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Orographic waves downwind of the Hawaiian Islands

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.

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Lake Effect Now Active Over Great Lakes

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).

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Tropical Storm Melissa forms in the Caribbean Sea

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]

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Fifty Loops for the 50th Anniversary of GOES

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

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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.

Alabama

1-minute GOES-16 Infrared (IR) Window images of severe thunderstorms that produced an EF-3 tornado near Birmingham: https://cimss.ssec.wisc.edu/satellite-blog/images/2021/01/210125_goes16_infrared_spcStormReports_AL_anim.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/39747

Alaska

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GOES-17 visible images showing the tidal motion of ice in Norton Sound: https://cimss.ssec.wisc.edu/satellite-blog/images/2022/02/NORTON_loop_GOES-17_2021149_160059_2021150_015949.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/40991

A low pressure system intensfying over the Gulf of Alaska, depicted by the GOES-17 water vapor band: https://cimss.ssec.wisc.edu/satellite-blog/images/2022/01/WV_PAC_loop_GOES-17_2022012_140032_2022013_130032_faster.mp4

Also see this resuspended volcanic ash example: https://cimss.ssec.wisc.edu/satellite-blog/archives/66940

Arizona

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1-minute GOES-17 Fire Temperature RGB imagery of the Tunnel Fire: https://cimss.ssec.wisc.edu/satellite-blog/images/2022/04/Tunnel_GOES-17_Rad_fire_temperature_abi_2022109_160117_2022111_083035.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/45859

Arkansas

Severe thunderstorms with SPC Storm Reports plotted over 1-minute GOES-16 IR Window imagery: https://cimss.ssec.wisc.edu/satellite-blog/images/2022/04/220411_goes16_infrared_spcStormReports_AR_anim.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/45730

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California

Smoke and thermal signatures of the County Fire as seen in the GOES-16 visible and shortwave infrared window bands: https://cimss.ssec.wisc.edu/satellite-blog/wp-content/uploads/sites/5/2018/07/180630_goes16_visible_shortwave_infrared_County_Fire_CA_anim.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/28722. Fog is also seen in the visible images, while the hottest shortwave IR pixels are colored in red.

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Colorado

Snow cover shown in GOES-16 Visible images and the Land Surface Temperature derived product : https://cimss.ssec.wisc.edu/satellite-blog/images/2024/11/241111_g16_vis_lst_sfcTemp_CO_snow_cover.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/61705

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Connecticut

GOES-16 Visible imagery of severe thunderstorms with SPC Storm Reports over-plotted: https://cimss.ssec.wisc.edu/satellite-blog/images/2021/11/211113_goes16_visible_spcStormReports_NJ_NY_CT_anim.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/43277

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Delaware

Fog as shown in GOES-16 Cloud Thickness derived product and Visible imagery: https://cimss.ssec.wisc.edu/satellite-blog/images/2021/03/210323_goes16_cloudThickness_visible_East_Coast_anim.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/40373

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Florida

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Florida is home to Cape Canaveral, from where all GOES Satellites were launched. The ABI can be used to view rocket launches, as in this animation showing the GOES-T launch with special GOES-17 30-second Mesoscale Sector scans https://cimss.ssec.wisc.edu/satellite-blog/images/2022/02/800x800_AGOES17_B1_ZOOM_V2_30SEC_animated_2022060_213625_188_2022060_215455_188_X_redo.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/44902

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.

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Georgia

SPC Storm Reports plotted on 1-minute GOES-16 Visible imagery of severe thunderstorms: https://cimss.ssec.wisc.edu/satellite-blog/wp-content/uploads/sites/5/2019/03/190303_goes16_visible_spcStormReports_AL_GA_anim.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/32150

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Hawaii

Derived multi-spectral Volcanic Ash Probability https://cimss.ssec.wisc.edu/satellite-blog/wp-content/uploads/sites/5/2022/11/GOES17AshProbability_0906_1301_28Nov_2022.gif (or https://cimss.ssec.wisc.edu/satellite-blog/images/2025/10/GOES17AshProbability_0906_1301_28Nov_2022.mp4) via https://cimss.ssec.wisc.edu/satellite-blog/archives/48881 Automated, multi-spectral derived products are key to be able to monitor such phenomena in realtime.

