Category Archives: Multiple Cloud Layers

When relatively low IFR probabilities are more likely to mean fog

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GOES-R IFR Probabilities over the Midwest, 1000 UTC on 27 August 2012

An important consideration when interpreting GOES-R IFR probabilities is the data being used to compute the probabilities.  When both model and satellite data are used, higher probabilities can result, as over south central and southeastern Wisconsin in the image above where probabilities exceed 90% in a region of IFR conditions.  The pixelated nature of the field there suggests that satellite data are being incorporated into the probability field.  When only model data are used, as over southern Lower Michigan in the image above, lower probabilities will result; however, IFR probabilities are nevertheless observed (as in Kalamazoo (KAZO), for example).  The blockier nature of the IFR probability field is showing that only model data are being used in that region.  The traditional brightness temperature field for that time is shown below. 

Traditional Brightness Temperature Difference Field over the lower Great Lakes, 1002 UTC 27 August 2012.

Fog over the Texas Panhandle

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GOES-R IFR Probabilities, and Surface Visibility/Ceilings, from 0432 UTC through 1432 UTC on 22 August 2012

GOES-R IFR Probabilities showed good spatial correlation with observed IFR conditions early in the morning on 22 August 2012.  The spatial characteristics of the field suggest that model-only predictions of the presence of fog/low stratus occurred in regions over west Texas that were overlain by higher clouds.  The Traditional brightness temperature difference product at 1101 UTC, for example, below, shows the characteristic signal of high clouds over parts of west Texas.  There is also a significant region of returns over north central OK/south central Kansas that is not associated with IFR conditions.  The GOES-R IFR product in that region correctly shows low probabilities.

Brightness Temperature Difference from GOES (10.7 – 3.9) at 1101 UTC 22 August.

The upper-air sounding from Amarillo from 1200 UTC confirms the presence of saturated conditions only in the boundary layer.

Skew-T/Ln P Thermodynamic diagram frmo 1200 UTC 22 August 2012 at Amarillo TX

Excellent example of the importance of model data

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GOES-R IFR Probabilities computed using GOES-East data (Upper Left), GOES-R IFR Probabilities computed using MODIS data (Upper Right), Surface Observations and Cloud Ceilings Above Ground level (Lower Left), Suomi-NPP VIIRS Brightness Temperature Difference field, 10.8  µm – 3.74 µm (Lower Right).  Times as indicated.

Three different satellite sensors — the GOES Imager on GOES-East, MODIS on Aqua, and VIIRS on Suomi/NPP — viewed data from the occurrence of Valley Fog over the Appalachians (and surroundings) early in the morning of 21 August.  A shortcoming of the Brightness Temperature Difference field in the lower left is immediately apparent:  no fog/low stratus is indicated where high clouds exist, even though observations do show IFR conditions.  In contrast, the fused product does show heightened probabilities underneath that high cloud deck.  Probabilities are not as high as they are where both satellite and model predictors can be used to evaluate the presence of fog and low stratus, and the resolution of the field is different, obviously limited to the horizontal resolution of the Rapid Refresh, meaning that small river valleys, that are very obvious in the regions where satellite data are used (and even much more obvious when high-resolution MODIS or VIIRS data are used).  Note also how GOES-R IFR Probabilities de-emphasize the signal over western Ohio, where IFR conditions are not reported.  Brightness temperature difference fields from MODIS and from GOES both see a signal there, as also shown in the VIIRS field, but these stratus clouds are not obstructing visibility.

Bottom line:  MODIS data’s higher resolution observes the big differences between river valleys and adjacent cloud-free ridge tops.  GOES-East has difficulty in resolving those differences.  So MODIS IFR fields better highlight river fog.  Model data can help discern between fog on the ground, and stratus that is off the ground.

