Assimilating spaceborne lidar dust extinction can improve dust forecasts

Escribano, Jerónimo; Di Tomaso, Enza; Jorba, Oriol; Klose, Martina; Gonçalves Ageitos, Maria; Macchia, Francesca; Amiridis, Vassilis; Baars, Holger; Marinou, Eleni; Proestakis, Emmanouil; Urbanneck, Claudia; Althausen, Dietrich; Bühl, Johannes; Mamouri, Rodanthi-Elisavet; Pérez García-Pando, Carlos

doi:https://doi.org/10.5194/acp-22-535-2022

Articles | Volume 22, issue 1

https://doi.org/10.5194/acp-22-535-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Dust aerosol measurements, modeling and multidisciplinary...

https://doi.org/10.5194/acp-22-535-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 22, issue 1

Research article

|

14 Jan 2022

Research article |

| 14 Jan 2022

Assimilating spaceborne lidar dust extinction can improve dust forecasts

Jerónimo Escribano, Enza Di Tomaso, Oriol Jorba, Martina Klose, Maria Gonçalves Ageitos, Francesca Macchia, Vassilis Amiridis, Holger Baars, Eleni Marinou, Emmanouil Proestakis, Claudia Urbanneck, Dietrich Althausen, Johannes Bühl, Rodanthi-Elisavet Mamouri, and Carlos Pérez García-Pando

Data sets

MYD15A2H MODIS/Aqua Leaf Area Index/FPAR 8-Day L4 Global 500 m SIN Grid V006 R. Myeni, Y. Knyazikhin, and T. Park https://doi.org/10.5067/MODIS/MYD15A2H.006

VIIRS/SNPP Deep Blue Aerosol L2 6-Min Swath 6 km, NASA Level 1 and Atmosphere Archive and Distribution System VIIRS Atmosphere Science Team, SSEC https://doi.org/10.5067/VIIRS/AERDB_L2_VIIRS_SNPP.011

Model code and software

MONARCH: Multiscale Online Nonhydrostatic AtmospheRe CHemistry model Version 2.0 (v2.0.0) M. Klose, O. Jorba, M. Gonçalves Ageitos, J. Escribano, M. L. Dawson, V. Obiso, E. Di Tomaso, S. Basart, G. Montané Pinto, F. Macchia, and C. Pérez García-Pando https://doi.org/10.5281/zenodo.5215467

Short summary

We explore the benefits and consistency in adding lidar dust observations in a dust optical depth assimilation. We show that adding lidar data to a dust optical depth assimilation has valuable benefits and the dust analysis improves. We discuss the impact of the narrow satellite footprint of the lidar dust observations on the assimilation.