Mapping of sea ice concentration using the NASA NIMBUS 5 ESMR microwave radiometer data 1972–1977
Bibliographic record
Abstract
Abstract. The Electrically Scanning Microwave Radiometer (ESMR) instrument on board the NIMBUS 5 satellite was a one channel microwave radiometer measuring the 19.35 GHz horizontally polarised brightness temperature (TB) from Dec. 11, 1972 to May. 16. 1977. The original tape archive data in swath projection have recently been made available online by NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Even though ESMR was a predecessor of modern multi frequency radiometers, there are still parts of modern processing methodology which can be applied to the data to derive the sea ice extent globally. Here we have reprocessed the entire data set using a modern processing methodology, that includes 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 re-sampling to daily grids, land-spill-over correction, application of climatological masks, setting of processing flags and estimation of sea ice extent, monthly means and estimation of trends. This sea ice dataset derived from NIMBUS 5 ESMR extends the sea ice record with an important reference from the mid 1970s. To make a consistent analysis of the sea ice development through time easier, the same grid and landmask as for EUMETSAT's OSI-SAF SMMR based sea ice CDR have been used for our ESMR dataset. SIC uncertainties have been included for further ease of 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) derived from the same ESMR dataset, with mean differences of 240.000 and 590.000 km2, respectively. The largest differences reach up to 2 million km2, when comparing monthly sea ice extents. Such large differences cannot be explained by the different grids and landmasks of the datasets alone, and must therefore also result from the difference in data filtering and algorithms, such as the dynamical tie-points and atmospheric correction. The new ESMR SIC data set has been released as part of the ESA Climate Change Initiative Programme (ESA CCI) and is publicly available at: http://dx.doi.org/10.5285/34a15b96f1134d9e95b9e486d74e49cf (Tonboe et al., 2023).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".