Author Archives: Scott Lindstrom

Fog over Southeast New England

Fog overspread much of southern New England overnight on 11-12 November 2014.

Downtown Boston, Fogbound, Wednesday Morning 12 Nov 2014 (Photo Credit: Blue Hill Observatory)

From the National Weather Service in Taunton, MA, late in the day on 11 November 2014:

000
WWUS81 KBOX 120001
SPSBOX

SPECIAL WEATHER STATEMENT
NATIONAL WEATHER SERVICE TAUNTON MA
701 PM EST TUE NOV 11 2014

CTZ002>004-MAZ002>024-026-NHZ011-012-015-RIZ001>008-120415-
HARTFORD CT-TOLLAND CT-WINDHAM CT-WESTERN FRANKLIN MA-
EASTERN FRANKLIN MA-NORTHERN WORCESTER MA-CENTRAL MIDDLESEX MA-
WESTERN ESSEX MA-EASTERN ESSEX MA-WESTERN HAMPSHIRE MA-
WESTERN HAMPDEN MA-EASTERN HAMPSHIRE MA-EASTERN HAMPDEN MA-
SOUTHERN WORCESTER MA-WESTERN NORFOLK MA-SOUTHEAST MIDDLESEX MA-
SUFFOLK MA-EASTERN NORFOLK MA-NORTHERN BRISTOL MA-
WESTERN PLYMOUTH MA-EASTERN PLYMOUTH MA-SOUTHERN BRISTOL MA-
SOUTHERN PLYMOUTH MA-BARNSTABLE MA-DUKES MA-NANTUCKET MA-
NORTHERN MIDDLESEX MA-CHESHIRE NH-EASTERN HILLSBOROUGH NH-
WESTERN AND CENTRAL HILLSBOROUGH NH-NORTHWEST PROVIDENCE RI-
SOUTHEAST PROVIDENCE RI-WESTERN KENT RI-EASTERN KENT RI-
BRISTOL RI-WASHINGTON RI-NEWPORT RI-BLOCK ISLAND RI-
INCLUDING THE CITIES OF…HARTFORD…WINDSOR LOCKS…UNION…
VERNON…PUTNAM…WILLIMANTIC…CHARLEMONT…GREENFIELD…
ORANGE…BARRE…FITCHBURG…FRAMINGHAM…LOWELL…LAWRENCE…
GLOUCESTER…CHESTERFIELD…BLANDFORD…AMHERST…NORTHAMPTON…
SPRINGFIELD…MILFORD…WORCESTER…FOXBORO…NORWOOD…
CAMBRIDGE…BOSTON…QUINCY…TAUNTON…BROCKTON…PLYMOUTH…
FALL RIVER…NEW BEDFORD…MATTAPOISETT…CHATHAM…FALMOUTH…
PROVINCETOWN…VINEYARD HAVEN…NANTUCKET…AYER…JAFFREY…
KEENE…MANCHESTER…NASHUA…PETERBOROUGH…WEARE…FOSTER…
SMITHFIELD…PROVIDENCE…WEST GREENWICH…WARWICK…BRISTOL…
NARRAGANSETT…WESTERLY…NEWPORT…BLOCK ISLAND
701 PM EST TUE NOV 11 2014

…PATCHY DENSE FOG POSSIBLE OVERNIGHT INTO WEDNESDAY MORNING…

A WARM AND MOIST AIRMASS BY NOVEMBER STANDARDS WAS OVER
CONNECTICUT… RHODE ISLAND…MASSACHUSETTS AND INTO NEW HAMPSHIRE
THIS EVENING. THIS COMBINED WITH LIGHT WINDS WILL RESULT IN PATCHY
DENSE FOG OVERNIGHT INTO WEDNESDAY MORNING. THERE IS SOME
UNCERTAINTY ON HOW WIDESPREAD THE FOG WILL BE. THUS A DENSE FOG
ADVISORY HAS NOT BEEN POSTED. HOWEVER AT LEAST SOME PATCHY DENSE
FOG IS LIKELY OVERNIGHT. THEREFORE MORNING COMMUTERS SHOULD PLAN
SOME EXTRA TIME TO REACH THEIR DESTINATION. IF FORECAST CONFIDENCE
ON WIDESPREAD DENSE FOG INCREASES LATER THIS EVENING A DENSE FOG
ADVISORY WILL BE ISSUED.

NOCERA

Subsequently, the NWS in Taunton tweeted two times about the fog.

