Different ways to see snow with GOES-16 imagery & RGBs – by Ed Szoke & Dan Bikos

This has not been a particularly snowy winter in New England and the Northeast, with a number of rain events during the season.  So naturally, now that “winter” is officially over a snowstorm hit much of the area on Monday 23 March.  Here are the snow totals as of Tuesday morning (24 March):

In this blog we will take a look at the different ways to view snow cover using satellite imagery during daytime hours, using a variety of satellite imagery and RGBs that are available for 1400 UTC on Tuesday 24 March.

Satellite imagery can be useful to see snow on the ground using visible imagery (Band 2 on GOES-16), but it can be hard to distinguish snow from clouds, as seen in the image below.

GOES-16 Band 2 Visible 0.64 µm image at 1401 UTC.

As noted earlier, one issue that often arises with using visible imagery to find snow cover is that it looks similar to clouds.  The above image points out a different issue in areas where there are lots of trees, in these areas snow cover does not stand out as well as it does, say, in the Great Plains.

With GOES-16 there are several other ways to detect snow on the ground during daytime hours.  One possibility is to use other visible bands, such as Band 5, the “snow/ice” band, shown below for the same time (1400 UTC).

GOES-16 Band 5 Snow/Ice 1.61 µm image at 1401 UTC.

Snow covered ground appears darker than bare ground, and one could argue that this band does make it a little easier to see the snow cover than band 2.  There are a variety of RGBs that help see snow, with two of them in fact designed to highlight snow on the ground.  Before we look at these RGBs we will check out a couple of RGBs that show the earth in color to see if that helps.  One of these is the RGB known as “Natural Color”, shown below for the same time.
GOES-16 Natural Color RGB image at 1401 UTC.

As the name implies, the Natural Color RGB (a baseline product that is available on AWIPS) attempts to show a view of the earth that one might see from space.  It is limited somewhat in that GOES-16 does not have the Green Channel, so the colors in this imagery are not as good as they could be from satellites that have all 3 color channels (red, blue and green), such as the Himawari satellites.  An interesting RGB that was developed at CIRA and is now widely available across the NWS (not on the SBN yet, as it is not a baseline product at this time) called GeoColor provides a closer representation of what a viewer would see from space (with additional corrections that reduce detrimental viewing effects and render the image clearer).  It does so by using a “synthetic” green channel that was derived by training the algorithm on Himawari imagery that does have the green channel.  An image for this case, also at 1400 UTC, is shown here:

GOES-16 CIRA GeoColor image at 1401 UTC.

Of the imagery shown so far, this RGB makes the snow cover easiest to see.  More information on this imagery can be found in this Quick Guide.  CIRA can provide instructions for getting it into AWIPS if you do not already have this product.

However, we still have the issue of distinguishing snow cover from clouds (again, not as big an issue for this case given the more splotchy appearance of the snow cover as a result of all the trees).  One RGB that is on AWIPS currently does highlight snow (and other features) during the daytime, known as the Day Snow/Fog RGB from EUMETSAT and NASA SPoRT, shown below.

GOES-16 EUMETSAT/SPoRT Day Snow/Fog RGB image at 1401 UTC.

This RGB distinguishes a number of features, as discussed in this Quick Guide.  Snow appears as a red-orange color.  The variety of colors used can be an issue for some folks, particularly those with some color blindness (like me/Ed!).  Another RGB that is an AWIPS baseline product that is also useful in distinguishing snow cover from other features is the Day Cloud Phase Distinction RGB, shown below at 1400 UTC.

GOES-16 Day Cloud Phase Distinction RGB image at 1401 UTC.

As the name implies, a primary use of this RGB is to highlight the difference between water-based and ice clouds.  This can be very useful for monitoring growing convective clouds to determine when they begin to glaciate, a necessary precursor to the production of lightning.  This RGB can also distinguish snow cover from clouds and bare ground, with snow appearing as shades of green.  Unfortunately, other features have shades of green and yellow (as described in the product’s Quick Guide), so some users will have issues separating these colors to find just the snow cover.

Our final RGB to consider is an experimental RGB developed by CIRA that only seeks to distinguish snow cover from clouds and ground (so simpler in what it is trying to do than the above two RGBs), with snow cover shown as white.  It is called the Day Snow/Cloud and Day/Snow Cloud Layer RGB (described here).  The basic Day Snow/Cloud RGB shows snow cover as white and clouds as yellow/green:

GOES-16 CIRA Snow/Cloud RGB image at 1401 UTC.

Now we can easily see the snow cover in the Northeast, a good portion of which is at this time (1400 UTC) obscured by clouds.  The slightly more complicated version of this RGB goes one step further to delineate ice clouds (appearing as magenta) from lower, water clouds (greenish/yellow).

GOES-16 CIRA Snow/Cloud Layer RGB image at 1401 UTC.

We expanded the domain of this image since higher clouds were not present in the more zoomed in view.  This RGB is available from CIRA to be ingested into AWIPS via the LDM and is in use at several WFOs; let us know if you would like to try it.

Please send us any comments on this and any of our other blogs.  Note that all the imagery shown here was downloaded from the CIRA Slider Tool, available at https://rammb-slider.cira.colostate.edu/ which displays real-time imagery for all the channels from several satellites as well as operational and experimental RGBs, as well as having an archive of data.



