Image of A tie-point zone group compaction schema for the geolocation data of S-NPP and NOAA-20 VIIRS SDRs to reduce file sizes in memory-sensitive environments

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A tie-point zone group compaction schema for the geolocation data of S-NPP and NOAA-20 VIIRS SDRs to reduce file sizes in memory-sensitive environments

The European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) disseminates weather and climate-related satellite data to its users via satellite broadcast. As part of this, the EUMETSAT Advanced Retransmission Service (EARS) provides Sensor Data Records (SDR) from the United States satellites Suomi National Polar-orbiting Partnership (S-NPP) and NOAA-20. Due to bandwidth constraints and for cost-reduction reasons for the upload to the disseminating satellite, a near-lossless compaction method has been developed for the geolocation data of the data capturing satellites’ Visible Infrared Imager Radiometer Suite (VIIRS) instrument. The geolocation data are compacted by storing only the data representation for so called tie-points which form tie-point zones and are grouped in tie-point zone groups. Compression factors of 120 for M-Band, 479 for I-Band, and 154 for Day/Night Band, not using HDF5 internal compression on neither the input nor the output, and 56 for M-Band, 210 for I-Band, and 89 for Day/Night Band, using HDF5 internal compression on both the input and output, respectively, are reached. The compaction process introduces an error of about 1 ​m RMSE for the position in latitude/longitude and of about 0.001° RMSE for the angular data. The concept of the tie-point zones and the quadratic interpolation schema with coefficients analytically derived from the scanning geometry forms the main elements of the method presented. The full algorithm and mathematical background needed are presented, and the algorithm can be highly parallelized. The method can likely be applied to any cross-track scanning sensor.

Ketersediaan
90551.136Perpustakaan BIG (Eksternal Harddisk)Tersedia
Informasi Detail
Judul Seri
Applied Computing and Geoscience - Open Access
No. Panggil
551.136
Penerbit
Amsterdam : Elsevier.,
Deskripsi Fisik
21 hlm PDF, 1.693 KB
Bahasa
Inggris
ISBN/ISSN
2590-1974
Klasifikasi
551.136
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
-
Edisi
Vol.6, June 2020
Subjek
Suomi National Polar-orbiting Partnership (SNPP)
Visible Infrared
Imager Radiometer Suite (VIIRS)
Data compression
Hierarchical Data Format version 5 (HDF5)
Tie-point zones
Info Detail Spesifik
-
Pernyataan Tanggungjawab
Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas
  • A tie-point zone group compaction schema for the geolocation data of S-NPP and NOAA-20 VIIRS SDRs to reduce file sizes in memory-sensitive environments
    The European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) disseminates weather and climate-related satellite data to its users via satellite broadcast. As part of this, the EUMETSAT Advanced Retransmission Service (EARS) provides Sensor Data Records (SDR) from the United States satellites Suomi National Polar-orbiting Partnership (S-NPP) and NOAA-20. Due to bandwidth constraints and for cost-reduction reasons for the upload to the disseminating satellite, a near-lossless compaction method has been developed for the geolocation data of the data capturing satellites’ Visible Infrared Imager Radiometer Suite (VIIRS) instrument. The geolocation data are compacted by storing only the data representation for so called tie-points which form tie-point zones and are grouped in tie-point zone groups. Compression factors of 120 for M-Band, 479 for I-Band, and 154 for Day/Night Band, not using HDF5 internal compression on neither the input nor the output, and 56 for M-Band, 210 for I-Band, and 89 for Day/Night Band, using HDF5 internal compression on both the input and output, respectively, are reached. The compaction process introduces an error of about 1 ​m RMSE for the position in latitude/longitude and of about 0.001° RMSE for the angular data. The concept of the tie-point zones and the quadratic interpolation schema with coefficients analytically derived from the scanning geometry forms the main elements of the method presented. The full algorithm and mathematical background needed are presented, and the algorithm can be highly parallelized. The method can likely be applied to any cross-track scanning sensor.
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