Category Archives: Alaska

GOES-R IFR Probabilities at High Latitudes

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GOES-R IFR Probabilities computed using GOES-15 and Aqua Data, both near 1300 UTC on 27 October 2014 (Click to enlarge)

GOES-R IFR Probabilities are created using both GOES-15 Imager and Terra/Aqua MODIS. The toggle above shows MODIS-based IFR Probabilities (computed using data from Aqua and the Rapid Refresh) and GOES-based IFR Probabilities (computed using data from GOES-15 and the Rapid Refresh). There are three regions in the fields that warrant comment.

(1) Over East-central Alaska and the Yukon, large values of MODIS-based IFR Probabilities are limited in area (and near stations — such as Northway Airport — that are reporting IFR or near-IFR conditions). GOES-based IFR Probabilities in that same region include a large area with modest values — around 50%. Limb brightening may have an effect at high latitudes on the brightness temperature difference fields that are used in the computation of IFR probabilities because limb brightening is a function of wavelength. MODIS data (which will have far less limb brightening) can be used as a good check on the IFR Probabilty fields computed from GOES.

(2) Over Southwestern Alaska, and into the eastern Aleutians, GOES-based and MODIS-based IFR Probabilities are very similar. In this region, multiple cloud layers prevent satellite data from being used as a predictor in the computation of GOES-R IFR Probabilities. Rapid Refresh data is the main predictor for low clouds/fog, so MODIS-based and GOES-based fields will look similar.

(3) In the northeastern part of the domain, over the Northwest Territories of Canada, MODIS-based and GOES-based IFR Probabilities are very high. Satellite data are being used as a predictor here, and the satellite-based signal is strong enough to overwhelm any limb-brightening. (Note that southern Northwest Territories and northern British Columbia are south of the MODIS scan).

Terra- and Aqua-based MODIS observations yield frequent observations that result in good spatial and temporal coverage for IFR Probability fields over Alaska. GOES-15 temporal coverage is better, but the frequent MODIS passes can be used to benchmark GOES-based IFR Probability fields that may be misrepresentative because of limb-brightening effects at high latitudes.

Use MODIS data at High Latitudes

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GOES-West IFR Probabilities, MODIS IFR Probabilities and surface topography over northern Alaska, ~2000 UTC on 14 January 2014 (click image to enlarge)

GOES pixels grow to large sizes at high latitudes, such as those found over northern Alaska. Consequently, the IFR Probabilities can give information that is difficult to interpret. Data from the polar-orbiting MODIS instrument (on board Aqua and Terra satellites), in contrast, have nominal 1-km resolutions even at high latitudes. In the toggle of imagery above, the MODIS IFR Probabilities suggests low clouds just north of the Brooks Range in northern Alaska. In contrast, the IFR Probabilities based on GOES-15 are difficult to interpret.

Consider using the MODIS-based IFR Probabilities. At very high latitudes, data from polar orbiters is more frequent than at mid-latitudes. Thus, there is a benefit from higher spatial resolution without an onerous loss of temporal resolution as happens in mid-latitudes.

IFR Probability fields in extreme cold

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GOES-R IFR Probabilities from GOES-15 with surface ceilings/visibilities and GOES-15 Brightness Temperature Difference (10.7 µm – 3.9 µm) Fields with surface plots at 1800 UTC 26 December 2013 (click image to enlarge)

The image toggle above shows IFR Probability and the Brightness Temperature Difference Field over northern Alaska. Plotted METAR observations show very cold surface temperatures in the -30 to -50 F range. At such cold temperatures, the pseudo-emissivity computation can become noisy because a very small change in 10.7 µm radiance (used to compute the 3.9 µm radiance) can cause a large change in 3.9 µm brightness temperature. (The effect is shown graphically below — a small change in radiance at 3.9 µm leads to a very large temperature change). This noise can lead to a speckled appearance to the IFR probability fields. This effect can also occur in the northern Plains of the United States when surface temperatures dip below zero.

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Radiance (y-axis) vs. Brightness Temperature (x-axis) for 3.8 µm (left) and 10.7 µm (right)

(update) Below is the IFR Probability in the heart of a Polar Airmass over northwestern Ontario.

