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Record W2120083670 · doi:10.1080/07038992.2015.1040876

Optimizing the Heliosat-II Method for Surface Solar Irradiation Estimation with GOES Images

2015· article· en· W2120083670 on OpenAlexaffvenue
Tommy Albarelo, Isabelle Marie-Joseph, Antoine Primerose, F. Seyler, Lucien Wald, Laurent Linguet

Bibliographic record

VenueCanadian Journal of Remote Sensing · 2015
Typearticle
Languageen
FieldComputer Science
TopicSolar Radiation and Photovoltaics
Canadian institutionsImpact
FundersNational Oceanic and Atmospheric Administration
KeywordsAlbedo (alchemy)SatelliteEnvironmental scienceTurbidityMean squared errorRemote sensingMeteorologyAtmospheric sciencesGeographyMathematicsPhysicsStatisticsGeology

Abstract

fetched live from OpenAlex

. The Heliosat-II method was developed to process Meteosat satellite images in order to assess the solar irradiation at surface. It is modified for its use with GOES satellite images in order to estimate the solar irradiation over French Guiana. Modifications include a change in the calibration formula, in the calculation of the cloud albedo, and in the values of the Linke turbidity factor. These modifications allow the improvement of the performances of the Heliosat-II model in Intertropical zones in French Guiana. The outcomes were compared to coincident measurements of hourly irradiation performed by ground stations. The results show that the use of the maximum apparent albedo as the cloud albedo (ρcmax), along with a fixed value of the Linke turbidity factor produces the best results; the bias was less than 23.61 Wh/m2 (5% of the mean value of the measurements) overall, and the RMSE was less than 109.39 W/m2 (24%) overall when using hourly values. In the case of daily means, the RMSE drops to less than 10.08 W/m2 (10%) overall, whereas the overall bias remains unchanged, as expected. The model performs better in clear skies than in cloudy skies. The results suggest that the Linke turbidity factor used in the Heliosat-II method does not reproduce well the attenuation of the solar radiation in French Guiana. The results were compared to those obtained from Heliosat II method and Meteosat images (HelioClim-3 database) and we found a better accuracy for GOES satellite data, which exhibits smaller viewing angle. The use of the Heliosat II method with GOES images allows assessment of the SSI with a good accuracy and high temporal and spatial resolutions.Résumé. La méthode Heliosat-II a été développée pour traiter des images du satellite Meteosat de façon à estimer l'irradiation solaire en surface. Elle a été modifiée pour être employée avec des images du satellite GOES de façon à estimer l'irradiation solaire au-dessus de la Guyane Française. Les modifications incluent un changement dans la formule d’étalonnage, dans le calcul de l'albédo des nuages et dans les valeurs de Trouble de Linke. Ces modifications permettent l'amélioration des performances du modèle Heliosat-II en zone intertropicale, notamment en Guyane Française. Les résultats ont été comparés à des mesures horaires coïncidentes d'irradiation horaire réalisées par des stations de mesure au sol. Les résultats montrent que l'utilisation de l'albédo apparent maximal en tant qu'albédo des nuages (ρcmax), ainsi qu'une valeur fixe de Trouble de Linke produit les meilleures estimations; le biais est inférieur à 23,61 Wh/m2 (5% de la valeur moyenne des mesures) dans l'ensemble et le RMSE est inférieur à 109,39 Wh/m2 (24%) dans l'ensemble en utilisant des valeurs horaires. Dans le cas de moyennes journalières, le RMSE vaut moins de 10,08 W/m2 (10%) alors que le biais a demeuré inchangé comme attendu. Le modèle fonctionne mieux en ciel clair qu'en ciel couvert. Les résultats suggèrent que le Trouble de Linke utilisé dans la méthode Heliosat-II ne reproduit pas correctement l'atténuation du rayonnement solaire en Guyane Française. Les résultats ont été comparés à ceux obtenus par la méthode Heliosat II avec les images Meteosat (HelioClim 3) et nous avons obtenu une meilleure précision pour les données issues du satellite GOES, qui présente un plus faible angle de visée. L'utilisation de la méthode Heliosat II avec des images GOES permet l'estimation de l'irradiation solaire de surface avec une bonne précision et à des résolutions temporelles et spatiales élevées

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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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.833
Threshold uncertainty score0.426

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.028
GPT teacher head0.267
Teacher spread0.239 · 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 designSimulation or modeling
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".

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Citations25
Published2015
Admission routes2
Has abstractyes

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