How well did the GOES-R IFR Probability Fields diagnose this fog event? Note the presence of high and mid-level clouds in the picture at top, from the morning of 12 November. Their signature should be in the IFR Probability fields as well, and that is the case.

GOES-R IFR Probabilities (Upper Left), GOES-East Brightness Temperature Difference (10.7 µm – 3.9 µm) (Upper Right), GOES-R Cloud Thickness (Lower Left) and GOES-R Low IFR Probability (Lower Right) (Click to animate)

In the animation above, the effect of high clouds is obvious on the IFR (and Low IFR) Probability fields: when mid-level or high clouds prevent satellite data from being used as a predictor in IFR Probability fields, then model data are the main predictors. Model data have coarse resolution relative to satellite data so the IFR Probability fields in those regions have a smoother look that is not at all pixelated. Note how a signal in of low clouds in the Brightness Temperature Difference Field (orange or yellow enhancement) nearly overlaps the more pixelated parts of the IFR and Low IFR Probability fields. Where cirrus/mid-level clouds are indicated in the brightness temperature difference fields, IFR and Low IFR Probability fields are smoother; these are also regions where the GOES-R Cloud Thickness (which field is of the thickness of the highest water-based cloud viewed by the satellite) is not computed because ice-based clouds are screening any satellite view of water-based clouds.

Note how southeastern Massachussetts in the animation above — under multiple cloud layers — has relatively small IFR and low IFR (LIFR) Probability values in a region where dense fog is reported. This arises because the model being used — the Rapid Refresh — to generate IFR Probability predictors is not saturating in the lower levels. It is important to remember that when satellite data are missing, only model data are used to generate GOES-R Fog/Low Stratus products. To rely on only the IFR Probability fields as an indication of the presence of fog is to believe the model simulation in that region is correct. Sometimes the model simulation is correct (this case, for example); in the present case, however, there were regions in southeastern Massachusetts where the model forecast did not accurately represent the observed conditions.

Suomi NPP overflew New England around 0700 UTC on 12 November, and the data collected are included in the image toggle below. Suomi NPP better resolves some of the smaller valleys in interior New England, and some of the sharp edges to the fields.

As above, but with a toggle between Suomi NPP VIIRS Day Night Band and Brightness Temperature Difference (11.45 µm – 3.74 µm) in the lower right. All data at ~0700 UTC 12 Nov (Click to enlarge)

Fog near Hanford California

Tweeted Message from the National Weather Service in HNX (Hanford, CA) showing GOES-R IFR Probabilities in the central Valley of California (click to enlarge)

The National Weather Service in Hanford tweeted the image, above, of IFR Probability this morning. How did the evolution of IFR Probabilities compare to that of brightness temperature difference fields?

The hourly animation, below, suggests that data from the Rapid Refresh model was likely crucial in determining exactly where the lowest visibility occurred; the brightness temperature difference field did not capture the horizontal extent of the narrow band of fog that developed to the east of Interstate 5 (The interstate is the purple line in the animation). Indeed, the brightness temperature difference field appears to offer little in the way of forecast value, and differences trend to zero as the sun starts to rise at the end of the animation. In contrast, both IFR and LIFR Probabilities have peak values where ceilings are obscured and visibilities are near zero, in and around Hanford, and those large values persist through sunrise.

Hourly GOES-R IFR Probabilities (Upper Left, computed with data from GOES-15) with ceilings and visibilities plotted, Hourly GOES-R LIFR Probabilities computed with data from GOES-15 (Lower Left), GOES-15 Brightness Temperature Difference (10.7µm – 3.9µm) (Upper Right), Suomi NPP Day Night Band and Brightness Temperature Difference (11.45µm – 3.74µm) (Lower Right), times as indicated (Click to enlarge)

Suomi NPP overflew the central Valley at 0938 UTC. The Day Night Band and the brightness temperature difference (11.45µm – 3.74µm) field, below, do not contain signatures of dense fog near Hanford.

As above, but at 0945 UTC, when Suomi NPP data were present (Click to enlarge)

The two-hour time-lapse video, below, shows the evolution of the Fog at the National Weather Service Office in Hanford on the morning of 4 November.

Fog over the Northeast under Cirrus Clouds

The system that produced cirrus to obscure satellite-based observations of low clouds and fog over the Southeast US on 29 October (link) had the same effect over the Northeast United States: Multiple Cloud Layers with an extratropical system will prevent satellites from identifying regions of low clouds and fog. Any kind of fog detection algorithm, then, must incorporate surface-based observations (as in a model, for example) to provide useful information when multiple cloud layers are present.

