Mapping of sea ice in 1975 and 1976 using the NIMBUS-6 Scanning Microwave Spectrometer (SCAMS)
Bibliographic record
Abstract
The Scanning Microwave Spectrometer (SCAMS) onboard the NIMBUS-6 satellite operated between 15 June 1975 and 1 June 1976. Its primary mission objective was to map tropospheric temperature profiles for improving weather predictions, measuring Brightness Temperature(s) ( T B s) at five different frequencies (22.235, 31.65, 52.85, 53.85 and 55.45 GHz). However, the top-of-the-atmosphere emission measured at the 22.235 and the 31.65 GHz radiometer channels on the satellite are dominated by surface emission over polar open water and sea ice and can therefore be used for mapping sea ice concentration (SIC) on large scale ( ∼ 100 km). Here we present a SIC and ice type data set, which is based on the T B observations of the two lowest frequencies of SCAMS (center frequencies at 22.235 & 31.65 GHz). While the SCAMS channels do not completely align with the usual frequencies for sea ice retrievals (19, 22 and 37 GHz) in modern processing methods, it is still possible to apply modern techniques to reduce noise in the data. The SIC dataset provides important insights into the sea ice concentration, extent and type of the mid 1970s, where other satellite datasets e.g. the NIMBUS-5 ESMR have gaps and irregular coverage. The SCAMS data has been processed following modern methods, including a regional noise reduction over open water using a simple radiative transfer model, land-spillover corrections and estimation of uncertainties, as well as dynamical tie-points to calibrate the algorithm. The data set has been resampled into daily files with EUMETSAT’s OSI-SAF and ESA CCI compatible daily grids and land masks, for easier comparison with other data sets, such as the modern multi-frequency period starting with NIMBUS-7 SMMR in October 1978 to present and the 1972–1977 period covered by the NIMBUS-5 ESMR with some interruptions. The SCAMS T B s were processed with a hybrid SIC algorithm, combining a one and a two-channel algorithm over open water and ice respectively. We find that the SIC calculated by the two-channel algorithm has more noise over water and low SIC areas than the single-channel algorithm. However, the two-channel algorithm does not systematically underestimate SIC in regions covered by Multi Year Ice (MYI) as the single channel algorithm does. A classification of sea ice types for First Year Ice (FYI) and MYI in the Northern Hemisphere (NH) proved successful, while it was also possible to identify two surface types A and B for the Southern Hemisphere (SH) sea ice, with different radiometric signatures. A comparison of monthly mean sea ice extent (SIE) with the NIMBUS-5 ESMR showed good alignment in the both hemispheres, where the SCAMS SIE is larger by 386 676 km 2 and 251 304 km 2 on average in the NH and SH, respectively. SCAMS fills a gap in the N5ESMR record including the Arctic SIE minimum and the Antarctic maximum in 1975.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 0.000 |
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 teacher head, 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".