Fog and Ice Fog over the southern Plains


GOES-R IFR Probabilities, hourly from 0400 through 1215 UTC on 5 December 2016 (Click to enlarge)

Dense Fog developed over the southern Plains early on Monday 5 December, and the GOES-R IFR Probability fields, above, were a tool that could be used to monitor the evolution of this event. A challenge presented on this date was the widespread cirrus (Here’s the 0700 UTC GOES-13 Water Vapor (6.5 µm) Image, for example) that prevented satellite detection of low clouds. The Brightness Temperature Difference fields, below, at 3-hourly intervals, also show a signature (dark grey/black in the enhancement used) of high clouds, although they are shifting east with time — by 1300 UTC there is a signature (orange/yellow in the enhancement used) of stratus clouds over central and eastern Oklahoma.

The IFR Probability fields, above, have a characteristic flat nature over Arkansas and Missouri, that is, a uniformity to the field, that is typical when model data are driving the probabilities. The more pixelated nature to the fields over Kansas and Oklahoma, especially near the end of the animation, typifies what the fields look like when both satellite data and model data are driving the computation of probabilities. Careful inspection of the fields over Arkansas shows regions — around Fayetteville, for example, around 1000 UTC where IFR Probabilities are too low given the observation at the airport of IFR conditions. This inconsistency gives information on either the small-scale nature of the fog (unlikely in this case) or on the accuracy of the Rapid Refresh model simulation that is contributing to the probabilities. In general, the Rapid Refresh model has accurately captured this event, and therefore the IFR Probabilities are mostly overlapping regions of IFR or near-IFR conditions. The region over southern Illinois that has stratus, and low probabilities of IFR conditions, for example. Adjacent regions have higher IFR Probabilities and lower ceilings and/or reduced visibilities. A screen shot from the National Weather Service, and from the Aviation Weather Center, at about 1300 UTC document the advisories that were issued for this event.


Brightness Temperature Difference fields (3.9 µm – 10.7 µm), 0400, 0700, 1000 and 1300 UTC on 5 December 2016 (Click to enlarge)

IFR Conditions over the High Plains of west Texas


GOES-13 Brightness Temperature Difference (3.9 µm – 10.7 µm, left) and GOES-R IFR Probability Fields (Right), hourly from 0215 through 1415 UTC on 2 December 2016 (Click to enlarge)

Near-IFR and and IFR Conditions developed over the High Plains of Texas on 2 December 2016, and a SIGMET for IFR conditions was issued as shown below.

The animation above shows plentiful cirrus (in the brightness temperature difference enhancement used in the imagery on the left, above, cirrus clouds are dark) over south Texas, with occasional breaks.  This makes continual monitoring via satellite of the developing stratus/fog field problematic:  the satellite cannot monitor what it is blocked from being observed by intervening cloud layers — in this case cirrus.  (Click here for a brightness temperature difference only animationClick here for an IFR Probability only animation)  Because IFR Probability fields include model-based data about saturation in the lower troposphere, in the form of Rapid Refresh model output, a useful and coherent signal can be generated underneath cirrus clouds.  The GOES-R IFR Probability signal can be better used for situational awareness and anticipation of the development of the IFR conditions shown below.

In the animation above, note the change between 1315 and 1415 UTC fields — in the Brightness Temperature Difference fields (1315 UTC ; 1415 UTC), this change arises because of increasing amounts of reflected solar 3.9 µm radiation:  this causes a sign change in the brightness temperature difference.  For IFR Probability fields (1315 UTC ; 1415 UTC), the change occurs because the Predictors used at night (1315 UTC) and during the day (1415 UTC) are different.


1605 UTC screen capture from Aviation Weather Center. Note IFR Sigmet over west Texas (Click to enlarge)

Dense Fog in Georgia and Florida


GOES-R IFR Probability Fields, 0000 – 1300 UTC on 25 November 2016 (Click to enlarge)

Light winds and a long November night allowed radiation fog formation over much of the deep south early on 25 November 2016. (1200 UTC Surface analysis is here). The Aviation Weather Center Website indicated widespread IFR Conditions, below, over the south, with the sigmet suggesting improving visibilities after 1400 UTC.

