the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
February 2017 extreme Saharan dust outbreak in the Iberian Peninsula: from lidar-derived optical properties to evaluation of forecast models
Abstract. An unprecedented extreme Saharan dust event was registered in winter time from 20 to 23 February 2017 over the Iberian Peninsula (IP). We report on aerosol optical properties observed under this extreme dust outbreak through remote sensing (active and passive) techniques. For that, EARLINET (European Aerosol Research LIdar NETwork) lidar and AERONET (AErosol RObotic NETwork) Sun-photometer Cimel CE 318 measurements are used. The sites considered are: Barcelona (41.38º N, 2.17º E), Burjassot (39.51º N, 0.42º W), Cabo da Roca (38.78º N, 9.50º W), Évora (38.57º N, 7.91º W), Granada (37.16º N, 3.61º W) and Madrid (40.45º N, 3.72º W).
In general, large aerosol optical depths (AOD) and low Ångström exponents (AE) are observed. An AOD of 2.0 at 675 nm is reached in several stations. Maximum values of AOD675 of 2.5 are registered in Évora. During and around the peak of AOD675, AEs close to 0 are measured. With regard to vertically-resolved aerosol optical properties, particle backscatter coefficients as high as 1.5∙10−5 m−1 sr−1 at 355 nm are recorded at every lidar stations. Mean lidar ratios are found in the range 40–55 sr at 355 nm and 34–61 sr at 532 nm during the event inside the dust layer. Mean particle and volume depolarization ratios are found to be very consistent between lidar stations. They range 0.19–0.31 and 0.12–0.26 respectively. The optical properties are also found very stable with height in the dust layer. Another remarkable aspect of the event is the limited height of the dust transport which is found between the ground and 5 km. Our vertically-resolved aerosol properties are also used to estimate the performances of two dust models, namely BSC-DREAM8b and NMMB/BSC-Dust, in order to evaluate their forecast skills in such intense dust outbreaks. We found that forecasts provided by the NMMB/BSC-Dust show a better agreement with observations than the ones from BSC-DREAM8b. The BSC-DREAM8b forecasts (24 h) present a large underestimation during the event. No clear degradation of the prognostics is appreciated in 24, 48, 72 h except for the Barcelona station.
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RC1: 'Just another report on a dust event over the Iberian Peninsula', Anonymous Referee #1, 25 Jun 2018
- AC1: 'Response to RC1', Alfonso Fernandez, 25 Sep 2018
- AC4: 'FINAL VERSION OF THE MANUSCRIPT', Alfonso Fernandez, 25 Sep 2018
- AC5: 'SUPPLEMENTARY MATERIAL OF THE MANUSCRIPT', Alfonso Fernandez, 25 Sep 2018
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RC2: 'Review of "February 2017 extreme Saharan dust outbreak in the Iberian Peninsula"', Anonymous Referee #2, 02 Aug 2018
- AC2: 'Response to RC2', Alfonso Fernandez, 25 Sep 2018
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RC3: 'Review of Fednández et al., on observing and modeling a dust outbreak in the Iberian Peninsula', Anonymous Referee #3, 14 Aug 2018
- AC3: 'Response to RC3', Alfonso Fernandez, 25 Sep 2018
-
RC1: 'Just another report on a dust event over the Iberian Peninsula', Anonymous Referee #1, 25 Jun 2018
- AC1: 'Response to RC1', Alfonso Fernandez, 25 Sep 2018
- AC4: 'FINAL VERSION OF THE MANUSCRIPT', Alfonso Fernandez, 25 Sep 2018
- AC5: 'SUPPLEMENTARY MATERIAL OF THE MANUSCRIPT', Alfonso Fernandez, 25 Sep 2018
-
RC2: 'Review of "February 2017 extreme Saharan dust outbreak in the Iberian Peninsula"', Anonymous Referee #2, 02 Aug 2018
- AC2: 'Response to RC2', Alfonso Fernandez, 25 Sep 2018
-
RC3: 'Review of Fednández et al., on observing and modeling a dust outbreak in the Iberian Peninsula', Anonymous Referee #3, 14 Aug 2018
- AC3: 'Response to RC3', Alfonso Fernandez, 25 Sep 2018
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Cited
3 citations as recorded by crossref.
- Mineralogy and physicochemical features of Saharan dust wet deposited in the Iberian Peninsula during an extreme red rain event C. Rodriguez-Navarro et al. 10.5194/acp-18-10089-2018
- Characterization of a Regional Dust Storm Using RAMAN Lidar Over the Western Indian Region K. Shukla et al. 10.1007/s12524-023-01778-x
- A neural network aerosol-typing algorithm based on lidar data D. Nicolae et al. 10.5194/acp-18-14511-2018