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GOES-R IFR Probabilities from GOES-13 with surface ceilings/visibilities and GOES-13 Brightness Temperature Difference (10.7 µm – 3.9 µm) Fields with METAR plots at 1215 UTC 29 December 2013 (click image to enlarge)

Fog/Low Stratus over southwest Alaska on November 8

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GOES-15-based GOES-R IFR Probabilities every half hour from 0300 UTC through 1530 UTC (click image to animate)

Fog and low stratus were present over southwestern Alaska early on November 8. How did the GOES-R IFR Probability fields perform compared to the heritage brightness temperature difference (in this case, 10.7 µm – 3.9 µm from GOES-15). Consider the airport PARS (southwest of Anvik — PANV and northwest of Aniak — PANI). IFR conditions are present there until 0900 UTC, when ceilings rise and IFR probabilities drop. Subsequently, IFR Probabilities increase again as a north-south oriented region of higher IFR probabilities moves over, and IFR conditions are again present by 1600 UTC. Further south, PAJZ and PAIG report IFR conditions when IFR Probabilities are high, and conditions improve as IFR Probabilities decrease. IFR Probabilities initially around PAIG have the characteristic flat field (and somewhat lower probability) associated with a region where high-level clouds are present. In these regions, only Rapid Refresh data can be used to compute the probability; because satellite predictors are not used, the computed IFR probabilities are lower.

Compare the IFR Probability field, above, to the Brightness Temperature Difference field, below, that has been color-enhanced to highlight regions where water-based clouds may be present. The IFR Probability field correctly reduces the regions where IFR conditions might be occurring. That is, the traditional brightness temperature difference field is plagued by many false positives. This is because mid-level stratus that is unimportant for transportation looks to a satellite to be very similar to low-level stratus that is important for transportation.

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GOES-15 Brightness Temperature Difference (10.7 µm – 3.9 µm) every hour from 0300 UTC through 1500 UTC (click image to animate)

MODIS data from the polar-orbiting satellites Terra and Aqua can also be used to compute IFR Probabilities, and MODIS data — although less frequent than the data from the geostationary GOES-15 — has far superior horizontal resolution (nominal MODIS resolution is 1 km at nadir) to GOES data (nominally 4 km at the sub-satellite point over the Equator) over Alaska. Small-scale features are much more likely to be detected in MODIS data, as shown below.

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GOES-15-based GOES-R IFR Probabilities (Upper Left), GOES-15 Brightness Temperature Difference Product (10.7 µm – 3.9 µm) (Upper Right), Suomi-NPP Brightness Temperature Difference (11.45 µm – 3.74 µm) and Day/Night band (Lower Left), MODIS-based GOES-R IFR Probabilities (Lower Right), all times as indicated (click image to enlarge)

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GOES-15-based GOES-R IFR Probabilities (Upper Left), GOES-15 Brightness Temperature Difference Product (10.7 µm – 3.9 µm) (Upper Right), Suomi-NPP Brightness Temperature Difference (11.45 µm – 3.74 µm) and Day/Night band (Lower Left), MODIS-based GOES-R IFR Probabilities (Lower Right), all times as indicated (click image to enlarge)

The Day/Night band from Suomi/NPP can sometimes be used to detect cloud features. However, when the Moon is not present to provide illumination, cloud detection is a challenge. In the toggle above between the Day/Night band and the brightness temperature difference from VIIRS (11.45 – 3.74), for example, there is little evidence of the apparent cloud edge that is visible both in VIIRS data, in GOES-15 data (Upper right) and in the IFR Probability fields from GOES (Upper Left) and MODIS (Lower Right).

IFR Conditions over southwest Alaska

Strong extratropical storms that move northward into the Gulf of Alaska, or into the Bering Sea, can bring IFR conditions to many parts of Alaska. However, they typically also bring multiple cloud layers that make traditional satellite-only methods of detecting fog and low stratus problematic. In cases like these, a fused product that incorporates model predictions of low-level saturation is helpful in defining just where IFR conditions are most likely.