The animation above shows the evolution of the GOES-R IFR Probability fields from just after sunset to just after sunrise. There is a good spatial match between observed IFR conditions and the developing field. IFR Probability can thus be a good situational awareness tool, identifying regions where IFR Conditions exist, or may be developing presently.


Screenshot of Aviation Weather Center Front Page, 1405 UTC on 25 November (Click to enlarge)

Did the GOES-13 Brightness Temperature Difference Field identify the fields? The animation below, from 0200-1300 UTC, shows a widespread signal that shows no distinguishable correlation with observed IFR conditions.  Note also how the rising sun at the end of the animation changes the difference field as more and more reflected solar radiation with a wavelength of 3.9 µm is present.  In addition, high clouds that move from the west (starting at 0400 UTC over Louisiana) prevent the satellite from viewing low clouds in regions where IFR conditions exist.


GOES-13 Brightness Temperature Difference field (3.9 µm – 10.7 µm), 0200-1300 UTC on 25 November 2016 (Click to enlarge)

The high clouds that prevent satellite detection of low clouds, as for example at 1100 UTC over parts of Alabama, cause a noticeable change in the IFR Probability fields, as shown in the toggle below.  Values over the central part of the Florida panhandle are suppressed, and the field itself has a flatter character (compared to the pixelated field over southern Georgia, for example, where high clouds are not present).  Even though high clouds prevent the satellite from providing useful information about low clouds in that region, GOES-R IFR Probability fields can provide useful information because of the fused nature of the product:  Rapid Refresh information adds information about low-level saturation there, so IFR Probability values are large.  In contrast, over southern Florida — near Tampa, for example, Rapid Refresh data does not show saturation, and IFR Probabiities are minimized even through the satellite data has a strong signal — caused by mid-level stratus.  Soundings from Tampa and from Cape Kennedy suggest the saturated layer is around 800 mb.


GOES-R IFR Probability fields and GOES-13 Brightness Temperature Difference Fields, 1100 UTC on 25 November 2016 (Click to enlarge)

GOES-R Cloud Thickness, below, related 3.9 µm emissivity to cloud thickness via a look-up table that was generated using GOES-West Observations of marine stratus and sodar observations of cloud thickness. The last pre-sunrise thickness field, below, is related to dissipation time via this scatterplot. The largest values in the scene below are around 1000 feet, which value suggests a dissipation time of about 3 hours, or at 1445 UTC.


GOES-R Cloud Thickness Field, 1145 UTC on 25 November 2016 (Click to enlarge)

IFR Probability vs. Brightness Temperature Differences over California


GOES-15 Brightness Temperature Difference Fields (3.9 µm – 10.7 µm), 0600-1200 UTC on 21 November 2016 (Click to enlarge)

GOES-15 Brightness Temperature Difference fields (3.9 µm – 10.7 µm, above, from 0600-1200 UTC on 21 November) can detect low clouds because water-based clouds do not emit 3.9 µm radiation as a blackbody, but they do emit 10.7 µm radiation as a blackbody. Consequently, the brightness temperature computed from 3.9 µm radiation is cooler than that computed by 10.7 µm radiation. The animation above depicts two challenges that arise from using brightness temperature difference fields. If mid-level clouds or cirrus are present, the satellite cannot view the low-level clouds that might be associated with fog. That is the case above over the San Joaquin Valley at the start of the animation. Later on, as the higher clouds move out, a strong signal develops everywhere. Brightness Temperature Difference fields are not giving useful information because alone they cannot distinguish between mid-level stratus and low stratus/fog.

