Dense Fog over Kansas

Dense Fog Advisory issued by Goodland Kansas on 19 August 2019 (Click to enlarge)

Dense Fog developed over portions of the central Plains on Monday morning, 19 August 2019; The Goodland Kansas office of the National Weather Service issued Dense Fog Advisories for part of their County Warning Area, as shown above.  A variety of satellite-based products are useful for monitoring dense for from satellite.  The one used for the longest time is the so-called Night Fog Brightness Temperature Difference field, shown below, that detects (in blue/cyan, in the default AWIPS color enhancement used) clouds that contain water droplets.  Small water droplets do not emit 3.9 µm radiation as a blackbody would, but they do emit 10.3 µm radiation as a blackbody.  Thus the brightness temperature that is computed by detecting via satellite the amount of radiation emitted and converting those numbers of photons to an emitting temperature (a conversion that does assume blackbody emission!) will be warmer for 10.3 µm radiation than for at 3.9 µm radiation.  An animation of that field is shown below.

GOES-16 ABI ‘Night Fog’ brightness temperature difference field (10.3 µm – 3.9 µm), 1026 – 1421 UTC on 19 August 2019 (Click to animate)

Note in the animation above that dense fog over the higher Plains or western Kansas and Nebraska is well-articulated by this field; dense fog over Iowa and eastern Nebraska is not depicted with clarity. The animation also shows a well-known feature of the Night Fog Brightness Temperature difference field — it loses a signal during sunrise as the amount of reflected 3.9 µm radiation increases.

GOES-R IFR Probability fields, below for the same time, better capture the horizontal extent of low ceilings and reduced visibilities (i.e., IFR conditions) in this case by marrying the brightness temperature difference field with low-level saturation as predicted by the Rapid Refresh model. If satellite detection suggests clouds are present — or likely — and low-level saturation is predicted by the model, then IFR Probabilities will be large. The animation below highlights the IFR conditions over western Nebraska and Kansas — but it also highlight IFR conditions in eastern Nebraska and western Iowa, regions that the Night Fog brightness temperature difference does not capture dramatically. Note that during the animation, IFR probability fields can change quickly — as updated Rapid Refresh data (that is, a more recent model run that is often more accurate) are incorporated into the algorithm.

GOES-R IFR Probabilities, 1026 – 1431 UTC on 19 August 2019 (Click to enlarge)

Because the Night Fog brightness temperature difference does not outline fog features well in eastern Nebraska and Iowa, the Nighttime microphysics Red/Green/Blue composite, that uses the Night Fog Brightness temperature difference as its green component (Night Time Microphysics Quick Guide), similarly does not outline them, unlike IFR Probability. Be alert to the strengths and weaknesses of the product used for detecting fog.

Night time Microphysics RGB, Night Fog Brightness Temperature Difference, and GOES-16 IFR Probabililty, all at 1026 UTC on 19 August 2019 (Click to enlarge)

Fog in the Mid-South on August 8th

GOES-R IFR Probability, 1002 UTC on 8 August 2019, along with surface reports of ceilings and visibility (Click to enlarge)

GOES-R IFR Probability fields from 1002 UTC on 8 August 2019, above, show a region of high probabilities mostly colocated with surface observations of ceilings and visibilities.  IFR conditions are widespread from north of Memphis TN to southern Illinois.  IFR Probability is a fused product, using both satellite imagery to detect low clouds and Rapid Refresh model data to identify regions of low-level saturation.  Where both indicators are present (for example, over extreme western Kentucky and adjacent regions of southern Illinois and northwest Tennessee), IFR Probabilities are very high and IFR conditions are observed.  There are also regions where only model data can be used — because satellites are not detecting low clouds (because high clouds are blocking the view);  such a case is over northwest Arkansas, where IFR Probabilities are high (albeit not as high as over western Kentucky) and the field is not pixelated like a satellite image can be.  IFR Conditions are observed there as well.  (Click here for a screen shot from later in the morning of the Paducah National Weather Service office front page showing Dense Fog Advisories in Tennessee, issued by the Memphis Forecast Office).

