NOAA satellites detect wildfires (June 17th)

 I have written about NOAA's GOES satellites in this blog including but not limited to how GOES  satellites can be used for wildfire detection (go here to read some of my posts). I just ran across a nice video, under two minutes from NOAA satellites about how NOAA's satellites, including but not limited to GOES 16 and 17 are used for wildfire detection. I knew that Bill Gabbert of Wildfire Today would post this video on his website, and he did on June 17th, you will want to read about this technology in his post. Nonetheless, as I have been interested in satellite technology, this was too good to pass up. So, I am sharing this video with you. When I wrote this post I knew that it is time to write another post about NOAA's satellites, I did so on June 23rd.

Direct link to video from NOAA Satellites on YouTube

Wildfire Detection Notification App at work in Tulsa OK WFO

After the launch of GOES-R in the fall of 2016, later known as GOES-16 and now known as GOES East, I spent some time over the winter and spring of 2017 learning a little about the capabilities of GOES-16, how this satellite will improve weather forecasting,  and finally learning a little about how GOES-16 and her sister satellites (GOES-S is due to launch in March 2018) are able to improve detections of wildfires. The result of these endeavors was an eight-part series: Application of GOES-16 for wildfire detection. In part 2, I introduced the Advanced Baseline Imager (ABI) in the GOES-R series of satellites with a video from NASA on the ABI. In part 3 I shared three examples of improved imagery with GOES-16.

With that as background and I moved on to writing about the Wildfire Detection Notification App (WFDN), developed by the National Weather Service (NWS) Norman OK Weather Forecast Office (WFO) in February 2016. In part 5 of my series on the Application of GOES-16 for wildfire detection I wrote about the WFDN Application, an application that uses GOES-16 for wildfire detection, that article may be found here.

I was very excited when the following video report from News On 6 out of Tulsa Oklahoma came across my desk this morning. The News On 6 video report is about how the the WFDN App is being used in the area served by the NWS Tulsa Oklahoma Weather Forecast Office (WFO), one of the NWS offices using the WFDN App (see part 6).The video is short, just under two minutes. My friend from the NWS Norman WFO liked the video report and agreed that I should share this with you. You will see how the WFDN helped first responders quickly respond to a grass fire in Mayes County in northeast Oklahoma last week.

Direct link to Youtube video from News on 6 published on February 16, 2018

Part 8 of 8 Application of GOES-16 for wildfire detection: Reflections on using GOES-16 for wildfire detection and the Experimental Wildfire Detection Notification App

Over the last several weeks as I was first learning about GOES-16 and then learning about the Experimental Wildfire Detection Notification  (WFDN) App, I have gained an appreciation for what the improvements in GOES-16 will bring to weather forecasting when she becomes operational as GOES-East in the Fall of 2017. I have tried to touch on these improvements in earlier articles and as applies to wildfire detection in this series. Looking at wildfire detections, I am very excited at the improvements that the Advanced Baseline Imager (ABI) in GOES-16 will mean to fighting wildfires.

The work that the NWS WFO at Norman OK and their state partners shows that the WFDN App means that in most cases, WFDN App dispatches come before 911 notifications, the lead time depending on how the WFO pushes WFDN to their local/state partners.

I have also gained an appreciation for how complicated satellite meteorology is. What I have learned about GOES-16 barely scrapes the surface of what GOES-16 will bring to weather forecasting.

