Category Archives: Multiple Cloud Layers

IFR Conditions under multiple cloud layers

GOES-R IFR Probabilities with surface observations of ceilings and visibilities (Upper Left), GOES-East Visible Image (Upper Right), GOES-East Brightness Temperature Difference (10.7 µm – 3.9 µm) (Lower Right), GOES-R Cloud Thickness (Lower Left), all near 2100 UTC (Click to enlarge)

IFR and near-IFR conditions existed near Duluth Minnesota during the day on 29 August 2014; How does information from the satellite help to diagnose the IFR conditions? Both the visible and brightness temperature difference fields, above, show widespread cloudiness, with convective features over Wisconsin and multiple cloud decks over Wisconsin and Minnesota. These multiple cloud decks show no apparent relationship the observed IFR or near-IFR conditions. In cases such as these, the low-level information available through the Rapid Refresh Model is key to providing information defining exactly where the lowest ceilings and visibilities exist. In the case above, that region is centered near Duluth, extending to the southwest. IFR Probabilities are elevated in regions where visibilities and ceilings are low, and they increase as the cloud ceiling increases. The image below shows Duluth Harbor at about 2140 UTC; the low ceiling is apparent (Source).

Fog over Chicago

GOES-R IFR Probability and GOES-13 Brightness Temperature Difference, 0915 UTC on 27 June (Click to enlarge)

A cold winter and cool Spring have caused Lake Michigan to be much cooler than normal (Linked-to Figure is on this page). Colder-than-normal lake temperatures and warm summer dewpoints are a recipe for fog, and that fog has persisted over and near Lake Michigan this month. (Click here for a video of fog moving over Chicago on 26 June). The imagery above shows the GOES-R IFR Probability toggling with the GOES-13 Brightness Temperature Difference field (10.7µm – 3.9µm) at 0915 UTC. Abundant high clouds (cirrus from convection over the Plains) makes the brightness temperature difference method of detecting low clouds problematic. Because IFR Probability computation includes surface information, however, a useful signal near Lake Michigan that captures the extent of the fog is produced.

When high clouds prevent satellite predictors from being used, the IFR Probability field is typically fairly smooth. That is the case over most of Lake Michigan. Breaks in the high clouds at 0915 UTC over Chicago allow for satellite predictors to be used. Where that happens, IFR probabilities are larger, and the IFR Probability field is more pixelated.

Click here for a blog entry at the Washington Post on the fog.

Advection fog over Lake Michigan

GOES-R IFR Probabilities computed from GOES-East (Upper Left), GOES-East Brightness Temperature Differences (10.7 µm – 3.9 µm) (Upper Right), GOES-R Cloud Thickness (Lower Left), GOES-R IFR Probabilities computed from MODIS, or GOES-East Visible Imagery, times as indicated on 29 April 2014 (click to enlarge)

The GOES-R IFR Probability fields computed from GOES-East captured the onset of Lake fog that moved onshore over eastern Wisconsin on April 29th. Multiple cloud layers associated with a strong extratropical cyclone precluded the use of the brightness temperature difference product (the heritage method of detecting fog/low stratus). However, the IFR Probability field aligns well with the reductions in visibility associated with the Lake fog. The character of the IFR Probability field can be used to infer whether of not satellite data predictors are being used. For example, the relatively flat field over southeast Wisconsin at the start of the animation is a region where satellite predictors are not used. The use of satellite predictors generally leads to a pixelated field. A flatter field as over southeast Wisconsin reflects the smoother model fields that are driving the probability field computation.

Cloud thickness is computed in regions where the highest cloud, as seen by the satellite, is a water-based cloud. And that is also usually the region where satellite predictors are used in the computation of IFR Probabilities. Note in the animation above how cloud thickness generally overlays regions of IFR Probability that are pixelated. Cloud thickness is not computed where only model data are used to compute IFR Probabilities. (Cloud thickness is also not computed in the hour or so around sunrise and sunset, during twilight conditions).

The slow northward movement of the fog bank is apparent in the first part of the animation above, from 0615 through 0745 UTC. Note also how the MODIS IFR Probability fields give a very similar solution to the GOES-13-based fields at 0745 UTC. Differences in resolution are apparent over southwest Wisconsin, however, where river valleys are more accurately captured by the MODIS fields.

