Estimating Surface Solar Irradiance from GOES Satellite with Particle Filter Model and Joint Probability Distribution
Bibliographic record
Abstract
. A satellite retrieval of surface solar irradiance (SSI) based on GOES satellite data is presented and validated for the French Guiana with 4 years of in situ measurements from 6 ground stations. We propose a particle filter approach combining GOES satellite observations and in situ data. We propose an original observation function based on a joint probability distribution and taking advantage of the characteristics of both involved types of data. The statistical results are compared with those of existing estimation methods and they are found to be in accordance. The daily bias ranges from −12 W/m2 (−6% of the mean of measurements) to 12 W/m2 (6%), depending on the stations. The daily root mean square difference ranges between 23 W/m2 (10%) and 27 W/m2 (15%). The correlation coefficient is close to 0.92. There is no seasonal climatic effect on the method, the bias and RMSD remain similar for both the rainy and the dry seasons, but better correlation is observed in the rainy season than in the dry season. Uncertainties are mainly due to the presence of specific tropical atmospheric conditions during the dry season.The method has been tested through various tests: cross-validation scheme has highlighted the potential generalization of the model in a temporal dimension; the ability of the method to effectively duplicate anomalies in surface solar irradiance time series has also been tested. The robustness of the results shows the interest of the method compared to other existing estimation methods. Because of its simplicity of implementation, this method opens new prospects in estimating surface solar irradiance. It is concluded that using a particle filter and original observation function method allow a high-quality surface solar irradiance estimation in French Guiana.Résumé. Une méthode d'estimation de l'irradiance solaire de surface (SSI) à partir de données satellites GOES est présentée et validée par comparaison avec 4 années de mesures in situ provenant de 6 stations météorologiques de Guyane Française. Dans cet article, nous proposons une approche par filtre particulaire combinant observations du satellite GOES et données in situ. Nous proposons une fonction d'observation originale basée sur une distribution de probabilité conjointe des 2 types de données. Les résultats statistiques sont conformes à ceux des méthodes existantes. Le biais journalier varie de −16 W/m2 (−6% de la moyenne des mesures) à 12 W/m2 (6%). L'erreur quadratique moyenne (RMSD) est comprise entre 23 W/m2 (10%) et 27 W/m2 (15%). Le coefficient de corrélation est de 0,92. La méthode est peu sensible aux variations climatiques saisonnières, le biais et RMSD restent similaires en saison des pluies comme en saison sèche, mais une meilleure corrélation est observée pendant la saison des pluies. Les incertitudes sont principalement dues à la présence de conditions atmosphériques tropicales spécifiques en saison sèche.La méthode a été soumise à différents tests: un processus de validation croisée a mis en évidence la possible généralisation du modèle dans une dimension temporelle, la capacité du modèle à reproduire efficacement des anomalies dans les séries temporelles d'irradiance solaire de surface a aussi été testée. La robustesse des résultats obtenus montrent l'intérêt du modèle par rapport aux autres méthodes d'estimation existantes. A cause de sa simplicité de mise en oeuvre cette méthode ouvre de nouvelles perspectives en matière d'estimation de l'irradiance solaire au sol. Nous concluons que l'utilisation d'un filtre particulaire combinée à une fonction d'observation originale permet une estimation de haute qualité de l'irradiance solaire au sol en Guyane Française.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".