Interpreting the GOES-R IFR Product in regions where Model and Satellite Predictors are used

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GOES-R IFR Probabilities computed from GOES-East and from the Rapid Refresh, hourly from 2202 UTC on 19 August through 1402 UTC on 20 August

The animation above demonstrates the different ways in which GOES-R probabilities can be detected, and the values that can occur.  Much of the East Coast was under a multi-layer cloud deck (as shown below in the color-enhanced 0632 GOES-East 10.7 µm image).  In those places, IFR probabilities will be computed using Rapid Refresh model output, and the character of the IFR probability field will differ from how the field looks when satellite data are used as IFR predictors.

For example, the smooth IFR probability field over North Carolina in this loop (especially from 0300 UTC to 0700 UTC) strongly indicates model-only predictors.  Before and after that time there are small regions that are more pixelated, suggesting satellite input.  At 0600 UTC, GOES-R IFR probabilities start to increase over West Virginia as overnight cooling allows temperatures to approach the dewpoint, and fog starts to develop.  Probabilities become quite high by sunrise over and near West Virginia, and IFR conditions are evident.  IFR probabilities where satellite and model data are used as predictors — as over West Virginia — are generally higher than regions where model data only are used (over North Carolina).  Model data will always be included in the computation of IFR probabilities.  In regions of multiple cloud layers, such as over North Carolina, it is used alone and IFR probabilities can be high.  In regions of low clouds, such as over West Virginia, the model underscores what the satellite data are telling and IFR probabilities will be even higher.  There can also be regions where the Satellite and Model predictors give conflicting information on the presence of fog/low stratus.  In these regions, IFR probabilities will be somewhat lower.

Note that the terminator is present in this loop, both at sunset (2345 UTC) and at sunrise (the 1100 UTC imagery).  The change in IFR probabilities that occur is evident as predictors that are used during the day change to or from those used at night.  At 1100 UTC, probabilities are increasing by about 18% over North Carolina.

GOES-13 10.7 µm imagery at 0632 UTC on 20 August 2012.

Fog and Low Clouds after evening Convection

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GOES-R IFR Probabilities (Upper Left), Brightness Temperature Difference (10.7 µm – 3.9 µm) (Upper Right), GOES-R Cloud Thickness (Lower Left), GOES-East 3.9 µm Brightness Temperature (Lower Right), for 0315 UTC to 1115 UTC on 17 August 2012

Post-sunset convection can supply the moisture that is needed for the development of overnight fog and low stratus.  But do those low clouds cause IFR conditions?  This example over NW Arkansas from the morning of 17 August shows GOES-R IFR Probabilities that successfully pinpoint the regions where IFR conditions are most likely and exclude regions where IFR conditions do not occur.  The brightness temperature difference field and the 3.9 µm both show convection before 0600 UTC. Subsequent to the convection, a brightness temperature difference signal that is consistent with fog/low stratus does develop over southern Kansas, and then over south-central Missouri and northwest Arkansas.  The GOES-R IFR Probabilities, however, suggest that IFR conditions are likely only over northwest Arkansas (where IFR conditions are observed).

This example also ably demonstrates the differences in the character of the IFR Probability field that occur when model data alone are used to predict IFR probabilities (Northeast Arkansas during most of the loop) vs. a combination of Satellite and Model data (Northwest Arkansas at the end of the loop).

Satellite and Model Predictors of Fog

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GOES-R IFR Probabilities (Upper left), Enhanced GOES-East 10.7 micrometer imagery (Upper right), Rapid Refresh Mean Relative Humidity (1000-850 mb) (Lower left), Composite Radar Reflectivity (Lower right).  Times as indicated.

A large convective system moved over Wisconsin during the morning of 16 August 2012 and it illustrates the importance of fused data in diagnosing IFR conditions.  The deep convective cloud precludes any satellite detection of low water-based clouds, so the traditional method of detecting fog/low stratus (the brightness temperature difference between 10.7 and 3.9 micrometers) cannot be used.  In this case, model data, in the form of Rapid Refresh Relative Humidity, is used to fill in regions where satellite predictors cannot help.  Note the observation of IFR conditions at Wisconsin Rapids (KISW);  this is a region of very high model relative humidity.  Model relative humidity is just as high over south-central Minnesota;  in that region, however, satellite predictors do exist, and they do not suggest fog/low clouds, so the IFR probability there is comparitively lower.

