In situ cloud and aerosol observations in the arctic region during the RadSnowExp campaign
Bibliographic record
Abstract
The high latitudes are likely to experience an increase in annual mean precipitation by the end of this century. However, the difficulties in data collection and limited numbers of monitoring stations have resulted in significant knowledge gaps about these regions. In the last decade, there has been an improvement in the characterization of cloud structures, microphysical properties and aerosol content at high-latitudes as a result of new research satellites such as CloudSat and Calipso and focused field studies using aircraft, ship and ground observations. While the details of the ice-nucleation mechanisms in Arctic clouds remain controversial, recent studies provide increasing evidence that aerosol population has an important role in these mechanisms.The RadSnowExp is a multi-platform and multi-sensor study organized by the European Space Agency (ESA) and conducted by the National Research Council of Canada (NRC) and Environment and Climate Change Canada (ECCC), to address the pressing need for provision of precipitation measurements, locally and globally. In this study we investigated and documented spatial and temporal variability, intensity and types of precipitation with complementary in situ and remote sensing measurements in the arctic region in the vicinity of Iqaluit (∼63N), Nunavut, Canada in November 2018, a month with the highest occurrence of frozen precipitation in this region. Our objectives included identification of remote sensing signatures, characterization of cloud microphysical properties and aerosol-cloud interactions, as well as, evaluation of the sensitivity of the instruments to particle size and morphology, evaluation of their spatial and temporal resolution and collection of data that can be used for future precipitation missions. In this study, we used the NRC Convair-580 twin-engine aircraft with wing-mounted pylons equipped withan array of commonly used cloud probes; e.g. Cloud Droplet Probe (CDP), Precipitation Imaging Probe (PIP), Fast Cloud Droplet Probe (FCDP), 2D-S, Nevzorov and aerosol instruments such as Ultra High SensitivityAerosol Spectrometer (UHSAS), Condensation Particle Counter (CPC), Single Particle Soot Photometer (SP2), Cloud Condensation Nuclei Counter (CCNC). The flights took place above cloud top, below cloud base, and in clouds at altitudes up to 7 km, totalling 30 hours of collected data. We present preliminary analysis of the multi-platform dataset of arctic thin and deep clouds. We report aerosol sizes and spatially resolved concentrations observed above and below clouds, and discuss their ability to act as CCN. Next, we present in situ single-particle data for cloud hydrometeor size distributions, ice crystal habits, and water content. These results are supplemented with the airborne lidar spatial characterization of the clouds. Last, we describe the overall microphysical picture for this set of flights and discuss the uncertainties of our measurements.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 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".