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Record W7055128617

Combining in situ observations and remote sensing data to determine the spatial extent of rain-on-snow events on the Brøgger peninsula

2023· other· en· W7055128617 on OpenAlexaboutno aff

Bibliographic record

Venueelib (German Aerospace Center) · 2023
Typeother
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsSnowpackSnowPeninsulaArcticSatelliteClimate changePrecipitationRadar
DOInot available

Abstract

fetched live from OpenAlex

Climate change is particularly impacting the Arctic, where the temperature increase is stronger than the global mean due to Arctic Amplification. Long-term observations at sites such as Ny-Alesund on the Brøgger’s peninsula in Svalbard allow understanding meteorological changes taking place in the Arctic. In the last decades, Ny Alesund was affected by a large increase of winter temperatures leading to occasional periods of positive temperatures lasting few days. As a result, the number of rainfall events also increased, contributing to an early degradation of the snowpack on the Brogger peninsula. Meteorological measurements such as at Ny Alesund allow to quantify the temporal variability of these “rain on snow” (ROS) events at specific points. The goal of this study is (i) to spatialize recent ROS events on the Brøgger peninsula during the period 2019-2022 using remote sensing radar data and (ii) to characterize the atmospheric origin of these events using anomalies of 500 hPa height or the identification of cyclonic systems. We use SAR satellite images from each event, mainly TSX and RCM images provided by German and Canadian Space Agencies. We processed the images with a thresholding method in order to find the spatial elevation limits between wet and dry snow after the events. PlanetScope optical images are used for snow extent validation. During the ROS episodes, the snow remains generally dry upstream of the glaciers, while at lower altitudes the snow on the peninsula is systematically wet. These ROS episodes are associated to cyclonic systems originated from the Northern Atlantic Ocean, and to a strong Z500 gradient from high pressure centered in Norway and low pressure centered in Greenland. These results are important to better characterize the origins and the spatial variability of ROS events and to evaluate the impact of these specific events on glaciers, permafrost, or ecology.

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)0.000
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 score0.048
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
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 head0.060
GPT teacher head0.311
Teacher spread0.250 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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