Author Archives: Scott Lindstrom

1-minute Imagery of Fog Dissipation

GOES-14 0.62 µm Visible images (click image to play animation)

GOES-14 0.62 µm Visible images (click image to play animation)

GOES-14 is in experimental SRSO-R mode for the next few weeks, and the 1-minute imagery it is yielding provides a close look at the dissipation of fog after sunrise. In this example, fog in the Wisconsin River Valley burns off. How did ‘conventional’ observations of the fog produced from GOES-13 and the Rapid Refresh Model observe this small region of fog? Hourly imagery of IFR Probabilities, below, show the development of highest probabilities along the Wisconsin River. This is a region where IFR conditions were observed around sunrise.

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GOES-R IFR Probabilities (click image to play animation)

GOES-R Cloud Thickness, below, also shows a signal over the Wisconsin River, with maximum cloud thickness around 800 ft. According to this chart, a fog with a thickness of 800 feet should burn off in between 1 and 2.5 hours. The GOES-14 animation confirms this prediction. Fog dissipates shortly after 1400 UTC. Note that the GOES-R Cloud Thickness loop, below, terminates at 1102 UTC, the last image before twilight conditions necessitate that the product not be computed.

GOES_CLD_THICK_20130814loop

GOES-R Cloud Thickness (click image to play animation)

GOES-14 views of Valley Fog in West Virginia on this same day are available here.

Late Summer Fog Detection over Alaska

GOES-15 0.62 µm Visible Imagery (Click to see animation)

GOES-15 0.62 µm Visible Imagery (Click to see animation)

It can be challenging to determine where fog and low stratus are problematic over Alaska. The visible imagery, above, shows an abundance of clouds (and, over the Northwest Territores, smoke) — but it’s difficult to guess which of the clouds are associated with low stratus or fog and accompanying low visibilities. Knowing where visibilities are low is vital in a state like Alaska where small aircraft provide vital transportation services.

After dark, increasingly frequent at this time of year in Alaska, the brightness temperature difference field (between brightness temperatures at 10.7 µm and 3.9 µm) can be used to identify water-based clouds because of differences at those two wavelengths in the emissivity properties of liquid water droplets that make up clouds. That animation is shown below. As with the visible imagery, the brightness temperature difference tells you about the top of the cloud. The important visibility restrictions occur at the bottom of the cloud.

GOES-15 Brightness Temperature Difference (10.7 µm - 3.9 µm) (Click to see animation)

GOES-15 Brightness Temperature Difference (10.7 µm – 3.9 µm) (Click to see animation)

GOES-R IFR Probabilities computed from GOES-15 and Rapid Refresh Data (Click to see animation)

GOES-R IFR Probabilities computed from GOES-15 and Rapid Refresh Data (Click to see animation)

Neither the visible nor brightness temperature difference loops above highlight cloudy regions with restricted visibility. The GOES-R IFR Probability product that fuses satellite information with model output, a loop of which is available above, does. Many offshore regions show high probabilities of IFR conditions. These are likely regions of advection fog where relatively moist air is flowing over cold ocean waters and being cooled to its dewpoint. (Click here to see a surface plot over northwest Alaska — dewpoints are in the 40s and 50s near the Bering Sea) An area of high IFR probabilities that occurs over land is over southwest Alaska between Dillingham and Bethel. Dillingham does report IFR and near-IFR conditions as the higher IFR probabilities rotate through. A close-up image of the IFR Probability, hourly, over southwest Alaska is shown below; note how ceilings and visibilities in Dillingham (PADL) lower as the IFR probabilities increase.

GOES-R IFR Probabilities computed from GOES-15 and Rapid Refresh Data (Click to see animation)

GOES-R IFR Probabilities computed from GOES-15 and Rapid Refresh Data (Click to see animation)

VIIRS data from Suomi/NPP can also be used to approximate where fog may occur. Both the Day/Night band and the Brightness Temperature Difference product can tell a forecaster where clouds exist. However, surface conditions beneath the clouds detected by Suomi/NPP may or may not be consistent with IFR conditions. In the animation below, a new algorithm has been applied to the Day/Night band to extract information in the transition zone between day and night. Some artifacts of that algorithm are evident (manifest as what appear to be cloud bands perpendicular to the satellite path), but useful information about clouds do appear elsewhere. The brightness temperature difference product is only computed where solar reflectance is essentially nil. GOES Imagery is included in the loop to show where sunrise has occurred.