June 21: Part 1 of 8: Application of GOES-16 for wildfire
detection: Introduction

June 23: Part 2 of 8: Application of GOES-16 for wildfire detection: A little about the
GOES-16 Advanced Baseline Imager

June 26: Part 3 of 8 Application of GOES-16 for wildfire detection: examples of improved imagery with GOES-16

June 28: Part 4 of 8 Application of GOES-16 for wildfire detection: wildfire detection
improved with GOES-16

June 30: Part 5 of 8 Application of GOES-16 for wildfire detection: February 18, 2016 wildfire danger in western OK and development of the Experimental Wildfire Detection Notification App

July 3: Part 6 of 8: Application of GOES-16 for wildfire detection: Experimental Wildfire Detection Notification App in use Spring 2017

July 5: Part 7 of 8 Application of GOES-16 for wildfire detection: Experimental Wildfire Detection Notification App making a difference

July 7: Part 8 of 8 Application of GOES-16 for wildfire detection: Reflections on using GOES-16 for wildfire detection and the Experimental Wildfire Detection Notification App (this article)

Part 7 of 8 Application of GOES-16 for wildfire detection: Experimental Wildfire Detection Notification App making a difference

I have exchanged several e-mails and have had a couple of telephone conversations with Todd Lindley (Science and Operations Officer, NWS Weather Forecast Office Norman OK about the Wildfire Detection Notification (WFDN) App. In a telephone conversation on May 1, 2017, I asked Todd if he could provide a couple of examples that I could share with you about how the Experimental WFDN App has made a difference. He discussed two examples that I will share with you.

February 8th 2017: The NWS Norman OK Forecaster analyzing GOES-16 wildfire detection images detected a hot spot in Logan County, OK. After the analysis and evaluation of these images was complete, the data released to Oklahoma for dispatch. Upon dispatch to Oklahoma, it turned out that the hot spot detected by GOES was a  structure fire in a very rural location. This WFDN dispatch was the only dispatch, there was no 911 dispatch. The house, which was not occupied at the time, was fully engulfed. 

February 23rd, 2017: In the period leading up to February 23rd, weather forecasters at WFO Norman OK used some historical wildfire outbreak data coupled with elevated wildfire danger conditions in the area to communicate extreme fire danger conditions to their State partners. The Oklahoma Forestry Services used this information to preposition ground and aviation resources (in this case Blackhawk helicopters) on February 22nd. 

On February 23rd Forecasters at NWS Norman OK analyzing GOES-16 images detected a hotspots, after analysis and evaluation, the data was released to their State partners for dispatch. One of the hot spots was near where OFS had prepositioned ground and aerial assets on February 22nd. Units responded quickly to the wildfire with good initial attack. The fire was contained at about 200 acres.. Similar outbreaks in nearby areas of Texas in the same time period grew to about 5 to 8,000 acres. The key to the containment of this particular fire at 200 acres was the prepositioning of ground and air resources coupled with the WFDN resulting in moving resources to the hotspot. The key was initial attack.

In part 8, I will conclude this series with some  of my own reflections on what I have learned in writing this series on the application of GOES-16 for wildfire detection and the development of the Experimental Wildfire Detection Notification App.

List of articles in this eight part series on the Application of GOES-16 for wildfire detection 


June 21: Part 1 of 8: Application of GOES-16 for wildfire detection: Introduction

June 23: Part 2 of 8: Application of GOES-16 for wildfire detection: A little about the GOES-16 Advanced Baseline Imager

June 26: Part 3 of 8 Application of GOES-16 for wildfire detection: examples of improved imagery with GOES-16

June 28: Part 4 of 8 Application of GOES-16 for wildfire detection: wildfire detection improved with GOES-16

June 30: Part 5 of 8 Application of GOES-16 for wildfire detection: February 18, 2016 wildfire danger in western OK and development of the Experimental Wildfire Detection Notification App

July 3: Part 6 of 8: Application of GOES-16 for wildfire detection: Experimental Wildfire
Detection Notification App in use Spring 2017

July 5: Part 7 of 8 Application of GOES-16 for wildfire detection: Experimental Wildfire
Detection Notification App making a difference (this article)

July 7: Part 8 of 8 Application of GOES-16 for wildfire detection: Reflections on using
GOES-16 for wildfire detection and the Experimental Wildfire Detection Notification App

Part 6 of 8: Application of GOES-16 for wildfire detection: Experimental Wildfire Detection Notification Ap in use Spring 2017

