Category Archives: Texas

Cloud Layers and Detection of IFR Conditions

GOES-16 Night Fog Brightness Temperature Difference (10.3 µm – 3.9 µm), Nighttime Microphysics RGB and GOES-16 IFR Probability at 1116 UTC on 22 February 2019; Surface observations of ceilings and visibilities at 1100 UTC are also plotted (Click to enlarge).

A strong storm embedded within a subtropical jet stream over the southern United States was associated with widespread fog on the morning of 22 February 2019. This screen-capture from this site shows Dense Fog Advisories over much of Georgia, and over regions near Dallas. Which products allowed an accurate depiction of the low ceilings and reduced visibilities?

The toggle above cycles between the Night Fog Brightness Temperature Difference (10.3 µm – 3.9 µm), which product identifies low clouds (cyan blue in the default AWIPS enhancement shown) because of differences in emissivity at 3.9 µm and 10.3 µm from small water droplets that make up stratus clouds, the Nighttime Microphysics RGB, which RGB uses the Night Fog Brightness Temperature Difference as it green component, and the GOES-16 IFR Probability product.  IFR conditions are defined as surface visibilities between 1 and 3 miles, and ceiling heights between 500 and 1000 feet above ground level.  The plotted observations help define where that is occurring.  Multiple cloud layers from Arkansas east-northeastward make a satellite-only detection of IFR conditions challenging.  IFR Probability gives useful information below cloud decks because model-based saturation information from the Rapid Refresh Model fill in regions below multiple cloud decks where satellite information about low clouds is unavailable.

The toggle below shows the same three satellite-based fields (Night Fog Brightness Temperature Difference, Nighttime Microphysics RGB and IFR Probability)  at the same time, but centered over Oklahoma.  In this case, the Rapid Refresh Data are used to screen out a region of elevated stratus over northeast Oklahoma. Note that these is little in the Night Fog Brightness Temperature Difference field to distinguish between the IFR and non-IFR locations.

GOES-16 Night Fog Brightness Temperature Difference (10.3 µm – 3.9 µm), Nighttime Microphysics RGB and GOES-16 IFR Probability at 1116 UTC on 22 February 2019; Surface observations of ceilings and visibilities at 1100 UTC are also plotted (Click to enlarge).

GOES-R IFR Probability over the southeast United States in this case is identifying regions of IFR conditions underneath multiple cloud decks (and also where only the low clouds are present) by incorporating low-level saturation information from the Rapid Refresh model. Over Oklahoma, non-IFR conditions under an elevated stratus deck are identified (and screened out in IFR Probability fields) by the lack of low-level saturation information in the Rapid Refresh.

Fog over the central United States

Night Fog Brightness Temperature Difference (10.3 µm – 3.9 µm) and Nighttime Microphysics RGB at 0507 UTC on 1 February 2019, and surface observations of ceilings and visibilities (Click to enlarge)

The toggle above displays the Night Fog Brightness Temperature Difference field (10.3 µm – 3.9 µm) and the Night Time Microphysics Red/Green/Blue (RGB) Product that uses the Night Fog Brightness Temprature Difference field as its green value. In the color enhancements above, cyan in the Night Fog Brightness Temperature Difference denotes positive values that occur because stratus clouds — that is, clouds that are made up of water droplets — do not emit 3.9 µm radiation as a blackbody. Consequently, the computation of brightness temperature (which assumes blackbody emission) results in a 3.9 µm brightness temperature that is cooler than at 10.3 µm (clouds are emitting 10.3 µm radiation very nearly like a blackbody).  Low clouds in the RGB that may or may not support IFR conditions range in color from light cyan (over Texas and Florida) to more orange and yellow (yellow over the Great Lakes were exceptionally cold air is in place).

The fields above are overpredicting where fog/low ceilings might be occurring because cloud top measurements from the Brightness Temperature Difference do not always give reliable guidance on cloud base.

By merging satellite information about clouds and cloud type with Rapid Refresh model information at about low-level saturation, GOES-R IFR Probability fields screen out regions where IFR conditions are unlikely;  the map suggests low ceilings and fog are most likely over Texas and Oklahoma.  The zoomed in image, below shows that IFR conditions are indeed occurring in this region.  Other regions with a strong signal in the Brightness Temperature Difference field — Tennessee, for example — show low IFR Probability and surface observations that do not show IFR conditions.

