Category Archives: New England

Fog in the Canadian Maritimes

GOES-16 ABI Band 2 (0.64 µm) from 0902-1312 UTC on 9 May 2018 (Click to enlarge)

GOES-16 Visible Imagery on the morning of 9 May 2018 shows the steady erosion of fog in/around the Bay of Fundy, and along coastal Maine.  The default 5-minute temporal cadence with the CONUS GOES-16 sector allows for a precise observation for when coastal fog will clear.

The satellite view of the fog in the bay was unobstructed by high clouds.  (except to the east of Nova Scotia, where a cirrus shield is apparent in the visible animation above, and in animation below)    Thus, the GOES-16 Night Fog Brightness Temperature Difference field (10.3 µm- 3.9 µm), below, could ably capture the fog’s presence and evolution.  The animation of that product, below, shows how the signal changes at sunrise as reflected solar 3.9 µm radiation overwhelms the brightness temperature difference (driven at night by differences in emissivity at 3.9 and 10.3 µm from cloud droplets): the sign flips.  Because the fog was captured in the Night Fog Brightness Temperature Difference, it was also present in the Nighttime Microsphysics RGB Composite (here), although the color associated with fog  changes as the sun rises, and clear skies also allowed the Day Snow Fog RGB Product to show the fog during the day (here).

None of the Satellite-based products can provide information on the likelihood of fog over the ocean to the east of Nova Scotia, however, because the presence of cirrus clouds there prevents the satellite from viewing low clouds.  What products can help with that?

GOES-16 ABI Night Fog Brightness Temperature Difference (10.3 µm – 3.9 µm) from 0817-1312 UTC on 9 May 2018 (Click to enlarge)

GOES-16 IFR Probability fields, below, combine together satellite and model information to determine where IFR Conditions are most likely.  Very high probabilities exist where other satellite fog detection products suggest the presence of fog/low stratus (and where surface observations confirm the presence of fog).  But there are also high probabilities over the ocean east of Nova Scotia where satellite-only fog detection fails because of the presence of high clouds;  this large signal is derived from Rapid Refresh data there that suggests low-level saturation. IFR Probability combines the strengths of both satellite data and model output to provide useful information to a forecaster.

GOES-16 IFR Probability (10.3 µm – 3.9 µm) from 0817-1322 UTC on 9 May 2018

(Thanks to Paul Ford, ECC Canada, for alerting us to this event)

Added: The GOES-16 ABI Band 3 (0.86 µm) “Veggie” Band, which has great land/sea contrast, shows the fog encroaching into the Bay of Fundy during the day on 8 May. Note in particular how the low clouds race up the west coast of Nova Scotia near sunset.

GOES-16 ABI Band 3 (0.86 µm) from 1512-2257 UTC on 8 May 2018 (Click to enlarge)

IFR Conditions in the Northeast

GOES-16 IFR Probability, 1007 UTC on 20 February 2018, along with surface observations of ceilings and visibilities (Click to enlarge)

A complex set of Low Pressure systems over the eastern half of the United States brought multiple cloud layers and IFR conditions to the northeastern United States on 20 February. The image above shows the IFR Probability field at 1007 UTC. IFR Conditions are apparent from the Chesapeake Bay northeastward through southeastern Pennsylvania and New York and coastal New England, as well as over southeastern Ontario Province in Canada and the Canadian Maritimes. These are also regions where IFR Probabilities are high, generally exceeding 80%. In regions where IFR conditions are not observed (Western Pennsylvania and Ohio, for example), IFR Probabilities are generally small.

When multiple cloud decks are present, as occurred on 20 February, satellite-only detection of low clouds is a challenge, as shown with by the brightness temperature difference field (10.3 µm – 3.9 µm), called the ‘Night Fog’ difference in AWIPS, below. High and mid-level clouds (grey/black in the enhancement used) make satellite detection of low-level stratus impossible.  So, for example, stations with IFR conditions over Long Island sit under a much different enhancement in the brightness temperature difference field compared to stations with IFR conditions over southern New Jersey and southeastern Pennsylvania.

