Impact of the Assimilation of CHAMP Refractivity Profiles on Environment Canada Global Forecasts
Bibliographic record
Abstract
Abstract The data assimilation system of Environment Canada (EC) is adapted to accept GPS radio occultation (GPSRO) data. Observations of this type are available with extensive coverage from several satellites. In this study, experiments are performed to compare the skill of EC’s three-dimensional variational data assimilation (3DVAR) system (including all data normally assimilated operationally), with and without the addition of radio occultation refractivity data from the Challenging Minisatellite Payload for Geophysical Research (CHAMP). These data were not available at the time studied as near-real-time (NRT) observations. However, data from this and other radio occultation missions are now available as NRT data, and the conditions (latency, reliability) are improving. It is expected that NRT GPSRO data from a number of satellite missions will continue to be available through the following years. The results of the assimilation tests are evaluated against the following three data types: radiosondes (temperature and dewpoint depression), satellite brightness temperatures (from the Advanced Microwave Sounding Unit-A), and GPS radio occultation refractivity profiles. For the 6-h forecasts, the differences between GPSRO observations and forecasts (O − F) are significantly reduced in the experiment that assimilates the GPSRO data. This reduction increases as the experiment proceeds in time, and stabilizes after a transient period of approximately 2 weeks, suggesting that the addition of GPSRO data to the assimilation system has a beneficial, persisting, and cumulative effect. This effect is more pronounced in the stratosphere than in the troposphere. In the stratosphere, the standard deviation of GPSRO (O − F) of the experiment that assimilates GPSRO decreases after the initial transient period by approximately 10%. This improvement can best be observed in the southern stratosphere where reductions of the order of 30% are common. This shows that, as a globally distributed and vertically well-resolved source of data, the GPSRO observations are not only useful for assimilation, but also as a tool to quantify the forecast skill of the assimilation system. Comparisons with radiometer and radiosonde data confirm the positive impact in these geographical areas. Longer-range forecasts (up to 6 days) also show a positive impact with similar geographical and altitude distribution.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".