GOES-R IFR Probability field, 0507 UTC on 1 February, along with surface reports of ceilings and visibility (Click to enlarge)

GOES-R IFR Probability field, 0507 UTC on 1 February, along with surface reports of ceilings and visibility zoomed in over the southern Plains (Click to enlarge)

Night Fog Brightness Temperature Difference (10.3 µm – 3.9 µm) and Nighttime Microphysics RGB at 1007 UTC on 1 February 2019, and surface observations of ceilings and visibilities (Click to enlarge)

The same relationships occur at 1007 UTC; the Night Fog Brightness Temperature Difference and Nighttime Microphysics RGB overpredict the regions of low clouds/fog; IFR Probability’s use of Rapid Refresh Data allows it to screen out regions where fog is not present, but stratus clouds are, and also add in regions where cirrus clouds prevent the detection of low clouds, but Rapid Refresh data suggests low-level saturation is present (such as over the Gulf of Mexico south of Louisiana).

The IFR Probability field is accurately outlining the region of IFR conditions.

GOES-R IFR Probability field, 1007 UTC on 1 February, along with surface reports of ceilings and visibility (Click to enlarge)

GOES-R IFR Probability field, 1007 UTC on 1 February, along with surface reports of ceilings and visibility zoomed in over the southern Plains (Click to enlarge)

IFR Probability discriminates between fog and elevated stratus over Texas

GOES-16 IFR Probability field, 1127 UTC on 13 February, along with observations of ceilings and visibility. (Click to enlarge)

GOES-16 IFR Probability fields on 13 February at 1127 UTC, above, suggest a clear difference in sky conditions between northeast Texas, where IFR Probabilities are very high, and where IFR conditions are widespread, and north-central Texas, around Dallas, where IFR Probabilities are small, and where ceilings and visibilities do not match IFR Conditions.

In contrast, the Brightness Temperature Difference field, below, (and the Nighttime Microphysics Red/Green/Blue product, shown here in a toggle with the Brightness Temperature Difference field) shows little difference in signal between the region of IFR conditions over northeast Texas and non-IFR conditions over Dallas and environs.

GOES-16 views the top of the cloud, and a region of fog and a region of stratus can look very similar in the Night Fog Brightness Temperature Difference. Because IFR Probability fields fuse satellite observations of low clouds with Numerical Model Output estimates of near-surface saturation, IFR Probabilities can differentiate between regions of elevated stratus (where near-surface saturation is not suggested by the model), such as near Dallas, and regions of stratus that is obstructing visibility (where near-surface saturation is suggested by the model).

A toggle of all three fields is shown at the bottom of this post.

GOES-16 Night Fog Brightness Temperature Difference (10.3 µm – 3.9 µm), 1127 UTC on 13 February 2018 (Click to enlarge)

GOES-16 IFR Probabilities, Night Fog Brightness Temperature Difference (10.3 µm – 3.9 µm) and NightTime Advanced Microphysics RGB, 1127 UTC on 13 February 2018 (Click to enlarge)

Dense Fog over the Texas High Plains

GOES-R IFR Probability fields, hourly from 0215-1115 UTC on 2 August 2017 (Click to enlarge)

GOES-R IFR Probabilities are computed using Legacy GOES (GOES-13 and GOES-15) and Rapid Refresh model information; GOES-16 data will be incorporated into the IFR Probability algorithm in late 2017

The National Weather Service in Lubbock issued Dense Fog Advisories (below) for parts of their CWA early in the morning on 2 August 2017.  GOES-R IFR Probability fields, above, show a slow increase in values over west Texas during the night of 1-2 August 2017, as visibilities drop and ceilings lower in the region.  This followed a band of showers that moved through the area around sunset on 1 August (Click here for a visible image from 0017 UTC on 2 August, from this site).  Highest IFR Probability values at the end of the animation generally overlay the Dense Fog Advisory.  As a situational awareness tool for the developing fog/low stratus, IFR Probability performed well.

 

GOES-16 data posted on this page are preliminary, non-operational and are undergoing testing

GOES-16 Brightness Temperature Difference field (10.3 µm – 3.9 µm) at 1117 UTC on 2 August 2017 (Click to enlarge)

The GOES-R IFR Probability fields above mostly show the small-scale variability (i.e., pixelation) that is common when both (legacy) GOES data and Rapid Refresh Data are used to produce a probability that IFR conditions will be present.  Some exceptions:  southeastern New Mexico at the end of animation (1115 UTC);  the yellow and orange region there overlain by mid-level or high clouds that prevent a satellite view of the low clouds.  The GOES-16 Brightness Temperature Difference (10.3 µm – 3.9 µm) field at 1117 UTC shows a signal of high clouds there (cyan / blue / purple enhancement showing negative values that typify thin cirrus in the Brightness Temperature Difference field at night).  The Green values in the color enhancement are positive values and correspond to stratus (composed of water droplets) clouds.  Because the Brightness Temperature Difference field shows a signal, the Advanced Nighttime Microphysics RGB will also have a signal for fog (the whitish/cyan color), as shown below.