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GOES-15 Brightness Temperature Difference Product (10.7 µm – 3.9 µm), times as indicated (click image to enlarge)

For example, the brightness temperature difference field, above, does not show a strong signal in regions where near-IFR conditions are present. In contrast, the IFR Probability field, below, that incorporates model fields that are influenced by surface features, better highlights the region of IFR conditions. It captures the edge of the fog/low stratus field over SW Alaska, and probabilities are highest in regions where IFR and near-IFR conditions exist. The relatively flat field over land is a typical feature of the IFR Probability when it is determined chiefly by model data. Because satellite data are not included in the predictors, the total probability is somewhat smaller. Where the satellite brightness temperature difference field does have a strong signal is where the IFR Probabilities are highest (over the Bering Sea).

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GOES-R IFR Probabilities derived from GOES-15 and Rapid Refresh data Brightness Temperature Difference Product (10.7 µm – 3.9 µm), times as indicated (click image to enlarge)

One shortcoming with IFR Probability is the pixel resolution at high latitudes. MODIS data can also be used to compute IFR Probabilities, and three comparisons between MODIS and GOES values are shown below. Alaska’s high latitudes means not only large GOES pixels, but also fairly frequent coverage from the polar-orbiting Terra and Aqua satellites that hold the MODIS instrument.

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Toggle between GOES-R IFR Probabilities derived from GOES-15 and from MODIS satellite data at ~0900 UTC 15 October (click image to enlarge)

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As above, but at ~1300 UTC (click image to enlarge)

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As above, but at ~1430 UTC (click image to enlarge)

IFR Conditions over southwest Alaska

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GOES-R IFR Probability computed from GOES-West (click image to play animation)

A relatively narrow band of high IFR probabilities developed over southwest Alaska, as shown in the animation above. Pay particular attention to the observations at St. Marys (PASM) and at Emmonak (PAEM). These two stations have near-IFR and IFR conditions as the high IFR Probability develops and curls over them.

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GOES-West Brightness Temperature Difference Field (click image to play animation)

The traditional method of detecting fog and low clouds, the brightness temperature difference between the 10.7 and 3.9 micrometer channels, does not distinguish between the region with IFR conditions and adjacent regions. Adding information from the Rapid Refresh model in this case fine-tuned the depiction of where the greatest restrictions to visibility exist.

Suomi-NPP Brightness temperature difference fields (below) had a similar deficiency in precisely highlighting the region with IFR conditions.

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Suomi-NPP VIIRS Brightness Temperature Difference Field (click image to play animation)

Late Summer Fog Detection over Alaska

GOES-15 0.62 µm Visible Imagery (Click to see animation)

GOES-15 0.62 µm Visible Imagery (Click to see animation)

It can be challenging to determine where fog and low stratus are problematic over Alaska. The visible imagery, above, shows an abundance of clouds (and, over the Northwest Territores, smoke) — but it’s difficult to guess which of the clouds are associated with low stratus or fog and accompanying low visibilities. Knowing where visibilities are low is vital in a state like Alaska where small aircraft provide vital transportation services.

After dark, increasingly frequent at this time of year in Alaska, the brightness temperature difference field (between brightness temperatures at 10.7 µm and 3.9 µm) can be used to identify water-based clouds because of differences at those two wavelengths in the emissivity properties of liquid water droplets that make up clouds. That animation is shown below. As with the visible imagery, the brightness temperature difference tells you about the top of the cloud. The important visibility restrictions occur at the bottom of the cloud.