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Can GLM Total Lightning help with warning for non-supercell (landspout) tornadoes? A case from Iowa on 29 May 2019.

by Ed Szoke and Dan Bikos

Total lightning (in-cloud and cloud-to-ground lightning) is available to forecasters from the Global Lightning Mapper (GLM) onboard GOES-16 and GOES-17.  Unlike cloud-to-ground lightning, the amount of in-cloud lightning is related to updraft strength, and various studies have seeked to relate in-cloud lightning to the potential for severe storms including tornadoes.  The relationship to supercell tornadoes is not clear, owing to the complexities involved in supercell tornadogenesis.  But non-supercell tornadoes have been shown to be closely related to updraft strength, implying that total lightning as measured by the GLM may have a possibility of helping with the issuing of warnings for these difficult to identify (with conventional radar signals) and predict tornadoes.  A VISIT module was developed in 2015 that explores this connection with a couple of cases (see this  VISIT session; note that we are in the process of updating this session with additional cases, stay tuned).  In this blog entry we will look at a couple of the tornadoes that occurred in Iowa on the afternoon of 29 May 2019 to see what GLM total lightning showed.  The Des Moines WFO has a nice summary of the event here.  We will be looking at tornadoes 2 and 3 in their summary.

It was certainly an interesting day in Iowa; the threat for “conventional” tornadoes and severe storms appeared to have moved east ahead of an unusually strong (for late May) upper level low moving northeastward out of the Rockies, which by late afternoon was centered over northwestern Iowa.

Fig. 1.  500 mb analysis valid at 22z/29 May from the SPC Mesoanalysis page.

Often the colder air aloft present with the core of an upper level low can provide the setup for shallow but intense convection that can produce severe weather or even tornadoes (either of the non-supercell variety or from “low-topped” supercells).  Indeed, forecasters at the Des Moines WFO recognized this possibility in the AFD issued at 3:39 AM CDT on 29 May: “While the instability will be limited and flow fields are not nearly as strong as yesterday, the proximity of the aforementioned surface low and its effects on low-level shear and convergence fields are suggestive of a conditional severe weather threat with any storms that do develop.  This would be in the form of pulsey/isolated wind/hail, but also the threat of an isolated tornado cannot be ruled out in such an environment.

And the Day 1 Hazardous Weather Outlook issued that morning stated:

“Scattered thunderstorms are possible this afternoon and evening.  Isolated severe storms may occur with quarter sized hail, 60 mph winds and a funnel cloud or landspout tornado possible.”

An upstream sounding from Omaha (OAX) at 00z/30 May shows the instability present in the lower parts of the troposphere.

Enough vertical wind shear exists to consider the possibility that some of the storms may have grown to be shallow supercells, so here we will only consider the first couple of tornadoes of those that occurred (the two labelled in the figure below), shown in this plot from SPC that covers the entire period (the summary issued by the Des Moines WFO counted 8 total tornadoes).

Below are a couple of pictures of the more northern of the two labeled tornadoes taken from the Des Moines WFO Twitter page, tornado #2 from the WFO summary, labeled the “Pocahontas tornado”.






















In the “old days” these funnel clouds were often referred to as “cold air funnels”, owing to their occurrence in a colder environment (with cold air aloft) not typical of tornado development.  But we know that in fact they can touch down (as they did in Iowa) and are legitimate tornadoes.  (Somewhere I saved a headline from the Madison, Wisconsin newspaper about a similar event, I think over 20 years ago, with the interesting headline “Cold air funnel hits car dealership”.  I guess that would be a tornado!  If I find it I’ll add to this post.)

So what did the GLM show for total lightning with these two tornadoes.  To answer this question we will look at a loop of a 4-panel from AWIPS with 3 types of GLM output in three of the panels (Flash Extent Density in the upper left, Average Flash Area in the upper right, and Total Optical Energy in the lower left, so far only Flash Extent Density has been correlated with non-supercell tornadoes) and the Day Cloud Phase Distinction RGB in the lower right (a Quick Guide on this product can be found here).  The circle represents the area covered by the AWIPS Meteogram Tracking tool, output from which we will show shortly.

The first tornado, the more southern of the two (labeled tornado #3 in the WFO summary, was the first tornado of the afternoon in Iowa and is a brief tornado that occurred at 2058z.  If you stop the loop at the very first image it is a minute after this tornado was observed.  It turns out that no lightning was observed with the parent cell, suggesting a very shallow storm perhaps not even (or barely) reaching the freezing level, since mixed precipitation (ice and water) is necessary for lightning to form.  Assuming the location of this tornado is correct, it occurred in the very southwest corner of the county and to the southwest of the county where the center of the meteogram tracking tool is located.  In agreement with no lightning being observed, this would be in the shallow cells located to the south of the more developed short line that did have glaciated clouds (more green and yellow colors) seen in the RGB image at 2059z.  So for this tornado the GLM did not provide any help.

For the second tornado (tornado #2 in the WFO summary) there is lightning that is observed by the GLM.  The tornado was observed beginning at 2149z and ending at 2101z within the circle on the 4-panel.  Concentrating on the Flash Extent Density in the upper left panel, we can see a sharp  increase at 2141z (stepping through the imagery will probably be necessary to pinpoint this), then a decrease up to the tornado time (note we are looking at 1-minute GLM products here, updated every minute).  If we look at the output from the Meteogram Tracking Tool in the image below, we can quickly get a more quantitative view of the lightning behaviour with time.

