Author Archives: Scott Lindstrom

Successive Suomi NPP Scans Show Stratus/Fog movement

Suomi NPP Day/Night Band at 0926 and 1106 UTC on 9 October 2014 (Click to enlarge)

(A Blog post on Suomi NPP Imagery over the western US from 10 October is available here).

Polar orbiters typically don’t give good temporal resolution, especially near the Equator. In mid-latitudes, however, Polar Geometry can yield views over a wide area on two successive scans. This happened along the West Coast early in the morning on 9 October. Two regions show noticeable changes in stratus between the two times: stratus/fog extends farther down the Salinas Valley at the southern edge of image, and stratus/fog expands over southwestern Puget Sound in Washington. The Brightness Temperature Difference field (11.35µm – 3.74µm) from Suomi NPP for the same times shows a similar evolution over the Salinas Valley, but the view of Washington is obscured by thin cirrus. These cirrus (that show up as black enhancements below) are mostly transparent in the Day Night Band but not in the infrared bands.

Suomi NPP Brightness Temperature Difference (11.35µm – 3.74µm) at 0926 and 1106 UTC on 9 October 2014 (Click to enlarge)

MODIS instruments onboard Terra and Aqua yield spectral data that can be used to generate GOES-R IFR Probability Fields. The animation below shows high-resolution imagery of where IFR Probabilities are highest, but only at three distinct times: 2152 UTC on 8 October and 0537 and 0949 UTC on 9 October. An increase in IFR Probabilities around Monterey Bay is apparent (and consistent with the Suomi NPP Observations above); IFR Probabilities also increase along the Oregon Coast and around Puget Sound.

MODIS-based GOES-R IFR Probabilities at 2152 UTC 8 October, 0547 UTC 9 October and 0949 UTC 9 October (Click to enlarge)

How do GOES-based observations complement the Polar Orbiter data above?

GOES-15 based GOES-R IFR Probabilities, hourly from 0200 through 1400 UTC, 9 October 2014 (Click to enlarge)

The hourly animation above shows the slow increase of IFR Probability in/around Monterey Bay, and also a push of higher IFR Probability onto the Oregon Coast that occurs after the last MODIS-based IFR Image shown farther up. (Higher IFR Probabilities also spill into San Francisco Bay). Do surface observations of ceilings and visibilities agree with the IFR Probability fields? The loops below, from Oregon (below) and Monterey Bay (bottom) suggest that they do. For example, Eugene OR (and stations north and south of Eugene) show IFR conditions as the high IFR Probability field moves in after 1100 UTC. GOES-based data is valuable in monitoring the motion of IFR Probability fields; keep in mind, though, that small-scale features may be lost. For example, it is difficult for GOES to resolve the Salinas Valley.

GOES-15 based GOES-R IFR Probabilities, hourly from 0200 through 1500 UTC, 9 October 2014, with surface and ceiling observations superimposed (Click to enlarge)

GOES-15 based GOES-R IFR Probabilities from 0400 through 1400 UTC, 9 October 2014, with surface and ceiling observations superimposed (Click to enlarge)

IFR Probability along the West Coast

MODIS data from Terra and Aqua, Suomi NPP data, and GOES-15 data yield different types of information that can be used to observe clouds in a region. Probabilities of IFR conditions — that is, what’s going on at cloud base, which is a part of the cloud the satellite cannot see — can be produced by combining satellite observations of cloud tops and data from a model (such as the Rapid Refresh) that includes accurate predictions low-level moisture.

GOES-based IFR Probabilities (Upper Left), GOES-15 Brightness Temperature Difference (10.7µm – 3.9µm) (Upper Right), MODIS-based IFR Probabilities (Lower Right) at 0646 UTC, 0919 and 1058 UTC (Click to enlarge)

The three MODIS images, above (bottom right), show the slow encroachment of higher IFR Probabilities east-southeastward along the Sonoma/Marin county border. The high-resolution imagery from MODIS allows a more accurate depiction of the sharp edges that can occur as marine stratus penetrates inland around topographic features. MODIS data also suggests reduced ceilings in San Francisco.