GOES-15 Brightness Temperature Difference (10.7 µm - 3.9 µm) (Click to see animation)

GOES-15 Brightness Temperature Difference (10.7 µm – 3.9 µm) (Click to see animation)

GOES-R IFR Probabilities computed from GOES-15 and Rapid Refresh Data (Click to see animation)

GOES-R IFR Probabilities computed from GOES-15 and Rapid Refresh Data (Click to see animation)

Neither the visible nor brightness temperature difference loops above highlight cloudy regions with restricted visibility. The GOES-R IFR Probability product that fuses satellite information with model output, a loop of which is available above, does. Many offshore regions show high probabilities of IFR conditions. These are likely regions of advection fog where relatively moist air is flowing over cold ocean waters and being cooled to its dewpoint. (Click here to see a surface plot over northwest Alaska — dewpoints are in the 40s and 50s near the Bering Sea) An area of high IFR probabilities that occurs over land is over southwest Alaska between Dillingham and Bethel. Dillingham does report IFR and near-IFR conditions as the higher IFR probabilities rotate through. A close-up image of the IFR Probability, hourly, over southwest Alaska is shown below; note how ceilings and visibilities in Dillingham (PADL) lower as the IFR probabilities increase.

GOES-R IFR Probabilities computed from GOES-15 and Rapid Refresh Data (Click to see animation)

GOES-R IFR Probabilities computed from GOES-15 and Rapid Refresh Data (Click to see animation)

VIIRS data from Suomi/NPP can also be used to approximate where fog may occur. Both the Day/Night band and the Brightness Temperature Difference product can tell a forecaster where clouds exist. However, surface conditions beneath the clouds detected by Suomi/NPP may or may not be consistent with IFR conditions. In the animation below, a new algorithm has been applied to the Day/Night band to extract information in the transition zone between day and night. Some artifacts of that algorithm are evident (manifest as what appear to be cloud bands perpendicular to the satellite path), but useful information about clouds do appear elsewhere. The brightness temperature difference product is only computed where solar reflectance is essentially nil. GOES Imagery is included in the loop to show where sunrise has occurred.

Suomi/NPP Day/Night band, Brightness temperature difference, and GOES Imager visible data, all near 1245 UTC on 8 August 2013 (Click to see animation)

Suomi/NPP Day/Night band, Brightness temperature difference, and GOES Imager visible data, all near 1245 UTC on 8 August 2013 (Click to see animation)

IFR Conditions over southwest mainland Alaska

Hourly GOES-R IFR Probability fields over southwest Alaska, 0500 to 1700 UTC 15 July 2013

The IFR Probability fields over southwest Alaska early on July 15 2013 show the influence of multiple cloud layers moving northward out of Bristol Bay and the Bering Sea starting near 0900 UTC.  IFR probabilities drop (because satellite data are not included as predictors when multiple cloud layers exist), and the field becomes flatter.  That is, it has less horizontal variability (especially compared to its pixelated nature when satellite data are included).  IFR conditions are largely confined to within the region of high IFR probabilities.

Two obvious boundaries are present in the field.  At 0800 UTC, a boundary extends southwest to northeast, with higher values to the north and west.  At 1500 UTC, a boundary extends southeast to northwest with higher values to the north and east.  In both cases, this line is the terminator, and daylight is occurring north of the line.  In general, IFR probabilities increase during the day (where they are diagnosed) because the cloud-clearing algorithm operates with a lot more certainty when visible imagery can be used to identify clouds.

MODIS data can be used in Alaska because Alaska’s high latitude ensures that MODIS overpasses are frequent (especially along the north slope of Alaska).  Three overpasses could be used between 0700 and 1415 UTC over this small region of southwest Alaska to give high-resolution depictions of IFR probabilities.  Using these high-resolution images with the high temporal resolution available from GOES-West gives a full description of the IFR Probability field over Alaska.  The 0832 UTC image suggests multiple cloud layers are still over Bristol Bay/the Bering Sea, and the 1415 UTC image shows both the day-night boundary and an IFR Probability field determined largely by model data.

MODIS-based GOES-R IFR Probabilities over southwest Alaska, 0654 UTC, 0832 UTC and 1415 UTC 15 July 2013

Fog detection in the land of the Midnight Sun

Brightness Temperature Difference (10.7 µm- 3.9 µm) from GOES-West (quarter-hourly timestep)

As above, but with a color enhancement applied (hourly timestep)

Over the continental United States, the brightness temperature difference can be used, sometimes, to approximate where fog and low clouds are present because water-based clouds (such as fog and stratus) have different emissivity properties for radiation at 10.7 µm and 3.9 µm.  In regions where the sun shines constantly, however, the brightness temperature difference product is harder to interpret because the abundance of scattered or reflected solar radiation with wavelengths near 3.9 µm.  In the examples above, the darker region (top animation) or lighter region (bottom animation) are regions of low clouds.  Regions of visibility obstruction — station PABA, for example (Barter Island on the shore of the Arctic Ocean) do not appear to be near fog/low stratus as indicated by the brightness temperature difference.

