This website works best with a newer web browser such as Chrome, Firefox, Safari or Microsoft
Edge. Internet Explorer is not supported by this website.
5-minute PACUS Sector GOES-18 (GOES-West) Mid-level Water Vapor and Upper-level Water Vapor images (above) displayed orographic waves downwind (southwest) of the Hawaiian Islands on 23 October 2025. With a ridge of high pressure north of Hawai’i (surface analyses), boundary layer flow from the northeast interacted with the topography of the island chain... Read More
GOES-18 Mid-level Water Vapor (6.9 µm) and Upper-level Water Vapor (6.2 µm) images, from 0601 UTC on 23 October to 0001 UTC on 24 October [click to play MP4 animation]
5-minute PACUS Sector GOES-18 (GOES-West) Mid-level Water Vapor and Upper-level Water Vapor images (above) displayed orographic waves downwind (southwest) of the Hawaiian Islands on 23 October 2025. With a ridge of high pressure north of Hawai’i (surface analyses), boundary layer flow from the northeast interacted with the topography of the island chain — generating vertically-propagating waves that were apparent in Mid-level (and to a lesser extent, even Upper-level) Water Vapor imagery (below).
GOES-18 Mid-level Water Vapor (6.9 µm) image and Topography [click to enlarge]
Northwest of the Big Island of Hawai’i, there was one Pilot Report of continuous light turbulence between the altitudes of 18000-20000 ft (below).
GOES-18 Mid-level Water Vapor (6.9 µm) image at 0001 UTC on 24 October, with a cursor sample of a Pilot Report at 0011 UTC [click to enlarge]
One item of interest was the diurnal change in thermal signatures of the Mauna Kea and Mauna Loa summits — transitioning from cooler (darker shades of blue) during the nighttime at 1201 UTC to warmer (brighter shades of yellow) during the daytime at 0001 UTC (below).
GOES-18 Mid-level Water Vapor (6.9 µm) images at 1201 UTC on 23 October and 0001 UTC on 24 October [click to enlarge]
Plots of Weighting Functions for the GOES-18 Mid-level and Upper-level Water Vapor spectral bands — derived using rawinsonde data from Hilo (PHTO) — showed the profiles of Band 08 and Band 09 weighting functions at 1200 UTC on 23 October to 0000 UTC on 24 October (below). The summits of Mauna Kea and Mauna Loa extend upward to near the 600 hPa pressure level — close to the level of strong weighting function contributions for Water Vapor spectral band 09 at each of those 2 times.
Weighting Function plots of GOES-18 Mid-level Water Vapor (6.9 µm / Band 09, cyan) and Upper-level Water Vapor (6.2 µm / Band 08, brown) spectral bands near the Big Island of Hawai`i [click to enlarge]
This diurnal variability of Mauna Kea and Mauna Loa thermal signatures in Water Vapor imagery has been discussed in previous blog posts here, here and here.
In a sign that the seasons are changing, the lake effect is now clearly visible over the Great Lakes. While most commonly associated with heavy snowfalls downwind of the Great Lakes, in these early pre-freezing times of the year it can be associated with the enhancement of rainfall as well.... Read More
In a sign that the seasons are changing, the lake effect is now clearly visible over the Great Lakes. While most commonly associated with heavy snowfalls downwind of the Great Lakes, in these early pre-freezing times of the year it can be associated with the enhancement of rainfall as well. The lake effect has two principal causes: colder air that originated over land is advected over warmer lake water causing latent heat transfer into the cold air mass, while the topographical lift and sudden increase in friction at the opposite shore forces additional upward motion, leading to precipitation enhancement. Lake effect clouds are generally shallow compared to more synoptically-forced precipitation systems.
The lake effect is easily detectable via satellite as parallel bands of clouds oriented along the direction of the wind flow. However, advanced tools allow us to gain some additional insight about the lake effect and its underlying processes from satellites. For example, here is the Day Cloud Phase Distinction RGB from GOES-19. In this product, three GOES ABI channels with various cloud microphysical characteristics are assigned the red, green, or blue color in the animation to help highlight various characteristics of the clouds. In this product, low level liquid-phase clouds are lavender or cyan, thicker liquid clouds are yellow, glaciating clouds are green, and thin cirrus are salmon.