The Flash Extent Density graph is in the middle panel on the right, with the sharp increase seen in the imagery shown on the plot at 2141z, 8 minutes before the tornado was first observed.  (We are assuming that the beginning tornado time is accurate.  It appears that the tornado was well observed, so this is probably a good assumption).  The Day Cloud Phase Distinction RGB also shows a rapidly growing tower that is glaciating for this cell that produced the tornado.  The tracking tool can be tricky to use in real time, but for this case (and probably a lot of non-supercell tornado cases since they often occur with fairly low winds aloft) the storm did not move much so the radius selected captured the entire storm without worrying about storm motion.  While the Flash Extent Density increase was apparent in the image display in the upper left panel, it might stand out even more on the tracking tool plot.

So how might one use this in a warning situation?  Certainly the GLM is only one piece of information to accompany other input.  Radar reflectivity and velocity might be good choices for a couple of the panels (although often the circulations with these types of tornadoes can only be seen if the storm is close to the radar), with the RGB shown above and the Flash Extent Density product in the other 2 panels of a 4-panel display.  For this case, here is another 4-panel with Composite Reflectivity in the upper left panel, Flash Extent Density in the upper right panel, but this time instead of displaying this GLM product in 1-minute intervals we have chosen a 5-min interval that is updated every minute, to see if this might make a mini “lightning jump” more apparent.  In the lower left is the Earth Networks lightning display, and in the lower right the MESH low-level rotation tracks.  Stepping through the composite reflectivity loop you may notice an increase in the composite reflectivity beginning at 2144z (it updates at 2-min intervals), which makes some sense if the GLM Flash Extent Density increase that we saw at 2141z represented an increase in the updraft strength, subsequently leading to a stronger storm.  The lightning increase we saw in the 1-min interval display in the earlier loop is not as noticable in this 5-min interval loop, suggesting the 1-min long interval updating every 1-min might be a better choice for this application.  The very minimal level of rotation noted in the track (lower right panel) implies there was probably not much of a rotational signal with this tornado or parent storm.

Our final loop is of GOES-16 visible (band 2, 0.64 microns) imagery at 5-min intervals (meso sectors were in other locations on this day) with an overlay of METARs.  Unfortunately the times are rather tiny owing to an issue with our AWIPS display, but the loop runs from 1821 to 2221z, so the storm of interest develops in the last quarter of the loop and we can see the rapid development during that period.  The observations indicate the cell developed within an area of converging flow between the southeast winds to the south and more northeasterly winds to the north.  Non-supercell tornadoes typically do form along some type of boundary, with studies often showing an incipient circulation (or circulations) forming along the convergence zone.  Such a low-level circulation can then be stretched vertically and if the updraft is strong enough a non-supercell tornado may develop (or “landspout” as it is sometimes called, owing to the similarities to waterspout formation).  We don’t have the data to know if such a low-level circulation existed in this case, but the idea of monitoring development along a boundary and then using the GLM data to help identify potential cells with more potent updrafts is a way in which this data may aide in predicting non-supercell tornadoes.  There is a more substantial convergence zone to the south of our cell running east from what appears to be the center of the surface low.  Perhaps showing us the difficulties involved with warning for non-supercell tornadoes, no tornado comes from this area (recall there was a short-lived tornado, Tornado #3 in the Des Moines WFO storm summary, but it appeared to occur to the south of this other line just before 2100z.

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Blowing Dust associated with 10-11 April 2019 Central US Strong Cyclone

By Ed Szoke and Dan Bikos

A rapidly intensifying cyclone developed in southeast Colorado late on April 10 as a strong upper-level wave moved out of the Rockies, not quite the “bomb cyclone” of 13 March 2019 but a very intense storm that brought blizzard conditions and widespread snow from the Central Plains to the Upper Midwest. On the southern end of the storm, very strong winds across the Southwest US resulted in a large area of blowing dust from northern Mexico / southern New Mexico into west Texas. We can see the blowing dust quite clearly as shades of yellow on a loop of the experimental GOES-16 CIRA DEBRA (Dynamic Enhancement with Background Reduction Algorithm) Dust product:

click here or on the image for the animation

The intensity of the yellow color is related to the confidence of dust detection. This product is currently available in real-time on the CIRA SLIDER site and is also being developed for AWIPS display. The blowing dust caused significant travel problems in west Texas, as documented in this video taken near Midland, TX from April 10.

There are other ways to view dust currently on AWIPS, for example:

Notice that the GeoColor imagery shows the dust better than visible channel 2. The Dust RGB product (developed by EUMETSAT) is on AWIPS and depicts dust in magenta / pink with other features denoted by various colors. The CIRA DEBRA Dust product is designed to just highlight dust with clouds appearing as in visible imagery during the day and a simple gray scale for IR at night.

A mesoscale sector was in place centered over New Mexico and the western half of Texas on April 10. Loops of GeoColor, the DEBRA dust product and EUMETSAT dust product are shown below:

click here or on the image for the GeoColor animation

click here or on the image for the DEBRA dust animation

click here or on the image for the EUMETSAT dust animation

The dust plume advected towards the northeast on 11 April as the cyclone tracked in the same direction, depositing dust onto the new fallen snow as far north as Minnesota and Wisconsin, as shown in the hourly animation of the DEBRA dust product below:

click here or on the image for the animation

It becomes more difficult to see the dust as it goes further north due to extensive cloudiness.  We speculate that we can still faintly see the dust because of some breaks in the clouds.  The same DEBRA dust product from SNPP VIIRS shows this more northern dust even better due to improved resolution compared to GOES-16:

There were numerous social media postings from different NWS WFOs in the affected region, for example see this post from NWS LaCrosse, WI WFO.

