Widespread mid- and upper-level cloudiness over the southern Plains associated with Tropical Storm Beta on 22 September 2020 make it difficult to use satellite data alone to identify where low clouds and fog might exist. This is a day where IFR conditions exist, as shown here, an image from this website. Where would you expect IFR conditions to exist within this field of view? The Night Fog brightness temperature difference, below, (and, by extension, the Nightime Microphysics RGB) shows scant information over eastern Texas/Oklahoma or ArkLaTex. IFR Probability fields, in contrast, have a definite signal of high probability.
The animation of the Night Fog Brightness Temperature Difference field, below, also highlights a challenge in using this product: increasing reflection of solar 3.9 radiation occurs as sunrise progresses, changing the character of the field. Further, soils can have emissivity properties that are similar to clouds, and a positive Night Fog Brightness Temperature difference signal results. (This is especially true in dry regions, such as west Texas; linked-to map from this website).
IFR Probability fields for the same time as the Night Fog Brightness Temperature Difference, above, show a consistent region where IFR conditions are most likely. The region over the high Plains of Texas that has a signal in the Brightness Temperature Difference field has low probabilities because in that region, the Rapid Refresh model is not suggesting widespread low-level saturation. In contrast, the Rapid Refresh model over east Texas/Oklahoma, western Louisiana and southwest Arkansas does show saturation.
What do observations shows? The hourly observations overlain on the IFR Probability fields, below, show that IFR and near-IFR conditions are widespread within the region of high IFR Probability. Outside that region, IFR conditions are rare.
GOES-R Fog/Low Stratus Products have been available in NWS Forecast Offices since 2012 via an LDM feed. GOES-16 versions for these products over the CONUS domain are now flowing over the Satellite Broadcast Network (SBN), effective 9 September 2020 (Announcement). Responsibility for this data feed is now at NESDIS following an extensive research-to-operations path. Fields distributed include Probability of: Marginal Visual Flight Rules (MVFR), Instrument Flight Rules (IFR) and Low Instrument Flight Rules (LIFR). In addition to these three probabilities (Click here to see an explanation), there is also a Low Cloud Thickness product that can be used to predict the dissipation time of radiation fogs.
IFR Probabilities, as shown above, are useful because they highlight regions under clouds where visibility restrictions are most likely. Loading it under a visible image and making the visible semi-transparent, as shown above, is a handy way to use the product. A forecaster responsible for transportation concerns can therefore focus their attention where it is needed, as defined by the IFR Probability field: IFR Probability is a good situational awareness tool.
Accessing the Fog/Low Stratus products via the SBN requires TOWR-S RPM v. 19 (It will be baselined in AWIPS v. 21.3.1 in 2021). GOES-17 (and GOES-16) IFR Probabilities are available at this website for the GOES-16 CONUS and GOES-17 PACUS sectors. Work is ongoing to product GOES-17 IFR Probabilities for Alaska.
Fog developed over western Kansas during the early morning of 14 August 2020 (helped along by above-normal precipitation in the past 30 days — shown here in an image created at this website). Low IFR Probability fields, above, show greatest probabilities of low ceilings and visibility restrictions in regions where they were observed: over western Kansas, with a sharp cut-off at the Colorado/Kansas border, and over western Oklahoma and the north Texas panhandle.
Compare the evolution of the Low IFR Probability field, above, to the evolution of the Night Fog brightness temperature difference (10.3 µm – 3.9 µm) field below.
The Night Fog field, below, has a region of strong return over western Kansas, but also two regions of weaker signals over central Kansas (where low clouds/fog are observed; note the regions in the brightness temperature difference field where the signal is very small, small grey pockets within the cyan, corresponding to the locations of towns in central Kansas) and over eastern Colorado (where low clouds/fog are not observed). Low IFR Probability fields are able to distinguish between the central Kansas and eastern Colorado because model predictions of low-level saturation are used to modulate the satellite-based signal: Low IFR Probability values are very small over Colorado (where the Rapid Refresh model is not predicting low-level saturation); values are larger over Kansas where ceiling and visibility reductions are occurring and where the Rapid Refresh model is suggesting low-level saturation is present). The brightness temperature difference field in Colorado might be driven by dry soils rather than low clouds. A brightness temperature difference signal can emerge at night because of soil emissivity differences (as noted earlier in this blog here).
Low IFR Probability fields are augmented underneath the convection that is apparent in the brightness temperature difference field over northwestern Arkansas. Satellite detection through the deep convection of low stratus in this region is impossible; the signal is driven by low-level saturation predicted by the Rapid Refresh model output.
As the sun rises, the brightness temperature difference field loses obvious cloud-detection signal because increasing amounts of reflected solar radiation (at 3.9 µm) overwhelm the emissivity-driven difference over low clouds at 3.9 µm and 10.3 µm. At some point after sunrise, the brightness temperature difference flips sign (and appears dark in the enhancement) because there is far more reflected solar radiation at 3.9 µm than at 10.3 µm.
