The Split Window Difference over Iowa

June 5th, 2018 |

GOES-16 ABI Split Window Difference (10.3 µm – 12.3 µm) at 1402 UTC on 5 June 2018 (Click to enlarge)

The Split Window Difference field (SWD, the 10.3 µm brightness temperature minus the 12.3 µm brightness temperature) can be used to identify regions of moisture and dust in the atmosphere.  (Click here for a previous blog post).  On 5 June 2018, the SWD showed a strong gradient over the upper Midwest, with large values over Iowa and relatively smaller values to the northeast over Wisconsin (and to the south over Missouri). Is this showing a moisture gradient between Iowa and Wisconsin? Do you trust its placement? Given that convection will frequently fire along the gradient of a field (HWT Link; Old HWT link), it’s important to trust the placement of the gradient.

The toggle below shows both the SWD and the (clear sky only) Baseline Derived Stability Lifted Index.  The Lifted Index shows negative values over the southern Plains, and also a lobe of instability stretching WNW-ESE from southwestern Minnesota to Chicago.  If you look carefully, you will note that the axis of instability in the Lifted Index is offset from the Split Window Difference field.  Why?

GOES-16 ABI Baseline Derived Stability Index Lifted Index and GOES-16 Split Window Difference (10.3 µm – 12.3 µm) at 1402 UTC on 5 June 2018 (Click to enlarge)

The toggles below show the Split Window Difference field and the Rapid Refresh Model estimates of moisture in the lowest 3 km of the atmosphere, followed by the Split Window Difference toggled with the Baseline Land Surface Temperature field. The maximum in moisture is along the northern edge of the Split Window Difference field, and aligns well with the Lifted Index (Toggle between those two is here).

The Split Window Difference better matches the Land Surface Temperature Baseline product, and that reinforces an important caveat in the use of the SWD to detect moisture: SWD is greatly influenced by the skin temperature. Gradients in surface temperature and gradients in moisture both will affect the Split Window Difference. Make sure you understand the underlying cause of the gradient in the Split Window Difference field.

Toggle between the GOES-16 ABI Split Window Difference (10.3 µm – 12.3 µm) and Mean 0-3km AGL Dewpoint from the Rapid Refresh Model, 1402 UTC on 5 June (Click to enlarge)

GOES-16 ABI Split Window Difference (10.3 µm – 12.3 µm) and Land Surface Temperature Baseline Product, 1402 UTC on 5 June 2018 (Click to enlarge)

By 2002 UTC on 5 June, the GOES-16 Lifted Index fields and the SWD more closely align, in part because the axis of moisture has shifted southward. See the toggle below.

GOES-16 ABI Baseline Lifted Index, Split Window Difference (10.3 µm – 12.3 µm) and 0-3 km AGL Rapid Refresh Dewpoint, 2002 UTC on 5 June 2018 (Click to enlarge)

Surface Cold Front over the High Plains of Texas

April 3rd, 2018 |

Hourly GOES-16 ABI Low-Level Water Vapor Infrared (7.34 µm) Imagery, and hourly observations, 0700-1600 UTC on 3 April 2018 (Click to enlarge)

A cold front moving southward along the western Great Plains showed a distinct signature in GOES-16 Water Vapor Imagery.  The hourly animation above, with surface observations, shows the front in the Low-Level Water Vapor passing over stations where winds shift from westerly and southwesterly to strong northerly.  The feature is far more trackable in GOES-16 ABI Imagery with a 5-minute cadence as is typical over CONUS, as shown below for both low-level water vapor infrared imagery (Band 10, 7.34 µm) and upper-level water vapor infrared imagery (Band 8, 6.19 µm). The infrared imagery allowed a precise determination of when the cold front would reach a location. (In fact, because a GOES-16 Mesoscale Sector was placed over west Texas, the time of arrival could be observed down to the minute, as shown in this animation of the clean window (10.3 µm) infrared imagery from GOES-16).

