Category Archives: Rocky Mountains

The Challenge of Satellite Fog Detection at Very Small Scales


Hazards as depicted by front page at 1400 UTC on 10 November 2016 (Click to enlarge)

Isolated regions of dense fog developed over eastern Oregon early in the morning on 10 November 2016, and one county — around Baker City — was placed in a Dense Fog advisory (Counties in the Willamette Valley of western Oregon, and near Glacier Park in Montana were placed under Dense Fog Advisories a bit later in the morning on 10 November). Click here to see a 1400 UTC mapping of IFR/LIFR conditions from the Aviation Weather Center.

600 AM MST THU NOV 10 2016

500 AM PST THU NOV 10 2016









What kind of Fog-detection products are available to assist a forecaster in seeing at a glance that fog is developing? How useful are they for small-scale features such as that in Baker County Oregon?

Brightness Temperature Difference Fields (3.9 µm – 10.7 µm) have historically been used to detect fog; the difference field keys on the Emissivity Differences that exist in water-based cloud droplets: they do not emit 3.9 µm radiation as a blackbody, but do emit 10.7 µm radiation more nearly as a blackbody, so computed brightness temperatures are different: cooler at 3.9 µm than at 10.7 µm. The Brightness Temperature Difference fields for 0900 and 1200 UTC are shown below (Note the seam — GOES-13 data are used east of the seam, GOES-15 data are used to the west). There is no distinct signal over Baker County, nor any pattern that can really help identify regions of fog. Cirrus is present over western Oregon (depicted as dark grey or black in this enhancement); satellite-only detection of low fog is not possible if cirrus prevents a view of the surface.


GOES Brightness Temperature Difference Fields (3.9 µm – 10.7 µm) at 0900 and 1200 UTC on 10 November 2016 (Click to enlarge)

For a small-scale event, the nominal 4-km pixel size on GOES-15 and GOES-13 (a size that is closer to 6-7 km over Oregon because of the distance from the sub-satellite point) may prevent satellite detection of developing fog. The toggle below shows Brightness Temperature Difference fields at 0928 UTC from MODIS on Aqua, as well as the GOES-R IFR Probability fields computed using the MODIS data.  As with GOES data, the presence of cirrus in the Brightness Temperature Difference field is obvious and shown by a black enhancement.  Little signal is present over Baker County.  (There is a strong signal, however, in the valleys of northwest Montana and northern Idaho — compare this to the GOES-based brightness temperature difference above).

Note:  MODIS resolution is 1-km;  data from the Advanced Baseline Imager (ABI) on GOES-R will have nominal 2-km resolution at the sub-satellite point.


MODIS Brightness Temperature Difference fields (3.7 µm – 11 µm) and MODIS-based GOES-R IFR Probability, 0928 UTC on 10 November 2016 (Click to enlarge)

What does the GOES-based GOES-R IFR Probability field show during the early morning hours of 10 November? The animation below, from 0800-1200 UTC, shows some returns in/around Baker County. It would have been difficult to use this product alone to diagnose this fog feature however. (It did do a better job of diagnosing the presence of fog over northwest Montana and western Oregon where Advisories were later issued).


GOES-R IFR Probability fields, hourly from 0800 – 1200 UTC on 10 November 2016 (Click to enlarge)

Fog over northeast Colorado backs into Denver International


GOES-R IFR Probability Fields, 1437 UTC on 31 August 2016, with surface observations of ceilings and visibilities (Click to enlarge)

GOES-R IFR Probability Fields over Colorado and Nebraska on the morning of 31 August 2016 show high IFR Probabilities in close proximity to Denver International Airport (DIA), which airport was reporting IFR conditions starting at 1237 UTC. Webcams to the southwest and northeast of the airport shortly after 1500 UTC confirm that the IFR conditions’ edge was very near the airport.

The hourly animation of GOES-R IFR Probability fields, below, shows the evolution of the field. Its motion could be used in a prognostic manner.


GOES-R IFR Probability fields, ~hourly from 0400 through 1400 UTC on 31 August 2016 (Click to enlarge). Surface observations of ceilings and visibility are also plotted.

