Author Archives: Scott Lindstrom

Radiation Fog over South Carolina

GOES-R IFR Probability Fields and surface reports of ceilings and visibilities, 0100-1000 UTC on 13 September 2016 (Click to enlarge)

High Pressure over the eastern United States allowed Radiation Fog to form over much of the southeast early on the morning of 13 September 2016. The GOES-R IFR Probability hourly animation, above, shows increasing probabilities of IFR conditions over much of North and South Carolina, with IFR conditions observed at many stations by sunrise (graphic from here). IFR Probabilities provided an earlier alert to the fog development (as such, it’s a good situational awareness tool) than was possible from the traditional brightness temperature difference field (see the 0400 UTC image below — click here for a much larger image) because of multiple cloud layers present over the Carolinas in the wake of departing showers. The enhancement for the brightness temperature difference field is such that clouds composed of water droplets are typically shaded orange or yellow. In the 0400 UTC brightness temperature difference field below (right), fog is not indicated over South Carolina.

GOES-R IFR Probability fields (left) and GOES-13 Brightness Temperature Difference Fields (right), both from 0400 UTC on 13 September 2016 (Click to enlarge)

GOES-R Cloud Thickness fields can be used to estimate fog dissipation time for radiation fog. This scatterplot shows a rough relationship between the last thickness field produced before twilight conditions and the dissipation time. That field is shown below — note that portions of eastern North Carolina have slipped into twilight conditions already by 1100 UTC. Maximum values over South Carolina are around 850 feet (near Greenville/Spartanburg), while those over North Carolina exceed 1200 (near Asheville). Fog dissipation should occur first over South Carolina, then over North Carolina.

GOES-R Cloud Thickness Field, 1100 UTC on 13 September 2016 (Click to enlarge)

Fog over northeast Colorado backs into Denver International

GOES-R IFR Probability Fields, 1437 UTC on 31 August 2016, with surface observations of ceilings and visibilities (Click to enlarge)

GOES-R IFR Probability Fields over Colorado and Nebraska on the morning of 31 August 2016 show high IFR Probabilities in close proximity to Denver International Airport (DIA), which airport was reporting IFR conditions starting at 1237 UTC. Webcams to the southwest and northeast of the airport shortly after 1500 UTC confirm that the IFR conditions’ edge was very near the airport.

The hourly animation of GOES-R IFR Probability fields, below, shows the evolution of the field. Its motion could be used in a prognostic manner.

GOES-R IFR Probability fields, ~hourly from 0400 through 1400 UTC on 31 August 2016 (Click to enlarge). Surface observations of ceilings and visibility are also plotted.

A similar event occurred on 22 September, see below from Mike Eckert and Amanda Terborg.

Fog under High Clouds

GOES-R IFR Probability Fields (Upper left), GOES-East Brightness Temperature Difference (3.9 – 10.7) (Upper Right), GOES-R Cloud Thickness (Lower Left) and GOES-East Water Vapor imagery (Lower Right), all at 1045 UTC on 18 August 2016. Surface observations of ceilings and visibilities at 1100 UTC are included in the upper right (Click to enlarge)

Dense Fog developed over southern Indiana on the morning of August 18 (and advisories were hoisted). The single image above demonstrates an advantage of GOES-R IFR Probability fields in determining the areal extent of fog: the traditional method of night-time fog detection from satellite fails in regions where cirrus clouds obscure the view of low clouds. That was the case over the Ohio River Valley where IFR conditions were occurring. GOES-R IFR Probability fields have a signal where high clouds exist in regions where Rapid Refresh model output shows low-level saturation, as over southwestern Indiana. Because satellite data cannot be used there to compute IFR Probabilities, the magnitude of the probability is smaller. Tailor your interpretation of the IFR Values based on the presence of high clouds. The presence of high clouds changes the character of the IFR Probability field, from a pixelated field where satellite data are present to a flatter field where only model data can be used.

