Category Archives: Wisconsin

Cloud Thickness and Dissipation Time


GOES-R Cloud Thickness Fields, 1130 UTC on 20 September 2016 (Click to enlarge)

GOES-R Cloud Thickness is created from a look-up table created from observations of 3.9 µm emissivity and sodar observations of cloud thickness off the west coast of the United States.  The product is not computed during twilight conditions when rapid changes in reflected solar radiation (either increases around sunrise or decreases around sunset).  The image above shows the GOES-R Cloud Thickness field over the midwest just before sunrise on 20 September 2016 (Radiation fog formed subsequent to late-afternoon and evening thunderstorms over Wisconsin and Illinois).  This scatterplot relates the last pre-sunrise value to dissipation time.  GOES-R Cloud thickness shows values over the Wisconsin River Valley in southwest Wisconsin, and over regions south of Military Ridge. Largest values — 1100 feet over Illinois and Iowa — suggest (from the scatterplot) a dissipation time of around 4 hours, which would be 1130 UTC (the time of the image) + 4 hours, or 1530 UTC.  There is also a region of thick clouds on northwest Indiana on the shore of Lake Michigan.  It’s these regions where you should expect large-scale fog/low clouds to dissipate last.   The animation below shows that to be true.  Fog over the river valleys is taking a bit longer to dissipate than expected, however. Note: navigation in the animation shows the effect of the loss of one star-tracker on GOES-13.


GOES-13 Visible (0.63 µm) animation, 1245-1515 UTC on 20 September 2016 (Click to enlarge)

The Day Night band on the VIIRS instrument on board Suomi NPP produces visible imagery at night that showed the regions of fog distinctly shortly after 0800 UTC on 20 September as shown below.


VIIRS Day/Night Band Visible (0.70 µm) Imagery from Suomi NPP at 0827 UTC on 20 September (Click to enlarge)

Maintaining a signal through sunrise


GOES-R IFR Probability fields, 1045, 1215 and 1300 UTC on 1 August 2016 (Click to enlarge)

A benefit of the GOES-R IFR Probability field is that a coherent signal is maintained through sunrise (or sunset). The traditional method of detecting fog that uses the brightness temperature difference between 10.7  µm and 3.9 µm cannot maintain a consistent signal through sunrise as the amount of reflected solar radiation with a wavelength of 3.9 µm increases, overwhelming the emissivity-driven differences between 10.7  µm and 3.9 µmbrightness temperatures that are observed at night. Consider the animation above, that shows GOES-R IFR Probability fields at 10:45 UTC, 12:15 UTC and 13:15 UTC. First: The GOES-R IFR Probability fields do a fine job of outlining where the lowest ceilings and poorest visibilities exist in this scene over Wisconsin both before sunrise and after.   The noticeable difference between the 1045 UTC and 1215 UTC fields is driven by a change in predictors that occurs as night transitions into day.

GOES-13 Brightness Temperature Difference fields (3.9 µm – 10.7 µm) are shown below. There is a strong signal at 1045 UTC, but little or no signal at 1215 UTC, before it returns (with opposite sign) at 1315 UTC.


GOES-13 Brightness Temperature Difference Fields (3.9 µm – 10.7 µm) at 1045 UTC, 1215 UTC and 1315 UTC. (Click to enlarge)

Fog over the Great Lakes


GOES-R IFR Probability fields computed with GOES-13 and Rapid Refresh Data, 1215 UTC on 26 May 2016 along with surface reports of Ceilings and visibilities (Click to enlarge)

High Dewpoint air (upper 50s and low- to mid-60s) has overrun the western Great Lakes, where water temperatures are closer to the mid 40s.  (Water Temperature from Buoy 45007 in southern Lake Michigan).  Advection fog is a result, and that fog can penetrate inland at night, or join up with fog that develops over night.  The image above shows the extent of low visibilities over the upper Midwest and the IFR Probability field early morning on the 26th of May. Lakes Michigan and Superior are diagnosed as socked in with fog. A similar field from 1945 UTC on 25 May similarly shows very high Probabilities over the cold Lakes. Expect high IFR Probabilities to persist over the western Great Lakes until the current weather pattern shifts.

