Category Archives: Multiple Cloud Layers

Dense Fog over Missouri

GOES-R IFR Probability Fields, and surface reports of Ceilings and Visibilities, hourly from 0315 to 1315 UTC on 15 August 2017 (Click to enlarge)

GOES-R IFR Probabilities are computed using Legacy GOES (GOES-13 and GOES-15) and Rapid Refresh model information; GOES-16 data will be incorporated into the IFR Probability algorithm in late 2017

IFR Conditions and Dense Fog developed over southeastern Missouri during the early morning on 15 August 2017, leading to the issuance of Dense Fog Advisories. GOES-R IFR Probabilities, above, showed increasing values as the ceilings lowered and visibilities dropped.  The IFR Probability fields over southern Missouri and northern Arkansas (and Kansas and Oklahoma) have the characteristic uniformity that arises when Rapid Refresh data alone are used to drive the IFR Probability values.  In these regions, high clouds (associated with convection over Arkansas) are blocking the satellite view of lower clouds.

Such high clouds will make it difficult for a satellite-only product to identify the regions of clouds.  For example, the Brightness Temperature Difference field below (10.3 µm – 3.9 µm) from GOES-16, color-enhanced so that low clouds are green and that cirrus (at night) are purple) shows widespread low cloudiness at the start of the animation, including some obvious river fog over Missouri (river valleys that are not well-resolved with GOES-13), but developing convection over Arkansas eventually prevents the view of low clouds.

GOES-16 data posted on this page are preliminary, non-operational and are undergoing testing

GOES-16 Brightness Temperature Difference field (10.3 µm – 3.9 µm), hourly from 0312 to 1312 UTC on 15 August 2017 (Click to enlarge)

Because the Brightness Temperature Difference field (helpfully called the ‘Fog’ Channel Difference in AWIPS) is challenged by high clouds in viewing the low clouds, RGB Products that use the brightness temperature difference field are also similarly impeded by high clouds.  Consider, for example, the stations in southern Missouri shrouded by the cirrus shield.  IFR Conditions are occurring there and a strong signal of that appears in the IFR Probability fields.

GOES-R IFR Probability computed with GOES-13 and Rapid Refresh Data, GOES-16 Fog (10.3 µm – 3.9 µm) Brightness Temperature Difference and GOES-16 Advanced Nighttime Microphysics RGB, all near 1115 UTC on 15 August 2017 (Click to enlarge)

Why Fused Data is better than Satellite Data alone in detecting IFR Conditions: Pennsylvania Example

GOES-R IFR Probability Fields, 0200-1100 UTC on 11 July 2017 (Click to enlarge)

GOES-16 data posted on this page are (still!) preliminary, non-operational data and are undergoing testing

GOES-R IFR Probabilities are computed using Legacy GOES (GOES-13 and GOES-15) and Rapid Refresh model information; GOES-16 data will be incorporated into the IFR Probability algorithm in late 2017

Low ceilings and reduced visibilities developed in and around thunderstorms from Ohio and Michigan into southwestern Ontario, upstate New York and northern Pennsylvania during the morning of 11 July 2017. (Reduced visibilities/lowered ceilings persisted past 15 UTC as shown in this image from here). Regions of IFR conditions over northwestern Pennsylvania are in regions of higher IFR Probability after 0500 UTC (over northwest Pennsylvania) and after 0700 UTC (over much of northern Pennsylvania) that have the characteristic flat field look that comes from having only Rapid Refresh Model output drive the Probability (because high clouds, as might occur downwind of Convection, prevent the satellite from seeing low clouds). GOES-R IFR Probability fields also retain a signal through sunrise, as shown in the this toggle between 1000 UTC and 1100 UTC, two times on either side of the terminator.

