Impact of Slant-Path Radiative Transfer in the Simulation and Assimilation of Satellite Radiances in Environment Canada’s Weather Forecast System
Bibliographic record
Abstract
Abstract Slant satellite-viewing geometry is investigated for the simulation and assimilation of radiances in Environment Canada’s weather forecast system. The standard approach is to extract from a short-term forecast (trial field) a one-dimensional vertical profile, located at the ground footprint of the observation location, to compute the model equivalent of the observation. Since in general, the lines of sight are not vertical, the observation operator can be improved by interpolating the trial field to the slant path. The interpolation relies on the estimation of horizontal gradients of atmospheric variables, extracted at each altitude layer, and representative of a neighborhood of approximately 100 km. Using these slant profiles of the trial fields yields significant improvements in the simulation and assimilation of radiance observations. The impact of this modification in assimilation and forecasts is evaluated over two periods of two months. Both short-term and long-term effects are found, obtained not only from the more accurate interpolation of the trial fields, but also from a revision of the observation error suggested by the lower observation minus trial field difference statistics. The error standard deviation of the geopotential height shows, in the upper troposphere and lower stratosphere, in 12–48-h forecasts, up to 10% reduction in the zonal value at higher latitudes, and 1%–2% reduction in the global value. Given these improved statistics, a revision of the radiance error statistics was explored. Applying these revised statistics suggests that the impact of a slant operator extends into longer range.
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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 | 0.001 | 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".