GOES-16 versions of the GOES-R Fog/Low Stratus products are now available in Real Earth (see this post also). (The GOES-17 versions will become available when deemed operational). At present, only the CONUS domain is rendered into Real Earth. The animation below can also be accessed at this link.
A large storm moving ashore in British Columbia (0900 UTC Map), shown above in MIMIC Total Precipitable Water (from this site), was accompanied by widespread high clouds over much of the Pacific Coast of the United States. The 1511 UTC image, below, shows GOES-16 “clean window” (10.3 µm) infrared imagery, with high clouds apparent.
Satellite-only detection of fog/low clouds will be challenged on this day by the abundance of high clouds that block the satellite’s view of low stratus decks. Indeed, the ‘Night Fog’ brightness temperature difference field, below, allows for only periodic glimpses of what is happening near the surface. There are indications of fog — but it is challenging even in the animation to determine the horizontal extent of the fog regions.
GOES-16 Low IFR Probability fields, below (note: GOES-17 IFR Probability fields are still undergoing testing in preparation for their being deemed operational) highlight two regions of visibility restrictions: One is off the coast of central California, and a another is a narrow ribbon of reduced visibilities in the Central Valley. This case highlights a strength of IFR Probability fields: You get a useful and consistent signal even if high clouds are blocking the satellite view of low clouds. This is because Rapid Refresh Model estimates of low-level saturation are incorporated into the Probability fields.
IFR Probability fields from early on 22 December 2020, above, show a region of High Probabilities over northwest Wisconsin, northeastern Minnesota and northwestern Ontario. In general, the observations of IFR conditions (ceilings between 1000 and 3000 feet, visibilities between 1 and 3 miles) match well with the highest IFR Probability. The western edge of the field has characteristics that suggest it becomes more model-defined (in this case, the Rapid Refresh model that supplies the low-level saturation information) with time: the field from western Wisconsin up through Minnesota becomes less and less pixelated with time, as satellite information is lost due to the incursion of higher clouds.
Note also: Lake Superior, warmer than the overlaying atmosphere, is diagnosed as having low probabilities of IFR conditions.
Brightness temperature Difference fields, shown below for the same times, have historically been used to detect low clouds. However, there is little correlation between the fields and the observations of IFR conditions for two principle reasons: Brightness Temperature Difference fields alone do not give information on the cloud base, and IFR conditions require low cloud bases; Higher clouds impede the detection of low-level clouds associated with IFR conditions, and high clouds are overspreading this scene.
The animation below also shows how the Brightness Temperature Difference signal is lost as increasing amounts of solar reflectance become present as the sun rises.
The toggle below shows GOES-16 IFR Probability and the Night Fog Brightness Temperature Difference (10.3 – 3.9) at 1301 UTC, before sunrise. There are significant regions of low clouds/fog over northeastern Minnesota that have little signal in the brightness temperature difference field — but there is a strong signal there in the IFR Probability field.
By 1501 UTC, below, when the sun is above the horizon, reflected solar radiation means that the Night Fog Brightness Temperature Difference default enhancement is no longer appropriate to detect low clouds. However, IFR Probability continues to outline regions of low clouds and fog.
GOES-R IFR Probability fields blend the strengths of satellite detection of clouds with the strengths of model detection of low-level saturation. In regions of high clouds, where the satellite cannot view low clouds (over northwest Wisconsin, for example), model data nevertheless gives a useful signal. Note also the lower IFR Probabilities over Ontario where low clouds are prevalent. Here, model data allows IFR probabilities to screen out regions of elevated stratus, which clouds are not so important as far as surface visibility restrictions go.
High Pressure (link), cold air, and proximity to warm Sea Surface Temperatures (image here, ACSPO SSTs from the Direct Broadcast Antenna at CIMSS) meant dense fog over southwestern Louisiana. The animation above shows Probabilty of Low IFR Conditions from 0400 UTC through sunrise. Low IFR Probability fields originate near Lake Charles and Franklin before consolidating into one large field over southern Louisiana. Surface observations of ceilings and visibilities also indicate dense fog.
An after-effect of the landfall of Hurricanes is the destruction of webcams that can be used to monitor fog, as noted in the Area Forecast Discussion from WFO Lake Charles, below, from 441 AM CST on 10 December. IFR Probability products in AWIPS combine model predictions of low-level saturation and satellite observations of low cloud to mitigate this loss of information. Regions where low IFR Probability values are high are likely regions that would have dense fog on webcams.
Night Fog Brightness Temperature difference (10.3 µm – 3.9 µm) fields, below, also show the region of stratus clouds. But this satellite-only product does not contain information on how dense the fog is underneath the cloud top (or even if the stratus cloud is also fog). The combination of model data and satellite data by IFR Probability products gives more information than satellite data alone.
The suite of IFR Probability products includes a GOES-R Cloud Thickness field, shown below overlain on top of a Night Fog Brightness Temperature Difference field. Cloud Thickness is not computed in the 90-120 minutes surrounding sunrise, and the terminator is apparent in the image below, where the Night Fog Brightness Temperature Difference becomes apparent. The thickest cloud (nearly 400 m) is northwest of Lake Ponchartrain. That is where the fog should dissipate last, perhaps.
