Dense Fog over the Texas High Plains

GOES-R IFR Probability fields, hourly from 0215-1115 UTC on 2 August 2017 (Click to enlarge)

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

The National Weather Service in Lubbock issued Dense Fog Advisories (below) for parts of their CWA early in the morning on 2 August 2017.  GOES-R IFR Probability fields, above, show a slow increase in values over west Texas during the night of 1-2 August 2017, as visibilities drop and ceilings lower in the region.  This followed a band of showers that moved through the area around sunset on 1 August (Click here for a visible image from 0017 UTC on 2 August, from this site).  Highest IFR Probability values at the end of the animation generally overlay the Dense Fog Advisory.  As a situational awareness tool for the developing fog/low stratus, IFR Probability performed well.

 

GOES-16 data posted on this page are preliminary, non-operational and are undergoing testing

GOES-16 Brightness Temperature Difference field (10.3 µm – 3.9 µm) at 1117 UTC on 2 August 2017 (Click to enlarge)

The GOES-R IFR Probability fields above mostly show the small-scale variability (i.e., pixelation) that is common when both (legacy) GOES data and Rapid Refresh Data are used to produce a probability that IFR conditions will be present.  Some exceptions:  southeastern New Mexico at the end of animation (1115 UTC);  the yellow and orange region there overlain by mid-level or high clouds that prevent a satellite view of the low clouds.  The GOES-16 Brightness Temperature Difference (10.3 µm – 3.9 µm) field at 1117 UTC shows a signal of high clouds there (cyan / blue / purple enhancement showing negative values that typify thin cirrus in the Brightness Temperature Difference field at night).  The Green values in the color enhancement are positive values and correspond to stratus (composed of water droplets) clouds.  Because the Brightness Temperature Difference field shows a signal, the Advanced Nighttime Microphysics RGB will also have a signal for fog (the whitish/cyan color), as shown below.

GOES-16’s better temporal and spatial resolution allow for more accurate monitoring of the development of small-scale features.  However, the shortcomings of using a Brightness Temperature Difference from satellite to monitor fog development should not be forgotten:  In regions of cirrus, satellite views of low stratus and fog are blocked.  In addition, over Texas and the rest of the High Plains, upslope flow can generate stratus over the central Plains that becomes fog over the High Plains as the terrain rises into the clouds.  The top of the stratus cloud and the fog bank in such a case can look very similar from satellite.

Advanced Microphysics RGB Composite at 1117 UTC on 2 August 2017 (Click to enlarge)

Below is a toggle between the 1115 UTC IFR Probability field, the GOES16 Brightness Temperature Difference Field, and the GOES16 Advanced Microphysics RGB Composite.

GOES-R IFR Probability fields computed with legacy GOES data and Rapid Refresh model output, GOES-16 Brightness Temperature Difference (10.3 µm – 3.9 µm) field and GOES-16 Advanced Microphysics RGB, all near 1115 UTC on 2 August 2017 (Click to enlarge)

 

Resolution: GOES-R IFR Probability Fields and GOES-16 Data

GOES-R IFR Probabilities computed with GOES-13 and Rapid Refresh Data, Hourly from 0215-1115 UTC on 31 July 2017 (Click to enlarge)

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

The animation above shows the evolution of GOES-R IFR Probability fields over West Virginia early on 31 July 2017, when IFR and Low IFR Conditions developed over much of the state. In addition to elevated probabilities over West Virginia, probabilities increased over eastern Virginia as well, where IFR conditions were not reported. The IFR probabilities over eastern Virginia diminished rapidly at sunrise, as indicated at the end of the animation.

Much of the fog on 31 July 2017 over West Virginia was valley fog. Legacy GOES (GOES-13 and GOES-15) has nominal 4-km resolution at the sub-satellite point, and this resolution can be insufficient to resolve the narrow valleys of the Appalachian Mountains.

GOES-16 data posted on this page are preliminary, non-operational and are undergoing testing

The GOES-16 Animation below shows the 10.3 µm – 3.9 µm Brightness Temperature Difference field for approximately the same time as above. The superior spatial resolution of GOES-16 is evident: tendrils of low clouds/fog are apparent in the animation that until sunrise highlights in green the clouds composed of water droplets (such as fog and stratus). A similar animation of the Nighttime Microphysics RGB Composite (here) similarly highlights stratus (as a whitish color) in the narrow river valleys.