Because high clouds inhibit the view of low stratus (and potential fog), products that rely on solely satellite data become ineffectual as a situational awareness tool. Consider the toggle below from 0800 UTC of GOES-R IFR Probabilities, the GOES-16 “Fog Product” Brightness Temperature Difference (10.3 µm – 3.9 µm) and the Advanced Nightime Microphysics RGB (that uses the Brightness Temperature Difference Product as one of its components). IFR Conditions over NW Pennsylvania are not diagnosed by the GOES-16 products that rely on the 10.3 µm – 3.9 µm Brightness Temperature Difference field. Where there is a clear field of view, all three products can highlight IFR conditions (in/around London Ontario, for example). Regions of low stratus — but not fog — are also highlighted over parts of upstate New York by the GOES-16 products. (Similar toggles at 0915 and 1100 UTC are available; note that the signal from GOES-16 becomes weak at 1102 UTC because increasing amounts of solar reflectance change the sign of the 10.3 µm – 3.9 µm Brightness Temperature Difference.

These examples typify why GOES-R IFR Probability fields typically have better statistics as far as IFR detection is concerned: Model data fills in regions where high clouds are present, and model data screens out regions where stratus is highlighted by a Brightness Temperature Difference field, but where fog does not exist.

GOES-R IFR Probabilities (computed using GOES-13 and Rapid Refresh Data), GOES-16 “Fog Product” Brightness Temperature Difference (10.3 µm – 3.9 µm) and the Advanced Nighttime Microphysics RGB, all around 0800 UTC on 11 July 2017 (Click to enlarge)

 

Methods of Fog Detection in the GOES-16 Era

GOES-16 ‘Fog Detection’ Channel Difference (10.3 µm – 3.9 µm), 0912 – 1132 UTC, 29 June 2017 (Click to enlarge)

GOES-16 data posted on this page are (still!) preliminary, non-operational data and are undergoing testing

The 16 channels on the GOES-R Series Advanced Baseline Imager (ABI) allow for many different channel combinations that can be used to detect atmospheric phenomena. The animation above shows the traditional method for detecting low stratus: the brightness temperature difference between the shortwave infrared (3.9 µm) and the cleanest longwave infrared (10.3 µm) windows. Cloudtops composed of water droplets are highlighted in the animation because they do not emit 3.9 µm radiation as a blackbody, but do emit 10.3 µm radiation as a blackbody; thus, the brightness temperature difference at night (when no reflected solar radiation at 3.9 µm is present) is positive. The range of the colorbar in the above animation is from -50 to +50 C; stratus appears as green over much of northern Wisconsin, Minnesota and North Dakota.  Higher cirrus clouds are cyan, and they interfere with the satellite detection of low clouds, especially over eastern North Dakota where IFR conditions were widespread (source), and where a Dense Fog Advisory existed.  Note the apparent disappearance of the fog signal — in green — as the sun rises.  Increasing amounts of reflected solar radiation are causing the brightness temperature difference value to switch sign from (10.3 µm – 3.9 µm) > 0 at night (because of emissivity differences) to (10.3 µm – 3.9 µm) < 0 during the day (because of solar reflectance).

The ‘Nighttime Microphysics Advanced RGB’ is also used as a fog detection device. In the animation below, low stratus (and by inference, fog) is highlighted in cyan, a signal that comes mostly from the ‘green’ part of the RGB, namely the Brightness Temperature Difference as shown above. Because the two products are linked by the 10.3 – 3.9 brightness temperature difference, shortcomings in that product as far as fog detection affect the RGB. Note how the fog signal erodes over Minnesota/Wisconsin as the sun rises, and how it is obscured by high clouds (dark purple/magenta) over North Dakota.

 

Note: GOES-R IFR Probabilities are computed using Legacy GOES (GOES-13 and GOES-15) and Rapid Refresh model information; GOES-16 data will be incorporated into the IFR Probability algorithm in late 2017

GOES-R IFR Probability fields, shown for this event below, were designed to mitigate detection issues noted above.  Where high clouds are present, meaning the satellite cannot detect low clouds, information about low-level saturation from the Rapid Refresh is used to assess whether or not fog is likely.    That low-level information from the model also can be used to distinguish between fog and elevated stratus that can look very similar from the top, as a satellite views it.  The fusing of model and satellite data makes for a product that has better statistics in detecting low ceilings and reduced visibilities.

