A geo‐statistical observation operator for the assimilation of near‐surface wind data
Bibliographic record
Abstract
Although many near‐surface wind observations are available, very few are assimilated over land mainly due to sub‐grid scale topographic interactions with the flow. The main objectives of this study are to understand the impact of near‐surface wind observations on the analysis and to point out aspects that need to be improved to make a better use of these observations. A geo‐statistical observation operator has been developed to correct for systematic and representativeness errors. Assimilation experiments were performed in a simplified context, assimilating only near‐surface wind observations over land in the ensemble‐variational data assimilation system developed at Environment Canada. Due to the background‐error covariances, the assimilation of near‐surface wind observations impacts the lower part of the atmosphere. The resulting correction has been evaluated by verifying the analyses against non‐assimilated collocated radiosonde data. This assessment also made it possible to estimate the observation error variance to strike a balance between having an important impact at the surface and maintaining a good vertical fit to upper air observations. Results from 1 month of assimilation experiments show that the geo‐statistical operator eliminates biases and significantly reduces representativeness errors as well as observation error correlations in the analysis, mainly over complex terrain. Results also show that flow‐dependent background error covariances from ensembles provide better vertical information propagation than static error statistics. Overall, the analysis fit to non‐assimilated collocated radiosonde observations is improved when assimilating wind observations from surface stations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 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 | 0.000 |
| Open science | 0.001 | 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".