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Di Tomaso, Enza
73  results:
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1

Modeling dust mineralogical composition: sensitivity to soi..:

Gonçalves Ageitos, María ; Obiso, Vincenzo ; Miller, Ron L....
Atmospheric Chemistry and Physics.  23 (2023)  15 - p. 8623-8657 , 2023
Link: https://doi.org/10.5194/..
 
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2

Comparison of dust optical depth from multi-sensor products..:

Mytilinaios, Michail ; Basart, Sara ; Ciamprone, Sergio...
Atmospheric Chemistry and Physics.  23 (2023)  9 - p. 5487-5516 , 2023
Link: https://doi.org/10.5194/..
 
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3

Assimilating spaceborne lidar dust extinction can improve d..:

Escribano, Jerónimo ; Di Tomaso, Enza ; Jorba, Oriol...
Atmospheric Chemistry and Physics.  22 (2022)  1 - p. 535-560 , 2022
Link: https://doi.org/10.5194/..
 
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4

Quantification of the dust optical depth across spatiotempo..:

Gkikas, Antonis ; Proestakis, Emmanouil ; Amiridis, Vassilis...
Atmospheric Chemistry and Physics.  22 (2022)  5 - p. 3553-3578 , 2022
Link: https://doi.org/10.5194/..
 
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5

The MONARCH high-resolution reanalysis of desert dust aeros..:

Di Tomaso, Enza ; Escribano, Jerónimo ; Basart, Sara...
Earth System Science Data.  14 (2022)  6 - p. 2785-2816 , 2022
Link: https://doi.org/10.5194/..
 
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6

MONARCH Regional Reanalysis of Desert Dust Aerosols: An Ini..:

, In: Air Pollution Modeling and its Application XXVIII; Springer Proceedings in Complexity,
Di Tomaso, Enza ; Escribano, Jerónimo ; Basart, Sara... - p. 241-247 , 2022
Link: https://doi.org/10.1007/..
 
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7

ModIs Dust AeroSol (MIDAS): a global fine-resolutiondust op..:

Gkikas, Antonis ; Proestakis, Emmanouil ; Amiridis, Vassilis...
Atmospheric Measurement Techniques.  14 (2021)  1 - p. 309-334 , 2021
Link: https://doi.org/10.5194/..
 
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8

Mineral dust cycle in the Multiscale Online Nonhydrostatic ..:

Klose, Martina ; Jorba, Oriol ; Gonçalves Ageitos, María...
Geoscientific Model Development.  14 (2021)  10 - p. 6403-6444 , 2021
Link: https://doi.org/10.5194/..
 
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9

Assimilation of MODIS Dark Target and Deep Blue observation..:

Di Tomaso, Enza ; Schutgens, Nick A. J. ; Jorba, Oriol.
Geoscientific Model Development.  10 (2017)  3 - p. 1107-1129 , 2017
Link: https://doi.org/10.5194/..
 
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10

Modeling dust mineralogical composition: sensitivity to soi..:

Gonçalves Ageitos, María ; Obiso, Vincenzo ; Miller, Ron L...
https://publikationen.bibliothek.kit.edu/1000167730.  , 2024
Link: https://publikationen.bi..
 
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11

Modeling dust mineralogical composition: sensitivity to soi..:

Ageitos, María Gonçalves ; Obiso, Vincenzo ; Miller, Ron, L...
info:eu-repo/semantics/altIdentifier/doi/10.5194/acp-23-8623-2023.  , 2023
Link: https://hal.science/hal-..
 
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12

MONARCH data presented in Klose et al. (2021), "Mineral dus..:

Klose, Martina ; Jorba, Oriol ; Gonçalves Ageitos, María...
https://publikationen.bibliothek.kit.edu/1000154875.  , 2023
Link: https://publikationen.bi..
 
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13

Modeling dust mineralogical composition: sensitivity to soi..:

Gonçalves Ageitos, María ; Obiso, Vincenzo ; Miller, Ron L...
info:eu-repo/grantAgreement/EC/H2020/821205/.  , 2023
Link: https://zenodo.org/recor..
 
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14

Modeling dust mineralogical composition: sensitivity to soi..:

Gonçalves Ageitos, María ; Obiso, Vincenzo ; Miller, Ron L...
Atmospheric Chemistry and Physics -- http://www.atmos-chem-phys.net/volumes_and_issues.html -- http://www.bibliothek.uni-regensburg.de/ezeit/?2069847 -- 1680-7324.  , 2023
Link: https://doi.org/10.5194/..
 
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15

Modeling dust mineralogical composition: sensitivity to soi..:

Ageitos, María Gonçalves ; Obiso, Vincenzo ; Miller, Ron, L...
info:eu-repo/semantics/altIdentifier/doi/10.5194/acp-23-8623-2023.  , 2023
Link: https://hal.science/hal-..
 
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