Category Archives: MODIS

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

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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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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

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.

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.

Low clouds over Louisiana

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GOES-R IFR Probabilities computed from GOES-East (Upper Left), GOES-East Brightness Temperature Difference (Upper Right), GOES-R IFR Probabilities computed from MODIS data (Lower Left), GOES-R Cloud Thickness computed from GOES-East (Lower Right), every half hour from 0015 UTC to 1615 UTC on June 7 2013

GOES-R IFR Probabilities are shown for northwest Louisiana and surrounding states.  At the beginning of the animation, GOES-R fields are mostly model-based over much of Louisiana — the north-south oriented edge of high clouds is plain in the brightness temperature difference field starting around 0315 UTC, and the GOES-R IFR Probability field over Louisiana has the characteristic flat look of a field produced from mostly model (Rapid Refresh) data.  Note also that the Cloud Thickness product is not shown when high clouds are present.  It is computed only for low clouds in non-twilight conditions.

As the high and mid-level clouds move to the east, two things happen to the GOES-R fields. One, the Cloud Thickness field starts to show values.  And second, the GOES-R IFR Probability field starts to acquire the pixelated character that is common when satellite data are part of the field.

The are of high IFR probabilities is smaller than the region of enhanced brightness temperature difference, which at 0900 UTC covers much of Louisiana and Arkansas.  Over Arkansas, however, model data are de-emphasizing the possibility of reduced surface visibilities.  The low clouds there are stratus, off the ground, not fog.  Thus the IFR probability field gives a more accurate representation of where visibility restrictions at the surface are possible.  This is an important consideration in aviation forecasting.

As above, but for 0400 UTC (top) and 0815 UTC (bottom)

IFR Probabilities computed from MODIS data (lower left) show very similar areal coverage compared to the GOES-Based IFR probability fields, but small-scale variations in the field are much more evident, as should be expected given the difference in pixel footprint between the two satellites.