The Low IFR Probability field by design includes temporal continuity around sunrise and sunset. This is most noticeable over central Kansas. The terminator sweep is noticeable in the field, but the values change only slowly in the hour surrounding the terminator. This temporal continuity is necessary because of the quick changes in detected 3.9 µm radiation that are occurring as solar reflectance changes occur.
Most of the posts on this blog discuss IFR Probability: The probability that IFR conditions are occurring. IFR, or Instrument Flight Rules conditions are defined as ceilings between 1000 and 3000 feet and/or visibilities between 1 and 3 miles. Two other Probability fields are created: MVFR Probabilities (MVFR, or Marginal Visual Flight Rules, are defined as ceilings between 3000 and 5000 feet and/or visibilities between 3 and 5 statute miles) and Low IFR Probabilities (LIFR, ceilings below 1000 feet and/or visibilities less than 1 mile). The animation above steps through the three fields from one time: MVFR Probability, IFR Probability and Low IFR Probability. As might be expected, MVFR Probability > IFR Probability > LIFR Probability.
Cursor readouts in AWIPS imagery are shown below; LIFR Probability fields are shown with the other two Probability fields are loaded underneath. The cursor readout (for the point just north and west of the upper left corner of the readout values) shows the relationship between the three fields. Low IFR Probability is shown in coral, IFR Probability in green, MVFR Probability in white. MVFR Probability values > IFR Probabilty values > LIFR Probability values.
Because IFR Probability includes both model information on low-level saturation and satellite information on low-level clouds, it can give an advanced warning on the development of fog when high clouds — at might occur behind departing convection, for example — prevent the satellite from viewing the low cloud beneath high clouds. Consider the animation below, that shows IFR Probability fields along with surface observations of ceilings and visibilities plotted so that regions with IFR conditions can be identified. IFR probability fields develop over northwest Indiana over the course of the night, especially over extreme northern Indiana where the east-west Indiana Toll road sits.
Compare the IFR Probability fields above to the Night Fog Brightness Temperature Difference (10.3 µm – 3.9 µm) field below. The field below historically has been used at night to identify regions of low clouds/fog — regions that are cyan in the enhancement are low clouds, regions that are black are high clouds. As the sun rises, the Night Fog brightness temperature difference field will struggle to identify regions of low clouds (although a different enhacement can be used to highlight the low cloud regions after sunrise). The satellite is not able to view low clouds over northern Indiana until the high clouds move out. Thus, a distinct indication of low clouds over the Indiana Toll road lags the suggestion of low clouds given by IFR Probability fields.
Consider the toggle below of GOES-R IFR Probability, Night Fog Brightness Temperature Difference and the Nighttime Microphysics RGB at 0601 UTC on 3 August.. IFR Probability fields show a developing region of IFR conditions (in yellow) over extreme NW Indiana. The two satellite-only detectors show only a scant suggestiong that fog is present. (A similar scenario is occurring near the bootheel of Missouri)
At 0901 UTC, below, all three indicators of low clouds are in better agreement over northwest Indiana (and over the Missouri Bootheel) as high clouds move from those areas.
Fields at 0931 and 1001 UTC continue this trend of stronger signals in the regions initially highlighted at 0601 UTC by the IFR Probability fields.
By 1131 UTC, below, increasing solar reflectance over the eastern part of the domain has changed the signal in both the Nighttime fog and Nighttime microphysics RGB over northern Indiana and Ohio. The GOES-R IFR Probability signal however is maintained.
GOES-17 ‘Night Fog’ Brightness Temperature Difference imagery, above, shows a stratus deck (cyan and blue in this default AWIPS enhancement) off the central California coast. Higher clouds (grey and black in the enhancement) are drifting over San Francisco bay, obscuring the GOES-17 satellite’s view of low clouds. These high clouds can present a challenge to aviation forecasters for San Francisco’s airport, and important airline hub because the nature of low clouds cannot be determined. (Note the striping in the image is an artifact of GOES-17’s malfunctioning Loop Heat Pipe). This animation also shows the effect of increasing amounts of reflected solar radiation on the Night Fog Brightness Temperature Difference signal.
GOES-17 IFR Probability fields, below, augment information at low levels by using model (Rapid Refresh) estimates of low-level saturation (as might be found in low stratus) in the computation of IFR Probability. The animation below shows that SFO was in a region of high — but not very high — IFR Probabililty. Note also how the signal is constant through the sunrise at the end of the animation.
The airport (KSFO) did not report IFR conditions on this morning.
