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
GOES-17 IFR Probability fields, above, show a thin region along the northern shore of Bristol and Kvichak Bays in southwestern Alaska, north of the Aleutian Peninsula, where IFR conditions are likely . Probabilities are highest around King Salmon (PAKN) and Igiugig (PAIG) northeast of King Salmon. Several nearby airports are not reporting observations (PAII — Egegik — just south of King Salmon; PATG –Togiak — along the northern shore of the Bay, east of PAEH (Cape Newenham). IFR probability uses satellite and model information to create an estimate of whether or not IFR conditions will be met in regions where observations are missing. Sometimes, as over Cape Newenham at the end of the animation, high clouds are present and only model data can be used to create the estimate.
The Night Fog Brightness Temperature Difference field, below, shows that low clouds (made up of water droplets) exist over the same region — but this one product cannot indicate whether the stratus deck observed is reducing visibility near the surface (where aviation interests require that information). The model data that are incorporated into IFR Probability in concert with satellite data allow for a better estimate of where visibility is reduced than do satellite data alone. This is especially important when the presence of high clouds, as at the end of the animation in the western part of this domain, makes it difficult for the satellite to view low clouds.
Toggles at 1000 UTC (above) and 1600 UTC (below) of IFR Probability, Low IFR Probability and Night Fog Brightness Temperature Difference, suggest that the greatest likehihood of reduced visibilities are not along the bay shore, but rather inland along the Kvichak River.
CIMSS is now creating GOES_17 IFR Probability fields for Alaska and will presently be distributing them to the Alaska Region. An example of the utility of the products occurred on 19 October over western Alaska. The toggle above between the Night Fog brightness temperature difference field and the IFR Probability field at 1310 UTC 19 October shows several regions where IFR Probability refines where ceilings and visibilities might restrict aviation — an important piece of information in Alaska.
The default Night Fog Brightness Temperature enhancement is constructed so that stratiform clouds containing water droplets are colored different shades of blue. Higher clouds are various shades of grey.
Consider station PANV — Anvik, AK, near 62.7 N, 160 W. This is a station reporting IFR conditions, and IFR probabilities are near 80%. However, it is also under high clouds; GOES-17 is prevented from viewing low clouds, but the Rapid Refresh model data used in IFR Probability is showing saturation. Model data fills in regions of IFR conditions where high (of mid-level) clouds prevent the satellite from viewing near-surface clouds. Note how IFR Probability is also able to distinguish — correctly — between the IFR conditions at Anvik with the more benign sky conditions to the north at St. Michaels (PAMK), Unalakleet (PAUN) and Shaktoolik (PFSH) along Norton Sound.
In contrast, station PASM — St. Mary’s AK, near 62 N, 163 W — is beneath a strong signal in the Night Fog brightness temperature difference field. However, IFR conditions are not reported, and IFR probabilities are near 40% (and decreasing abruptly to the south). In this region, IFR Probability fields are screening out a region of mid-level stratus.
Station PAMC — McGrath, AK, near 63 N, 155 W — shows near-IFR conditions with a local minimum IFR Probability near 40% and a strong signal of stratus clouds in the Night Fog Brightness Temperature difference. For this station it would be prudent to see how IFR Probabilities were changing with time.
IFR Probability combines the strengths of satellite detection of low clouds with the strength of Rapid Refresh model predictions of low-level saturation to create a product useful in regimes with single or multiple cloud layers.
Detecting stratus at night, and thereby inferring the presence of fog, usually involves the Night Fog Brightness Temperature Difference (10.3 µm – 3.9 µm) field that identifies clouds made up of water droplets owing to the droplets’ different emissivity properties at 10.3 µm (droplets emit energy at that wavelength mostly as a blackbody) and at 3.9 µm (droplets do no emit energy at that wavelength as a blackbody). This difference field is a crucial component in the Night Time Microphysics Red-Green-Blue (RGB) Product as evinced in the toggle above. The regions shown to have low clouds (blue and cyan in the Brightness Temperature Difference field, pale yellow in the RGB) are not necessarily those regions with IFR conditions, i.e., where fog and low ceilings are present. The satellite can sense the top of the cloud, but it is a challenge to infer from the satellite data alone where the cloud base sits.
GOES-R IFR Probability fields combine satellite information with model estimates of low-level saturation. An accurate model simulation can allow the product to highlight regions of low ceilings (where fog is more likely) and screen out mid-level stratus. Consider the toggle below, and note how is emphasizes regions where observations show low ceilings and/or reduced visibilities (Blue Canyon airport northwest of Lake Tahoe and Paso Robles airport). Note also how the signal at Bakersfield, at the southern end of California’s Central Valley, is de-emphasized.
GOES-R IFR Probability fields provide a consistent signal for low ceilings and reduced visibility. The fields marry the strengths of satellite detection and model data.
The Cooperative Institute for Meteorological Satellite Studies (CIMSS) is (as noted in this blog post) testing GOES-17 IFR Probability fields in the AWIPS environment in preparation for their deployment to interested offices (via an LDM feed). The GOES-17 field, above, at 1106 UTC, suggests stratus offshore of San Francisco but higher ceilings over the city and the bay. Webcam views of the city (source), and of Alcatraz Island (source), below, from around 630 AM PST, also suggest relatively high ceilings over the city and the bay.
GOES-16 is also providing IFR Probability over the west coast of the United States. The toggle below between GOES-17 and GOES-16 shows how the oblique view from GOES-16 and the effects of parallax can perhaps place the probability in the wrong place. Parallax errors shift the clouds towards the sub-satellite point. Parallax effects can be explored at this website.
The GOES-17 Advanced Baseline Imager (ABI) is currently showing the effects of inadequate imager cooling by the faulty Loop Heat Pipe on board the spacecraft. At times between 1100 and 1500 UTC, as shown below for 1401 UTC, stripes will appear in the IFR Probability field. Manifestations of the Loop Heat Pipe issue will continue with increasing impact into early March, at which time Eclipse Season will mitigate the issue until April.
GOES-17 IFR Probability fields are available over the CONUS domain at this website.
The animation above, of GOES-16 IFR Probability, shows high IFR values in regions over western Washington and Oregon, and those high values correlate spatially very well with surface observations of low ceilings and reduced visibilities.b You will note a couple things in the animation: The field is mostly stationary, with slight adjustments on every hour. Those small changes reflect the change in the model fields (hourly Rapid Refresh) that are used to complement satellite estimates of low clouds in the computation of IFR Probabilities.
On this day, the satellite did not view many low clouds (some are apparent in southwestern Oregon). The flat field (vs. the more pixelated view over southwest OR, and also occasionally in the west-northwest flow coming in off the Pacific) suggests only model data are being used. The stepwise changes on the hour also suggest that.
The animation of the Night Fog Brightness Temperature Difference (10.3 µm – 3.9 µm) field, below, has a signal that is consistent with the lack of observed stratus over the coastal Pacific Northwest. Note also how the Night Fog Brightness temperature difference field flips sign as the Sun comes up and the 3.9 µm signal becomes larger due to reflected solar radiance with a wavelength of 3.9 µm.
GOES-16 views of the Pacific Northwest show fairly large pixel sizes. NOAA/CIMSS scientists have been creating GOES-17 IFR Probability for the past couple weeks, and this product will become available via an LDM field in the near future.