Category Archives: Multiple Cloud Layers

Dense Fog in Georgia and Florida

ifrprob_25nov2016_0000_1300anim

GOES-R IFR Probability Fields, 0000 – 1300 UTC on 25 November 2016 (Click to enlarge)

Light winds and a long November night allowed radiation fog formation over much of the deep south early on 25 November 2016. (1200 UTC Surface analysis is here). The Aviation Weather Center Website indicated widespread IFR Conditions, below, over the south, with the sigmet suggesting improving visibilities after 1400 UTC.

The animation above shows the evolution of the GOES-R IFR Probability fields from just after sunset to just after sunrise. There is a good spatial match between observed IFR conditions and the developing field. IFR Probability can thus be a good situational awareness tool, identifying regions where IFR Conditions exist, or may be developing presently.

awc_1400utc_25nov2016_ifr

Screenshot of Aviation Weather Center Front Page, 1405 UTC on 25 November (Click to enlarge)

Did the GOES-13 Brightness Temperature Difference Field identify the fields? The animation below, from 0200-1300 UTC, shows a widespread signal that shows no distinguishable correlation with observed IFR conditions.  Note also how the rising sun at the end of the animation changes the difference field as more and more reflected solar radiation with a wavelength of 3.9 µm is present.  In addition, high clouds that move from the west (starting at 0400 UTC over Louisiana) prevent the satellite from viewing low clouds in regions where IFR conditions exist.

btd_25nov2016_0200_1300anim

GOES-13 Brightness Temperature Difference field (3.9 µm – 10.7 µm), 0200-1300 UTC on 25 November 2016 (Click to enlarge)

The high clouds that prevent satellite detection of low clouds, as for example at 1100 UTC over parts of Alabama, cause a noticeable change in the IFR Probability fields, as shown in the toggle below.  Values over the central part of the Florida panhandle are suppressed, and the field itself has a flatter character (compared to the pixelated field over southern Georgia, for example, where high clouds are not present).  Even though high clouds prevent the satellite from providing useful information about low clouds in that region, GOES-R IFR Probability fields can provide useful information because of the fused nature of the product:  Rapid Refresh information adds information about low-level saturation there, so IFR Probability values are large.  In contrast, over southern Florida — near Tampa, for example, Rapid Refresh data does not show saturation, and IFR Probabiities are minimized even through the satellite data has a strong signal — caused by mid-level stratus.  Soundings from Tampa and from Cape Kennedy suggest the saturated layer is around 800 mb.

ifr_btd_25nov2016_1100toggle

GOES-R IFR Probability fields and GOES-13 Brightness Temperature Difference Fields, 1100 UTC on 25 November 2016 (Click to enlarge)

GOES-R Cloud Thickness, below, related 3.9 µm emissivity to cloud thickness via a look-up table that was generated using GOES-West Observations of marine stratus and sodar observations of cloud thickness. The last pre-sunrise thickness field, below, is related to dissipation time via this scatterplot. The largest values in the scene below are around 1000 feet, which value suggests a dissipation time of about 3 hours, or at 1445 UTC.

cloudthickness_25nov2016_1145

GOES-R Cloud Thickness Field, 1145 UTC on 25 November 2016 (Click to enlarge)

IFR Probability Fields let you peek beneath the Cirrus

btd_0215-1215_27oct2016anim

GOES-13 Brightness Temperature Difference (3.9 µm – 10.7 µm) fields, hourly from 0215-1215 UTC on 27 October (Click to enlarge)

If you rely on satellite data alone to anticipate the development of IFR conditions — fog, low ceilings, and reduced visibilities — then the presence of widespread cirrus, shown here with the GOES-13 6.5 µm image, makes situational awareness difficult. At a glance, can you tell in the animation of brightness temperature difference, above, hourly from 0215 through 1215 UTC, where IFR Conditions are occurring? The widespread cirrus, present in the enhancement as dark grey and black, prevents the satellite from viewing any fog development, hence making this brightness temperature difference field, traditionally used to detect the development of fog and low stratus, unsuitable for large-scale situational awareness.