A similar event occurred on 22 September, see below from Mike Eckert and Amanda Terborg.09222016-den_fog

Fog and Low Stratus in Idaho’s Snake River Valley


GOES-R IFR Probability, hourly from 0500 through 1500 on 8 February 2016 (Click to enlarge)

River Valleys will be prone to fog when they are capped by strong High Pressure, as occurred early in the morning on 8 February 2016 over Idaho. The animations, above and below, show GOES-R IFR Probability fields and GOES Brightness Temperature Difference Fields, respectively. GOES-R IFR Probability fields include information from the Rapid Refresh about near-surface saturation. Compare IFR Probabilities at Burley Municipal Airport (KBYI) in Cassia County with those at Jerome County Airport (KJER) between 0800 and 1100 UTC, when visibilities and ceilings at the two airports vary. IFR Probabilities in general are higher when reported ceilings and visibilities are consistent with IFR conditions, and they are lower when IFR conditions are not reported.

Extensive mid-level stratus over the eastern portions of northern Idaho and western Montana have reduced values of IFR Probabilities (compared to the Snake River Valley where IFR Probabilities are large). This is a benefit of a fused product: Information from two sources combined is more powerful than either of the two sources individually. IFR Probability fields also have superior results when mid-level or higher clouds overspread an area. This occurs around 1200-1300 UTC over the northeastern portion of the Snake River Canyon around Rexburg and Idaho Falls, locations that maintain IFR (or near-IFR) conditions although the brightness temperature difference field has little signal. IFR Probabilities remain enhanced in the region, however, with a signal — a less pixelated, flatter field — that suggests only Rapid Refresh Data are being used as predictors of IFR.


GOES Brightness Temperature Difference (10.7µm – 3.9µm), hourly from 0500 through 1500 on 8 February 2016 (Click to enlarge)

Dense Fog Advisories in southern Idaho

Dense Fog Advisories were issued over southern Idaho early on 19 January 2016. GOES-R IFR Probability fields, below, show high probabilities in this region. Surface observations are not common over northern Utah/southern Idaho, and the hourly animation does show IFR Conditions observed near the high IFR Probabilities over southern Idaho and northern Utah (Logan, UT — KLGU — at 41:47 N, 111:51 W, in far northern Utah from 0300 – 1300 UTC; Elko NV — KEKO — at 40:50 N, 115:47 W also shows IFR conditions).  The Snake River Valley in Idaho also shows high IFR Probabilities during parts of the animation.

GOES-R IFR Probability fields fuse together information from GOES-15 (or, in the eastern part of the USA, GOES-13) and Rapid Refresh Data. Highest probabilities occur where the satellite detects low (water-based) clouds and where the Rapid Refresh Model predicts low-level saturation. High IFR Probabilities don’t necessarily guarantee the presence of IFR Conditions — but they are a flag that should prompt a forecaster to be alert to the possibility that IFR conditions are present nearby or are developing. When dense fog is valley-based as might happen in the Rocky Mountains, GOES Pixel footprints and Rapid Refresh model resolution are sometimes too coarse to resolve completely the fog. MODIS data (bottom) has 1-km horizontal resolution and IFR Probabilities computed from MODIS data are more likely to resolve small valleys.


GOES-R IFR Probability fields computed from Rapid Refresh Model Output and GOES-15 Satellite Data, hourly from 0200 – 1500 UTC, 19 January 2016 (Click to enlarge)


GOES-R IFR Probability computed from MODIS data and Rapid Refresh Model output, 1023 UTC 19 January 2016 (Click to enlarge)

Lead Time with GOES-R IFR Probabilities and Brightness Temperature Difference

A small region of dense fog developed over northeast Colorado and western Nebraska during the early morning on June 1st 2015. How did the GOES-R and traditional products handle this event? The animation below shows IFR Probabilities from 0730-0800 UTC on 1 June. Probabilities jump from <10% to about 20% at 0800 UTC in a region centered on Holyoke, CO, just south of I-76 in northeast Colorado. The Brightness Temperature Difference Field for the same 3 times, below the IFR Probabilities, shows a signal moving over the region but not substantially changing. (From this, one could conclude that the Rapid Refresh model data might be driving increase in the IFR Probability field)


GOES-R IFR Probabilities, 0730-0800 UTC on 1 June (Click to enlarge)


GOES-East Brightness Temperature Difference fields (10.7 µm – 3.9 µm) at 0730, 0745 and 0800 UTC, 1 June 2015 (Click to enlarge)


Toggle between GOES-R IFR Probabilities and GOES-East Brightness Temperature Difference Fields, 0915 UTC on 1 June (Click to enlarge)

By 0915 UTC (above), IFR Probabilities and GOES-13 Brightness Temperature Difference fields show a strong signal over NE Colorado where IFR Conditions occur/are developing. IFR Probability fields have provided more lead-time in the development of this region of low ceilings and visibilities. By 1100 UTC (below), a stronger, more widespread signal is apparent in both fields. At 1230 UTC (bottom), the rising sun has altered the brightness temperature field so it gives no useful information on low clouds; this highlights an advantage of GOES-R IFR Probability fields: A consistent signal through sunrise.


