Atmospheric constituents forecast and analysis using an ...
6th EnKF Workshop Studying impacts of the Saharan Air Layer on hurricane development using WRF-Chem/EnKF Jianyu(Richard) Liang Yongsheng Chen York University Saharan Air Layer (SAL) 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 00Z25th, August, 2010 Hurricane Earl (2010) +METEOSAT-7/GOES-11 combined Dry Air/SAL Product (source: University of WisconsinCIMSS) Dust Impact on Atmosphere 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. 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 25th , August to 4th September, 2010. (Cangialosi 2011)
Official track forecast from 00Z 26th , August to 00Z 30 th, August. (Cangialosi 2011) Model and Data Assimilation System 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 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 Assimilating MODIS AOD (1) Generating ensemble perturbations in meteorological fields Randomly draw from 3DVAR error covariance (2) Generating ensemble perturbations in chemistry a) Use existing dust product to reduce spin-up problem MOZART-4 : output from MOZART (driven by NASA GMAO GEOS-5 model). b) Random perturbation of aerosol initial and time-dependent boundary condition
MOZART-4 AOD 00Z20th MODIS AOD (3) Data assimilation cycles Cycle 6-hourly for 4 days ( from 20th-24th) , assimilate conventional and MODIS AOD observations 12Z23th 18Z23th
MODIS coverage AOD Prior vs. Observation 00Z24th Model AOD Total Spread RMSE MODIS AOD
(4) Model Forecast 00Z24th 00Z27th Control Sea level pressure With MODIS Model Forecast
00Z27th Temperature (With MODIS) Temperature difference (With MODIS Control) Assimilating AIRS data 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 Temperature (oC) from AIRS 850hPa Relative humidity from AIRS Temperature RMSE and Bias From Aug. 20th to Aug.24th , assimilating conventional observation and AIRS temperature, specific humidity observation together
Diagnostics in assimilating AIRS temperature . rmse: Post Bias: Post rmse: Prior Bias: Prior Analysis difference sea level pressure
Sea level pressure. 00Z 24th,August Control With AIRS Model Forecast 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 Ensemble track (no AIRS) Best track AIRS mean track AIRS ensemble track Model Forecast minimum sea level pressure maximum wind speed
Control AIRS With airs Best track Best track AIRS Control The thermal properties of SAL have significant effects on hurricane
behavior ! 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
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