Suomi/NPP Day/Night band, Brightness temperature difference, and GOES Imager visible data, all near 1245 UTC on 8 August 2013 (Click to see animation)

Suomi/NPP Day/Night band, Brightness temperature difference, and GOES Imager visible data, all near 1245 UTC on 8 August 2013 (Click to see animation)

Marine Stratus moves over Sacramento

GOES-based GOES-R IFR Probabilities (Upper Left), GOES-West Brightness Temperature Difference (Upper Right, 10.7 µm - 3.9 µm), GOES-R Cloud Thickness (Lower Left), Suomi/NPP Brightness Temperature Difference (click image to play animation)

GOES-based GOES-R IFR Probabilities (Upper Left), GOES-West Brightness Temperature Difference (Upper Right, 10.7 µm – 3.9 µm), GOES-R Cloud Thickness (Lower Left), Suomi/NPP Brightness Temperature Difference (click image to play animation)

GOES-based IFR Probabilities track the incursion of a deck of marine stratus into metropolitan Sacramento and its surroundings on the morning of August 7. The cloud movement is also captured in the GOES-West (GOES-15) Brightness Temperature Difference fields. Note, however, how the GOES-R IFR Probability fields correctly suppress the signal in regions surrounding the stratus deck. These are regions where emissivity differences at 10.7 and 3.9 µm are more likely due to soil variability than to the presence of small liquid clouds droplets.

Suomi/NPP overflew the region at approximately 0945 UTC. The brightness temperature difference field for that time is shown here. The sharp edges of the stratus deck are readily apparent.

Although IFR Probabilities are high in this cloud bank, widespread IFR conditions were not reported. Much of the stratus was apparently elevated off the ground.

Some forecast models ably captured the evolution of this cloud field. A brightness temperature difference field from the 0000 UTC 7 August NSSL WRF, shown here (Courtesy of Dan Lindsey, NOAA) shows a cloud field evolving between 0900 and 1900 UTC on 7 August. Combining the forecast model output with real-time IFR Probability observations allows anticipation and monitoring of an evolving stratus deck.

Fog detection in the Upper Midwest

GOES-based GOES-R IFR Probabilities (Upper Left), GOES-East Brightness Temperature Difference (Upper Right, 10.7 µm - 3.9 µm), MODIS-based GOES-R IFR Probabilities (Lower Left), GOES-based GOES-R Cloud Thickness (Lower Right) (click image to play animation)

GOES-based GOES-R IFR Probabilities (Upper Left), GOES-East Brightness Temperature Difference (Upper Right, 10.7 µm – 3.9 µm), MODIS-based GOES-R IFR Probabilities (Lower Left), GOES-based GOES-R Cloud Thickness (Lower Right) (click image to play animation)

As nights lengthen in the upper Midwest in late Summer, the probability of fog development increases, especially on nights after light rainfall. The hourly animation, above, shows the gradual areal increase in high IFR probabilities that occurs as surface visibilities fall. Several aspects to the animation bear investigation. Note, for example, that at the start of the animation, the brightness temperature difference field, the traditional method used for fog detection, has a strong signal over eastern Illinois and western Indiana, a region where IFR conditions are not reported. This is a region of stratus clouds. Rapid Refresh model data that are incorporated into the GOES-R algorithm screens out these mid-level clouds; IFR probabilities in that region are correctly negligible. The end of the animation, 1215 UTC, occurs after sunrise, and reflected 3.9 µm solar radiation is affecting the brightness temperature difference field. The solar radiation complicates the use of the brightness temperature difference field as the sun rises. (Note also in that 1215 UTC image that GOES-R Cloud Thickness is not computed as it is during twilight conditions).

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The VIIRS instrument on board Suomi/NPP includes a day/night band that uses reflected Earth Glow and reflected lunar light to detect clouds. When the moon has set (or near times of the new moon — and the new moon occurs on August 6th, the date of these images), the scant illumination from Earthglow only makes low cloud detection a challenge. The brightness temperature difference product will still detect water-based cloudiness, however, as shown in the toggle above. However, the brightness temperature difference product does not include information on the cloud base, only on the cloud top. Incorporation of Suomi/NPP data into the GOES-R IFR Probability algorithm is ongoing.

