Identification of snowfall microphysical processes from vertical gradients of polarimetric radar variables
Bibliographic record
Abstract
The accurate representation of snowfall is still a challenge for weather forecast and climate models. It mostly relies on the parameterization of microphysical processes that govern snowfall growth and decay. Recently, strong discrepancies have been pinpointed between different microphysical schemes in cold precipitations over Antarctica, questioning the reliability of surface mass balance assessments. A better understanding and an improved parameterization of these processes require the acquisition of observational data, which nonetheless remains difficult in polar or mountainous regions due to the remote location and harsh meteorological conditions. Polarimetric radars offer continuous measurements of precipitation with a large spatial coverage, retrieving information about the microphysical processes that govern its evolution. This study presents a new method, called Process Identification based on Vertical gradient Signs (PIVS), to spatially identify the occurrence of the dominant microphysical processes (aggregation and riming, crystal growth, sublimation) governing snowfall evolution from polarimetric radar scans. We first propose a theoretical framework to asses in which meteorological conditions a vertical analysis of the radar signal reflects the underlying microphysical processes. Then PIVS identifies aggregation and riming, crystal growth and sublimation based on the sign of the local vertical gradients of reflectivity ZH and differential reflectivity ZDR We then applied our method on two frontal snowfall cases, one in Adélie land, Antarctica and one in the Taebaeck mountains, South Korea. We successfully compare PIVS results with an hydrometeor classification and with snowflake observations using a Multi-Angle Snowflake Camera. In Antarctica, PIVS indicates that crystal growth dominates above 2500m a.g.l., aggregation and riming prevail between 1500m and 2500m a.g.l., and sublimation occurs mainly below, concurring with previous studies stating that snowflakes preferentially sublimate in the relatively dry katabatic boundary layer. In south Korea, the structure is similar although the altitudes are shifted, with aggregation and riming between 4000m and 4800m a.g.l., sublimation below and crystal growth above. Moreover, the statistical analysis of different radar variables provides quantitative information to further characterize the microphysical processes of interest. Finally, we highlight further possible improvements of the method - notably the addition of complementary polarimetric variables - and illustrate the potential of PIVS to evaluate the microphysical schemes in numerical models.
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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 | 0.000 | 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) | 0.001 | 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".