Studying impacts of the Saharan Air Layer on hurricane development using WRF-Chem/EnKF Jianyu(Richard) Liang Yongsheng Chen 6th EnKF Workshop York University

+METEOSAT-7/GOES-11 combined Dry Air/SAL Product (source: University of Wisconsin- CIMSS) 00Z25 th, August, 2010 Definition: Saharan air and mineral dust, warm, dry Origin: from near the coast of Africa Duration : late spring to early fall Coverage : in the North Atlantic Ocean Vertical extend : can reach around 500 hPa height Hurricane Earl (2010) Saharan Air Layer (SAL)

Dust inside SAL plays an important role in weather forecast and climate. (1) Indirect effect: modification of the cloud droplet concentration and size distribution (Twomey, 1977; Albrecht, 1989). (2) Direct effect: change radiation budget by absorbing and scattering solar radiation. Dust Impact on Atmosphere

SAL Impact on Hurricane Positive impact: Enhance easterly waves growth and potentially cyclongenesis (eg., Karyampudi and Carlson, 1988) Negative impact: 1)Bring dry and warm air into mid-level of tropical storms, thus increase stability 2)Enhance the vertical wind shear to suppress the developments of tropical storms (eg., Dunion and 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.

Hurricane Earl (2010) Hurricane Earl best track from 25 th, August to 4 th September, (Cangialosi 2011) Official track forecast from 00Z 26 th, August to 00Z 30 th, August. (Cangialosi 2011)

Model : WRF-Chem model Model Configuration: grid size: 36 km, 310X163X57 RRTMG radiation scheme Mellor-Yamada Nakanishi and Niino Level 2.5 PBL Grell 3D cumulus Lin microphysics scheme GOCART simple aerosol scheme Data assimilation: Data Assimilation Research Testbed (DART) Assimilate MODIS aerosol optical depth (AOD) at 550 nm in addition to conventional observations Localization in variables and space Adaptive inflation 20 members Model and Data Assimilation System

DA Experiments In order to represent SAL accurately in the model, two data sets (MODIS AOD and AIRS T&Q) are assimilated into the model. Experiments: Control: Assimilating conventional obs only MODIS: Assimilating MODIS AOD AIRS: Assimilating AIRS temperature and specific humidity retrievals

a) Use existing dust product to reduce spin-up problem MOZART-4 : output from MOZART (driven by NASA GMAO GEOS-5 model). MODIS AOD MOZART-4 AOD 00Z20 th Assimilating MODIS AOD (1) Generating ensemble perturbations in meteorological fields Randomly draw from 3DVAR error covariance (2) Generating ensemble perturbations in chemistry b) Random perturbation of aerosol initial and time-dependent boundary condition

(3) Data assimilation cycles Cycle 6-hourly for 4 days ( from 20 th -24 th ), assimilate conventional and MODIS AOD observations MODIS coverage 12Z23 th 18Z23 th AOD Prior vs. Observation

00Z24 th Model AODMODIS AOD RMSE Total Spread

(4) Model Forecast 00Z24 th 00Z27 th Control With MODIS Sea level pressure

00Z27 th Temperature (With MODIS) Temperature difference (With MODIS – Control) Model Forecast

Relative humidity from AIRSTemperature ( o C) from AIRS Dust direct and indirect effect can be reflected in the temperature and humidity field of the SAL, which can be observed by satellites such as AIRS (Atmospheric Infrared Sounder). If we assimilate the AIRS observations, what kind of impacts they can have on the hurricane development? 00Z 23 th 850hPa Assimilating AIRS data

From Aug. 20 th to Aug.24 th, assimilating conventional observation and AIRS temperature, specific humidity observation together Diagnostics in assimilating AIRS temperature. Bias: Post Bias: Prior rmse: Prior rmse: Post Temperature RMSE and Bias

Sea level pressure. 00Z 24 th,August Control With AIRS Analysis difference –sea level pressure

After the data assimilation, two forecasts have been made, from 24 th to 29 th, August. a)Control: from the initial condition which come from assimilating conventional observation alone. b)AIRS: from the initial condition which come from assimilating ARIS data and conventional observation together; Hurricane track No AIRS mean track Best track AIRS mean track Ensemble track (no AIRS) AIRS ensemble track Model Forecast

minimum sea level pressure With airs maximum wind speed Best track AIRS Best track Control The thermal properties of SAL have significant effects on hurricane behavior ! Model Forecast

Summary (1)Assimilating MODIS AOD can influence hurricane Earl (2010) significantly in this case. (2)The AIRS observations were assimilated into the model. This can improve the accuracy of the temperature and humidity field in the WRF model. The ensemble track and intensity forecasts have been improved significantly. (3)In this case study, considering dust direct effect alone may not be enough to represent SAL thermal property, and its subsequence impact on hurricane development.

Future Works (1) Considering dust indirect effect by employing different chemistry schemes such as MOSAIC, which includes interactions between aerosols and microphysics processes. (2) Assimilating MODIS AOD on top of conventional observations and AIRS retrievals to assess the added value of MODIS AOD