Category Archives: Cloud Thickness

Fog Development near Lake Michigan

GOES_IFR_PROB_20131010_0615

GOES-13-based GOES-R IFR Probabilities (Upper Left), GOES-13 Brightness Temperature Difference Product (10.7 µm – 3.9 µm) (Upper Right), GOES-13-based GOES-R Cloud Thickness (Lower Left), Suomi/NPP Brightness Temperature Difference (Lower Right), all near 0615 UTC on 10 October (click image to enlarge)

The GOES-R IFR Probability product gave useful advance warning to the development of fog near Lake Michigan’s eastern shore overnight. The image above, from 0615 UTC, shows a flat brightness temperature difference field over the lakeshore counties in Wisconsin and Illinois (values are from -7.1 to -7.3); there are two regions of high values in the IFR Probability field, however: Near Manitowoc WI (values up to 29%) and over southeast WI and northeast IL (values near 20%). So by 0615 UTC on 10 October, IFR Probabilities are suggestive of a nascent fog development.

GOES_IFR_PROB_20131010_0702

As above, but for 0702 UTC on 10 October (click image to enlarge)

Forty-five minutes later, at 0702 UTC (above), IFR Probabilities have increased dramatically in eastern WI even as the brightness temperature difference field remains flat. Thus, the Rapid Refresh Data is accurately capturing the development of low-level saturation in the atmosphere, and that is influencing the IFR probability field. In addition, the GOES-R Cloud Thickness field is suggesting that the cloud bank is 500-600 feet thick. The strip of enhanced brightness temperature difference paralleling the Lake Michigan shore in lower Michigan is an artifact of the co-registration error between the 10.7 µm and 3.9 µm band detectors on GOES-13. Between 0656 UTC and 0734 UTC, visibility at Manitowoc, WI (KMTW), dropped from 5 to 3/4 statute miles. The visibility at Burlington WI (KBUU) dropped from 4 to 1 statute miles between 0600 and 0700 UTC, and Waukegan, IL (KUGN) reported a visibility of 1/4 mile at 0552 UTC and 0652 UTC.

GOES_IFR_PROB_20131010_0802

As above, but for 0802 UTC on 10 October (click image to enlarge)

At 0802 UTC, the GOES-East brightness temperature difference field shows greater differences over the region of SE Wisconsin where the fog is developing. Accordingly, the IFR probability increases past 80% IFR Probabilities are near 70% in Manitowoc County (and Manitowoc reported 1/2-mile visibility at 0834 UTC). Compare the GOES-East and Suomi/NPP Brightness Temperature Difference Fields; note the lack of a signal in the Suomi/NPP field along the western shore of Lake Michigan, confirming the co-registration error present in GOES-13.

GOES_IFR_PROB_20131010_1145

As above, but for 1145 UTC on 10 October (click image to enlarge)

The last pre-sunrise image, 1145 UTC, shows a definite signal of fog/low stratus in both the IFR Probability field and in the Brightness Temperature Difference field. However, the early detection in the IFR Probability field gives a nice head’s up to the forecaster. Note also in this image how the strong signal in the brightness temperature difference field that arises because of the co-registration error can contaminate the IFR Probability field. The Cloud Thickness in this field has been related to dissipation time, as shown in this chart. The maximum thickness of 1000 feet predicts a dissipation time around 1500 UTC. The 1445 and 1515 UTC GOES-13 visible images are shown below.