More extensive coverage of the dust can be found in this article from the BBC

Posted in Blowing Dust Detection (Split-window technique) | Leave a comment

Rapid ice cover development over the eastern Great Lakes in late January 2019

An Arctic outbreak occurred in late January 2019 over the Great Lakes which caused not only lake-effect snowfall but a rapid increase in ice coverage across the lakes, particularly over shallower lakes.

First, we’ll look at GOES-16 imagery prior to the Arctic outbreak. GOES-16 GeoColor imagery on January 25 (click to open a larger window):

The clouds associated with lake-effect snowbands show up well, however ice cover on the lake can be difficult to see since the white colored ice may blend in with clouds of the same color. In order to help make the discrimination between ice cover and clouds, the CIRA Snow/Cloud Layers product can be viewed (click to open a larger window):

In the product, low/water clouds generally appear as yellow or possibly greenish while high/ice clouds appear as magenta. Snow on the ground will appear white. Ice cover on the lakes appear white or bluish/white.

Next, we’ll analyze the same imagery after the Arctic outbreak across the Great Lakes. First, the GeoColor product (click to open a larger window):

and the CIRA Snow/Cloud Layers product (click to open a larger window):

Compare this loop with the same loop observed on January 25 and you can readily see the increase in ice coverage across the Great Lakes, in particular over Lake Erie. Lake Erie is the most shallow of the Great Lakes making it quicker to develop ice cover and in fact lake surface temperatures on January 25 were near freezing. Ice cover significantly reduces sensible/latent heat fluxes which helps explain why there is a lack of lake-effect clouds across Lake Erie on the February 1 imagery. Finally, a mesovortex is observed over Lake Michigan moving northward.

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Daytime fog over snow in Wyoming on 21 November 2018

By Ed Szoke and Dan Bikos

On 21 November 2018, here is the GOES-16 visible (0.64 micron) imagery:

Visible loop:
click here or on the image for the animation

Do you see any fog in this imagery?

How about in this product, do you see any fog?

click here or on the image for the animation

This is the experimental CIRA Snow/Cloud Layers product. The purpose of the product is to distinguish clouds from snow cover on the ground during the daytime hours. In the product, low/water clouds generally appear as yellow or possibly greenish (as in this case) while high/ice clouds appear as magenta. Snow on the ground will appear white. This product uses a number of GOES-16 ABI bands (0.47, 0.64, 1.37, 1.6, 2.24, and 10.3 microns) as a RGB product with additional calculations. Note in our loop over central Wyoming we see a region that is green/brown in color over the white snow field that is decreasing in areal extent over time, indicating low cloud or fog. This product is currently on the RAMMB SLIDER page, however will be experimentally available on AWIPS via LDM from CIRA.

A product that is currently available in AWIPS is the Day Cloud Phase Distinction RGB which is shown here:

click here or on the image for the animation

in this product, snow on the ground appears green while low cloud or fog will appears lavender to cyan in color during the daytime. The reason is that we have relatively large contribution from the blue component with liquid cloud, small contribution from red component and large contribution from green component since it’s reflective in the visible band. The combination contributes to the lavender color for the low cloud / fog.

For this case, the METAR site at Riverton, WY reported fog as seen in this METAR plot at 1600 UTC:

A list of the observations from Riverton during the morning are shown here:

KRIW 211853Z AUTO 00000KT 6SM BR CLR M07/M09 A3016 RMK AO2 SLP279 I1001 T10721089
KRIW 211753Z AUTO 00000KT 6SM BR SCT002 M09/M10 A3018 RMK AO2 SLP291 I6001 T10941100 11094 21161 58001 $
KRIW 211727Z AUTO 00000KT 5SM BR FEW002 M11/M11 A3018 RMK AO2 T11061106 $
KRIW 211717Z AUTO 19004KT 4SM BR BKN002 M11/M11 A3019 RMK AO2 T11061111 $
KRIW 211707Z AUTO 00000KT 9SM SCT002 M10/M11 A3018 RMK AO2 T11001106 $
KRIW 211653Z AUTO 00000KT 8SM BKN001 M11/M11 A3019 RMK AO2 SLP293 I1001 T11061111 $
KRIW 211639Z AUTO 00000KT 3SM BR OVC001 M11/M12 A3019 RMK AO2 T11061122 $
KRIW 211633Z AUTO 00000KT 1SM BR OVC001 M11/M12 A3019 RMK AO2 T11111122 $
KRIW 211553Z AUTO 03003KT 1/4SM FZFG VV001 M13/M15 A3019 RMK AO2 SLP303 T11331150 $
KRIW 211543Z AUTO 00000KT 1/4SM FZFG VV001 M13/M14 A3019 RMK AO2 T11331144 $

Posted in GOES Low Cloud / Fog Imagery | Leave a comment

Dry…but not THAT dry!

The GOES-16 water vapor imagery for all 3 channels showed a narrow band of very warm brightness temperatures (implying sinking air and a dry atmosphere) on Friday morning (1502 UTC) 9 Feb 2018.

GOES-16 6.9 micron (“mid-level”) water vapor image at 1502 UTC/9 Feb 2018.

GOES-16 6.2 micron water vapor image (“high level”) at 1502 UTC/9 Feb 2018.

