Editorial Type:
Article
Article Type:
Research Article

Wavelet Compression Technique for High-Resolution Global Model Data on an Icosahedral Grid

Ning Wang NOAA/Earth System Research Laboratory, and Cooperative Institute for Research in the Atmosphere, Colorado State University, Boulder, Colorado

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Jian-Wen Bao NOAA/Earth System Research Laboratory, and Cooperative Institute for Research in the Atmosphere, Colorado State University, Boulder, Colorado

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Jin-Luen Lee NOAA/Earth System Research Laboratory, and Cooperative Institute for Research in the Atmosphere, Colorado State University, Boulder, Colorado

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Fanthune Moeng NOAA/Earth System Research Laboratory, and Cooperative Institute for Research in the Atmosphere, Colorado State University, Boulder, Colorado

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Cliff Matsumoto NOAA/Earth System Research Laboratory, and Cooperative Institute for Research in the Atmosphere, Colorado State University, Boulder, Colorado

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Print Publication:
01 Sep 2015
DOI:
https://doi.org/10.1175/JTECH-D-14-00217.1
Page(s):
1650–1667
Restricted access

Abstract

Modern Earth modeling systems often use high-resolution unstructured grids to discretize their horizontal domains. One of the major challenges in working with these high-resolution models is to efficiently transmit and store large volumes of model data for operational forecasts and for modeling research.

A newly developed compression technique is presented that significantly reduces the size of datasets produced by high-resolution global models that are discretized on an icosahedral grid. The compression technique is based on the wavelet transform together with a grid rearrangement algorithm and precision-controlled quantization technology. The grid rearrangement algorithm converts an icosahedral grid to a set of 10 rhombus grids that retain the spatial correlation of model data so that a three-dimensional wavelet transform can be effectively applied. The precision-controlled quantization scheme guarantees specified precision of compressed datasets.

The technique is applied to the output of a global weather prediction model, the Flow-Following, Finite-Volume Icosahedral Model (FIM) developed by NOAA’s Earth System Research Laboratory. Experiments show that model data at 30-km resolution can be compressed up to 50:1 without noticeable visual differences; at specified precision requirements, the proposed compression technique achieves better compression compared to a state-of-the-art compression format [Gridded Binary (GRIB) with JPEG 2000 packing option]. In addition, model forecasts initialized with original and compressed initial conditions are compared and assessed. The assessment indicates that it is promising to use the technique to compress model data for those applications demanding high fidelity of compressed datasets.

Corresponding author address: Ning Wang, NOAA/ESRL, R/GSD1, 325 Broadway, Boulder, CO 80305. E-mail: ning.wang@noaa.gov

Abstract

Modern Earth modeling systems often use high-resolution unstructured grids to discretize their horizontal domains. One of the major challenges in working with these high-resolution models is to efficiently transmit and store large volumes of model data for operational forecasts and for modeling research.

A newly developed compression technique is presented that significantly reduces the size of datasets produced by high-resolution global models that are discretized on an icosahedral grid. The compression technique is based on the wavelet transform together with a grid rearrangement algorithm and precision-controlled quantization technology. The grid rearrangement algorithm converts an icosahedral grid to a set of 10 rhombus grids that retain the spatial correlation of model data so that a three-dimensional wavelet transform can be effectively applied. The precision-controlled quantization scheme guarantees specified precision of compressed datasets.

The technique is applied to the output of a global weather prediction model, the Flow-Following, Finite-Volume Icosahedral Model (FIM) developed by NOAA’s Earth System Research Laboratory. Experiments show that model data at 30-km resolution can be compressed up to 50:1 without noticeable visual differences; at specified precision requirements, the proposed compression technique achieves better compression compared to a state-of-the-art compression format [Gridded Binary (GRIB) with JPEG 2000 packing option]. In addition, model forecasts initialized with original and compressed initial conditions are compared and assessed. The assessment indicates that it is promising to use the technique to compress model data for those applications demanding high fidelity of compressed datasets.

Corresponding author address: Ning Wang, NOAA/ESRL, R/GSD1, 325 Broadway, Boulder, CO 80305. E-mail: ning.wang@noaa.gov
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