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Studying the Impact of Saharan Dust on Tropical Cyclone Evolution using WRF/ Chem and EnKF

Studying the Impact of Saharan Dust on Tropical Cyclone Evolution using WRF/ Chem and EnKF. Jianyu Liang (York U.) Yongsheng Chen (York U.) Zhiquan Liu (NCAR). Acknowledge: Avelino Arellano , Ziqiang Jiang, Yongxin Zhang. Image: NASA. Saharan Air Layer (SAL).

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Studying the Impact of Saharan Dust on Tropical Cyclone Evolution using WRF/ Chem and EnKF

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  1. Studying the Impact of Saharan Dust on Tropical Cyclone Evolution using WRF/Chem and EnKF Jianyu Liang (York U.) Yongsheng Chen (York U.) Zhiquan Liu (NCAR) Acknowledge: Avelino Arellano, Ziqiang Jiang, Yongxin Zhang

  2. Image: NASA

  3. Saharan Air Layer (SAL) Definition: elevated layer of Saharan air and mineral dust, warm, dry, and enhanced easterly jet to the south Origin: begin from near the costal of Africa. Under the influence of African easterly waves, the air mass often moved towards west from the North African coast ( Burpee 1972) Duration : The SAL usually originate form late spring and remain exist to early fall. Coverage : It cover a very large region in the North Atlantic Ocean Vertical extend : During the summer, the dry ,well mixed SAL can reach around 500 hPa height (Calson and Prospero, 1972).

  4. Impact of SAL on Tropical Cyclones • Positive impact: • Enhance easterly waves growth and potentially cyclongenesis • (eg., Karyampudi and Carison, 1988) • Negative impact: • Bring dry and warm air into tropical storms, thus increase stability • Enhance the vertical wind shear to suppress the developments of tropical storms • (eg., Dunionand Velden2004; Sun et al. 2009) • Objectives: • Use WRF-CHEM and DART to quantify the impact of SAL on TCs. • Hurricane Earl (2010) is chosen to be the first case.

  5. Methodology • 1)WRF-CHEM model • The chemistry component including dust variables in addition to the meteorological component; • both components use the sametime steps, grid , transport schemes, and the same physics schemes for subgrid-scale transport (Grell, etc. 2005). • GOCART dust • 2) DART • Assimilate MODIS aerosol optical depth (AOD) at 550 nm in addition to conventional observations • Localization in variables and space • Fixed prior covariance inflation

  6. Hurricane Earl case Figure 1 Hurricane Earl best track from 25th , August to 4th September, 2010. ( FromCangialosi 2011) Figure 2. Forecast from the model from 0000 UTC 26th , August to 0000 UTC 30, August. ( From Cangialosi 2011)

  7. Figure 3. +METEOSAT-7/GOES-11 combined Dry Air/SAL Product (source: University of Wisconsin-CIMSS) ,red A indicate the position of hurricane Earl . (a) 25th, August. (b) 26th, August.

  8. Temperature (oC) from AIRS. at 1000hPa Temperature (oC) from AIRS. at 850hPa

  9. Relative humidity from AIRS. at 1000hPa Relative humidity from AIRS. at 850hPa

  10. Optical_Depth_Land_And_Ocean_Mean(0~1) from MODIS L3 product . a) 23, August . b) 24th, August

  11. Resolution: 36 km West-east: 310; North-South: 163; Vertical: 57 GOCART simple aerosol scheme , RRTMG radiation scheme, Mellor-Yamada Nakanishi and Niino Level 2.5 PBL, Grell 3D cumulus, Lin microphysics scheme Ensemble: 20 members

  12. Experiment Design • Generating ensemble perturbations in chemistry • spin up for 20 days starting from 00UTC, 01 August 2010 • updating meteorological fields by FNL every 6 hours • spin-up cycle stops at 20,August , 2012 • 2) Generating ensemble perturbation in meteorological fields • Randomly draw from 3DVAR error covariance • 3) Data assimilation cycles and forecast • First, assimilate conventional observations 6-hourly for 1 day • Then, cycle 6-hourly for 4 days • a) Assimilate conventional observations only • b) Assimilate conventional and MODIS AOD observations • Finally, forecast with/without chemistry using WRF-CHEM

  13. Standard deviation of Modis AOD from model at 00UTC, 21 August 2012. average observaton error~ 0.2

  14. Dust size bin 1 (assimilate modis, level 11) 12UTC, 24,August, 2010 Dust size bin 1 with modis-without modis

  15. Relative humidity (assimilate modis , level 11) 12UTC, 24,August, 2010 Relative humidity difference with modis-without modis

  16. Temperature (assimilate modis , level 11) 12UTC, 24,August, 2010 Temperature difference with modis-without modis

  17. Compare hurricane evolution in different experiments MU 00UTC 27, August ,2010 Surface dry pressure perturbation Assimilate MODIS, with chemistry No MODIS assimilation , chemistry Assimilate MODIS, no chemistry

  18. 00UTC 28 Surface wind speed Assimilate MODIS, with chemistry No MODIS assimilation , chemistry Assimilate MODIS, no chemistry

  19. Summary • Simple GOCART scheme in WRF/CHEM can represent the SAL to some extend. • MODIS AOD product can be assimilated into the model. It can change the chemistry field and impact on the meteorological field through the chemistry interaction with meteorological field • Dust can influence the hurricane intensity significantly in this case • Future work • Use different chemistry schemes such as MOSAIC , which includes interaction between the aerosols and the microphysics processes • Conduct more case study and understand the physical mechanism of dust impact on the tropical cyclone formation and evolution .

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