The launch of GOES-16 in November 2016, which continues in operational testing, means that images from the GOES-16 Advanced Baseline Imager (ABI) are available to NWS weather forecasters at WFO Norman OK in 2017 for use with the Experimental Wildfire Detection Notification (WFDN) App. There are times when GOES-16 is unavailable because of operational testing, when that happens imagery is available from GOES-14 in Super Rapid Scan (SRSOR) mode (see the June 30th article in this series for more information about GOES-14 in SRSOR mode). The WFDN App continues to help first responders respond to wildfires quickly as this official from the Oklahoma Forestry Services shared with Todd Lindley of the NWS Weather Forecast Office Norman OK: “The timeliness and location accuracy of the detected wildfires prompted timely communication with local resources. Having this information encourages rapid size up and allocation of resources prioritization and efficient assignment of aerial and heavy equipment” (Todd Lindley January 27 2017 e-mail with author).

GOES-16 imagery (or GOES-14 in SRSOR mode) flows directly into AWIPS and NWS radar. The next step is important, that is, a NWS forecaster has to analyze and evaluate the GOES-16 imagery before the data is released via the WFDN App software for dispatch to state (or local) officials. NWS staffing in times of wildfire danger is similar to staffing in other severe weather events such as tornadoes. The WDFN App was modified and improved in the spring 2017 by the NWS Norman OK, one of the key improvements is that the WFDN App auto-populates. There are no added costs to the WFOs, at least there are no added costs to the WFO Norman OK.

The NWS Weather Forecast Office (WFO) Norman OK partners with the Oklahoma Forestry Service (OFS), the Oklahoma Department of Emergency Management (ODEM), the Texas A&M Forest Service. NWS Norman send WFDN SMS to e-mail notifications are sent to two OFS Chiefs, the Oklahoma Department of Emergency Management Watch Officers, and a Chief from the Texas A&M Forest Service, they in turn transmit the WFDN App to local first responders. Speaking of the partnership between NWS WFO staff and State/County wildfire agencies, Todd Lindley told me that the use of the WFDN App “requires a deep level of cooperation between State/County Agencies and the NWS WFO staff” (May 1, 2017 phone call with author). In most counties in the NWS Norman OK forecast area the use of the WFDN App has translated to 5 to 10 minutes lead time ahead of E-911 notifications.

The WFO Norman OK continued to use the WFDN App during their Late Winter/Early Spring 2017 wildfire season, and they will continue to use the WFDN beyond the Late Winter/Early Spring 2017 wildfire season. In addition, other NWS WFOs are using the WFDN App. The WFDN App can be modified by each WFO for their own use. The use of the WFDN has spread to other NWS WFOs, and there are still more WFOs that have expressed interest in the WFDN. Other WFOs that are currently using the WFDN App include: NWS Amarillo TX and NWS Tulsa OK. In Amarillo TX, after the imagery is analyzed and evaluated by their forecasters, the WFDN notifications are released directly to local agencies. This means that local first responders in the Amarillo TX region receive the notification 15 to 20 minutes in advance of E-911 notifications because unlike in the Norman OK area (5 to 10 minutes ahead of E-911), the WFDN goes directly to the local agencies.

The WFOs of the NWS have always been involved in forecasting fire weather through the issuance of fire weather forecasts and red flag warnings. The development of the Experimental Wildfire Detection Notification App (WFDN) changes this for Norman, Amarillo, and Tulsa WFOs, Todd Lindley of WFO Norman OK explains:

If I could make a final summarizing statement about the Experimental WFDNs, it is that there is not a change in overall NWS fire weather services. However, the development of the Experimental WFDN, a prototype, does mean that in in addition to providing fire weather services they are taking on an experimental and more active tactical role in routing firefighting resources directly to newly detected fires (May 1 June 5, 2017 e-mails with author).

In part 7,  I will share a couple of examples of how the Experimental Wildfire Detection Notification made a difference in the Spring of 2017 in Oklahoma.

