Starting 21 October 2021, GOES-R IFR Probability fields are archived (for both GOES-16 and GOES-17, Full Disk and CONUS/PACUS domains) at NOAA CLASS. These fields are available under the drop-down menu: Choose ‘GOES-R Series L2+ Enterprise Products (GRABINDE)’, as shown below.
Clicking on ‘>>Go‘ after selecting the product takes a user to the page below, from which page ABI Fog/Low Stratus products can be obtained.
The files retrieved from CLASS will be netCDF files with a filename structure such as this:
The file name above is for GOES-16 data from 0811 UTC on 22 October 2021. Such data can be displayed in (for example) Panoply, as shown below. Additionally, one could configure an AWIPS session to accept the fields.
The netCDF files include many different 2-dimensional fields: IFR Probability, MVFR Probability, Low IFR Probability, Cloud Thickness, Maximum Relative Humidity between the surface and 500 feet AGL, Maximum Relative Humidity between the surface and 3000 feet AGL, Band 7 (3.9) Emissivitiy, and many more. A Full Disk image below (courtesy Tim Schmit, NOAA STAR), shows IFR Probability values on 27 October 2021 at 0000 UTC.
The animation above steps through the Night Fog brightness temperature difference (10.3 µm – 3.9 µm), the Nighttime Microphysics RGB (which RGB has as its green component the Night Fog brightness temperature difference) and the GOES-17 IFR Probability fields at 1100 UTC on 24 September 2021. (Note that the Night Fog Brightness Temperature difference and Night Microphysics RGB are at reduced resolution)
A low pressure system is moving onshore through southeastern Alaska. The animation includes two regions that demonstrate particular strengths of the IFR Probability field: over inland and coastal southeastern Alaska, east of Anchorage and west of Yakutat, where low clouds are diagnosed under the multiple cloud layers; over western Alaska where low clouds are diagnosed by the brightness temperature difference field — and color-enhanced as cyan/blue — but where fog observations are not occurring. Over western Alaska, model data allows the IFR Probability to screen out the region of elevated stratus.
Animations of the brightness temperature difference field, the night time microphysics RGB and the IFR Probability are shown below.
On this day, GOES_17 IFR Probability fields were better able to highlight regions of low ceilings and visibilities over Alaska by combining the strength of satellite detection of clouds and the ability of Rapid Refresh model simulations to predict where low-level saturation is most likely. Note that in the animation of the IFR Probability fields there are slight changes on the hour that are related to incorporation of new model output into the computation of the fields.
GOES-17 IFR Probability fields are slated to be operational as of 13 October 2021.
Examine the four-panel animation above. Fog with IFR conditions developed over night over North Dakota. IFR Probability fields, upper left in the four-panel, do a credible job of outlining where the restrictions to visibility are (including the low ceilings). Indeed, a strong signal develops in the IFR Probability field before one develops in the Night Fog brightness temperature difference. In addition, there is a strong signal in the brightness temperature difference field (10.3 µm – 3.9 µm) and also in the night time microphysics RGB over northern Minnesota, but observations suggest that the stratiform clouds highlighted there by those two products are not hugging the ground, that is, they are elevated stratus vs. fog. Also note how the signal diminishes as the sun rises, as expected. The bottom left panel shows GOES-R Cloud Thickness, and it suggests that the fog over North Dakota is fairly thin. Rapid burnoff-should occur.
Some of the stations in the Dakotas are reporting visibility restrictions due to smoke. IFR Probability fields do not provide information for that kind of IFR conditions: Smoke detection with infrared imagery is very challenging, and the Rapid Refresh model being used (to identify regions of fog by assessing low-level saturation) does not predict smoke. The image below, taken from the CSPP Geosphere site (link), shows the widespread smoke, and also the patches of stratus and mid-level clouds as depicted in the scene above.
The animation above shows various methods typically used to detect fog/low stratus in the early morning. Night Fog Brightness Temperature differences, bottom left, and Night Time microphysics, bottom right, are both satellite-only detection systems; a shortcoming might be that satellite data is challenged in detecting cloud bases — satellites view the cloud top. Additionally, the signal is lost as the sun rises. IFR Probability (upper right) includes information (from the Rapid Refresh model) on low-level saturation, so perhaps that field better defines the scattered pockets of fog apparent on this morning. However, the Rapid Refresh model resolution is 13-km, and a valley fog might not be well-resolved in the model.