At the end of the two animations above, for example, how confident will a satellite analyst or forecaster be that there is dense fog over eastern North Dakota?  How about the analyst/forecaster using IFR Probability fields? IFR Probabilities maintain a signal for fog over the entire region from North Dakota to Wisconsin, even through sunrise and under high clouds.

GOES-R IFR Probabilities, 0915 to 1115 UTC on 29 June 2017 (Click to enlarge)

Fog Detection Methods when Cirrus Clouds are present

GOES-R IFR Probability fields, 0545, 0800, 1000 and 1300 UTC on 13 June 2017 (Click to enlarge)

GOES-16 data posted on this page are (still!) preliminary, non-operational data and are undergoing testing

Note: GOES-R IFR Probabilities are computed using Legacy GOES (GOES-13 and GOES-15) and Rapid Refresh model information; GOES-16 data will be incorporated into the IFR Probability algorithm in late 2017

Reduced visibilities and lowered ceilings — IFR and Low IFR conditions — occurred over a wide stretch of the southeastern United States on the morning of 13 June 2017.  The animation above shows enhanced IFR Probabilities aligned northwest to southeast from central Alabama to northwest Florida, the region where IFR Conditions develop.  The flat nature of the field suggests that satellite data are not widely available as a predictor for low clouds on this morning, and that is because of widespread cirrus clouds over the southeastern United States (Click here to see the 0545 and 1300 UTC GOES-13 Water Vapor Imagery with cold brightness temperatures over the southeast).  When high clouds are present, satellite-only detection of low clouds is not feasible.

For example, the brightness temperature difference field from GOES-13 (3.9 µm – 10.7 µm), below, has little predictive value for the northwest-to-southeast-oriented feature of low clouds/reduced visibilities because cirrus clouds are blocking the view.

GOES-13 Brightness Temperature Difference (3.9 µm – 10.7 µm) at 0545, 0800, 1000 and 1315 UTC on 13 June 2017 (Click to enlarge)

Various other detection techniques that rely on only satellite data similarly will be challenged by the presence of high clouds. For example, the Nighttime Microphysics RGB (From this site) shows little signal of fog (cyan/white in the RGB composite) over Georgia and Florida. Intermittent signals will appear occaionally as high clouds thin to allow the low-cloud signal through. The GOES-16 Brightness Temperature Difference field (10.33 µm – 3.9 µm) similarly is challenged by the presence of high clouds. The example shown at 1300 UTC shows a negative value because of reflectance off of high clouds.

Because the IFR Probability fields include surface information in the form of output from the Rapid Refresh model (saturation in the lowest part of the model is used as a predictor for the presence of fog), IFR Probability fields can fill in regions where satellite data cannot be used to detect low clouds because of the presence of high clouds. In regions where high clouds are present, satellite-only detection of fog and low stratus will always be a challenge.

Dense Fog under high clouds in the Deep South

GOES-13 Brightness Temperature Difference (3.9 µm – 10.7 µm) Values at 0815 UTC on 23 May 2017 (Click to enlarge)

Note: GOES-R IFR Probabilities are computed using Legacy GOES (GOES-13 and GOES-15) and Rapid Refresh model information; GOES-16 data will be incorporated into the IFR Probability algorithm in late 2017

The legacy method of detecting fog/low clouds from satellite is the Brightness Temperature Difference product that compares computed brightness temperatures at 3.9 µm and at 10.7 µm. At night, because clouds composed of water droplets do not emit 3.9 µm radiation as a blackbody, the inferred 3.9 µm brightness temperature is colder than the brightness temperature computed using 10.7 µm radiation. In the image above, the brightness temperature difference has been color-enhanced so that clouds composed of water droplets are orange — this region is mostly confined to southeast Texas. Widespread cirrus and mid-level clouds are blocking the satellite view of low clouds. IFR and near-IFR conditions are widespread over east Texas, Louisiana and Mississippi. The GOES-R IFR Probability field, below, from the same time, suggests IFR conditions are likely over the region where IFR conditions are observed.