To begin with, the ASOS observations plotted on the above animation show northeasterly winds on either side of the Lake Michigan shore, being driven by a large midlatitude cyclone centered over northern Quebec (see the surface map here to know more about the synoptic situation). Bands of shallow convection are visible from the central axis of Lake Michigan streaming to the southeast. These clouds are a light cyan to periwinkle in color in this RGB, which indicates that they are likely low-level liquid clouds. This is consistent with what we’d expect for lake effect precipitaiton, especially in mid-October.
As the clouds propagate toward the Michigan shore, they become more congested and are changing to a greenish-yellow. This color shift indicates that they clouds are deepening and changing phase to ice at their tops. In fact, these clouds are growing deep enough to precipitate. Since surface temperatures on the western edge of the lower peninsula of Michigan are well into the mid 40s, this takes the form of rain. A contemporaneous view of the NEXRAD radar mosaic is seen below.
Of course, the key factor in the development of lake effect precipitation is a difference in the temperature of the air that originated over the land relative to the temperature of the water surface. The Great Lakes Surface Enivronmental Analysis produced by our colleagues at NOAA’s Great Lakes Environmental Research Laboratory shows that mid-lake temperatures are in the low-to-mid 60s.
However, the surface temperatures over Wisconsin depicted in the initial animation show that the airmass likely has a temperature in the mid 40s. That is a substantial difference between the lake and the air that is advecting over it, and so it should not be a surprise that this is having an impact on the precipitation patterns.
As noted above, lake effect precipitation is shallow. One way to illustrate this is by looking at two adjacent radars. The minimum elevation angle for a NEXRAD weather radar is typically set to 0.5 degrees above the horizon while the surface of the earth is curving away from the beam. This means the further away from a radar one goes, the farther above the surface the bottommost radar beam is. While deep precipitation systems are tall enough to be easily visible from multiple radars, shallow systems can often sneak below the minimum beam height. Here, contemporaneous radar images from Grand Rapids, Michigan(left), and Detroit (right), are shown. Note how the Detroit radar is unable to detect the rain in the western half of the state: it’s just too shallow to appear on that radar.
The lake effect will likely be contributing to rain and snow over the next few months as the large thermal mass of the lake generally means it remains warmer than the surrounding land for much of the fall and winter. (In the spring, the temperature gradient reverses and lake breezes are born instead).
Tropical Storm Melissa developed in the Caribbean Sea on 21 October 2025 — and 1-minute Mesoscale Domain Sector GOES-19 (GOES-East) Infrared and Visible images (above) showed that most of the deep convection (which often exhibited abundant GLM-detected lightning activity) remained west of Melissa’s surface center of circulation, due to moderate west-southwesterly deep-layer wind... Read More
1-minute GOES-19 Infrared and Visible images with plots of GLM Flash Points, from 1401-2230 UTC on 21 October [click to play MP4 animation]
Tropical Storm Melissa developed in the Caribbean Sea on 21 October 2025 — and 1-minute Mesoscale Domain Sector GOES-19 (GOES-East) Infrared and Visible images (above) showed that most of the deep convection (which often exhibited abundant GLM-detected lightning activity) remained west of Melissa’s surface center of circulation, due to moderate west-southwesterly deep-layer wind shear (below).
GOES-19 Infrared images, with an overlay of contours and streamlines of deep-layer wind shear at 0200 UTC on 22 October
Melissa was moving across very warm waters, where high values of Sea Surface Temperature and Ocean Heat Content were present (below).
Sea Surface Temperature on 21 October, with a plot of Melissa’s track ending at 1800 UTC
Ocean Heat Content on 21 October, with a plot of Melissa’s track ending at 1800 UTC
Surface scatterometer data from OCEANSAT and ASCAT (below) indicated that the strongest winds existed east of the storm center.
