Author Archives: Scott Lindstrom

IFR Conditions over Georgia on a Summer Morning

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Animation of GOES-East Water Vapor Imagery (6.7 µm), Brightness Temperature Difference Product (10.7 µm – 3.9 µm) and GOES-R IFR Probability computed with GOES-East data, 1000 UTC on 11 July 2013

The satellite animation, above, shows ample evidence of multi-layered clouds over Georgia and surrounding states, in a region where ceilings and visibilities approached/exceeded IFR conditions.  The traditional method of determining regions of fog/low stratus — the brightness temperature difference between the 10.7 µm and 3.9 µm channels — gives no information here because low clouds are screened by higher ice-phase clouds.

GOES-R IFR Probability fields merge information from GOES Imager data and the Rapid Refresh Model.  Even if GOES Imager data gives little information, GOES-R IFR Probability fields will give valuable information because they are also use information from the Rapid Refresh model.  Because the IFR Probability fields don’t include satellite data, probabilities are lower.  The large region of yellow — IFR Probabilities around 40% — sits over many stations that are reporting IFR conditions.   Note how IFR Probabilities are higher over North Carolina where satellite data are being used in the computation of the field — but there are fewer reports there of IFR conditions (despite the higher probability).  Temper the interpretation of the IFR Probabilities with knowledge of what is being used to compute them.

The evolution of IFR Probability fields can give a Head’s Up to deteriorating conditions in the atmosphere.  Note in the hourly animation below how probabilities initially do increase over regions that subsequently have IFR or near-IFR conditions.  At the end of the animation, there is an obvious boundary between different probabilities over northeast Georgia (Orange values around 55%) and western Georgia (values around 40%).  That southeast-to-northwest boundary shows where nighttime predictors are being used (to the west) vs. daytime predictors (to the east) in the computation of IFR Probabilities.

GOES-R IFR Probabilities (hourly) from 0200 through 1100 UTC, 11 July 2013

Resolution and Cloud Depth

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GOES-R Cloud Thickness computed from GOES-East and from MODIS data, ~0815 UTC 9 July 2013

The resolution and view angle of MODIS, compared to the GOES Imager, means that smaller features are better resolved and more accurately navigated.  In the example above, the Kickapoo River in Vernon, Richland and Crawford Counties in southwest Wisconsin is clearly delineated in the MODIS product, with a small ribbon of values from 800-1000 feet, but not in the GOES where values are closer to 600.  Differences along the coast of Lake Michigan are also evident.  MODIS detects a thick cloud bank off the coast of Sheboygan County (the cloud thickness is near 1000 feet);  GOES detection has thicknesses of 800 feet in that region, but the values are shifted onshore because of parallax and the co-registration error that exists between the 10.7 µm and 3.9 µm  channels on the GOES-13 Imager.

If you are using Cloud Thickness to estimate fog dissipation, the difference between 1000 and 800 feet equates to 60-90 minutes.

Lake Superior Plus High Dewpoints Means Fog

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GOES-R IFR Probabilities computed from GOES-East, and surface observations of ceilings/visibilities (Upper Left), GOES-East Brightness Temperature Difference (10.7 µm – 3.9 µm) (Upper Right), GOES-R Cloud Thickness computed from GOES-East (Lower Left), GOES-R IFR Probabilities computed from MODIS (Lower Right)

The combination of cold Lake surface temperatures in the 40s and 50s over Lake Superior and mid-Summer dewpoints in the 60s to near 70 is a recipe for fog over the upper midwest. North winds behind a complex of thunderstorms fostered the development of fog and low stratus over the upper midwest early in the morning on July 8th, as shown in the imagery above.

The GOES-R IFR Probability fields seamlessly track the expansion of fog/low stratus in the region around western Lake Superior;  highest probabilities are confined to regions where IFR or near-IFR conditions are observed.  The IFR Probabilities also downplay regions where the brightness temperature difference field is showing a signal (northwest Minnesota) but where visibility obscurations are small.

