Studying impacts of the Saharan Air Layer on hurricane development using WRF- Chem / EnKF

Studying impacts of the Saharan Air Layer on hurricane development using WRF-Chem/EnKF

Jianyu(Richard) LiangYongsheng Chen

6th EnKF Workshop

York University

+METEOSAT-7/GOES-11 combined Dry Air/SAL Product (source: University of Wisconsin-CIMSS)

00Z25th, August, 2010

Definition: Saharan air and mineral dust, warm, dry

Origin: from near the coast of AfricaDuration : late spring to early fallCoverage : in the North Atlantic OceanVertical 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 HurricanePositive 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 stability2) 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 25th , August to 4th September, 2010. (Cangialosi 2011)

Official track forecast from 00Z 26th , 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 AODMOZART-4 AOD 00Z20th

Assimilating MODIS AOD (1) Generating ensemble perturbations in meteorological fieldsRandomly 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 cyclesCycle 6-hourly for 4 days ( from 20th-24th) , assimilate conventional and MODIS

AOD observations

MODIS coverage12Z23th

18Z23th

AOD Prior vs. Observation

00Z24thModel AOD MODIS AOD

RMSE

Total Spread

(4) Model Forecast

00Z24th

00Z27th

Control With MODISSea level pressure

00Z27th Temperature (With MODIS) Temperature difference (With MODIS – Control)

Model Forecast

Relative humidity from AIRSTemperature (oC) 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 23th 850hPa

Assimilating AIRS data

From Aug. 20th to Aug.24th , 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 24th,August

Control With AIRS

Analysis difference –sea level pressure

After the data assimilation, two forecasts have been made, from 24th to 29th , 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 trackAIRS

AIRS

Best track

Control

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