1. GOES-R Fire and Moisture Data Viewed with McIDAS-V
Joleen Feltz
Cooperative Institute for Meteorological Satellite Studies
Tim Schmit, Tom Whittaker, Tom Rink, Jessica Staude, Elaine Prins and the McIDAS Help Desk Team

2. Goals
Use McIDAS-V instead of writing data from McIDAS-X and completing analysis in other software.
Highlight the advantage in fire detection of higher spatial resolution and higher saturation temperature of GOES-R.
View GOES-R water vapor data in a new way.

3. Simulations of Southern California Fires
GOES-R and GOES-I/M
Simulations of Southern California Fires
GOES-12 Simulated 3.9 micron Data
Padua/Grand Prix Fires
Date: 27-Oct Time: 09:50 UTC
GOES-R Simulated 3.9 micron Data
Padua/Grand Prix Fires
Date: 27-Oct Time: 09:50 UTC
Project Goals: The goal of this project was to show the enhanced ability to detect fires through better spatial resolution and a higher saturation temperature of GOES-R over the current GOES systems. In the past, the data would be display in McIDAS-X, the brightness temperatures would be placed in a file and then the brightness temperature data would have to be mainupulated in excel to create these 3-D surfaces of brightness temperature data. Tim Schmit proposed that it would be great to do all of this in McIDAS, allowing us to keep the display the surfaces over the actual hot spots in the imagery. This would/could also save a large amount of time in transferring the data between two different programs.
Brightness Temperature (K)
Figure courtesy of Elaine Prins

4. Option 1: Image Display Over Topography
Faster than other options: a good choice for finer resolution data
Better choice for large datasets
Visual definition is based on image pixels
At 2 km GOES-R data is too coarse for the texture mapping used to create the display
There are a couple of ways create a 3-D image of brightness temperature data. Here is the Image Display over Topography. This was not chosen because the peak of the color bar does not line up with the vertical peak.

5. Option 2: Color Filled Contours Over Topography
Memory intensive: must restrict dataset size
Developers are addressing the use of memory resources
Better for lower resolution data
Here the color bar and vertical peak line up well. I find it easier to change contour interval than it is to add and adjust color bar breakpoints.

6. Simulation of Grand Prix Fire/Southern California
GOES-12 and GOES-R ABI
Simulation of Grand Prix Fire/Southern California
GOES-12
GOES-R ABI
GOES-12
GOES-R ABI
Here are two different fires. What we can see is a more defined peak in the higher resolution data and it is easier to pick out individual hotspots. In addition, the higher saturation tempearture of the ABI allows us to see that the fire is around 370K.

7. Advantages
One data analysis tool is used which has excellent navigation.
The individual images can be linked so zoom, pan and rotation can be done as a group.
Flexible – Can change location of data within program if first choice is not what was desired.
Point 1&2: Great for comparing data from two different, but co-navigated satellites

8. More Information http://dcdbs.ssec.wisc.edu/mcidasv/forums/
MUG Forum: Tips and Tricks – Displaying satellite data fields in three dimensions
MUG Forum: Bundles – Color Filled Contours over Topography

9. GOES-R ABI Moisture Channels
UW/CIMSS
What next?

10. UW/CIMSS

11. UW/CIMSS

12. UW/CIMSS