This is a case where the model information that is included in this fused product (that includes both satellite observations where possible and model predictions) fills in regions where cirrus and mid-level clouds obstruct the satellite’s view of low clouds.. As a situational awareness tool, GOES-R IFR Probability can give a more informed representation of where restricted visibilities and ceilings might be occurring.

IFR Conditions continued into early morning as noted in this screenshot from the Aviation Weather Center.

GOES-R IFR Probability fields, 0815 UTC on 23 May 2017 (Click to enlarge)

IFR Conditions under multiple cloud decks in the Upper Midwest

GOES-R IFR Probability Field, along with observations of surface visibility and ceiling heights, 1100 UTC on 17 May 2017 (Click to enlarge)

Note: GOES-R IFR Probabilities are computed using Legacy GOES (GOES-13 and GOES-15) and Rapid Refresh model information; GOES-16 data will be incorporated into the IFR Probability algorithm in late 2017

The GOES-16 data posted on this page are preliminary, non-operational data and are undergoing testing

A morning screenshot from the Aviation Weather Center website shows a wide region of IFR and Low IFR Conditions reported over the Upper Midwest, from eastern North Dakota eastward to Lake Superior. The GOES-R IFR Probability field, above, from 1100 UTC on 17 May 2017, shows high probabilities over that region.

Over much of Minnesota and Michigan, the character of the IFR Probability field is flat.  This is typical of IFR Probability when high clouds prevent satellite data from being used as a statistical predictor for IFR Conditions.  If high clouds are present, the satellite cannot detect the presence of low clouds, and the chief predictor of IFR conditions will therefore be model data that typically does not vary strongly from gridpoint to gridpoint when IFR conditions are present.  The pronounced boundary apparent in the IFR Probability field that extends nortwestward from Green Bay in Wisconsin is the boundary between night-time predictors (to the west) and daytime predictors (to the east).

GOES-R IFR Probability values are largest in the region of IFR conditions over North Dakota.  In this region, high clouds are not present and the satellite is able to detect low clouds, and that information is part of the computation of IFR Probabilities.  Note also the region in east-central Minnesota where satellite data are also being used in the computation of IFR Probabilities;  the resultant field there is pixelated.

The toggle below shows the brightness temperature difference field between the shortwave and longwave infrared window channels from GOES-13 (3.9 µm and 10.7 µm) and GOES-16 (3.9 µm and 10.33 µm).  This brightness temperature difference field is used to detect stratus, and by inference fog, because stratus cloud tops composed of water droplets emit radiation around 10.3-10.7 µm as a blackbody, but do not emit 3.9 µm radiation as a blackbody.  Satellite detection of radiation, and computation of the inferred temperature of the emitting surface, assumes blackbody emissions.  Consequently, the brightness temperature computed using detected 3.9 µm radiation is colder than that computed using 10.7 µm (or 10.33 µm) radiation.

Both satellites capture the region of low stratus/fog over North Dakota, and the superior spatial resolution of GOES-16 is apparent. Note, however, that neither satellite can detect low clouds associated with dense fog in regions of higher clouds — over Michigan’s Keewenaw Peninsula, for example, or along the shore of Lake Superior in Minnesota. This is an unavoidable shortcoming of satellite-only-based detection of low clouds/fog.

GOES-13 (3.9 µm – 10.7 µm) and GOES-16 (10.33 µm – 3.9 µm) Brightness Temperature Difference fields, 1100 UTC on 17 May 2017 (Click to enlarge)

IFR Probabilities with a strong storm in Maine

GOES-R IFR Probabilities, 0100-1000 UTC on 7 April 2017, along with surface reports of ceilings and visibilities (Click to enlarge)

Note:  GOES-R IFR Probabilities are computed using Legacy GOES (GOES-13 and GOES-15) and Rapid Refresh model information;  GOES-16 data will be incorporated into the IFR Probability algorithm in late 2017

A strong storm over the northeastern United States produced widespread IFR conditions over that region.  The storm was also accompanied by multiple cloud layers, however, and that made diagnosis of regions low clouds/fog difficult.  For these cases, a fused data approach is vital — using model information (in the case of IFR Probability, above, the model is the Rapid Refresh) to provide information at low levels allows for a better tool to alert a forecaster to the presence of reduced visibilities.

