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Record W4403901363 · doi:10.5194/amt-17-6301-2024

An improved geolocation methodology for spaceborne radar and lidar systems

2024· article· en· W4403901363 on OpenAlexaff
Bernat Puigdomènech Treserras, Pavlos Kollias

Bibliographic record

VenueAtmospheric measurement techniques · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsMcGill University
FundersEuropean Space AgencyJet Propulsion LaboratoryNational Aeronautics and Space Administration
KeywordsGeolocationRemote sensingLidarEnvironmental scienceRadarComputer scienceMeteorologyGeologyGeographyTelecommunications

Abstract

fetched live from OpenAlex

Abstract. Geolocation and co-registration methodologies are essential for the accurate interpretation of observations from spaceborne remote sensors. In preparations for the Earth Cloud Aerosol and Radiation Explorer (EarthCARE), here, we refine the definition of these techniques and present various examples of geolocation assessments. The geolocation methods build upon earlier work; however, they introduce several improvements that have increased the reliability of the geolocation accuracy. The EarthCARE active-sensor geolocation methods use coastlines and significant elevation gradients in both statistical and numerical ways. The effectiveness of the proposed geolocation methods was tested using the extensive record of CloudSat and CALIPSO observations. The EarthCARE active-sensor geolocation methods were effective in identifying and correcting a short period of CloudSat observations when the star tracker was not operating properly. In addition, the geolocation methods were able to reproduce the excellent geolocation record of the CloudSat and CALIPSO missions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.589
Threshold uncertainty score0.855

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.040
GPT teacher head0.283
Teacher spread0.243 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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

Citations3
Published2024
Admission routes1
Has abstractyes

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