GOES-16 Brightness Temperature Difference Fields (10.3 µm – 3.9 µm), hourly from 0312 – 1112 UTC on 31 July 2017 (Click to enlarge)

This Toggle between the GOES-R IFR Probability and the GOES-16 Brightness Temperature Difference field at 1015 UTC suggests how the IFR Probability Fields will better handle small valley fogs when GOES-16 data are used in the algorithm.

Why Fused Data is better than Satellite Data alone in detecting IFR Conditions: Pennsylvania Example

GOES-R IFR Probability Fields, 0200-1100 UTC on 11 July 2017 (Click to enlarge)

GOES-16 data posted on this page are (still!) preliminary, non-operational data and are undergoing testing

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

Low ceilings and reduced visibilities developed in and around thunderstorms from Ohio and Michigan into southwestern Ontario, upstate New York and northern Pennsylvania during the morning of 11 July 2017. (Reduced visibilities/lowered ceilings persisted past 15 UTC as shown in this image from here). Regions of IFR conditions over northwestern Pennsylvania are in regions of higher IFR Probability after 0500 UTC (over northwest Pennsylvania) and after 0700 UTC (over much of northern Pennsylvania) that have the characteristic flat field look that comes from having only Rapid Refresh Model output drive the Probability (because high clouds, as might occur downwind of Convection, prevent the satellite from seeing low clouds). GOES-R IFR Probability fields also retain a signal through sunrise, as shown in the this toggle between 1000 UTC and 1100 UTC, two times on either side of the terminator.

Because high clouds inhibit the view of low stratus (and potential fog), products that rely on solely satellite data become ineffectual as a situational awareness tool. Consider the toggle below from 0800 UTC of GOES-R IFR Probabilities, the GOES-16 “Fog Product” Brightness Temperature Difference (10.3 µm – 3.9 µm) and the Advanced Nightime Microphysics RGB (that uses the Brightness Temperature Difference Product as one of its components). IFR Conditions over NW Pennsylvania are not diagnosed by the GOES-16 products that rely on the 10.3 µm – 3.9 µm Brightness Temperature Difference field. Where there is a clear field of view, all three products can highlight IFR conditions (in/around London Ontario, for example). Regions of low stratus — but not fog — are also highlighted over parts of upstate New York by the GOES-16 products. (Similar toggles at 0915 and 1100 UTC are available; note that the signal from GOES-16 becomes weak at 1102 UTC because increasing amounts of solar reflectance change the sign of the 10.3 µm – 3.9 µm Brightness Temperature Difference.

These examples typify why GOES-R IFR Probability fields typically have better statistics as far as IFR detection is concerned: Model data fills in regions where high clouds are present, and model data screens out regions where stratus is highlighted by a Brightness Temperature Difference field, but where fog does not exist.

GOES-R IFR Probabilities (computed using GOES-13 and Rapid Refresh Data), GOES-16 “Fog Product” Brightness Temperature Difference (10.3 µm – 3.9 µm) and the Advanced Nighttime Microphysics RGB, all around 0800 UTC on 11 July 2017 (Click to enlarge)

 

What GOES-16 Resolution will bring to IFR Probability

GOES-16 Brightness Temperature Difference field (10.3 µm – 3.9 µm) at 1247 UTC on 5 July 2017 (Click to enlarge)

GOES-16 data posted on this page are preliminary, non-operational data and are undergoing testing

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 continue to be created using legacy GOES (GOES-13 and GOES-15) data. This is slated to continue through late 2017. The toggle above, over Oregon, hints at how the change in resolution in GOES-16, even far from the sub-satellite point, will likely improve GOES-R IFR Probability performance in regions where topography can constrain low clouds and fog.  The GOES-16 Brightness Temperature Difference field, above, is color enhanced so that positive values (that is, where the brightness temperature at 10.3 µm is warmer than the 3.9 µm brightness temperature, which regions indicate cloud tops composed of water droplets, i.e., stratus) are whitish — and the data shows stratus/fog along the Oregon Coast, with fingers of fog advancing up small valleys.  The image below shows the GOES-R IFR Probability field for the same time (Click here for a toggle).

GOES-R IFR Probability fields show strong probabilities where the Brightness Temperature Difference field above is indicating low clouds.  This is not surprising as the morning fog on this date was not overlain by higher clouds.  However, the resolution inherent in the legacy GOES (inferior resolution compared to GOES-16), shows up plainly as a blocky field.  When GOES-R IFR Probability fields are computed using GOES-16 data, the IFR Probability field resolution will match the GOES-16 resolution.  (Click here for a aviationweather.gov observation of IFR / Low IFR conditions on the morning of 5 July).

