IMPACT OF MODIS WINDS ON THE GLOBAL NWP SYSTEM OF THE
Bibliographic record
Abstract
Global wind field measurements are essential to improve our knowledge of atmos-pheric dynamics, including atmospheric transport processes of energy, water and airbourne particles. Unfortunately, coverage of wind observations is rather poor over the oceans and the polar regions. Only a few regular wind measurements are made along coastal areas of the Arctic, Antarctica and the interior of Canada, Alaska, Russia and Northern Europe, but there is little or no coverage of the interior of Antarctica, Greenland or the Arctic Ocean. Poor knowledge of the polar wind field is a major cause of larger than normal analysis and forecast errors in these regions, leading to occasional forecast “busts ” in areas like Europe, influenced by synoptic disturbances originating in polar regions. Recently a new satellite-derived wind product, developed at the Cooperative Institute for Meteorological Satellite Studies (CIMSS), has become available, which provides information on polar wind fields. The winds are derived by tracking features in the IR window band at 11 µm and in the water vapour (WV) band at 6.7 µm from the Moderate Resolution Imaging Spectrodiameter (MODIS) instrument on board the polar-orbiting satellites Terra and Aqua. Wind vector heights are assigned by using either the IR windows, CO2 slicing or the H2O intercept method (Key et al., 2002). Results of the NOGAPS model are used as a first guess wind field. MODIS winds are available in areas north of 65o N and 65o S.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".