Monthly Archives: January 2016

Near-IFR conditions over South Carolina


GOES-R IFR Probability (Upper Left), GOES-R Cloud Thickness (Lower Left), GOES-East Brightness Temperature Difference (10.7µm – 3.9µm) (Upper Right), GOES-East Water Vapor (6.5µm) at night, Visible Imagery (0.65µm) in day (Lower Right), hourly from 0315 UTC through 1400 UTC 29 January 2015 (Click to enlarge)

The animation above shows hourly imagery, from 0315 through 1400 UTC on 29 January 2016 (Click here for a faster animation). GOES-R IFR Probabilities are moving northeastward from South Carolina into North Carolina throughout the animation. Changes in surface observations of ceilings and visibilities closely match the motion of the GOES-R IFR Probability field. This is a case where the GOES-13 Brightness Temperature Field also is giving information about the presence of low clouds; GOES-R IFR Probability lends confidence to a forecaster that the stratus deck is actually fog because GOES-R IFR Probability includes information about low-level saturation (from the Rapid Refresh Model that is one of the predictors used in the statistical model used to generate IFR Probability fields).

In the early part of the animation, higher clouds are present and can be detected both in the water vapor imagery and in the brightness temperature difference (high clouds are dark in the enhancement curve used). When high clouds are present, GOES-R Cloud Thickness (in the lower left of the figures above) will not be determined as it is derived from an empirical relationship between the 3.9 emissivity of low clouds and cloud thickness that was determined from sodar observations of cloud thickness off the West Coast of the USA. High clouds prevent a determination of this relationship. Regions where GOES-R Cloud Thickness is not computed at night because of high clouds correspond very well with regions where GOES-R IFR Probability is determined by Rapid Refresh Model Output only.

Fog in California’s Central Valley


GOES-R IFR Probability Fields and GOES Brightness Temperature Difference (10.7 µm – 3.9 µm) at 0500 UTC, 25 January 2016 (Click to enlarge)

Fog developed over California’s central Valley during the early morning of 25 January 2016.  The toggle above shows the GOES-R IFR Probability field and the GOES Brightness Temperature Difference field at 0500 UTC, before any IFR conditions were reported. Note that the brightness temperature difference field reports large regions of water-based clouds over Nevada — IFR Probability fields screen out this region because low-level saturation is not occurring in the Rapid Refresh Model.  The inference should be that any cloud present there is mid-level stratus rather than fog. That screening of water-based clouds continues at 0700 UTC, below.  Around Hanford, IFR Probability values have increased as ceilings and visibilities have lowered.


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


As above, but at 0900 UTC (Click to enlarge)

By 0900 UTC, above, high clouds have moved over Hanford (shown as dark streaks in the brightness temperature difference field enhancement) where IFR conditions are occurring. IFR Probability fields correctly continue to maintain a strong signal around Hanford even though brightness temperature difference fields cannot detect low clouds because of the presence of high clouds. Thus, GOES-R IFR Probability in that region is controlled by Rapid Refresh Model output that successfully predicted the presence of low-level saturation. By 1100 UTC, below, the high clouds have moved south, and GOES-R IFR Probability values rebound. When both Satellite data and Model data can be used as predictors, IFR Probability field values are larger than when only satellite data or only model data are used as predictors.


As above, but at 1100 UTC (Click to enlarge)


As above, but at 1300 UTC (Click to enlarge)

By 1300 UTC, the region of IFR Conditions near Hanford has expanded — and a second region has developed closer to Sacramento.  IFR Probability fields continue to screen out regions that the brightness temperature difference field alone suggests are regions of low stratus.  Surface-based observations do show regions of mid-level stratus over Nevada, around San Francisco, and over northern California, three regions where the brightness temperature difference signal is strong.  The higher terrain of the Sierra Nevada to the east of the Central Valley also shows higher IFR Probability as might be expected where terrain rises up into mid-level stratus.

The 1500 UTC images, below, continue these trends.  By 1700 UTC, at bottom, there is enough solar radiation reflecting off the clouds that the brightness temperature difference has flipped sign.  The IFR Probability field, however, has a consistent signal over the persistent region of low clouds/fog around Hanford and around Sacramento.


As above, but at 1500 UTC (Click to enlarge)


As above, but at 1700 UTC (Click to enlarge)

Dense Fog and Freezing Fog in Montana and North Dakota


GOES-R IFR Probability Fields, 0300-1300 UTC on 20 January 2015 (Click to enlarge)

Dense Fog over North Dakota and Freezing Fog over eastern Montana prompted the issuance of Dense and Freezing Fog advisories early Wednesday Morning.    GOES-R IFR Probabilities, above, computed from data from the GOES-13 Imager and from Rapid Refresh Model output, capture the growth/evolution of the fog field. In general, the IFR Probability field captures the area of reduced visibilities (with some exceptions, such as KHEI (Hettinger, ND, near the border with South Dakota is southwest North Dakota).