The Cloud Thickness information suggests that any clouds are thin, and that morning burn-off will be speedy. That is indeed what happened, as shown by the 1500 UTC image below (taken from the CSPP Geosphere site). Recall that the Cloud Thickness product is not produced in the times that surround sunrise (or sunset).
Mid-day observations over southeastern New England (and the offshore islands) show widespread fog. The toggle above shows mid-day visible imagery and GOES-16 IFR Probability. It’s very difficult to assess from the satellite imagery (especially on a day such as 4 June when high clouds are also present) alone where the reduced visibilities sit. Because IFR PRobability includes information on low-level saturation from the Rapid Refresh model, a better estimate of the horizontal extent of fog results. High IFR Probability values are widespread along the south coast of New England and the offshore islands where IFR and Low IFR conditions were observed. IFR Probability is a useful Situational Awareness tool. It can also be useful to load the imagery such that the IFR Probability is underneath a semi-transparent visible image, as shown below.
The toggle above compares Night Fog Brightness Temperature difference fields at 0901 UTC on 11 May over Colorado and surrounding states. Both fields can be used to identify regions of stratus/low stratus — and by inference, fog. At this time, however, it is a challenge to use these satellite-only products because of widespread mid/upper-level clouds over eastern Colorado, Kansas and Nebraska. Note how the blue/cyan regions in the Night Fog Brightness Temperature Difference field show through into the Nighttime Microphysics RGB; the green component of that RGB is the Night Fog Brightness Temperature Difference field.
On this day, the high clouds prevented the Brightness Temperature Difference alone from highlighting regions of fog. IFR Probability fields, below from the same time, show that this field better outlined the region of low clouds/fog over the High Plains. The field incorporates both Night Fog Brightness temperature difference fields — to determine where stratus clouds might be — and Rapid Refresh model estimates of low-level moisture — to determine where saturation might be occurring. Where the satellite detects stratus clouds, and where the Rapid Refresh model suggests low-level saturation, high IFR Probability (as over southeastern Colorado) results, in accordance with, on this day, observations. If high clouds are present (as over much of eastern Colorado), IFR Probability can still occur because the Rapid Refresh data shows low-level saturation. Note also that the extensive cloud cover over Kansas and Nebraska — elevated stratus — does not have a strong signal in the IFR Probability fields.
This product blends the strengths of satellite observations and the strength of model estimates.
GOES-17 IFR Probability fields, above, show a large region of high probabilities to the south and west of Alaska over the Bering Sea. This region of low clouds is encroaching onto shore and demonstrates how the field could be used to predict the onset of lower ceilings. The Night Fog brightness temperature difference animation, below, spanning the same times, documents how high clouds can make mask the satellite detection of low clouds. The animation also shows how the signal changes when the Sun rises (mot noticeable after about 1600 UTC). The Night Fog Brightness Temperature difference field is a component of the Nighttime Microphysics RGB; if the Night Fog Brightness Temperature difference cannot detect low fog because of high clouds, then the Nighttime Microphysics RGB similarly will not detect low clouds.
The toggles below of Night Fog Brightness Temperature difference and GOES-17 IFR Probability at 1400 UTC (below) and 1620 UTC (bottom) underscore how the IFR Probability field can give useful information in regions underneath high clouds. If you were scheduled to be on a boat in the Bering Sea on this day, for example, would you expect any visibility?
The imagery below, from webcams at Togiak (at 1700 UTC) (source), show the low clouds along the coast.
GOES-17 IFR Probability fields over southwestern Alaska and the Aleutians, shown above, characterize several areas of likely IFR conditions on 7 March. One region is over the Bering Sea, north of the Aleutians. This is a region where multiple cloud layers prevent the satellite from viewing low clouds. However, the Rapid Refresh model simulation there does show saturation at low levels; large values of IFR Probability are the result. When model fields control the IFR Probability values, there is little pixel-to-pixel variability and field has a uniform look. This example also shows values from two different models — the Alaska version of the Rapid Refresh (over most of the domain) and the GFS model over parts of Asia.
A second region of high IFR Probabilities is over land in southwest Alaska. There, an absence of high clouds means that satellite detection of low clouds occurs. There is good agreement between IFR Probability fields and observations of ceilings and visibility. For example, note how IFR Probability decreases around PAUN (Unalakeet) as the visibility improves and ceilings lift.
