An intercomparison of EarthCARE cloud, aerosol and precipitation retrieval products
Notice bibliographique
Résumé
Abstract. The mission of the Earth cloud, aerosol and radiation explorer (EarthCARE) mission to observe cloud, aerosol, precipitation and radiation using four complementary instruments requires the development of many single-instrument and synergistic algorithms for the retrieval of geophysical quantities. The retrieval products employ one or more of the cloud profiling radar (CPR), atmospheric lidar (ATLID) and multispectral imager (MSI), while the broadband radiometer (BBR) places the retrieved quantities in the context of the atmospheric radiation budget. To facilitate the development and evaluation of the ESA EarthCARE production model prior to launch, sophisticated instrument simulators have been developed to produce realistic synthetic EarthCARE measurements from the output of cloud-resolving model simulations. While acknowledging that the physical and radiative representation of cloud, aerosol and precipitation in the test scenes are based on numerical models, the opportunity to perform a detailed evaluation wherein the model ``truth'' is known has provided rare insights into the performance of EarthCARE's instruments and retrieval algorithms. This level of omniscience will not be available for the evaluation of in-flight EarthCARE retrieval products, even during validation activities coordinated with ground-based and airborne measurements. In this study we intercompare EarthCARE retrieval products from within the ESA production model both statistically across all simulated EarthCARE granules, and using timeseries of data from an individual scene. The comparison between the retrieved quantities helps to illustrate the strengths and limitations of the single-instrument retrievals, and the degrees to which the synergistic retrieval and composite products can represent the entire atmosphere of clouds, aerosols and precipitation. We show that radar-lidar synergy has the greatest impact in ice clouds; when compared with single-instrument radar and lidar retrievals, the synergistic ATLID-CPR-MSI cloud, aerosols, and precipitation (ACM-CAP) product accurately retrieves profiles of both ice water content and effective radius. While liquid cloud is difficult to detect directly from spaceborne remote sensors, especially in complex and layered scenes, the synergistic retrieval benefits from combined constraints from lidar backscatter, solar radiances and radar path-integrated attenuation, but still exhibits a high degree of random error. For precipitation retrievals, the CPR cloud and precipitation product (C-CLD) and ACM-CAP have similar performance when well-constrained by CPR measurements. The greatest differences are in coverage, with ACM-CAP reporting retrievals in the melting layer, and in heavy precipitation where the radar is dominated by multiple scattering and attenuation). Aerosol retrievals from ATLID compensate for a high degree of measurement noise in a number of ways, with the ATLID extinction, backscatter and depolarization (A-EBD) product and ACM-CAP demonstrating similar performance in the test scenes. The multispectral imager (MSI) cloud optical properties (M-COP) product performs very well in unambiguous cloud layers; similarly, the MSI aerosol optical thickness (M-AOT) product performs well where the possibility of contamination by cloud signal is very low. A summary of the performance of all retrieval products is provided, and may help to inform the selection of EarthCARE data products by future users.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».