GOES-16 7.3 micron water vapor image (“low level”) at 1502 UTC/9 Feb 2018.

This narrow zone of sinking air is on the southern (anticyclonic) side of a very strong upper level jet draped across the CONUS, seen in the 1200 UTC 300 mb analysis below.  The dry slot stretches back into the Pacific south of the CONUS jet.

Notice how the brightness temperatures within the narrow zone are quite distinct, being at the very warm end of the scale (for this time of year).  So does seeing these warm brightness temperatures at all 3 water vapor channels indicate a lack of moisture through the column.  Well, not necessarily.  Check out the surface plots below at 1200 UTC and 1500 UTC on 9 Feb, with corresponding GeoColor imagery for these same times.  Bluish clouds in the GeoColor imagery indicate water clouds, which goes along with the low overcast conditions shown in the METAR plot.  By 1500 UTC the GeoColor nighttime imagery has morphed into visible imagery, verifying the low clouds streaming north from the Gulf of Mexico.  GeoColor imagery is not currently a baseline product, but CIRA can provide it to your WFO if you would like to try it.

Surface (METAR) plot at 12 UTC on 9 Feb 2018. Note the overcast conditions extending across northeast TX into southeast OK.


GOES-16 GeoColor image at 1202 UTC on 9 Feb. Lower level (water) clouds are bluish, while ice clouds are white. City lights are shown during nighttime imagery.


Surface (METAR) plot at 15 UTC on 9 Feb 2018.


GeoColor image at 1502 UTC 9 Feb.


The satellite imagery and METAR plots indicate relatively deep low level moisture that extends northwards across a portion of the very warm/dry slot shown in the water vapor imagery.  The 1200 UTC 9 Feb sounding from Dallas (DFW) in northeastern TX confirms the low level moisture (note that DFW at 1200 UTC reported scattered clouds at 1800 ft (AGL) and a 3000 ft solid cloud deck).

Dallas sounding at 1200 UTC on 9 Feb.


The moist layer extends from the surface to just above 900 mb.  Why is it that this moisture is not detected by the water vapor bands, even the lowest one?  The answer lies in what the weighting functions look like for the different water vapor channels for this sounding.  You can see what these look like on the real-time CIMSS site at https://cimss.ssec.wisc.edu/goes/wf

For the Dallas sounding shown above the weighting functions are shown in the plot below for the 3 water vapor channels.


The high level water vapor channel has a single weighting function peak around 400 mb, while the other two channels have a dual peak.  Even though the sounding showed that conditions were quite dry above the low level moist layer, there is just enough moisture to saturate the other two channels before reaching this low level moist layer aob 900 mb.  The low-level water vapor image (7.3 micron band) appears to have a small contribution nearly to the surface, and indeed, if you look closely at that image (above) you do see a slight difference in the color within the warm/dry band in northeastern Texas.  But for the most part the main message from the water vapor imagery would be a lack of moisture, yet clearly there is a low-level saturated layer.  This case indicates that interpreting water vapor imagery in regards to the amount of moisture present is not straightforward.  An earlier blog (go here to see the blog) showed an even more extreme case near San Diego using GOES-15 Sounder imagery (basically the same 3 water vapor channels that are on GOES-16, but at far worse horizontal resolution (10-km; and temporal resolution of course).  In the San Diego case all 3 channels suggested dry conditions, but in fact the San Diego sounding had a record level of Precipitable Water (PW) for the time of year, but the moisture, while deeper than in this case, was all present below where the 3 channels saturated.  This again could be seen using the weighting function profiles.

Bottom line – caution must be used when inferring a moisture profile using water vapor imagery, and the imagery does not indicate the actual value of moisture that may be present.  So is there a to determine how much moisture is present in the atmosphere (besides the raob)?  Indeed there is such a product, known as the Advected Layered Precipitable Water (ALPW) product.  The ALPW product uses data from Polar orbiting satellites that have instrumentation that detects the amount of moisture (PW) in the atmosphere, with the ability to also see through clouds (which saturate water vapor imagery, unless of course we have a case like this one where the channels saturate before reaching the level of the clouds).  The advantage of looking at the ALPW product versus looking at ABI water vapor bands is that it provides a quantitative value for the moisture in a given layer without the need to think about weighting function profiles or other complications (e.g., zenith angle) that affect your interpretation of assessing moisture from ABI water vapor bands alone.  The cursor readout function on AWIPS can be used with the ALPW imagery to get read-outs of the PW values for each layer.  Forecasters are familiar with the Total PW product on AWIPS, but more recently CIRA has developed the ALPW product, which is an “advected” version of the LPW product where the moisture present in 4 different layers is displayed.  The ALPW product for 1500 UTC on 9 Feb is shown below (at times there are missing swaths in the imagery and this was the case over the area of interest at 1200 UTC, so 1500 UTC imagery is used).  This image is from AWIPS2, using the 4-panel display to see each individual layer in one image.  The imagery is available at 3-h intervals, and real-time imagery can be seen at http://cat.cira.colostate.edu/sport/layered/advected/lpw.htm .   Training for this product is available at the VISIT site at http://rammb.cira.colostate.edu/training/visit/training_sessions/advected_layer_precipitable_water_product/

The ALPW image above depicts very dry conditions in the upper two layers (aob 700 mb), consistent with the sounding at Dallas and the water vapor imagery (which had the most contribution in these layers per the weighting functions).  But the ALPW does show a nice moisture plume extending north/north-northeast into northeastern Texas, southeastern Oklahoma and into Arkansas in the lowest layer (surface to 850 mb).  In the next higher layer most of this moisture is just making it towards the Dallas areas.  In practice, the most information can come from using the GOES-16 water vapor imagery, with its much higher time and space resolution, in concert with the ALPW imagery.  The ALPW imagery is not currently an AWIPS baseline product, but if you would like to try it at your WFO contact Dan Bikos or myself and it can be delivered to AWIPS over the LDM in real-time.



