IFR Conditions over North Dakota

GOES-13 Brightness Temperature Difference and GOES-R IFR Probability at 0615 UTC on 24 April 2017, along with surface observations (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 late-season snow storm was affecting North Dakota on Monday 24 April. Snow amounts were modest and IFR Conditions were widespread.   The multiple clouds layers associated with the storm meant that satellite detection of low clouds/fog was difficult.  The toggle above shows Brightness Temperature Difference (3.9 µm – 10.7 µm) and GOES-R IFR Probabilities.  The IFR Probability field distinctly outlines the region where visibilities and ceilings are restricted by the storm.  It is very difficult to discern from the Brightness Temperature Difference product where low clouds/fog exist.  Because the IFR Probability field incorporates surface information (that is, low-level saturation as predicted by the Rapid Refresh Model), it is better able to alert a forecaster to the presence of IFR or near-IFR conditions.

Dense Fog along the Florida Gulf Coast

GOES-R IFR Probability Fields, hourly from 0415-1107 UTC on 17 April 2017 (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.

GOES-R IFR Probability fields, above, show increases in IFR Probability starting around midnight local time. The IFR Probability fields generally outline the regions where fog occurred (and which led to the issuance of dense fog advisories to the east of Mobile). How did the brightness temperature difference field capture this event? Brightness Temperature Difference fields have been used historically to identify regions of stratus.

GOES-13 Brightness Temperature Difference fields (3.9 µm – 10.7 µm), hourly from 0415-1107 UTC on 17 April 2017 (Click to enlarge)

The brightness temperature difference field, above, shows a slow increase in negative values (negative because the brightness temperature computed from emitted 3.9 µm radiation is cooler than that computed from emitted 10.7 µm radiation over clouds composed of water because such clouds do not emit 3.9 µm radiation as a blackbody), but careful inspection of the field shows IFR conditions in regions outside the largest signal (highlighted in this enhancement in yellow).  This occurs mostly were cirrus clouds are indicated (represented as dark regions in the enhancement, over central Mississippi, for example).  In such regions, the IFR Probability fields can maintain a signal of IFR conditions because low-level saturation is predicted by the Rapid Refresh Model.

This fused product combines the strengths of both inputs:  Satellite detection of low clouds, and model prediction of low-level saturation.

As the sun rises, the amount of reflected solar radiation at 3.9 µm increases, and the sign of the brightness temperature difference changes from negative at night over water clouds to positive.  This toggle below shows the brightness temperature difference at 1107 and 1145 UTC.  The decrease in signal does not necessarily mean fog is decreasing.

GOES-13 Brightness Temperature Difference fields (3.9 µm – 10.7 µm) at 1107 UTC and 1145 UTC on 17 April 2017 (Click to enlarge)

A toggle of the IFR Probability and the Brightness Temperature Difference at 1145 UTC, below, shows that the IFR Probability fields can maintain a useful signal during the time of rapid changes in reflected solar radiation with a wavelength of 3.9 µm.

GOES-R IFR Probability and GOES-13 Brightness Temperature Difference fields (3.9 µm – 10.7 µm) at 1145 UTC on 17 April 2017 (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.

Low IFR and IFR Conditions over the central United States

Note: GOES-R IFR Probabities continue to be computed with GOES-13 and GOES-15 data only. Incorporation of GOES-16 data will occur near the end of 2017.

Front Page from the Aviation Weather Center webpage (http://www.aviationweather.gov) at 1155 UTC 3 April 2017. Stations with IFR / LIFR conditions are indicated by red / magenta. (Click to enlarge)

A wide swath of Dense Fog Advisories were hoisted over the central part of the United States on Monday 3 April 2017 in association with southerly flow and a variety of fronts. The Aviation Weather Center front page (screen-captured, as shown above), showed Low IFR and IFR conditions throughout the central Plains.

The development of the Low IFR Conditions coincided with an increase in Low IFR Probabilities during the night, as shown by the stepped animation below showing data from 0200, 0500 and 0915 UTC. Low IFR Probabilities over Iowa, Nebraska and Kansas, part of a smooth yellow field, are driven solely by Rapid Refresh Data in a region where high and mid-level clouds are preventing the satellite from observing low clouds.

