open access publication

Article, 2024

Mapping of sea ice concentration using the NASA NIMBUS 5 Electrically Scanning Microwave Radiometer data from 1972–1977

Earth System Science Data, ISSN 1866-3516, 1866-3508, Volume 16, 3, Pages 1247-1264, 10.5194/essd-16-1247-2024

Contributors

Kolbe, Wiebke Margitta 0000-0002-7782-8581 (Corresponding author) [1] [2] Tonboe, Rasmus Tage 0000-0003-1463-4832 [2] Stroeve, Julienne Christine 0000-0001-7316-8320 [3] [4] [5]

Affiliations

  1. [1] Danish Meteorological Institute
  2. [NORA names: DMI Danish Meteorological Institute; Governmental Institutions; Denmark; Europe, EU; Nordic; OECD];
  3. [2] Technical University of Denmark
  4. [NORA names: DTU Technical University of Denmark; University; Denmark; Europe, EU; Nordic; OECD];
  5. [3] University College London
  6. [NORA names: United Kingdom; Europe, Non-EU; OECD];
  7. [4] University of Colorado Boulder
  8. [NORA names: United States; America, North; OECD];
  9. [5] University of Manitoba
  10. [NORA names: Canada; America, North; OECD]

Abstract

Abstract. The Electrically Scanning Microwave Radiometer (ESMR) instrument onboard the NIMBUS 5 satellite was a one-channel microwave radiometer that measured the 19.35 GHz horizontally polarized brightness temperature (TB) from 11 December 1972 to 16 May 1977. The original tape archive data in swath projection have recently been made available online by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Even though the ESMR was a predecessor of modern multi-frequency radiometers, there are still parts of modern processing methodologies which can be applied to the data to derive the sea ice extent globally. Here, we have reprocessed the entire dataset using a modern processing methodology that includes the implementation of pre-processing filtering, dynamical tie points, and a radiative transfer model (RTM) together with numerical weather prediction (NWP) for atmospheric correction. We present the one-channel sea ice concentration (SIC) algorithm and the model for computing temporally and spatially varying SIC uncertainty estimates. Post-processing steps include resampling to daily grids, land-spillover correction, the application of climatological masks, the setting of processing flags, and the estimation of sea ice extent, monthly means, and trends. This sea ice dataset derived from the NIMBUS 5 ESMR extends the sea ice record with an important reference from the mid-1970s. To make it easier to perform a consistent analysis of sea ice development over time, the same grid and land mask as used for EUMETSAT's OSI-SAF SMMR-based sea-ice climate data record (CDR) were used for our ESMR dataset. SIC uncertainties were included to further ease comparison to other datasets and time periods. We find that our sea ice extent in the Arctic and Antarctic in the 1970s is generally higher than those available from the National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC), which were derived from the same ESMR dataset, with mean differences of 240 000 and 590 000 km2, respectively. When comparing monthly sea ice extents, the largest differences reach up to 2 million km2. Such large differences cannot be explained by the different grids and land masks of the datasets alone and must therefore also result from the differences in data filtering and algorithms, such as the dynamical tie points and atmospheric correction. The new ESMR SIC dataset has been released as part of the ESA Climate Change Initiative (ESA CCI) program and is publicly available at https://doi.org/10.5285/34a15b96f1134d9e95b9e486d74e49cf (Tonboe et al., 2023).

Keywords

Antarctic, Archive Center, Arctic, CCI, Climate Change Initiative, Distributed Active Archive Center, ESA, ESA CCI, ESA Climate Change Initiative, EUMETSAT, Earth Sciences Data, Electrically Scanning Microwave Radiometer, GES, GES DISC, Goddard Earth Sciences Data, NASA, NASA Goddard Earth Sciences Data, NSIDC, National, National Snow, Nimbus, Nimbus 5 satellite, Nimbus-5 ESMR, TB, algorithm, analysis, archival data, atmospheric correction, center, change initiatives, climate data record, comparison, comparison to other datasets, concentration, correction, daily grids, data, data filtering, data recording, dataset, development, differences, disk, dynamic tie points, electricity, estimation, extent, filter, flag, grid, ice concentration, ice dataset, ice development, ice extent, ice records, implementation, initiation, instrument, km2, land, land masking, maps, mask, mean, methodology, microwave radiometer, microwave radiometer data, mid-1970s, model, monthly means, monthly sea ice extent, numerical weather prediction, period, point, post-processing step, pre-processing filters, prediction, process, processing methodology, project, radiative transfer model, radiometer, radiometer data, records, scanning microwave radiometer, sea, sea ice concentration, sea ice datasets, sea ice development, sea ice extent, sea ice records, snow, steps, tape, tie points, time, time periods, transfer model, trends, uncertainty, weather prediction

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