Home > Publications database > Compression of meteorological reanalysis data using multiresolution analysis and their application to trajectory calculations > print

001     1043671
005     20250731202237.0
037 _ _ |a FZJ-2025-02971
041 _ _ |a English
100 1 _ |a Kolb, Adrian
|0 P:(DE-HGF)0
|b 0
|e Corresponding author
111 2 _ |a Platform for Advanced Scientific Computing
|g PASC25
|c Brugg
|d 2025-06-16 - 2025-06-18
|w Switzerland
245 _ _ |a Compression of meteorological reanalysis data using multiresolution analysis and their application to trajectory calculations
260 _ _ |c 2025
336 7 _ |a Conference Paper
|0 33
|2 EndNote
336 7 _ |a Other
|2 DataCite
336 7 _ |a INPROCEEDINGS
|2 BibTeX
336 7 _ |a conferenceObject
|2 DRIVER
336 7 _ |a LECTURE_SPEECH
|2 ORCID
336 7 _ |a Conference Presentation
|b conf
|m conf
|0 PUB:(DE-HGF)6
|s 1753943020_27882
|2 PUB:(DE-HGF)
|x After Call
520 _ _ |a The 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).
536 _ _ |a 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)
|0 G:(DE-HGF)POF4-5111
|c POF4-511
|f POF IV
|x 0
536 _ _ |a ADAPTEX - Adaptive Erdsystemmodellierung mit stark reduzierter Berechnungsdauer für Exascale-Supercomputer (16ME0670)
|0 G:(BMBF)16ME0670
|c 16ME0670
|x 1
700 1 _ |a Khosrawi, Farahnaz
|0 P:(DE-Juel1)196659
|b 1
700 1 _ |a Hoffmann, Lars
|0 P:(DE-Juel1)129125
|b 2
700 1 _ |a Müller, Siegfried
|0 P:(DE-HGF)0
|b 3
856 4 _ |u https://pasc25.pasc-conference.org/about/conference/
909 C O |o oai:juser.fz-juelich.de:1043671
|p VDB
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 1
|6 P:(DE-Juel1)196659
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|6 P:(DE-Juel1)129125
913 1 _ |a DE-HGF
|b Key Technologies
|l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action
|1 G:(DE-HGF)POF4-510
|0 G:(DE-HGF)POF4-511
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Enabling Computational- & Data-Intensive Science and Engineering
|9 G:(DE-HGF)POF4-5111
|x 0
914 1 _ |y 2025
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)JSC-20090406
|k JSC
|l Jülich Supercomputing Center
|x 0
980 _ _ |a conf
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)JSC-20090406
980 _ _ |a UNRESTRICTED

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21