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Validation for ESA’s Aeolus Mission using the in-situ instruments at Canadian Arctic and reanalysis data

2020· article· en· W3043145035 on OpenAlexaffabout
Chih‐Chun Chou, Paul J. Kushner, Zen Mariani, Peter Rodriguez, Christopher G. Fletcher

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsUniversity of WaterlooEnvironment and Climate Change CanadaUniversity of Toronto
Fundersnot available
KeywordsRadiosondeLidarEnvironmental scienceMeteorologyRayleigh scatteringAtmospheric sciencesRemote sensingPhysicsGeographyOptics

Abstract

fetched live from OpenAlex

ESA’s Aeolus mission, launched in August 2018, is designed to capture tropospheric wind profiles on a global scale in near-real time. The Aeolus lidar system, Atmospheric LAser Doppler INstrument (ALADIN), uses two modes of lidar-driven active scattering, Mie and Rayleigh scattering channels, to retrieve horizontal line-of-sight (HLOS) winds under both clear and cloudy conditions. ESA Aeolus aims to improve numerical weather and climate prediction, and to advance understanding of atmospheric circulation and weather systems. This presentation will describe the Canadian validation activities for ESA Aeolus level-2B product, coordinated by the University of Toronto’s Department of Physics and Environment and Climate Change Canada (ECCC). The main focus is the evaluation of Aeolus overpasses using the fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF ERA5), and in-situ measurements at Environment and Climate Change Canada’s (ECCC) Iqaluit and Whitehorse supersites where several wind sensing instruments are co-located. It will compare the Aeolus HLOS winds with the profiles of wind vector from regular radiosonde launches, line-of-sight winds from Doppler Lidar and Ka-Band Radar. The accuracy of the Aeolus measurements is analyzed based on the type of scattering and natural variability of the wind on different levels. The radiosonde measures the profiles of temperature, relative humidity, pressure, and winds twice a day with a vertical resolution of 15 m up to 30 km. On the other hand, the Mie scattered 1.5 micron Doppler Lidar retrieves LOS winds at every 3 m as well as aerosol backscatter and depolarization ratio every 5 minutes up to 3 km. Lastly, for every 10 minutes, the dual-polarization Doppler Ka-Band Radar measures the LOS wind speed and direction, cloud and fog backscatter, and depolarization ratio up to a range of 25 km with a vertical resolution of 10 m. The wind profiles were directly compared to the profiles derived from other instruments or reanalysis. The vertical structure of the Aeolus winds, for example the wind shear, will also be compared and discussed. The validation results showed that Aeolus is providing some promising initial products and that the ERA5 reanalysis is the most consistent dataset with the Aeolus wind measurements from level-2B product.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.136
Threshold uncertainty score0.273

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.067
GPT teacher head0.276
Teacher spread0.209 · 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
Published2020
Admission routes2
Has abstractyes

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