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Record W7100026535

The assimilation of AMSU and SSM/I brightness temperatures in clear skies at the Meteorological Service of Canada

2015· article· en· W7100026535 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsnot available
Fundersnot available
KeywordsAdvanced Microwave Sounding UnitRadiosondeData assimilationWater vaporSatelliteRadiancePrecipitable waterBrightness temperatureGeostationary Operational Environmental Satellite
DOInot available

Abstract

fetched live from OpenAlex

In recent years, the assimilation of satellite data has become a vital component of the global and regional assimilation systems at the Canadian Meteorological Centre (CMC). Moreover, the direct assimilation of satellite radiance measurements from AMSU-A, AMSU-B, and the GOES water vapor channel has resulted in notable improvements in the short and medium range CMC forecasts. This has been demonstrated in Observation System Experiments conducted by CMC. In preparation for the operational assimilation of Special Sensor Microwave Imager (SSM/I) data in the 4D-Var global analysis system at CMC, two 3D-Var experiments are conducted. In the first experiment, the assimilation of clear-sky, open-ocean brightness temperatures from the 7 SSM/I microwave channels is added to the operational configuration of the global analysis system. In the second experiment, stricter filtering of AMSU data is applied together with the addition of the SSM/I data. More specifically, AMSU-A CH3 (50.3 GHz) is removed due to its non-negligible sensitivity to clouds, and more aggressive filtering of AMSU-B CH2 (150.0 GHz), CH3 (183.3±1 H GHz), CH4 (183.3±3 H GHz), and CH5 (183.3±7 H GHz) is invoked using CH2 to identify cloudy pixels. In the current quality control procedures for AMSU-B, an effective precipitation screen is present, however, there is no method of detecting and removing cloudy observations. In both experiments, improvements are evident in the analysed integrated water vapour, surface wind speed, and daily precipitation rate fields when compared against independent observations. Furthermore, for the second experiment gains are realized in the forecasts when validated against radiosonde data. Other indicators such as anomaly correlation, RMSE, and QPF scores show a net positive effect. Overall, the second experiment shows better results than the first. In particular, the additional filtering of AMSU-B CH2-5 is identified as an important modification to the current operational configuration.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.992
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.025
GPT teacher head0.214
Teacher spread0.190 · 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 designBench or experimental
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
Published2015
Admission routes1
Has abstractyes

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