In the animation above, Maine intially shows IFR Probabilities around 50% — but the flat nature of the field should alert a user to the fact that satellite predictors cannot be included in the computation of IFR Probabilities because high clouds are preventing a satellite view of low clouds.  Accordingly, the computed Probability is lower.  In contrast, high clouds are not present over southern New England at the start of the animation, and IFR Probabilities are much larger there:  both satellite and model predictors are used. As the high clouds lift north from northern New England the region of higher IFR Probabilities expands from the south.

Note the influence of topographic features on the IFR Probability field.  The Adirondack Mountains and St. Lawrence Seaway have higher and lower Probabilities, respectively, because of the higher terrain in the Mountains, and the lower terrain along the St. Lawrence.

An example of why fused data are important is shown below.  Look at the conditions in Charlottetown, on Prince Edward Island, in the far northeast part of the domain.  Between 0315 and 0400, ceilings and visibilities deteriorate as IFR Probabilities increase.  The brightness temperature difference field, at bottom, shows no distinct difference between those two times because the clouds being viewed are high clouds.

Dense Fog over the Carolinas

GOES-13 Brightness Temperature Difference (3.9 µm – 10.7 µm), GOES-R IFR Probability and GOES-R Cloud Thickness at 1115 UTC on 17 January 2017 (Click the enlarge)

Dense Fog Advisories (National Weather Service Website) and IFR SIGMETs (Aviation Weather website) were issued early in the morning for dense fog over the southeastern United States.  The toggle above from 1115 UTC on 17 January shows the Brightness Temperature Difference field (3.9 µm – 10.7 µm), the GOES-R IFR Probability Field, and the GOES-R Cloud Thickness fields associated with this dense fog event.  Note the presence of high clouds over northern South Carolina and western North Carolina — the dark region in the Brightness Temperature Difference enhancement — prevents the brightness temperature difference field from highlighting that region of reduced ceilings/visibilities.  The GOES-R Cloud Thickness field is not computed under cirrus either, as it relates 3.9 µm emissivity of water-based clouds to cloud thickness (based on a look-up table generated using data from a SODAR off the West Coast of the United States).  If cirrus blocks the view, then, neither the Brightness Temperature Difference field nor the GOES-R Cloud Thickness field can give useful information about low clouds.

In contrast, the GOES-R IFR Probability field does give useful information in regions where cirrus clouds (and low clouds/fog) are present — because Rapid Refresh information about the lower troposphere can be used.  IFR Probability values will be smaller in those regions because satellite predictors are unavailable, and the Probability incorporates both predictors from satellites and from Rapid Refresh model output — if the satellite predictors are missing because of cirrus, the IFR Probability values will be affected. Despite the smaller values, however, the IFR Probability fields in regions of cirrus are giving useful information for this event.

GOES-R Cloud thickness fields can be used to estimate Fog dissipation using the last GOES-R Cloud Thickness field produced before twilight conditions at sunrise (shown below for this case). (GOES-R Cloud Thickness is not computed during twilight conditions because of rapidly changing 3.9 µm emissivity related to the reflected solar radiation as the sun rises, or as it sets). This scatterplot gives the relationship between thickness and dissipation time after the Cloud Thickness time stamp (1215 UTC in this case).  In this case, the thickest fog is near Athens GA;  the algorithm predicts that clearing should happen there last, at about 1515 UTC.