As above, but at 0919 UTC only, with a toggle of MODIS Brightness Temperature Difference (11µm – 3.7 µm) and IFR Probability (Bottom Right) (Click to enlarge)

The 0919 UTC MODIS pass (from Aqua) happened to pass at a time when the GOES-West brightness temperature difference field was contaminated by stray light. The toggle above shows the difference between MODIS and GOES Brightness Temperature Difference fields at that time. MODIS is only detecting a signal where low clouds are present (or where soil differences allow the emissivitiy differences that can show up in the brightness temperature difference field, in this case over Nevada)

Suomi NPP overflew California at 1000 UTC, and those images are below. The brightness temperature difference field, and the Day Night band show clouds offshore, and city lights in and around San Francisco Bay and Monterey Bay. Regions of stratus have moved inland towards southern Sonoma County, over San Francisco, and over Monterey. There is a slight brightness temperature difference signal in the Suomi NPP data that extends down the Salinas Valley as well; it’s difficult to perceive cloudiness in the Day Night band in that region however.

As above, but with a toggle of Suomi NPP Brightness Temperature Difference and Day Night Band in the lower left, data at 1021 UTC (Click to enlarge)

The great strength of GOES data is its ability to monitor continuously the West Coast. Trends are therefore more easily observed. The hourly loop, below, shows that along much of the west coast, marine stratus stayed off shore through the night. Higher IFR Probabilities are also confined to regions where ceilings and visibilities were reduced. This is an improvement on the brightness temperature difference fields for the same time that have strong signals over much of the central Valley (for example, at 0500 and 1300 UTC, as well as at 0915 UTC, above).

GOES-15 IFR Probabilities, hourly from 0400 through 1300 UTC, 6 October 2014 (Click to enlarge)

MODIS vs. GOES-based IFR Probabilities

MODIS-based and GOES-based IFR Probability fields at ~0300 UTC on 03 October 2014 (click to enlarge)

A great benefit of a polar-orbiting satellite, such as Terra, or Aqua, or Suomi NPP, is that they provide very high-resolution imagery. The toggle above shows the early development of overnight fog over the mountainous terrain of Pennsylvania. The MODIS IFR Probability resolves with clarity the small river valleys of north-central Pennsylvania. Both MODIS and GOES suggest higher probabilities over the elevated terrain (the Laurel Highlands near Johnstown and regions around Bradford). A later overpass, below, just after 0700 UTC, shows the expansion of the regions of high probability has occurred in both MODIS and GOES-based products, but the MODIS-based product continues better to resolve the river valleys, such that Probabilities are higher over River Valleys (over the Pine Creek basin of north-central Pennsylvania, for example).

MODIS-based and GOES-based IFR Probability fields at ~0700 UTC on 03 October 2014 (click to enlarge)

Suomi NPP also provides high-resolution imagery, and sometimes orbital geometry allows two consecutive orbits – about 90 minutes apart — to view the same region, as below over Pennsylvania. This also shows the general increase in fog/low stratus over the eastern two-thirds of the state. (IFR Probability algorithms do not yet include Suomi NPP data).

Suomi NPP Brightness Temperature Difference (11.35µm – 3.74µm) Fields at 0617 and 0756 UTC 03 October 2014 (click to enlarge)

Polar orbiters lack good routine temporal resolution, a shortcoming that can be a significant drawback. For example, none of the satellites are overhead just before sunrise, a time when the start of the morning commute might demand information. For that, GOES data (with its 15-minute temporal resolution) is essential. The 1145 UTC image, below, shows IFR Probabilities over the region at that time, and GOES data are routinely used to monitor the evolution of IFR Probability fields over the course of the night.

GOES-based IFR Probability fields at 1145 UTC on 03 October 2014 (click to enlarge)

Fog over the Piedmont of Virginia

GOES-R IFR Probabilities (Upper Left), GOES-R Cloud Thickness (Lower Left), Brightness Temperature Differences from GOES-13 (Top Right, 10.8 µm – 3.9 µm) and from Suomi-NPP (Bottom Right, 11.35 µm -3.74 µm , all at 0715 UTC 30 September (Click to enlarge)

Radiation fog developed over the Piedmont of Virgina on the morning of 30 September in a region with little pressure gradient. The image above, from 0715 UTC, shows IFR Probability to be very high (from 85-95%) over the Virginia Piedmont. Numerous stations in the region were reporting IFR visibilities and ceilings. Brightness Temperature Difference products are also highlighting the region (Note the advantage in the Suomi NPP Brightness Temperature Difference field that ably captures fine detail related to Ridge/Valley topography over the Appalachians); the advantage of IFR Probability fields is that it includes surface information so that someone using the field can be more certain that the stratus detected by the satellite is in contact with the ground as fog. The animation below shows the evolution of the fields from 0200 through 0700 UTC.