In the visible imagery at the end of the animation (1355 UTC), the rapid saturation of moisture-laden air moving northward from Indiana over the cold waters of southern Lake Michigan is very apparent.

Fog/Low Stratus near the Gulf of Maine

GOES-R IFR Probabilities computed from GOES-13 and surface plots of ceilings/visibilities, times as indicated (click to enlarge)

When relatively high dewpoints move over the cold waters of the Gulf of Maine in Spring, advection fog can form. Sometimes this happens underneath clear skies, sometimes it happens underneath high clouds. In both cases, the IFR Probability field should give a reasonable answer — but how can that prediction be validated? In the example above, the apparent fog/low cloud bank propagates off to the east, and when it is fully ashore near Yarmouth, NS, visibilities drop from near-IFR to IFR conditions. The IFR probability field has an appearance that suggests plenty of high clouds are overlaying the visibility-restricting lower clouds, yet a consistent signal of higher probabilities of IFR conditions is maintained in/around the Gulf of Maine northward into Maine.

Identifying regions of fog under cirrus

GOES-R IFR Probabilities computed from GOES-13 and GOES-East Brightness Temperature Differences (10.7 µm – 3.9 µm) at 0500 UTC on 5 March 2014 (click to enlarge)

Fog developed overnight over Tennessee on March 5th, but Cirrus clouds prevented the traditional brightness temperature difference product from observing low-level water-based clouds. It is for events like this that the IFR Probability fields (that incorporate surface-based information by way of the Rapid Refresh Model) is important. The IFR Probability fields use predictors from the Rapid Refresh model to showcase where low ceilings and reduced visibilities are most likely. Satellite predictors are unavailable where cirrus clouds are present and the probability field shows lower values. So when you see low values, make sure you understand why the values are low: is it because cirrus clouds are present?

A side-by-side animation of the IFR Probabilities and the Brightness Temperature Difference Field is presented below. The effects of cirrus on the Probability field is obvious.

GOES-R IFR Probabilities computed from GOES-East (left) and GOES-East Brightness Temperature Differences (10.7 µm – 3.9 µm) (right), hourly from 0500 UTC through 1515 UTC on 5 March 2014 (click to enlarge)

Fog over northeast Florida and coastal Georgia and South Carolina

GOES-R IFR Probabilities computed from GOES-13 (Upper left); GOES-East Brightness Temperature Differences (10.7 µm – 3.9 µm) (Upper Right); MODIS-based IFR Probabilities or VIIRS-based Brightness Temperature Difference (11.35 µm – 3.74 µm) (Lower Left); GOES-R Cloud Thickness computed from GOES-East (Lower Right) (click to play animation)

Cold air has swept down the east coast into northern Florida, and the leading edge of that cold air, marked by a shift to northeasterly winds and low clouds, shows up well in the GOES-R IFR Probability fields, displayed above, because the airmass with the northeasterly winds also included low clouds/fog. Note in the animation how IFR conditions develop in Jacksonville as the higher IFR probabilities slide southward. Similarly, IFR conditions diminish over Savannah as IFR Probabilities drop.

This is a case for which the heritage method of detecting fog had difficulties because multiple cloud layers existed. For example, a stratus deck over central Florida shows up very well in the brightness temperature difference field from both GOES and VIIRS, but IFR conditions are not initially seen there (and GOES-R IFR Probabilities are small). The GOES-R Cloud Thickness is not computed in regions with multiple cloud layers, typically, because it shows the thickness of the highest water-based cloud layer. If any overlaying cloud layer at high levels contains ice, the field is not computed.

Advection Fog over the Upper Midwest

Toggle between nighttime GOES-R IFR Probabilities from GOES-13 and GOES-13 Brightness Temperature Differences (10.7 µm – 3.9 µm) at 0215 UTC on 4 December 2013 (click image to enlarge)

Dense advection fog developed in the upper midwest on Tuesday 3 December 2013 and persisted into December 4th as a Colorado Cyclone moved into central Wisconsin, drawing moist air over cold ground. The IFR Probability Product, a product that fuses together the 3.9 µm pseudo-emissivity (nighttime only) from satellites (a signal similar to the 3.9-11 µm brightness temperature difference field, which gives little information on low clouds in this situation) with model data from the Rapid Refresh (which suggests widespread fog), accurately depicts the large region of advection fog that led to dense fog advisories over parts of Wisconsin and Iowa and surrounding states (see below).