Also see GOES-18 imagery of the deadly Lahaina, Maui wildfirehttps://cimss.ssec.wisc.edu/satellite-blog/images/2023/08/230808_230809_goes18_shortwaveInfrared_HI_anim.mp4

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Idaho

Wildfire smoke as seen in this CIMSS True Color RGB imagery: https://cimss.ssec.wisc.edu/satellite-blog/images/2024/12/ID_GOES-17_RadC_cimss_true_color_2022252_140117_2022253_015617.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/47831

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Illinois

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

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Indiana

During arctic outbreaks, northwest Indiana frequently experiences lake effect snow bands. Multi-spectral RGB imagery from GOES-East can highlight which cloud bands are most likely to produce heavy snow. GOES-16 Cloud Phase Distinction RGB images: https://cimss.ssec.wisc.edu/satellite-blog/images/2024/01/MI_GOES-16_RadC_cloud_phase_distinction_2024019_140117_2024019_220117.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/56535

Aircraft “hole punch” (or cavum) clouds https://cimss.ssec.wisc.edu/satellite-blog/wp-content/uploads/sites/5/2017/12/171221_goes16_visible_snow-ice_IL_IN_hole_punch_clouds_anim.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/26446 GOES Visible imagery is on the top panel, while Near-IR Snow/Ice imagery is on the bottom panel.

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Iowa

One of the most powerful derechos of the 2020s formed over western Iowa on 10 August 2020 and then moved eastward (Here’s a summary from the Quad Cities NWS; Cedar Rapids was particularly hard-hit; WFO IND noted the anniversary). A CIMSS Satellite Blog post: https://cimss.ssec.wisc.edu/satellite-blog/archives/37938 includes 1-minute imagery and plots of SPC Storm Reports of the system as it crossed Iowa. https://cimss.ssec.wisc.edu/satellite-blog/images/2020/08/200810_goes16_visible_spcStormReports_Midwest_Derecho_anim.mp4

GOES-16 infrared imagery (Band 13, 10.3), 0901 UTC on 10 August 2020 – 0601 UTC 11 August 2020

Here is an animated gif version of the above animation. There are several other CIMSS Satellite Blog posts on Derechos.

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Kansas

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.

Severe thunderstorms: https://cimss.ssec.wisc.edu/satellite-blog/images/2024/05/240519_goes16_visibleInfraredSandwichRGB_localStormReports_KS_2_anim.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/59341

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Kentucky

Thunderstorms that produced heavy rainfall and flooding in July 2022, as seen by GOES-16: https://cimss.ssec.wisc.edu/satellite-blog/images/2022/07/220727_220728_goes16_infrared_surfacePlots_KY_flooding_anim.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/47406

A river valley fog example: https://cimss.ssec.wisc.edu/satellite-blog/archives/42165

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Louisiana

Hurricane Katrina in August of 2005, as seen by GOES-12: https://cimss.ssec.wisc.edu/satellite-blog/images/2024/12/logoLL_logoLR_GOES12_Katrina_loop_GOES-12_2005235_001500_2005242_034500.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/19402

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Maine

Wildfire smoke as seen in two of the GOES-16 spectral bands — Visible and Near-Infrared Cirrus: https://cimss.ssec.wisc.edu/satellite-blog/wp-content/uploads/sites/5/2017/08/170817_goes16_visible_cirrus_Canadian_smoke_anim.mp4 (Preliminary, Non-operational) via https://cimss.ssec.wisc.edu/satellite-blog/archives/24736

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Maryland

Winter storm via the GOES-16 Cloud Type RGB: https://cimss.ssec.wisc.edu/satellite-blog/images/2024/02/240213_goes16_dayCloudTypeRGB_Northeast_US_snow_cover_anim.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/57171

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Massachusetts

GOES-16 Set of Nor’ Easters: https://cimss.ssec.wisc.edu/goes/abi/youtube/loops/20_1080x1920_AGOES16_B1_SHCS_FD_FOUREASTER_2018_loop_59s.mp4

