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:
1) Bring dry and warm air into tropical storms, thus increase
stability
2) 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.
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:
(a) 25th, August.
University of Wisconsin-CIMSS) ,red A indicate the position of hurricane Earl .
(b) 26th, August.
Temperature (oC) from AIRS. at 1000hPa
Temperature (oC) from AIRS. at 850hPa
Relative humidity from AIRS. at 1000hPa
Relative humidity from AIRS. at 850hPa
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, MellorYamada Nakanishi and Niino Level 2.5 PBL, Grell 3D cumulus, Lin
microphysics scheme
Ensemble: 20 members
Experiment Design
1)
a.
b.
c.
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
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
00UTC 28
Surface wind speed
Assimilate MODIS, with chemistry
No MODIS assimilation , chemistry
Assimilate MODIS, no chemistry
Summary
1) Simple GOCART scheme in WRF/CHEM can represent the SAL
to some extend.
2) 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
3) Dust can influence the hurricane intensity significantly in this
case
Future work
1) Use different chemistry schemes such as MOSAIC , which
includes interaction between the aerosols and the
microphysics processes
2) Conduct more case study and understand the physical
mechanism of dust impact on the tropical cyclone formation
and evolution .