GOES-R Cloud Thickness, 1215 UTC on 17 January 2017 (Click to enlarge)

Fog over South Texas

Toggle between Brightness Temperature Difference (3.9µm – 10.7µm) and GOES-R IFR Probability fields, 2300 UTC on 11 December 2016 (Click to enlarge)

Dense Fog developed over south Texas during the early morning of 12 December 2016 (IFR Sigmet from this website shown here ; Advisories from the weather.gov website shown here). The toggle above shows in the brightness temperature difference field a signature of high clouds — and where those high clouds exist, IFR Probability fields rely on Rapid Refresh Model data to diagnose where IFR conditions might be occurring, or where IFR conditions might develop. The animation of Brightness Temperature Difference fields from 0215 through 1115 UTC, below, shows that the high clouds over south Texas diminished with time: by 0815 UTC only low stratus is present over south Texas.  But is that stratus also hugging the ground — that is, is it fog?  From the satellite’s perspective, the top of a stratus deck and the top of a fog bank can look very similar.

GOES-13 Brightness Temperature Difference (3.9µm – 10.7µm), 0215 through 1115 UTC on 12 December (Click to enlarge)

GOES-R IFR Probability fields give a more complete estimate about the presence of fog/low stratus because Rapid Refresh data and satellite data are used to diagnose the probability of IFR conditions. If the Rapid Refresh model shows low-level saturation, then the presence of stratus clouds also likely indicates the presence of fog; conversely, if the Rapid Refresh Model does not show low-level saturation, then the presence of stratus cloud need not indicate the presence of fog. IFR Probability fields below, from 0215 through 1115 UTC, start off regions with uniform values where only Rapid Refresh data are used in the algorithm — where high clouds block the satellite view of low clouds/fog. As the high clouds dissipate, the field acquires larger values because there is higher confidence of the presence of clouds (in part because satellite data can be used to observe them). In addition, these larger values have pixel-sized variability because of variability in the satellite observations.

IFR conditions are observed latest over far south Texas — this is also where IFR Probabilities are slowest to reach large values.

Fog and Ice Fog over the southern Plains

ifrp_0400_1215_05dec2016anim

GOES-R IFR Probabilities, hourly from 0400 through 1215 UTC on 5 December 2016 (Click to enlarge)

Dense Fog developed over the southern Plains early on Monday 5 December, and the GOES-R IFR Probability fields, above, were a tool that could be used to monitor the evolution of this event. A challenge presented on this date was the widespread cirrus (Here’s the 0700 UTC GOES-13 Water Vapor (6.5 µm) Image, for example) that prevented satellite detection of low clouds. The Brightness Temperature Difference fields, below, at 3-hourly intervals, also show a signature (dark grey/black in the enhancement used) of high clouds, although they are shifting east with time — by 1300 UTC there is a signature (orange/yellow in the enhancement used) of stratus clouds over central and eastern Oklahoma.

The IFR Probability fields, above, have a characteristic flat nature over Arkansas and Missouri, that is, a uniformity to the field, that is typical when model data are driving the probabilities. The more pixelated nature to the fields over Kansas and Oklahoma, especially near the end of the animation, typifies what the fields look like when both satellite data and model data are driving the computation of probabilities. Careful inspection of the fields over Arkansas shows regions — around Fayetteville, for example, around 1000 UTC where IFR Probabilities are too low given the observation at the airport of IFR conditions. This inconsistency gives information on either the small-scale nature of the fog (unlikely in this case) or on the accuracy of the Rapid Refresh model simulation that is contributing to the probabilities. In general, the Rapid Refresh model has accurately captured this event, and therefore the IFR Probabilities are mostly overlapping regions of IFR or near-IFR conditions. The region over southern Illinois that has stratus, and low probabilities of IFR conditions, for example. Adjacent regions have higher IFR Probabilities and lower ceilings and/or reduced visibilities. A screen shot from the National Weather Service, and from the Aviation Weather Center, at about 1300 UTC document the advisories that were issued for this event.

btd_0400_1300_05dec2016step

Brightness Temperature Difference fields (3.9 µm – 10.7 µm), 0400, 0700, 1000 and 1300 UTC on 5 December 2016 (Click to enlarge)