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

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

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

Background Dust detection uses the IR and visible bandsDust 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 groundDust 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 landThere 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 usedMODIS (36 channels, 6 used) and GOES (5 channels, 4 used) data was used

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

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

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

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

Zhoa et al (2010) Algorithm Test for good data Reflectance(.47,.64,.68,1.38) >0 Brightness Temperatures (3.9,11,12)>0 Test for water free pixels BT11µm − BT12µm ≤ − 0.5 BT3.9µm − BT11µm ≥ 20 R1.38µm < 0.05 Test for Dust BT3.9µm − BT11µm ≥ 25 or ((R.64- R.47)/(R.64+R.67))^2/(R.47^2)<.08 ((R.86- R.64)/(R.86+R.64))^2/R.64^2>.005

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

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

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

Adjustments after trial and error Before: BT11 − BT12 ≤ − 0.5 R1.38 < 0.05 and BT3.9 – BT11 <= 25 or Reflectivity 1 <.08 Reflectivity 2 >.005 After: BT11 − BT12 ≤ 0 BT3.9 – BT11 <= 10 R1.38 <.1 Reflectivity 1 <.1 Overall the Following Occurred: (1) Brightness temperature differences relaxed (2) Reflectivity conditions were relaxed and simplified (3) Reflectivity and brightness temperature conditions were combined

Results for 4/19/2008

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

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

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

Goes algorithm Theory: focus on BT differences where there aren’t clouds MODIS 36 bands Used 6 1km resolution Level 1B 1 or 2 passes a day GOES 5 bands Used 4 1km for vis and 4 km for IR (dust algorithm 4 km) Goes 8 – 15 Data every 15 to 30 minutes MODIS: BT11µm − BT12µm ≤ 0 BT3.9 – BT11 <= 10 R1.38µm <.1 Reflectivity 1 <.1 GOES: BT11µm − BT12µm ≤ 0 BT3.9 – BT11 <= 10 R.64 cloud mask Dust retrieval will be lower resolution More false positives over “dusty” terrain since reflectivity constraints were removed

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

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

Questions?