Jianyu Liang (York U.) Yongsheng Chen (York U.) Zhiquan Liu (NCAR) 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) 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).

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. So far,

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

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)

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.

Temperature (oC) from AIRS. at 1000hPa

Relative humidity from AIRS. at 1000hPa

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

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

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

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

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

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

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

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

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

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 .