The toggle below compares the IFR Probability field with the GOES-16 ‘Night Fog’ brightness temperature difference.  There is no consistent signal in the brightness temperature difference field to indicate fog on the ground.  The color enhancement for the brightness temperature difference is created so that teal to blue is positive.  Clouds made of liquid water droplets — such as stratus, or fog — will have a strong positive value because of the emissivity properties of small water droplets.  (That is, those droplets emit 10.3 µm radiation mostly as a blackbody would, but do not emit 3.9 µm radiation as a blackbody would).  Note in particular the very dark enhancement over northwest Arkansas (suggestive of cirrus there that will block any satellite view of low clouds) and the grey enhancement over west central Tennessee, north of Memphis, where mid-level clouds are similarly blocking a good view of the stratus at the surface.

GOES-16 IFR Probability and GOES-16 ‘Night Fog’ Brightness Temperature Difference field (10.3 µm – 3.9 µm) at 1002 UTC on 8 August 2019 (Click to enlarge)

The Nighttime Microphysics RGB can also be used to alert forecasters to fog — but a main component of that detection is the Night Fog brightness temperature difference field, and where the Night Fog brightness temperature difference fails to identify fog, as above, Nighttime Microphysics will as well, as shown in the toggle below.  Which color is useful for identifying fog in the RGB below?  Almost all of them!

Fog and Low Stratus under cirrus

Night Fog Brightness Temperature Difference (10.3 µm – 3.9 µm), 0801-1311 UTC on 7 May 2019 (Click to animate)

Consider the animation above, of the Brightness Temperature Difference product (10.3 µm – 3.9 µm) centered on Colorado on the morning of Tuesday 7 May 2019. The surface observations show widespread IFR conditions, but because of widespread high clouds over the region, the brightness temperature difference shows little signal that is consistent with low clouds (blue or cyan in this enhancement). There isn’t much horizontal spatial correlation between observations of low ceilings/reduced visibility and the Brightness Temperature Difference product. When low clouds are overlain by high clouds, don’t expect a satellite detection of low clouds to work. Note also how the Brightness Temperature Difference field loses its signal as the sun rises and a general increase in the amount of reflected solar shortwave (3.9 µm) infrared radiation increases.

GOES-R IFR Probability includes near-surface information that is useful, especially when mid-level or high clouds obscure the satellite view of low clouds. Rapid Refresh estimates of near-surface saturation are used to gauge the probability of IFR conditions. In the case on 7 May 2019, that information allowed GOES-R IFR Probability to approximate exactly where the lowest ceilings and more reduced visibilities were. There is therefore a much better spatial correlation between the location of surface observations showing IFR conditions, and the IFR Probability field. This has a tacit implication on how well the Rapid Refresh Model is simulating the evolution of the atmosphere. Note also that in contrast to the Brightness Temperature Difference field, a consistent signal is maintained through sunrise.

GOES-R IFR Probability, and surface observations of ceilings/visibility, 0801-1311 UTC on 7 May 2019 (Click to animate)

Because the NightTime Microphysics RGB relies on the Night Fog Brightness Temperature difference field, it rises or falls on fog detection on the shoulders of the Brightness Temperature Difference field. On this day, it fell. Click here for the Nighttime Microphysics RGB animation; the animation below toggles between the three fields — Night Fog Brightness Temperature Difference, Nighttime Microphysics RGB, and IFR Probability — at 1002 UTC on 7 May 2019.