GOES-R Cloud Thickness can be used to estimate fog dissipation time (using this scatterplot, where the thickness values are from the last pre-sunrise scene). That field, however, is only produced where the satellite has an unimpeded view of the low clouds (therefore, where cirrus clouds are present, as over the Ohio River Valley, Cloud Thickness is not produced). Note the line parallel to the terminator over eastern Ohio: GOES-R Cloud Thickness is not produced during twilight times around sunrise or sunset. This 1045 UTC image is the final one over Indiana before sunrise. Maximum thickness values are just over 1000 feet over southwest Indiana, suggesting a dissipation time of about three hours, that is, around 1345 UTC.

Changes in Model Fields show up in IFR Probability

When GOES-R IFR Probability fields are governed solely by Rapid Refresh model output because of thick cloudiness (as was the case over Illinois on 15 August 2016), there can be changes in the field at the top of the hour that are related to changes in the Rapid Refresh model output — that is, changes in which hour Rapid Refresh Model is used. The toggle above shows the IFR Probability fields at 1045 UTC and 1100 UTC on 15 August. Both fields are characterized by smooth values that come with IFR Probability that is driven by Rapid Refresh model output, output that is smooth and not pixelated like satellite data. It’s pretty noticeable, however, that values increase (from ~39% to ~52%) in those 15 minutes. Why?

The image below shows Rapid Refresh Model Predictions of 1000-700 mb Relative Humidity at 1100 UTC from the 0800 UTC model run (that is, a 3-hour forecast, left) and from the 0900 UTC model run (that is, a 2-hour forecast, right). Relative Humidity Values from the 0800 UTC Run (interpolated to 1045) are used in the computation of IFR Probabilities at 1045 UTC; values from the 0900 UTC Run are used in the computation of IFR Probabilities at 1100 UTC. It’s not this relative humidity field (value from 1000-700 hPa) precisely that is used, but rather maximum values in the vertical. Certainly there are changes in the predicted low-level relative humidity field at 1100 UTC between the sequential model runs; it’s more likely that saturation is occurring in the later model run, and that greater likelihood of saturation is reflected in the change of IFR Probability from 1045 UTC (when 0800 UTC Rapid Refresh Model fields are used) to 1100 UTC (when 0900 UTC Rapid Refresh Model fields are used).

Rapid Refresh model predictions of 1000-700 mb Relative Humidity; 3-hour forecast from the 0800 UTC Rapid Refresh Model (left) and 2-hour forecast from the 0900 UTC Rapid Refresh Model (Right), both from 15 August 2016 (Click to enlarge)

IFR Probabilities and SRSO-R Visible Imagery over Nebraska

GOES-R IFR Probability, hourly from 0600 through 1400 UTC on 9 August 2016 [Click to enlarge]

GOES-R IFR Probabilities show the development of IFR-producing stratus and fog over central and western Nebraska between midnight and dawn on 9 August 2016. The character of the field suggests that satellite data and Rapid Refresh Model output are both contributing to IFR Probability fields; IFR Probability fields will look far flatter in appearance (just one color) when model fields only are used.

When the Sun rises, the predictors that are used to compute IFR Probabilities change, and that change is evident in the 1245 UTC image below. Night-time predictors are being used to the west of the obvious boundary through central Nebraska, and day-time predictors are being used to the east. It’s more common for IFR Probability values to increase when Day-Time predictors are used, but on 9 August values decreased. (You can see from the animation above that the IFR Probability values subsequently rebounded)

GOES-R IFR Probability at 1245 UTC on 9 August 2016 [Click to enlarge]

GOES-R Cloud Thickness can be used to estimate when fog/low stratus will dissipate (using this scatterplot). The image below shows the last Cloud Thickness before twilight conditions (during twilight conditions, GOES-R Cloud Thickness is not computed — over Nebraska at this time of year, that’s generally about 2 hours starting around 1145 UTC). The largest values are around 1280 feet, which corresponds to a dissipation time of more than four hours, meaning the fog/low clouds should persist to at least 1530 UTC!