List of articles in this eight part series on the Application of GOES-16 for wildfire detection


June 21: Part 1 of 8: Application of GOES-16 for wildfire detection: Introduction

June 23:  Part 2 of 8: Application of GOES-16 for wildfire detection: A little about the GOES-16 Advanced Baseline Imager

June 26: Part 3 of 8 Application of GOES-16 for wildfire detection: examples of improved imagery with GOES-16

June 28: Part 4 of 8 Application of GOES-16 for wildfire detection: wildfire detection improved with GOES-16

June 30: Part 5 of 8 Application of GOES-16 for wildfire detection: February 18, 2016 wildfire danger in western OK and development of the Experimental Wildfire Detection Notification App

July 3: Part 6 of 8: Application of GOES-16 for wildfire detection: Experimental Wildfire Detection Notification App in use Spring 2017 (this article)

July 5: Part 7 of 8 Application of GOES-16 for wildfire detection: Experimental Wildfire Detection Notification App making a difference

July 7: Part 8 of 8 Application of GOES-16 for wildfire detection: Reflections on using GOES-16 for wildfire detection and the Experimental Wildfire Detection Notification App

Part 5 of 8 Application of GOES-16 for wildfire detection: Feb 18, 2016 wildfire danger in western OK and development of the Experimental Wildfire Detection Notification Application

In this article I will introduce the Experimental Wildfire Detection Notification (WFDN) application first developed and used in the NWS Norman OK WFO on February 18, 2016. The WFDN was first used in conjunction with GOES-14 in super rapid scan mode. After GOES-16 was launched, and while she was still in operational testing, the WFDN was and still is being used by National Weather Service Weather Forecast Office in Norman OK in conjunction with GOES-16.


Todd Lindley, Science Operations Officer with the National Weather Service Weather Forecast Office (WFO) in Norman Oklahoma is the senior author of a 2016 paper in the Journal of Operational Meteorology: T. Todd Lindley, Aaron R. Anderson, Vivek N. Mahale, Thomas S. Curl, William E Line, Scott S. Lindstrom and A. Scott Bachmeier. 2016. Wildfire Detection Notifications for Impact-Based Decision Support Services in Oklahoma Using Geostationary Super Rapid Scan Satellite Imagery. Journal of Operational Meteorology, 4 (14), 182-191, http://nwafiles.nwas.org/jom/articles/2016/2016-JOM14/2016-JOM14.pdf. I have been privileged to have exchanged e-mails and had a telephone conversation with Mr. Lindley about an Experimental Wildfire Detection Notification Application (WFDN) that he and his colleagues have written about in this 2016 paper. Unless otherwise noted, this post is based on Lindley et al’s 2016 paper.


In February 2016, GOES-16 (GOES-R) was not yet launched. GOES-14, the in-orbit spare, was operating in what is known as super rapid scan mode (SRSOR), an experimental mode where GOES-14 can take special one-min imagery. GOES-14 was operating in this SRSOR mode in mid to late February 2016 when there were wildfires in OK. Lindley et al explain how this works in their 2016 paper:

Although not capable of the improved spatial or spectral attributes of the Advanced Baseline Imager (ABI) of GOES-16, the GOES-14 imager was operated by NOAA in SRSOR mode during several multi-week periods spanning late 2012 through early 2016. These SRSOR windows have demonstrated the high-temporal resolution sampling capability of the GOES-R ABI when operating in mode 3, known as “flex mode,” by providing 1-min imagery. The SRSOR data have been utilized in algorithm development, in various NWS field offices and national centers, and in operational support of experiments including those in the NWS’s Operations Proving Ground and Hazardous Weather Testbed. Experimental use of SRSOR and the operational benefits of high- temporal resolution satellite imagery is well documented for numerous phenomena including fog and low stratus, convective storms, wildland fire and smoke, and tropical cyclones (Lindley et al (2016, p, 184).

