Probability Maps for the Visualization of Assimilation Ensemble Flow Data

Handle URI:
http://hdl.handle.net/10754/558297
Title:
Probability Maps for the Visualization of Assimilation Ensemble Flow Data
Authors:
Hollt, Thomas; Hadwiger, Markus ( 0000-0003-1239-4871 ) ; Knio, Omar; Hoteit, Ibrahim ( 0000-0002-3751-4393 )
Abstract:
Ocean forecasts nowadays are created by running ensemble simulations in combination with data assimilation techniques. Most of these techniques resample the ensemble members after each assimilation cycle. This means that in a time series, after resampling, every member can follow up on any of the members before resampling. Tracking behavior over time, such as all possible paths of a particle in an ensemble vector field, becomes very difficult, as the number of combinations rises exponentially with the number of assimilation cycles. In general a single possible path is not of interest but only the probabilities that any point in space might be reached by a particle at some point in time. In this work we present an approach using probability-weighted piecewise particle trajectories to allow such a mapping interactively, instead of tracing quadrillions of individual particles. We achieve interactive rates by binning the domain and splitting up the tracing process into the individual assimilation cycles, so that particles that fall into the same bin after a cycle can be treated as a single particle with a larger probability as input for the next time step. As a result we loose the possibility to track individual particles, but can create probability maps for any desired seed at interactive rates.
KAUST Department:
Physical Sciences and Engineering (PSE) Division; Applied Mathematics and Computational Science Program
Publisher:
The Eurographics Association
Journal:
Workshop on Visualisation in Environmental Sciences (EnvirVis)
Conference/Event name:
Visualization in Environmental Sciences 2015 (EnvirVis 2015)
Issue Date:
25-May-2015
DOI:
10.2312/envirvis.20151090
Type:
Conference Paper
Additional Links:
https://diglib.eg.org/handle/10.2312/envirvis.20151090.043-047
Appears in Collections:
Conference Papers; Applied Mathematics and Computational Science Program; Physical Sciences and Engineering (PSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorHollt, Thomasen
dc.contributor.authorHadwiger, Markusen
dc.contributor.authorKnio, Omaren
dc.contributor.authorHoteit, Ibrahimen
dc.date.accessioned2015-06-21T09:28:24Zen
dc.date.available2015-06-21T09:28:24Zen
dc.date.issued2015-05-25en
dc.identifier.doi10.2312/envirvis.20151090en
dc.identifier.urihttp://hdl.handle.net/10754/558297en
dc.description.abstractOcean forecasts nowadays are created by running ensemble simulations in combination with data assimilation techniques. Most of these techniques resample the ensemble members after each assimilation cycle. This means that in a time series, after resampling, every member can follow up on any of the members before resampling. Tracking behavior over time, such as all possible paths of a particle in an ensemble vector field, becomes very difficult, as the number of combinations rises exponentially with the number of assimilation cycles. In general a single possible path is not of interest but only the probabilities that any point in space might be reached by a particle at some point in time. In this work we present an approach using probability-weighted piecewise particle trajectories to allow such a mapping interactively, instead of tracing quadrillions of individual particles. We achieve interactive rates by binning the domain and splitting up the tracing process into the individual assimilation cycles, so that particles that fall into the same bin after a cycle can be treated as a single particle with a larger probability as input for the next time step. As a result we loose the possibility to track individual particles, but can create probability maps for any desired seed at interactive rates.en
dc.publisherThe Eurographics Associationen
dc.relation.urlhttps://diglib.eg.org/handle/10.2312/envirvis.20151090.043-047en
dc.rightsArchived with thanks to The Eurographics Associationen
dc.titleProbability Maps for the Visualization of Assimilation Ensemble Flow Dataen
dc.typeConference Paperen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.identifier.journalWorkshop on Visualisation in Environmental Sciences (EnvirVis)en
dc.conference.datethe 25. and 26. May 2015en
dc.conference.nameVisualization in Environmental Sciences 2015 (EnvirVis 2015)en
dc.conference.locationCagliari, Sardinia/Italyen
dc.eprint.versionPublisher's Version/PDFen
kaust.authorHollt, Thomasen
kaust.authorHadwiger, Markusen
kaust.authorKnio, Omaren
kaust.authorHoteit, Ibrahimen
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