GOES_IFR_4Panel_Northeast_29October2014_00_12

GOES-based GOES-R IFR Probabilities (Upper Left), GOES-13 Brightness Temperature Difference (10.7µm – 3.9µm) (Upper Right), GOES-based GOES-R Cloud Thickness (Lower Left), MODIS-based GOES-R IFR Probabilities (Lower Right) (Click to enlarge)

WFOs in the northeast issued Dense Fog Advisories for the morning of 29 October (and retweeted fog images from the public). (Link) The animation above shows the evolution of GOES-based GOES-R IFR Probabilities and GOES-13 Brightness Temperature Difference fields. Little information can be gleaned from the brightness temperature difference fields; however, the IFR Probability does show high probabilities where IFR and near-IFR conditions develop from coastal Massachusetts northeastward through coastal Maine. The flat character of the IFR Probability field occurs because Rapid Refresh model data are being used as predictors in the computation of IFR Probability, and those model fields do not vary quickly. When satellite data are also used — as over Quebec at the start of the animation — the IFR Probability fields have a pixelated character.

IFR Probabilities increase on the last frame of the animation. This occurs because the region becomes sunlit, and cloud-clearing abilities increase. The algorithm to compute IFR Probability therefore has greater confidence that clouds are present and probabilities increase.

Fog over the Southeast under Cirrus Clouds

Toggle between GOES-13 Brightness Temperature Difference (10.7µm – 3.9µm) and GOES-based GOES-R IFR Probabilities at 1015 UTC 29 October, with ceilings and visibilities plotted (Click to enlarge)

On the morning of 28 October 2014, Fog developed over the southeast under clear skies. On the morning of 29 October, Fog developed under cirrus clouds. When cirrus clouds are present, the brightness temperature difference product gives no information on low clouds, and the GOES-R IFR Probability fields rely on model data only to provide information. The toggle above shows IFR Probability Fields that overlap the region of reduced ceilings/visibilities in coastal South Carolina. Because model data are the primary predictor used, the field is much smoother (less pixelated) than when satellite data can also be used as a predictor.

Fog Development, Detection and Dissipation in the Southeast

GOES-based GOES-R IFR Probability (Upper Left), GOES-East Brightness Temperature Difference (10.7µm – 3.9µm) (Upper Right), GOES-R Cloud Thickness (Lower Left), MODIS-based GOES-R IFR Probability (Lower Right) (Click to Enlarge)

Fog and low ceilings developed over the southeast United States during the morning hours of 28 October 2014. The animation above, showing data each hour, shows IFR Probabilities developing initially near Charleston South Carolina then overspreading much of South Carolina, Georgia and Mississippi. In contrast, the brightness temperature difference field has a positive signal over most of those states. IFR Probability correctly screens out regions where Brightness Temperature Difference suggests low clouds are present but where ceilings and visibilities do not meet IFR criteria. IFR Probabilities are highest where the brightness temperature difference signal has the largest value, however.

The animation includes one hour (0700 UTC) with a MODIS-based IFR Probability field, shown below. MODIS- and GOES-based fields show similar patterns, but edges are sharper in the higher-resolution data.

As above, but at 0700 UTC (Click to enlarge)

Suomi NPP overflew the southeast in the early morning as well. The toggle below of the Day Night Band and the Brightness Temperature Difference shows two things plainly: The near-New Moon has not yet risen and lunar illumination is missing; therefore, only Earth-glow and City Lights are providing light, and clouds are very difficult to detect. As with GOES data, the Suomi NPP Brightness Temperature Difference field overpredicts where low clouds and fog and creating IFR conditions.

As above, but at 0647 UTC, with Suomi NPP Brightness Temperature Difference (11.45µm – 3.74µm) and Day Night Band imagery in the bottom right (Click to enlarge)

GOES-R Cloud Thickness Fields can be used to predict when fog will burn off (Using this scatterplot as a first guess). The image below is the last pre-sunrise GOES-R Cloud Thickness field over the Southeast. The Thickest clouds are over east-central Mississippi and central North Carolina, so that is where fog should linger the longest. The short animation, at bottom, showing the 1415 and 1515 UTC shows that to be the case. Note that fog/low clouds over Mississippi are moving eastward and lingering, perhaps because the high clouds above them are reducing insolation.