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Michigan

Bore over Lake Superior in GOES-16 Visible imagery: https://cimss.ssec.wisc.edu/satellite-blog/images/2024/12/RedVisLakeSuperiorFog-20170710_101218_194718anim.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/24420

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Minnesota

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)

Blowing snow example: https://cimss.ssec.wisc.edu/satellite-blog/images/2024/12/GOES-19_RadC_blowing_snow_2024339_142117_2024339_220117.gif via https://cimss.ssec.wisc.edu/satellite-blog/archives/61934

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Mississippi

Severe thunderstorms https://cimss.ssec.wisc.edu/satellite-blog/images/2024/04/240410_goes16_visible_spcStormReports_TX_LA_MS_AL_anim.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/58370

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Missouri

Severe convection as seen by 1-minute Mesoscale Sector GOES-16 Visible and Infrared imagery: https://cimss.ssec.wisc.edu/satellite-blog/images/2023/05/230506_goes16_visible_infrared_localStormReports_MO_anim.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/52201

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Montana

30-second GOES-18 Visible imagery of severe thunderstorms https://cimss.ssec.wisc.edu/satellite-blog/images/2022/07/220709_goes18_visible_spcStormReports_MT_anim.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/47146

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Nebraska

Haboob over Nebraska in May 2022 https://cimss.ssec.wisc.edu/satellite-blog/images/2024/12/NE_GOES-16_RadC_cimss_true_color_2022132_150117_2022132_235617.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/46385

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Nevada

Cherrywood Fire from May 2021 as seen by GOES-17 in 4-panels: https://cimss.ssec.wisc.edu/satellite-blog/images/2021/05/210520_goes17_shortwaveInfrared_visible_goes16_firePower_fireTemperature_Cherrywood_Fire_NV_anim.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/40926

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New Hampshire

Mountain waves and banner clouds in October 2018 as seen by GOES-16: https://cimss.ssec.wisc.edu/satellite-blog/wp-content/uploads/sites/5/2018/10/181025_goes16_waterVapor_Northeast_US_anim.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/30441

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New Jersey

Special (research) GOES-14 Super Storm Sandy in 2012: https://cimss.ssec.wisc.edu/satellite-blog/images/2024/12/logos_GOES14_SRSOR_B1_2012299_174500_2012304_224500_UTC.mp4 More on Hurricane Sandy and SRSOR imagery which was used to prepare users for the eventual routine GOES-R ABI 1-minute imagery.

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New Mexico

GOES data are helpful in identifying regions of blowing dust that can occur either with strong low pressure systems or with thunderstorm outflow. For example, this GOES-18 animation (https://cimss.ssec.wisc.edu/satellite-blog/images/2024/06/Haboob_GOES-18_RadM1_cimss_true_color_night_dust_2024171_210030_2024172_035927.mp4) shows dust caused by a thunderstorm downdraft moving westward across New Mexico (for more information, see this CIMSS Satellite Blog post: https://cimss.ssec.wisc.edu/satellite-blog/archives/60275 .

This CIMSS Satellite Blog post (https://cimss.ssec.wisc.edu/satellite-blog/archives/64237 ) shows dust moving northeastward across New Mexico in response to the circulation around a strong low pressure system. Here’s the animation: https://cimss.ssec.wisc.edu/satellite-blog/images/2025/04/250417_goes19_trueColorRGB_dustRGB_NM_TX_blowing_dust_anim.mp4

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New York

A heavy rain event over NYC in September 2023: https://cimss.ssec.wisc.edu/satellite-blog/archives/54772 that was also a Forecast Decision Training Division (FDTD) Webinar ( https://rammb2.cira.colostate.edu/training/visit/satellite_webinar/fdtd_webinar/2024-01-10/ and https://www.youtube.com/watch?v=8v9qUynltyc ) as well as a Satellite Book Club presentation (https://www.youtube.com/watch?v=KuO-xsy3S-s&list=PLJzZC8w9vPV1NSHVBtMqOEP0VxlDmh5WA&index=61).