Compare the areal extent of the IFR Probability field with that from the Brightness Temperature Difference from GOES, below. The presence of multiple cloud layers prevents any satellite from viewing low clouds, and satellite-only products therefore give little information about near-surface events in much of western North Dakota and Montana.  When Rapid Refresh Model output is controlling the GOES-R IFR Probability field, as happened in this case over Montana and western North Dakota, the IFR Probability will have lower values and a less pixelated look that reflects the coarser model resolution and model smoothing.


GOES Brightness Temperature Difference fields, 0300-1300 UTC, 20 January 2016 (Click to enlarge)

Dense Fog Advisories in southern Idaho

Dense Fog Advisories were issued over southern Idaho early on 19 January 2016. GOES-R IFR Probability fields, below, show high probabilities in this region. Surface observations are not common over northern Utah/southern Idaho, and the hourly animation does show IFR Conditions observed near the high IFR Probabilities over southern Idaho and northern Utah (Logan, UT — KLGU — at 41:47 N, 111:51 W, in far northern Utah from 0300 – 1300 UTC; Elko NV — KEKO — at 40:50 N, 115:47 W also shows IFR conditions).  The Snake River Valley in Idaho also shows high IFR Probabilities during parts of the animation.

GOES-R IFR Probability fields fuse together information from GOES-15 (or, in the eastern part of the USA, GOES-13) and Rapid Refresh Data. Highest probabilities occur where the satellite detects low (water-based) clouds and where the Rapid Refresh Model predicts low-level saturation. High IFR Probabilities don’t necessarily guarantee the presence of IFR Conditions — but they are a flag that should prompt a forecaster to be alert to the possibility that IFR conditions are present nearby or are developing. When dense fog is valley-based as might happen in the Rocky Mountains, GOES Pixel footprints and Rapid Refresh model resolution are sometimes too coarse to resolve completely the fog. MODIS data (bottom) has 1-km horizontal resolution and IFR Probabilities computed from MODIS data are more likely to resolve small valleys.


GOES-R IFR Probability fields computed from Rapid Refresh Model Output and GOES-15 Satellite Data, hourly from 0200 – 1500 UTC, 19 January 2016 (Click to enlarge)


GOES-R IFR Probability computed from MODIS data and Rapid Refresh Model output, 1023 UTC 19 January 2016 (Click to 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)

Fog over the High Plains under Multiple Cloud Layers


GOES-13 IFR Probability fields, 0100-1400 UTC, 6 January 2015, and surface observations of ceilings and visibility (Click to enlarge)

National Weather Service offices in El Paso and in Lubbock issued dense fog advisories for portions of their respective CWAs early on 6 January 2016. Moisture from a storm entering from the Pacific Ocean (as suggested by this graphic showing the GFS 00-h analysis from 1200 UTC showing the 300-mb jet) means multiple cloud layers. Higher clouds make satellite-only detection of low clouds difficult, and the animation above shows regions (north of Roswell — KROW — at the end of the animation, for example) where the flat GOES-R IFR Probability field suggests only Rapid Refresh data are diagnosing the probability of IFR conditions.

The Brightness Temperature Difference fields, below, for the same times as the IFR Probability fields at top, miss regions of reduced visibility under multiple cloud layers, and also have strong returns in regions of mid-level stratus. Because GOES-R IFR Probability incorporates information about saturation in the lowest 1000 feet of the atmosphere (in the form of Rapid Refresh Model Output), regions that are saturated in low levels that cannot be diagnosed as foggy by satellite alone because of mid-level/high-level clouds can be flagged as likely having IFR conditions, and regions that are flagged by the satellite as having stratus (stratus and fog can look very similar to a satellite) can be screened out if low-level saturation is not diagnosed in the model. Both of these actions serve to increase the GOES-R IFR Probability’s ability to diagnose correctly areas of low clouds/fog relative to brightness temperature difference fields alone.

One other advantage to GOES-R IFR probability fields: They are not strongly affected by changing amounts of 3.9 µm radiation around sunrise/sunset. In the animation below, note how at 1400 UTC (just past sunrise) the brightness temperature difference signal is collapsing. This is because increasing amounts of reflected solar 3.9 µm radiation are changing the difference between observed 10.7 µm and 3.9 µm radiation above water-based clouds. Little change occurs in the IFR Probability fields at this time however.


GOES-13 Brightness Temperature Difference (10.7µm – 3.9µm) fields, 0100-1400 UTC, 6 January 2015, and surface observations of ceilings and visibility (Click to enlarge)