Trajectory validation using tracers of opportunity such as fire plumes and dust episodes

1. Trajectory validation using tracers of opportunity such as fire plumes and dust episodes Narendra Adhikari March 26, 2007 ATMS790 Seminar (Dr. Pat Arnott)

2. Outline • Introduction • Data and Methods • Case Studies -Fire episodes -Dust episodes

3. Introduction • Problem with model trajectories • Errors associated • Physical (inadequate data: spatial and temporal) • Computational (numerical truncation) • Forecast (error from forecast data) • HYSPLIT trajectory model –for evaluation

4. Introduction- contd.HYSPLIT • The HYSPLIT(Hybrid Single-Particle Lagrangian Integrated Trajectory) created and maintained by NOAA ARL • The HYSPLIT model is a complete system for computing trajectories, complex dispersion and deposition simulations using either puff or particle approaches

5. Introduction- contd.Objectives • Main objective of the study is the model trajectory validation, accuracy evaluation • Model trajectory evaluation using tracer of opportunity like wildfire smoke plumes, wind blown dust trails etc. • The major effort will be given to estimate plume height estimation for model input

6. Introduction- contd.Importance? • Model trajectory can be used for forecasting • Accidental toxic chemical release plume path/time • Wildfire plume path, downwind dispersion and level of pollution that might risk the personal health • How accurate are the model trajectory calculation/forecasting? • Accurate estimation of trajectory run starting position is important issue to evaluate trajectory model • Quantify errors associated with model trajectory

7. Focus In this presentation • Tracers of opportunity • Dust and smoke plumes

8. Data Model satellite data EDAS MODIS FNL GOES MM5 MISR CALIPSO

9. GOES & MODIS visible imagery • GOES (Geostationary Operational Environmental Satellite) • Image every 15 minutes • Ground resolution of 1 km • MODIS (MODerate resolution Imaging Spectroradiometer) • Twice Daily from two satellites (Terra and Aqua) • Ground resolution 250 m

10. Use of GOES to identify dust plumes • 15 minute time resolution is helpful for fire/dust episodes • GOES longwave IR difference: channel 4 (10.7 um) minus channel 5 (12um) is helpful to identify dust plumes

11. GOES Aerosol Data • GOES Aerosol/Smoke Product (GASP) Aerosol Optical Depth (AOD) • A measure of the aerosol column amount (ground to space) • Data available: every 30 minutes • GASP AOD can be used to identify fire plumes that are not visible in GOES visible images

12. MISR Data • Multi-angle Imaging SpectroRadiometer • MISR collects imagery simultaneously at 9 different views toward the earth’s surface • Cloud/aerosol height information • Temporal resolution ~9days

13. CALIPSO Data • Satellite lidar operated in 532nm and 1064nm • Measures attenuated backscatter from atmosphere • Shows vertical cross-section of atmospheric aerosols and clouds • Vertical resolution of profiles between 30 and 60m

14. Examples of Fire plume satellite Image MODIS visible image

15. Satellite visible image of fire plume

16. GASP, Aerosol Optical Depth Smoke plume from Northern CA fire

17. Analyzing fire and dust plumes View from satellite Illustrates complexity in estimating plume’s leading edge Shows problems in estimating plume height

18. Fire, Dust Plume separation- an example Image processing to isolate plume Estimate plume extent & centerline

19. HYSPLIT trajectory run from different position/Height Fire location Backtrajectory starting positions

20. MISR Satellite image of fire plume- an example

21. Visible Image of Dust episode Clouds El Paso Clouds Dust plumes

22. Dust trails compare with HYSPLIT model trajectory Background image produced by subtracting GOES channel 4 from channel 5 44 km 262 km Green 500m Blue 1000m

23. Web Cam picture of El Paso looking south Clear conditions around noon

24. Web Cam picture of El Paso Dust storm around 3 PM Haze attributed to blowing dust

25. Wind Gusts during this episode in the El Paso Area Peak gust of 56 mph

26. Hourly PM10 for El Paso Area

27. African DustExample

28. CALIPSO LIDAR Passing through dust region Focus on this region

29. CALIPSO LIDAR vertical cross section through dust region 30km 20km African Coastline 10km Over Ocean Over Ocean surface

30. CALIPSO vertical cross-section magnified

31. Challenges in Wind Trajectory Evaluations Using GIS tools to Test accuracy Statistical Method of Accuracy Test Trajectory Validation using tracer of opportunity Satellite data / images of fire plumes Estimation of Plume top height Results of trajectory Accuracy Assign accuracy index Outcome / applications MISR Stereographic Image to derive height CALIPSO LIDAR for Plume height and Vertical spread Model Run Part Model Run Part Trajectory -HYSPLIT Assign height and time for the trajectory model Dispersion -HYSPLIT Final Plume centerline And centroid height

32. Acknowledgement Thanks for the Advisors: Prof. Mark Green A. Prof. Dave DuBois

33. Questions/comments? Thank you