Toggle between GOES-R IFR Probabilities and GOES-East Brightness Temperature Difference Fields, 1100 UTC on 1 June (Click to enlarge)


Toggle between GOES-R IFR Probabilities and GOES-East Brightness Temperature Difference Fields, 1230 UTC on 1 June (Click to enlarge)

Use moving IFR Probability Fields as a forecast aid


GOES-R IFR Probabilities, hourly from 0200 through 1300 UTC on 7 April 2015 (Click to enlarge)

Denver International Airport had a period of restricted visibility during the morning of 7 April, starting around 0830 UTC, when northeast winds ushered in low ceilings and reduced visibilities. High Probabilities in the IFR Probability fields shift west and south with time, demonstrating how the fields can be used to anticipate the development of IFR conditions.

Freezing Fog over Idaho’s Snake River Valley


GOES-R IFR Probabilities over southern Idaho, hourly from 0300 through 1800 UTC on 14 January 2015 (Click to enlarge)

High Pressure over the Rocky Mountains, and its associated inversion, has trapped moisture at low levels, including along the Snake River in southern Idaho. As a consequence, fog is prevalent at low levels, and cold temperatures are allowing for the development of freezing fog. (Click here for a National Weather Service advisory from the Pocatello WFO).

The GOES-R IFR Algorithm shows high probabilities over the Snake River Valley where the Fog/Freezing Fog was occuring — Twin Falls and Rexburg both reported freezing fog during the animation above. The Brightness Temperature Difference field, below, also captures the areal extent of the low stratus deck.


GOES Brightness Temperature Difference fields (10.7µm – 3.9µm) over southern Idaho, hourly from 0400 through 1800 UTC on 14 January 2015 (Click to enlarge)

Extended Period of Fog/Low Stratus over the Arizona


GOES-13 Visible Imagery at 1600 and 2300 UTC from 6 December through 13 December 2014 (Click to enlarge)

Northern Arizona experienced a wet start to December (From 2-4 December, Flagstaff received 1.79″, Winslow received 0.80″ and Grand Canyon Airport received 0.41″). When High Pressure and an inversion then settled over the region (an animation of surface weather charts is here), the stage was set for a prolonged period of fog and low stratus, as noted here, for example. The visible imagery, above, testifies to the persistence of the low clouds and fog. It is apparent on 6 December during the day, and persists through the 12th. Visible imagery also shows the presence of high clouds; the presence of those high clouds makes ongoing detection of lower clouds difficult. In addition, both the visible imagery and brightness temperature difference product (10.7 µm – 3.9 µm on GOES-13, historically and still routinely used to detect water-based clouds) give information about the top of the cloud. There is great difficulty in using this information to infer a surface visibility or ceiling (that is, information about the bottom of the cloud).

Fused products have an advantage of incorporating surface-based data (assimilated into the model — the Rapid Refresh in this case) to provide information on whether saturation is occurring in the lowest kilometer of the atmosphere. If that is the case, IFR Probabilities will be larger. The animation below shows Brightness Temperature Difference products and IFR Probabilities at 3 different times (~0700, ~1700 and ~2300) during the days on 8-11 December. IFR Probability fields continually show a strong signal in the region of fog/low stratus over eastern Arizona; the brightness temperature difference field does not, as it is affected by cirrus clouds and by solar reflectivity during the day. Clear skies on 10-11 December at night did allow the brightness temperature difference product to highlight the low clouds over Arizona.


GOES-13 Brightness Temperature Difference (Left, 10.7 µm – 3.9 µm) and IFR Probability (Right) from 8 December through 11 December at ~0700, ~1700 and ~2300 UTC. The Brightness Temperature Difference is enhanced so that fog/low stratus are yellow/orange/red at night, black during the day. (Click to enlarge)

Ice Fog causes Flight Diversions at Denver International

Ice Fog at Denver International Airport on Sunday 30 November resulted in the diversion of almost 50 flights. (News Link) From the link:

Sunday morning fog caused about 46 flights scheduled to land at Denver International Airport to be diverted, airport officials said.