Fog Detection under Cirrus

MODIS Brightness Temperature Difference and MODIS-based GOES-R IFR Probabilities, 30 July 2013, 0835 UTC

MODIS Brightness Temperature Difference and MODIS-based GOES-R IFR Probabilities, 30 July 2013, 0835 UTC

The toggle above switches between a brightness temperature difference field and a GOES-R IFR Probability field (both from MODIS) over Missouri and Kansas (including the busy airport in Kansas City).  The cirrus shield over the convective complex over Missouri obscures any satellite view of low clouds.  North and west of that cirrus shield, over Nebraska, Kansas and Iowa, the brightness temperature difference indicates clouds comprised of water droplets (that is, stratus or fog).  Ceilings and visibilities underneath the cirrus canopy, and within the stratus deck show regions of IFR conditions. Rapid Refresh model data that is part of the GOES-R IFR Probability algorithm is able to alert a viewer (or forecaster) to the possibility of Fog/Low stratus in areas underneath the cirrus. Probabilities under the cirrus canopy are lower because satellite-based predictors are not used in the computation of probabilities.

 

Unusual late-July High Plains Fog

GOES_IFR_PROB_20130729_loop

Fog and low stratus developed over the High Plains under easterly (upslope) flow in the early morning hours of July 29, 2013, and the GOES-R IFR Probability fields ably discriminated between regions of stratus and visibility-restricting low stratus/fog.  Note in the imagery above how the Brightness Temperature Difference field (upper right) includes a strong signal over eastern Nebraska where visibility restrictions/low ceilings are not present.  Fusing the satellite data with Rapid Refresh model data allows the MODIS and GOES-based GOES-R IFR Probability fields to more accurately depict the regions of low visibilities/ceilings.  Note that the brightness temperature difference product from MODIS, below (Lower Right), also highlights the mid-level stratus over eastern Nebraska.

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Low clouds in the Pacific Northwest

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GOES-R IFR Probabilities computed from GOES-West (Upper Left), GOES-West Brightness Temperature Difference (10.7 µm – 3.9 µm) (Upper Right), GOES-R Cloud Thickness from GOES-West (Lower Left), GOES-R IFR Probabilities computed from MODIS (Lower Right), hourly from 0500 through 1200 UTC on 23 July 2013.

Hourly GOES-R IFR Probabilities ably capture the creep of low fog and stratus into Puget sound from the north — through the strait of Juan de Fuca, and from the south, with low clouds eventually surrounding the Olympic Mountains.  Part of the input into the GOES-R IFR Probability algorithm is the brighness temperature difference shown in the upper right, and note that it used alone overpredicts where low clouds/fog might be occurring.  Including Rapid Refresh data allows the GOES-R algorithm to restrict — correctly — the IFR conditions to where they are most widespread.

Suomi-NPP data from the VIIRS instrument includes a Day/Night band that on July 23rd, when the moon was approaching full, neatly outlines both the cloudy regions — where moonlight is reflected off clouds — and those regions where light is being emitted (that is, cities).

As above, but with the VIIRS Day/Night band in the bottom right, at 0923 and 1103 UTC.

Suomi/NPP VIIRS data also includes 10.8 and 3.74 µm imagery, so a brightness temperature difference product can be computed (work is in progress to incorporate Suomi/NPP data into the GOES-R algorithm suite so an NPP-based IFR probability can be developed.  The toggle below compares the day/night band data with the brightness temperature difference produce at 0923 UTC.

Cloud thickness as a Predictor for dissipation time

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GOES-R IFR Probabilities (Upper Left) computed from GOES-East data, Brightness temperature difference (GOES-East, 10.7 – 3.9 ), GOES-R Cloud Thickness (Lower Left), Suomi-NPP Day/Night Band (Lower Right).  All images hourly, 0515 UTC through 1115 UTC on 22 July 2013.

When fog forms overnight because of radiational cooling to the dewpoint, the thickness of the fog is related to the time it will take for the fog to dissipate.  In general, thick fog dissipates more slowly than thin, and because thick fog is generally surrounded by thinner fog, a region of fog/low stratus will generally erode from the outside in.  The animation above shows the gradual development and expansion of fog overnight over Iowa, Illinois and Missouri, with thicknesses over southeastern Iowa eventually reaching values of 1100 feet!

The relationship between time of dissipation and fog depth is shown on this chart, where the depth is the last pre-twilight image.  For 22 July 2013, that image is at 1045 UTC, shown below.  A value of 1100 feet corresponds to a dissipation time of 2-3 hours after sunrise.

As above, but for 1045 UTC 22 July 2013

The visible animation, below, shows dissipation between 1400 and 1500 UTC.

GOES-13 Visible imagery, 1215 through 1545 UTC 22 July 2013

Suomi/NPP data showed a snapshot of the fog development in this region at ~0800 UTC.