GOES_R_CloudThickness_1130UTC_09August2016

GOES-R Cloud Thickness just before Twilight Conditions over Nebraska, 1130 UTC on 9 August [Click to enlarge]

GOES-14 was observing Nebraska at 1-minute intervals on 9 August, as part of GOES-14’s SRSO-R. The animation from dawn (~1204) through 1610 UTC is below. Fog Dissipation is mostly complete by 1600 UTC.

GOES-14 Visible Imagery (0.62 µm) from 1204-1610 UTC [Click to view very large animation]

IFR Probability and Aviation

GOES-R IFR Probability, 1400 UTC on 5 August 2016, along with surface reports of ceilings and visibilities (Click to enlarge)

GOES-R IFR Probability describes regions where IFR Conditions are likely. For example, the IFR Probability field above, from 1400 UTC on 5 August 2016, shows high probabilities over part of the Piedmont from Virginia southwestward into Georgia. Observations confirm that IFR Conditions (and near-IFR conditions) exist in this region of higher probabilities.

The Aviation Weather Center maintains a website with products that dovetail nicely with IFR Probability fields. For example, the screenshot below shows stations reporting IFR Conditions (in red) and Low IFR Conditions (in magenta) ( a CWA-issued polygon on IFR conditions is included). The overall extent of the IFR Conditions in the image above and plotted below is also roughly similar. The G-AIRMET of IFR Conditions, bottom, also shows overlap with the IFR Probability field, as expected.

Aviation Weather Center screenshot from 1456 UTC showing region of IFR and Low IFR Probability over the southeastern and mid-atlantic states (Click to enlarge)

Graphical Depiction of IFR G-AIRMET and MTN OBS (Mountaintop Obscuration) G-AIRMET at 1500 UTC on 5 August 2016 (Click to enlarge)

Maintaining a signal through sunrise

GOES-R IFR Probability fields, 1045, 1215 and 1300 UTC on 1 August 2016 (Click to enlarge)

A benefit of the GOES-R IFR Probability field is that a coherent signal is maintained through sunrise (or sunset). The traditional method of detecting fog that uses the brightness temperature difference between 10.7 µm and 3.9 µm cannot maintain a consistent signal through sunrise as the amount of reflected solar radiation with a wavelength of 3.9 µm increases, overwhelming the emissivity-driven differences between 10.7 µm and 3.9 µmbrightness temperatures that are observed at night. Consider the animation above, that shows GOES-R IFR Probability fields at 10:45 UTC, 12:15 UTC and 13:15 UTC. First: The GOES-R IFR Probability fields do a fine job of outlining where the lowest ceilings and poorest visibilities exist in this scene over Wisconsin both before sunrise and after. The noticeable difference between the 1045 UTC and 1215 UTC fields is driven by a change in predictors that occurs as night transitions into day.

GOES-13 Brightness Temperature Difference fields (3.9 µm – 10.7 µm) are shown below. There is a strong signal at 1045 UTC, but little or no signal at 1215 UTC, before it returns (with opposite sign) at 1315 UTC.

GOES-13 Brightness Temperature Difference Fields (3.9 µm – 10.7 µm) at 1045 UTC, 1215 UTC and 1315 UTC. (Click to enlarge)

Comparing GOES-R IFR Probability Fields and Webcam Observations over Alaska

GOES-R IFR Probability, 1345 UTC on 19 July 2016, and 1400 UTC Observations of surface Visibility. Blue arrows point to, from left to right, Wainwright, Barrow, and Deadhorse Alaska (Click to enlarge)

The FAA website that includes webcams over much of Alaska provides a great opportunity to match IFR Probability fields with surface observations. In the image above, Wainwright AK shows a 3-mile visibility, but Barrow and Deadhorse farther east report 10-mile visibilities. All three stations are very near high IFR Probabilities — the red/orange region has IFR Probabilities exceeding 90%. What to the webcams at the three stations show? Screen captures from the three webcams are below. Webcams from both Barrow and Deadhorse show non-IFR conditions. Wainwright shows a dense fog through which the sun is dimly visible. These webcam observations are in general agreement with the GOES-based IFR Probability fields: only Wainwright is in a region where IFR Probabilities are consistently high.