GOES-R IFR Probability field computed from GOES-15 data at 1245 UTC on 5 July 2017 (Click to enlarge)

A similar set of figures for California at the same time is below.  The toggle is here, and the aviationweather.gov screen capture is here.

GOES-16 Brightness Temperature Difference field (10.3 µm – 3.9 µm) at 1247 UTC on 5 July 2017 (Click to enlarge)

GOES-R IFR Probability field computed from GOES-15 data at 1245 UTC on 5 July 2017 (Click to enlarge)

 

Methods of Fog Detection in the GOES-16 Era

GOES-16 ‘Fog Detection’ Channel Difference (10.3 µm – 3.9 µm), 0912 – 1132 UTC, 29 June 2017 (Click to enlarge)

GOES-16 data posted on this page are (still!) preliminary, non-operational data and are undergoing testing

The 16 channels on the GOES-R Series Advanced Baseline Imager (ABI) allow for many different channel combinations that can be used to detect atmospheric phenomena. The animation above shows the traditional method for detecting low stratus: the brightness temperature difference between the shortwave infrared (3.9 µm) and the cleanest longwave infrared (10.3 µm) windows. Cloudtops composed of water droplets are highlighted in the animation because they do not emit 3.9 µm radiation as a blackbody, but do emit 10.3 µm radiation as a blackbody; thus, the brightness temperature difference at night (when no reflected solar radiation at 3.9 µm is present) is positive. The range of the colorbar in the above animation is from -50 to +50 C; stratus appears as green over much of northern Wisconsin, Minnesota and North Dakota.  Higher cirrus clouds are cyan, and they interfere with the satellite detection of low clouds, especially over eastern North Dakota where IFR conditions were widespread (source), and where a Dense Fog Advisory existed.  Note the apparent disappearance of the fog signal — in green — as the sun rises.  Increasing amounts of reflected solar radiation are causing the brightness temperature difference value to switch sign from (10.3 µm – 3.9 µm) > 0 at night (because of emissivity differences) to (10.3 µm – 3.9 µm) < 0 during the day (because of solar reflectance).

The ‘Nighttime Microphysics Advanced RGB’ is also used as a fog detection device. In the animation below, low stratus (and by inference, fog) is highlighted in cyan, a signal that comes mostly from the ‘green’ part of the RGB, namely the Brightness Temperature Difference as shown above. Because the two products are linked by the 10.3 – 3.9 brightness temperature difference, shortcomings in that product as far as fog detection affect the RGB. Note how the fog signal erodes over Minnesota/Wisconsin as the sun rises, and how it is obscured by high clouds (dark purple/magenta) over North Dakota.

 

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, shown for this event below, were designed to mitigate detection issues noted above.  Where high clouds are present, meaning the satellite cannot detect low clouds, information about low-level saturation from the Rapid Refresh is used to assess whether or not fog is likely.    That low-level information from the model also can be used to distinguish between fog and elevated stratus that can look very similar from the top, as a satellite views it.  The fusing of model and satellite data makes for a product that has better statistics in detecting low ceilings and reduced visibilities.

At the end of the two animations above, for example, how confident will a satellite analyst or forecaster be that there is dense fog over eastern North Dakota?  How about the analyst/forecaster using IFR Probability fields? IFR Probabilities maintain a signal for fog over the entire region from North Dakota to Wisconsin, even through sunrise and under high clouds.

GOES-R IFR Probabilities, 0915 to 1115 UTC on 29 June 2017 (Click to enlarge)

Fog Detection Methods when Cirrus Clouds are present

GOES-R IFR Probability fields, 0545, 0800, 1000 and 1300 UTC on 13 June 2017 (Click to enlarge)

GOES-16 data posted on this page are (still!) preliminary, non-operational data and are undergoing testing

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

Reduced visibilities and lowered ceilings — IFR and Low IFR conditions — occurred over a wide stretch of the southeastern United States on the morning of 13 June 2017.  The animation above shows enhanced IFR Probabilities aligned northwest to southeast from central Alabama to northwest Florida, the region where IFR Conditions develop.  The flat nature of the field suggests that satellite data are not widely available as a predictor for low clouds on this morning, and that is because of widespread cirrus clouds over the southeastern United States (Click here to see the 0545 and 1300 UTC GOES-13 Water Vapor Imagery with cold brightness temperatures over the southeast).  When high clouds are present, satellite-only detection of low clouds is not feasible.