The GOES-R IFR Probability field combines Rapid Refresh data and GOES-West brightness temperature difference information (and other information, such as a cloud mask) to more accurately portray the horizontal extent of visibility obstructions.  The animation below suggests high probability of IFR conditions along the Arctic Ocean shore in northern Alaska.

There are regions at present over Alaska of significant visibility obstructions due to smoke.  These visibility obstructions are not predicted by the GOES-R Fog/Low Stratus product.  Smoke detection typically incorporates a brightness temperature difference between 10.7 µm and 12.0 µm, and the GOES-15 Imager does not include 12.0 µm detection.

GOES-R IFR Probabilities computed from GOES-West, along with surface ceilings and visibilities.  Times as indicated.

A visible image of the north slope of Alaska, below, shows fog and low clouds very near the Arctic shore.

GOES-15 Visible Imagery, 1700 UTC 28 June 2013

FLS Advecting Northwest Towards the Eastern Aleutians

Area of focus in the Eastern Aleutians in Southern Alaska

Due to the location of Alaska, geostationary satellite data can sometimes be difficult to work with. This is because the large satellite angle causes the satellite footprint to increase, thus decreasing the spatial resolution of the data. However, even though the spatial resolution is not ideal the temporal resolution allows geostationary satellite data to remain useful. The example below shows and area of fog/low stratus (FLS) moving northwestward toward the eastern Aleutians in southern Alaska.

GOES-R IFR probabilities computed using GOES-15 (left) and MODIS (right) around 08:20Z on May 30, 2013. Note that the images are rotated so true north is actually oriented from the lower left corner of the images to the upper right corner.

In the image above the orange and darker red colors indicate areas with a high probability of FLS over the northern Gulf of Alaska. The difference in spatial resolution stands out when comparing the images, however, it should be noted that the same areas of higher probabilities in the MODIS image are also picked up in the GOES image, just at a coarser resolution. Although the MODIS image looks more detailed than GOES, temporal trends can not be discerned from a single image. The next available MODIS pass over the area was at 12:26Z.

GOES-R IFR probabilities computed using GOES-15 (left) and MODIS (right) around 12:26Z on May 30, 2013. Note that the images are rotated so true north is actually oriented from the lower left corner of the images to the upper right corner. 

In the 12:26Z image the FLS deck moved northwest over the eastern Aleutians as indicated by the higher probabilities that are now over the southeastern side of the Aleutians. This is confirmed by the surface station in Chignik, AK, which reported a ceiling of 400 ft at 12:00Z when it reported no ceiling at 08:00Z. Once both MODIS passes were available the NW movement of the FLS could be observed. However, in the 4 hours between the two MODIS passes it would be hard to forecast whether the IFR conditions would move into the area or stay out at sea.

On the other hand, GOES data was available about every 15 minutes. By animating the GOES images such as this, the slow NW movement of the FLS can be tracked and a better approximation of when IFR conditions will reach the Aleutians can be made. IFR conditions were first reported at Chignik, AK at 09:00Z, just as the higher GOES-R IFR probabilities reached the coastline. Also from the animation, it appears that the eastern Aleutians block the movement of the FLS deck from continuing over to the NW side into the Bering Sea. This blocking might be difficult to interpret from the two MODIS images alone.

Alaska has the largest coastline in the U.S. (more than twice the size of the entire lower 48, including Hawaii) where hazardous areas of FLS can move onshore. Although the high spatial resolution available from MODIS provides more detail in a single scene than GOES, the high temporal resolution that GOES offers makes finding trends in the movement of hazardous low clouds possible. This is another example of how looking at the GOES-R FLS products using GOES and MODIS together can be more useful than using only GOES or MODIS.