Because the Brightness Temperature Field cannot view the low clouds, the Nighttime Microphysics RGB (shown below the Brightness Temperature Difference field) similarly cannot identify all regions of low, warm clouds — typically yellow or cyan in that RGB.

Night Fog Brightness Temperature Difference field (10.3 µm – 3.9 µm) at 1007 UTC on 20 February 2018, along with surface observations of ceilings and visibilities (Click to enlarge)

NightTime Microphysics RGB at 1007 UTC on 20 February 2018, along with surface observations of ceilings and visibilities (Click to enlarge)

IFR Probabilities with a strong storm in Maine

GOES-R IFR Probabilities, 0100-1000 UTC on 7 April 2017, along with surface reports of ceilings and visibilities (Click to enlarge)

Note:  GOES-R IFR Probabilities are computed using Legacy GOES (GOES-13 and GOES-15) and Rapid Refresh model information;  GOES-16 data will be incorporated into the IFR Probability algorithm in late 2017

A strong storm over the northeastern United States produced widespread IFR conditions over that region.  The storm was also accompanied by multiple cloud layers, however, and that made diagnosis of regions low clouds/fog difficult.  For these cases, a fused data approach is vital — using model information (in the case of IFR Probability, above, the model is the Rapid Refresh) to provide information at low levels allows for a better tool to alert a forecaster to the presence of reduced visibilities.

In the animation above, Maine intially shows IFR Probabilities around 50% — but the flat nature of the field should alert a user to the fact that satellite predictors cannot be included in the computation of IFR Probabilities because high clouds are preventing a satellite view of low clouds.  Accordingly, the computed Probability is lower.  In contrast, high clouds are not present over southern New England at the start of the animation, and IFR Probabilities are much larger there:  both satellite and model predictors are used. As the high clouds lift north from northern New England the region of higher IFR Probabilities expands from the south.

Note the influence of topographic features on the IFR Probability field.  The Adirondack Mountains and St. Lawrence Seaway have higher and lower Probabilities, respectively, because of the higher terrain in the Mountains, and the lower terrain along the St. Lawrence.

An example of why fused data are important is shown below.  Look at the conditions in Charlottetown, on Prince Edward Island, in the far northeast part of the domain.  Between 0315 and 0400, ceilings and visibilities deteriorate as IFR Probabilities increase.  The brightness temperature difference field, at bottom, shows no distinct difference between those two times because the clouds being viewed are high clouds.

IFR Conditions over Maine

GOES-R IFR Probability and with surface observations of ceilings and visibilities, 1045 UTC on 13 February 2017 (Click to enlarge)

A strong storm off the East Coast of the United States produced a variety of winter weather over Maine on 13 February 2017, including Blizzard conditions. Although ceilings and visibilities above show IFR or near-IFR conditions at 1045 UTC, GOES-R IFR probabilities over Maine are small (less than 20%). Why?

The image below from this site shows Cloud Type, Low-Level Saturation, IFR Probability, and the Nighttime Microphysics.  Both Ice clouds and falling snow are widespread over Maine. GOES-R IFR Probabilities typically assume saturation with respect to water.   The Gray, ME morning sounding shows maximum RH (with respect to water) at only 94% (Link).  Assuming saturation with respect to water rather than with respect to ice may be a source of error that will have to be investigated in the future.

Note that after sunrise, IFR Probabilities increased over Maine to values between 30 and 45% (Link).

Satellite-derived Cloud Type (upper left), Maximum Low-Level Relative Humidity in the Rapid Refresh (upper right), GOES-R IFR Probability (lower left) and Nighttime microphysics (lower right), all from 1045 UTC on 13 February (Click to enlarge)

IFR Conditions over Maine


GOES-R IFR Probability, and surface plots of ceilings and visibility, 0500-1215 UTC on 12 June 2016 (Click to enlarge)

IFR Probability fields, above (a slower animation is here), show high probabilities of IFR Conditions over much of Maine, but a definite western edge is also present, moving eastward through New Hampshire and Vermont and reaching western Maine by 1215 UTC. The screen capture below, from this site, shows IFR (station models with red) and Low IFR Conditions (station models with magenta) over much of southern Maine at 1200 UTC on 12 June in advance of a warm front.

