Publication Abstracts

Di Tomaso et al. 2022

Di Tomaso, E., J. Escribano, S. Basart, P. Ginoux, F. Macchia, F. Barnaba, F. Benincasa, P.-A. Bretonnière, A. Buñuel, M. Castrillo, E. Cuevas, P. Formenti, M. Gonçalves-Ageitos, O. Jorba, M. Klose, L. Mona, G. Montané, M. Mytilinaios, V. Obiso, M. Olid, N. Schutgens, A. Votsis, E. Werner, and C. Pérez García-Pando, 2022: MONARCH regional reanalysis of desert dust aerosols: An initial assessment. In Air Pollution Modeling and its Application XXVIII. C. Mensink and O. Jorba, Eds., Springer Proceedings in Complexity, Springer International, pp. 241-247, doi:10.1007/978-3-031-12786-1_33.

Aerosol reanalyses are a well-established tool for monitoring aerosol trends, for validation and calibration of weather chemical models, as well as for the enhancement of strategies for environmental monitoring and hazard mitigation. By providing a consistent and complete data set over a sufficiently long period, they address the shortcomings of aerosol observational records in terms of temporal and spatial coverage and aerosol speciation. These shortcomings are particularly severe for dust aerosols. A 10-year dust aerosol regional reanalysis has been recently produced on the Barcelona Supercomputing Center HPC facilities at the high spatial resolution of 0.1°. Here we present a brief description and an initial assessment of this data set. An innovative dust optical depth data set, derived from the MODIS Deep Blue products, has been ingested in the dust module of the MONARCH model by means of a LETKF with a four-dimensional extension. MONARCH ensemble has been generated by applying combined meteorology and emission perturbations. This has been achieved using for each ensemble member different meteorological fields as initial and boundary conditions, and different emission schemes, in addition to stochastic perturbations of emission parameters, which we show is beneficial for dust data assimilation. We prove the consistency of the assimilation procedure by analyzing the departures of the assimilated observations from the model simulations for a two-month period. Furthermore, we show a comparison with AERONET coarse optical depth retrievals during a period of 2012, which indicates that the reanalysis data set is highly accurate. While further analysis and validation of the whole data set are ongoing, here we provide a first evidence for the reanalysis to be a useful record of dust concentration and deposition extending the existing observational-based information intended for mineral dust monitoring.

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BibTeX Citation

  author={Di Tomaso, E. and Escribano, J. and Basart, S. and Ginoux, P. and Macchia, F. and Barnaba, F. and Benincasa, F. and Bretonnière, P.-A. and Buñuel, A. and Castrillo, M. and Cuevas, E. and Formenti, P. and Gonçalves-Ageitos, M. and Jorba, O. and Klose, M. and Mona, L. and Montané, G. and Mytilinaios, M. and Obiso, V. and Olid, M. and Schutgens, N. and Votsis, A. and Werner, E. and Pérez García-Pando, C.},
  editor={Mensink, C. and Jorba, O.},
  title={MONARCH regional reanalysis of desert dust aerosols: An initial assessment},
  booktitle={Air Pollution Modeling and its Application XXVIII},
  publisher={Springer International},
  series={Springer Proceedings in Complexity},

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RIS Citation

ID  - di05200z
AU  - Di Tomaso, E.
AU  - Escribano, J.
AU  - Basart, S.
AU  - Ginoux, P.
AU  - Macchia, F.
AU  - Barnaba, F.
AU  - Benincasa, F.
AU  - Bretonnière, P.-A.
AU  - Buñuel, A.
AU  - Castrillo, M.
AU  - Cuevas, E.
AU  - Formenti, P.
AU  - Gonçalves-Ageitos, M.
AU  - Jorba, O.
AU  - Klose, M.
AU  - Mona, L.
AU  - Montané, G.
AU  - Mytilinaios, M.
AU  - Obiso, V.
AU  - Olid, M.
AU  - Schutgens, N.
AU  - Votsis, A.
AU  - Werner, E.
AU  - Pérez García-Pando, C.
ED  - Mensink, C.
ED  - Jorba, O.
PY  - 2022
TI  - MONARCH regional reanalysis of desert dust aerosols: An initial assessment
BT  - Air Pollution Modeling and its Application XXVIII
T3  - Springer Proceedings in Complexity
SP  - 241
EP  - 247
DO  - 10.1007/978-3-031-12786-1_33
SN  - 978-3-031-12786-1
PB  - Springer International
CY  - Cham
ER  -

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