GOES13_VIS_10Oct2013loop

GOES-13 Visible Imagery at 1445 UTC and 1515 UTC on 10 October (click image to enlarge)

IFR Conditions in the Southeast

GOES_IFR_PROB_20131004loop

GOES-13-based GOES-R IFR Probabilities (Upper Left), GOES-13 Brightness Temperature Difference Product (10.7 µm – 3.9 µm) (Upper Right), GOES-13-based GOES-R Cloud Thickness (Lower Left), Suomi/NPP Brightness Temperature Difference (Lower Right), all times as indicated (click image to enlarge)

High Pressure of the southeast US allowed for clear skies and light winds overnight, and radiation fog developed over coastal portions of eastern Georgia. Because high clouds were present, the traditional method for detecting fog and low stratus, the brightness temperature difference between 10.7 µm and 3.9 µm on GOES could not capture the entire areal extent of the cloud. Fog is initially reported in eastern Georgia where IFR Probabilities are increasing underneath an ice-phase cloud deck that prevents the GOES satellite from seeing the development of low clouds and fog. Accordingly, the IFR probabilities are lower than they would be if satellite data were included. The uniform nature of the field is testament to the use of Rapid Refresh Data to drive the IFR Probability field. Shortly after 0600 UTC, a satellite signal develops over South Carolina as the high clouds shift to the south. When this happens, IFR probabilities increase (and acquire a more pixelated look). Regions under high clouds with IFR conditions persist through the end of the loop, however. It’s important to have a fused data product that allows two different complementary fields to diagnose where IFR conditions are likely. Where the brightness temperature difference cannot be used, Rapid Refresh Data gives vital information. Where brightness temperature difference data can be used, the Rapid Refresh Data can fine-tune things.

GOES_IFR_PROB_20131004_1115

As above, but for 1115 UTC (click image to enlarge)

The 1115 UTC image, above, shows the GOES-R cloud thickness just before twilight conditions that accompany sunrise prohibit its computation. The cloud thickness in a radiation fog is related to dissipation time, and the cloud thickness is shown to be quite thin: Thickest values, in blue, are around 800 to 900 feet. This scatterplot relates cloud thickness to dissipation time, and it suggests a dissipation time of 1-2 hours. However, the visible imagery animation, below, shows actual dissipation occurred shortly after 1500 UTC. Note that there is considerable spread in that predictive scatterplot.

GOES13_4October2013_loop

GOES-13 Visible Imagery, hourly at 1315, 1415 and 1515 UTC on 4 October (click image to enlarge)

Fog over Kansas

GOES_IFR_PROB_20130917_0832

GOES-R IFR Probability (Upper Left), GOES-East Brightness Temperature Difference (Upper Right), GOES-R Cloud Thickness (Lower Left), Suomi/NPP Day/Night Band (Lower Right), all imagery at ~0830 UTC on 17 September 2013 (click image to enlarge)

Light winds with a small upslope component allowed for the formation of fog over the High Plains on the morning of 17 September 2017. The image above shows the GOES-R IFR Probability, GOES-East Brightness Temperature Difference (10.7 µm – 3.9 µm), the GOES-R Cloud Thickness and the Day/Night band from Suomi/NPP that provides for nighttime visible imagery. In the imagery above, a large region over southeastern Kansas is overlain by higher ice-based clouds (likely cirrus) such that the brightness temperature difference product does not give the signal that is common with fog and low stratus (in the enhancement used here, fog and low stratus occur where the brightness temperature difference is colored orange or yellow). The Day/Night band visible imagery also suggests high clouds over southeast Kansas. Surface observations do show reduced visibilities, at or near IFR conditions. In this region, the IFR Probability Product gives useful information by using Rapid Refresh Data to diagnose the possibility of low-level fog. The probabilities are smaller — in the 40- to 50% range — but that is because no satellite data are being used as predictors. IFR Probabilities are very high only if both predictors — satellite and Rapid Refresh — are associated with high probability of fog/low stratus.

Note also how the Cloud Thickness product yields no information where high clouds are present. The Cloud Thickness is the thickness of the highest liquid cloud layer; the presence of ice clouds or mixed phase clouds precludes the determination of how thick a water cloud is because the satellite cannot view the water-based cloud.