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Lots of smoke moving south – a comparison of GeoColor with other bands for smoke visualization on 1 Aug 2017

The GOES-16 data posted on this page are preliminary, non-operational data and are undergoing testing.  Users bear all responsibility for inspecting the data prior to use and for the manner in which the data are utilized.

Fires have been burning for some time in Montana as well as western Canada, as seen in the latest large fire incident map for 1 August 2017 below.



A broad upper-level ridge over the southwest to south-central CONUS has generally kept the smoke on an eastward trajectory.


500 mb plot and analysis from 1200 UTC on 26 July 2017.

A significant change to the pattern is occurring this week with a retrogression of the upper level ridge towards the West Coast (coincident with the very hot weather across the Pacific NW), allowing for shortwave troughs moving out of Canada to deepen into the central and eastern CONUS.  This has changed the flow across the High Plains southward through the eastern Rockies from west-southwest to north/northeast, as seen in the 500 mb analysis from this morning (1200 UTC/1 August, below).

500_170801_12 500 mb plot and analysis from 1200 UTC on 1 August 2017.

Smoke from fires can be a health hazard and if thick enough may negatively influence the development of convection, so tracking and monitoring the smoke is a concern for forecasters.   In the pre-GOES-16 era we know that smoke is typically most apparent on visible imagery near sunrise or sunset (as well as for an observer on the ground).  There are many bands and products available now with GOES-16, and here we take a look at a few of these from early (1317 UTC on 1 August) this morning to see the differences in the appearance of smoke.

First we look at a type of imagery developed at CIRA known as GeoColor.  Using a layering technique it combines 0.64 µm (Band 2) visible imagery with a “True Color” background during the daytime, and 10.35 µm (Band 10) IR imagery (along with 10.35-3.9 µm imagery to highlight fog and low clouds) with a static image of nighttime lights during the night.  This allows for a seamless transition from day to night when viewing a loop of the imagery.  Unique to GeoColor is the True Color background, which without a special algorithm developed using Himawari imagery would not be possible, since GOES-16 does not have a green band.  GeoColor creates a synthetic green band and by using this is able to make a very realistic looking image of the daytime surface, similar to what one would see if on the International Space Station.  GeoColor is available for display in AWIPS-2 through the LDM, currently at a reduced temporal resolution of 15 minutes.  If interested in the product send an email to myself (Edward.J.Szoke@noaa.gov) or Dan Lindsey (Dan.Lindsey@noaa.gov).  Dan presented a recent FDTD GOES-16 Applications Webinar (also known as a VISIT Satellite Chat) on GeoColor and some of its applications (the powerpoint can be downloaded and a recording viewed at http://rammb.cira.colostate.edu/training/visit/satellite_chat/ ).  You can also view real-time GeoColor imagery, as well as all the GOES-16 bands and some RGBs, online at http://rammb-slider.cira.colostate.edu/ ).  The GeoColor image from earlier today at 1317 UTC (1 August) is shown below.



GOES-16 GeoColor image at 1317 UTC on 1 August 2017.

We see both the nighttime and daytime version of the GeoColor imagery in the image at 1317 UTC, with nightlights visible for some of the cities from Utah and Arizona westward.  In the daytime portion of the imagery the smoke is nicely seen extending from northeastern Colorado northwards to Montana and then east across the Northern Plains.  Now let’s contrast this image with the traditional visible imagery (Band 2, and 0.64 µm) from GOES-16 for the same time.



GOES-16 Band 2 (visible 0.64 µm) image at 1317 UTC on 1 August 2017.

Some of the thicker smoke is visible across the northern portion of the image from Montana to North Dakota, but most of the smoke is not easily seen.  Smoke can typically be seen better in the visible imagery at 0.47 µm (Band 1 on GOES-16) because it has higher reflectance in the presence of atmospheric aerosols compared to Band 2.


GOES-16 Band 1 (visible 0.47 µm) image at 1317 UTC on 1 August 2017.

For this case we do not see much difference between Band 2 and for both images the extent of the smoke is hard to determine compared to the GeoColor image.  An RGB image developed by CIMSS known as Natural Color (available in AWIPS-2) attempts to replicate True Color without using the complexities involved in the GeoColor imagery.  As seen in the Natural Color image below, while the background colors show many features of terrain and vegetation, they are not as “true” a representation as one gets with GeoColor.  However, often the colors of the background image make it easier to discern smoke and dust when compared to the visible imagery bands 1 and 2.


GOES-16 Natural Color RGB image at 1317 UTC on 1 August 2017.

For this case some of the smoke is easier to see across the northern portion of the image, but again we do not have the extent of smoke (especially thinner smoke layers) that were seen in the GeoColor image.