IFR Probabilities are not as large over southern upper Michigan or over north-central Wisconsin, regions where multiple cloud layers mean that the satellite component does not contribute to IFR Probability computation, resulting in a smaller value.  Note that Cloud Thickness is not initially computed there either:  Cloud Thickness describes the thickness of the lowest water-based cloud layer in non-twilight conditions.  If there are multiple cloud layers that include mixed-phase of ice clouds, cloud thickness is not computed.  Note also that cloud thickness is not computed in the hours around sunrise (i.e., during twilight conditions).

By 1702 UTC, the final image, the Summer Sun has burned off much of the fog and low stratus, with the exception being along the shorline of Lake Superior.  MODIS-based IFR Probabilities have much sharper edges because of the higher resolution of the MODIS instrument compared to the GOES Imager.

Fog Development over central Illinois

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GOES-R IFR Probabilities computed from GOES-East, hourly from 0215 UTC through 1115 UTC on July 5 2013

GOES-R IFR Probabilities show a characteristic increase over central Illinois as radiation fog develops in the early morning hours of July 5 2013.  Probabilities are initially low, but gradually increase, and spread, as the fog develops.  The IFR probability field over Tennessee, Indiana and Kentucky has the characteristic flat look of a field produced mainly from model fields:  the probability field is flat, and IFR probabilities are low.  There are regions — such as near Nashville at the end of the animation — where the field includes satellite data;  IFR Probabilities there are larger and the IFR Probability field has a more pixelated appearance.

If multiple cloud layers are present, you should not expect the GOES-R Cloud Thickness product to yield a value.  GOES-R Cloud Thickness diagnoses the thickness of the highest water-based cloud in non-twilight conditions.  If ice clouds (or mixed phase) clouds are present, cloud thickness will not be computed.  The toggle below shows cloud thickness, GOES-R IFR Probabilities, and the Brightness Temperature Difference (10.7 µm – 3.9 µm)

GOES-R IFR Probabilities, GOES-R Cloud Thickness, and GOES-East Brightness Temperature Difference, 0730 UTC on 5 July 2013

There a several things of note in the image above.  IFR Probability field in central Illinois have higher values south of the peak in the brightness temperature difference field, where the lowest visibilities and ceilings are reported.  GOES-R Cloud Thickness is unavailable in regions underneath the high clouds in the eastern part of the imagery — over Tennessee and Kentucky, except where there are holes in the clouds.  Note how these regions of diagnosed GOES-R Cloud Thickness overlap with regions of pixelated GOES-R IFR Probability fields.  In both fields, Satellite data are being used for the computation.

Fog Detection on Oregon’s West Coast

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Toggle between MODIS-based and GOES-West-based IFR Probabilities, ~0630 UTC 01 July 2013

The high-resolution MODIS data likely gives a more accurate interpretation of visibility obstructions near the surface because it better resolves the sharp edges to fog/low stratus that can occur along the west coast of the United States, as exemplified by this scene centered on Oregon.  Look at North Bend, OR, for example.  In a hole in the MODIS field, but with relatively high probability in the GOES field.  The MODIS field also highlights ship fields in the stratus deck off the coast.

MODIS data is useful for high spatial resolution, but the temporal resolution at one point is not good.  The swath width on Suomi-NPP is wide enough, however, that sometimes two sequential polar passes will overlap over the northern United States, giving a 90-minute time-step with high-resolution data.

Brightness Temperature Difference, 11 – 3.74 from VIIRS data on Suomi/NPP.

The animation above shows how the low clouds, as detected using the Brightness Temperature Difference between 3.74 and 11 channels from VIIRS, are moving inland btween 0935 and 1116 UTC on 1 July.  The brightness temperature difference also shows a general increase offshore, so the likelihood of any breaks in the low clouds is decreasing.  The Day/Night band imagery shows a similar change in low clouds along the coast, although the view is somewhat obscured because this region is in the stray light zone (that is, the satellite is illuminated by the sun even though it is over a region where it is still night).

Day/Night band imagery along the west coast, 0935 and 1116 UTC on 1 July
GOES-R IFR probabilities, 0930 and 1115 UTC, 1 July 2013

GOES-R Probabilities computed from the GOES-West imager also show an expansion in the region where IFR probabilities are highest.  Thus, the expansion in low clouds detected by the VIIRS instruments on Suomi/NPP likely corresponds an increase in the area with lowered visibilities.