GOES-R IFR Probability fuses satellite data with Rapid Refresh Model output and allows a product that in essence peeks beneath the cirrus because near-surface saturation predicted in the Rapid Refresh Model allows the IFR Probability product to have a strong signal where fog might be developing. Consider the animation below, that covers the same spatial and temporal domain as the brightness temperature difference animation above. IFR Probability increases over inland southeast Georgia in concert with the development of low ceilings/reduced visibilities. It gave a few hours alert to the possibility that IFR conditions would be developing.  Note in the animation below that the 1215 UTC image includes IFR Probabilities computed using daytime predictors and nighttime predictors.  There is therefore a discontinuity in the field values over central Georgia at 1215 UTC, the end of the animation.

Compare the animation below to the one above.  Which yields better situational awareness for the developing fog field?

ifr_0215-1215_27oct2016anim

GOES-R IFR Probability Fields, hourly from 0215 through 1215 UTC on 27 October (Click to enlarge)

The Aviation Weather Center plot (below) highlights the presence of an IFR SIGMET over the region at 1324 UTC on 27 October.

aviationweather1324utc27oct2016

Aviation Weather Center plot showing MVFR, IFR and LIFR stations over Georgia, along with an IFR Sigmet at 1324 UTC 27 October 2016 (Click to enlarge)

IFR Probability Screens out mid-level Stratus

ifrbtd_18october_1100_southeast_toggle

GOES-13 Brightness Temperature Difference (3.9 µm- 10.7 µm) and GOES-R IFR Probability fields computed with GOES-13 and Rapid Refresh Data, 1100 UTC 18 October. Plots of ceilings and surface visibilities are included (Click to enlarge)

GOES-R IFR Probability fields often to a better job (compared to brightness temperature difference fields) in outlining exactly where low ceilings and reduced visibilities are occurring because IFR Probability fields include information about low-level saturation from the Rapid Refresh model. That information about near-surface saturation allows the IFR Probability algorithms to screen out regions where only mid-level stratus is occurring. A low fog — a stratiform cloud of water droplets that sits on/near the surface — and a mid-level stratus deck (also a stratiform cloud of water droplets) can look very similar in a brightness temperature difference field. In the example above, consider much of northeastern Alabama and northern Georgia. There is a strong return in the brightness temperature difference field because mid-level stratus is present — but IFR Probabilities are small because the Rapid Refresh does not diagnose low-level saturation in the region. Compare Brightness Temperature Difference returns over northeast Alabama and over extreme western North Carolina — to the west of Asheville. IFR Conditions are observed over western North Carolina, and IFR Probabilities are high there. In general, the region with high IFR Probabilities in the toggle above includes stations that are reporting IFR or near-IFR conditions. Most stations outside the region of high IFR Probability are not showing IFR Conditions, even though they may be in a region with the Brightness Temperature Difference signal is large.

A similar story can be told farther west at 0800 UTC, shown below. Focus on the region with a strong Brightness Temperature Difference signal over southeast Arkansas. IFR Conditions are not occurring under that mid-level stratus deck, and IFR Probabilities are very low. Similarly, IFR Probabilities are small over Oklahoma and north-central Texas because the Rapid Refresh Model is not showing low-level saturation in those regions; IFR Probabilities cannot be large when low-level saturation is not indicated in the model.