Wainwright AK webcams at 1413 UTC on 19 July 2016 (Click to enlarge)

Barrow AK webcams at 1413 UTC on 19 July 2016 (Click to enlarge)

Deadhorse AK webcams at 1413 UTC on 19 July 2016 (Click to enlarge)

==============================================================================

GOES-R IFR Probabilities, 1645 UTC on 19 July 2016, and 1700 UTC Observations of Ceilings/Visibilities (Click to enlarge)

The image above is of GOES-R IFR Probabilities over the Aleutians, along with surface observations of ceilings and visibilities. In general, IFR Probabilities are a bit higher on the northern side of the Aleutians (False Pass, Cold Bay, Nelson Lagoon, Port Heiden, Pilot Point) than the southern side (Perryville, King Cove). That is confirmed with the screen captures of webcam imagery, seen below. The webcam scenes are aligned from west to east, starting at False Pass AK and ending at Pilot Point.

Webcam imagery from False Pass AK (PAKF) at 1653 UTC, 19 July 2016 (Click to enlarge)

Webcam imagery from Cold Bay, AK (PACD) at 1654 UTC on 19 July 2016 (Click to enlarge)

Webcam imagery from King Cove, AK, (PAVC) at 1655 UTC on 19 July 2016 (Click to enlarge)

Webcam imagery from Nelson Lagoon, (PAOU) 1656 UTC on 19 July 2016 (Click to enlarge)

Webcam Imagery from Perryville, AK, 1659 UTC on 19 July 2016 (Click to enlarge)

Webcam imagery from Port Heiden, AK, (PAPH) 1657 UTC on 19 July 2016 (Click to enlarge)

Webcam Imagery from Pilot Point, AK, (PAPN) at 1658 UTC on 19 July 2016 (Click to enlarge)

Fog Detection at Very High Latitudes

GOES-R IFR Probability Fields, 0930-1230 UTC on 14 July 2016 (Click to enlarge)

The low Sun Angle at very high latitudes during Summer presents a challenge to the GOES-R IFR Probability algorithm. Solar backscatter from clouds in the visible and near-infrared (3.9 µm) channels make traditional fog detection methods (Brightness Temperature Difference between 10.7 µm and 3.9 µm) problematic, but they also are a challenge for GOES-R IFR Probability. At mid-latitudes, when 3.9 µm radiation is changing quickly because of rapid changes associated with a rising (or setting) sun, the ‘satellite’ portion of GOES-R IFR Probability will be frozen for a time step or two. In the GOES-R Algorithm, temporal data (in other words, data from previous times) are used starting at the beginning of the terminator transition periods (that is, when the sun is rising or setting) and it’s used until valid data, either day or night predictors, depending on whether it’s sunrise or sunset, is again available. At mid-latitudes (as seen in the example at the bottom of this post), the transition period is typically less than thirty minutes in length. At high latitudes, however, when the sun lingers near the horizon for hours, the use of temporal data can stretch out for several hours and products will not be updated during that time. This effect happens only in the months surrounding the Summer solstice.

Consider the animation above, of GOES-R IFR Probability north of Alaska, over the Arctic Ocean. IFR Probabilty varies with each time step over much of the image, especially, for example, near the lower left corner (a region that includes the Brooks Range). There is a significant region, though, where values do not change over the three hours of the animation — this is because of the low sun angle and the difficulty in relating the 3.9 µm emissivity to a fog property. (You can detect at the end of the animation changes starting to impinge on the region of a static signal from the west) The three hour animation from 1100-1400 UTC, below, shows changes in the field over much of the domain over the Arctic Ocean.

Barrow Alaska (PABR) develops IFR conditions during this animation as low clouds move south from the ocean. GOES-R IFR Probability fields suggest that the fog does not penetrate far inland. Much of northern Alaska had record high temperatures on 13 July (It was 85 in Deadhorse, for example) with light south/southwest winds that would keep the ocean stratus offshore. Webcam views at Barrow (from this link) confirm the presence of IFR conditions.

