Synchronous starphotometry and lidar measurements at Eureka in High Canadian Arctic
Bibliographic record
Abstract
Abstract. We present recent progress related to the night-time retrievals of aerosol and cloud optical depth using starphotometry over the PEARL (Polar Environmental Atmospheric Research Laboratory) station at Eureka (Nunavut, Canada) in the High Arctic (80° N, 86° W). In the spring of 2011 and 2012, the SPSTAR starphotometer was employed to acquire aerosol optical depth (AOD) measurements while vertical aerosol and cloud backscatter coefficient profiles were acquired using the CANDAC Raman Lidar (CRL). Several events were detected and characterized using starphotometry-lidar synergy: aerosols (short term aerosol events on 9 and 10 March 2011); a potential multi-night aerosol event across three polar nights (13–15 March 2012), a thin cloud event (21 February 2011) and a very low altitude ice crystals (10 March 2011). Using a simple backscatter coefficient threshold criterion we calculated fine and coarse (sub and super-micron) mode AODs from the vertically integrated CRL profiles. These were compared with their starphotometry analogues produced from a spectral deconvolution algorithm. The process-level analysis showed, in general, good agreement in terms of the physical coherence between high frequency starphotometry and lidar data. We argue that R2 (coefficient of determination) is the most robust means of comparing lidar and starphotometer data since it is sensitive to significant optico-physical variations associated with these two independent data sources while being minimally dependent on retrieval and calibration artifacts. Differences between the fine and course mode components of the starphotometry and lidar data is clearly also useful but is more dependent on such artifacts. Studying climatological seasonal aerosol trends necessitates effective cloud-screening procedures: temporal and spectral cloud screening of starphotometry data was found to agree moderately well with temporal cloud screening results except in the presence of thin homogeneous cloud. We conclude that better screening conditions can be implemented to arrive at a robust method for combined temporal/spectral cloud-screening of starphotometer (and possibly sunphotometer) data. In general, as our understanding of process-level details increases with growing datasets, we will inevitably have more confidence in bulk climatological analyses of ground-based and satellite retrievals of aerosol parameters where conditions are less than ideal because of the weakness of the polar winter aerosol signal.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".