The scatterplot below can be used to estimate when radiation fog might dissipate. It relates the last pre-sunrise observation of GOES-R Cloud Thickness, as shown above, to the number of hours until burn-off. A value of almost 400 m suggests a burnoff nearly four hours after the image above: 1651 UTC. The Thickness field also suggests fog dissipation will be more rapid over southwestern Louisiana than over regions near Lake Ponchartrain. Animations of the Night Fog Brightness Temperature, above, and the visible imagery, below, show that the estimate was a good one on this day.
The IFR Probability products include Low IFR Probability, shown above, IFR Probability, and Marginal VFR (MVFR) Probability, in addition to Cloud Thickness. The toggle below, from 0906 UTC, shows that MVFR Probability and Low IFR Probability fields were co-located. As expected, probability of MVFR conditions are greater than probabilities of Low IFR conditions.
Added: Lake Charles WFO also tweeted out this excellent image of a Fog Bow as the fog dissipated!
GOES-17 IFR Probability fields, above, show a thin region along the northern shore of Bristol and Kvichak Bays in southwestern Alaska, north of the Aleutian Peninsula, where IFR conditions are likely . Probabilities are highest around King Salmon (PAKN) and Igiugig (PAIG) northeast of King Salmon. Several nearby airports are not reporting observations (PAII — Egegik — just south of King Salmon; PATG –Togiak — along the northern shore of the Bay, east of PAEH (Cape Newenham). IFR probability uses satellite and model information to create an estimate of whether or not IFR conditions will be met in regions where observations are missing. Sometimes, as over Cape Newenham at the end of the animation, high clouds are present and only model data can be used to create the estimate.
The Night Fog Brightness Temperature Difference field, below, shows that low clouds (made up of water droplets) exist over the same region — but this one product cannot indicate whether the stratus deck observed is reducing visibility near the surface (where aviation interests require that information). The model data that are incorporated into IFR Probability in concert with satellite data allow for a better estimate of where visibility is reduced than do satellite data alone. This is especially important when the presence of high clouds, as at the end of the animation in the western part of this domain, makes it difficult for the satellite to view low clouds.
Toggles at 1000 UTC (above) and 1600 UTC (below) of IFR Probability, Low IFR Probability and Night Fog Brightness Temperature Difference, suggest that the greatest likehihood of reduced visibilities are not along the bay shore, but rather inland along the Kvichak River.
GOES-16 IFR Probability fields, above, show very high IFR Probability values over the Willamette Valley of Oregon to the south of Portland; surface observations in the region show IFR conditions from Salem (KSLE) southward through Eugene (KEUG) to Roseburg (KRBG). IFR Probability is also large along the coast, with IFR conditions reported at Newport (KONP) and North Bend (KOTH). The 10.3 µm – 3.9 µm “Night Fog” Brightness Temperature Difference field, below, also has a modest signal in the Valley, and along the coast, giving a qualitative (but not quantitative) estimate of fog.
GOES-R Cloud Thickness (labeled as Fog Depth in TOWR-S Build 19) can be used to estimate radiation fog dissipation time. Values are not computed during the time of rapidly-changing reflected solar radiance (i.e., for the times around sunrise and sunset); the last pre-sunrise value can be used in concert with this scatterplot to estimate burn-off times. Values at 1451 UTC in the Willamette valley peak at around 380 m. This suggests a burn-off time (if this is radiation fog and not advection fog) of about two hours according to the scatterplot below, i.e., shortly before 1700 UTC.
Visible imagery, below, at 1601 and 1706 UTC do show a trend towards clearing. The scatterplot can underestimate clearing time if (1) the sun angle is lower than at the points used to create the scatterplot or (2) if the fog is not strictly a radiation fog. In either event, however, the values from day to day should give useful information. For example, if today’s last pre-sunrise cloud thickness is greater than yesterday’s, then the burn-off time today should be later than yesterday.
Fog/low stratus dissipated shortly after the 1901 UTC image shown above.
GOES-16 IFR Probability fields, above, from 0901 UTC on 27 October 2020 (in a toggle with the Night Fog Brightness Temperature Difference), outline two regions of likely IFR conditions over Virginia and surrounding states. Southern Virginia shows restricted conditions, as does the high terrain along the spine of the Appalachians, extending up to Johnstown PA. (Surface observations largely agree with IFR Probability fields: IFR Probability is high where ceilings are low and visibilities are restricted; IFR Probability is low in regions where IFR conditions are not occurring).
Night Fog Brightness Temperature Difference fields (10.3 µm – 3.9 µm), struggle to identify regions of IFR conditions — elevated stratus (over western Virginia and western Maryland, for example) has a similar signal to fog over southeastern Virginia. There are also regions of high clouds above the low stratus over Maryland and Virginia surrounding middle Chesapeake Bay. In such regions, high IFR Probability is being driven by Rapid Refresh model predictions of low-level saturation.