An interesting forecast product that is being run experimentally at NOAA/ESRL/GSD is a version of the HRRR with a chemistry formulation that is initialized for fires at this time using information from VIIRS.  The HRRR-Smoke model is run in non-operational mode four times per day out to 36-h and output can be found at https://rapidrefresh.noaa.gov/hrrr/HRRRsmoke/ .  A forecast from the 00z/1 August run valid at 13 UTC is shown next.

trc1_int_f13 HRRR-smoke 13-h forecast of vertically integrated smoke valid at 13 UTC on 1 August.

The southern extent of the thinner smoke layer across eastern Colorado in the forecast is in good agreement with the GeoColor imagery shown earlier.  The forecast from the same run out to 36-h, shown below, indicates the smoke is expected to continue moving southward into the Central and Southern Plains.


HRRR-smoke 36-h forecast of vertically integrated smoke valid at 12 UTC on 2 August.

Finally, a look at the extent of the fires using imagery that highlights the fire hot spots. First a look at the traditional 3.9 µm (Band 7) imagery from GOES-16, which of course has greater resolution (2 km) and can resolve higher temperatures than imagery from the previous GOES.  An image from this afternoon is shown next.


GOES-16 Band 7 (3.9 µm) image at 2232 UTC on 1 August 2017.

A simple grey color table is used in this imagery, so all fires appear as white dots, which can be seen at numerous spots from central Idaho through western Montana, with another fire in far northeastern Washington and another across the Canadian border.  Other color tables are available that discriminate some of the temperatures that can be seen with the GOES-16 39 micron imagery.  See, for example, the images posted by Bill Line in his blog on the Southern Plains fires on 6 March 2017 at https://satelliteliaisonblog.com/2017/03/06/meso-sectors-shift-west-for-fire-severe-weather/   Also within that blog Bill shows how fire hot spots can also appear in the near-IR 2.25 and 1.61 µm bands (Bands 5 and 6).  CIRA has developed a Fire Temperature RGB that combines information from the three shortwave IR bands (ABI Bands 5, 6 and 7) to provide information on fire intensity.  In this RGB composite, the red component is Band 7 (3.9 µm), the green component is Band 6 (2.25 µm) and the blue component is Band 5 (1.6 µm).  As a general rule, the more intense a fire is burning, the more radiation it emits at shorter wavelengths.  Band 7 is capable of detecting most fires.  Less intense fires will only be detected in Band 7 and appear bright red.  Moderately intense fires will be detected by both Band 7 and Band 6 and appear orange to yellow, depending on intensity.  The most intense fires will be detected by all three bands and appear white.  Note that the range detected by this RGB will exceed the temperature range from the Band 7/3.9 µm imagery.  The Fire Temperature RGB image for the same time this afternoon is shown next.



GOES-16 Fire Temperature RGB image at 2232 UTC on 1 August 2017.

The same fires are seen in this imagery at various colors, indicating the varying intensities of these fires.  The Fire Temperature RGB should be available on your AWIPS-2.


The smoke from the Canadian and Montana (and other) fires also moved into the Pacific Northwest.  The link below is to a GeoColor loop over the Pacific Northwest that covers 1-2 August.


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Dust event in the El Paso vicinity on 4 April 2017

The GOES-16 data posted on this page are preliminary, non-operational data and are undergoing testing.  Users bear all responsibility for inspecting the data prior to use and for the manner in which the data are utilized.

During the afternoon of 4 April 2017, strong westerly winds and low relative humidity was observed in west Texas, southern New Mexico and northern Mexico as seen in the surface observations at 2100 UTC:


The winds were in response to a deepening low near the Texas Panhandle.  Although none of the observations in the plot above show blowing dust, METAR sites such as ELP (El Paso, TX) included some periods of blowing dust in the late morning and afternoon hours.  Here we explore how well the areas of blowing dust were seen in GOES-16 satellite imagery, using 3 different loops during the afternoon hours of 4 April.

The first loop (below) goes from 1827 to 2157 UTC and displays GOES-16 visible imagery (0.64 microns/Channel 2).


Do you see any evidence of blowing dust in this loop?

Refer to the static image below which contains a yellow circle, refer to the visible loop above and look in the region of this yellow circle.



The blowing dust is extremely subtle in the static image and even in the animation, no other areas of dust are obvious in the loop.

Sometimes features such as blowing dust show quite well in the CIRA True Color product.  True Color imagery approximates the response of normal human vision, providing a depiction of the satellite-observed scene.  The natural color of the background often makes it easier to see certain features when compared to the standard 0.64 micron visible imagery. Since there is no green channel on GOES-16, CIRA creates a “synthetic” green band to make this product, more information is provided at:


The GOES-16 True Color product:


Does the True Color product improve our ability to see dust in this case?


If we return to the same region (yellow circle), this dust plume appears a little more obvious relative to the visible band only.  North of this plume you may be able to discern additional plumes of dust, however they are still fairly subtle.

Certain products have been developed that are designed to highlight features such as dust plumes.  One example is the band difference product between the 10.3 micron and 12.3 micron bands.  The difference product would show a negative value in the presence of dust, which is shown as purple in the color table shown in the loop below:


With this in mind, do you see any additional dust plumes in this product?


You should now be able to discern multiple east-west oriented dust plumes (purple color).  Some of the plumes have a distinct source region associated with them.  This product helps us to see dust plumes that was not obvious in the other imagery.  Even in the GOES-16 era with 0.5 km visible band, dust may not be discernible without the help of a product designed to highlight dust.  Examples of other dust products that are under development include one by CIRA known as the DEBRA dust product with real-time data available at:




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22 March 2017 GOES-16 imagery in the northeast US into southeast Canada

The GOES-16 data posted on this page are preliminary, non-operational data and are undergoing testing.  Users bear all responsibility for inspecting the data prior to use and for the manner in which the data are utilized.

