Machine learning-driven characterization and prescription  of aerosol optical properties for atmospheric models

do Rosário, Nilton Évora; Longo, Karla M.; Toso, Pedro H.; Freitas, Saulo R.; Yamasoe, Marcia A.; Rodrigues, Luiz Flávio; Medeiros, Otavio; Velho, Haroldo Campos; Menezes, Isilda da Cunha; Miranda, Ana Isabel

doi:10.5194/gmd-19-2691-2026

Articles | Volume 19, issue 7

https://doi.org/10.5194/gmd-19-2691-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-19-2691-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 19, issue 7

Development and technical paper

|

10 Apr 2026

Development and technical paper |

| 10 Apr 2026

Machine learning-driven characterization and prescription of aerosol optical properties for atmospheric models

Nilton Évora do Rosário, Karla M. Longo, Pedro H. Toso, Saulo R. Freitas, Marcia A. Yamasoe, Luiz Flávio Rodrigues, Otavio Medeiros, Haroldo Campos Velho, Isilda da Cunha Menezes, and Ana Isabel Miranda

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BibTeX: 265
EndNote: 316

Views and downloads (calculated since 16 Apr 2025)

Month	HTML	PDF	XML	Total
Apr 2025	180	18	8	206
May 2025	142	38	4	184
Jun 2025	94	30	4	128
Jul 2025	68	26	6	100
Aug 2025	862	32	12	906
Sep 2025	3,482	10	16	3,508
Oct 2025	164	16	4	184
Nov 2025	114	28	4	146
Dec 2025	64	56	6	126
Jan 2026	56	64	4	124
Feb 2026	50	56	8	114
Mar 2026	118	64	6	188
Apr 2026	416	144	17	577
May 2026	205	59	8	272
Jun 2026	35	13	5	53
Jul 2026	93	33	8	134
Aug 2026	73	33	18	124
Sep 2026	276	85	56	417
Oct 2026	82	42	26	150

Cumulative views and downloads (calculated since 16 Apr 2025)

Month	HTML	PDF	XML	Total
Apr 2025	180	18	8	206
May 2025	142	38	4	184
Jun 2025	94	30	4	128
Jul 2025	68	26	6	100
Aug 2025	862	32	12	906
Sep 2025	3,482	10	16	3,508
Oct 2025	164	16	4	184
Nov 2025	114	28	4	146
Dec 2025	64	56	6	126
Jan 2026	56	64	4	124
Feb 2026	50	56	8	114
Mar 2026	118	64	6	188
Apr 2026	416	144	17	577
May 2026	205	59	8	272
Jun 2026	35	13	5	53
Jul 2026	93	33	8	134
Aug 2026	73	33	18	124
Sep 2026	276	85	56	417
Oct 2026	82	42	26	150

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Total article views: 7,641 (including HTML, PDF, and XML) Thereof 7,584 with geography defined and 57 with unknown origin.

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Short summary

This study maps aerosol regimes over the Iberian Peninsula using AERONET data and machine learning. Five types were identified, from Saharan dust to smoke, highlighting differences in particle size and absorption. Combining observations with model data improves aerosol representation in climate simulations, reducing uncertainties and enhancing understanding of regional air quality and climate impacts.