GOES-R IFR Probability that uses present GOES (GOES-13 and GOES-15) data in the computation of GOES-R IFR Probability fields was designed in anticipation of GOES-16 data that are now flowing to National Weather Service Forecast offices. Click here for a description of the Brightness Temperature Difference field values that are available now in AWIPS from GOES-16.
GOES-R FLS products are currently derived from GOES-13 and GOES-15 data. A GOES-16 version of the GOES-R FLS products will not be available until later in 2017.
GOES-13 Brightness Temperature Difference (3.9 µm – 10.7 µm), GOES-R IFR Probability and GOES-R Cloud Thickness at 1115 UTC on 17 January 2017 (Click the enlarge)
Dense Fog Advisories (National Weather Service Website) and IFR SIGMETs (Aviation Weather website) were issued early in the morning for dense fog over the southeastern United States. The toggle above from 1115 UTC on 17 January shows the Brightness Temperature Difference field (3.9 µm – 10.7 µm), the GOES-R IFR Probability Field, and the GOES-R Cloud Thickness fields associated with this dense fog event. Note the presence of high clouds over northern South Carolina and western North Carolina — the dark region in the Brightness Temperature Difference enhancement — prevents the brightness temperature difference field from highlighting that region of reduced ceilings/visibilities. The GOES-R Cloud Thickness field is not computed under cirrus either, as it relates 3.9 µm emissivity of water-based clouds to cloud thickness (based on a look-up table generated using data from a SODAR off the West Coast of the United States). If cirrus blocks the view, then, neither the Brightness Temperature Difference field nor the GOES-R Cloud Thickness field can give useful information about low clouds.
In contrast, the GOES-R IFR Probability field does give useful information in regions where cirrus clouds (and low clouds/fog) are present — because Rapid Refresh information about the lower troposphere can be used. IFR Probability values will be smaller in those regions because satellite predictors are unavailable, and the Probability incorporates both predictors from satellites and from Rapid Refresh model output — if the satellite predictors are missing because of cirrus, the IFR Probability values will be affected. Despite the smaller values, however, the IFR Probability fields in regions of cirrus are giving useful information for this event.
GOES-R Cloud thickness fields can be used to estimate Fog dissipation using the last GOES-R Cloud Thickness field produced before twilight conditions at sunrise (shown below for this case). (GOES-R Cloud Thickness is not computed during twilight conditions because of rapidly changing 3.9 µm emissivity related to the reflected solar radiation as the sun rises, or as it sets). This scatterplot gives the relationship between thickness and dissipation time after the Cloud Thickness time stamp (1215 UTC in this case). In this case, the thickest fog is near Athens GA; the algorithm predicts that clearing should happen there last, at about 1515 UTC.
GOES-R Cloud Thickness, 1215 UTC on 17 January 2017 (Click to enlarge)
Screenshot from Charleston WFO, 1230 UTC 4 August 2015
GOES-R IFR Probability fields computed from GOES-13 and Rapid Refresh Data, hourly from 0400 through 1215 UTC 4 August 2015 (Click to enlarge)
Dense fog developed over the piedmont of South Carolina/Georgia on 4 August 2015 in the wake of departing convection. The GOES-R IFR Probability fields, shown above hourly from 0400 to 1215, do parallel the development of the reduced ceilings and visibilities. Brightness Temperature Difference fields, below, from 0615 to 1215 UTC, do not show a strong fog signal until after 0800 UTC, yet IFR conditions at that time stretch from Walterboro SC (KRBW) southeastward to Eastman GA (KEZM) and Baxley GA (KBHC). GOES-R IFR Probabilities therefore give a better head’s up to a forecaster tasked with monitoring ceilings and visibilities.
Suomi NPP overflew the Southeast United States at ~0730 UTC on 4 August. Ample illumination from the waning three-quarter moon showed cloudiness over southeastern coastal South Carolina and adjacent parts of Georgia but the brightness temperature difference field does not suggest that these are all water-based clouds (such clouds generally fall in the yellow or orange part of the enhancement).
Suomi NPP Day Night Band visible (0.70 µm) image, 0732 UTC 04 August 2015 (click to enlarge)
Suomi NPP Brightness temperature Difference field (11.45 µm – 3.74 µm), 0732 UTC on 4 August 2015 (click to enlarge)
MODIS data from Terra and Aqua satellites can also be used to compute GOES-R IFR Probability fields, and two MODIS swaths were produced over South Carolina/Georgia early on August 4. Toggles between the 0337 Terra-based GOES-R IFR Probability Field and the 0755 UTC Aqua-based GOES-R IFR Probability fields are below. The larger values from MODIS — especially at 0755 UTC — suggest the fog was initially at small-scale horizontally. The 1-km resolution pixels from MODIS better capture any small-scale features.
MODIS-based (Terra) and GOES-based (GOES-13) GOES-R IFR Probability fields at ~0340 UTC on 04 August 2015 (click to enlarge)
MODIS-based (Aqua) and GOES-based (GOES-13) GOES-R IFR Probability fields at ~0800 UTC on 04 August 2015 (click to enlarge)
Multiple cloud decks — shown in the toggle, below, of Suomi NPP Day Night Band and Brightness Temperature Difference (11.45 µm – 3.74 µm) — prevented the traditional brightness temperature difference product from providing useful information. GOES-R IFR Probabilities, shown ~hourly in an animation above do highlight the region of developing IFR conditions. Low ceilings and reduced visibilities are commonplace in regions where IFR Probabilities are increasing over night. The predictors that are included to compute the IFR Probabilities are mostly model-based because of the multiple cloud layers that are present, and the IFR Probability field is somewhat flat as a result. Note that GOES-R IFR probabilities increase at the very end of the animation; when daytime predictors are used, probabilities are a bit higher than when nighttime predictors are used.