GOES-R IFR Probability Fields, every 15 minutes, from 0500 UTC through 2200 UTC on 30 November 2014, along with surface reports of ceilings and visibility (Click to enlarge)

The GOES-R IFR Probability field gave useful anticipatory information for this event. The animation above shows a line of high IFR Probability moving southward and westward. Stations within the highest IFR Probability reported freezing fog (e.g. Sidney Nebraska (KSNY) at 0800 UTC, Akron, CO (KAKO) at 1200, 1300 and 1400 UTC and Kit Carson Airport in Burlington CO (KITR) at 1400 UTC). When the region of higher IFR Probability abuts up against Denver International (KDEN), then, at 1600 UTC, the Freezing Fog that occurred should not surprise. This region of enhanced IFR Probability persisted near Denver International through 2200 UTC.

The METARS, listed below, show the onset of the freezing fog (FZFG). Note that times are boldface in black, and fog-related observations are boldfaced in red:

KDEN 301353Z 13005KT 10SM FEW110 BKN220 07/M13 A2981 RMK AO2
SLP068 T00721128 $=
KDEN 301453Z 17007KT 10SM FEW110 SCT220 06/M11 A2984 RMK AO2
SLP082 FG BANK DSNT NW-SE T00561111 51034 $=
KDEN 301539Z 07016KT 1/2SM R35L/P6000FT FZFG FEW001 FEW110
SCT220 M06/M08 A2988 RMK AO2 WSHFT 1505 FG FEW001
T10611078 $=
KDEN 301542Z 07017KT 1/4SM R35L/P6000FT FZFG FEW001 FEW110
SCT220 M06/M07 A2989 RMK AO2 WSHFT 1505 FG FEW001 VIS W
1/2 T10611072 $=
KDEN 301553Z 07019KT 1/4SM R35L/2200VP6000FT FZFG VV002
I1000 T10611072 $=
KDEN 301630Z 07020KT 1/8SM R35L/1400V2200FT FZFG VV001
M06/M07 A2993=
KDEN 301637Z 08019KT 1/4SM R35L/1400V2400FT FZFG VV001
M06/M07 A2994 RMK AO2 PK WND 07026/1633 TWR VIS 1/4
I1004 T10611072 $=
KDEN 301651Z 08021G28KT 1/4SM R35L/1000V1600FT FZFG VV002
M07/M07 A2995 RMK AO2 PK WND 07028/1643 I1004 $=
KDEN 301653Z 08023KT 1/4SM R35L/1000V1400FT FZFG VV002
M07/M07 A2996 RMK AO2 PK WND 07028/1643 SLP145 I1004
T10671072 $=

(Click here to see a more English Language Listing; Click here to see a meteorogram)


GOES-13 Brightness Temperature Difference fields (10.7µm – 3.9µm) from 0800 through 1600 UTC on 30 November 2014 (Click to enlarge)

For comparison, the Brightness Temperature Difference Field is shown above. Terrain-induced cirrus clouds largely obscured the view of low clouds from satellite in this case. Thus, the incorporation of surface information via the Rapid Refresh model was key to producing an IFR Probability field with useful content.

Visible Imagery from GOES-13 (below) and GOES-15 (bottom) show the cirrus and the underlying low clouds. The steady southward advancement of the low clouds is consistent with the motion of the IFR Probability fields.


GOES-13 Visible Imagery (0.63µm) animation, 1400-1800 UTC on 30 November 2014 (Click to enlarge)


GOES-15 Visible Imagery (0.62µm) animation, 1400-1800 UTC on 30 November 2014 (Click to enlarge)

IFR Probabilities over the Texas Panhandle


GOES-R IFR Probabilities (Upper Right), GOES-13 Brightness Temperature Difference (10.7 µm – 3.9 µm) (Upper Left), MODIS IFR Probabilities (Lower Left), Suomi NPP Brightness Temperature Difference (11.35 µm – 3.74 µm) and Day Night Band (Lower Right), all near 0930 UTC 2 September 2014 (Click to enlarge)

GOES-R IFR Probabilities (from GOES and from MODIS) over the Great Plains and southern Rockies indicated one region where IFR conditions were most likely: over the Texas panhandle, where IFR conditions were reported. There is a strong signal in the GOES-based Brightness Temperature Difference field there (and in the Suomi NPP Brightness Temperature Difference field) as well. There is also a Brightness Temperature difference signal in regions where IFR conditions are not occurring; in those locations, stratus is present, or (over the Rockies) emissivity differences in the dry soil are present, both of which conditions will lead to a signal in the brightness temperature difference that is unrelated to surface visibility and ceilings. This is therefore another example showing how incorporation of model data that accurately describes saturation (or near-saturation) in the lowest model layers can help the GOES-R IFR Probability more accurately depict where IFR conditions are present.