A combination of fog and smoke reduced visibilities along I-95 S in southern Volusia County Florida. A series of fatal crashes (news report; this tweet suggests the accident was near Florida State Route 442; second tweet). News report suggest the smoke that helped seed the dense fog resulted from controlled burns. The Band 7 imagery (3.9 µm) above, does not show strong evidence of burns (the Fire Detection and Characterization Algorithm — FDCA — similarly showed no information in Volusia County), nor of the fire that occurred at the crash scene as vehicles burned. The first crashes occurred around 0630 UTC.

The Nighttime microphysics RGB is often used to highlight regions of fog. On this day, however, no obvious signal of fog (fog typically appears as a color between cyan and yellow, as noted here) is apparent.

The Night Fog brightness temperature difference (the ‘green’ component of the RGB above) also can be used to detect fog. GOES-R IFR Probability fields use satellite data (and model data) to outline regions of fog. The toggle below includes the night time microphysics RGB, the night fog brightness temperature difference, and the IFR Probability fields at 0631 UTC, near the time of the crash. Satellite data provided little detection for this very thin combination of smoke and fog. For this case, it would be better to rely on things like webcams.

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There was a very timely Suomi-NPP overpass as shown below. The timestamp is at 0632 UTC, which is when the satellite first was broadcasting data to the Direct Broadcast antenna at CIMSS; the satellite was viewing central Florida around 0642 UTC, based on this orbit calculation (from this site). The slider does suggest a small temperature difference as might be caused by fog over southern Volusia County. (Click here to see a toggle — and here to see a very fast toggle).

A brightness temperature difference field between I05 and I04 on this date was created using McIDAS-V in this blog post.

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A similar incident of fog — possibly enhanced by smoke — causing a multi-vehicle accident occurred in Osceola County (not far to the south of Volusia County) Florida on 13 March 2007. In that case, higher-resolution MODIS imagery was a bit more helpful in helping to highlight an area of nocturnal fog formation.

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Turbulence probability is an aviation tool created (using machine learning techniques) at CIMSS to diagnose the likelihood of Moderate Or Greater (MOG) turbulence at least once during a 10-minute period. The image above shows the 1455 UTC MOG Turbulence Probability along with pilot reports (PIREPS) of turbulence, taken from this website. Turbulence Probability fields are also in AWIPS, and the 1500 UTC image is shown below.

There are frequently features in the water vapor imagery (or in visible, or near-infrared, imagery during the day) that have a known association with turbulence. The turbulence probability field is toggled below with the 6.19 µm upper-level water vapor infrared imagery, and beneath that with the 1.38 µm “cirrus band” near-infrared imagery. Striated features that are apparent in, for example, this event from 28 February, are not obvious.

MOG Turbulence Probability also includes information from the GFS: temperature, height, and winds from 850 mb to 50 mb! (Such GFS fields are especially important when using GOES-17 data during times when the Loop Heat Pipe malfunctions leads to data loss over the Pacific.) The toggles below show the relationship between the Turbulence Probability and upper-tropospheric stability (as diagnosed by the 400-300 mb lapse rate), and the also the pressure on the 1.5 PVU surface. Turbulence is occurring just Equatorward of a slope in the tropopause, in a region of weak stability.

GOES Data can also be used to diagnose wind speeds in the upper-troposphere. Peak winds in the 350-450 mb layer, shown in a toggle below with the MOG Turbulence Probability, are 100-120 knots from southeastern North Dakota into northwestern Wisconsin.

How did this potential for turbulence (and strong winds) affect air traffic? Consider the path of one late-morning flight, Delta Flight 867 from Minneapolis to Seattle. Its path is shown below for February 28th, and for March 2nd, courtesy of FlightAware. The 2 March flight was diverted south to avoid the strong winds and potential for turbulence.

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You might ask: Why are the upper-tropospheric lapse rates shown? The reason is because they align the use of the 6.19 µm imagery. The (new and improved!!) CIMSS Weighting Function page shows that at 1200 UTC, much of the signal in the upper-level water vapor imagery (in brown in the plot below) around Minneapolis was coming from the 300-400 mb layer. A plot using GFS data at 45^o N, 95^o W, shows a similar distribution.

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AWIPS imagery in this blog post was created using the NOAA/NESDIS TOWR-S AWIPS Cloud Instance.

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GOES-T was launched from the Kennedy Space Center in Florida at 2138 UTC on 01 March 2022 — and distinct reflectance and/or thermal signatures of the Atlas V rocket launch were evident in 30-second images from all 16 ABI spectral bands of GOES-16 (GOES-East) (above).

One of the more interesting aspects was the long trail of superheated air + water vapor in the wake of the Atlas V booster engines, which could be seen drifting slowly northward in GOES-16 Shortwave Infrared (Band 07, 3.9 µm) and Upper-level Water Vapor (Band 09, 6.2 µm) images (below). The warmest 3.9 µm Shortwave Infrared brightness temperature sensed by GOES-16 was 38.78ºC at 2139 UTC.

30-second scan were also available from GOES-17 (GOES-West) — reflectance and/or thermal signatures were also evident in imagery from all 16 of those ABI spectral bands (below). The warmest 3.9 µm Shortwave Infrared brightness temperature sensed by GOES-17 was 38.58ºC at 2139 UTC. 

A comparison of “Red” Visible (0.64 µm) images from GOES-17 and GOES-16 is shown below — and as in the 16-band examples above, the images are displayed in the native projection of each satellite (in other words, they are not re-mapped to a common map projection). Due to the much higher oblique viewing angle from GOES-17, parallax made the rocket condensation plume appear much longer (and extend farther to the east).

However, a toggle between GOES-16 and GOES-17 Shortwave Infrared images at 21:38:55 UTC (below) — both displayed in a common map projection — revealed the large eastward displacement of the Atlas V rocket booster engine thermal signature with GOES-17 (the parallax shift magnitude was 35 km).

The Atlas V rocket’s rapid rate of ascent was apparent when looking at the first 1 minute (at 30-second intervals) of GOES-16 True Color RGB images visualized using CSPP GeoSphere (below).

GOES-16 Plume RGB imagery (below) is an effective product that aids in the identification of both the rocket condensation plume and the booster engine thermal signature.

 

True Color RGB images from GOES-16 (above) and GOES-17 (below) highlighted the rocket condensation plume. 

Additional imagery and information on the GOES-T launch can be found on the Satellite Liaison Blog.

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The image above (from 2100 UTC on 28 February 2022) and below (from 0930 UTC on 1 March 2022) toggle between low-level estimates of 1000-mb dewpoint and GOES-16 IFR Probability fields. Is there a general relationship between the two? At first blush, it does seem like the IFR Probability fields are affected by the strong gradient in low-level temperature, where the dewpoint drops from the teens (^oC, grey/blue to cyan in the enhancement) to the single digits (purple and white in the enhancement). Note that SSTs in the region where the 1000-mb dewpoints are in the single digits are between 10 and 14 C at both ~2100 UTC 28 February and ~0930 UTC 1 March (ACSPO SSTs at the link are derived from Direct Broadcast data from CIMSS and are available via an LDM feed).

The toggle below compares gridded NUCAPS estimates of 1000-mb relative humidity with Low IFR Probability fields. There again seems to be a relationship. How robust that relationship is is to be determined. This is the first in a series of blog posts that compares these two fields, as part of a way of better forecasting fog over the oceans.

AWIPS imagery in this blog post was created using the NOAA/NESDIS TOWR-S AWIPS Cloud Instance.

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