A Comparison of Surface Layer and Surface Turbulent Flux Observations over the Labrador Sea with ECMWF Analyses and NCEP Reanalyses
Bibliographic record
Abstract
Comparisons are made between a time series of meteorological surface layer observational data taken on board the R/V Knorr, and model analysis data from the European Centre for Medium-Range Weather Forecasting (ECMWF) and the National Centers for Environmental Prediction (NCEP).The observational data were gathered during a winter cruise of the R/V Knorr, from 6 February to 13 March 1997, as part of the Labrador Sea Deep Convection Experiment.The surface layer observations generally compare well with both model representations of the wintertime atmosphere.The biases that exist are mainly related to discrepancies in the sea surface temperature or the relative humidity of the analyses.The surface layer observations are used to generate bulk estimates of the surface momentum flux, and the surface sensible and latent heat fluxes.These are then compared with the model-generated turbulent surface fluxes.The ECMWF surface sensible and latent heat flux time series compare reasonably well, with overestimates of only 13% and 10%, respectively.In contrast, the NCEP model overestimates the bulk fluxes by 51% and 27%, respectively.The differences between the bulk estimates and those of the two models are due to different surface heat flux algorithms.It is shown that the roughness length formula used in the NCEP reanalysis project is inappropriate for moderate to high wind speeds.Its failings are acute for situations of large air-sea temperature difference and high wind speed, that is, for areas of high sensible heat fluxes such as the Labrador Sea, the Norwegian Sea, the Gulf Stream, and the Kuroshio.The new operational NCEP bulk algorithm is found to be more appropriate for such areas.It is concluded that surface turbulent flux fields from the ECMWF are within the bounds of observational uncertainty and therefore suitable for driving ocean models.This is in contrast to the surface flux fields from the NCEP reanalysis project, where the application of a more suitable algorithm to the model surface-layer meteorological data is recommended.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".