GOES-16 ABI Low-Level Water Vapor Infrared Imagery (7.34 µm), 0832-1637 UTC on 3 April 2018 (Click to animate)

GOES-16 ABI Upper-Level Water Vapor Infrared Imagery (6.19 µm), 0832-1637 UTC on 3 April 2018 (Click to animate)

Visible Imagery after sunrise (below) shows that some surface cloudiness was associated with this feature — but other parts were clear.

GOES-16 ABI “Red” Visible Imagery (0.64 µm), 1252-1637 UTC on 3 April 2018 (Click to animate)

It is not common for surface features to appear in the Upper-Level Water Vapor Imagery, even when the surface is near 900 mb, as over the High Plains of west Texas. Weighting Functions show from which layers in the atmosphere energy detected by the satellite originates. The Weighting function from Amarillo TX at 1200 UTC on 3 April is shown below.  The low-level water vapor weighting function — shown in magenta — shows contributions from the surface, but the upper-level water vapor weighting function — shown in green, shows contributions ending about 200 mb above the surface, at around 700 mb.  A conclusion might be that the depth of the cold air quickly increases to around 200 mb behind the front.  Thus is can appear in the Upper-Level water vapor imagery.   The cold front passes Amarillo (here is a meteorogram) shortly before 1200 UTC (and before the Radiosonde was launched).  The radiosonde from Dodge City Kansas, however, at 1200 UTC, shows a cold layer about 200 mb thick.  (Here is the Amarillo Sounding for the same time;  it’s shown in the Weighting Function plot below as well).

Clear-Sky Weighting Functions from Amarillo TX, 1200 UTC on 3 April 2018 (Click to enlarge)

Interpretation of water vapor imagery is simplified if you use information from weighting functions to understand the three-dimensional aspect of the water vapor imagery.

Moisture Changes as viewed in the Cirrus Channel

March 14th, 2018 |

GOES-16 ABI Band 3 (0.86 µm) Reflectance, hourly from 1632-1932 UTC on 14 March 2018 (Click to enlarge)

Skies were clear over much of the southern Plains on 14 March 2018, as noted in the animation above that shows hourly GOES-16 ABI Channel 3 (0.86 µm) Imagery. Differences in absorption/reflectance between water and land yield excellent discrimination between lakes and land over Oklahoma and adjacent states.  GOES-16 ABI “Cirrus Channel” (Band 4, at 1.38 µm) shows little reflectance in the area over Oklahoma, except where cirrus clouds are present over western Oklahoma.  The rest of Oklahoma is dark because water vapor in the atmosphere is absorbing energy at 1.38 µm. An animation — also at hourly intervals — is shown below.  This uses the default enhancement in AWIPS, with reflectance values between 0 and 50 shown.

GOES-16 ABI Band 4 (1.37 µm) Reflectance, hourly from 1632-1932 UTC on 14 March 2018 with default AWIPS Enhancement (Click to enlarge)

If you alter the Band 4 enhancement to change the bounds from 0-50 (the default) to 0-2 (!), as was done in the animation below showing data every 5 minutes, a gradient in reflectance becomes apparent, and surface features — specifically lakes — over central Oklahoma that are initially present slowly become obscured as the gradient moves to the east. This gradient shows differences in moisture. The atmosphere that is moving into eastern Oklahoma from central Oklahoma is slightly more moist.  (Compare the morning sounding at Amarillo, for example, with a total precipitable water of 0.38″ to the morning sounding at Little Rock, with a total precipitable Water of 0.14″)

GOES-16 ABI Band 4 (1.37 µm) Reflectance, from 1632-1947 UTC on 14 March 2018 with default AWIPS Enhancement modified as described in text (Click to animate)

GOES-16 data includes channel differences and level 2 products that also confirm the slow increase in moisture. The Split Window Difference field, shown below with the default enhancement (Click here to see the same animation with the Grid MidRange Enhanced enhancement), and the Total Precipitable Water, at bottom, show a slow increase in moisture. These increases were above the surface: surface dewpoints in this region (source) were not increasing greatly.