For example, the brightness temperature difference field from GOES-13 (3.9 µm – 10.7 µm), below, has little predictive value for the northwest-to-southeast-oriented feature of low clouds/reduced visibilities because cirrus clouds are blocking the view.

GOES-13 Brightness Temperature Difference (3.9 µm – 10.7 µm) at 0545, 0800, 1000 and 1315 UTC on 13 June 2017 (Click to enlarge)

Various other detection techniques that rely on only satellite data similarly will be challenged by the presence of high clouds. For example, the Nighttime Microphysics RGB (From this site) shows little signal of fog (cyan/white in the RGB composite) over Georgia and Florida. Intermittent signals will appear occaionally as high clouds thin to allow the low-cloud signal through. The GOES-16 Brightness Temperature Difference field (10.33 µm – 3.9 µm) similarly is challenged by the presence of high clouds. The example shown at 1300 UTC shows a negative value because of reflectance off of high clouds.

Because the IFR Probability fields include surface information in the form of output from the Rapid Refresh model (saturation in the lowest part of the model is used as a predictor for the presence of fog), IFR Probability fields can fill in regions where satellite data cannot be used to detect low clouds because of the presence of high clouds. In regions where high clouds are present, satellite-only detection of fog and low stratus will always be a challenge.

Dense Fog under high clouds in the Deep South

GOES-13 Brightness Temperature Difference (3.9 µm – 10.7 µm) Values at 0815 UTC on 23 May 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

The legacy method of detecting fog/low clouds from satellite is the Brightness Temperature Difference product that compares computed brightness temperatures at 3.9 µm and at 10.7 µm. At night, because clouds composed of water droplets do not emit 3.9 µm radiation as a blackbody, the inferred 3.9 µm brightness temperature is colder than the brightness temperature computed using 10.7 µm radiation. In the image above, the brightness temperature difference has been color-enhanced so that clouds composed of water droplets are orange — this region is mostly confined to southeast Texas. Widespread cirrus and mid-level clouds are blocking the satellite view of low clouds. IFR and near-IFR conditions are widespread over east Texas, Louisiana and Mississippi. The GOES-R IFR Probability field, below, from the same time, suggests IFR conditions are likely over the region where IFR conditions are observed.

This is a case where the model information that is included in this fused product (that includes both satellite observations where possible and model predictions) fills in regions where cirrus and mid-level clouds obstruct the satellite’s view of low clouds.. As a situational awareness tool, GOES-R IFR Probability can give a more informed representation of where restricted visibilities and ceilings might be occurring.

IFR Conditions continued into early morning as noted in this screenshot from the Aviation Weather Center.

GOES-R IFR Probability fields, 0815 UTC on 23 May 2017 (Click to enlarge)

IFR Conditions under multiple cloud decks in the Upper Midwest

GOES-R IFR Probability Field, along with observations of surface visibility and ceiling heights, 1100 UTC on 17 May 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

The GOES-16 data posted on this page are preliminary, non-operational data and are undergoing testing

A morning screenshot from the Aviation Weather Center website shows a wide region of IFR and Low IFR Conditions reported over the Upper Midwest, from eastern North Dakota eastward to Lake Superior. The GOES-R IFR Probability field, above, from 1100 UTC on 17 May 2017, shows high probabilities over that region.

Over much of Minnesota and Michigan, the character of the IFR Probability field is flat.  This is typical of IFR Probability when high clouds prevent satellite data from being used as a statistical predictor for IFR Conditions.  If high clouds are present, the satellite cannot detect the presence of low clouds, and the chief predictor of IFR conditions will therefore be model data that typically does not vary strongly from gridpoint to gridpoint when IFR conditions are present.  The pronounced boundary apparent in the IFR Probability field that extends nortwestward from Green Bay in Wisconsin is the boundary between night-time predictors (to the west) and daytime predictors (to the east).

GOES-R IFR Probability values are largest in the region of IFR conditions over North Dakota.  In this region, high clouds are not present and the satellite is able to detect low clouds, and that information is part of the computation of IFR Probabilities.  Note also the region in east-central Minnesota where satellite data are also being used in the computation of IFR Probabilities;  the resultant field there is pixelated.

