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Record W4236721468 · doi:10.5194/tcd-8-4195-2014

Cloud and precipitation properties from ground-based remote sensing instruments in East Antarctica

2014· preprint· en· W4236721468 on OpenAlexaff
Irina Gorodetskaya, Stefan Kneifel, Maximilian Maahn, Kristof Van Tricht, Jan H. Schween, Susanne Crewell, Nicole Van Lipzig

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsMcGill University
FundersVlaamse regeringUniversität zu KölnFonds Wetenschappelijk OnderzoekBelgian Federal Science Policy OfficeDeutsche ForschungsgemeinschaftKU LeuvenBundesministeriums für Verkehr, Bau und Stadtentwicklung
KeywordsSnowEnvironmental scienceOvercastPrecipitationObservatoryCeilometerAtmospheric sciencesDrizzleMeteorologyClimatologyLiquid water contentCloud baseSkyRemote sensingGeologyCloud computingLidarGeographyPhysics

Abstract

fetched live from OpenAlex

Abstract. A new comprehensive cloud-precipitation-meteorological observatory has been established at Princess Elisabeth base, located in the escarpment zone of Dronning Maud Land, East Antarctica. The observatory consists of a set of ground-based remote sensing instruments (ceilometer, infrared pyrometer and vertically profiling precipitation radar) combined with automatic weather station measurements of near-surface meteorology, radiative fluxes, and snow accumulation. In this paper, the observatory is presented and the potential for studying the evolution of clouds and precipitating systems is illustrated by case studies. It is shown that the synergetic use of the set of instruments allows for distinguishing ice, mixed-phase and precipitating clouds, including some information on their vertical extent. In addition, wind-driven blowing snow events can be distinguished from deeper precipitating systems. Cloud properties largely affect the surface radiative fluxes, with liquid-containing clouds dominating the radiative impact. A statistical analysis of all measurements (in total 14 months mainly occurring in summer/autumn) indicates that these liquid-containing clouds occur during as much as 20% of the cloudy periods. The cloud occurrence shows a strong bimodal distribution with clear sky conditions 51% of the time and complete overcast conditions 35% of the time. Snowfall occurred 17% of the cloudy periods with a predominance of light precipitation and only rare events with snowfall > 1 mm h−1 water equivalent (w.e.). Three of such intensive snowfall events occurred during 2011 contributing to anomalously large annual snow accumulation. This is the first deployment of a precipitation radar in Antarctica allowing to assess the contribution of the snowfall to the local surface mass balance. It is shown that on the one hand large accumulation events (>10 mm w.e. day−1) during the measurement period of 26 months were always associated with snowfall, but that on the other hand snowfall did not always lead to accumulation. In general, this promising set of robust instrumentation allows for improved insight in cloud and precipitation processes in Antarctica and can be easily deployed at other Antarctic stations.

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)0.000
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 score0.040
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.219
Teacher spread0.179 · 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

Citations23
Published2014
Admission routes1
Has abstractyes

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