The character of the IFR probability field is much less pixelated in regions where model data only are used as predictors.  When satellite data and model data are used, as over northwest Wisconsin, for example, the pixelated nature of the satellite data becomes apparent.

The Challenge of Identifying Fog in River Valleys

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Animation of GOES-R IFR probabilities computed from GOES-East (upper left), Brightness Temperature difference (11 – 3.9) from GOES-East (upper right), Surface visibilities and ceilings and GOES-East Visible imagery (lower left), GOES-R IFR probabilities computed from MODIS (lower right)

Radiation fog that forms first — or only — in river valleys is a challenge to detect.  In the example above from Pennsylvania for the morning of August 8 2012, the satellite signal starts to appear over the West Branch of the Susquehanna around 0500 UTC.  It is very unlikely that a numerical model with a horizontal resolution of (only) 13 km — such as the Rapid Refresh — will be able to forecast the development of such a small-scale feature, so the satellite observations are key.  Nominal 4-km resolution of the infrared channels is the principle limiting factor in detection.

The presence of high clouds over southeast Pennsylvania precludes the detection by satellite of low clouds/fog, so the GOES-R IFR product there is driven primarily by model output, and the scale of the river fogs are simply too small to be simulated in the model.  Fog probabilities do increase near Selinsgrove (KSEG) around daybreak because the model relative humidity does reach high enough values.  This also happens over southeast Pennsylvania.  Typically, model relative humidities in the lowest kilometer of the model must be greater than 80-85% for a strong GOES-R IFR signal to be present.

GOES-R IFR probabilities computed from GOES-East (upper left), Brightness Temperature difference (11 – 3.9) from GOES-East (upper right), Surface visibilities and ceilings and GOES-East Visible imagery (lower left), GOES-R IFR probabilities computed from MODIS (lower right), all valid at 0700-0715 UTC 8 August 2012

Note that at 0715 UTC there is a comparison between the better resolution of the MODIS imagery.  When GOES-R is operational, resolution will be in between that of current GOES and MODIS.  The MODIS GOES-R IFR probabilities are much higher, and show the different river valleys far more cleanly than present GOES.

IFR Probabilities under clouds

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GOES-R IFR Probabilities (from GOES-East) (upper left), GOES-East Brightness Temperature Difference 11 µm – 3.9 µm (upper right), Plot of Ceiling (AGL) and visibility (lower left), Enhanced 11 µm brightness temperature (lower right) from 0815 UTC from 1845 UTC on 7 August 2012

When atmospheric conditions support multiple cloud layers and low-level fog, the heritage method of fog/low cloud detection (the brightness temperature difference between the 10.8 and 3.9 µm channels) will yield little information because the highest cloud will likely include ice, and the brightness temperature difference exploits emissivity differences in water clouds.  In these regions, the model output is vital to predict IFR probabilities.  Because satellite predictors are giving no information in these regions, however, the overall probabilities are likely to be lower than the case where both satellite and model suggest the presence of fog.  Expect the highest probabilities when both satellite model suggest the presence of fog.  If only one of the predictors (satellite data or model data) suggests the presence of fog, IFR probabitilies will be lower.

In the example above, periodic episodes of IFR conditions are occurring over Georgia.  Most of the IFR probability signal is coming from the Rapid Refresh, and there are hints that the model output is not quite reproducing the observed weather.  If satellite data are not used as a predictor in the GOES-R IFR Probability product (because of high clouds), interpretation of the GOES-R IFR product is complicated by Rapid Refresh performance.

IFR Probabilities under a Thick Cloud Deck

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GOES-R IFR Probabilities at 1132 UTC with 1200 UTC surface Observations (Upper left), GOES-East Brightness Temperature Difference (10.7 micrometer brightness temperature – 3.9 micrometer brightness temperature) at 1130 UTC (upper right), GOES-East 10.7 micrometer brightness temperature (lower left) and GOES-East Visible imagery at 1130 UTC (lower right).