GOES-16’s better temporal and spatial resolution allow for more accurate monitoring of the development of small-scale features.  However, the shortcomings of using a Brightness Temperature Difference from satellite to monitor fog development should not be forgotten:  In regions of cirrus, satellite views of low stratus and fog are blocked.  In addition, over Texas and the rest of the High Plains, upslope flow can generate stratus over the central Plains that becomes fog over the High Plains as the terrain rises into the clouds.  The top of the stratus cloud and the fog bank in such a case can look very similar from satellite.

Advanced Microphysics RGB Composite at 1117 UTC on 2 August 2017 (Click to enlarge)

Below is a toggle between the 1115 UTC IFR Probability field, the GOES16 Brightness Temperature Difference Field, and the GOES16 Advanced Microphysics RGB Composite.

GOES-R IFR Probability fields computed with legacy GOES data and Rapid Refresh model output, GOES-16 Brightness Temperature Difference (10.3 µm – 3.9 µm) field and GOES-16 Advanced Microphysics RGB, all near 1115 UTC on 2 August 2017 (Click to enlarge)

 

Dense Fog Advisories along the western Gulf Coast

GOES-R IFR Probability fields, hourly from 0145-1345 UTC on 08 February 2017, along with surface observations of visibility and ceiling height (Click to enlarge)

Dense Fog developed along the western Gulf Coast early on the morning of 8 February 2017, leading to the issuance of Dense Fog Advisories (graphic from this site) and of IFR Conditions (graphic from this site).  The animation above shows the expansion of the field of high IFR Probabilities northwestward from the Gulf of Mexico starting at 0145 UTC.  IFR Conditions reported in concert with the arrival of higher IFR probabilities.  Relatively high IFR Probability values also develop over northern MIssissippi and Alabama.

The traditional method of detecting low clouds at night, the brightness temperature difference field computed using brightness temperatures at 3.9 µm and 10.7 µm detects water-based clouds because of the different emissivity properties of the water-based cloud at those two wavelengths.  If ice clouds (at high levels) or mixed phase clouds (at mid-levels) exist, however, the satellite cannot view the low clouds.  This was the case on 8 February over northern Mississippi and northern Alabama, and also occasionally over Louisiana and Texas.  The toggle below from 0945 UTC, between the GOES-R IFR Probability field and the Brightness Temperature Difference field, shows several regions where Brightness Temperature Difference field enhancements do not indicate low clouds (over northwestern Mississippi, for example); in these regions, IFR Probabilities are nevertheless large because Rapid Refresh model data shows saturation in the lowest 1000 feet of the atmosphere, strongly suggestive of high IFR Probabilities, and that predictor serves to increase the value of the IFR Probability. The animation of the Brightness Temperature Difference fields is at the bottom of this blog post; compare it to the IFR Probability fields at the top. The IFR Probability algorithm capably fills in regions under high clouds/mid-level clouds where the satellite cannot view low clouds.  It gives a more consistent (and more accurate) depiction of the spread of the low clouds/fog.

Brightness Temperature Difference (3.9 µm – 10.7 µm) and GOES-R IFR probability at 0945 UTC on 8 February 2017 (Click to enlarge)

Another difficulty with Brightness Temperature Difference fields occurs around sunrise when increasing amounts of reflected solar radiation at 3.9 µm cause a sign change in the brightness temperature difference field (reflected 3.9 µm radiation increases as the sun rises and the computed brightness temperature therefore changes because reflected solar radiation at 10.7 µm is minimal;  emissivity-related differences between the two bands are overwhelmed).  The toggle below compares 1245 UTC and 1345 UTC Brightness Temperature Values.

Brightness Temperature Difference (3.9 µm – 10.7 µm) at 1245 and 1345 UTC on 8 February 2017 (Click to enlarge). Decreases in the brightness temperature differences occur at 1345 UTC because of increases in reflected solar radiation at 3.9 µm.