OCEANSAT scatterometer winds at 1710 UTC on 21 October
GOES-19 Infrared image at 0121 UTC on 22 October, with a cursor sample of 30-knot ASCAT winds [click to enlarge]
GOES-19 Infrared image at 0121 UTC on 22 October, with a cursor sample of 30-knot ASCAT winds [click to enlarge]
===== 22 October Update =====
1-minute GOES-19 Infrared images with plots of GLM Flash Points, from 1801 UTC on 21 October to 1800 UTC on 22 October [click to play MP4 animation]
24 hours of 1-minute GOES-19 Infrared images + GLM Flash Points (above) showed that while the areal coverage of deep convection associated with Tropical Storm Melissa had been increasing, it still remained displaced to the east of the storm center (due to persistent westerly wind shear).
Cloud-top infrared brightness temperatures of convective overshooting tops closer to the center of Melissa have been as cold as -96.57ºC below).
GOES-19 Infrared image with GLM Flash Points at 1347 UTC, with a cursor sample of the coldest cloud-top infrared brightness temperature at that time [click to enlarge]
Loading map … let cimssInteractiveMapBaseUrl = 'https://cimss.ssec.wisc.edu/satellite-blog/wp-content/themes/cimss'; To celebrate the 50th anniversary of the GOES-A launch (GOES-A became GOES-1 on reaching geostationary orbit), this blog post contains one or two satellite animations (or images) for each of the 50 states. More on GOES-1 through GOES-19.There were experimental geostationary imagers (ATS and SMS) that preceded the first GOES.... Read More
Loading map …
To celebrate the 50th anniversary of the GOES-A launch (GOES-A became GOES-1 on reaching geostationary orbit), this blog post contains one or two satellite animations (or images) for each of the 50 states. More on GOES-1 through GOES-19.
There were experimental geostationary imagers (ATS and SMS) that preceded the first GOES. In fact, what was going to be SMS-3 became GOES-1. Learn more about the the history of GOES in these book chapters (chapter 2 and chapter 1.05: Schmit, T.J., Goodman, S.J., Daniels, J., Rachmeler, L.A., 2026. GOES: Past, Present, and Future. In: Liang, S. (Ed.), Comprehensive Remote Sensing, vol. 1. Elsevier, pp. 126–166. https://dx.doi.org/10.1016/B978-0-443-13220-9.00051-2). More on the history of satellites.
US Geostationary Imagers over the decades. Note the finer spatial resolutions and more spectral bands over time.
Florida’s location in the tropics means that tropical cyclones frequently affect the state. Sometimes they make landfall in an area where previous storms have occurred, allowing interesting satellite comparisons such as this one between 2022’s Ian and 2004’s Charley: https://cimss.ssec.wisc.edu/satellite-blog/archives/48129. This kind of comparison between different storms has also been done near Jamaica, as shown in this blog post: https://cimss.ssec.wisc.edu/satellite-blog/archives/60196
GOES-R series routine 5-minute scanning allows for precise knowledge of fog‘s increase, as shown in this animation of the Nighttime Microphysics RGB. Note that surface observations show widespread fog in regions where the RGB doesn’t suggest fog (i.e., where the cyan color isn’t present). GOES-R IFR Probability fields, a product that combines satellite information with Rapid Refresh model estimates of low-level saturation, does indicate high probabilities of IFR conditions throughout the observed fog field.
GOES continuously monitors the Earth and its atmosphere. When something new and unexpected occurs, GOES imagery or products can help with situational awareness. Consider, for example, this Chemtool facility fire in northern Illinois in 2021. GOES-16 Near-Infrared imagery identified the (dark) smoke plume’s transport and areal extent. This was also a case where both GOES satellites (East and West) were useful; although GOES-East gave a better view of the smoke plume, that dense smoke plume masked the view of the fire thermal signature. GOES-West however had a continuous view of the fire and could be used to determine how the fire intensity might be changing in time. https://cimss.ssec.wisc.edu/satellite-blog/images/2021/06/GOES-16_RadC_C03_2021165_120000_2021165_192500.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/41094
In addition to severe severe thunderstorms, the Spring season will often bring agricultural burning that can be monitored very closely from GOES, as shown in this blog post. These agricultural burns can also show up in the Next Generation Fire System events dashboard here.