Split Window Difference (10.3 µm – 12.3 µm) from 1632 – 1947 UTC on 14 March (Click to enlarge)

GOES-16 Total Precipitable Water Baseline Product, 1632-1947 UTC on 14 March 2018 (Click to enlarge)

Transitory Solar Reflectance in GOES-R Series Imagery

March 5th, 2018 |

GOES-16 Visible (0.64 µm) animation, 1637-1732 UTC on 5 March 2018 (Click to enlarge)

Animations of GOES-16 Visible, near-Infrared and shortwave Infrared over North America shortly before the Vernal Equinox, and shortly after the Autumnal Equinox, (that is, when the Sun is overhead in the Southern Hemisphere) show bright spots that propagate quickly from west to east (these features were first noted by Frank Alsheimer of the National Weather Service). The animation above shows the visible imagery (0.64 µm) over the Continental United States on 5 March 2018 (Click here for a slower animation speed). Brightening over regions between 30 and 40 N between 1637 UTC and 1732 UTC is apparent. The animation below of the shortwave infrared (3.9 µm) shows slight warming (Click here for a slower animation), as might be expected with reflected solar energy. The brightening is also apparent in the Band 4 “Cirrus”  (1.37 µm) — in fact, a closer look at southern Colorado reveals the bright signature of sunlight reflecting off solar panels at the Alamosa Solar Generating Facility (Google maps).

GOES-16 Shortwave Infrared (3.9 µm) animation, 1637-1732 UTC on 5 March 2018 (Click to enlarge)

The increased reflectance can cause the ABI Clear Sky Mask to mis-characterize clear regions as cloudy (See the animation below; click here for a slower animation). Thus, Cloud properties (Cloud-top Height, Temperature, Pressure, etc.) can be identified in clear regions.

GOES-16 Clear Sky Mask (White: Clouds ; Black : No Clouds) from 1637 UTC – 1732 UTC on 5 March 2018 (Click to enlarge)

The bright spots in the visible, and warms spots in the shortwave infrared, occur when the Earth’s surface, the GOES Satellite and the Sun are aligned on one line. If you were within the bright spot with a powerful telescope trained on the Sun, you would see the GOES Satellite transecting the solar disk. The location of these bright spots changes with season: they appear in the Northern Hemisphere shortly before the (Northern Hemisphere) vernal equinox and shortly after the (Northern Hemisphere) autumnal equinox. Similarly, they appear in the Southern Hemisphere shortly before the (Southern Hemisphere) vernal equinox and shortly after the (Southern Hemisphere) autumnal equinox. On the Equinox, the bright spots are centered on the Equator.

This animation (courtesy Daniel Lindsey, NOAA/CIRA and Steve Miller, CIRA) shows where the reflection disk moves during the days around the Northern Hemisphere Autumnal Equinox; a similar animation for the Northern Hemisphere vernal equinox would show a disk starting at the North Pole and moving southward with time.

The animation below (from this link that is used for calibration exercises), shows the difference in reflectance (Bands 1-6) or Brightness Temperature (Bands 7-16) between 1657 and 1652 UTC on 3 and 5 March 2018. Two things are apparent: The centroid of the largest difference in solar reflectance has moved southward in those two days, as expected; the effect of this solar backscatter is most obvious in the visible, near-infrared and shortwave infrared channels (that is, bands 1-7 on the ABI).  The effect is most pronounced in clear skies.

Time Difference in each of the 16 ABI Channels (1657 – 1652 UTC) on 3 and on 5 March 2018 (Click to enlarge)

This reflectance feature is also detectable in legacy GOES Imagery. However, the great improvements in detection and calibration in the GOES-R Series ABI (and AHI on Himawari-8 and Himawari-9) and the better temporal resolution with the GOES-R Series allows for better visualization of the effect.

The feature also shows up in “True Color” Imagery, shown below (from this site). Geocolor imagery (shown here), from CIRA, also shows the brightening.

CIMSS Natural True Color Animation ending 1757 UTC on 5 March 2018 (Click to enlarge)

Thanks to Daniel Lindsey and Tim Schmit, NOAA/ASPB, Steve Miller, CIRA and Mat Gunshor, CIMSS, for contributions to this blog post.