Monthly Archives: December 2013

Fog over snow in the upper midwest

Suomi/NPP Brightness Temperature Difference (11.35 µm – 3.74 µm) Brightness Temperature Difference and Day/Night Band, 0840 UTC 27 December 2013 (click image to enlarge)

At times of low lunar illumination, it can become increasingly difficult to discern regions of clouds and snow in the Suomi/NPP day/night band, shown above, toggling with the brightness temperature difference. Nevertheless, careful perusal of the image reveals cloud edges over northeastern Wisconsin and through central lower Michigan that are confirmed by the brightness temperature difference field. In contrast, the whiter region that stretches southwestward from Des Moines towards extreme northeastern Kansas has no signal in the brightness temperature difference field. This is snow on the ground (vs. little snow to the northwest).

GOES IFR Probabilities computed with GOES-13 data, hourly from 0615 UTC through 1302 UTC, 27 December 2013 (click image to enlarge)

IFR Probability fields and GOES-Based brightness temperature difference fields are produced to aid in the detection of low clouds. In the animation above, higher IFR probabilities are centered on north-central Wisconsin where, intially, IFR conditions are not quite met (according to the plotted observations of ceiling and visibilities). However, as the night progresses, ceilings lower and visibilities decrease as IFR conditions do develop in regions where IFR Probabilities are high. The IFR probabilities roughly overlap the region where IFR conditions exist.

Note that encroachment of higher clouds in from the west, starting around 0800 UTC, means that satellite data cannot be used in the IFR Probability algorithm. Because only model data are used, IFR probabilities drop from values at/above 80% to values between 50 and 60% even as IFR conditions come to be more widespread. For this reason, it is important when interpreting IFR Probabilities to be alert to the presence of high clouds.

IFR Probabilities give a much more better approximation of where fog/low stratus may be occurring than a simple brightness temperature difference field. The toggle between the GOES-R IFR Probabilities and the GOES-13 Brightness Temperature Difference, below, gives testimony to this.

IFR Probability fields in extreme cold

GOES-R IFR Probabilities from GOES-15 with surface ceilings/visibilities and GOES-15 Brightness Temperature Difference (10.7 µm – 3.9 µm) Fields with surface plots at 1800 UTC 26 December 2013 (click image to enlarge)

The image toggle above shows IFR Probability and the Brightness Temperature Difference Field over northern Alaska. Plotted METAR observations show very cold surface temperatures in the -30 to -50 F range. At such cold temperatures, the pseudo-emissivity computation can become noisy because a very small change in 10.7 µm radiance (used to compute the 3.9 µm radiance) can cause a large change in 3.9 µm brightness temperature. (The effect is shown graphically below — a small change in radiance at 3.9 µm leads to a very large temperature change). This noise can lead to a speckled appearance to the IFR probability fields. This effect can also occur in the northern Plains of the United States when surface temperatures dip below zero.

Radiance (y-axis) vs. Brightness Temperature (x-axis) for 3.8 µm (left) and 10.7 µm (right)

(update) Below is the IFR Probability in the heart of a Polar Airmass over northwestern Ontario.

GOES-R IFR Probabilities from GOES-13 with surface ceilings/visibilities and GOES-13 Brightness Temperature Difference (10.7 µm – 3.9 µm) Fields with METAR plots at 1215 UTC 29 December 2013 (click image to enlarge)

The influence of high clouds

GOES-R IFR Probabilities from GOES-15 (upper left), GOES-15 and GOES-13 Brightness Temperature Difference (10.7 µm – 3.9 µm) Fields (upper right), GOES-R Cloud Thickness from GOES-15 (lower left), GOES-13 and GOES-15 6.7 µm enhanced water vapor imagery (lower right), all at ~0700 UTC 16 December 2013 (click image to enlarge)

High clouds in the atmosphere limit the ability of satellites to sense the presence of low clouds, as this example from December 16 2013 on the coast of Oregon demonstrates. Both the brightness temperature difference product and the water vapor imagery show signatures that accompany high cirrus. When cirrus is present, the brightness temperature difference field cannot be used to isolate regions of fog and low stratus because the satellite is detecting radiation from the highest emitting surface (the cirrus) not the fog/low stratus beneath. The IFR Probability field, however, uses both cloud information and Rapid Refresh Data, and the model data can fill in regions where satellites give no useful information, such as the lower Columbia River Valley around Astoria. Because satellite data are not used as a predictor, probabilities are lower. Remember how the presence of high clouds affects things when you interpret the IFR Probability fields.

GOES-R Cloud Thickness is not computed under high clouds. The GOES-R Cloud Thickness is the thickness of the highest water-based cloud deck. If a cirrus deck is present, or if twilight conditions are present, GOES-R Cloud Thickness is not computed.

Pseudo-emissivity at 3.9 µm

The traditional (or heritage) method of detecting fog/low clouds is a brightness temperature difference (BTD) product. The difference between brightness temperatures at 10.7 µm and 3.9 µm highlights water-based clouds because those clouds do not emit as a blackbody at 3.9 µm, so the inferred temperature (computed assuming a blackbody emission) is colder than that temperature computed using 10.7 µm radiation, because clouds emit radiation at 10.7 µm more like a blackbody.

At night, the GOES-R IFR Probabilities use a pseudo-emissivity at 3.9 µm in lieu of the 3.9-11µm BTD to highlight regions of water-based clouds at low levels. The 3.9 µm Pseudo-emissivity is the ratio of the observed radiance at 3.9 µm to a computed 3.9 µm blackbody radiance that is based on the observed 10.7 µm brightness temperature. In other words, the observed 10.7 µm brightness temperature is computed from the 10.7 µm radiance. That computed brightness temperature is then used to compute a 3.9 µm radiance that would be detected if the emitter was a blackbody.

The 3.9 µm pseudo-emissivity produces a satellite signature for low water clouds similar to the brightness temperature difference but is used instead because it is less sensitive to scene temperature. Skill scores for fog/low stratus detection are higher when the pseudo-emissivity is used in the algorithm to find regions of low clouds/fog than when the brightness temperature difference field is used.

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.

GOES-R Fog Product Examples

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