Night Fog Brightness Temperature Difference (10.3 µm – 3.9 µm), Nighttime Microphysics RGB and GOES-R IFR Probability fields, 1001 UTC on 7 May 2019 (Click to enlarge)

Fog surrounding convection in the Midwestern United States

GOES-16 IFR Probability and GOES-16 ‘Night Fog’ Brightness Temperature Difference (10.3 µm – 3.9 µm) at 1001 UTC on 2 May 2019, along with surface observations of ceilings and visibility (Click to enlarge)

Widespread Fog occurred over the midwestern United States on Thursday morning, 2 May 2019, with some Dense Fog Advisories issued. Some of the fog developed under an extensive cirrus canopy associated with strong convection over north-central Illinois, and those cloud layers make satellite-based detection of stratus near the surface (i.e., fog) a challenge.  The toggle above between the Night Fog Brightness Temperature Difference (10.3 µm – 3.9 µm) and GOES-16 IFR Probability highlights the difficulty in using the brightness temperature difference alone to outline regions of fog. In contrast, the Rapid Refresh model information on low-level saturation allows IFR Probability to alert a forecaster to the likelihood of fog in regions underneath cirrus, regions such as southeastern Illinois and southwestern Indiana.

There is an excellent spatial correlation between regions with high IFR probability and low ceilings/reduced visibilities, and also with regions with low IFR probability and non-IFR conditions (central Iowa, for example, and central Illinois southwestward into central Missouri).

Because the Night Fog Brightness Temperature difference is a central feature of the Nighttime Microphysics RGB product — Night Fog Brightness Temperature difference is the green part of that RGB, as apparent in the toggle below — Nighttime Microphysics also is challenged to identify fog in regions under cirrus

Advection Fog and Multiple Cloud Layers

GOES-16 Night Fog Brightness Temperature Difference (10.3 µm – 3.9 µm), Low IFR Probability and Advanced Nighttime Microphysics RGB at 0837 UTC on 13 March 2019 (Click to enlarge)

An intense early Spring storm produced blizzard conditions over the western Great Plains on 13 March 2019 (See this blog post for example). The southerly flow in advance of the storm moved moist air over a dense snowpack over the upper Midwest, resulting in dense Advection Fog. The animation above cycles between the Night Fog Brightness Temperature Difference (10.3 µm – 3.9 µm), the Advanced Nighttime Microphysics RGB that uses the Night Fog Brightness Temperature Difference as the ‘green’ and GOES-16 Low IFR Probabilities.

At 0837 UTC, high clouds had not yet covered all of the upper Midwest, and the satellite still viewed stratus clouds over Wisconsin and Minnesota (bright cyan in the Night Fog Brightness Temperature difference enhancement). Note the difference in the Low IFR Probability field in regions where high clouds are present (Iowa and states to the south and west) and where they are not. Low IFR Probability has a pixelated look over Wisconsin because modest satellite pixel-level variability is included in the IFR probability field. Over Iowa, in contrast, satellite data gives no information about reductions to visibility under the thick cloud cover; low-level data from the Rapid Refresh model drives the Low IFR Probability field there. Probabilities are high in regions where observations suggest IFR/Low IFR conditions are present. This suggest the Rapid Refresh is simulating the evolution of the atmosphere well over the Plains.

The Advanced Nighttime Microphysics RGB can detect low clouds — but only when high clouds are not in the way. For this case, the RGB signal over Wisconsin is consistent with stratus and fog, but over Iowa the signal is consistent with cirrus that is occurring there. Surface restrictions in visibility and lowered ceilings are similar in the two states, and the IFR Probability field has similar values in the two states.

By 1502 UTC on 13 March (below), high clouds had overspread the entire Midwest; Low IFR Probability relies almost entirely on Rapid Refresh Model estimates of low-level saturation.  That is why the field over northern Minnesota and northern Wisconsin is flat.  Some holes in the high clouds are suggested by the pixelated look of the field in Iowa and southern Wisconsin.

GOES-16 Low IFR Probability, 1502 UTC on 13 March 2019 (Click to enlarge)

Cloud Layers and Detection of IFR Conditions

GOES-16 Night Fog Brightness Temperature Difference (10.3 µm – 3.9 µm), Nighttime Microphysics RGB and GOES-16 IFR Probability at 1116 UTC on 22 February 2019; Surface observations of ceilings and visibilities at 1100 UTC are also plotted (Click to enlarge).