The toggle below shows the brightness temperature difference field between the shortwave and longwave infrared window channels from GOES-13 (3.9 µm and 10.7 µm) and GOES-16 (3.9 µm and 10.33 µm).  This brightness temperature difference field is used to detect stratus, and by inference fog, because stratus cloud tops composed of water droplets emit radiation around 10.3-10.7 µm as a blackbody, but do not emit 3.9 µm radiation as a blackbody.  Satellite detection of radiation, and computation of the inferred temperature of the emitting surface, assumes blackbody emissions.  Consequently, the brightness temperature computed using detected 3.9 µm radiation is colder than that computed using 10.7 µm (or 10.33 µm) radiation.

Both satellites capture the region of low stratus/fog over North Dakota, and the superior spatial resolution of GOES-16 is apparent. Note, however, that neither satellite can detect low clouds associated with dense fog in regions of higher clouds — over Michigan’s Keewenaw Peninsula, for example, or along the shore of Lake Superior in Minnesota. This is an unavoidable shortcoming of satellite-only-based detection of low clouds/fog.

GOES-13 (3.9 µm – 10.7 µm) and GOES-16 (10.33 µm – 3.9 µm) Brightness Temperature Difference fields, 1100 UTC on 17 May 2017 (Click to enlarge)

Dense Fog from the Ohio Valley to North Carolina

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.

The weather.gov website on Wednesday morning 10 May 2017 showed two dense fog advisories, one near Cincinnati, OH and one near Greensboro, NC. The aviation weather website showed an IFR Sigmet in between the two regions of dense fog. The fog formed along a stationary front that sat over the region.

How well did GOES-R IFR Probabilities and GOES-13 Brightness Temperature Difference fields capture this event? The animation of GOES-R IFR Probability, below, computed using data from GOES-13 and the Rapid Refresh Model, shows enhanced probabilities early in the evening that increased with time. The orientation of the field — from west-northwest to east-southeast — aligns well with the regions of developing fog.

GOES-R IFR Probability fields, 0100, 0400 and 0700 – 1100 UTC on 10 May 2017 (Click to enlarge)

The brightness temperature difference field, below, did not perform as well in outlining the region of low ceilings/reduced visibilities because of the presence of high clouds that interfered with the ability to detect low clouds. Consequently, the highest brightness temperature differences (3.9 µm – 10.7 µm) do not align so well with the regions of developing fog. Note also that at the end of the animation — 1100 UTC — increasing amounts of reflected solar 3.9 µm radiation is changing the character of the field from negative to positive. In contrast, the IFR Probability fields (above) maintain a consistent signal through sunrise.

GOES-13 Brightness Temperature Difference fields (3.9 µm – 10.7 µm), 0700-1100 UTC on 10 May 2017 (Click to enlarge)

IFR Probability and Low IFR Probability in the Pacific Northwest

GOES-R IFR Probability fields, hourly from 0300 through 1500 UTC on 4 May 2017 (Click to enlarge)

GOES-R Low IFR Probability fields, hourly from 0300 through 1500 UTC on 4 May 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.

Dense fog with IFR and Low IFR Conditions occurred along the Oregon and Washington Coasts early on 4 May 2017. The animations above show the evolution of IFR Probability and Low IFR Probability. Note that IFR Conditions/Low IFR Conditions mostly occurred where Probabilities were high, with a few exceptions (KSMP, Stampede Pass, WA; KKLS, Kelso WA at 1400 UTC). Both IFR and Low IFR Probabilities show a general areal increased between 0800 and 0900; this can be traced to a big increase in the brightness temperature difference that occurred between 0845 and 0900 UTC (shown here) that is likely due to stray light intruding into the satellite detectors. (Brightness Temperature Difference values decreased after 0900 UTC — note that the Brightness Temperature Difference enhancement has color starting when ‘counts’ in the image reach -6).

Low IFR Probabilities do a particularly good job above of outlining the regions of visibility and ceiling restrictions along the coasts of Oregon and of Puget Sound.  Note also that a strip of missing satellite data exists at 1100 UTC over northern Washington.  When satellite data are missing completely, IFR Probabilities are not computed.

A difficulty in using Brightness Temperature Difference fields is shown below. The 1300 and 1400 UTC Brightness Temperature Difference fields show an apparent decrease in low clouds detected as the sun rises (in reality, the amount of reflected 3.9 radiation is increasing as the Sun rises). Fog persists through sunrise as shown in the observations; IFR Probabilities (and Low IFR Probabilities) maintain a signal throughout sunrise.

GOES-15 Brightness Temperature Difference (3.9 µm – 10.7 µm) at 1300 and 1400 UTC on 4 May 2017 followed by GOES-R IFR Probability fields at 1300 and 1400 UTC on 4 May 2017 (Click to enlarge)