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Cloud band hugging the shore of Lake Michigan

The animation above shows true-color imagery from the CSPP Geosphere site (direct link to animation). Of particular interest is the cloud band that forms along the western shore of Lake Michigan, especially near Chicago, and north of Milwaukee. The toggle below of the True Color imagery and Cloud Top Height suggests the cloud tops are... Read More

GOES-16 True Color imagery from the CSPP Geosphere site, 1531-2031 UTC on 21 March 2024

The animation above shows true-color imagery from the CSPP Geosphere site (direct link to animation). Of particular interest is the cloud band that forms along the western shore of Lake Michigan, especially near Chicago, and north of Milwaukee. The toggle below of the True Color imagery and Cloud Top Height suggests the cloud tops are at or above 700 mb (see this 1951 UTC image of Cloud Top Height), perhaps too high to be affected by the Lake Michigan. A curious feature (at least to me) of this animation is that the cloud band does not appear underneath the high clouds over southeast Wisconsin. Does that mean that solar forcing plays some role in this band’s development?

GOES-East True Color imagery and Cloud Top Height, 2115 UTC on 21 March 2024 (Click to enlarge)

This event was brought to our attention by Gino Izzi at WFO LOT (i.e., Chicago) via this email that concludes:

visible imagery starting just before 18z this standing wave looking mid level cloudiness formed on the southwestern shores of Lake Michigan. Eventually some subtle waves form just a hair downstream over the lake and the standing wave seems to propagate upwind just a bit. These wave clouds appear to be around 10kft (~700mb) and looking at ACARS soundings this is at the top of a deep isothermal layer based at around 850mb. The strength of the inversion around 850mb should preclude any lake or marine influence from affecting cloudiness at 10kft. Any idea why these standing waves are there and apparently connected to the western shores of Lake Michigan (even up into central WI) when thermodynamically the lake shouldn\'t be influencing cloudiness that high up.

He followed up the email with NAM-12 output, below, showing a pretty good simulation of the rising/sinking motion couplet that might accompany such a band of clouds. In addition, this ACARS sounding from a plane landing at O’Hare, approaching from the southwest, shows stable air below 700 mb

3-h forecasts of omega (dp/dt) valid at 2100 UTC on 21 March 2024: 500 mb (upper left), 600 mb (upper right), 700 mb (lower right), 800 mb (lower left) (Click to enlarge)

Mid-level water vapor infrared (Band 9, 6.95 µm) imagery below, from Scott Bachmeier, CIMSS, suggests cooler cloud tops along the Lake Michigan shoreline, but also warming upstream of that, suggesting, perhaps that downward motion is occurring immediately upwind of the cloud band. (Note also how a break in the cooler brightness temperatures develops over southeast Wisconsin).

GOES-East mid-level water vapor infrared (band 9, 6.95 µm) imagery, 1736-2216 UTC on 21 March 2024 (Click to enlarge)

West-to-east oriented cross sections of RAP40 model Omega and Winds along Baseline A-A’ (below) showed a fairly deep column of elevated subsidence (darker shades of blue to violet) just inland over southern Wisconsin, which roughly corresponded to the band of subsidence (warmer/dryer air, darker shades of blue) seen in GOES-16 Water Vapor imagery.

West-to-east oriented cross sections of RAP40 model Omega and Winds along Baseline A-A’, every hour from 1800-2200 UTC (courtesy Scott Bachmeier, CIMSS) [click to enlarge]

GOES-16 Water Vapor image at 2101 UTC, with Baseline A-A’ moved southward across the Chicago area [click to enlarge]

Relocating Baseline A-A’ farther south across the Chicago area, note the colder signature of the cloud band (brighter shades of white) seen just inland on the 2101 UTC Water Vapor image (above), along with a signature of subsidence (darker shades of blue) immediately to the west — the 2100 UTC RAP40 Omega field (below) depicted an elevated couplet of downward (blue) / upward (green) motion located over those two Water Vapor features.

RAP40 model cross section of Omega and Winds at 2100 UTC, along Baseline A-A’ across the Chicago area [click to enlarge]


This write-up concludes with more questions than answers. The exact answer why the standing wave develops is not yet clear.

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Gridded NUCAPS in AWIPS include data from MetopC

The incorporation of MetopC NUCAPS files into AWIPS also means that MetopC Gridded NUCAPS fields are available for forecasters to help diagnose the state of the atmosphere. The toggle above shows two swaths of data, one from MetopC over China, and one from NOAA-20 over the western Pacific. The imagery also includes estimates of the 850-500 mb lapse rates in... Read More

NUCAPS Sounding Availability, 0050 UTC on 19 March 2024 and gridded NUCAPS fields of 500-mb Temperature at 0138 UTC on 19 March 2024 (Click to enlarge)

The incorporation of MetopC NUCAPS files into AWIPS also means that MetopC Gridded NUCAPS fields are available for forecasters to help diagnose the state of the atmosphere. The toggle above shows two swaths of data, one from MetopC over China, and one from NOAA-20 over the western Pacific. The imagery also includes estimates of the 850-500 mb lapse rates in two regions (that I accessed through AWIPS’ Product Browser). Those are viewed a bit more closely below.