Brightness Temperature Difference Fields can also show stratus over the Great Lakes, of course, but only if multiple cloud layers between the top of the stratus and the satellite do not exist. Convection over the upper Midwest overnight on 25-26 May frequently blocked the satellite’s view of the advection fog. The toggle below, from 0515 UTC on 26 May, shows how model data from the Rapid Refresh is able to supply guidance on IFR probability even in the absence of satellite information about low stratus over the Lakes.


GOES-13 Brightness Temperature Difference Fields and GOES-R IFR Probability fields, 0515 UTC on 26 May 2016 (Click to enlarge)

Low Ceilings and Reduced Visibilities over the Upper Midwest


GOES-R IFR Probability, 0515-1315 UTC on 26 April, with surface observations of ceilings and visibility (Click to enlarge)

GOES-R IFR Probability fields, above, expand southwestward across the upper midwest as ceilings lower and visibilities reduce.  The fields offered quick guidance on where the lowest ceilings were occurring and how the field of low clouds was evolving.  After sunrise (1215 and 1315 UTC imagery), IFR Probability values increased but continued to show a coherent signal over the region of lowest ceilings and smallest visibility.

The Brightness Temperature Difference fields for the same times, below, have structures that have echoes in the IFR Probability fields. The depiction of low ceilings and visibilities associated with the largest brightness temperature difference values (the deepest orange-red in the enhancement) is lost, however, as reflected 3.9 µm radiation alters the brightness temperature difference field. By 1315 UTC, the end of the animation, only the IFR Probability field is giving useful information about the low ceilings and reduced visibilities.


GOES-13 Brightness Temperature Difference (10.7 µm – 3.9 µm) Fields, hourly from 0515 through 1315 UTC, 26 April 2016 (Click the enlarge)

Dense Fog over the Upper Midwest


SSEC WebCam, north-facing, at 1555 UTC on 8 January 2016 (Click to enlarge)

A storm in the Midwest that has drawn moist air northward (dewpoints exceed freezing over snowcover over much of the upper midwest) has caused advection fog over a wide area of the upper midwest. (The WebCam at SSEC in Madison WI (source) is shown above)  Dense Fog Advisories (below) were issued by the Davenport, Des Moines, Lincoln and LaCrosse WFOs. Extratropical storm systems are usually accompanied by multiple cloud layers that prevent the satellite from viewing low stratus. For such events as these, only a fog-detection product that includes surface-based information will be useful. GOES-R IFR Probability fields, below, neatly outline the region of lowest visibilities and ceilings.  As the highest probabilities push to the east over Wisconsin and Illinois, visibilities and ceilings both drop.

Other aspects of the animation below require comment. IFR Probability fields use predictors based on both satellite and model data; if one of those predictors cannot be used (satellite data, for instance, in regions where high clouds that mask the view of the near-surface), IFR Probability values will be suppressed. The relatively flat nature of the GOES-R IFR probability field over Iowa is characteristic of a field controlled mainly by Rapid Refresh Model output. But there are embedded regions of greater values of IFR Probability that propagate northward: these are regions where breaks in the higher/mid-level clouds allow the satellite to view low clouds, and satellite predictors are available to the algorithm, and probabilities can therefore be larger. Similarly, as the sun rises — at the end of the animation — IFR Probability in general increases as visible imagery can be used for more confident cloud-clearing. The algorithm yield higher probabilities of IFR Conditions because there is more confidence that a cloud is actually present.

GOES-13 Brightness Temperature Difference values are shown below the IFR Probability field. There is little relationship between the Brightness Temperature Difference field and the reduced surface visibility/lowered ceilings. Note also how the character of the brightness temperature difference field changes as reflected solar radiance becomes important at sunrise.


Hourly GOES-R IFR Probability fields, 0200-1500 UTC on 8 January 2015 (Click to enlarge)


GOES-13 Brightness Temperature Difference (10.7 µm – 3.9 µm) Fields, 0800-1500 UTC on 8 January 2015 (Click to enlarge)

Are IFR Conditions Present?