February 18, 2016 was a busy day at the NWS Norman OK Weather Forecast Office. They were monitoring both wildfire danger and existing wildfires in their forecast area. GOES-14 in SRSOR mode (one-minute imagery) was available to them where data and images were being fed into their AWIPS computer system. I’ll let Todd Lindley, Science and Operations Officer at the National Weather Service in Norman, OK explain:

We had a request by Oklahoma Forestry Services (OFS) to provide a courtesy call as we detected new fire on the morning of 18 February 2016.  It just so happened that we were ingesting 1-min SRSOR data that day as part of an experimental window in preparation for GOES-R/16.  My Meteorologist-in-Charge had the vision that morning to suggest that this was an opportunity to ‘innovate’.  We quickly brainstormed on how best to do that, and the Experimental Wildfire Detection Notification (WFDN) App was born (May 10, 2017 e-mail with author).

Specifically, the NWS Norman OK Weather Forecast Office Information Technology staff quickly wrote a Python application where after the satellite imagery was analyzed by NWS forecasters for wildfire hotspots. After the imagery was analyzed, the NWS forecasters could transmit wildfire detection notifications through AWIPS to a list of predetermined OFS officials by SMS e-mail to text. The SMS includes the latitude and longitude of the wildfire hotspot plus a link to the local weather forecast. Todd was nice enough to send me a sample of one of the Experimental Wildfire Detection Notifications for you to look at:



Courtesy of NWS Norman OK Weather Forecast Office


Among the wildfires in the February 18, 2016 wildfire outbreak was the Buffalo Fire that ultimately burned 17,280 acres in Northwest Oklahoma. GOES-14 in SRSOR mode detected wildfire hotspots that would not have been detected by GOES East (GOES-13) and GOES West (GOES-15). The high resolution capabilities of GOES-14 in SRSOS mode and the WFDN App meant an 18 to 23 minute advantage leading to improved response time by responding fire departments and wild land firefighters.

A total of eight wildfire notifications were transmitted by WFO Norman on 18 February 2016. Post-event feedback from OFS stated that these notifications were 'key contributors to a measure of effectiveness in response to very aggressive and fast-paced fire activity' and that the dissemination of this information ‘enhanced situation awareness’ and ‘permitted contact [with] a few departments in advance of 911 calls.’ It was additionally noted that 'fire location often plays a role in resource allocation priority' and that text messages enabled a timely dispatch of resources and aided in prioritization of fires ‘with structures and improvements at risk' (D. Daily 2016 personal communication cited in Lindley et al, 2016, 185).

After the February 18th wildfire outbreak, the Oklahoma Department of Emergency Management developed a GIS based display system that monitors wildfires and allocation of equipment and firefighters. During the remainder of the 2016 late-winter early-spring wildfire season the WFO Norman OK continued to use GOES-13 (GOES East) or GOES-14 in SRSOR mode. After analyses by NWS Forecasters at WFO Norman OK, the imagery was fed into AWIPS, and to the WFDN App and many notifications were received by first responders prior to 911 calls.


The WFDN App continued to make a difference in wildfire response time in NW Oklahoma during the rest of the late-winter early-spring wildfire season in NW OK:

… continued use of text notifications on 5 April 2016 prompted the following feedback from Major County Emergency Manager, Tresa Lackey: ‘We were very grateful when NWS detected a fire south of Bouse Junction and was able to route forestry planes to the location...to assist in fire suppression. Our resources were spread thin already fighting fires across the county. The extra help in the fire detection and suppression really saved us. Fire- fighters were able to contain the fire before the wind [shift] later that evening’ (Lindley et al, 2016, 189).

I will write about the further developments of the Experimental Wildfire Detection Notification App by WFO Norman OK and other WFO offices in 2017 in part 6.