Fog detection in the land of the Midnight Sun

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Brightness Temperature Difference (10.7 µm- 3.9 µm) from GOES-West (quarter-hourly timestep)

As above, but with a color enhancement applied (hourly timestep)

Over the continental United States, the brightness temperature difference can be used, sometimes, to approximate where fog and low clouds are present because water-based clouds (such as fog and stratus) have different emissivity properties for radiation at 10.7 µm and 3.9 µm.  In regions where the sun shines constantly, however, the brightness temperature difference product is harder to interpret because the abundance of scattered or reflected solar radiation with wavelengths near 3.9 µm.  In the examples above, the darker region (top animation) or lighter region (bottom animation) are regions of low clouds.  Regions of visibility obstruction — station PABA, for example (Barter Island on the shore of the Arctic Ocean) do not appear to be near fog/low stratus as indicated by the brightness temperature difference.

The GOES-R IFR Probability field combines Rapid Refresh data and GOES-West brightness temperature difference information (and other information, such as a cloud mask) to more accurately portray the horizontal extent of visibility obstructions.  The animation below suggests high probability of IFR conditions along the Arctic Ocean shore in northern Alaska.

There are regions at present over Alaska of significant visibility obstructions due to smoke.  These visibility obstructions are not predicted by the GOES-R Fog/Low Stratus product.  Smoke detection typically incorporates a brightness temperature difference between 10.7 µm and 12.0 µm, and the GOES-15 Imager does not include 12.0 µm detection.

GOES-R IFR Probabilities computed from GOES-West, along with surface ceilings and visibilities.  Times as indicated.

A visible image of the north slope of Alaska, below, shows fog and low clouds very near the Arctic shore.

GOES-15 Visible Imagery, 1700 UTC 28 June 2013

IFR Probability Fields capture back-door cold front in New England

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Visible imagery around 0000 UTC on 27 June 2013 showing the slow advance of a backdoor coldfront over eastern New England.  The yellow arrows at the end highlight the front over New Hampshire.

A back-door coldfront moved westward through New England late in the day on June 26th, bringing with it cooler air and lowered ceilings.  How well did the IFR Probability field capture the low ceilings that came with the cooler air?

GOES-R IFR Probability fields computed from GOES-East, hourly from 2345 UTC on 26 June to 0445 UTC on 27 June.

Intially (2345 UTC), high probabilities of IFR conditions were limited to the cold waters east of New England. This is reasonable given the high dewpoints (upper 60s Fahrenheit) that prevailed New England on the 26th.  Light westerly winds would move that moist air over the cold Gulf of Maine, and advection fog would form.  As the backdoor front moved across the region, IFR probabilities increased as visibilities declined.  The animation of the IFR probability fields captures the leading edge of the maritime air.

Resolution: GOES vs. MODIS and Suomi/NPP over Appalachia

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Brightness Temperature Difference (11µm – 3.74µm) at 0621 and 0750 UTC on 20 June 2013.  Data from VIIRS instrument on Suomi/NPP
Brightness Temperature Difference (10.7 µm – 3.9 µm) at 0625 and 0755 UTC on 20 June 2013.  Data from Imager GOES-East.

The GOES Imager, with a nominal (sub-satellite point) resolution of 4 km, has trouble detecting fog when that fog forms over very narrow valleys, as are common over the central Appalachians of the eastern United States.  Compare the views from the GOES Imager (the bottom images) to the view from the Suomi/NPP VIIRS instrument that has 1-km resolution.  VIIRS is much better able to capture the dendritic nature of valley fog, and also to detect it at all when the horizontal scale is very small (for example, the southwest-to-northeast oriented valleys in extreme southwest Virginia).  Thus, a signal will appear first in the high-resolution 1-km polar orbiter data, sometimes several hours before it appears in the coarser-resolution GOES Imager data.