Suomi NPP Visible (0.70 µm) Day Night Band Imagery and Infrared Brightness Temperature Differences (11.45 µm – 3.74 µm), 0731 UTC on 24 November 2015 (Click to enlarge)

Low clouds (with a sharp southern edge) were over northern Wisconsin during the early morning of 24 November 2015. Are IFR Conditions present? Can you tell from the satellite imagery alone? The cloud bank stretched over northern Wisconsin seems thick compared to the bank of clouds over northeastern Wisconsin (centered on southern Green Bay). The city lights of Duluth are not visible in the same way that the city lights of Green Bay are in the Day Night band imagery. Clouds in general are distinct with the near-full moon providing ample illumination.

Both GOES and MODIS Brightness Temperature Difference fields, below, show a signal consistent with low clouds over most of northern WI and adjacent regions.  But are there IFR Conditions?


GOES-13 Brightness Temperature Difference (10.7 µm – 3.9 µm) Fields (0730 UTC) and MODIS Brightness Difference Fields (11 µm – 3.9 µm) (0749 UTC)

IFR Probability fields blend the information available from satellite (are water-based clouds present?) with model output to yield a refined diagnostic of IFR Conditions. If there is saturation in the lowest levels (the lowest 1000 feet) of the model, then Probabilities of IFR Conditions are increased. If the lowest levels of the model are relatively dry, in contrast, then IFR Probabilities are reduced. On the morning of 24 November, the latter condition occurred over northern Wisconsin. IFR Probabilities computed from MODIS and GOES-13 satellite values are shown below. Probabilities are very low over most of Wisconsin where mid-level stratus (with varying bases) was present: IFR conditions were not generally observed in the regions where water-based clouds were indicated by the satellite. Mid-level stratus can look, from the top, very similar to fog, but it’s impossible for the satellite alone to discern what’s happening at the cloud base. Model data helps the IFR Probability algorithm screen out regions where mid-level stratus is occurring.


MODIS-based GOES-R IFR Probabilities (0751 UTC), GOES-13-based GOES-R IFR Probabilities (0731 UTC), and GOES-based GOES-R IFR Probabilities with surface observations of ceilings and visibilities (Click to enlarge)

Late Spring in the Great Lakes: Fog


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

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


Cloud Thickness as a Predictor of Fog Dissipation, part II

This post showed examples of Cloud Thickness and how its use as a predictor for dissipation time might be incorrect because of synoptic or mesoscale forcing.

Radiation fog formed in River Valleys of Wisconsin early in the morning on 22 September, and the image below shows the final Cloud Thickness field computed before twilight conditions developed over western Wisconsin (twilight conditions are already occurring over eastern Wisconsin).


GOES-R Cloud Thickness (of the highest liquid water cloud layer) just before sunrise, 22 September 2014

Cloud Thickness values near LaCrosse, WI, are around 900 feet; values are closer to 1200 feet over northeast Wisconsin along the St. Croix River. The chart suggests a dissipation time over the southwest part of Wisconsin of around 3 hours, and more than 4 hours over northwestern Wisconsin. The animation below shows that those estimates were accurate.


GOES-13 Visible (0.63 µm) Animation over Wisconsin, 22 September, 1215-1515 UTC (Click to animate)

Cloud Thickness as a Predictor of Fog Dissipation


GOES-R Cloud Thickness over Wisconsin and surrounding States, 18 September 2014, just before sunrise (Click to enlarge)

GOES-R Cloud Thickness can be used as a predictor for dissipation time of Radiation Fog, using this chart and the thickness (as above) from the last pre-dawn GOES-R Cloud Thickness field (Recall that GOES-R Cloud Thickness is not computed in the few hours of twilight surrounding sunrise or sunset; in the image above, twilight has reached lower Michigan but not yet Wisconsin). However, it’s important to remember that the chart is valid for radiation fog. Other forcings might cause fog to dissipate (or persist).

In the example above, Cloud Thickness values ranges from around 700 over southwest Wisconsin to as much as 1400 over north-central Wisconsin. Most of south-central Wisconsin (cyan) has values around 1200. According to the best-fit line, that suggests a burn-off time of more than 5 hours (although those values are extrapolated; note that no values that large went into the creation of the best-fit line) over WI, except over southwestern WI where a burn-off time of less than 1 hour is predicted. Did that work out?