As above, but with Suomi NPP Day/Night band in lower Right. Hourly imagery from 0200 through 0700 UTC on 30 September 2014 (Click to enlarge)

GOES-R Cloud Thickness (the thickness of the highest water-based cloud) can be used to estimate when fog/low clouds will burn off. The estimate is most accurate for strict radiation fog. GOES-R Cloud Thickness is only computed for water-based clouds during non-twilight times (in other words, it is not computed in the hours surrounding twilight at both sunrise and sunset). The last value of Cloud Thickness before morning twilight (shown below) can be used in concert with this scatterplot to guess when clouds might dissipate. Values over eastern Virginia, near Richmond, exceeding 1100 feet, correspond to a dissipation time 4+ hours after 1100 UTC, or at 1500 UTC. In this case, that value was an underestimate.

As above, but for 1100 UTC 30 September 2014

GOES-13 Visible imagery, hourly between 1215 and 1715 UTC 30 September 2014 (Click to enlarge)

Comparing IFR Probabilities and Brightness Temperature Differences over Wisconsin

GOES-R IFR Probabilities computed from GOES-East (Upper Left), GOES-13 Brightness Temperature Difference (10.7 µm – 3.9 µm) (Upper Right), GOES-R Cloud Thickness computed from GOES-East (Lower Left), GOES-R IFR Probabilities computed from MODIS (Lower Right), hourly from 0300 through 1200 UTC 26 September (Click to enlarge)

Fog developed over southeast Wisconsin during the morning of 26 September 2014. The GOES-R IFR Probability fields did a better job of detecting the hazard than did the traditional method of satellite-based fog detection (brightness temperature difference) in part because of the presence of higher clouds. (Fog/low stratus also generated IFR conditions over northeast Ohio). The animation above also includes a MODIS-based IFR Probability field. In fact, three separate MODIS-based fields were created, one at 0430 UTC, one at 0700 UTC, and one at 0845 UTC, shown below (Note that the MODIS-based IFR toggles with the MODIS Brightness Temperature Difference field). MODIS data confirms the small-scale nature of the fog event over southeast Wisconsin. (Note also how the MODIS-based data at 0700 UTC, below, are able to resolve the small river valleys in Pennsylvania in ways the GOES data cannot.)

Note how the IFR Probability fields ignore regions of mid-level stratus, such as over northeast Ohio along Lake Erie.

As above, but for 0430 UTC, and with the MODIS Brightness Temperature Difference in the lower right (Click to enlarge)

As above, but for 0700 UTC, and with the MODIS Brightness Temperature Difference in the lower right (Click to enlarge)

As above, but for 0845 UTC, and with the MODIS Brightness Temperature Difference in the lower right (Click to enlarge)

Suomi NPP Overflew the region as well. GOES-R IFR Probability algorithms do not yet incorporate Suomi NPP data; when that happens, an early morning snapshot that complements MODIS overpasses will be available.

As up top, but with Suomi NPP Brightness Temperature Difference (11.35 µm- 3.74 µm) in the bottom right, times at 0647 and 0831 UTC 26 September (Click to enlarge)

Cloud Thickness as a Predictor of Fog Dissipation, part II

This post showed examples of Cloud Thickness and how its use as a predictor for dissipation time might be incorrect because of synoptic or mesoscale forcing.

Radiation fog formed in River Valleys of Wisconsin early in the morning on 22 September, and the image below shows the final Cloud Thickness field computed before twilight conditions developed over western Wisconsin (twilight conditions are already occurring over eastern Wisconsin).

GOES-R Cloud Thickness (of the highest liquid water cloud layer) just before sunrise, 22 September 2014

Cloud Thickness values near LaCrosse, WI, are around 900 feet; values are closer to 1200 feet over northeast Wisconsin along the St. Croix River. The chart suggests a dissipation time over the southwest part of Wisconsin of around 3 hours, and more than 4 hours over northwestern Wisconsin. The animation below shows that those estimates were accurate.