List of articles in this eight part series on the Application of GOES-16 for wildfire detection


June 21: Part 1 of 8: Application of GOES-16 for wildfire detection: Introduction


June 23:  Part 2 of 8: Application of GOES-16 for wildfire detection: A little about the GOES-16 Advanced Baseline Imager


June 26: Part 3 of 8 Application of GOES-16 for wildfire detection: examples of improved imagery with GOES-16


June 28: Part 4 of 8 Application of GOES-16 for wildfire detection: wildfire detection improved with GOES-16


June 30: Part 5 of 8 Application of GOES-16 for wildfire detection: February 18, 2016 wildfire danger in western OK and development of the Experimental Wildfire Detection Notification App (this article)


July 3: Part 6 of 8: Application of GOES-16 for wildfire detection: Experimental Wildfire Detection Notification App in use Spring 2017


July 5: Part 7 of 8 Application of GOES-16 for wildfire detection: Experimental Wildfire Detection Notification App making a difference


July 7: Part 8 of 8 Application of GOES-16 for wildfire detection: Reflections on using GOES-16 for wildfire detection and the Experimental Wildfire Detection Notification App

Part 4 of 8 Application of GOES-16 for wildfire detection: wildfire detection improved with GOES-16

One of the 16 spectoral channels on GOES-16, channel 7, 3.9 µm, detects wildfire hot spots among other things.. Listen to Ivan Csiszar, a physical scientist with the NOAA Center for Satellite Applications, discuss wildfire detection using GOES-16 in the following video:


Direct link to video

For those of you who want to dig a little deeper, I will share three CIMSS Blog entries below with images from GOES-13 and GOES-16 of wildfires. Note the higher resolution from the GOES-16 Advanced Baseline Imager. One of my Operational Meteorologist friends from the National Weather Service  told me that their posts explain things very well. When looking at the imagery, it is important to note that red/yellow colors in the imagery depict high intensity fires and the black colors depict smaller fires. See the March 6th imagery of the Grass Fires in KS, OK, and TX for some good imagery of high intensity fires, recall that the Northwest Complex (KS, OK, TX) burned over 782,000 acres.


I think that the April 11th imagery is the best of the three at representing  the higher resolution of GOES-16 as compared to GOES-13. The March 16 imagery depicts a smaller wildfire hotspot, you will see red/yellow colors, depicting a high intensity fire at about 5 seconds on the GOES-16 ABI image.

CIMSS Blog, April 11, 2017 Fires (prescribed burns) in Eastern KS and OK

CIMSS Blog, March 6, 2017 Grass Fires-KS, OK, TX

CIMSS Blog, March 19, 2017 GOES-16 Mesoscale Sectors: Improved monitoring of fire activity

Finally, I recently contacted NOAA Satellites and Information Services on their Facebook Page (which is very nice and I highly recommend it. I told them about the article this article, asking them if they had any videos comparing GOES-13 with GOES-16. NOAA Satellites provided this link to their animations (currently page 6 of 8 pages). You may need to scroll to find two videos, one is of GOES-16 and GOES-13 images of Grass Fires in Florida and the other is of GOES-16 and GOES-13 Comparison Punch Clouds. I did not know what a punch cloud is, so I asked one of my meteorologist friends from the National Weather Service who told me punch clouds are circular or elliptical holes in clouds that can form when supercooled water begins to freeze. Note, depending on when you are arriving at this article, it is possible that the provided link may have different images, but if you look around on different pages you should find the two referenced images.