Using both Satellite Data and Model Data accentuates the strengths of both. That’s the power of a fused data product.

ifrbtd_18october_0800_louisiana_toggle

GOES-13 Brightness Temperature Difference (3.9 µm – 10.7 µm) and GOES-R IFR Probability fields computed with GOES-13 and Rapid Refresh Data, 0800 UTC 18 October. Plots of ceilings and surface visibilities are included (Click to enlarge)

Fog/Low Ceilings over Southwest Georgia

GOESR_IFRP_0200_1300_27June2016anim

GOES-R IFR Probability fields on 27 June 2016 at 0200, 0400, and then hourly from 0700 through 1300 UTC (Click to enlarge)

Late-day thunderstorms on 26 June 2016 set the scene for the development of fog overnight over southwestern Georgia. The animation above shows the GOES-R IFR Probability fields.  An enhancement in the fields that is initially driven by Rapid Refresh Model data showing near-saturation at low levels is apparent at 0200 UTC.  As clouds associated with the departing convection dissipate, satellite data could also be used as input into the IFR Probability fields.  The toggle below of GOES-13 Brightness Temperature Difference fields (3.9 µm – 10.7 µm), at 0200 and 0400 UTC, shows the appearance of low-level clouds as mid-level and higher clouds (dark in the enhancement used) dissipate.  By 0400 UTC, when satellite pixels finally start to suggest low clouds, fog had already started to develop.  IFR Probability fields gave an early alert to the possibility of fog development on this day that was not possible from satellite data alone.

GOES_BTD_0200_0400_27June2016toggle

GOES-R Cloud Thickness Fields can give a hint to when radiation fog, as in this event, will dissipate in accordance with this scatterplot. The image below shows the GOES-R Cloud Thickness at 1030 UTC, the last field computed before twilight conditions (indeed, the boundary showing that boundary is readily apparent over eastern Georgia), with values exceeding 900 feet in some places over southwest Georgia.  Based on the scatterplot, that suggests a dissipation time of just over 2 hours (based on the best fit line, but note the scatter in dissipation times associated with cloud thicknesses of 900 feet:  just over an hour to almost 4 hours!) so clear skies would be expected by 1300 UTC.  The animation of visible imagery, here, shows that fog persisted just a bit longer than that, dissipating shortly after 1400 UTC.  GOES-R Cloud Thickness field is an empirical relationship between 3.9 µm emissivity and cloud thickness that is based on SODAR observations off the west coast.  The scatterplot was created based on past observations limited to the southeast part of the US and parts of the Great Plains.

GOESR_CLDT_1030_27June2016

GOES-13 Cloud Thickness, 1030 UTC on 27 June 2016 (Click to enlarge)

Low Ceilings and reduced visibilities over the Ohio Valley

1200UTC_OhioValley24June2016

Surface Observations at 1200 UTC on 24 June 2016 (Click to enlarge)

A screen capture from this site at 1215 UTC on 24 June 2016, above, shows IFR Conditions (Red) and Low IFR Conditions (Purple) over the upper Ohio River Valley and surrounding states.  The IFR Probability field for the same time, below, shows high probabilities in roughly the same regions that have IFR or Low IFR conditions.  The Brightness Temperature Difference field, also displayed in the toggle below, gives little information at this time of day.  A benefit of the GOES-R IFR Probability field is that it contains a coherent signal through sunrise.

GOES_IFR_BTD_1215_24June2016toggle

GOES-R IFR Probability fields and GOES-13 Brightness Temperature Difference Fields (3.9 µm – 10.7 µm) at 1215 UTC on 24 June 2016 (Click to enlarge)

The toggle at 0915 UTC, below, before sunrise, shows a second benefit of IFR Probability fields: a useful signal in regions with cirrus clouds. High clouds, of course, prevent GOES-13 from viewing the development of fog/low stratus near the surface. The Rapid Refresh model data on low-level saturation that are part of the IFR Probability Field computations give quality information in regions of cirrus. In the example below, developing IFR conditions are depicted (the yellow enhancement that shows IFR Probabilities around 40%) over much of northern Kentucky and southern Ohio.  This is under a region of cirrus (black in the enhancement used for the brightness temperature difference) north of a convective system that sits over southeastern Kentucky and eastern Tennessee.