Suomi NPP Day Night Band visible imagery and Brightness Temperature Difference (11.45 µm – 3.74 µm) at 0818 UTC, 19 May 2015 (Click to enlarge)
GOES-R IFR Probabilities, hourly from 0200 through 1300 UTC on 7 April 2015 (Click to enlarge)
Denver International Airport had a period of restricted visibility during the morning of 7 April, starting around 0830 UTC, when northeast winds ushered in low ceilings and reduced visibilities. High Probabilities in the IFR Probability fields shift west and south with time, demonstrating how the fields can be used to anticipate the development of IFR conditions.
Webcam image of South Padre Island, TX, 11 AM 1 April 2015 (click to enlarge)
Webcam imagery from South Padre Island between 10 and 11 AM on 1 April (above, from this site) showed dense fog that had rolled in from the sea. This is likely an advection fog formed as humid air over the Gulf of Mexico moved over relatively cooler shelf water. SSTs in the region were in the upper 60s (Fahrenheit) as depicted by the image below.
Sea-surface Temperatures, early morning 1 April 2015 (Click to enlarge)
GOES-R IFR Probabilities, below, suggested the presence of the IFR conditions that existed at the coast. Very high probabilities are concentrated near South Padre Island, and spread north and northeastward, with highest values hugging the shoreline.
GOES-R IFR Probabilities computed from GOES-East and Rapid Refresh Data, 1545 UTC on 1 April 2015 (Click to enlarge)
Brightness Temperature Difference fields at the same time gave little surface information because of the presence of high clouds.
GOES-East Brightness Temperature Difference Field, 1545 UTC on 1 April 2015 (Click to enlarge)
A similar event occurred in March 2015 along the Florida Atlantic Coast. (Link).
GOES-East 10.7 µm imagery at 0630 UTC on 9 March 2015 (Click to enlarge)
The 10.7 µm imagery from GOES-East (Band 4) shows a cloud signature that is typical of a developing low-pressure system that one might find to the east of an open wave at 300 mb. Using satellite data alone to deduce (or perceive) the presence of IFR conditions in this case will be difficult. GOES Sounder Estimates of Total Precipitable Water in the region (from here) give little information.
GOES-R IFR Probability fields blend together satellite information with moisture information from the Rapid Refresh (also included: slowly varying fields such as Sea Surface Temperature and surface emissivity). The animation below, showing the field at 0615, 0915 and 1215 UTC, demonstrates the product’s ability to distinguish between regions with cloud (Louisiana) and regions with fog/low stratus (Much of east Texas, stretching up into central Arkansas; observations show IFR or near-IFR conditions widespread in this area). Because satellite data are unavailable for the product (only water-based cloud information is used, typically, and multiple cloud layers associated with deep clouds include mixed phase and ice clouds) the fields are fairly uniform. Where holes in the clouds appear — over the Rio Grande at 0915 UTC and over parts of south Texas at 1215 UTC — IFR Probabilities look more pixelated and Probabilities are higher. The larger IFR Probabilities occur because satellite predictors are incorporated into the algorithm where low clouds are detected.
GOES-R IFR Probability fields and surface plots of ceiling/visibility and the NWS Frontal Analysis at ~0600, ~0900 and ~1200 UTC on 9 March (Click to embiggen)
Webcam image of Fog along I-90 in central South Dakota near Kimball, after sunrise on 17 September 2014
The toggle below shows the Brightness Temperature Difference Field from GOES-13 (10.7 µm – 3.9 µm) and the GOES-R IFR Probability fields computed using data from GOES-13 and the Rapid Refresh Model. The Brightness Temperature Difference field detects the presence of water-based clouds (yellow and orange in the enhancement used) and works because such clouds have difference emissivity properties at 3.9 µm and 10.7 µm. Temperatures inferred from the 3.9 µm radiation detected are cooler than those temperatures inferred from the 10.7 µm radiation because water-based clouds do not emit 3.9 µm radiation as blackbodies. The Brightness Temperature Difference field gives information about the top of the cloud only, however, and it typically overestimates regions of fog/low stratus. That is the case on the morning of 17 September 2014. For example, IFR Conditions are not reported over much of southeastern South Dakota or western Minnesota along Interstate 90. The IFR Probability algorithm is correctly minimizing the influence of the strong brightness temperature difference signal there because Rapid Refresh Data is not showing boundary-layer saturation. The highest IFR Probabilities in South Dakota are associated with reported IFR Conditions, for example at Chamberlain, SD (west of Kimball SD and also in Brule County) and at Aberdeen in northeastern South Dakota.
GOES-R IFR Probabilities (Upper Left), GOES-R Low IFR Probabilities (Upper Right), GOES-R MVFR Probabilities (Lower Left) and GOES-R Cloud Thickness (Lower Right), time as indicated (Click to enlarge)
The default colorbars for GOES-R IFR (Instrument Flight Rules) Probabilities, Low IFR Probabilities and MVFR (Mostly Visible Flight Rules) Probabilities change colors at different break points, as shown above (by where the arrows are, for example). IFR Probabilities switch from white to orange/yellow at around 40%, vs. 30% for LIFR and 55% for MVFR. A statistical analysis of the surface observations was used in the development of the colorbars. Highest skill at detecting the category was assigned the reddest colors. One could therefore infer from this that there is in general less skill in detecting MVFR conditions than LIFR conditions. For a given event, however, as shown above, MVFR probabilities will in general be larger than IFR or LIFR probabilities.
Let the color of the colorbar guide the interpretation. Black and white values mean the category (IFR conditions, for example) is unlikely, yellow means it’s possible, and red means it is highly likely. MVFR probabilities have higher thresholds than LIFR probabilities because of skill differences in predicting the different visibilities associated with the sky conditions.