many spatially well resolved but temporally not well resolved storms that steady-state assumption could not account for. To reliably track simulated storms in CAM outputs, this study proposed an object-based method with two new features. First, the method explicitly estimated the probability of each probable track based on either its immediate past and future motion or a reliable “first-guess motion” derived from storm climatology or near-storm environmental variables. Second, object size was incorporated into the method to help identify temporally not well resolved storms and minimize false tracks derived for them. Parameters of the new features were independently derived from a storm evolution analysis using 2-min Multi-Radar Multi-Sensor (MRMS) data and hourly CAM forecasts produced by the University of Oklahoma (OU) Multiscale data Assimilation and Predictability Laboratory (MAP) from May 2019. The performance of the new method was demonstrated with hourly MRMS and CAM forecast examples from May 2018. A systematic evaluation of four severe weather events indicated 99% accuracy achieved for over 600 hourly MRMS tracks derived with the proposed tracking method.Fan HanXuguang WangMDPI AGarticleconvective storm trackingconvection allowing modelssevere weatherMeteorology. ClimatologyQC851-999ENAtmosphere, Vol 12, Iss 1535, p 1535 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	convective storm tracking convection allowing models severe weather Meteorology. Climatology QC851-999
spellingShingle	convective storm tracking convection allowing models severe weather Meteorology. Climatology QC851-999 Fan Han Xuguang Wang An Object-Based Method for Tracking Convective Storms in Convection Allowing Models
description	The steady-state assumption commonly used in object-based tracking algorithms may be insufficient to determine the right track when a convective storm goes through a complicated evolution. Such an issue is exacerbated by the relatively coarse output frequency of current convection allowing model (CAM) forecasts (e.g., hourly), giving rise to many spatially well resolved but temporally not well resolved storms that steady-state assumption could not account for. To reliably track simulated storms in CAM outputs, this study proposed an object-based method with two new features. First, the method explicitly estimated the probability of each probable track based on either its immediate past and future motion or a reliable “first-guess motion” derived from storm climatology or near-storm environmental variables. Second, object size was incorporated into the method to help identify temporally not well resolved storms and minimize false tracks derived for them. Parameters of the new features were independently derived from a storm evolution analysis using 2-min Multi-Radar Multi-Sensor (MRMS) data and hourly CAM forecasts produced by the University of Oklahoma (OU) Multiscale data Assimilation and Predictability Laboratory (MAP) from May 2019. The performance of the new method was demonstrated with hourly MRMS and CAM forecast examples from May 2018. A systematic evaluation of four severe weather events indicated 99% accuracy achieved for over 600 hourly MRMS tracks derived with the proposed tracking method.
format	article
author	Fan Han Xuguang Wang
author_facet	Fan Han Xuguang Wang
author_sort	Fan Han
title	An Object-Based Method for Tracking Convective Storms in Convection Allowing Models
title_short	An Object-Based Method for Tracking Convective Storms in Convection Allowing Models
title_full	An Object-Based Method for Tracking Convective Storms in Convection Allowing Models
title_fullStr	An Object-Based Method for Tracking Convective Storms in Convection Allowing Models
title_full_unstemmed	An Object-Based Method for Tracking Convective Storms in Convection Allowing Models
title_sort	object-based method for tracking convective storms in convection allowing models
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/dbb2bfcf0f614ffcab670501fc5af70d
work_keys_str_mv	AT fanhan anobjectbasedmethodfortrackingconvectivestormsinconvectionallowingmodels AT xuguangwang anobjectbasedmethodfortrackingconvectivestormsinconvectionallowingmodels AT fanhan objectbasedmethodfortrackingconvectivestormsinconvectionallowingmodels AT xuguangwang objectbasedmethodfortrackingconvectivestormsinconvectionallowingmodels
_version_	1718412986723336192

An Object-Based Method for Tracking Convective Storms in Convection Allowing Models

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