Developing a Dust Retrieval Algorithm

1. Developing a Dust Retrieval Algorithm Jeff Massey aka “El Jeffe”

2. Motivation • Dust can cause the snowpack to melt out a month in advance causing many water management issues • Need a better understanding of processes behind how dust plumes originate and where they originate from

3. Dust Events timing Most common in spring Occur an average of 4 times a year Most common in afternoon

4. Background • Dust detection uses the IR and visible bands • Dust can only be remotely detected during the day (zenith angle < 80), when clouds aren’t present, and when there is no snow or ice on the ground • There are different detection schemes over the ocean and land, this project is only concerned with land • MODIS (36 channels, 6 used) and GOES (5 channels, 4 used) data was used

5. IR bands: Split window technique • Dust has a higher spectral absorption at 11 microns than 12 microns • Opposite for clouds • Brightness temperature differences can detect dust. • Less pronounced in thick dust near the surface since transmission distinction is weaker • Similarly, dust has higher absorption at 3.9 microns and lower absorption at 11 microns than clouds

6. Visible Light difference • Dust becomes increasingly absorptive with decreasing visible wavelengths (absorbs more blue light) • This method is most effective over water since land surface can look similar to dust

7. Utah Specific Dust detection limitation • Optically thick dust near the surface produces small BT differences • Utah dust is from nearby point sources that usually does not leave the boundary layer SLC

8. Additional limitations • Algorithm may need tuning for different seasons as brightness temperatures change • False positives tend to show up over cold ground (mountains), or desert areas • Areas far away from nadir are more likely to have false positives

9. Zhoa et al (2010) Algorithm

10. 4/19/2008 at 19Z (1pm MDT) Strong SW winds over Utah and Nevada (v>25kts) ahead of land falling Pacific trough Clear skies over majority of area Dust plumes identifiable on visible image making algorithms easier to test Near solar noon so reflectivity adjustment errors should be low Multiple dust plumes over different regions make for an interesting event

11. Zhoa Algorithm Looks like all this did was detect deserts and mountains.

12. What went wrong? • To get brightness temperature I inverted the Planck function, thus assuming the earth is a blackbody at these wavelengths • Wavelength differences: • Location differences • They used Mexico to test their algorithm • Different season? • Did they assume dust was above BL?

13. Adjustments after trial and error Overall the Following Occurred: (1) Brightness temperature differences relaxed (2) Reflectivity conditions were relaxed and simplified (3) Reflectivity and brightness temperature conditions were combined

14. Results for 4/19/2008

15. Comparison with AVHRR algorithm Note: images are about an hour apart. MODIS is 18Z, AVHRR is 19z

16. Other events: Top: non-dust event Upper right: 3/22/2009 Lower right: 3/21/2011

17. 3/21/2011 compared to navy algorithm (only a couple of weeks archived)

18. Goes algorithm Theory: focus on BT differences where there aren’t clouds Dust retrieval will be lower resolution More false positives over “dusty” terrain since reflectivity constraints were removed

20. 4/19/08 14:45Z to 4/20/08 01:15Z every 15 to 30 minutes

21. Conclusions • “All data is bad, but some is useful” • Data cannot be fully trusted, but GOES makes it easier to separate dust from false positives • Important tool for researching dust event case studies

22. Questions?