GOES-R Low IFR Probabilities at 0200, 0500, 0915 UTC on 3 April 2017 (Click to enlarge)

GOES-R IFR Probabilities, below, generally cover the same region as Low IFR Probabilities shown above, but have larger values. As with the Low IFR Probabilities, model data only are determining the probabilities over much of Iowa, Nebraska and Kansas, a region where the probability field is uniform and flat, especially at the end of the animation.

GOES-R IFR Probabilities at 0200, 0500, 0915 UTC on 3 April 2017 (Click to enlarge)

GOES-R Low IFR Probability is shown below with surface observations superimposed. There is a good relationship between high probabilities and observed IFR and Low IFR conditions.

GOES-R IFR Probability fields, 0945 UTC on 3 April 2017 along with surface observations of ceilings and visibilities at 1000 UTC (Click to enlarge)

Note: GOES-R IFR Probabities continue to be computed with GOES-13 and GOES-15 data only. Incorporation of GOES-16 data will occur near the end of 2017.

Fast-moving Fog over northeast Montana

GOES-R IFR Probability fields computed with GOES-13 and Rapid Refresh Data, 1400-1500 UTC on 24 March 2017 (Click to enlarge)

Rains over Montana earlier this month (data from this site) (along with snowmelt) caused substantial flooding on Big Muddy Creek in the extreme northeast part of the state. Saturated soils in that region have increased the likelihood of fog, and fog was indicated by IFR Probability in that region on the morning of 24 March as shown above.

GOES-16 Visible Imagery showed the fog speedily moving down Big Muddy Creek. An animation using GOES-13 Visible imagery (0.64 µm) is shown below. The GOES-16 CONUS cadence is every five minutes; it is every 15 minutes with GOES-13, except when Full Disks are being scanned (14:45 UTC) or when housekeeping is occurring (15:30 UTC).

GOES-13 and GOES-16 visible data both show quick movement of the fog. For this case, it was harder to judge motion from the IFR Probability fields. This could be related to Infrared and model resolution; the creek valley might be too narrow for the satellite infrared data and for the model.

GOES-13 VIsible (0.64 µm) imagery, 1415-1615 UTC. Sheridan County Montana is outlined in the first image. Fog advancing down Big Muddy Creek is apparent

Dense Fog in Louisiana

GOES-R IFR Probabilities computed using GOES-13 and Rapid Refresh Data, 0400-1000 UTC on 20 March 2017 (Click to enlarge)

Note: GOES-R FLS products are currently derived from GOES-13 and GOES-15 data. A GOES-16 version of the GOES-R FLS products will not be available until later in 2017.

Dense Fog advisories were issued for much of central and southern Louisiana (screenshot taken from this site) on Monday morning, 20 March 2017;  IFR and Low IFR conditions were widespread (screenshot from this site).  The animation above shows the development of IFR Probabilities in concert with the development of IFR conditions. A strength of the IFR Probability field on this day was that it indicated the possibility of fog development some time before satellite brightness temperature difference fields. Low-level saturation was (correctly) occurring in the Rapid Refresh model, and that helped increase IFR Probability values.

Consider the toggle below, showing, the IFR Probability and Brightness Temperature Difference fields at 0500 UTC. The Brightness Temperature Difference field over coastal Louisiana at 0500 shows little indication of fog development. An animation brightness temperature difference fields that matches the 0400-1000 UTC timeframe shown above for IFR Probabilities is below. Although a strong brightness temperature difference signal is present in Texas, it does take some time for the signal to develop over Louisiana. IFR Probabilities were more helpful for situational awareness on this day.

IFR Probabilities and GOES-13 Brightness Temperature Difference Fields (3.9 µm – 10.7 µm), 0500 UTC on 20 March 2017 (Click to enlarge)

GOES-13 Brightness Temperature Difference (3.9 µm – 10.7 µm), 0400-1000 UTC on 20 March 2017 (Click to enlarge)

Marine Stratus over southern California

GOES-R IFR Probability, hourly from 0300 to 1300 UTC, 13 March 2017 (Click to enlarge)

Note:  GOES-R FLS products are currently derived from GOES-13 and GOES-15 data.  A GOES-16 version of the GOES-R FLS products will not be available until later in 2017.