These resolution issues that are apparent in the Brightness Temperature Difference fields, above, the traditional method of detecting fog and low stratus, carry over to the GOES-R IFR Probability fields.  Imagery below, from 0745 UTC on 20 June 2013 suggests that the higher-resolution MODIS data better captures the structure of fog in mountain valleys.  Note also the horizontal shift in the field that occurs because of the GOES parallax shift.

GOES-R IFR Probability fields computed from GOES-East and from Aqua MODIS data, 0745 UTC on 20 June 2013

Fog/Low Stratus over southern California viewed by many Satellites

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Suomi-NPP VIIRS imagery at 0857 UTC 17 June 2013.  Imagery includes the Day/Night band (including regions north of Southern California within the Stray Light zone) and the brightness temperature difference between 11 µm and 3.74 µm

There are a variety of ways to detect fog and low stratus using satellites.  The imagery above uses VIIRS data aboard the Suomi/NPP satellite.  Both the Day/Night band and the brightness temperature difference product show a region of clear skies west of the Channel Islands, with low clouds hugging the coast from Los Angeles southward.  There are also low cloud signals in the brightness temperature difference field over the deserts of California, Arizona and Mexico.

MODIS-based imagery at 0853 UTC 17 June 2013.  The brightness temperature difference (10.8 µm – 3.9 µm ) and MODIS-based GOES-R IFR Probabilities

MODIS data also hints at a clear pocket west of the Channel Islands, and shows fog/stratus extending southward from Los Angeles along the coast.  Whereas the brightness temperature difference also shows a signal over the deserts of California, Arizona and Mexico, the GOES-R IFR probability field suggests probabilities for IFR conditions are enhanced only along the north coast of the Gulf of California.  The other signals over land are likely related to emissivity property differences in the dry soils over the deserts.  MODIS data does show the sharp edge to the fog/low stratus deck that has moved onshore over coastal northern Baja California.  That sharp edge demonstrates an advantage of 1-km MODIS data.

GOES-West Brightness Temperature Difference (10.7 µm – 3.9 µm ) and GOES-R IFR Probabilities computed from GOES-West data, 0900 UTC 17 June 2013

GOES-West data also suggest a clear spot west of the Channel Islands, with fog and low stratus that extends southward along the coast from Los Angeles.  The brightness temperature difference signal over the deserts of the southwest is not in the IFR probability field because the Rapid Refresh model data does not show low-level saturation (save for that small region along the north coast of the Gulf of California).  The cloud edge along the Pacific Coast is not quite so sharp as it is in the MODIS data because the pixel size of GOES is larger.  GOES data does have an advantage over MODIS, however:  it views the scene every 15 minutes so temporal changes can be monitored.

Lake Michigan Fog

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Fog from Lake Michigan is a year-round hazard for travel in eastern Wisconsin.  One of the biggest crashes on an interstate in Wisconsin occurred in October 2002 on I-43 near Sheboygan as fog moved onshore.  Ten died in that event that included 25 vehicles.

Rain overnight followed by partial clearing led to widespread fog over the upper Midwest on June 10, 2013, and the IFR Probability products described the horizontal extent of the lowest visibilities.  Those low visibilities hugged the coast of Lake Michigan in eastern Wisconsin.

GOES-R IFR Probabilities, 0402 – 1345 UTC on 10 June 2013, computed from GOES-East

GOES-R IFR probabilities increase in two regions overnight:  over Iowa and in the western Wisconsin River Valleys, and over Lake Michigan.  Reduced visibilities are reported in and around Lake Michigan, and as the tweet up top shows, dense fog was reported along highways as well.  Water temperatures in the upper 40s over central Lake Michigan promote the development of fog, as dewpoints over Wisconsin were near 60.

GOES-R IFR Probabilities computed from GOES-East (Upper Left) and from MODIS (Lower Left);  GOES-East Brightness Temperature Difference Product (Upper Right), all from approximately 0845 UTC on 10 June 2013

The GOES Imager usually cannot resolve small river valleys.  Polar-orbiting data, however, usually can.  The MODIS-based IFR probability from 0847 UTC better resolves the Wisconsin River Valley over southwest Wisconsin, for example.