Careful inspection of the IFR Probability animation shows a field at 1000 UTC that is very speckled/pixelated. This likely results from cloud shadowing. The combination of a very low sun and multiple cloud layers resulted in many dark regions in the visible imagery that the cloud masking may have interpreted as clear regions. (Click here for a toggle between Visible Imagery and GOES-R IFR Probabilities at 1000 UTC).


Surface plot at 1200 UTC 12 June 2016. See text for details (Click to enlarge)

Low IFR Probability fields are also computed by the GOES-R Algorithms. Values are typically smaller than IFR Probability. Plots of Low IFR and IFR Probabilities at 0700 and 1215 UTC are shown below.


GOES-R Low IFR Probability and GOES-R IFR Probability, 0700 and 1215 UTC (Click to enlarge)

IFR Conditions with a Coastal Storm


GOES-R IFR Probabilities, hourly from 0400 through 1515 UTC, 5 February 2016 (Click to enlarge)

IFR Conditions frequently occur with storms along the East Coast. Satellite detection of such conditions is very difficult because of the multiple cloud layers that accompany cyclogenesis. The IFR Probabilities, above, have a character that reflects their determination solely from Rapid Refresh Data. That is, Satellite Predictors were not considered over much of New England because of the presence of multiple cloud layers, as suggested in the Water Vapor animation below.

IFR Probability fields are initially entirely offshore in the animation above, and IFR conditions are not observed over southern New England.  Note how IFR probabilities initially increase over land over southern New Jersey and then quickly move northeastward into southern New England as IFR Conditions develop.  Because satellite predictors are unavailable in these regions (on account of the many clouds layers), the simultaneous development of high IFR Probabilities with observed IFR Conditions argues for a good simulation of the observed weather by the Rapid Refresh.  Fused data products such as IFR Probability fields join the strengths of different systems to provide a statistically more robust field than is possible from the individual pieces.

When daytime arrives — at around 1215 UTC in the animation above — a distinct transition is apparent in the GOES-R IFR Probability fields.  This occurs because Satellite Data — visible satellite data — can be used during daytime to articulate the regions of cloudiness with more precision.  Because cloudiness in general is better defined, IFR Probability fields (that require the presence of clouds) increase somewhat, and the color table used emphasizes that change.


GOES-13 Water Vapor 3-hourly Animation, 0400-1300 UTC, 5 February 2016 (Click to enlarge)

Dense Fog over New England


GOES-13 Brightness Temperature Difference Fields (10.7 µm – 3.9 µm), hourly from 0515 through 1315 UTC 11 December 2015 (Click to enlarge)

The brightness temperature difference field (10.7 µm – 3.9 µm), above, over New England during the morning of 11 December 2015 shows extensive cirrus cloud cover (a hole in the high clouds moves over Southern New England just before sunrise). Surface observations of ceilings and visibility show widespread IFR conditions, yet cirrus and mid-level clouds prevent a diagnosis of the low clouds.

GOES-R IFR Probability fields for the same times, below, do show a strong signal (that is, higher probability of IFR conditions) in regions where IFR conditions are observed or where they have recently developed.  Because GOES-R IFR Probability fields incorporate information from the Rapid Refresh about low-level saturation.  Even if clouds decks obscure the view of near-surface clouds, as in this case, GOES-R IFR Probability fields, because they fuse together satellite data and surface information from the Rapid Refresh model, can provide useful information.


