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Enregistrement W4393834033 · doi:10.5281/zenodo.7311704

EarthCARE level-2 demonstration products from simulated scenes

2022· dataset· en· W4393834033 sur OpenAlexaffabout
Gerd‐Jan van Zadelhoff, Howard W. Barker, Edward Baudrez, Sebastian Bley, Nicolas Clerbaux, Jason N. S. Cole, Jos de Kloe, Nicole Docter, Carlos Doménech, David P. Donovan, Jean‐Louis Dufresne, Michael Eisinger, J. Fischer, Raquel García-Marañón, Moritz Haarig, Robin J. Hogan, Anja Hünerbein, Pavlos Kollias, Rob Koopman, Nils Madenach, Shannon Mason, René Preusker, Bernat Puigdomènech Treserras, Zhipeng Qu, Manuel Ruiz-Saldaña, Mark W. Shephard, Almudena Velázquez-Blazquez, Najda Villefranque, Ulla Wandinger, Ping Wang, Tobias Wehr

Notice bibliographique

RevueZenodo (CERN European Organization for Nuclear Research) · 2022
Typedataset
Langueen
DomaineEarth and Planetary Sciences
ThématiqueGeophysics and Gravity Measurements
Établissements canadiensMcGill UniversityEnvironment and Climate Change Canada
Organismes subventionnairesnon disponible
Mots-clésComputer scienceEnvironmental scienceRemote sensingComputer graphics (images)Geography