Brightness Temperature Difference (3.9 µm – 10.7 µm), 0145 – 1345 UTC on 8 February 2017, along with surface observations of ceilings and visibility (Click to enlarge)

Widespread IFR Conditions over the Plains

GOES-R IFR Probability Fields, hourly from 0115-1315 UTC (Click to enlarge)

A cyclone over the southern Plains, in addition to causing severe weather over Texas on 15 January also generated widespread IFR Conditions over the southern Plains, as shown below in screengrabs from the Aviation Weather Center and from the National Weather Service. An overnight Water Vapor image (here) testifies to the ubiquitous presence of high clouds over the Plains; in such cases with widespread high clouds, low-cloud detection by satellite is a big problem. A strength of the GOES-R IFR Probability field is that it is a fused data product, incorporating both satellite information (not particularly useful for much of the overnight hours on 15-16 January) and Rapid Refresh model data that can be used to discern conditions near the surface. When the Rapid Refresh model suggests saturation is occurring near the surface (in, say, the lowest 1000 feet of the model atmosphere), IFR Probabilities will be large. They won’t be as large as they might be if both satellite and model data suggest low clouds are present, but useful information emerges in the IFR Probability fields, above, where the Rapid Refresh is predicting low-level saturation. IFR Probabilities are large over much of the southern Plains where IFR conditions are observed. This is the region where the color enhancement is orange.

The low pressure system develops such that high clouds diminish over Texas and Oklahoma. When that happens, the IFR Probability fields change in two ways. First, values increase because satellite data and model data can be used as predictors. When only model data can be used, IFR Probability fields will have smaller values. Secondly, the character of the IFR Probability field takes on a more pixelated appearance because the satellite data values will vary from pixel to pixel. In contrast, when only model data can drive the IFR Probability field (for example, over Kansas at the beginning of the animation), the IFR Probability fields vary quite slowly from pixel to pixel in part because of model smoothing.

Screen Capture from Aviation Weather Center (left, showing widespread IFR Conditions) and from Weather.Gov (right, showing Dense Fog Advisories in grey) (Click to enlarge)

The toggle below includes sampling over Abilene, TX (KABI), a station at the edge of the IFR Probability field. IFR Probabilities are relatively constant at ~40% for the two hours shown, but station conditions change from IFR to VFR. IFR Probabilities at Abilene become quite small by 1315 UTC, at the end of the animation above.

GOES-R IFR Probability at 1015 and 1215 UTC on 16 January 2017. Station conditions at KABI are indicated by the sample probe (Click to enlarge)

Stratus over Texas

GOES-13 Visible (0.64 µm) Imagery, 1945 UTC on 6 January 2017 and surface observations of ceilings and visibilities (click to enlarge)

Visible imagery over Texas shows an extensive stratus deck blanketing the southern and eastern portions of the state.  Can you tell at a glance — without looking at the observations — if the stratus is extending to the surface?  The animation below shows how GOES-R IFR Probabilities describe the scene, with highest IFR Probabilities offshore (where dense fog is observed over the warm water).  Higher Probabilities also hug the high terrain of eastern Mexico, where IFR conditions are also reported (at Monclova, ID MMMV, where a 500-foot ceiling and 3-mile visibility is reported).  The toggle below cycles through the visible and GOES-R IFR Probability fields and also includes terrain.

GOES-R IFR Probability provides useful situational awareness information during the daytime as well as at night.

GOES-13 Visible (0.64 µm) Imagery, 1945 UTC on 6 January 2017 and surface observations of ceilings and visibilities, and with surface analysis superimposed, as well as GOES-R IFR Probabilities (1945 UTC) and Terrain (click to enlarge)

Fog over South Texas

Toggle between Brightness Temperature Difference (3.9µm – 10.7µm) and GOES-R IFR Probability fields, 2300 UTC on 11 December 2016 (Click to enlarge)

Dense Fog developed over south Texas during the early morning of 12 December 2016 (IFR Sigmet from this website shown here ; Advisories from the weather.gov website shown here). The toggle above shows in the brightness temperature difference field a signature of high clouds — and where those high clouds exist, IFR Probability fields rely on Rapid Refresh Model data to diagnose where IFR conditions might be occurring, or where IFR conditions might develop. The animation of Brightness Temperature Difference fields from 0215 through 1115 UTC, below, shows that the high clouds over south Texas diminished with time: by 0815 UTC only low stratus is present over south Texas.  But is that stratus also hugging the ground — that is, is it fog?  From the satellite’s perspective, the top of a stratus deck and the top of a fog bank can look very similar.