GOES-R IFR Probability Fields, hourly from 0515 through 1315 UTC 11 December 2015 (Click to enlarge)

IFR Conditions with a strong extratropical Cyclone


GOES-R IFR Probability Fields (from GOES-13 and the Rapid Refresh Model), 1300 and 1400 UTC on 12 November 2015 (click to enlarge)

Strong Extratropical Cyclones, such as the one affecting the central and eastern United States on 11-12 November 2015 (1200 UTC Map on 12 November is here), generate multiple levels of clouds that make it difficult to detect fog/low stratus from satellite, because intervening cloud layers get in the way. GOES-R IFR Probability fields, however, because they incorporate near-surface information from the Rapid Refresh Model, can yield useful information about the likelihood of IFR Conditions. The toggle above shows data at 1300 and 1400 UTC over the northeast United States.  Highest IFR Probabilities are removed from the coastlines of New England — as observations confirm.  Note how the Adirondack and Catskill regions have higher probabilities, where terrain is reaching up into the stratus deck.  Features in the IFR Probability field are strongly related to surface-based observations of fog and low stratus.

The toggle below shows IFR Probability fields and GOES-13 Brightness Temperature Differences as 1300 UTC.  Features in the brightness temperature difference field have no relationship to surface-based observations of fog and low stratus.


GOES-R IFR Probability Fields (from GOES-13 and the Rapid Refresh Model) and GOES-13 Brightness Temperature Difference fields (10.7µm – 3.9µm), 1300 UTC on 12 November 2015 (click to enlarge)

IFR Conditions over eastern Massachusetts and the Cape


GOES-R IFR Probability, 0315 through 1215 UTC on 22 June 2015 (Click to enlarge)

Sea fog penetrated inland over eastern New England overnight. How did GOES-R IFR Probabilities depict the event, and how did those fields compare to the traditional fog-detection method, brightness temperature differences between the 10.7 µm and 3.9 µm channels? The animation above shows the IFR Probabilities, and they neatly outline the regions of low ceilings and reduced visibilities.

In contrast, Brightness Temperature Difference fields, shown below, are troubled by two different factors in these loops. Around 0700-0800 UTC, thin cirrus over southern Cape Cod impedes the satellite view of low clouds (Click here for a toggle between the two fields at 0722 UTC); brightness temperature difference fields yield little information when that happens. (GOES-R IFR Probability values drop when the Satellite component cannot be used; make certain when interpreting the values that you are aware of the presence/absence of high clouds!) In addition, the brightness temperature difference field loses features around sunrise, when solar radiation with a wavelength around 3.9 µm increases. GOES-R IFR Probability fields maintain a coherent signal through sunrise, however.

Careful inspection of the animation above does reveal some stations where IFR Conditions occur and IFR Probabilities are low. For example, Lebanon NH in the Connecticut River Valley reports IFR Conditions intermittently. Small-scale valley fog is a challenge for both GOES detection and for Rapid Refresh detection.


GOES-13 Brightness Temperature Difference Fields (10.7 µm – 3.9 µm), ~0315-1215 UTC on 22 June 2015 (Click to enlarge)

Dense Fog over eastern Maine


GOES-R IFR Probability Fields, hourly from 0315 to 1215 UTC on 10 June 2015 (Click to enlarge)

A cold front that moved across Maine early in the morning on 10 June 2015 was accompanied by dense fog. Dense Fog Advisories were hoisted over eastern Maine (screenshot from; screenshot from the Caribou ME National Weather Service). The hourly imagery of IFR Probabilities, above, showed high probabilities over eastern Maine (where surface observations are scant). Note how the back edge of the high IFR Probabilities, after the frontal passage, correlates will with the timing of rising ceilings and reduced visibilities.  This occurs at Augusta (KAUG), Bangor (KBGR) and Millinocket (KMLT), for example.

The traditional method of detecting fog and low cloud, the brightness temperature difference field that compares values at 10.7 µm and 3.9 µm , had difficulty indicating regions of fog for two reasons on the morning of 10 June.  An animation of the product is below.  There were high clouds present that interfered with the satellite’s view of low clouds.   (IFR Probability can still give a useful signal in this case because of information that comes from the Rapid Refresh Model).  In addition, the ever-earlier sunrise in early June supplies enough 3.9 µm radiation at the end of the animation to flip the sign of the brightness temperature difference field and the distinct signal of low water-based clouds is lost.


Brightness Temperature Difference Fields (10.7 µm – 3.9 µm), hourly from 0315 through 1215 UTC on 10 June 2015 (Click to enlarge)