GOES-13 Visible (0.63 µm) Animation over Wisconsin, 22 September, 1215-1515 UTC (Click to animate)

Cloud Thickness as a Predictor of Fog Dissipation

GOES_R_Cloud_Thickness_1137UTC_18Sep2014

GOES-R Cloud Thickness over Wisconsin and surrounding States, 18 September 2014, just before sunrise (Click to enlarge)

GOES-R Cloud Thickness can be used as a predictor for dissipation time of Radiation Fog, using this chart and the thickness (as above) from the last pre-dawn GOES-R Cloud Thickness field (Recall that GOES-R Cloud Thickness is not computed in the few hours of twilight surrounding sunrise or sunset; in the image above, twilight has reached lower Michigan but not yet Wisconsin). However, it’s important to remember that the chart is valid for radiation fog. Other forcings might cause fog to dissipate (or persist).

In the example above, Cloud Thickness values ranges from around 700 over southwest Wisconsin to as much as 1400 over north-central Wisconsin. Most of south-central Wisconsin (cyan) has values around 1200. According to the best-fit line, that suggests a burn-off time of more than 5 hours (although those values are extrapolated; note that no values that large went into the creation of the best-fit line) over WI, except over southwestern WI where a burn-off time of less than 1 hour is predicted. Did that work out?

The animation below shows fog/low stratus moving towards the southwest with time. The cool and damp northeasterly flow from the Great Lakes into Wisconsin (surface map at 1800 UTC on 18 September) suppressed the heating necessary to reduce the relative humidity and foster fog evaporation. Perhaps the fog initially formed as advection fog; however, the northeasterly flow that developed early in the morning on 18 September came from a synoptic set-up that allowed fog to persist longer than the GOES-R Cloud Thickness algorithm suggests. This is not an uncommon occurrence. Clouds did not burn off over south-central WI until after 1800 UTC. During September, delayed burn-off of morning clouds can significantly affect the day-time temperature.

Half-hourly visible imagery over Wisconsin, 1215-2045 UTC on 18 September (Click to animate)

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Low clouds and fog redeveloped during the morning of the 19th of September as well. This occurred during persistent southerly flow in advance of a low pressure system over the Northern Plains. The hourly animation of IFR Probabilities, below, shows IFR Probabilities developing over the course of the early morning of the 19th between 0315 and 1215 UTC. The animation shows a gradual overspreading of the IFR Probability field with higher clouds moving in from the west. (Here is a toggle between IFR Probability and GOES-13 Brightness Temperature Difference Fields at 1115 UTC; note how smooth the field is over much of WI where only Rapid Refresh model data can be used in the computation of the IFR Probability).

GOES-R IFR Probability fields, hourly from 0315-1215 UTC on 19 September (Click to animate)

When high clouds overspread the scene, GOES-R Cloud Thickness is not computed. Thus, the last image before twilight, below, shows Cloud Thickness in only a few locations, but those values over southeast Wisconsin exceed 1200 feet, suggesting a burn-off of around 1615 UTC — 5 hours after this last Cloud Thickness image. In this case, that is an overestimate because the southerly winds over WI promote mixing, and the fog quickly dissipates after sunrise. It’s important to consider the synoptic forcing when you use Cloud Thickness. The last Cloud Thickness field and its use as a predictor for fog dissipation (using this chart) is most useful for radiation fog. The visible imagery animation at the bottom shows that the fog dissipated by 1415 UTC.

GOES-R Cloud Thickness just before Sunrise (1115 UTC on 19 September 2015) (Click to enlarge)

GOES-13 Visible Imagery, 1215-1615 UTC on 19 September (Click to animate)

Fog over South Dakota

The NWS Office in Sioux Falls tweeted a picture of fog near Kimball, SD, in Brule County (below).

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.

Stratus and Fog over the northeast and mid-Atlantic States

GOES-13 Color-Enhanced Brightness Tempreature Difference Fields (10.7 µm – 3.9 µm), hourly from 0200 through 1100 UTC, 15 September 2014 (Click to enlarge)

Brightness Temperature Difference Fields from GOES-13 show large regions over Pennsylvania and surrounding states during the early morning hours of September 15th. (Note that the image at 0515 UTC, not in the loop above, shows the effect of stray light). If you look at the ceilings and visibilities in the imagery above, you will note that many regions where stratus/fog are indicated by the brightness temperature difference field (over upstate NY, for example), do not in fact show anything near IFR conditions. Always recall that the satellite is seeing the top of the cloud deck; whether or not that cloud extends to the surface is beyond the capability of present satellite systems. (You can infer it sometimes, of course, especially if the signal is confined to a narrow river valley, as occurs in the animation above: The Ohio River along the northern panhandle of West Virginia shows up very well).