In contrast, the GOES-R IFR Probability fields suggest the likelihood of IFR conditions in three distinct regions: Along the Sierra Nevada, where terrain is likely to rise up into the Cloud Base, in the San Joaquin Valley, and along the coastal range. Largest values of IFR Probability do occur where (or near where) ceilings and visibilities are reduced and can help a forecaster restrict interest to where it is actually warranted. IFR Probability fields combine satellite observations of stratus with Rapid Refresh model predictions of low-level saturation, so IFR Probability fields are better able to highlight regions of low stratus and fog.


GOES-R IFR Probability Fields, 0600-1200 UTC on 21 November 2016 (Click to enlarge)

The Challenge of Satellite Fog Detection at Very Small Scales


Hazards as depicted by front page at 1400 UTC on 10 November 2016 (Click to enlarge)

Isolated regions of dense fog developed over eastern Oregon early in the morning on 10 November 2016, and one county — around Baker City — was placed in a Dense Fog advisory (Counties in the Willamette Valley of western Oregon, and near Glacier Park in Montana were placed under Dense Fog Advisories a bit later in the morning on 10 November). Click here to see a 1400 UTC mapping of IFR/LIFR conditions from the Aviation Weather Center.

600 AM MST THU NOV 10 2016

500 AM PST THU NOV 10 2016









What kind of Fog-detection products are available to assist a forecaster in seeing at a glance that fog is developing? How useful are they for small-scale features such as that in Baker County Oregon?

Brightness Temperature Difference Fields (3.9 µm – 10.7 µm) have historically been used to detect fog; the difference field keys on the Emissivity Differences that exist in water-based cloud droplets: they do not emit 3.9 µm radiation as a blackbody, but do emit 10.7 µm radiation more nearly as a blackbody, so computed brightness temperatures are different: cooler at 3.9 µm than at 10.7 µm. The Brightness Temperature Difference fields for 0900 and 1200 UTC are shown below (Note the seam — GOES-13 data are used east of the seam, GOES-15 data are used to the west). There is no distinct signal over Baker County, nor any pattern that can really help identify regions of fog. Cirrus is present over western Oregon (depicted as dark grey or black in this enhancement); satellite-only detection of low fog is not possible if cirrus prevents a view of the surface.


GOES Brightness Temperature Difference Fields (3.9 µm – 10.7 µm) at 0900 and 1200 UTC on 10 November 2016 (Click to enlarge)

For a small-scale event, the nominal 4-km pixel size on GOES-15 and GOES-13 (a size that is closer to 6-7 km over Oregon because of the distance from the sub-satellite point) may prevent satellite detection of developing fog. The toggle below shows Brightness Temperature Difference fields at 0928 UTC from MODIS on Aqua, as well as the GOES-R IFR Probability fields computed using the MODIS data.  As with GOES data, the presence of cirrus in the Brightness Temperature Difference field is obvious and shown by a black enhancement.  Little signal is present over Baker County.  (There is a strong signal, however, in the valleys of northwest Montana and northern Idaho — compare this to the GOES-based brightness temperature difference above).

Note:  MODIS resolution is 1-km;  data from the Advanced Baseline Imager (ABI) on GOES-R will have nominal 2-km resolution at the sub-satellite point.


MODIS Brightness Temperature Difference fields (3.7 µm – 11 µm) and MODIS-based GOES-R IFR Probability, 0928 UTC on 10 November 2016 (Click to enlarge)

What does the GOES-based GOES-R IFR Probability field show during the early morning hours of 10 November? The animation below, from 0800-1200 UTC, shows some returns in/around Baker County. It would have been difficult to use this product alone to diagnose this fog feature however. (It did do a better job of diagnosing the presence of fog over northwest Montana and western Oregon where Advisories were later issued).