As above, but with a toggle of the Day/Night band and a Brightness Temperature Difference, all imagery at 0802 UTC 22 July 2013.

Fog development over the lower Mississippi River valley as viewed by different satellites

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GOES-R IFR Probabilities computed from GOES-East (Upper Left), GOES-East Brightness Temperature Difference (10.7 µm – 3.9 µm) (Upper Right), GOES-R Cloud Thickness (Lower Left), GOES-East Visible Imagery (Lower Right), all images hourly from 0415 UTC through 1415 UTC 19 July 2013

Hourly imagery of GOES-R IFR Probabilities show the development of high probabilities in a region where low clouds and fog develop to cause IFR and near-IFR conditions in and around the lower Mississippi River Valley.  The traditional method of detecting low stratus will be hampered by an abundance of high and mid-level clouds (as evidenced in the brightness temperature difference product, above, and in the day/night band imagery, below).  Different polar-orbiting satellites can give snapshots at high spatial resolution that describe the fog/low cloud fields.  The Suomi/NPP overpass at  0715 UTC (early in the night for radiation fog development), shows the abundance of high clouds over the western part of the domain, for example, that are illuminated by the setting half-moon.  It is difficult to discern low clouds in regions where IFR probabilities are high.

As above, but with Suomi/NPP Day/Night Band (Bottom Right), 0715 UTC.

 MODIS data are used to produce IFR probabilities, and those images are shown below.  The later of the images matches the time of the Suomi/NPP band shown above.

As above, but with MODIS-based GOES-R IFR Probabilities (Lower Right), at 0445 and 0715 UTC 19 July 2013

MODIS-based IFR probabilities are higher than GOES-based probabilities over the west-central part of Mississippi where satellite data are included in the predictors.  This occurs when the MODIS-based satellite signal of low clouds is stronger than that from GOES, which difference in signal can occur because of the finer resolution of the MODIS data.  In central Arkansas and northern Louisiana, however, where high clouds are present (and therefore where Satellite data are not used in the computation of the IFR Probability), the GOES-based and MODIS-based fields are nearly identical.

AVHRR data (below) can also be used to compute brightness temperature differences, but those data are not yet incorporated into the GOES-R algorithms.  However, the trend of the brightness temperature difference field can be used to monitor trends in low clouds/fog.  High clouds will obscure the view, however.  It is very difficult to infer a change in the amount of fog, and the concomitant decreases in visibility, based on the brightness temperature difference changes displayed below.

As above, but with the AVHRR Brightness Temperature Difference (10.8 – 3.74), bottom right, at 0654 UTC, 0837 UTC and 1051 UTC on 19 July 2013.

IFR Conditions over southwest mainland Alaska

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Hourly GOES-R IFR Probability fields over southwest Alaska, 0500 to 1700 UTC 15 July 2013

The IFR Probability fields over southwest Alaska early on July 15 2013 show the influence of multiple cloud layers moving northward out of Bristol Bay and the Bering Sea starting near 0900 UTC.  IFR probabilities drop (because satellite data are not included as predictors when multiple cloud layers exist), and the field becomes flatter.  That is, it has less horizontal variability (especially compared to its pixelated nature when satellite data are included).  IFR conditions are largely confined to within the region of high IFR probabilities.

Two obvious boundaries are present in the field.  At 0800 UTC, a boundary extends southwest to northeast, with higher values to the north and west.  At 1500 UTC, a boundary extends southeast to northwest with higher values to the north and east.  In both cases, this line is the terminator, and daylight is occurring north of the line.  In general, IFR probabilities increase during the day (where they are diagnosed) because the cloud-clearing algorithm operates with a lot more certainty when visible imagery can be used to identify clouds.

MODIS data can be used in Alaska because Alaska’s high latitude ensures that MODIS overpasses are frequent (especially along the north slope of Alaska).  Three overpasses could be used between 0700 and 1415 UTC over this small region of southwest Alaska to give high-resolution depictions of IFR probabilities.  Using these high-resolution images with the high temporal resolution available from GOES-West gives a full description of the IFR Probability field over Alaska.  The 0832 UTC image suggests multiple cloud layers are still over Bristol Bay/the Bering Sea, and the 1415 UTC image shows both the day-night boundary and an IFR Probability field determined largely by model data.

MODIS-based GOES-R IFR Probabilities over southwest Alaska, 0654 UTC, 0832 UTC and 1415 UTC 15 July 2013