GOES-R Cloud Thickness, 1115 UTC on 28 October 2014 (Click to enlarge)

GOES-13 Visible Imagery, 1415 and 1515 UTC, 28 October 2014 (Click to enlarge)

GOES-R IFR Probabilities at High Latitudes

GOES-R IFR Probabilities computed using GOES-15 and Aqua Data, both near 1300 UTC on 27 October 2014 (Click to enlarge)

GOES-R IFR Probabilities are created using both GOES-15 Imager and Terra/Aqua MODIS. The toggle above shows MODIS-based IFR Probabilities (computed using data from Aqua and the Rapid Refresh) and GOES-based IFR Probabilities (computed using data from GOES-15 and the Rapid Refresh). There are three regions in the fields that warrant comment.

(1) Over East-central Alaska and the Yukon, large values of MODIS-based IFR Probabilities are limited in area (and near stations — such as Northway Airport — that are reporting IFR or near-IFR conditions). GOES-based IFR Probabilities in that same region include a large area with modest values — around 50%. Limb brightening may have an effect at high latitudes on the brightness temperature difference fields that are used in the computation of IFR probabilities because limb brightening is a function of wavelength. MODIS data (which will have far less limb brightening) can be used as a good check on the IFR Probabilty fields computed from GOES.

(2) Over Southwestern Alaska, and into the eastern Aleutians, GOES-based and MODIS-based IFR Probabilities are very similar. In this region, multiple cloud layers prevent satellite data from being used as a predictor in the computation of GOES-R IFR Probabilities. Rapid Refresh data is the main predictor for low clouds/fog, so MODIS-based and GOES-based fields will look similar.

(3) In the northeastern part of the domain, over the Northwest Territories of Canada, MODIS-based and GOES-based IFR Probabilities are very high. Satellite data are being used as a predictor here, and the satellite-based signal is strong enough to overwhelm any limb-brightening. (Note that southern Northwest Territories and northern British Columbia are south of the MODIS scan).

Terra- and Aqua-based MODIS observations yield frequent observations that result in good spatial and temporal coverage for IFR Probability fields over Alaska. GOES-15 temporal coverage is better, but the frequent MODIS passes can be used to benchmark GOES-based IFR Probability fields that may be misrepresentative because of limb-brightening effects at high latitudes.

GOES-R IFR Probabilities as a Weather System Moves Out

GOES-13 Water Vapor (6.5µm), Brightness Temperature Difference (10.7µm – 3.9µm) and GOES-based IFR Probabilities, all at 0615 UTC 23 October 2014, with surface observations of ceilings and visibilities (click to enlarge)

When a baroclinic system moves through a CWA, drops precipitation and then exits after sunset, the stage is often set for the development of radiation fog. The animation above cycles through the 0615 UTC imagery: GOES-13 Water Vapor (6.5 µm), GOES-13 Brightness Temperature Difference (10.7µm – 3.9µm) and GOES-based GOES-R IFR Probabilities. The upper-level reflection of a surface cold front moving through eastern Nebraska and western Iowa (link) is obvious in the (infrared) water vapor imagery, and also in the brightness temperature difference field.

The difficulty that arises with multiple cloud layers that invariably accompany these systems is that the mid- and high-level clouds do not allow for an accurate satellite-only-based depiction of low stratus and fog. GOES-R IFR Probabilities allow for that kind of depiction because near-surface saturation is considered in the computation of IFR Probabilities (using data from the Rapid Refresh Model). Thus, IFR Probabilities correctly suggest the presence of reduced visibilities over extreme northwestern Iowa and they alert a forecasters to the possibility of fog over much of western Iowa.

GOES-based IFR Probabilities, 03-13 UTC 23 October 2014 (Click to enlarge)

The animation of GOES-R IFR Probability fields, above, shows the steady increase in probability that accompanied the reduction in ceilings and visibilities. The character of the IFR Probability fields is testimony to the data that are used to create them. The fairly flat fields over Iowa early in the animation mean that satellite data cannot be used as a predictor (because of the multiple cloud levels that are present there, as apparent in the animation below). Instead, the model fields (that are fairly flat compared to satellite pixels) are used and horizontal variability in the field is small. In addition, IFR Probability values themselves are somewhat smaller because fewer predictors can be used.

