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Record W2129641766 · doi:10.1029/2007gl031536

Considerations for spaceborne 94 GHz radar observations of precipitation

2007· article· en· W2129641766 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGeophysical Research Letters · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPrecipitation Measurement and Analysis
Canadian institutionsEnvironment and Climate Change CanadaMcGill University
Fundersnot available
KeywordsRadarSnowPrecipitationRemote sensingEnvironmental scienceSampling (signal processing)Doppler radarGlobal Precipitation MeasurementRain and snow mixedAttenuationMeteorologyGeologyComputer scienceOpticsPhysicsTelecommunications

Abstract

fetched live from OpenAlex

Spaceborne 94 GHz radars offer sufficient sensitivity to observe all types of precipitation and their associated clouds without stretching the instrument requirements. In this study, considerations for precipitation classification and detection from space using 94 GHz radars are presented. First, a technique that uses the path‐integrated attenuation normalized to the depth of the rain layer, the snow‐integrated reflectivity and the reflectivity difference from snow to rain to discriminate convective and stratiform profiles is proposed. Second, we present a critical view of sampling issues for precipitation and Doppler measurements from space at 94 GHz. A new sampling strategy for spaceborne 94 GHz radars with alternating cloud and precipitation modes is discussed that can improve our ability to detect and measure precipitation without losing sight of the main objective of deploying such high frequency radars in space, to map the global distribution of clouds.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.798
Threshold uncertainty score0.326

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.137
GPT teacher head0.333
Teacher spread0.196 · 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