Using Airborne Measurements to Evaluate Forecasts of Freezing Drizzle Aloft Results from the Wintre-Mix Field Campaign
Bibliographic record
Abstract
Icing associated with the accretion of supercooled large droplets (SLD) serves as a significant hazard to aviation activities. Forecasting SLD events is complex due in part to the limitations of numerical weather prediction models. The Winter Precipitation Type Research Multiscale Experiment (WINTRE-MIX) aimed to comprehend such limitations within numerical forecasts of precipitation type (p-type) through the use of extensive observations during events with multiple p-types. To investigate precipitation processes aloft in winter storms with near-freezing surface conditions, research flights were orchestrated with the National Research Council of Canada Convair-580 aircraft. This study examines a WINTRE-MIX intensive observing period that took place on 07–08 March 2022 and coincided with a warm frontal passage through northern New York and into southern Quebec. As the Convair ascended in altitude, widespread freezing drizzle (FZDZ) connected with SLD icing was observed with abnormally cool cloud-top temperatures as low as -15°C. This study evaluates the High-Resolution Rapid Refresh (HRRR) model forecasts of FZDZ against Convair-580 observations. The HRRR is favored within the aviation meteorology community for its 3-km grid spacing, its frequent hourly data assimilation, and its use of complex microphysics schemes with the ability to diagnose multiple hydrometeor categories. Icing conditions are examined using in-situ aircraft observations by analyzing ice detector frequency, liquid water content, cloud number concentration, and rain number concentration, and diagnosed hydrometeor types which are compared to their model-simulated equivalent. Additionally, Weather Research and Forecasting (WRF) model simulations explore sources of inconsistencies between observations and model runs. Airborne W-band radar profiles collected by the Convair-580 are compared to simulated radar reflectivity. HRRR and control (CTRL) WRF predominately simulate snow and ice hydrometeors at flight level which contrasts observations of FZDZ and SLD icing. The presence of snow hydrometeors throughout the vertical column suggests that seeding from the upper-level cloud inhibits collision-coalescence. Frozen hydrometeors will serve as ice nuclei or lead to the evaporation of liquid through the Bergeron-Wegener-Findeisen process. This leads to the removal of available liquid in the atmosphere. Two sensitivity experiments are performed by removing moisture above an average altitude of 5092 m and between average altitudes of 4076 m and 5836 m to explore the influence of seeding from the upper-layer cloud. The reduction of snow mixing ratios and improvements to rain mixing ratios at locations of observed icing demonstrate the biases introduced by the seeding mechanisms.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".