Cropped Screenshot from http://www.crh.noaa.gov at 1414 UTC on 4 December 2013 that shows widespread Dense Fog Advisories over the Upper Midwest

Daytime GOES-R IFR Probabilities computed from GOES-13, 2145 UTC on 3 December 2013 (click image to enlarge)

The dense fog was present late in the day on December 3rd, 2013, and the IFR Probability fields reflected that. However, in the image above, there are isolated pixels with very low probabilities mixed in with the high probabilities over Wisconsin and surrounding states where advection fog was widespread. Why?

This storm had multiple cloud layers, which can make detection of low cloud difficult from satellite (above) when sun angles are low (sunrise/sunset). The IFR Probability image above is at 3:45 PM local time, and the sun is low in the sky. Deep shadows are being cast and the dark shadowed regions in the visible are misinterpreted by the cloud-clearing algorithm as clear skies. During the day the GOES-R fog/low stratus algorithm relies on the cloud mask to determine where clouds are. Where clear skies are detected (erroneously, in this case), IFR Probabilities are not calculated because fog/low stratus are not expected to be present. Thus, if you see pixelated fields such as the one above, and the sun is low in the sky, this likely means cloud shadows are causing the cloud mask to erroneously return clear sky, which in turn leads to very low IFR probabilities. The animation below cycles through IFR Probability and visible imagery (with a regular enhancement and with a low-light enhancement). After sunset, the cloud shadows are gone and the probability field fills in (as can be seen in the 0215 UTC imagery at the top of this post).

Daytime GOES-R IFR Probabilities computed from GOES-13 at 2145 UTC on 3 December 2013 and the corresponding Visible Imagery (click image to enlarge)

Fog Detection under Cirrus

GOES-R IFR Probabilities from GOES-13 (Upper Left), GOES-13 Brightness Temperature Difference Product (10.7 µm – 3.9 µm) (Upper Right), GOES-R Cloud Thickness from GOES-13 (Lower Left), MODIS-based IFR Probabilities (Lower Right), Times as indicated (click image to enlarge)

Dense Fog developed over the southern Plains overnight, and the case demonstrates how the Fused data product is able to give a useful signal of IFR probabilities even in regions where high clouds preclude the detection of low clouds by satellite. The fog was widespread and dense enough to warrant Dense Fog Advisories from Tulsa, Norman and Topeka forecast offices. See below, for example.

000
FXUS64 KTSA 020953
AFDTSA

AREA FORECAST DISCUSSION
NATIONAL WEATHER SERVICE TULSA OK
353 AM CST MON DEC 2 2013

.DISCUSSION…
DENSE FOG CONTINUES THIS MORNING ACROSS MUCH OF THE CWA. GIVEN THE
TIME OF YEAR /LOW SUN ANGLE/ AND THE FACT THAT SOME HIGH CLOUDS ARE
STREAMING INTO THE AREA FROM THE NW /REDUCED INSOLATION AND
DELAYED MIXING/…THINK IT MAY TAKE A LITTLE LONGER THAN
PREVIOUSLY EXPECTED TO GET RID OF THE FOG. WE HAVE EXTENDED THE
DENSE FOG ADVISORY UNTIL 11 AM. ONCE THE FOG BURNS OFF…SHOULD
BE A PLEASANT DAY WITH UNSEASONABLY WARM TEMPS AND FAIRLY LIGHT
WIND. COULD BE SOME MORE FOG TUESDAY MORNING IN SOME PLACES BUT A
LITTLE MORE WIND MAY KEEP IT FROM GETTING AS DENSE AND AS
WIDESPREAD AS IT IS THIS MORNING. SLIGHTLY WARMER TEMPS IN STORE
TUESDAY WITH SOME PLACES LIKELY IN THE 70S. WARM AND WINDY
CONDITIONS WILL RESULT IN AN INCREASING FIRE WEATHER CONCERN.