IFR Conditions developed early on March 13th 2017 as Marine Stratus moved over the southern California. This is a typical occurrence that nevertheless requires timely monitoring because of the impact of fog on transportation. The Brightness Temperature Difference fields, below, show the tops of the clouds. Water clouds do not emit 3.9 µm radiation as a blackbody, but they do emit 10.7 µm radiation nearly as a blackbody. Result: Inferred brightness temperatures (a computation that assumes blackbody emission from the source) are cooler and 3.9 µm than at 10.7 µm, and a difference field will highlight clouds made up of water droplets, i.e., stratus. If the stratus is at the surface, fog is a result. Low IFR Probability fields, above, include surface information in the form of low-level relative humidity fields from the Rapid Refresh model. Only where saturation near the surface is indicated by the model will Low IFR Probabilities be large.

In the animation above, IFR conditions develop along the coast and penetrate inland as Low IFR Probabilities increase.  Probabilities are decreasing by 1300 UTC.

IFR and Low IFR Conditions are shown in this plot from the Aviation Weather CenterThis toggle, from 0300 UTC, shows both Low IFR Probability and IFR Probability.  As might be expected, IFR Probabilities exceed Low IFR Probabilities

GOES-15 Brightness Temperature Difference (3.9 µm – 10.7 µm) Fields, hourly from 0300 to 1300 UTC on 13 March 2017 (Click to enlarge)

GOES-16 Data are Flowing

GOES-R IFR Probability that uses present GOES (GOES-13 and GOES-15) data in the computation of GOES-R IFR Probability fields was designed in anticipation of GOES-16 data that are now flowing to National Weather Service Forecast offices. Click here for a description of the Brightness Temperature Difference field values that are available now in AWIPS from GOES-16.

GOES-R FLS products are currently derived from GOES-13 and GOES-15 data.  A GOES-16 version of the GOES-R FLS products will not be available until later in 2017.

Dense fog in Ohio, Indiana and Illinois

Toggle between 1100 UTC GOES-R IFR Probability fields and GOES-13 Brightness Temperature Difference (3.9 µm – 10.7 µm) fields, with surface observations of ceilings and observations. (Click to enlarge)

Dense Fog developed over central/southern Ohio, Indiana and Illinois on the morning of 20 February 2017 (Screen shot from here; IFR Depiction from here). The toggle above includes the GOES-R IFR Probability fields; large values of IFR Probability overspread the region of low ceilings and visibilities. In contrast, high clouds (dark grey in the enhancement used) are preventing the Brightness Temperature Difference field from articulating where the fog/low ceilings might be occurring. Because satellite data cannot be used as a predictor at 1100 UTC, the character of the IFR Probability field is mostly uniform, lacking the pixelation that occurs when low clouds can be viewed from the satellite (as over southwestern Pennsylvania, West Virginia, central Kentucky, parts of central Illinois, and elsewhere).

High clouds have not moved into Ohio and Indiana at 0500 UTC on 20 February as the fog was developing (they are present over Illinois, however). The toggle below, from that time, shows low probabilities of IFR conditions over Ohio, and only a few reports of IFR conditions, mostly over central and southern Ohio.

Toggle between 0500 UTC GOES-R IFR Probability fields and GOES-13 Brightness Temperature Difference (3.9 µm – 10.7 µm) fields, with surface observations of ceilings and observations. (Click to enlarge)

Toggle between 0700 UTC GOES-R IFR Probability fields and GOES-13 Brightness Temperature Difference (3.9 µm – 10.7 µm) fields, with surface observations of ceilings and observations. (Click to enlarge)

By 0700 UTC (above), IFR conditions are becoming more widespread as IFR Probabilities increase.  The dark regions in the Brightness Temperature Difference fields over Wisconsin and Lower Michigan and surrounding regions show the advance of high clouds.  By 1000 UTC, those clouds have overspread Ohio and Indiana, and the brightness temperature difference field loses utility as far as low-cloud detection goes.  Because the IFR Probability fields incorporate low-level saturation information from the Rapid Refresh Model, however, IFR Probability fields can continue to provide a useful signal when high clouds are present or move in.

Toggle between 1000 UTC GOES-R IFR Probability fields and GOES-13 Brightness Temperature Difference (3.9 µm – 10.7 µm) fields, with surface observations of ceilings and observations. (Click to enlarge)

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)