Monthly Archives: May 2015

Fog over Coastal California

GOES-R IFR Probabilities computed from GOES-West and Rapid Refresh Data, 0300-1200 UTC on 29 May 2015 (Click to enlarge)

GOES-R IFR Probability fields are challenged most days by the diurnal penetration of coastal fog and stratus that occurs overnight along the California Coast. In the animation above, IFR Probabilities increase in regions along the coast, and also in valleys (such as the Salinas Valley) where fog moves inland. Note above how Monterey, Watsonville and Paso Robles all show IFR (or near-IFR) conditions as the IFR Probabilities increase. The same is true farther north at Santa Rosa and at Marin County Airport, and farther south at Avalon, Ontario, Point Mugu and LA International. IFR Probability fields routinely do capture these common fog events.

The Brightness Temperature Difference Field (10.7 µm – 3.9 µm), below, captures the motion of these low clouds as well. However, numerous ‘false positive’ signals occur over the central Valley of California (likely due to differences in soil emmissivities). The GOES-R IFR Probability field can screen these regions out because the Rapid Refresh data in the region does not show saturation in the lowest kilometer. Note also how the Brightness Temperature Difference field gives little information about low clouds where high clouds are present (over the Pacific Ocean in the images below). IFR Probability fields, however, do maintain a strong signal there because data from the Rapid Refresh strongly suggests the presence of low clouds/fog.

GOES Brightness Temperature Difference Fields, 0400-1200 UTC on 29 May 2015 (Click to enlarge)

Suomi NPP makes an overflight over the West Coast each day around 1000 UTC, and the toggle of the Day Night Band and the Brightness Temperature Difference field (11.45 µm – 3.74 µm) is shown below. The moon at this time was below the horizon, so illumination of any fog is scant; the brightness temperature difference field does highlight regions of water-based clouds (that is, stratus); however, it does not contain information about the cloud base. In other words, it’s difficult to use the brightness temperature difference product alone to predict surface conditions.

1010 UTC Imagery from Suomi NPP VIIRS Instrument: Day Night Visible Band (0.70µm) and Brightness Temperature Difference Field (11.45µm – 3.74µm) (Click to enlarge)

GOES-14 is in SRSO-R mode, and its view today includes the west coast. The animation below shows the erosion of the fog after sunrise at 1-minute intervals. (Click here for mp4, or view it on YouTube). (Click here for an animation centered on San Francisco).

GOES-14 Visible (0.6263 µm) animation, 29 May 2015 [click to play very very large animation]

Late Spring in the Great Lakes: Fog

GOES-R IFR Probability fields, 2100 UTC on 26 May 2015, along with surface observations of ceilings and visibility (Click to enlarge)

Sea or lake surface temperatures are part of the algorithm used to create IFR Probability fields. The cold Great Lakes in late May are a prime location for advection fog, and IFR Probability fields will blanket the Great Lakes with high values under southerly, moist flow. It is not uncommon to see all of the Lakes bright orange/red. Note in the image above how Manitowoc WI and Charlevoix MI both have IFR Conditions. In addition, Dense Fog advisories were issued north of Milwaukee to the tip of Door County.

Post-thunderstorm Fog over Mississippi

GOES-R IFR Probability Fields, ~hourly, from 0300 through 1200 UTC on 19 May 2015 (Click to enlarge)

Thunderstorms moved through Mississippi (See this animation — from this Blog Post — of SRSO-R 1-minute imagery from 18 May), and the low-level moisture left behind allowed Dense Fog to form, and Dense Fog advisories were issued.

Multiple cloud decks — shown in the toggle, below, of Suomi NPP Day Night Band and Brightness Temperature Difference (11.45 µm – 3.74 µm) — prevented the traditional brightness temperature difference product from providing useful information. GOES-R IFR Probabilities, shown ~hourly in an animation above do highlight the region of developing IFR conditions. Low ceilings and reduced visibilities are commonplace in regions where IFR Probabilities are increasing over night. The predictors that are included to compute the IFR Probabilities are mostly model-based because of the multiple cloud layers that are present, and the IFR Probability field is somewhat flat as a result. Note that GOES-R IFR probabilities increase at the very end of the animation; when daytime predictors are used, probabilities are a bit higher than when nighttime predictors are used.