GOES-R IFR Probability fields, hourly from 0800 – 1200 UTC on 10 November 2016 (Click to enlarge)

IFR Conditions over the Deep South


GOES-R IFR Probability Fields, 0100-0500 UTC on 31 October 2016 (Click to enlarge)


GOES-13 Brightness Temperature Difference Fields (3.9 µm – 10.7 µm), 0100-0500 UTC on 31 October 2016 (Click to enlarge)

Compare the two animation from 0100-0500 above, showing GOES-R IFR Probability fields  (top) and GOES-13 Brightness Temperature Difference fields (bottom) from shortly after sunset on 30 October 2016 until Midnight.  IFR Probability shows very little signal at first, and IFR conditions are rare (Jack Edwards Airport near Gulf Shores AL report IFR conditions).  IFR Probabilities increase slowly in the next 4 hours, especially in regions where IFR conditions develop.  In contrast, the trend in the Brightness Temperature Difference field is a slow decrease in areal coverage with little spatial correlation between a strong signal and IFR reports.  These animations demonstrate a strength of IFR Probabilities:  By combining satellite information with Rapid Refresh predictions of low-level saturation, a better estimate of visibility restrictions can be created.

Subsequent to 0500 UTC, in the animations shown below, IFR Probability fields expanded as IFR conditions developed over western Louisiana and southern/eastern Texas;  a strong signal develops in the brightness temperature difference field in these regions as well.  Note the lack of signal in the GOES-R IFR Probability field over Alabama and Mississippi where Brightness Temperature Difference fields show a consistent signal (and where IFR Conditions are not present).   Brightness Temperature Difference signals over those states may be related to changes in emissivity properties that occur during severe drought, as discussed here.


GOES-R IFR Probability fields, 0500-1215 UTC on 31 October 2016 (Click to enlarge)


GOES-13 Brightness Temperature Difference Fields (3.9 µm – 10.7 µm), 0500-1215 UTC on 31 October 2016

GOES-R Cloud Thickness relates future dissipation of fog to present observations of Cloud Thickness. The last pre-sunrise GOES-R Cloud Thickness field is related to dissipation time in this scatterplot. (GOES-R Cloud Thickness is not computed during twilight times surrounding sunrise and sunset)  The animation below shows the thickest clouds over south-central Texas; fog over Louisiana and coastal Texas is comparatively thin. Dissipation should occur last over interior Texas.


GOES-R Cloud thickness every half hour from 1145-1245 UTC on 31 October 2016 (click to enlarge)

IFR probabilities were noted by the Aviation Weather Center, and Dense Fog Advisories were issued along the Gulf Coast for this case.

IFR Probability Fields let you peek beneath the Cirrus


GOES-13 Brightness Temperature Difference (3.9 µm – 10.7 µm) fields, hourly from 0215-1215 UTC on 27 October (Click to enlarge)

If you rely on satellite data alone to anticipate the development of IFR conditions — fog, low ceilings, and reduced visibilities — then the presence of widespread cirrus, shown here with the GOES-13 6.5 µm image, makes situational awareness difficult. At a glance, can you tell in the animation of brightness temperature difference, above, hourly from 0215 through 1215 UTC, where IFR Conditions are occurring? The widespread cirrus, present in the enhancement as dark grey and black, prevents the satellite from viewing any fog development, hence making this brightness temperature difference field, traditionally used to detect the development of fog and low stratus, unsuitable for large-scale situational awareness.

GOES-R IFR Probability fuses satellite data with Rapid Refresh Model output and allows a product that in essence peeks beneath the cirrus because near-surface saturation predicted in the Rapid Refresh Model allows the IFR Probability product to have a strong signal where fog might be developing. Consider the animation below, that covers the same spatial and temporal domain as the brightness temperature difference animation above. IFR Probability increases over inland southeast Georgia in concert with the development of low ceilings/reduced visibilities. It gave a few hours alert to the possibility that IFR conditions would be developing.  Note in the animation below that the 1215 UTC image includes IFR Probabilities computed using daytime predictors and nighttime predictors.  There is therefore a discontinuity in the field values over central Georgia at 1215 UTC, the end of the animation.

Compare the animation below to the one above.  Which yields better situational awareness for the developing fog field?


GOES-R IFR Probability Fields, hourly from 0215 through 1215 UTC on 27 October (Click to enlarge)

The Aviation Weather Center plot (below) highlights the presence of an IFR SIGMET over the region at 1324 UTC on 27 October.


