A Lagrangian Backward Air Parcel Trajectories Clustering Framework

Many studies concerning atmosphere moisture paths use Lagrangian backward air parcel trajectories to determine the humidity sources for specific locations. Automatically grouping trajectories according to their geographical position simplifies and speeds up their analysis. In this paper, we propose...

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Main Authors: Iulia-Maria Rădulescu, Alexandru Boicea, Florin Rădulescu, Daniel-Călin Popeangă
Format: Article
Language:English
Published: MDPI AG 2021-12-01
Series:Water
Subjects:
Online Access:https://www.mdpi.com/2073-4441/13/24/3638
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author Iulia-Maria Rădulescu
Alexandru Boicea
Florin Rădulescu
Daniel-Călin Popeangă
author_facet Iulia-Maria Rădulescu
Alexandru Boicea
Florin Rădulescu
Daniel-Călin Popeangă
author_sort Iulia-Maria Rădulescu
collection DOAJ
description Many studies concerning atmosphere moisture paths use Lagrangian backward air parcel trajectories to determine the humidity sources for specific locations. Automatically grouping trajectories according to their geographical position simplifies and speeds up their analysis. In this paper, we propose a framework for clustering Lagrangian backward air parcel trajectories, from trajectory generation to cluster accuracy evaluation. We employ a novel clustering algorithm, called DenLAC, to cluster troposphere air currents trajectories. Our main contribution is representing trajectories as a one-dimensional array consisting of each trajectory’s points position vector directions. We empirically test our pipeline by employing it on several Lagrangian backward trajectories initiated from Břeclav District, Czech Republic.
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spelling doaj.art-ee7efd16491c4d7ab887785699bb1c6b2023-11-23T11:02:06ZengMDPI AGWater2073-44412021-12-011324363810.3390/w13243638A Lagrangian Backward Air Parcel Trajectories Clustering FrameworkIulia-Maria Rădulescu0Alexandru Boicea1Florin Rădulescu2Daniel-Călin Popeangă3Computer Science and Engineering Department, University Politehnica of Bucharest, 060042 Bucharest, RomaniaComputer Science and Engineering Department, University Politehnica of Bucharest, 060042 Bucharest, RomaniaComputer Science and Engineering Department, University Politehnica of Bucharest, 060042 Bucharest, RomaniaComputer Science and Engineering Department, University Politehnica of Bucharest, 060042 Bucharest, RomaniaMany studies concerning atmosphere moisture paths use Lagrangian backward air parcel trajectories to determine the humidity sources for specific locations. Automatically grouping trajectories according to their geographical position simplifies and speeds up their analysis. In this paper, we propose a framework for clustering Lagrangian backward air parcel trajectories, from trajectory generation to cluster accuracy evaluation. We employ a novel clustering algorithm, called DenLAC, to cluster troposphere air currents trajectories. Our main contribution is representing trajectories as a one-dimensional array consisting of each trajectory’s points position vector directions. We empirically test our pipeline by employing it on several Lagrangian backward trajectories initiated from Břeclav District, Czech Republic.https://www.mdpi.com/2073-4441/13/24/3638Lagrangian backward trajectoriesclusteringHYSPLITposition vectors
spellingShingle Iulia-Maria Rădulescu
Alexandru Boicea
Florin Rădulescu
Daniel-Călin Popeangă
A Lagrangian Backward Air Parcel Trajectories Clustering Framework
Water
Lagrangian backward trajectories
clustering
HYSPLIT
position vectors
title A Lagrangian Backward Air Parcel Trajectories Clustering Framework
title_full A Lagrangian Backward Air Parcel Trajectories Clustering Framework
title_fullStr A Lagrangian Backward Air Parcel Trajectories Clustering Framework
title_full_unstemmed A Lagrangian Backward Air Parcel Trajectories Clustering Framework
title_short A Lagrangian Backward Air Parcel Trajectories Clustering Framework
title_sort lagrangian backward air parcel trajectories clustering framework
topic Lagrangian backward trajectories
clustering
HYSPLIT
position vectors
url https://www.mdpi.com/2073-4441/13/24/3638
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