A strong storm embedded within a subtropical jet stream over the southern United States was associated with widespread fog on the morning of 22 February 2019. This screen-capture from this site shows Dense Fog Advisories over much of Georgia, and over regions near Dallas. Which products allowed an accurate depiction of the low ceilings and reduced visibilities?

The toggle above cycles between the Night Fog Brightness Temperature Difference (10.3 µm – 3.9 µm), which product identifies low clouds (cyan blue in the default AWIPS enhancement shown) because of differences in emissivity at 3.9 µm and 10.3 µm from small water droplets that make up stratus clouds, the Nighttime Microphysics RGB, which RGB uses the Night Fog Brightness Temperature Difference as it green component, and the GOES-16 IFR Probability product.  IFR conditions are defined as surface visibilities between 1 and 3 miles, and ceiling heights between 500 and 1000 feet above ground level.  The plotted observations help define where that is occurring.  Multiple cloud layers from Arkansas east-northeastward make a satellite-only detection of IFR conditions challenging.  IFR Probability gives useful information below cloud decks because model-based saturation information from the Rapid Refresh Model fill in regions below multiple cloud decks where satellite information about low clouds is unavailable.

The toggle below shows the same three satellite-based fields (Night Fog Brightness Temperature Difference, Nighttime Microphysics RGB and IFR Probability)  at the same time, but centered over Oklahoma.  In this case, the Rapid Refresh Data are used to screen out a region of elevated stratus over northeast Oklahoma. Note that these is little in the Night Fog Brightness Temperature Difference field to distinguish between the IFR and non-IFR locations.

GOES-16 Night Fog Brightness Temperature Difference (10.3 µm – 3.9 µm), Nighttime Microphysics RGB and GOES-16 IFR Probability at 1116 UTC on 22 February 2019; Surface observations of ceilings and visibilities at 1100 UTC are also plotted (Click to enlarge).

GOES-R IFR Probability over the southeast United States in this case is identifying regions of IFR conditions underneath multiple cloud decks (and also where only the low clouds are present) by incorporating low-level saturation information from the Rapid Refresh model. Over Oklahoma, non-IFR conditions under an elevated stratus deck are identified (and screened out in IFR Probability fields) by the lack of low-level saturation information in the Rapid Refresh.

Advection Fog in Warm Air Advection Regimes

‘Night Fog’ Brightness Temperature Difference field (10.3 μm – 3.9 μm) and GOES-R IFR Probability at 1202 UTC on 4 February 2019 (Click to enlarge)

Advection Fog during thaws, when very cold surfaces are overrun by air with dewpoints above freezing, can be very dense, and very difficult to detect via satellite; typically advection fog accompanies extratropical cyclones and their accompanying multiple cloud layers. The toggle above compares the ‘Night Fog’ Brightness Temperature Difference field (10.3 μm – 3.9 μm), historically used to detect low stratus because of radiation emissivity differences of clouds made up of water droplets at those two wavelengths, and GOES-R IFR Probability which fuses information from the satellite — not particularly useful in this case as far as low-level visibility is concerned — with information about low-level saturation from the Rapid Refresh Model. GOES-R IFR Probability gives a much more accurate depiction of exactly where the reduced visibilities and lowered ceilings are present, a vital piece of information for aviation (for example).

In addition, Low IFR Probability suggests where the lowest ceilings and greatest visibility reductions occur. The toggle below compares IFR Probability and Low IFR Probability at 1202 UTC (Here’s the toggle at 1642 UTC).  As expected, the region of Highest Low IFR Probability is contained within the region of highest IFR probability;  values of Low IFR Probability are somewhat smaller than those for IFR Probability (the same colorscale is used for both products).