Résumé

récupéré en direct d'OpenAlex

Overview The EarthCARE satellite combines four instruments, a Cloud Profiling Radar (CPR), an Atmospheric Lidar (ATLID), a Multispectral Imager (MSI) and a Broadband Radiometer (BBR), from which many products will be generated on the properties of clouds, aerosols, precipitation and radiation. The dataset in this repository consists of test products generated from simulated 3D scenes produced by the Canadian Global Environmental Multiscale (GEM) model. This includes both Level 1 (L1) "input" products containing simulated satellite measurements, and Level 2 (L2) "output" products produced by running the various European retrieval algorithms on the inputs. The dataset has been produced as part of the European Space Agency funded "CARDINAL" project involving numerous European and Canadian scientists, and there are several versions representing the evolution of the algorithms during preparation for the launch of EarthCARE. The scenes and algorithms are discussed in detail in a Special Issue of the journal Atmospheric Modelling Techniques (AMT), so the overview here is limited to a summary of what is contained in this dataset. Directory structure The three top-level directories inside the zip file are for the three simulated scenes (except for the C-APC directory, described in a separate section below), each of which represent a single 6000-km long EarthCARE granule: Halifax: A swath over the Atlantic Ocean from the Caribbean to the Labrador Sea, passing close to Halifax in Nova Scotia Baja: A swath passing over the Rocky Mountains and the Baja California peninsula in Mexico Hawaii: A swath over the Pacific Ocean passing close to Hawaii Each of these directories contains three further directories: input: simulated level-1 instrument data output: level-2 meteorological products generated from the input data logs: text files logging the progress of the algorithms as they generated the level-2 data The input and output directories contain subdirectories for each product, and these each contain two files: A NetCDF4/HDF5 file with the suffix "h5" containing the retrieved data An XML file with the suffix "HDR" containing a description of the variables in the file (reproducing metadata already in the NetCDF4/HDF5 file) Input products (L1) The level-1 products are named Y-ZZZ where Y indicates the source of the data (A=ATLID, C=CPR, M=MSI, B=BBR and X=auxiliary) and ZZZ is a shortened version of the product name: A-NOM: Nominal ATLID measurements C-NOM: Nominal CPR measurements M-RGR: Regridded MSI measurements B-NOM: Nominal BBR measurements averaged to various scales B-SNG: Single-pixel BBR measurements X-JSG: Definition of the Joint Standard Grid on to which several of the observations are interpolated X-MET: Meteorological data from ECMWF Single-instrument output products (L2a) The naming convention is the same as the L1 data products. A-AER: ATLID aerosol profiles A-ALD: ATLID aerosol layer descriptor A-CTH: ATLID cloud top height A-EBD: ATLID extinction, backscatter and depolarization A-FM: ATLID feature mask A-ICE: ATLID ice cloud properties A-TC: ATLID target classification C-CD: CPR corrected Doppler velocity C-CLD: CPR cloud properties C-FMR: CPR feature mask and reflectivity C-TC: CPR target classification M-AOT: MSI aerosol optical thickness M-CM: MSI cloud mask M-COP: MSI cloud optical and physical properties Multi-instrument output products (L2b) The level-2b output products are named YY-ZZZ where YY is 2-4 character code conveying which of the four instruments were used and ZZZ is the shortened version of the product name. AC-TC: ATLID-CPR target classification AM-ACD: ATLID-MSI aerosol column descriptor AM-CTH: ATLID-MSI cloud top height BM-RAD: BBR-MSI broadband radiances (unfiltered) ACM-3D: ATLID-CPR-MSI constructed 3D scene ACM-CAP: ATLID-CPR-MSI synergistic retrieval of cloud, aerosol and precipitation ACM-COM: ATLID-CPR-MSI composite of single-instrument cloud and aerosol retrievals ACM-RT: ATLID-CPR-MSI radiative fluxes and heating rates computed on the retrievals BMA-FLX: BBR-MSI-ATLID broadband fluxes ACMB-DF: ATLID-CPR-MSI-BBR difference between radiances and fluxes computed from retrievals (ACM-RT) and measurements (BM-RAD, BMA-FLX) CPR Antenna Pointing Correction (C-APC) product From version 10.01 of the dataset, an additional top-level "C-APC" directory contains test data for the CPR Antenna Pointing Correction product, which will be generated via statistical analysis of a larger sample of C-NOM data. The generated information about mis-pointing of the radar antenna will then be used to improve interpretation of subsequent radar Doppler observations. There are two subdirectories: GEM_scenes: contains a file used in subsequent radar processing of the three GEM scenes, although in these scenes it has been assumed that there is no mis-pointing to correct, indicated by the file containing missing data and zeros. Antenna_mispointing_example: contains input and output files (in further subdirectories) illustrating what the data would look like with mis-pointing present. It was generated by artificially modifying the Doppler veclocities in the original Baja, Halifax and Hawaii scenes, concatenating them to generate a full orbit, and then running the C-APC algorithm to characterize the mis-pointing. Filename format The data filenames and directories have the following name format: ECA_EXAA_YYY_ZZZ_LL_OBSERVATION-TIME_GENERATION-TIME_VVVVVV where: "ECA" indicates EarthCARE. EXAA indicates the file class: ESA, Latency N/A, Simulator, Baseline N/A. YYY is a three-character code indicating the instrumental source of the data. The 1-4 character codes defined in the sections above are converted to 3 character codes as follows: A -> ATL, C -> CPR, M -> MSI, B -> BBR, X -> AUX, AM -> AM_, AC -> AC_, BM -> BM_, ACMB -> ALL. ZZZ is a three-character code abbreviating the full name of the product, as defined in the sections above. LL represents the level of the data from 1B, 1C, 1D, 2A and 2B. OBSERVATION-TIME is a code representing the date and time the observations were taken in the form yyyymmddThhmmssZ. Note that for the present datasets the dates are fictional future dates. GENERATION-TIME is a code of the same form representing the time that the processor was run to produce the data product. VVVVVV indicates the orbit number and frame letter. Further information may be obtained from the papers in the special issue of AMT. The description here may be expanded in future. Contacts: Gerd-Jan van Zadelhoff and Robin Hogan

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Jeu de données · Signal consensuel: Jeu de données
Score de désaccord entre enseignants0,036
Score d'incertitude au seuil0,122

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,004
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,000
Communication savante0,0020,002
Science ouverte0,0030,002
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0360,024

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.

Tête enseignante Opus0,077
Tête enseignante GPT0,235
Écart entre enseignants0,159 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreJeu de données

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations11
Publié2022
Routes d'admission2
Résumé présentoui

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