Gradient boosting machine learning to improve satellite-derived column water vapor measurement error.
Journal
Atmospheric measurement techniques
ISSN: 1867-1381
Titre abrégé: Atmos Meas Tech
Pays: Germany
ID NLM: 101622298
Informations de publication
Date de publication:
2020
2020
Historique:
entrez:
16
11
2020
pubmed:
17
11
2020
medline:
17
11
2020
Statut:
ppublish
Résumé
The atmospheric products of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm include column water vapor (CWV) at a 1 km resolution, derived from daily overpasses of NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard the Aqua and Terra satellites. We have recently shown that machine learning using extreme gradient boosting (XGBoost) can improve the estimation of MAIAC aerosol optical depth (AOD). Although MAIAC CWV is generally well validated (Pearson's
Identifiants
pubmed: 33193906
doi: 10.5194/amt-13-4669-2020
pmc: PMC7665162
mid: NIHMS1642057
doi:
Types de publication
Journal Article
Langues
eng
Pagination
4669-4681Subventions
Organisme : NIEHS NIH HHS
ID : P30 ES023515
Pays : United States
Organisme : NIEHS NIH HHS
ID : R00 ES023450
Pays : United States
Organisme : NIH HHS
ID : UH3 OD023337
Pays : United States
Déclaration de conflit d'intérêts
Competing interests. The authors declare that they have no conflict of interest.
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Atmos Meas Tech. 2020;13(9):4669-4681
pubmed: 33193906