The GOES-R series routine 5-minute scanning allowed for timely monitoring of snow melt over southwest Minnesota in early December 2021, as shown in the animation below — see this CIMSS Satellite Blog post. The animation shows Band 2 (Visible, 0.64 µm), True Color RGB imagery, the Day Cloud Phase Distinction RGB, and the Day Snow/Fog RGB. You can view the mp4 below, or an animated gif.
GOES-16 views of melting snow over southwestern Minnesota, 1501-2001 UTC (Note data from 1601-1656 are missing). Band 2, Visible at 0.64 µm (upper left) ; True Color RGB imagery (upper right) ; Day Cloud Phase Distinction RGB (lower left) ; Day Snow/Fog RGB (lower right)
Pennsylvania’s many river-filled valleys are common areas for fog development. The high temporal resolution of GOES-R (as shown in the animation below from a special observing session with GOES-14) means you can observe the dissipation to the closest minute.
South Carolina was visited by a swarm of tornadoes before sunrise on 13 April 2020 (SPC Storm Reports) as a strong cold front moved through the state. The animation below shows GOES-16 Mesoscale Sector 1-minute imagery. Most of the tornadoes occurred between 0900 and 1000 UTC. A large-scale view of this system is shown below. The forecast office in Columbia (WFO CAE) published a Storyboard on this event.
GOES-16 Clean Window Infrared (Band 13, 10.3) imagery 0600 UTC on 13 April 2020 – 1200 UTC on 13 April 2020 (Meso Sector)
West Texas can be a prime wildfire environment, and GOES-R’s fine temporal scanning abilities allows for precise monitoring of the evolution of fires. That kind of information is vital to ensure the safety of those responding to the fire. An excellent example is the Smokehouse Creek fire in 2024: https://cimss.ssec.wisc.edu/satellite-blog/archives/57431, the largest fire in areal extent in Texas history; it burned more than 1600 square miles, an area larger than the state of Rhode Island.
GOES data continuously monitors the evolution of the atmosphere, and machine learning tools have been developed (at CIMSS, CIRA and elsewhere) to help forecasters (in the National Weather Service and elsewhere) anticipate the development of lightning. One of the more important channels that fosters the ability of satellite data to anticipate lightning is GOES-R’s Band 5 (that is, observations at 1.61) that can highlight phase changes during the day based on changes in reflectivity from clouds made up of water droplets vs. clouds made up of ice crystals. Both the Day Cloud Phase Distinction and Day Cloud Type RGBs include band 5 for that reason. LightningCast probabilities, plotted below on top of Day Cloud Phase Distinction RGB imagery, shows a slow increase in lightning possibilities in advance of a GLM observation at the end of the animation (See more, including this animated gif https://cimss.ssec.wisc.edu/satellite-blog/wp-content/uploads/sites/5/2024/07/G16DCPD_LtgCast_GLMFED-20240716_1616_to_1931anim.gif at this CIMSS satellite blog post https://cimss.ssec.wisc.edu/satellite-blog/archives/60337 )
GOES-16 Day Cloud Phase Distinction RGB, and LightningCast probability contours, 1616-1931 UTC on 16 July 2024
Both McIDAS-X and Geo2Grid (as well as AWIPS) software was used in generating these images, using data via the UW/SSEC Data Services. More about GOES-16. Thanks to CIMSS Satellite Blog contributors Scott Bachmeier and Tim Schmit, in addition to Mat Gunshor and Alexa Ross. Credit to Bill Bellon for coding up the map.
Click on the logo to go to the NOAA/NESDIS Official 50th Anniversary of GOES web page.