The animation below shows fog/low stratus moving towards the southwest with time. The cool and damp northeasterly flow from the Great Lakes into Wisconsin (surface map at 1800 UTC on 18 September) suppressed the heating necessary to reduce the relative humidity and foster fog evaporation. Perhaps the fog initially formed as advection fog; however, the northeasterly flow that developed early in the morning on 18 September came from a synoptic set-up that allowed fog to persist longer than the GOES-R Cloud Thickness algorithm suggests. This is not an uncommon occurrence. Clouds did not burn off over south-central WI until after 1800 UTC. During September, delayed burn-off of morning clouds can significantly affect the day-time temperature.


Half-hourly visible imagery over Wisconsin, 1215-2045 UTC on 18 September (Click to animate)


Low clouds and fog redeveloped during the morning of the 19th of September as well. This occurred during persistent southerly flow in advance of a low pressure system over the Northern Plains. The hourly animation of IFR Probabilities, below, shows IFR Probabilities developing over the course of the early morning of the 19th between 0315 and 1215 UTC. The animation shows a gradual overspreading of the IFR Probability field with higher clouds moving in from the west. (Here is a toggle between IFR Probability and GOES-13 Brightness Temperature Difference Fields at 1115 UTC; note how smooth the field is over much of WI where only Rapid Refresh model data can be used in the computation of the IFR Probability).


GOES-R IFR Probability fields, hourly from 0315-1215 UTC on 19 September (Click to animate)

When high clouds overspread the scene, GOES-R Cloud Thickness is not computed. Thus, the last image before twilight, below, shows Cloud Thickness in only a few locations, but those values over southeast Wisconsin exceed 1200 feet, suggesting a burn-off of around 1615 UTC — 5 hours after this last Cloud Thickness image. In this case, that is an overestimate because the southerly winds over WI promote mixing, and the fog quickly dissipates after sunrise. It’s important to consider the synoptic forcing when you use Cloud Thickness. The last Cloud Thickness field and its use as a predictor for fog dissipation (using this chart) is most useful for radiation fog. The visible imagery animation at the bottom shows that the fog dissipated by 1415 UTC.


GOES-R Cloud Thickness just before Sunrise (1115 UTC on 19 September 2015) (Click to enlarge)


GOES-13 Visible Imagery, 1215-1615 UTC on 19 September (Click to animate)

Advection fog over Lake Michigan


GOES-R IFR Probabilities computed from GOES-East (Upper Left), GOES-East Brightness Temperature Differences (10.7 µm – 3.9 µm) (Upper Right), GOES-R Cloud Thickness (Lower Left), GOES-R IFR Probabilities computed from MODIS, or GOES-East Visible Imagery, times as indicated on 29 April 2014 (click to enlarge)

The GOES-R IFR Probability fields computed from GOES-East captured the onset of Lake fog that moved onshore over eastern Wisconsin on April 29th. Multiple cloud layers associated with a strong extratropical cyclone precluded the use of the brightness temperature difference product (the heritage method of detecting fog/low stratus). However, the IFR Probability field aligns well with the reductions in visibility associated with the Lake fog. The character of the IFR Probability field can be used to infer whether of not satellite data predictors are being used. For example, the relatively flat field over southeast Wisconsin at the start of the animation is a region where satellite predictors are not used. The use of satellite predictors generally leads to a pixelated field. A flatter field as over southeast Wisconsin reflects the smoother model fields that are driving the probability field computation.

Cloud thickness is computed in regions where the highest cloud, as seen by the satellite, is a water-based cloud. And that is also usually the region where satellite predictors are used in the computation of IFR Probabilities. Note in the animation above how cloud thickness generally overlays regions of IFR Probability that are pixelated. Cloud thickness is not computed where only model data are used to compute IFR Probabilities. (Cloud thickness is also not computed in the hour or so around sunrise and sunset, during twilight conditions).

The slow northward movement of the fog bank is apparent in the first part of the animation above, from 0615 through 0745 UTC. Note also how the MODIS IFR Probability fields give a very similar solution to the GOES-13-based fields at 0745 UTC. Differences in resolution are apparent over southwest Wisconsin, however, where river valleys are more accurately captured by the MODIS fields.

In the visible imagery at the end of the animation (1355 UTC), the rapid saturation of moisture-laden air moving northward from Indiana over the cold waters of southern Lake Michigan is very apparent.