GOES-R IFR Probability fields, 1100-1400 UTC on 14 July 2016 (Click to enlarge)

When GOES-R is flying, data the 8.4 µm channel will be incorporated into the GOES-R IFR Probability algorithm. This channel (Band 11 on the GOES-R ABI) is not so adversely affected by reflected solar radiation as the 3.9 µm channel so the unchanging nature of GOES-R IFR probability fields will be mitigated.

This stationarity in the GOES-R IFR Probability is apparent at mid-latitudes as well. Careful inspection of the animation below, from 1300-1400 UTC on 14 July 2016, shows a region of static IFR Probability fields off the coast of northern Oregon between 1315 and 1345 UTC.

GOES-R IFR Probability, 1300-1400 UTC on 14 July 2016 (Click to enlarge)

MODIS and GOES IFR Probabilities over Alaska

GOES-R IFR Probabilities computed using GOES-15 pixels over Alaska suffer from problems inherent in any Geostationary Data Product at high latitudes: Pixel sizes are large. In addition, ‘limb brightening’ — that is, the shift in a brightness temperature towards cooler values because the path length of photon towards the satellite travels through more of the upper (colder) troposphere (a cooling that is also dependent on wavelength being sensed) — affects the brightness temperature difference product that is used to detect water-based clouds. MODIS data from Terra and Aqua has a much higher spatial resolution and a superior view angle. It’s fairly simple to use both MODIS data to get an idea of conditions in and around Alaska, and then use GOES data to approximate the temporal change. Terra and Aqua view Alaska frequently (link) — it’s uncommon to go more than 6 hours without a view.

The toggles below show a series of MODIS IFR Probabilities and corresponding GOES-15 IFR Probabilities from late on 8 July 2016 through mid-day on 9 July 2016. From 2100 UTC to 1400 UTC — 17 hours — there are 7 separate MODIS views of Alaska, and they show similar features. For example, the high terrain of the Brooks Range is apparent: larger values of IFR Probabilities are noted there. The same is true over south central Alaska, over the Alaska Range in between Anchorage and Fairbanks. Interpretation of IFR Probability fields over the State require this background knowledge of Topography.

MODIS-based and GOES-15-based GOES-R IFR Probability fields, 2100 UTC on 8 July 2016 (Click to enlarge)

MODIS-based and GOES-15-based GOES-R IFR Probability fields, 2245 UTC on 8 July 2016 (Click to enlarge)

MODIS-based and GOES-15-based GOES-R IFR Probability fields, 2300 UTC on 8 July 2016 (Click to enlarge)

MODIS-based and GOES-15-based GOES-R IFR Probability fields, 0645 UTC on 9 July 2016 (Click to enlarge)

MODIS-based and GOES-15-based GOES-R IFR Probability fields, 0815 UTC on 9 July 2016 (Click to enlarge)

MODIS-based and GOES-15-based GOES-R IFR Probability fields, 1230 UTC on 9 July 2016 (Click to enlarge)

MODIS-based and GOES-15-based GOES-R IFR Probability fields, 1400 UTC on 9 July 2016 (Click to enlarge)

The scenes above suggest that most IFR Conditions near Alaska are offshore during the early morning of 9 July. On 11 July 2016, some of those regions of reduced visibility crept onshore, as shown in the plot below from this site, where surface stations are color-coded by Flight Rules: Red and Magenta denote IFR and Low IFR conditions.

Surface METARS, 1700 UTC on 11 July 2016 (Click to enlarge)

GOES-15-based IFR Probability fields from near that time show high probabilities along the coastline of Alaska. Note that the presence of IFR Conditions can also be deduced from this set of webcams! Consider, for example, this webcam site just west of Prudhoe Bay, in a region where GOES-based IFR Probabilities are high.

GOES-15-based GOES-R IFR probability fields, 1700 UTC on 11 July 2016, along with surface observations of ceilings and visibilities (Click to enlarge)

GOES-R Fog Product Examples

Fog detection fusing GOES, Terra/Aqua or Suomi/NPP Satellite data with Model output