GOES-R IFR Probability and Low IFR Probability at 1202 UTC on 4 February 2019 (Click to enlarge)

Fog over the central United States

Night Fog Brightness Temperature Difference (10.3 µm – 3.9 µm) and Nighttime Microphysics RGB at 0507 UTC on 1 February 2019, and surface observations of ceilings and visibilities (Click to enlarge)

The toggle above displays the Night Fog Brightness Temperature Difference field (10.3 µm – 3.9 µm) and the Night Time Microphysics Red/Green/Blue (RGB) Product that uses the Night Fog Brightness Temprature Difference field as its green value. In the color enhancements above, cyan in the Night Fog Brightness Temperature Difference denotes positive values that occur because stratus clouds — that is, clouds that are made up of water droplets — do not emit 3.9 µm radiation as a blackbody. Consequently, the computation of brightness temperature (which assumes blackbody emission) results in a 3.9 µm brightness temperature that is cooler than at 10.3 µm (clouds are emitting 10.3 µm radiation very nearly like a blackbody).  Low clouds in the RGB that may or may not support IFR conditions range in color from light cyan (over Texas and Florida) to more orange and yellow (yellow over the Great Lakes were exceptionally cold air is in place).

The fields above are overpredicting where fog/low ceilings might be occurring because cloud top measurements from the Brightness Temperature Difference do not always give reliable guidance on cloud base.

By merging satellite information about clouds and cloud type with Rapid Refresh model information at about low-level saturation, GOES-R IFR Probability fields screen out regions where IFR conditions are unlikely;  the map suggests low ceilings and fog are most likely over Texas and Oklahoma.  The zoomed in image, below shows that IFR conditions are indeed occurring in this region.  Other regions with a strong signal in the Brightness Temperature Difference field — Tennessee, for example — show low IFR Probability and surface observations that do not show IFR conditions.

GOES-R IFR Probability field, 0507 UTC on 1 February, along with surface reports of ceilings and visibility (Click to enlarge)

GOES-R IFR Probability field, 0507 UTC on 1 February, along with surface reports of ceilings and visibility zoomed in over the southern Plains (Click to enlarge)

Night Fog Brightness Temperature Difference (10.3 µm – 3.9 µm) and Nighttime Microphysics RGB at 1007 UTC on 1 February 2019, and surface observations of ceilings and visibilities (Click to enlarge)

The same relationships occur at 1007 UTC; the Night Fog Brightness Temperature Difference and Nighttime Microphysics RGB overpredict the regions of low clouds/fog; IFR Probability’s use of Rapid Refresh Data allows it to screen out regions where fog is not present, but stratus clouds are, and also add in regions where cirrus clouds prevent the detection of low clouds, but Rapid Refresh data suggests low-level saturation is present (such as over the Gulf of Mexico south of Louisiana).

The IFR Probability field is accurately outlining the region of IFR conditions.

GOES-R IFR Probability field, 1007 UTC on 1 February, along with surface reports of ceilings and visibility (Click to enlarge)

GOES-R IFR Probability field, 1007 UTC on 1 February, along with surface reports of ceilings and visibility zoomed in over the southern Plains (Click to enlarge)

Fog over the Northeast: Where is it?

GOES-16 ABI Band 02 (0.64 µm), Band 13 (10.3 µm), Day Fog Brightness Temperature (3.9 µm – 10.3 µm), Day Snow Fog RGB and Band 5 (1.61 µm) at 1602 UTC on 28 December 2018 (Click to enlarge)

The animation above cycles through the GOES-16 Visible Imagery (Band 2, 0.64 µm), Band 13 (Clean Window Infrared, 10.3 µm), the Day Fog Brightness Temperature Difference (3.9 µm – 10.3 µm), the Day Snow Fog Red Green Blue (RGB) Composite and the Snow/Ice near-Infrared channel (Band 5, 1.61 µm) that is the green component of the Day Snow Fog RGB. (That’s very apparent in this toggle between the Day Snow Fog RGB and the 1.61 µm) Do any of these products give you a good idea of where IFR conditions (Low ceilings and reduced visibilities) are occurring?

Consider the toggle of visible imagery below, with and without surface observations of ceilings and visibility. It is a difficult prospect to relate the top-of-cloud reflectance (which is what the visible imagery gives you!) to the ceilings beneath the cloud.