Suomi NPP Day Night Band visible imagery and Brightness Temperature Difference (11.45 µm – 3.74 µm) at 0818 UTC, 19 May 2015 (Click to enlarge)

IFR Probabilities with a back-door cold front

GOES-R IFR Probabilities, hourly from 0200 through 1015 UTC on 18 May 2015 (click to enlarge)

Easterly winds south of a High over the Canadian Maritimes ushered in cool, moist air over the Northeastern United States early on Monday. IFR Probabilities, above, show the progress of the low ceilings and reduced visibilities that accompanied the change in air mass. The low clouds eventually penetrated to the Delaware River, as shown in the GOES-14 SRSO-R animation here (from this blog post).

In the animation above, observations of ceilings and visibilities over southern New England approach IFR conditions quickly as the IFR Probability ‘front’ passes through.

Fog in the Florida Panhandle

Dense Fog Advisories were issued for the northwestern part of the Florida Panhandle overnight (Link). How did various fog-alert satellite products perform for this case?

The brightness temperature difference product, below, that works because clouds comprising water droplets have different emissivity properties at 3.9 µm and 10.7 µm, did not provide much guidance for this event. This is because multiple cloud layers prevented the satellite from seeing down to the developing stratus/fog deck near the surface. The cloud deck persisted over Alabama, but eroded over northwest Florida by 0700 UTC. (Click here for an animation of GOES-Sounder Cloud Heights). In addition, fog persists through sunrise, and the rising sun is emitting 3.9 µm radiation; that makes interpretation of the brightness temperature difference field problematic.

Brightness Temperature Difference Fields (10.7 µm – 3.9 µm) from GOES East, ~hourly from 0400 through 1215 UTC on 12 May 2015 and surface observations of ceilings and visibilities (Click to enlarge)

The GOES-R IFR Probability field, below, provided better information about the developing cloud field. Probabilities increase as the fog develops, and the spatial coverage of the enhanced IFR Probabilities better matches the regions where Dense Fog Advisories were issued. In addition, the strong signal persists through sunrise.

GOES-R IFR Probability Fields computed from Rapid Refresh and GOES-13 Data, ~hourly from 0400 through 1215 UTC, and surface observations of ceilings and visibilities (Click to enlarge)

Fog along the East Coast

GOES-R IFR Probability Fields computed from GOES-East and Rapid Refresh Data, hourly from 2300 7 May through 1200 8 May, and surface observations of ceilings/visibility (Click to enlarge)

GOES-R IFR Probability Fields showed large values at sunset over the Atlantic Ocean east of New Jersey and the Delmarva Peninsula. As night progressed, that fog penetrated inland. The IFR Probability field accurately depicts the region where visibilities due to fog were reduced. The 0400 UTC image in the animation above (reproduced below), has qualities that highlight the benefit of a fused product. The Ocean to the east of the southern Delmarva peninsula is overlain with multiple cloud layers that make satellite detection of low clouds/fog problematic. In this region, satellite data cannot be used as a predictor and the GOES-R IFR Probability field is a flat field. Because the GOES-R IFR Probability product includes information from the Rapid Refresh model (2-3 hour model forecasts, typically) and because saturation is indicated in the lowest 1000 feet of the model, IFR Probabilities over the ocean are high in a region where satellite data cannot be used as a predictor.

As above, but for 0400 UTC 8 May 2015 only (Click to enlarge)

GOES-R IFR Probabilities can also be computed using MODIS data, which data has better spatial resolution than GOES (1-km vs. nominal 4-km). The toggle below of the MODIS brightness temperature difference and the GOES-R IFR Probability shows a very sharp edge to the expanding fog field over New Jersey.

MODIS Brightness Temperature Difference (11µm – 3.9µm) and MODIS-based GOES-R IFR Probabilities, ~0250 UTC on 8 May, 2015 (Click to enlarge)

The Gulf Stream is apparent in the Brightness Temperature Difference field above and IFR Probabilities are high over the ocean between the coast and the Gulf Stream. In the absence of observations, how much should those high IFR Probabilities be believed. There is high dewpoint air (mid-50s Fahrenheit) along the East Coast at this time, and advection fog could be occurring, for example. Suomi NPP also overflew the region shortly after midnight. The toggle below, of brightness temperature difference and the Day Night Band confirms the presence of (presumably) low clouds over the cold Shelf Water of the mid-Atlantic bight.