An example of river valley fog and snow cover is shown above: https://cimss.ssec.wisc.edu/satellite-blog/wp-content/uploads/sites/5/2018/04/180501_goes16_visible_snow_ice_NY_Catskills_anim.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/27893

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North Carolina

Tropical Storm Ophelia in September 2023: https://cimss.ssec.wisc.edu/satellite-blog/images/2023/09/230922_goes16_visible_infared_TS_Ophelia_anim.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/54632

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North Dakota

An annual possibility along North Dakota’s eastern border is flooding from the Red River of the North. Satellite information is vital to monitor such inundations. Combined ABI/VIIRS products (available at this website) allow for the monitoring, as shown here: https://cimss.ssec.wisc.edu/satellite-blog/archives/45947 ; SAR data can also be used for very high-spatial resolution flood monitoring: https://cimss.ssec.wisc.edu/satellite-blog/archives/46140 . Multi-spectral data are very important when monitoring floods, especially observations at 0.87 and/or 1.61 micrometers (an early example with MODIS data: https://cimss.ssec.wisc.edu/satellite-blog/archives/7894).

Light snow from lake effect clouds as seen in GOES-19 Day Cloud Phase Distinction RGB imagery: https://cimss.ssec.wisc.edu/satellite-blog/images/2024/11/241125_goes19_dayCloudPhaseDistinctionRGB_ND_lake_effect_clouds_anim.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/61872

Satellite observations of blowing snow in very cold airmasses (when visibility restrictions from blowing snow can rapidly become life-threatening) are very important over North Dakota — one example is shown in this CIMSS Satellite Blog post: https://cimss.ssec.wisc.edu/satellite-blog/archives/44612 ; Note that for this case, Blizzard Warnings were modified based on the Satellite Observations, as detailed in this Satellite Liaison Blog post: https://satelliteliaisonblog.wordpress.com/2022/02/11/more-northern-plains-blowing-snow/

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Ohio

Cleveland is an active port on Lake Erie. GOES data can be used to monitor ship tracks as they move through lake ice, as shown in the animation (https://cimss.ssec.wisc.edu/satellite-blog/images/2022/02/220215_goes16_visible_Lake_Erie_ship_track_anim.mp4“), from this blog post: https://cimss.ssec.wisc.edu/satellite-blog/archives/44646“.

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Oklahoma

Grass fires (hottest pixels colored red to yellow) and smoke plumes https://cimss.ssec.wisc.edu/goes/abi/youtube/loops/17_ABI_BAND7_13_FIRE_COMBO_OK_loop_2017065_170029_2017066_045929_fast.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/23297

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Oregon

Wildfire smoke as seen in CIMSS True Color RGB imagery: https://cimss.ssec.wisc.edu/satellite-blog/images/2024/12/OR_GOES-17_RadC_cimss_true_color_2022252_140117_2022253_015617.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/47831

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Pennsylvania

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.

https://cimss.ssec.wisc.edu/satellite-blog/wp-content/uploads/sites/5/2014/08/GOES14_VIS_18August2014loop.gif as in this blog post.

Snow squalls https://cimss.ssec.wisc.edu/satellite-blog/images/2022/02/220219_goes16_dayCloudPhaseDistinctionRGB_PA_snow_squalls_anim.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/44742

Also see: https://cimss.ssec.wisc.edu/satellite-blog/archives/45461

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Rhode Island

This animation https://www.ssec.wisc.edu/~scottl/data/GOES-16_ABI_RadC_C02_20210607_1501_to_2356_RhodeTLanim.gif (See more at this blog post https://cimss.ssec.wisc.edu/satellite-blog/archives/41031) shows the evolution of fog moving over Block Island. Of particular note, especially to sailors, is the development of overlapping waves downwind of Block Island. Note also how this imagery can be used to pinpoint where over southern Rhode Island beaches you might be disappointed by the weather on this day! Snow during a Nor’easter: https://cimss.ssec.wisc.edu/satellite-blog/images/2020/12/201216_201217_goes16_waterVapor_Noreaster_anim.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/39306

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South Carolina

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)

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South Dakota

“River effect” clouds during an arctic outbreak: https://cimss.ssec.wisc.edu/satellite-blog/images/2024/01/240113_goes16_daySnowFogRGB_SD_river_effect_snow_anim.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/56436

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Tennessee

GOES-16 Visible and Infrared images (with GLM lightning observations) of severe thunderstorms https://cimss.ssec.wisc.edu/satellite-blog/images/2024/05/240508_goes16_visible_infrared_glmFlashExtentDensity_warningPolygons_TN_anim.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/59059

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Texas

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.

