Assimilation of Wind Profiler Data in the Canadian Meteorological Centre’s Analysis Systems
Bibliographic record
Abstract
Abstract Real-time horizontal wind observations from the National Oceanic and Atmospheric Administration’s (NOAA’s) Profiler Network (NPN) are assessed in preparation for their assimilation in the Canadian Meteorological Centre (CMC) analysis systems. As a first step, radiosonde winds from 20 stations were compared to the central U.S. profiler stations over the 2001/02 winter season. It was found that profilers are at least as good as conventional radiosonde data. The 2001/02 winter season data were also used to examine the vertical correlation structure of the observation error for profilers. Using a statistical analysis of innovations, the observation error standard deviation of the wind components is estimated as 2.2 m s−1 and the vertical correlation length is approximately 500 m. These results suggest that the data are vertically correlated because they are available every 250 m. Therefore, a thinning process is proposed in which one out of three data are selected in the vertical for each station. Since January 2004, a close monitoring of NPN profiler data revealed significant errors at some stations in the lower and upper troposphere. Consequently, a monthly blacklist of NPN profilers is built based on data from the previous month. A data impact study with both the three-dimensional variational data assimilation (3DVAR) and four-dimensional variational data assimilation (4DVAR) analysis systems was conducted using data from the 2003/04 winter season in which the vertical thinning was tested. It was found that the vertical thinning improves slightly the 6-h forecast error, especially in the 4DVAR over the central United States in which 6 times more profilers are assimilated. The impact of the vertical thinning is found to be neutral in the 3DVAR. Also, the impact of profiler data is significant over the central U.S. domain compared to a control run with the only difference being the addition of profiler data. These results were sufficiently good to implement NPN profilers in both the CMC global and regional analysis systems with the thinning process in fall of 2004.
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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 | 0.001 |
| 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".