IFR Probabilities are more pixelated in nature over southern Nebraska where satellite-based predictors could be used. IFR conditions are widespread in that region where IFR Probabilities exceed 90%. Note how IFR probabilities are smaller over Kansas, in a region of mid-level stratus (but not fog). The brightness temperature difference field there maintains a strong signal. IFR probability fields do a superior job of distinguishing between mid-level stratus and low stratus/fog (compared to the brightness temperature difference field). This is because a mid-level stratus deck and a fog bank can look very similar from the top, but an accurate Rapid Refresh model simulation of that atmosphere will have starkly different humidity profiles.

GOES-13 Brightness Temperature Difference (10.7µm – 3.9µm) Fields hourly from 0300 through 1300 UTC 23 October, including surface observations of ceilings and visibilities (Click to enlarge)

At 1400 UTC (below), the rising sun (and its abundant 3.9 µm energy that can be easily scattered off clouds) causes the sign of the brightness temperature difference field to flip. (Compre the 1400 UTC Brightness Temperature Difference Field below to the final Brightness Temperature Difference field (1300 UTC) in the animation above!) However, the IFR Probability field maintains its character through sunrise.

GOES-13 Brightness Temperature Difference (10.7µm – 3.9µm) and GOES-based IFR Probabilities, all at 1400 UTC 23 October 2014, with surface observations of ceilings and visibilities (click to enlarge)

The NSSL WRF accurately suggested that fog/low stratus was possible. The brightness temperature difference field from the model run, below, at 0900 UTC, shows a strong signal of low water-based clouds over western Iowa and Nebraska. (This link shows the latest model run (initialized at 0000 UTC) of Brightness Temperature Difference fields, with output from 0900 through 1200 UTC of the following day)

Simulated Brightness Temperature Difference fields, 0900 UTC 23 October 2014, from the 0000 UTC NSSL WRF Model Run (Click to enlarge)

MODIS-based IFR Probability Fields were also available for this event, at 0824 UTC, below. As with the GOES, there is a noticeable (very!) difference between regions where Satellite Predictors are being used in the computation of IFR Probabilities (Nebraska) and regions where Satellite Predictors are not being used in the computation of IFR Probabilities (Iowa). The superior resolution of the MODIS data also suggests that River Valleys in eastern Nebraska are more likely foggy than adjacent land. The Elkhorn River between Norfolk and O’Neill, for example, shows up in the MODIS-based IFR Probability field as a thread of higher IFR Probability.

MODIS-based IFR Probability at 0824 UTC, 23 October 2014 (Click to enlarge)

GOES-R IFR Probabilities refine the Brightness Temperature Difference Signal

Toggle of GOES Brightness Temperature Difference (10.7µm – 3.9µm) and GOES-R IFR Probabilities computed from GOES data, 1100 UTC 23 October 2013, with surface observations of ceilings and visibilities (Click to enlarge)

The toggle above, of Brightness Temperature Difference and GOES-R IFR Probabilities, shows how the traditional method of fog/low stratus detection — brightness temperature difference fields — can overpredict where fog and low stratus are actually observed. There are two main reasons for this: First, in regions of dry soil (over the Desert Southwest, for example), emissivity differences in soil can trigger a large difference in satellite-perceived brightness temperature at 10.7µm and 3.9µm that leads to a fog-like signal; second, in regions of water-based clouds, the signal is for the cloud top only. The satellite signal gives little information about the thickness of the cloud or of the cloud base. In the example above, mid-level stratus over central Kansas southward through central Oklahoma into central Texas yield a brightness temperature difference signal similar to regions of low clouds over the northern Texas panhandle. Compare the observations at Dalhart TX (KDHT) and Alva, OK (KAVK), for example.

The fused product (GOES-R IFR Probabilities) yields a statistically superior picture of the region of low stratus and fog because of the use of Rapid Refresh Data. These model data include the effects of surface-based observations and can therefore screen regions where low clouds are not actually present. GOES-R IFR Probabilities therefore give a better estimate of exactly where the low clouds present a hazard to — for example — aviation.

Fog over the Ozarks and southern Plains

GOES-IFR Probabilities, computed from GOES-13 and Rapid Refresh, hourly from 0100 through 1300 UTC on 21 October 2014 (Click to enlarge)

Fog and stratus developed overnight over the Ozark Mountains and southern Plains. The hourly loop of GOES-R IFR Probabilities shows the development and expansion of visibility and ceiling reductions over the area. How do these fields compare to other measures of fog? Brightness Temperature Difference fields, below, generally overestimate the regions of fog. The 0200 and 0800 UTC brightness temperature difference fields, below, are toggled with the IFR Probabilities; the inclusion of surface information via the Rapid Refresh Model correctly limits the positive brightness temperature difference to regions where fog and low stratus are most likely. The satellite-only signal overpredicts regions of reduced visibilities because it can only see the top of the cloudbank; this offers little information about the cloud ceilings!