GOES_IFR_PROB_20130917_1215

As above, but at 1215 UTC (click image to enlarge)

The 1215 UTC image shows the effect of twilight conditions moving westward across Kansas — GOES-R Cloud Thickness is not computed during twilight conditions that occurring over eastern Kansas, although they are still computed around Dodge City, where the computed cloud thickness is just over 1000 feet thick. The 1200 UTC Sounding from Dodge City, below, does show a nearly-saturated layer at the surface (about 927 mb, or about 2700 feet ASL) up to about 870 mb (4600 feet ASL).

KDDC_20130917_1200

Upper Air Sounding from Dodge City, KS, 1200 UTC (click image to enlarge)

What does MODIS-type Resolution get you?

MODIS-based IFR Probabilities and Cloud Thickness, 0657 UTC on 15 August 2013

MODIS-based IFR Probabilities and Cloud Thickness, 0657 UTC on 15 August 2013

The 1-km data available from the MODIS (above) that is on the Terra and Aqua satellites allows much better resolution than the nominal 4-km resolution from GOES-East and GOES-West (below). The higher resolution on MODIS yields better depiction of dendritic valley fog patterns in mountainous regions. Extremes in cloud thickness will be deeper with MODIS data as well. (In this example, MODIS-based cloud depths reach 1300 feet, vs. 900 feet in GOES) In addition, because fog/low stratus generally starts at small scales and grows in size, MODIS is more likely to detect the early stages of fog (if a serendipitous overpass occurs). Thus, a forecaster can be alert to subsequent development in the GOES data with its better temporal resolution.

GOES-based IFR Probabilities and Cloud Thickness, 0657 UTC on 15 August 2013

GOES-based IFR Probabilities and Cloud Thickness, 0702 UTC on 15 August 2013

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.

GOES_IFR_PROB_20130814loop

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.

Resolution and Cloud Depth

Leave a reply
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

Leave a reply
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

Leave a reply
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.

Low clouds over Louisiana

Leave a reply
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.

Identifying regions of fog underneath multiple cloud layers

Leave a reply
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-R IFR Probabilities computed from MODIS (Lower Right).  All imagery around 0400 UTC 20 May 2013

When multiple cloud layers are present, the traditional method of detecting fog, the brightness temperature difference between the 10.7 µm and 3.9 µm channels on the GOES Imager, will fail.  For such a configuration of clouds, the GOES-R IFR Probability field will yield information because it also uses information from the Rapid Refresh model to predict whether fog is possible.  The image above contains regions where both model and satellite data are used to compute the IFR probability, and where model data only are used.  How can you differentiate between the two?

Regions over southwest North Dakota are not overlain by high clouds.  In those regions, a strong signal in the brightness temperature difference fields is present.  There is also a north-south oriented signal over extreme southeast North Dakota and northeast South Dakota.  In both of those regions, the Cloud Thickness product is predicting a thickness.  Such a prediction works only when low clouds are visible by the satellite.

The GOES-R IFR Probability field, in the upper left, contains regions where both satellite and model are used (and these mostly overlay the regions where the Cloud Thickness field is present) and where only model data are used (because the satellite signal for low clouds is blocked by mid- and high-level clouds).  The horizontal homogeneity of the field over northeast North Dakota is characteristic of GOES-R IFR Probability fields that are determined largely by model data only.  Compare that to the more pixelated field over southwest North Dakota where Cloud Thickness fields are also computed:  Pixelation is a hallmark of the use of satellite data in the prediction of the IFR Probability.

Hourly evolution of GOES-R IFR Probability (with surface plots of ceiling/visibility) over North Dakota, 2202 UTC 19 May through 1402 UTC 20 May 2013

The GOES-R IFR probability field accurately depicts the region of IFR conditions over northeast North Dakota that is separate from southeast North Dakota where higher ceilings/visibilities are present.  (Consider the observations at Jamestown, ND (KJMS), for example).  As nighttimes progresses, IFR probabilities increase over most of the state.  The switch from daytime predictors (initially) to nighttime predictors is apparent in the 0202 UTC image (the terminator slants southwest to northeast).  The switch back to daytime predictors occurs between the 1102 UTC and 1215 UTC imagery.