GOES-16 ABI Band 02 (0.64 µm) with an without surface observations of ceilings and visibility at 1602 UTC on 28 December 2018 (Click to enlarge)

GOES-16 IFR Probability fields blend satellite observations of cloud with Rapid Refresh model data that predicts saturation near the surface. That model data, incorporated into a statistical prediction of IFR conditions, allows the field to outline the regions where low ceilings and reduced visibilities occur, as shown in the toggle below with and without observations. (Click here to see the Visible and IFR Probability fields toggled). The inclusion of near-surface saturation values extracted from the Rapid Refresh model allows the IFR Probability field to discriminate between low ceilings/fog — as over central Pennsylvania, Massachusetts and central Ohio (among other places) — and mid-level stratus — as over southwestern Pennsyvlania and surrounding Lakes Erie and Ontario (among other places).

GOES-16 IFR Probability with surface observations of ceilings/visibilities at 1602 UTC on 28 December 2018 (Click to enlarge)

Fog over the Pacific Northwest: Where is it?

GOES-17 imagery in this blog post is made from GOES-17 Data that are preliminary and non-operational!

Night Time Fog Brightness Temperature Difference (10.3 µm – 3.9 µm) from GOES-16 and (preliminary, non-operational) GOES-17, 1202 UTC on 3 December 2018 (Click to enlarge)

The toggle above shows GOES-16 and GOES-17 Night Fog Brightness Temperature difference fields over the Pacific Northwest shortly after 1200 UTC on 3 December 2018.  The Pacific NW is a lot farther from the sub-satellite point (nadir) of GOES-16 (75.2 West Longitude) than from the sub-satellite point (nadir) of GOES-17 (at 137.2 W Longitude).  Thus, the GOES-16 view is has inferior spatial resolution.  There are also different parallax shifts for clouds between the two views.  The scene includes plenty of stratus, based on the observations, and isolated pockets of IFR conditions:  Spokane WA, Stampede Pass, WA, Pendleton OR, Medford OR, Salem OR.  It’s difficult to tell at a glance from the Brightness Temperature Difference field where the lowest ceilings and poorest visibilities are — because the satellite sees the top of the cloud, and it’s difficult to infer cloud base properties from infrared imagery of the cloud top.

The Advanced Nighttime Microphysics RGB can also be used to detect low ceilings, because its green component is the Night Fog Brightness Temperature Difference as shown above.  The toggle below compares the GOES-16 and GOES-17 RGBs.  Again, it is difficult to pinpoint at a glance where the IFR conditions are occurring in this product.

There are subtle differences in the colors of the RGB from the two satellites.  These are related to the distance from the sub-satellite point.  Limb cooling will cause the brightness temperatures from the GOES-16 Clean Window to be slightly cooler than the GOES-17 values, and that will affect the color:  The Clean Window is the Blue component of the RGB.  In addition, the Split Window Difference values will be slightly different because of the different amounts of limb cooling in 12.3 µm and 10.3 µm brightness temperatures.  GOES-17 data also includes striping that is being addressed with ongoing calibration work with the satellite.

Advanced Nighttime Microphysics RGB from GOES-16 and GOES-17 at 1202 UTC on 3 December 2018 (Click to enlarge)

The toggle below shows, (from GOES-16 data) the 10.3 µm – 3.9 µm Brightness Temperature Difference, the Nighttime Microphysics RGB, and the IFR Probability fields at 1202 UTC. IFR Probability fields include information about low-level saturation from the Rapid Refresh model, and that information allows the product to screen out regions of mid-level stratus; thus, the region of IFR conditions is better captured.  There are three main regions:  eastern WA southwestward to Pendleton OR, Seattle southward into Oregon, and the peaks of central Washington.

GOES-16 Night Fog Brightness Temperature Difference (10.3 µm – 3.9 µm), Nighttime Microphysics RGB and IFR Probability, 1202 UTC on 3 December 2018 (Click to enlarge)