Next up in part 5: wildfire danger in Norman in western OK (February 18, 2016) and the development of the Experimental Wildfire Detection Notification App


List of articles in this eight part series on the Application of GOES-16 for wildfire detection


June 21: Part 1 of 8: Application of GOES-16 for wildfire detection: Introduction

June 23:  Part 2 of 8: Application of GOES-16 for wildfire detection: A little about the GOES-16 Advanced Baseline Imager

June 26: Part 3 of 8 Application of GOES-16 for wildfire detection: examples of improved imagery with GOES-16

June 28: Part 4 of 8 Application of GOES-16 for wildfire detection: wildfire detection improved with GOES-16 (this article)

June 30: Part 5 of 8 Application of GOES-16 for wildfire detection: February 18, 2016 wildfire danger in western OK and development of the Experimental Wildfire Detection Notification App

July 3: Part 6 OF 8: Application of GOES-16 for wildfire detection: Experimental Wildfire Detection Notification App in use Spring 2017

July 5: Part 7 of 8 Application of GOES-16 for wildfire detection: Experimental Wildfire Detection Notification App making a difference

July 7: Part 8 of 8 Application of GOES-16 for wildfire detection: Reflections on using GOES-16 for wildfire detection and the Experimental Wildfire Detection Notification App

Part 3 of 8 Application of GOES-16 for wildfire detection: examples of improved imagery with GOES-16.

There is a growing amount of images from GOES-16 available on the internet. GOES-16 is undergoing testing as I write this; all of these images that you see on the internet are non-operational, preliminary data. 

The three primary sources that I go to are (1) NOAA’s Satellite and Information Service , and (2) a GOES-R mission page with a data and imagery page. Third, a few months ago, a couple of my Operational Meteorologist friends from the National Weather Service suggested that I take a look at the CIMSS out of University of Wisconsin at Madison, saying that they have good information on GOES-16 and other satellites. The CIMSS has a blog with images from GOES-16 and other satellites. I have spent hours on all three sites. 

Before I move to how GOES-16 can be used for wildfire detections, I want to show you a couple of examples of the differences between GOES-13 and GOES-16. On the theory that one picture (or short video) is worth a thousand words, I am steering you to two entries from the CIMSS Blog.

CIMSS Blog, April 4, 2017, lake effect clouds, GOES-13 and GOES-15 images (left and right) will look similar. The resolution in the GOES-16 image in the center will be clearer. Note the cloud you are looking at is not very big. 

CIMSS Blog, April 4, 2017, fog/stratus dissipation Again, the fog and stratus in the GOES-16 image will be clearer.

List of articles in this eight part series on the Application of GOES-16 for wildfire detection



June 21: Part 1 of 8: Application of GOES-16 for wildfire detection: Introduction

June 23:  Part 2 of 8: Application of GOES-16 for wildfire detection: A little about the GOES-16 Advanced Baseline Imager

June 26: Part 3 of 8 Application of GOES-16 for wildfire detection: examples of improved imagery with GOES-16 (this article)

June 28: Part 4 of 8 Application of GOES-16 for wildfire detection: wildfire detection improved with GOES-16

June 30: Part 5 of 8 Application of GOES-16 for wildfire detection: February 18, 2016 wildfire danger in western OK and development of the Experimental Wildfire Detection Notification App

July 3: Part 6 of 8: Application of GOES-16 for wildfire detection: Experimental Wildfire Detection Notification App in use Spring 2017

July 5: Part 7 of 8 Application of GOES-16 for wildfire detection: Experimental Wildfire Detection Notification App making a difference

July 7: Part 8 of 8 Application of GOES-16 for wildfire detection: Reflections on using GOES-16 for wildfire detection and the Experimental Wildfire Detection Notification App

Part 2 of 8: Application of GOES-16 for wildfire detection: A little about the GOES-16 Advanced Baseline Imager

I want to begin with a little background on our geostationary weather satellites. Most of you know that we have a new geostationary weather satellite was launched last fall. GOES-16 -then known as GOES-R), was launched on November 19, 2016. GOES-16 is the first in the GOES-R series of satellites, GOES R-T. GOES-S is undergoing pre-launch testing and will launch in 2018. As I write this, GOES-16 is still under going in-orbit operational testing. GOES-16 represents the sixth generation of NOAA’s Geostationary satellites. The fifth generation is GOES 13-15 (GOES N - P). GOES-13 is also known as GOES East, GOES-15 is also known as GOES West, and GOES-14 is an in-orbit spare. I wrote about GOES 13-15 on November 30, 2016. I wrote a little more about GOES-16 here.