Convection developed over the upper Midwest and northern Plains during the early morning hours of 25 July 2012.  Deep convective clouds preclude the ‘traditional’ brightness temperature difference method of fog detection:  emissions from the low-level water-based clouds cannot be seen by the satellite because of high-level cirrus clouds associated with thunderstorm anvils.  IFR conditions nevertheless can occur and can be predicted using model-based predictors in the fused GOES-R IFR probability product.  The case above is an excellent example.

The bottom two images show the tradiational satellite imagery, telling the tale of a departing mesoscale system.  It leaves in its wake low clouds over North Dakota and Manitoba that are detected by the traditional product, and notice how the GOES-R IFR probabilities are highest here, because satellite and model predictors both agree.  Under the convective cloud canopy, probabilities are lower:  around 40% in central North Dakota (where night-time predictor relationships are being used) and around 55% over the Arrowhead of Minnesota (where daytime predictor relationships are being used);  the terminator boundary is very obvious in the IFR Probability figure.  There is an excellent overlap between the GOES-R IFR Probabilities and reported IFR conditions that is impossible to get in this case with satellite information alone.

Fog over Louisiana and Mississippi

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GOES-R IFR Probabilities computed from GOES-East Imager data (upper left), GOES-East brightness temperature difference (upper right), GOES-R Cloud Thickness of the highest liquid water cloud layer (bottom left), Suomi/NPP Day/Night band (bottom right), all from 0830-0900 UTC on 23 July 2012.
Enhanced 11-micrometer imagery, 0831 UTC 23 July 2012

Fog formed over the southern Mississippi Valley in the early morning of July 23 in a region where high clouds associated with a westward-tracking wave made detection difficult via the traditional brightness temperature difference method.  The imagery above shows relatively high IFR probabilities over southwestern Louisiana where IFR conditions are occurring.  Two items should jump out.  The IFR probabilities are highest where both satellite and model predictors are high, and that occurs in west-central Louisiana.  In regions to the south and west, where higher clouds exist (and satellite predictors are therefore low), probabilities are a bit lower in a region where only model predictors are being used.  However, IFR conditions are present.  Note how the character of the IFR probability field changes from the region where satellite data are used (much more spatially variable) to the region where mostly model data are used (more spatially uniform).  It is very important when interpreting the probability fields to be aware of the presence of high clouds that limit the inclusion of satellite data in the predictors.

GOES-R IFR Probabilities computed from GOES-East Imager data (upper left), GOES-East brightness temperature difference (upper right), GOES-R Cloud Thickness of the highest liquid water cloud layer (bottom left), Suomi/NPP Day/Night band (bottom right), all from 1145-1200 UTC on 23 July 2012.
GOES-R IFR Probabilities computed from GOES-East Imager data (upper left), GOES-East brightness temperature difference (upper right), GOES-R Cloud Thickness of the highest liquid water cloud layer (bottom left), Suomi/NPP Day/Night band (bottom right), all from 1215 UTC on 23 July 2012.

The two images above show how the probabilities change as the predictors used change from nighttime values (at 1145 UTC) to daytime values (1215 UTC).  At 1145 UTC, probabilities over Louisiana are near 40%, and these probabilities are driven largely by model data, because of high clouds.  There are several airports reporting IFR conditions at 1200 UTC.  Probabilities jump to around 55% at 1215 UTC.

GOES-R IFR Probabilities computed from GOES-East Imager data (upper left), GOES-East brightness temperature difference (upper right), GOES-R Cloud Thickness of the highest liquid water cloud layer (bottom left), GOES-East Visible imagery (bottom right), all from around 1300 UTC on 23 July 2012.

At 1300/1400 UTC, the GOES-R IFR probabilities and cloudt thickness fields neatly overlap the visible imagery observations of cloudiness over Mississippi and over western Louisiana, with a pronounced break in central Louisiana.