Suomi NPP Brightness Temperature Difference (11.35 µm – 3.74 µm) and Suomi NPP Day Night Band Visible Imagery (0.70 µm) at night, 0643 UTC on 8 May, 2015

Dense Fog over the Midwest US

Fog and low clouds developed north of a stationary front draped across the midwest early in the morning on May 6th. Dense fog advisories were hoisted from Iowa to northwestern Ohio.

The animation above shows the increase in IFR Probabilities overnight as the dense fog developed. Note the difference in IFR Probability that arises when satellite data can be used as a predictor (that is, when the developing fog/low stratus is not overlain by higher clouds). Northwest Ohio until about 1200 UTC is a region where low clouds are viewed. There, satellite predictors can be used in the computation of IFR Probability fields. Accordingly, values are larger and there is more small-scale variability (the field looks more pixelated). In contrast, the field over Iowa for much of the animation is relatively flat. Here, even through values are comparatively low, interpret them knowing that satellite predictors are not being used because of the presence of middle/higher clouds that preclude the ability of satellite detection of low clouds.

An animation of the traditional brightness temperature difference field, here, from 0500-1000 UTC (after 1100 UTC, increasing reflected solar radiation makes the brightness temperature difference field less useful as a fog/stratus detection device). Compare the regions where IFR Probabilities are largest with the regions of strong Brightness Temperature Difference Signals.

IFR Probability fields above define the region of reduced visibilities very well. This suggests the Rapid Refresh model and the satellite (where its use was possible) were both in accord with the development of fog/low stratus in this region of the country.

The 1115 UTC image in the animation above, shown below, includes the day/night boundary artifact. This is the straight line, roughly parallel to the terminator (it will stretch directly north-south on the Equinoxes), that parallels the Lake Michigan shoreline at Chicago. To the right, daytime predictors are used and IFR Probabilities are somewhat larger (67% vs. 51%) than they are where nighttime predictors are used to the west.

Fog over Nebraska under high clouds

GOES-R IFR Probabilities, 0315-1215 UTC, 5 May 2015 (Click to enlarge)

Dense Fog developed over the Hastings, Nebraska WFO overnight, leading to the issuance of Dense Fog Advisories. The GOES-R IFR Probabilities, above, show a steady increase in probabilities over the night as the fog develops. The relatively flat nature of the IFR Probability field is characteristic of GOES-R IFR Probabilities that do not include information from satellite (that is, only model fields are being used here to educe IFR probabilities). IFR Probability fields are a fused product, typically blending information from model fields and from satellite data. However, this was a case of fog developing under an extensive cirrus shield so that satellite data were not used as a predictors. The 10.7µm – 3.9µm Brightness Temperature Difference field, shown below, gives no information about surface conditions. That the IFR Probability fields neatly overlap the region of developing IFR conditions is testimony to the accuracy of the model field in simulating the lower part of the troposphere.

When only model data are used, as above, features in the field that are parallel to surface topography contours can become evident in the GOES-R IFR Probability fields. This is related to interpolation of the lowest 1000 feet of model relative humidity fields (moisture information that is used as a predictor in the computation of the IFR Probability) in regions of sloping topography.

In the animation above, note that the IFR Probabilities increase in the final frame. Over most of the scene, at 1215 UTC, the sun has risen and Daytime Predictors are being used to compute IFR Probabilities. (The dividing line between Daytime — to the east — and nighttime — to the west — is visible stretching north-northwest to south-southeast from the extreme northeast corner of Colorado). IFR Probabilities are somewhat higher during the day (compared to night) because visible imagery is incorporated into the satellite predictors; more accurate cloud clearing means that IFR Probabilities increase just a bit.

GOES-13 Brightness temperature difference fields (10.7 µm – 3.9 µm) over the Great Plains, 0630-1300 UTC, 5 May 2015 (Click to enlarge)

Suomi NPP overflew Nebraska, giving a view of the extensive cirrus shield. The Day Night Band gave crisp imagery as the Moon was very nearly full.

Suomi NPP Day Night Band Visible Imagery (0.70 µm) at 0740 UTC on 5 May 2015 (Click to enlarge)

GOES-R Fog Product Examples

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