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Record W2147822475 · doi:10.1175/jamc-d-12-0286.1

Validation of Environment Canada and NOAA UV Index Forecasts with Brewer Measurements from Canada

2013· article· en· W2147822475 on OpenAlexaffabout
Huixia He, Vitali Fioletov, D. W. Tarasick, Thomas W. Mathews, Craig S. Long

Bibliographic record

VenueJournal of Applied Meteorology and Climatology · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsEnvironment and Climate Change Canada
FundersNational Oceanic and Atmospheric Administration
KeywordsEnvironmental scienceMeteorologyIndex (typography)IrradianceCloud coverSkyStandard deviationAtmospheric sciencesClimatologyStatisticsCloud computingMathematicsGeographyComputer sciencePhysicsGeology

Abstract

fetched live from OpenAlex

Abstract Ground-based ultraviolet (UV) irradiance measurements by Brewer spectrophotometers at 10 sites across Canada are compared with UV index forecasts for the same locations from Environment Canada (EC) and NOAA. For the EC forecast validation, summertime (May–August) data for the period from 1996 to 2009 are used. Comparison with NOAA forecasts is made for the more limited period of May–August 2006 and 2007. Several statistical measures are used, including the mean and the standard deviation of differences, correlation coefficients, and the probability of detection and false-alarm rate for prediction of high (UV index of 6 or above) values. For most conditions, only modest differences are found between the two forecasting systems; that is, UV index forecasts reported in the United States and Canada for Canadian sites are compatible. In general, the physically based NOAA system, which started operation in 2005, performs better than the semiempirical EC model, developed in the mid-1990s. The difference in model performance is not large under clear-sky and light-cloud conditions, but the EC model underperforms relative to the NOAA model under heavy-cloud and rainy conditions. Both the EC and the NOAA forecast models tend to overestimate UV under clear-sky and light-cloud conditions. Under heavy-cloud and rainy conditions, the EC model underestimates UV values, with about 30% of all forecasts under these conditions being 2 or more units below observations. NOAA forecasts tend to overestimate UV index values under these conditions.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.008
GPT teacher head0.165
Teacher spread0.157 · 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.

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

Citations24
Published2013
Admission routes2
Has abstractyes

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