An equivalent neutral wind observation operator for variational assimilation of scatterometer ocean surface wind data
Bibliographic record
Abstract
Abstract This article describes a new observation operator developed for improved variational data assimilation of scatterometer ocean wind vectors. The forward operator is designed explicitly to take into account the equivalent neutral calibration of scatterometer wind retrievals. The corresponding tangent linear and adjoint (TL/AD) operators are developed for the calculation of near‐surface increments based explicitly on parametrizations of surface‐layer turbulent momentum transfer. Tangent linear and adjoint operators are formulated using the Jacobian of the forward operator estimated using the perturbation method. Various TL/AD configurations are investigated in more detail to assess the impact of sensitivities affecting the vertical gradient of surface‐layer temperature during assimilation of ocean surface wind data. It is argued that these sensitivities should be neglected in the context of a stand‐alone atmospheric prediction system. The complete form of the operator has, however, the potential to be a key component of a coupled atmosphere–ocean data assimilation system. Two data assimilation experiments were performed using distinct scatterometer observation operators, i.e. new equivalent neutral versus the simpler operational configuration using stability‐dependent (real) background winds. Associated medium‐range forecasts were run for a complete impact assessment. The impact of the new operator is tracked through the entire data assimilation and forecast process by comparing analyses and forecasts from both experiments. A significant sensitivity of the data assimilation system to the configuration of the observation operator has been found. Modest but beneficial impacts are obtained with the equivalent neutral wind observation operator with respect to short‐range forecasts of ocean surface winds and medium‐range forecasts of extratropical geopotential heights. © 2012 Crown in the right of Canada. Published by John Wiley & Sons Ltd.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".