Texas has a long Gulf coastline, and tropical cyclones are a common occurrence. Hurricane Harvey, in 2017, was an early example of a tropical system being monitored with Geostationary Lightning Mapper data, as shown in this blog post: https://cimss.ssec.wisc.edu/satellite-blog/archives/24841 . Harvey’s effects on SE Texas were also monitored via VIIRS’ Day Night Band sensor on Suomi-NPP: https://cimss.ssec.wisc.edu/satellite-blog/archives/24924. A similar Day Night Band comparison with Hurricane Beryl in 2024 is shown in this National Weather Association short course: https://rammb2.cira.colostate.edu/training/2024-nwa-satellite-workshop/

GOES-16 images showing splitting convection in 2018: https://cimss.ssec.wisc.edu/goes/abi/youtube/loops/04_ABI_BAND2_SPLIT_loop_2018084_200104_2018085_012904_17s.mp4

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Utah

30-second imagery from GOES-17, to monitor the development of convection during a radar outage: https://cimss.ssec.wisc.edu/satellite-blog/images/2022/06/220623_goes17_visible_UT_CO_KGJT_radar_outage_anim.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/46981

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Vermont

Wave clouds and snow squalls https://cimss.ssec.wisc.edu/satellite-blog/images/2024/12/VT_GOES-16_RadC_cloud_phase_distinction_2022058_123117_2022058_222617.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/44873

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Virginia

Rocket Plume and Shadow https://cimss.ssec.wisc.edu/satellite-blog/wp-content/uploads/sites/5/2019/11/WALLOPS_Vis_loop.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/34945

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Washington

Preliminary, Non-operational GOES-16 Fog advection https://cimss.ssec.wisc.edu/satellite-blog/images/2024/12/Fog_GOES-16_RadC_day_snow_fog_2017140_173207_2017141_032707.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/23981

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West Virginia

Snow cover https://cimss.ssec.wisc.edu/satellite-blog/images/2021/01/210128_goes16_visible_daySnowFogRGB_dayCloudPhaseDistinctionRGB_VA_NC_snow_cover_anim.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/39766

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Wisconsin

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

In 2018, fire at a refinery in Superior Wisconsin was captured by GOES-East (and JPSS) satellite imagery. GOES imagery allows Emergency Managers to determine where any evacuations might have to occur. A CIMSS Satellite Blog post on this event is here: https://cimss.ssec.wisc.edu/satellite-blog/archives/27868. The response of WFO Duluth to this event was discussed at the FDTD Satellite Applications webinar here https://rammb2.cira.colostate.edu/training/visit/satellite_webinar/fdtd_webinar/2019-07-17/ and on YouTube here: https://www.youtube.com/watch?v=h4BImH-zJio

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Wyoming

Hail Swath produced by a severe thunderstom: https://cimss.ssec.wisc.edu/satellite-blog/images/2023/07/230711_goes16_nighttimeMicrophysicsRGB_WY_SD_anim.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/53389

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Other

Washington, D. C.

Severe weather: https://cimss.ssec.wisc.edu/satellite-blog/images/2025/10/PSv3Readout5July2022_2000_to_2200step.mp4 https://cimss.ssec.wisc.edu/satellite-blog/images/2025/10/G16B13-20220705_2001_to_2256anim.mp4 via https://cimss.ssec.wisc.edu/satellite-blog/archives/47110

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Puerto Rico

Hurricane Maria https://cimss.ssec.wisc.edu/satellite-blog/wp-content/uploads/sites/5/2017/09/GOES16_RedVis-20170920_1017_1117anim.gif via https://cimss.ssec.wisc.edu/satellite-blog/archives/25338

American Samoa

Flash flooding example: https://cimss.ssec.wisc.edu/satellite-blog/archives/59796

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H/T

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.

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