New Methods for Data Storage of Model Output from Ensemble Simulations

AbstractData storage and data processing generate significant cost for weather and climate modeling centers. The volume of data that needs to be stored and data that are disseminated to end users i...

[1]  J. G. Esler,et al.  Estimation of the local response to a forcing in a high dimensional system using the fluctuation-dissipation theorem , 2013 .

[2]  Francesco De Simone,et al.  Evaluating lossy data compression on climate simulation data within a large ensemble , 2016, Geoscientific Model Development.

[3]  Julian M. Kunkel,et al.  Data Compression for Climate Data , 2016, Supercomput. Front. Innov..

[4]  Linus Magnusson,et al.  Factors Influencing Skill Improvements in the ECMWF Forecasting System , 2013 .

[5]  Marc G. Genton,et al.  Compressing an Ensemble With Statistical Models: An Algorithm for Global 3D Spatio-Temporal Temperature , 2016, Technometrics.

[6]  Charles S. Zender,et al.  The compression–error trade-off for large gridded data sets , 2017 .

[7]  Martin Isenburg,et al.  Fast and Efficient Compression of Floating-Point Data , 2006, IEEE Transactions on Visualization and Computer Graphics.

[8]  Ning Wang,et al.  Wavelet Compression Technique for High-Resolution Global Model Data on an Icosahedral Grid , 2015 .

[9]  Wayne Luk,et al.  On the use of programmable hardware and reduced numerical precision in earth‐system modeling , 2015, Journal of advances in modeling earth systems.

[10]  David B. Stephenson,et al.  Statistical methods for interpreting Monte Carlo ensemble forecasts , 2000 .

[11]  Charles S. Zender.  Bit Grooming: statistically accurate precision-preserving quantization with compression, evaluated in the netCDF Operators (NCO, v4.4.8+) , 2016 .