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Record W3008685239 · doi:10.82308/40309

Snow study at Centre for Atmospheric Research Experiments : variability of snow fall velocity, density and shape

2008· article· en· W3008685239 on OpenAlexaboutno aff
Eunsil Jung

Bibliographic record

VenueeScholarship@McGill (McGill) · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
Fundersnot available
KeywordsSnowMeteorologyAtmospheric sciencesEnvironmental scienceAtmospheric researchPhysical geographyClimatologyGeologyGeography

Abstract

fetched live from OpenAlex

In this work, snow data, collected at the Centre for Atmospheric Research Experiments (CARE) site during the winter of 2005/06 as part of the Canadian CALIPSO/CloudSat Validation Project (C3VP) were analyzed with various goals in mind: 1) investigate the effects of surface temperature and system depth on the snow fall velocity and particle size distribution, 2) find the variables that control the relationships between snow fall velocity and size (control variables), 3) retrieve the coefficient and the exponent in the power-law mass-size relations used in snow reflectivity, 4) estimate vertical air motion and 5) describe the shape of snowflakes that can be used in polarimetric studies of snow. It also includes considerable calibration work on the Hydrometeor Velocity and Shape Detector (HVSD); as well as sensitivity testing for radar calibration and Mie-scattering effect on snow density. Snow events were classified into several categories according to the radar echo vertical extent (H), surface and echo top temperatures (T s, Tt), to find their effects on snow fall velocity and particle size distribution. Several case studies, including situations of strong turbulence, were also examined. Simple and multiple correlation analyses between control variables and parameters of the power-law size-velocity relationship were carried out for 13 snow cases having a high R2 between their mean snowflakes fall velocity and the v-D fitted curve, in order to find the control variables of power-law v-D relations. Those cases were all characterized by single layered precipitation with different echo depth, surface and echo top temperatures. Results show that the exponent "b" in v-D power-law relationship has little effect on the variability of snow fall velocity; all control variables (T s, Tt, H) correlate much better to the coefficient "a" than to the exponent "b", leading to a snow fall velocity that can be simulated with a varying coefficient "a" and a fixed exponent "b" (v=aD0.15) with good accuracy. Coefficient "a" and exponent "b" for a generic snow v-D relationship were also examined. The results indicate that coefficient "a" of generic snow represents the most frequent coefficient "a" during the events, while the exponent "b" does not show any representative. Retrieval of the coefficient "a" and exponent "b" in a power-law mass-size relation, which eventually affects the snow reflectivity, was conducted by minimizing the root mean square (RMS) of differences in reflectivity between Vertically pointing McGill X-band Radar (VertiX) and HVSD. Minima of reflectivity differences lay on a diagonal direction of a diagram of the coefficient "a" (x-axis) versus exponent "b" (y-axis). It is shown that as the system deepens, the slope gets less steep. In addition, coefficient and exponent for this mass-size relation change with time, and snow density derived from several combined snow events does not explain the average snow density of the period. A method to retrieve vertical air motion with good accuracy using VertiX and HVSD is suggested. Several snow shape parameters and relations between the area ratio (Ar) and size of snowflakes (Ar-D relation) are investigated with snow dimensions defined in various ways. These Ar-D relations will be used as a guideline in snow density models.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.069
GPT teacher head0.278
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2008
Admission routes1
Has abstractyes

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