slide1 n.
Skip this Video
Loading SlideShow in 5 Seconds..
Forecasting PM 2.5 PowerPoint Presentation
Download Presentation
Forecasting PM 2.5

play fullscreen
1 / 23
Download Presentation

Forecasting PM 2.5 - PowerPoint PPT Presentation

deion
59 Views
Download Presentation

Forecasting PM 2.5

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. 3-Dimensional Validation of Satellite AOD Products andthe Numerical Aerosol Forecast Models that Use Them Raymond Hoff, J. Engel-Cox, R. Rogers, N. Jordan, K. McCann, K. Mubenga Physics, MEES and JCET UMBC S. Palm, J. Spinhirne GSFC

  2. Forecasting PM2.5 • NOAA-EPA MOU: 2013 PM Forecasts available to the public • 2006 PM forecasting goes into beta testing mode • How do you calibrate/validate these forecasts?

  3. Surface Data - Real Time: AIRNoW CAMMS TEOM Nephelometer Beta Attenuation Been there, done that…..ozone says this just doesn't work Courtesy Jim Szykman, EPA

  4. The problem with 4D-VAR assimilation • "Everybody says they should do something about getting data for 4D-VAR, but no one wants to do anything about it" • Two examples follow where it is obvious that 3D and 4D solutions are needed: • Alaskan Fires of 2004 • California Fire of 2003

  5. A good IDEA Courtesy: CIMSS, UW

  6. Linking optical properties and mass concentration Engel-Cox et al. 2004

  7. Old Town TEOM MODIS AOD Baltimore, MD Summer 2004 July 21 Mixed down smoke July 9 High altitude smoke PM2.5 (g/m3) MODIS Aerosol Optical Depth Courtesy EPA/UWisconsin

  8. Smoke mixing in Maryland20-22 July 2004

  9. U.S. Air Quality (The Smog Blog), http://alg.umbc.edu/usaq Daily posts NASA satellite images, EPA data, etc. Index & Links Over 1,000,000 hits over 19 months ~ 10,000 visits per month ~800 unique visitors per week including EPA, NASA, NOAA, & States

  10. Data for: September 1, 2004 Click on a REALM Participant for their LIDAR data. http://alg.umbc.edu/REALM

  11. REALM: Wisconsin lidar for July 2004 Eloranta, U. Wisc

  12. July 17 July 18 July 19 MODIS

  13. 19 July 2004 21 July 2004

  14. Low cloud 02:00 13:35 Smoke Midcloud Example 2: GLAS and the California Fires of October 2003 MODIS AQUA 19:43 UTC October 30

  15. 10-5 5 10-6 Smoke 10-6

  16. October 31 12:08 23:01

  17. MODIS 17:05 UTC

  18. This is tough…in 2D or 3D or 4D • Where was the smoke 6 hours previously? • We built a tool (CALIPSO-MORPH or C-MORPH)

  19. Backward movie

  20. Forward Movie

  21. The easy validation problems are over • Now it gets tough…. • Cal/Val has to be 3D+ to be able to integrate multiple profiling sensors • Cal/Val is going to have to involve a 4Danalysis tool • UMBC/NOAA/NASA/EPA/UW/CDC are collaborating on "3D-AQS", an attempt to integrate multisensor data into the EPA decision support system, AQS

  22. Backup

  23. July 16-22, 2004: Evidence of Effects of Long Range Transport Originating Outside the Modeled Domain Evolution of Model and Observed Aerosol Optical Depth MODIS 7/16 7/17 7/18 7/19 7/20 7/22 Model 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 • Transport from outside the domain influences observed PM concentrations which • are grossly under-predicted during this period • Model picks up spatial signatures ahead of the front • Under predictions behind the front (due to LBCs)