The toggle between the Night Fog Brightness temperature difference and IFR Probability field at 1210 UTC, below, reveals differences as well. The Brightness Temperature Difference field shows uniformity (except for a path of high clouds centered on Dillingham). IFR Probability fields show that the low clouds and reduced visibilities are not so widespread: note the conditions near McGrath and Sleetmute — IFR Probabilities are low, and IFR conditions are not present.
The animation of Night Fog Brightness temperature below demonstrates challenges in using the field. When high clouds overspread a region, the low cloud signal is lost; perhaps because the fog lifts, and perhaps not. The signal is also lost as the sun rises: the emissivity differences that drive the difference field at night are overwhelmed by solar reflectance as the sun rises.
GOES-16 Night Fog Brightness Temperature Difference (BTD) fields (10.3 µm – 3.9 µm), above, show different cloud types in and around South Carolina before and through sunrise on 1 March 2021. Before sunrises, low clouds — stratus and fog — are characterized by blue/cyan/aqua colors in this enhancement. The positive brightness temperature difference arises because of emissivity differences in small cloud droplets for shortwave (3.9 µm) and longwave (10.3 µm) infrared radiation. Negative brightness temperature differences occur for higher clouds.
The presence of high clouds interferes with the satellite’s ability to view low clouds. Although one can infer a low cloud’s presence from an animation (and the assumption that the cloud doesn’t substantively change when a high cloud overspreads it), it’s a bit more difficult to extrapolate the cloud base from the cloud top behavior. In other words: Are the low clouds highlighted actually fog? Note also how the BTD enhancement changes as reflected solar radiation around sunrise starts to overwhelm any differences attributable to emissivity.
IFR Probability fields, shown below, combine the strength of satellite detection of low clouds with the strength model depiction of low-level saturation. IFR Probabilities will be quite high where satellites detect low clouds, and where Rapid Refresh model simulations show near-surface saturation. But if high clouds are in the way, IFR Probabilities can still be large if the Rapid Refresh model shows low-level saturation. This is occurring off the coasts of South and North Carolina. Note also: IFR Probability fields aren’t changing radically as the sun rises.
The toggle above compares the Night Fog BTD with the Night time microphysics RGB (an RGB that takes as its green component the Night Fog BTD), and the GOES-16 IFR Probability fields at 1001 UTC on 1 March, shortly before sunrise. There is an obvious relationship between the RGB color and the regions of low clouds highlighted in the BTD by blue/cyan/aqua colors; the color of the RGB is tempered by the cloud temperature, however: the blue component of the RGB is the longwave infrared brightness temperature, and that is different over the ocean compared to over southwestern Georgia (for example). Note also how IFR Probability shows large values under the deep clouds over northwest South Carolina/western North Carolina.
Why is fog occurring offshore? Moist air over South Carolina is moving over relatively cool shelf waters, and cooled to its dewpoint; advection fog is the result. The toggle below shows surface dewpoints in the low 60s over coastal South Carolina. Sea-surface temperatures, shown at bottom from VIIRS at 0700 UTC and GOES-16 at 1300 UTC, show cool shelf waters with surface temperatures in the 50s (F).
Clean Window infrared imagery from GOES-17, above, shows a cyclonic storm making landfall over the southeast Alaska peninsula. Multiple cloud levels can be inferred from this animation, and satellite detection of low clouds (and stratus), as reported in sparse METAR observations, is a challenge. Note also the occasional striping that suggests the Loop Heat Pipe on GOES-17 is not cooling the satellite (The Cooling Timeline — every 15 minutes for a Full Disk, is being used at the start of the animation).
In particular, the GOES-based ‘Night Fog ‘ Brightness Temperature Difference field, below, commonly used alone, and as part of the night time microphysics RGB, does not show a consistent signal (cyan in the enhancement) associated with low clouds/stratus/fog — because higher clouds (grey in the applied enhancement) are interfering with the view.
GOES-17 IFR Probability combines satellite information with Rapid Refresh model (resolution: 11 km) predictions of low-level saturation. More recent model data are incorporated every hour; you might notice that fields adjust slightly at the top of the hour as that happens.
IFR Probability fields show that the likelihood of IFR conditions are extending southward along the coastal range with time, and increasing in the Inside Passage as well. Note also how IFR Probability is generally larger near mountain tops: it is created with knowledge of topography