Use IFR Probability fields to identify regions that have low ceilings and reduced visibilities when Visible imagery (in the day) and Night Fog Brightness Temperature difference (at night). During the day time, this can be achieved by displaying both images at once.
CIMSS is now creating GOES_17 IFR Probability fields for Alaska and will presently be distributing them to the Alaska Region. An example of the utility of the products occurred on 19 October over western Alaska. The toggle above between the Night Fog brightness temperature difference field and the IFR Probability field at 1310 UTC 19 October shows several regions where IFR Probability refines where ceilings and visibilities might restrict aviation — an important piece of information in Alaska.
The default Night Fog Brightness Temperature enhancement is constructed so that stratiform clouds containing water droplets are colored different shades of blue. Higher clouds are various shades of grey.
Consider station PANV — Anvik, AK, near 62.7 N, 160 W. This is a station reporting IFR conditions, and IFR probabilities are near 80%. However, it is also under high clouds; GOES-17 is prevented from viewing low clouds, but the Rapid Refresh model data used in IFR Probability is showing saturation. Model data fills in regions of IFR conditions where high (of mid-level) clouds prevent the satellite from viewing near-surface clouds. Note how IFR Probability is also able to distinguish — correctly — between the IFR conditions at Anvik with the more benign sky conditions to the north at St. Michaels (PAMK), Unalakleet (PAUN) and Shaktoolik (PFSH) along Norton Sound.
In contrast, station PASM — St. Mary’s AK, near 62 N, 163 W — is beneath a strong signal in the Night Fog brightness temperature difference field. However, IFR conditions are not reported, and IFR probabilities are near 40% (and decreasing abruptly to the south). In this region, IFR Probability fields are screening out a region of mid-level stratus.
Station PAMC — McGrath, AK, near 63 N, 155 W — shows near-IFR conditions with a local minimum IFR Probability near 40% and a strong signal of stratus clouds in the Night Fog Brightness Temperature difference. For this station it would be prudent to see how IFR Probabilities were changing with time.
IFR Probability combines the strengths of satellite detection of low clouds with the strength of Rapid Refresh model predictions of low-level saturation to create a product useful in regimes with single or multiple cloud layers.
The New York City area has 3 major international hubs for which ceiling/visibility observations and prediction are critical to efficient operations. IFR Probability fields use both satellite data and Rapid Refresh model data and can supply information about low-level conditions even where mid-level or upper-level clouds obscure a satellite’s view of low clouds. The example above shows very slow northeastward progress of an area of potential IFR conditions towards New York.
Satellite-only data, below, in the form of the night fog brightness tempreature difference from GOES-16, does not give a useful signal for the low clouds along the east coast. A conclusion: Use IFR Probability to monitor the progress of low clouds when multiple cloud decks are present.
The toggle above, between IFR Probability and the Brightness Temperature Difference demonstrates and underscores (1) how IFR Probability can fill in regions under low clouds (in Delmarva and New Jersey, for example), and screen out regions with mid-level stratus (over eastern Lake Ontario the surrounding land, and over eastern Ohio, for example).
IFR Probability fields are supplied to AWIPS (TOWR-S Build 19) via the SBN.
The extensive cirrus shield from Hurricane Delta in the Gulf of Mexico made difficult the satellite-based detection of stratus and low clouds over much of the Deep South on Friday 9 October. The Night Fog Brightness Temperature Difference is also affected by increased solar reflectivity at 1246 UTC in the imagery above. The low clouds over southern Illinois are no longer detected, for example, and the high cirrus of Delta becomes much darker — in both cases because of increased reflectivity of 3.9 µm solar radiation.
The toggle below between the Night Fog Brightness tempreature difference and the Band 4 near-infrared “Cirrus” channel just after sunrise (1246 UTC) shows the extensive cirrus signal in both images. Widespread cirrus is not uncommon, thus the difficulty in detection is not uncommon
GOES-R IFR Probability fields, below, compared to the Night Fog Brightness Temperature fields highlight how IFR Probability, which fields include information about low-level saturation in the Rapid Refresh model, capably fills in regions of fog/low stratus underneath high clouds. It is useful for situational awareness in fog detection when satellite imagery is showing only upper-level clouds. For example, note the large values of IFR Probability in western Kentucky, under the high cirrus, or in northeast Texas! There is good spatial correlation between high IFR Probabilities and reduced ceilings/visibilities. The correlation between the Night Fog Brightness Temperature difference field and reduced ceilings/visibilities is smaller because of low visibility under cirrus (as occurring over northwestern TN and much of LA, for example). Note that IFR Probability fields also give information where IFR conditions are not occurring under cirrus: central Mississippi, for example.
IFR Probability fields through sunrise (below) show a consistent signal, in part because of the Rapid Refresh model information on saturation.
The CIMSS-driven LDM feed that has supplied GOES-16 IFR Probability fields (and before GOES-R’s launch, GOES-13 and GOES-15 IFR Probability fields) to NWS offices will be terminated on or about 20 October. Operational creation of GOES-16 IFR Probability fields has shifted to NOAA/NESDIS, and the fields are now sent over the SBN to forecast offices. TOWR-S Build 19 is required to access and display these fields in AWIPS.