001043671 001__ 1043671
001043671 005__ 20250731202237.0
001043671 037__ $$aFZJ-2025-02971
001043671 041__ $$aEnglish
001043671 1001_ $$0P:(DE-HGF)0$$aKolb, Adrian$$b0$$eCorresponding author
001043671 1112_ $$aPlatform for Advanced Scientific Computing$$cBrugg$$d2025-06-16 - 2025-06-18$$gPASC25$$wSwitzerland
001043671 245__ $$aCompression of meteorological reanalysis data using multiresolution analysis and their application to trajectory calculations
001043671 260__ $$c2025
001043671 3367_ $$033$$2EndNote$$aConference Paper
001043671 3367_ $$2DataCite$$aOther
001043671 3367_ $$2BibTeX$$aINPROCEEDINGS
001043671 3367_ $$2DRIVER$$aconferenceObject
001043671 3367_ $$2ORCID$$aLECTURE_SPEECH
001043671 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1753943020_27882$$xAfter Call
001043671 520__ $$aThe storage requirements for meteorological reanalysis data have increased significantly in recent years. To address the challenges of handling these large data sets, efficient compression techniques are required. In addition, the error of the compressed data should be as small as possible. Although lossless compression algorithms exist, the resulting data are still too large. Conversely, lossy compression formats allow a small file size, but are often not able to control the error relative to the original data. We propose a multiresolution-based grid adaptation as an alternative method for lossy compression. To do this, we perform a multiresolution analysis using multiwavelets on a hierarchy of nestedgrids. This method provides us with local information on the differences between successive refinement levels. Since smooth regions have small local differences, we apply hard thresholding to resolve these regions on a coarser grid. Thus, the data is projected onto an adaptive grid where only regions with steep gradients or discontinuities have a high resolution, which significantly reduces the file size. We present how data compression is achieved by applying multiresolution-based grid adaptation using ERA5 meteorological reanalysis data. We additionally discuss the implementation of this method into the Lagrangian model for Massive-Parallel Trajectory Calculation (MPTRAC).
001043671 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001043671 536__ $$0G:(BMBF)16ME0670$$aADAPTEX - Adaptive Erdsystemmodellierung mit stark reduzierter Berechnungsdauer für Exascale-Supercomputer (16ME0670)$$c16ME0670$$x1
001043671 7001_ $$0P:(DE-Juel1)196659$$aKhosrawi, Farahnaz$$b1
001043671 7001_ $$0P:(DE-Juel1)129125$$aHoffmann, Lars$$b2
001043671 7001_ $$0P:(DE-HGF)0$$aMüller, Siegfried$$b3
001043671 8564_ $$uhttps://pasc25.pasc-conference.org/about/conference/
001043671 909CO $$ooai:juser.fz-juelich.de:1043671$$pVDB
001043671 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)196659$$aForschungszentrum Jülich$$b1$$kFZJ
001043671 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)129125$$aForschungszentrum Jülich$$b2$$kFZJ
001043671 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5111$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x0
001043671 9141_ $$y2025
001043671 920__ $$lyes
001043671 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
001043671 980__ $$aconf
001043671 980__ $$aVDB
001043671 980__ $$aI:(DE-Juel1)JSC-20090406
001043671 980__ $$aUNRESTRICTED