GOES_IFR_BTD_0915_24June2016toggle

GOES-R IFR Probability fields and GOES-13 Brightness Temperature Difference Fields (3.9 µm – 10.7 µm) at 0915 UTC on 24 June 2016 (Click to enlarge)

The waning full moon provided ample illumination for the Suomi NPP Day/Night Band Imagery, shown below, from 0736 UTC on 24 June 2016.  The cirrus shield, mid-level clouds and developing valley fogs are all apparent.

NPP_DNB_0736_24June2016

Suomi NPP Day/Night band imagery, 0736 UTC on 24 June 2016 (Click to enlarge)

Multiple Cloud Layers and Topography

IFR_1315_22Apr2016

GOES-R IFR Probability Fields, 1315 UTC on 22 April 2016, along with surface observations of ceilings and visibilities (Click to enlarge)

The 1315 UTC image of GOES-R IFR Probabilities, above, shows an axis of higher probabilities aligned with the topography of the Sierra Nevada. Note that Blue Canyon (KBLU) is the sole station reporting IFR Conditions. Did conventional satellite data capture this event? The Water Vapor (6.5µm) and Brightness Temperature Difference fields (10.7µm – 3.9µm), below, do not show evidence of low clouds;  indeed, the cirrus signature in the water vapor must mask any satellite observation of low clouds banked along the Sierra Nevada. Thus a fused product that combines model data and satellite data (such as IFR Probability fields) must be used, and the relatively flat nature of the IFR Probability field above confirms that Rapid Refresh information on low-level saturation is the reason why IFR Probability values are elevated along the mountains.

WV_BTD_1315_22Apr2016

GOES-15 Water Vapor (6.5 µm), left, and GOES-15 Brightness Temperature Difference Field (10.7 µm – 3.9 µm), right, at 1315 UTC 22 April 2016 (Click to enlarge)

IFR Probability Fields earlier in the night did have a satellite component to them. The values at 0300 and 0600, below, show the gradual encroachment of cirrus from the south and west over the low clouds along the Sierra Nevada. After 0600 UTC, only model data were used over the Sierra as high-level cirrus blocked the satellite view.

IFR_BTD_03_06_22Apr2016

Brightness Temperature Difference (10.7µm – 3.9µm) Fields (Left) and GOES-R IFR Probaility Fields (Right) from 0300 (Top) and 0600 (Bottom) on 22 April 2016 (Click to enlarge)

Reduced Visibilities under multiple cloud layers in the Northern Plains

GIFR_P_1115_18Apr2016

GOES-R IFR Probabilities at 1115 UTC and 1100 UTC surface observations of ceiling and visibility (Click to enlarge)

The image above shows GOES-R IFR Probabilities over North/South Dakota and Minnesota shortly before sunrise on Monday April 18 2016.  There is a distinct difference in the field between western Minnesota and eastern North and South Dakota that occurs because Rapid Refresh model fields (low-level saturation) are used as a predictor of IFR Probability.  Reduced visibilities and ceilings are reported where IFR Probabilities exceed 50% (the orange shading).  In contrast, the window channel and water vapor imagery for the same time, below, gives little indication that fog and low ceilings are present over eastern North and South Dakota:  satellite views of the lowest levels are blocked by mid- and upper-level clouds.   Fusing model data and satellite data into one predictor yields a superior product for detection of low ceilings and reduced visibilities.

3PanelToggle_1115anim.IR4IR3IFRP

GOES Infrared imagery (10.7 µm and 6.5 µm) and GOES-R IFR Probability fields, 1115 UTC on 18 April (Click to enlarge)

GOES-R IFR Probability screens out mid-level stratus

BTD_1215_29March2016

GOES-13 Brightness Temperature Difference (10.7 µm – 3.9 µm) at 1215 UTC on 29 March 2016 (Click to enlarge)

Consider the brightness temperature difference field above, from 1215 UTC on 29 March 2016. A strong signal that indicates water-based clouds extends from central Oklahoma to west Texas, and also from south Texas to west Texas. Are these water-based clouds obscuring visibilities at the surface? Over much of the region they are not. The Brightness Temperature Difference field gives information about the top of the cloud, but not the cloud base.