One of the instruments on GOES-16 is the Advanced Baseline Imager (aka ABI), go here to read a brief description about improvements in the GOES-R series ABI. NOAA and NASA have a nice short fact sheet that introduces GOES-R (GOES-16) ABI, it may be found here. This is one of many fact sheets on the GOES-R series. Some of you may be interested in a listing of GOES-R ABI products on the GOES-R products page, a sub-page accessible from the GOES-R mission page.


Finally, please take three minutes to watch this video, made in 2013, describing the ABI on the GOES-R series:



Direct link to video on Youtube


List of articles in this eight part series on the Application of GOES-16 for wildfire detection

June 21: Part 1 of 8: Application of GOES-16 for wildfire detection: Introduction

June 23: Part 2 of 8: Application of GOES-16 for wildfire detection: A little about the GOES-16 Advanced Baseline Imager (this article)

June 26: Part 3 of 8 Application of GOES-16 for wildfire detection: examples of improved imagery with GOES-16

June 28: Part 4 of 8 Application of GOES-16 for wildfire detection: wildfire detection improved with GOES-16

June 30: Part 5 of 8 Application of GOES-16 for wildfire detection: February 18, 2016 wildfire danger in western OK and development of the Experimental Wildfire Detection Notification App

July 3: Part 6 of 8: Application of GOES-16 for wildfire detection: Experimental Wildfire Detection Notification App in use Spring 2017

July 5: Part 7 of 8 Application of GOES-16 for wildfire detection: Experimental Wildfire Detection Notification App making a difference

July 7: Part 8 of 8 Application of GOES-16 for wildfire detection: Reflections on using GOES-16 for wildfire detection and the Experimental Wildfire Detection Notification App

Part 1 of 8: Application of GOES-16 for wildfire detection: Introduction

Regular readers of my blog will know that I have been following GOES-16 since it was launched, then known as GOES-R, on November 19, 2016. As I learned more about GOES-16 I wondered what improvements GOES-16 and her sister satellites (GOES R-T) would bring to the detection of wildfires.   One exciting use of GOES-16 for wildfire detection is the development of an Experimental Wildfire Detection Notification Application that was first developed and used by the National Weather Service Weather Forecast Office in Norman Oklahoma. Learning about the Experimental Wildfire Detection Notification Application lead to this eight-part series on an application of GOES-16 for wildfire detection. This introduction is part 1 of 8, the rest of the articles in the series are listed below.

I start off in part 2 with a short article on the GOES-16 Advanced Baseline Imager, followed by two articles on improvements in wildfire detection with GOES-16. I then turn to the development and use of the Experimental Wildfire Detection Notification Applications in parts 5 through 7, followed by my own brief reflections in part 8.


As I post each article in the series, I will update this article with links to each article in the series.

June 23: Part 2 of 8: Application of GOES-16 for wildfire detection: A little about the GOES-16 Advanced Baseline Imager

June 26: Part 3 of 8 Application of GOES-16 for wildfire detection: examples of improved imagery with GOES-16

June 28: Part 4 of 8 Application of GOES-16 for wildfire detection: wildfire detection improved with GOES-16

June 30: Part 5 of 8 Application of GOES-16 for wildfire detection: February 18, 2016 wildfire danger in western OK and development of the Experimental Wildfire Detection Notification App

July 3: Part 6 of 8: Application of GOES-16 for wildfire detection: Experimental Wildfire Detection Notification App in use Spring 2017

July 5: Part 7 of 8 Application of GOES-16 for wildfire detection: Experimental Wildfire Detection Notification App making a difference

July 7: Part 8 of 8 Application of GOES-16 for wildfire detection: Reflections on using GOES-16 for wildfire detection and the Experimental Wildfire Detection Notification AppWildfire Detection Notification App