1. Transport Simulation of the April 1998 Chinese Dust Event Prepared by: Bret A. Schichtel And Rudolf B. Husar Center for Air Pollution Impact and Trend Analysis (CAPITA) Washington University Saint Louis, Missouri April 16, 1998 SeaWiFS imageSeaWiFS

2. Introduction On April 15th, 1998 an unusually intense dust storm began in the western Chinese Province of Xinjiang. By April 20, the dust front covered a 1000 mile stretch on the east coast of China and within five days it moved across the Pacific impacting the West Coast. This event was closely monitored by the air quality community using satellite, aircraft and surface based measurements.air quality community A number of organizations have taken advantage of this unique “natural tracer experiment“ to test and validate global transport models, including the Naval Research Laboratory, Euro-Mediterranean Centre on Insular Coastal Dynamics and NOAA’s Climate Monitoring & Diagnostics Laboratory.Naval Research LaboratoryEuro-Mediterranean Centre on Insular Coastal DynamicsNOAA’s Climate Monitoring & Diagnostics Laboratory This analysis adds to the body of the Chinese dust simulations by simulating the transport of the Chinese dust cloud from April 19 – April 30 using the CAPITA Monte Carlo Model driven by the FNL global meteorological data to. The simulation is evaluated against TOMS aerosol index and surface PM10 and PM2.5 measurements.CAPITA Monte Carlo ModelFNL global meteorological data

3. CAPITA Monte Carlo Model A diagnostic tool that uses the Monte Carlo approach to simulate and investigate the roles of air pollutant emissions, transport and kinetics on air quality. Airmass transport and diffusion, forward or backward in time, is simulated by tracking the movement of “particles”. Meteorological winds advect the particles in 3-D space, while the particles are randomly distributed throughout the atmospheric boundary layer simulating vertical mixing. Kinetics are simulated using pseudo first order transformation rates which are a function of meteorological variables, e.g. solar radiation, precipitation, temperature The model is routinely driven by met. data generated by the National Weather Service and NOAA’s Air Resources LaboratoryAir Resources Laboratory

4. CAPITA Monte Carlo Model - Transport Advection: Particles are moved in 3-D space using the input meteorological data’s mean wind field. Horizontal Dispersion: Eddy diffusion coefficients which vary depending on time of day randomly displace the particle horizontally. Vertical Dispersion: Intense vertical mixing within the mixing layer is simulated by uniformly distributing particle from the ground to the mixing height. No vertical dispersion is applied to particles above mixing layer.

5. CAPITA Monte Carlo Model - Kinetics Chemistry: Pseudo first order transformation rates, function of meteorological variables, such as solar radiation, temperature, water vapor content Deposition dry and wet: Pseudo first order rates equations Dry deposition function of hour of solar radiation, Mixing Hgt Wet deposition function of precipitation rate Note: in this application no kinetics were applied to the particles

6. FNL Meteorological Data Archive The FNL meteorological data were used as input into the Monte Carlo Model. The FNL data is a product of the Global Data Assimilation System (GDAS), which uses the Global spectral Medium Range Forecast model (MRF) to assimilate multiple sources of measured data and forecast meteorology. 129 x 129 Polar Stereographic Grid with ~ 190 km resolution. 12 vertical layers on constant pressure surfaces from 1000 to 50 mbar 6 hour time increment Upper Air Data: 3-D winds, Temp, RH Surface Data includes: pressure, 10 meter winds, 2 meter Temp & RH, Momentum and heat flux Data is available from 1/97 to present.

7. FNL Meteorological Data Processing The Monte Carlo model requires the input meteorological data to have a terrain following vertical coordinate system and an estimate of the mixing height. Therefore, the data were reprocessed: The vertical coordinate system was converted from constant pressure surfaces to 18 constant height surfaces above the ground from 10 to 11000 meters. The 10 meter winds and 2 meter temperature and RH surface variables were incorporated into the upper air variables. The vertical velocity was converted from mbar/sec to meter/sec. An estimate of the mixing layer height was computed from a modified bulk Richardson number that accounts for mixing due to convective and mechanical processes. The mixing height was limited to 4 km.

8. Chinese Dust Cloud Transport Simulation TOMS and SeaWiFS satellite images were used to identify the location where a large dust cloud was formed over northern China and southern Mongolia. In the Monte Carlo model this dust “source region” was seeded with 60 sources releasing 15 particles every 2 hours. The particles were released during the day time on April 18 and 19 simulating the April 19 th dust cloud. The cloud was tracked until April 30. The location of the particle sources used to simulate the April 19 th dust cloud

9. Comparison of the Dust Cloud Transport Simulation to TOMS Aerosol IndexTOMS Aerosol Index The set of images below compares the dust cloud transport simulation to the TOMS aerosol Index. The position of the dust cloud in the simulation is identified by the particles. The particles have been colored based upon their height with blue particles less than a 1 km and red particles above 7 km. The transport simulation and TOMS data depict similar daily patterns of the dust cloud as it moves from Asia to the US. Both sets of data show the dust cloud moving completely off of the coast of Asia on the 23 rd stretching two thirds to the US, and first impacting North America’s west coast on the 25 th. The simulation shows a large number of elevated particles to the north of the domain, i.e. Siberia and Alaska throughout the period. Evidence of dust this far north is only seen in TOMS on the 23 rd and 24 th between Alaska and Siberia. The northern elevated particles moved to the Gulf of Alaska on the 25 th and stretched to the center of the Pacific by the 28 th. The TOMS data do not show evidence of dust in this region. However, both TOMS and the transport simulation show evidence of dust north of Hawaii during these four days.

10. April 19 April 20 April 21 April 22 April 23 Transport Simulation TOMS Aerosol Index (Blue Particles 7 km)TOMS Aerosol Index

11. April 24 April 25 April 26 April 27 April 28 Transport Simulation TOMS Aerosol Index (Blue Particles 7 km)TOMS Aerosol Index

12. The transport simulation shows that the dust cloud over the West Coast was elevated on the 25 th and 26 th and did not reach the surface until April 27 th. This corresponds with surface measurements which did not show elevated particulate matter concentrations until April 27 th. Comparison of the Dust Cloud Transport Simulation to Surface PM Data

13. Transport Dynamics of the April 19 th Dust Cloud The dust cloud moved out into the Pacific on the 20 th where it was entrained into a low pressure system and rapidly transported eastward. On the 23 rd, the southern part of the dust cloud was entrained in a high pressure system. These particles sank to the surface and remained in the center of the Pacific for the duration of the simulation. The remaining part of the dust cloud was transported to the West Coast on the 25 th, where one part of the cloud headed southward along the California coast and the other continued eastward over Canada. Click on image to view Gif animation Down load the AVI animation (2 megs)AVI animation

14. Conclusions The Monte Carlo Model driven by the FNL winds is able to reproduce the dust cloud pattern as it is transport from Asia to the US as identified by the TOMS data.