Fog developed over North and South Carolina (some of this region has been cloudy and wet for much of the past week; here is a weekly precipitation total from this site) on the morning of 19 June 2020; the screenshot above, from this site, shows a sigmet related to the IFR conditions present:
How did GOES-R IFR Probability capture this event? The animation below, from 0900 to 1306 UTC, shows generally high IFR Probabilities over most of the region. There are stations where IFR conditions are occurring and IFR Probabilities are low: the Columbus County Municipal Airport (KCPC, in southeast North Carolina), for example, shows obstructed ceilings and reduced visibility. This might be a localized sub-pixel scale fog related to the small streams near the airport there. A similarly small-scale fog event may be happening at Macon County airport (K1A5) in western North Carolina. The 0901 UTC Brightness Temperature Difference field shows a signal consistent with valley fog along the Little Tennessee River (see image at bottom)
Note how the signal shows little discernible impact from the rising of the Sun. A strength of this product is that uniformity — in contrast to the Night Fog Brightness Temperature difference field.
The 4-panel image below shows the ‘Night Fog’ Brightness Temperature Difference (10.3 µm – 3.9 µm, top) at 0901 and 1056 UTC and the IFR Probability fields, also at 0901 and 1056 UTC. IFR probability shows an expansion in the region of low ceilings reduced visibilities, as might be expected to occur around sunrise. The Night Fog Difference field shows a decrease in signal related to the increasing amount of reflected 3.9 µm solar insolation.
Dense Fog was widespread over southeast Virginia on 27 May 2020. The toggle above compares the Night Fog Brightness Temperature Difference field, Night Time Microphysics RGB, and GOES-R IFR Probabilities computed with GOES-16 data and Rapid Refresh Model output, all at 0801 UTC. A challenge with satellite-only detection of low ceilings/poor visibility is that high clouds get in the way. For example, consider the satellite-only signal at Richmond, with 1/4-mile visibility and 200-foot ceilings. Low clouds are not easily detected in that region by satellite; only the GOES-R IFR Probability field (which blends satellite detection of clouds with model estimates of low-level saturation) correctly suggests the transportation hazard that is present there (and extending some distance to the north and west!)
The animation of the night Fog brightness temperature difference field, above, the ‘green’ component of the night time microphysics RGB, shown below, shows a second challenge of using this product for fog detection: the signal is lost (or, at best, changes) as the Sun rises. The RGB product shows the same challenges in fog/low stratus detection that are present in the channel difference field above: high clouds mask the signal, and the signal changes as the sun rises. (Contrails show up very nicely in both however.)
GOES-R IFR Probability fields, below, do a much better job characterizing the horizontal extent of the low clouds early in the animation, and in highlighting the region where visibility reductions persist through sunrise, namely from Hampton Roads northeastward along the coast.
Much of the central Plains of the United States was under easterly flow during the night on 12-13 May 2020, as suggested by the surface analysis above. Strong High Pressure over the Great Lakes and lower pressures over the Rocky Mountains drove that upslope flow. Consequently, IFR conditions were common over much of the Plains, as shown below.
The animation, below, of IFR Probability (overlain with surface observations and ceilings and visibilities) shows high probabilities. There is good correlation between regions with High IFR Probabilities — red and orange — and regions with low ceilings and reduced visibilities. The highest probabilities, dark red in the enhancement, occur where satellite data shows low clouds and where model data shows low-level saturation. Note that there is a trend towards less satellite information in the IFR Probability field with time over central Oklahoma and south-central Kansas because of developing convection.
The night time microphysics RGB, shown below, is also used to detect regions of low clouds; they typically appear yellowish at this time of year. The challenge with the RGB use in highlighting IFR conditions is two-fold: high clouds will mask the low cloud signal (as in the case of developing convection over Oklahoma, or with cirrus over Colorado and Nebraska). The RGB can also struggle to differentiate between fog and low stratus. Note also in this animation how the effect of the rising sun becomes apparent at the end of the animation over the eastern third of the image.
In the toggle below, the signal of low clouds in the RGB, yellow/cyan over the Texas panhandle (for example) has an echo in the IFR Probability field. In the absence of high clouds, both products will show regions of low clouds. The Night Time Microphysics RGB gives extra information about the cloud type in regions where high clouds are blocking the satellite view of low clouds (the purple region over Colorado/Nebraska and the reddish regions over Kansas and Oklahoma). In contrast, the IFR Probabilty field in those regions gives no information on the character of the higher cloud; the IFR Probability field in those regions is flat (i.e., not pixelated), the hallmark of IFR Probability that is derived chiefly from model fields, so all an analyst can tell is that a high cloud is present.
Frontal passage with extratropical cyclones will frequently be accompanied by low ceilings and reduced visibilities, that is, IFR conditions. A loop of visible imagery, above, suggests a surface cyclone (surface analyses are shown below) but it is difficult to determine from the imagery where low ceilings and reduced visibilities are present. Continue reading →