Satellite detection of this fog event was constrained by the presence of two upper-level cloud decks. At the beginning of the animation, above, high clouds associated with the subtropical jet are over the southern quarter of the domain plotted. These high clouds quickly shift southward, and the region in the brightness temperature difference product that is consistent with detection of fog/low stratus (that is, low water-based clouds) expands to the south (surface observations suggest the low stratus clouds were present earlier, but masked by the higher clouds). Later in the animation, high clouds sag southward into the northern part of the domain. When this happens, low stratus/fog (indicated in observations by IFR conditions) are not detected by GOES because the higher ice clouds block the view of the scene. However, the IFR Probability fields that use both satellite data and output from the Rapid Refresh Model continue to depict a likely region (confirmed by the observations) of reduced visibilities. IFR Probabilities do drop, of course, as satellite data cannot be used to confirm the presence of low clouds. Knowledge of why the probabilities drop is vital to the interpretation of the field: You have to know that the high clouds are present, either by looking at the satellite data, or by understanding that the character of the IFR Probability field changes to one that is less pixelated when satellite data cannot be included because of ice clouds above the low stratus deck.

For a large-scale event like this, MODIS-based IFR Probabilities overlap well with GOES-Based IFR Probabilities, as shown in the image above. In cases like this sometimes individual river valleys will show up with slightly elevated IFR Probabilities (or cloud thicknesses).

The GOES-R Cloud Thickness field is computed for the highest water-based cloud detected (during non-twilight conditions — that is, not during the hour or so surrounding sunrise and sunset). Note how well the thickest clouds — over northeast OK, surrounding Tulsa — correlate with the strongest Brightness Temperature Difference, both in GOES and in Suomi/NPP data (below). Note also how the Cloud Thickness field is not computed in regions where higher ice-based clouds are present.

GOES-R IFR Probabilities from GOES-13 (Upper Left), GOES-13 Brightness Temperature Difference Product (10.7 µm – 3.9 µm) (Upper Right), GOES-R Cloud Thickness from GOES-13 (Lower Left), Suomi/NPP Brightness Temperature Difference from VIIRS (10.35 µm – 3.74 µm) (Lower Right), Times near 0802 UTC as indicated (click image to enlarge)

Cloud Thickness can be used to predict the time of fog dissipation, using this scatterplot/relationship. If sun angle is limited by the season, or if solar insolation is limited by higher clouds, you might adjust the first guess for dissipation to a later time.

IFR Conditions over southwest Alaska

Strong extratropical storms that move northward into the Gulf of Alaska, or into the Bering Sea, can bring IFR conditions to many parts of Alaska. However, they typically also bring multiple cloud layers that make traditional satellite-only methods of detecting fog and low stratus problematic. In cases like these, a fused product that incorporates model predictions of low-level saturation is helpful in defining just where IFR conditions are most likely.

GOES-15 Brightness Temperature Difference Product (10.7 µm – 3.9 µm), times as indicated (click image to enlarge)

For example, the brightness temperature difference field, above, does not show a strong signal in regions where near-IFR conditions are present. In contrast, the IFR Probability field, below, that incorporates model fields that are influenced by surface features, better highlights the region of IFR conditions. It captures the edge of the fog/low stratus field over SW Alaska, and probabilities are highest in regions where IFR and near-IFR conditions exist. The relatively flat field over land is a typical feature of the IFR Probability when it is determined chiefly by model data. Because satellite data are not included in the predictors, the total probability is somewhat smaller. Where the satellite brightness temperature difference field does have a strong signal is where the IFR Probabilities are highest (over the Bering Sea).

GOES-R IFR Probabilities derived from GOES-15 and Rapid Refresh data Brightness Temperature Difference Product (10.7 µm – 3.9 µm), times as indicated (click image to enlarge)

One shortcoming with IFR Probability is the pixel resolution at high latitudes. MODIS data can also be used to compute IFR Probabilities, and three comparisons between MODIS and GOES values are shown below. Alaska’s high latitudes means not only large GOES pixels, but also fairly frequent coverage from the polar-orbiting Terra and Aqua satellites that hold the MODIS instrument.

Toggle between GOES-R IFR Probabilities derived from GOES-15 and from MODIS satellite data at ~0900 UTC 15 October (click image to enlarge)

As above, but at ~1300 UTC (click image to enlarge)

As above, but at ~1430 UTC (click image to enlarge)

Fog over Kansas

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.

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).

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

GOES-R Fog Product Examples

Fog detection fusing GOES, Terra/Aqua or Suomi/NPP Satellite data with Model output