The GOES-R IFR Probability field for the same time, below, has screened out much of the region of mid-level stratus over Oklahoma and Texas. This can occur because IFR Probability fields include information from the Rapid Refresh Model. If that model does not indicated low-level saturation, IFR Probabilities will not be large. In the example shown, large values of IFR Probabilities are restricted to regions where IFR or near-IFR conditions are occurring.

IFRP_1215_29March2016

GOES-R IFR Probability Fields at 1215 UTC on 29 March 2016 (Click to enlarge)

Widespread IFR conditions over the Upper Midwest

GOESR_IFRP_0215_1215stepanim

GOES-R IFR Probability Fields, hourly from 0215-1215, 14 March 2016 (Click to enlarge)

A rain-dampened boundary layer allowed fog to form over much of the upper midwest on early Monday March 14 2016 (as a baggy low pressure system moved eastward). GOES-R IFR Probability fields, above, captured the slow expansion of the region of IFR conditions.  The character of the IFR Probability Field varies from smooth (over northern Illinois at 0215 UTC, for example (link)) to pixelated (over southern Minnesota at the same time).  This is related to whether the model only is used as a predictor (over northern Illinois) because of high clouds that prevent the satellite from viewing low clouds or whether model and satellite data are both used as predictors (over southern Minnesota).  The toggle below, of IFR Probability Fields and Brightness Temperature Difference fields at 1000 UTC on 14 March, underscores this relationship between IFR Probability and Brightness Temperature Difference fields.

In the animation above, IFR conditions are in general observed where IFR Probability fields suggest their presence.

GOES_IFR_BTD_14March2016_1000ztoggle

GOES-R IFR Probability and GOES-13 Brightness Temperature Difference Field, 1000 UTC on 14 March 2016 (Click to enlarge)

Nebraska Fog

IFRP-0415_1515anim_22feb2016

GOES-R IFR Probability fields, hourly from 0415 to 1515 UTC on 22 February, along with surface observations of ceilings and visibilities (Click to enlarge)

Dense Fog developed in two regions over Nebraska overnight, as shown in the screen capture below (from 1445 UTC) showing Dense Fog Advisories. GOES-R IFR Probabilities discerned the differences between the two regions, as shown in the animation above. The fog feature near the Missouri River (the boundary between Iowa and Nebraska) developed first in a region where high clouds were also present. That high clouds are present is apparent from the flat nature (that is, not pixelated) of the IFR Probability field at, say, 0415 and 0515 over western Iowa. The second region of fog, over south-central Nebraska, develops under clear skies as evidences by the (initially) very pixelated field. Towards the end of the animation high clouds overspread that region also. When that happens, IFR Probability values drop (because only model predictors can be used in the computation of IFR Probability in regions where high clouds exist). At the end of the animation, largest IFR Probabilities overlap the two regions where Dense Fog Advisories were issued. The region in between the advisories has lower Probabilities.

NWSFronPage

Compare the IFR Probability field, above, to Brightness Temperature Difference, below. Brightness Temperature Difference fields identify regions of Fog and Stratus, but near-surface information cannot be extrapolated (consistently) from cloud-top information. Thus, many stations with ceilings and visibilities far better than IFR conditions are in regions where a strong Brightness Temperature Difference signal exists (for example, KONL in northeastern Nebraska), The incorporation of surface information via Rapid Refresh model predictions of near-surface saturation allows the IFR Probability fields to better outline regions of IFR conditions only.

BTD_1300UTC_22feb2016

GOES-13 Brightness Temperature Difference (10.7 µm – 3.9 µm), 1300 UTC on 22 February 2016 (click to enlarge)