GOES-13 Brightness Temperature Difference (3.9µm – 10.7µm), 0215 through 1115 UTC on 12 December (Click to enlarge)

GOES-R IFR Probability fields give a more complete estimate about the presence of fog/low stratus because Rapid Refresh data and satellite data are used to diagnose the probability of IFR conditions. If the Rapid Refresh model shows low-level saturation, then the presence of stratus clouds also likely indicates the presence of fog; conversely, if the Rapid Refresh Model does not show low-level saturation, then the presence of stratus cloud need not indicate the presence of fog. IFR Probability fields below, from 0215 through 1115 UTC, start off regions with uniform values where only Rapid Refresh data are used in the algorithm — where high clouds block the satellite view of low clouds/fog. As the high clouds dissipate, the field acquires larger values because there is higher confidence of the presence of clouds (in part because satellite data can be used to observe them). In addition, these larger values have pixel-sized variability because of variability in the satellite observations.

IFR conditions are observed latest over far south Texas — this is also where IFR Probabilities are slowest to reach large values.

IFR Conditions over the High Plains of west Texas

goes_btd_ifr_0215_1415_02dec2016anim

GOES-13 Brightness Temperature Difference (3.9 µm – 10.7 µm, left) and GOES-R IFR Probability Fields (Right), hourly from 0215 through 1415 UTC on 2 December 2016 (Click to enlarge)

Near-IFR and and IFR Conditions developed over the High Plains of Texas on 2 December 2016, and a SIGMET for IFR conditions was issued as shown below.

The animation above shows plentiful cirrus (in the brightness temperature difference enhancement used in the imagery on the left, above, cirrus clouds are dark) over south Texas, with occasional breaks.  This makes continual monitoring via satellite of the developing stratus/fog field problematic:  the satellite cannot monitor what it is blocked from being observed by intervening cloud layers — in this case cirrus.  (Click here for a brightness temperature difference only animationClick here for an IFR Probability only animation)  Because IFR Probability fields include model-based data about saturation in the lower troposphere, in the form of Rapid Refresh model output, a useful and coherent signal can be generated underneath cirrus clouds.  The GOES-R IFR Probability signal can be better used for situational awareness and anticipation of the development of the IFR conditions shown below.

In the animation above, note the change between 1315 and 1415 UTC fields — in the Brightness Temperature Difference fields (1315 UTC ; 1415 UTC), this change arises because of increasing amounts of reflected solar 3.9 µm radiation:  this causes a sign change in the brightness temperature difference.  For IFR Probability fields (1315 UTC ; 1415 UTC), the change occurs because the Predictors used at night (1315 UTC) and during the day (1415 UTC) are different.

awc_1600utc_2december2016

1605 UTC screen capture from Aviation Weather Center. Note IFR Sigmet over west Texas (Click to enlarge)

Persistent fog and freezing fog over New Mexico and Texas

GOES_R_IFR_30Dec2015anim_0415_1745

GOES-R IFR Probability fields, 0415-1745 UTC on 30 December 2015 (Click to enlarge)

Fog with sub-freezing temperatures developed over New Mexico and west Texas early on the 30th of December, and persisted into mid-day. How well did conventional (and newer) algorithms designed to detect fog perform? The GOES-R IFR Probability fields, above, hourly from 0415 UTC through 1745 UTC on 30 December, show highest IFR Probabilities initially along the Pecos River in New Mexico. Airports at both Artesia and Roswell reported IFR conditions continuously during the period shown. IFR Conditions developed over Texas west of a line from about Breckenridge (in Stephens County) to Vernon Texas (in Wilbarger County). The southern extent of the ice fog was near a Midland (in Midland County) to Coleman (in Coleman County) line. High IFR Probabilities were common over Texas where the Fog/Freezing Fog was occurring.

The Brightness Temperature Difference field for the same times are shown below. The Brightness Temperature Difference field captures the presence of water-based clouds along the Pecos River in New Mexico — both at night (orange enhancement) and during the day (black enhancement). The Brightness Temperature Difference field tells you something about the top of the cloud only, however; it cannot give information about the cloud base. (In contrast, the GOES-R IFR Probability product, because it fuses satellite data with surface information derived from Rapid Refresh Model output, a distinction between mid-level stratus and low fog is possible). In addition, there are regions in the brightness temperature difference field where no strong signal occurs even though fog is present (Hobbs, NM in Lea County and Seminole TX in Gaines County, for example).

BTD_30Dec2015anim_0415_1745

GOES-13 Brightness Temperature Difference Fields (10.7 µm – 3.9 µm), 0415-1745 UTC on 30 December 2015 (Click to enlarge)