Aviation Weather Center plot showing MVFR, IFR and LIFR stations over Georgia, along with an IFR Sigmet at 1324 UTC 27 October 2016 (Click to enlarge)

IFR Probability Screens out mid-level Stratus


GOES-13 Brightness Temperature Difference (3.9 µm- 10.7 µm) and GOES-R IFR Probability fields computed with GOES-13 and Rapid Refresh Data, 1100 UTC 18 October. Plots of ceilings and surface visibilities are included (Click to enlarge)

GOES-R IFR Probability fields often to a better job (compared to brightness temperature difference fields) in outlining exactly where low ceilings and reduced visibilities are occurring because IFR Probability fields include information about low-level saturation from the Rapid Refresh model. That information about near-surface saturation allows the IFR Probability algorithms to screen out regions where only mid-level stratus is occurring. A low fog — a stratiform cloud of water droplets that sits on/near the surface — and a mid-level stratus deck (also a stratiform cloud of water droplets) can look very similar in a brightness temperature difference field. In the example above, consider much of northeastern Alabama and northern Georgia. There is a strong return in the brightness temperature difference field because mid-level stratus is present — but IFR Probabilities are small because the Rapid Refresh does not diagnose low-level saturation in the region. Compare Brightness Temperature Difference returns over northeast Alabama and over extreme western North Carolina — to the west of Asheville. IFR Conditions are observed over western North Carolina, and IFR Probabilities are high there. In general, the region with high IFR Probabilities in the toggle above includes stations that are reporting IFR or near-IFR conditions. Most stations outside the region of high IFR Probability are not showing IFR Conditions, even though they may be in a region with the Brightness Temperature Difference signal is large.

A similar story can be told farther west at 0800 UTC, shown below. Focus on the region with a strong Brightness Temperature Difference signal over southeast Arkansas. IFR Conditions are not occurring under that mid-level stratus deck, and IFR Probabilities are very low. Similarly, IFR Probabilities are small over Oklahoma and north-central Texas because the Rapid Refresh Model is not showing low-level saturation in those regions; IFR Probabilities cannot be large when low-level saturation is not indicated in the model.

Using both Satellite Data and Model Data accentuates the strengths of both. That’s the power of a fused data product.


GOES-13 Brightness Temperature Difference (3.9 µm – 10.7 µm) and GOES-R IFR Probability fields computed with GOES-13 and Rapid Refresh Data, 0800 UTC 18 October. Plots of ceilings and surface visibilities are included (Click to enlarge)

IFR Probability Motion as a forecast tool


GOES-R IFR Probability Fields, 0200-1400 UTC on 13 October 2016 along with surface observations of ceilings/visibility (Click to enlarge)

Because GOES-R IFR Probability fields are computed with the same time latency as GOES imagery, motion of the IFR Probability fields can have predictive value.  In the animation above, higher GOES-R IFR Probability  is moving eastward;  IFR Conditions are reported as the higher IFR conditions move overhead (consider, for example, Bowling Green, KY, or Clarksville, TN), and ceilings / visibilities improve as the band of higher IFR conditions moves eastward from a station (over southern Illinois, for example).

IFR Probabilities over Louisiana


GOES-13 Brightness Temperature Difference Fields and GOES-13-based GOES-R IFR Probability fields, 1107 UTC on 6 October (Click to enlarge)

The toggle above between the GOES-R IFR Probability fields at 1107 UTC on 6 October, and the corresponding Brightness Temperature Difference field from GOES-13, is an example of the strength of the GOES-R IFR Probability field. By fusing Satellite Data with model (Rapid Refresh) estimates of low-level saturation, the Probability field is able to differentiate between regions where Brightness Temperature Difference fields are showing a signal but where widespread low-level fog is not occurring (Mississippi) from regions where Brightness Temperature Difference Fields show a signal and where IFR conditions are present (Louisiana and Texas).  An IFR SIGMET was issued associated with the Fog over Louisiana and Texas.