By Ed Szoke and Dan Bikos

On 22 March 2017 the GOES-16 visible band at 0.64 micron over Maine and southeast Canada shows many different cloud motions across different scales:


We identify some of these on the following image with 1600 UTC METARs overlaid:


The mesolow can be seen in the animation entering the scene and moving northeastward.  This mesolow is likely along a cold front with the synoptic surface low to the north over New Brunswick.  The brown dashed line above corresponds to a line of convection seen in the animation, this line is along the cold front moving eastward.  Ahead of the cold front in the warm sector we observe wave clouds over Nova Scotia early in the loop that transition to unstable cloud streets followed by convection.   Over north central Maine, we observe clouds at different heights.  There are higher level clouds moving northward ahead of the upper-level trough, meanwhile underneath those clouds we see low-level banded clouds moving relatively quickly to the southeast (in low-level cold advection).

The cloud heights can be more easily identified by looking at multiple channels on GOES-16.  In this 4-panel:


We are looking at 0.64 microns (Band 2 / visible) in the upper-left, 1.6 microns (Band 5 / snow / ice) in the upper-right, 1.38 microns (Band 4 / cirrus) in the lower-left, and 0.87 micron (Band 3 / veggie) in the lower-right.

Note the discrimination between low (liquid) versus high (ice) clouds for the following bands:

1.6 microns: High (ice) clouds appear darker / gray and low (liquid) clouds appear lighter (white).

1.38 microns:   High (ice) clouds appear lighter (white), whereas low (liquid) clouds are much more subtle and may appear dark.

The 0.87 micron (veggie) band is useful here in that Nova Scotia shows up much more clearly compared to the 0.64 micron (visible) band since vegetation surfaces have high reflectance and water surfaces have low reflectance at this wavelength.

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A look at water vapor imagery from GOES-16

The GOES-16 data posted on this page are preliminary, non-operational data and are undergoing testing.  Users bear all responsibility for inspecting the data prior to use and for the manner in which the data are utilized.

By Ed Szoke and Dan Bikos

Among the 16 channels on GOES-16 are 3 water vapor channels at 2 km resolution (at NADIR), compared to a single water vapor channel on GOES-15 or GOES-13 at 4 km resolution.  Here we take a look at the new water vapor imagery over and near Colorado from Monday 6 March 2017.  We’ll use the 4-panel display from AWIPS II at the Boulder NWS WFO, with the lower resolution GOES single water vapor image in the lower right panel.  A series of three 4-panel images is shown below at hourly intervals from 1702 UTC to 1902 UTC.

Ed4panelWV1702_screenCapture Ed4panelWV1802_screenCapture


Ed4panelWV1902_screenCaptureIn the 4-panels above, GOES-16 water vapor imagery is channel 10 (7.34 µm), in the upper left panel, channel 9 (6.95 µm) in the upper right panel, channel 8 (6.19 µm) in the lower left panel, and from GOES-15 the single water vapor band at 6.5 µm.  A strong trough is passing across Colorado at this time, as seen in the RAP analysis of 500 mb heights and temperature below.  Many wave features on various scales are seen in all the imagery, but more distinctly with the higher resolution GOES-16 imagery.


Notice the very cold air near and just behind the trough axis, which passes across the Front Range of Colorado at this time (1800 UTC).  The strong warming/drying signature in northeastern Colorado is just south of the center of a smaller scale circulation associated with the overall trough.  Meanwhile farther to the east very warm brightness temperatures are seen across the Texas Panhandle into southwestern Kansas behind the dryline that had pushed eastward.  Plunging dewpoints and strong SW winds in this area created very high fire danger and numerous grassland fires developed and rapidly spread, as detailed in other blogs accessible through the VISIT homepage.

A strong jet accompanied this trough, as seen in the 250 mb wind speed and height analysis below.


One interesting feature that was quite evident in the looping of the imagery was a small piece of cirrus that moved rapidly from the UT/AZ border at 17z to just WSW of the 4-corners point at 1802z to ESE of the 4-corners by the last image at 1902z.  We can see this feature also in this loop of the channel 10 (7.34 µm) lower-level water vapor imagery from GOES-16: http://rammb.cira.colostate.edu/templates/loop_directory.asp?data_folder=training/visit/loops/6mar17/B10&loop_speed_ms=80

At first we were a bit unsure as to what this was since it was moving so much faster than all the other cloud features.  In the higher resolution imagery it was quite apparent as a small “blocky” feature.  Using the tracking feature on AWIPS II we estimated a motion of ~115 kts, which is in agreement with the wind speed analysis of the stronger winds with the leading edge of the main jet.

Some of the other bands from GOES-16 were used to confirm the feature was a patch of cirrus.  These are shown in the 4-panel below.


This 4-panel from AWIPS II has 4 different channels from GOES-16:  channel 2 V=visible imagery (0.64 µm), in the upper left panel, the channel 4 “Cirrus band” (1.38 µm) in the upper right panel, channel 14 IR (11.2 µm) in the lower left panel, and channel 5 “snow/ice” imagery (1.61 µm) in the lower right panel.  In this case the cirrus and IR channels most clearly define the patch of cloudiness as an ice cloud.








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