GOES-13 Brightness Temperature Difference (10.7 µm – 3.9 µm) and GOES-based IFR Probabilities at 0200 UTC, 21 October 2014 (Click to enlarge)

GOES-13 Brightness Temperature Difference (10.7 µm – 3.9 µm) and GOES-based IFR Probabilities at 0800 UTC, 21 October 2014 (Click to enlarge)

Brightness Temperature Difference fields are occasionally contaminated by stray light in the signal. This happened on 21 October at 0400 UTC. The Brightness Temperature Difference fields at 0345, 0400 and 0415 UTC are shown below, with the GOES-R IFR Probabilities for the same time follow. Note how Stray Light contamination does bleed into the GOES-R IFR Probability field; if there is a large change over 15 minutes in the IFR Probability signal, consider the possible reasons for that change. Stray light contamination is a strong candidate if the signal is near 0400-0500 UTC with GOES-East. There are regions in the IFR Probability fields where even the strong — but meteorologically unimportant — brightness temperature difference signal during stray light is not enough to overcome the information from the Rapid Refresh model that denies the possibility of low-level saturation (for example, in northern Kansas or southern Oklahoma).

GOES-13 Brightness Temperature Difference (10.7 µm – 3.9 µm) at 0345, 0400 and 0415 UTC, 21 October 2014 (Click to enlarge)

GOES-based IFR Probabilities at 0345, 0400 and 0415 UTC, 21 October 2014 (Click to enlarge)

MODIS data from either Terra or Aqua can give important early alerts to the development of Fog/Low Stratus. Because of its superior resolution to GOES, the character of the developing fog can be depicted with more accuracy. The MODIS-based IFR Probability, below, in a toggle with the GOES-based IFR Probability at the same time, distinctly shows that the fog development at 0430 UTC is starting in the small valleys of the Ozarks of northwest Arkansas. GOES-based IFR Probabilities give a broader signal; certainly if you are familiar with the topography of the WFO you can correctly interpret the coarse-resolution GOES data, but the MODIS data spares you that necessity.

GOES- and MODIS-based IFR Probabilities at ~0430 UTC, 21 October 2014 (Click to enlarge)

Suomi NPP data can also be used to compute IFR Probabilities, but those data are not yet computed for AWIPS. The Ozarks were properly positioned on 21 October to be scanned by two successive orbits of Suomi NPP (one of the benefits of Suomi NPP’s relatively broad scan), and the brightness temperature difference fields (11.45µm – 3.74µm) at 0715 and 0900 UTC are shown below. As with MODIS, the strong signal in the river valleys is apparent. (The Day Night band from Suomi NPP for this event does not show a strong signal because the near-new Moon provides no illumination at 0745 or 0900 UTC: it hasn’t even risen yet.)

Suomi NPP Brightness Temperature Difference (11.45 µm – 3.74 µm) and 0715 and 0900 UTC, 21 October 2014 (Click to enlarge)

IFR Probabilities with a strong extratropical cyclone

GOES-R IFR Probabilities over the upper Midwest, 1515 UTC on 13 October 2014, along with surface reports of Ceilings and Visibilities, and HPC Frontal / Pressure analyses (Click to enlarge)

Strong low pressure systems can cause IFR conditions over large areas, but the multiple cloud layers that accompany extratropical cyclogenesis make difficult the observation of low stratus, because higher cloud decks are invariably in the way of the satellite’s view. For such systems, inclusion of Rapid Refresh Data as a way of detecting low-level saturation is a must. In the imagery above, note that the highest IFR Probabilities are between the warm front (that emerges from the low in Missouri and stretches into Illinois and Indiana) and the trough that extends north of the low in Missouri.

A zoomed-in view of the above image, below, centered over Iowa, does show good spatial correlation between observed IFR conditions and high IFR Probabilities. This suggests that the Rapid Refresh Model is accurately simulating the evolution of the strong storm in Missouri.

As above, but zoomed in over Iowa (Click to enlarge)

GOES-R Fog Product Examples

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