GOES-13 IFR Probability Fields, hourly from 0200 through 1100 UTC, 15 September 2014 (Click to enlarge)

IFR Probability fields for the same time do a better job of highlighting only where reduced ceilings and visibilities are present. For example, the region of stratus over upstate New York is screened out, as well as the region over southern and southeastern Virginia. Probabilities are also quite high over the Ohio River Valley, where river fog is likely occurring. Note that IFR Probabilities over southwestern Indiana at the end of the animation have the characteristic look (a flat field) associated with IFR Probabilities created without the benefit of satellite data.

MODIS data were able to provide a a high-resolution image of this scene in the middle of the night. As with GOES, MODIS identified the large region of stratus over upstate New York and over southeastern Virginia, and the IFR Probabilities correctly screened out those stratus clouds. River Valleys show up distinctly along the Ohio River downriver from Pennsylvania; smaller IFR Probabilities surround the rivers. Sometimes MODIS data can give an early alert to the development of fog; in the present case, when MODIS overflew the region, fog development was at sufficiently large a scale that GOES-13 could also detect it.

MODIS Color-Enhanced Brightness Tempreature Difference Fields (11 µm – 3.9 µm) and IFR Probability Fields at 0722 UTC 15 September 2014 (Click to enlarge)

Suomi NPP data also viewed the developing river fogs, both in the day/night band, and in the brightness temperature difference (11.35 µm – 3.74 µm), below. At present, IFR Probabilities are not computed from Suomi NPP satellite data.

Suomi NPP Color-Enhanced Brightness Tempreature Difference Fields (11.35 µm – 3.74 µm) and Day Night band visible imagery at 0653 UTC 15 September 2014 (Click to enlarge)

IFR Probability when an extratropical storm passes by

The approach of an extratropical cyclone, such as the Colorado Cyclone in this animation in the upper Midwest on 10 September 2014, will frequently result in areas of IFR or near-IFR conditions. However, the many cloud layers that accompany these baroclinic disturbances will always make difficult the task of identifying (using satellite imagery) regions of low stratus and fog. Consider the animation below of Brightness Temperature Difference (10.7µm – 3.9µm) fields (a traditional method of detecting water-based clouds) over the Upper Midwest on 10 September.

Color-enhanced Brightness Temperature Difference fields (10.7µm – 3.9µm), hourly from 0100 UTC to 1400 UTC on 10 September 2014 (Click to enlarge)

Interpretation of this loop is time-consuming. Not only is there little distinct signal related to observed IFR and near-IFR conditions, but the rising sun (at the end of the animation) causes the Brightness Temperature Difference to flip sign, altering the enhancement. There are regions where the Brightness Temperature Difference field detects water-based clouds that may be associated with fog or stratus, chiefly over the western third of the domain (and especially over the Dakotas) in the later half of the animation.

Compare the animation above to the loop of IFR Probabilities for the same time period below. IFR Probabilities are highest where near-IFR or IFR conditions are present, and the IFR Probability field screens out regions where low stratus (but not fog) is present, such as over the Dakotas at the end of the animation. Regions where IFR Probability fields have a flat character — such as over Wisconsin around sunrise — are where only Rapid Refresh model data (but not satellite data) are used as predictors, and the field does not have pixel-scale variability. Because fewer predictors are used, the magnitude of the IFR Probability is smaller than in regions where both satellite and model data can be used as Predictors. Thus, a flat field (over eastern Wisconsin at the end of the animation, or over Iowa at the beginning) has values that should be interpreted differently from similar values in regions where both satellite and model data can be used in the computation of IFR Probabilities.

GOES-R IFR Probability fields (Computed from GOES-13 and Rapid Refresh Data), hourly from 0100 UTC to 1400 UTC on 10 September 2014 (Click to enlarge)

GOES-R Fog Product Examples

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