The time of the MetopC and NOAA-20 overpasses was near 0140 UTC on 19 March. Himawari-9 Band 13 (Clean Window infrared, 10.4 µm) and Air Mass RGB imagery (taken from Real Earth) show evidence of a polar front over China. Much of the West Pacific is in a tropical airmass.

Himawari-9 Band 13 (Clean Window infrared, 10.41 µm, left) and Air Mass RGB (right), at 0140 UTC on 19 March 2024 (Click to enlarge)

MetopC Gridded NUCAPS fields, below, show the very cold (ca. -40oC) 500-mb temperatures and steep lapse rates associated with the upper level front over China.

MetopC Gridded NUCAPS Lapse Rates (850-500 mb, color shaded) and 500-mb temperatures (contoured in green), 0138-0140 UTC on 19 March 2024 (Click to enlarge)

At about the same time, NOAA-20 saw very steep lapse rates each of Japan, but relatively warmer 500-mb temperatures (i.e., the lower levels were likely warmer).

NOAA-20 Gridded NUCAPS Fields (color-shaded 850-500 mb lapse rates and green-contoured 500-mb temperatures), 0138-0152 UTC on 19 March 2024

Use gridded NUCAPS fields to determine what’s going on in the troposphere in regions where other sources of data are scarce, and also over data-rich regions where more thermodynamic information will be useful.

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Pyrocumulonimbus clouds in Sichuan, China

10-minute JMA Himawari-9 AHI “Red” Visible (0.64 µm), Shortwave Infrared (3.9 µm) and “Clean” Infrared Window (10.4 µm) images (above) showed the formation of 2 pyrocumulonimbus (pyroCb) clouds spawned by a wildfire in Sichuan Province, China on 16 March 2024. The initial (smaller, shorter-lived) pyroCb developed at 0820 UTC —... Read More

Himawari-9 “Red” Visible (0.64 µm, top), Shortwave Infrared (3.9 µm, middle) and “Clean” Infrared Window (10.4 µm, bottom) images, from 0600-1100 UTC on 16 March [click to play animation | MP4]

10-minute JMA Himawari-9 AHI “Red” Visible (0.64 µm), Shortwave Infrared (3.9 µm) and “Clean” Infrared Window (10.4 µm) images (above) showed the formation of 2 pyrocumulonimbus (pyroCb) clouds spawned by a wildfire in Sichuan Province, China on 16 March 2024. The initial (smaller, shorter-lived) pyroCb developed at 0820 UTC — exhibiting cloud-top 10.4 µm infrared brightness temperatures of -40ºC and colder, denoted by the shades of blue — with the second (larger, somewhat longer-lived) pyroCb developing at 0910 UTC. The wildfire began to make a rapid northeastward run during the ~5 hour period shown in the animation. The fire environment across that region became more favorable as wind speeds increased with the approach of a deepening surface cyclone, as shown in surface analyses (the pyroCb-producing wildfire was located near 30º N latitude, 131º E longitude).

Himawari-9 True Color RGB images created using Geo2Grid (below) showed the east-northeast transport of the wildfire smoke plume, with its embedded pyroCb cloud.

JMA Himawari-9 True Color RGB images, from 0610-1050 UTC on 16 March [click to play animated GIF | MP4]

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Widespread Convection over the central U.S.

It’s spring time on the Great Plains, which means strong dynamical systems that often bring blizzards as well as severe thunderstorms. A long-wave trough with an embedded 850-500mb jet streak created cyclogenesis in western Oklahoma and southwest Kansas on March 13, with associated low-level moisture return into eastern Kansas.The strong... Read More

It’s spring time on the Great Plains, which means strong dynamical systems that often bring blizzards as well as severe thunderstorms. A long-wave trough with an embedded 850-500mb jet streak created cyclogenesis in western Oklahoma and southwest Kansas on March 13, with associated low-level moisture return into eastern Kansas.

The strong instability and excellent wind shear spawned rapidly growing thunderstorms (some of which are highlighted in this blog post). ProbSevere LightningCast, which is a deep-learning model that uses GOES ABI to predict next-hour lightning, showed high probabilities on this convection 20-30 minutes before the first flashes.

LightningCast probability of lightning (contours), GOES-16 Natural Color imagery, and GOES-16 GLM flash-extent density (blue-to-yellow foreground pixels) over eastern Kansas.

While LightningCast is scheduled to become operational in NOAA in later 2024, new enhancements will be evaluated in NOAA’s Hazardous Weather Testbed in 2024, including the probability of >=10 flashes in the next 60 min, and a GOES-West-only model (trained on GOES-18 data). One important aspect that will be evaluated are lightning dashboards. These time series of the probability of lightning and observed GLM flashes can serve meteorologists providing impacts-based decision support for large venues like stadiums, concerts, fairs, and amusement parks. The dashboard below shows the progression of LightningCast probabilities and GOES-16 Geostationary Lightning Mapper (GLM) flashes near Kansas City International Airport as storms approached.

Time series of LightningCast 1-min (yellow) and 5-min (red) probabilities of lightning in 60 min, and GLM observed flash counts within 10 miles (small blue points) and 5 miles (large blue points) of Kansas City International Airport.

As storms became severe, the Kansas City metro region got hammered with hailstones up to 3″ in diameter, while tornadoes touched down between Manhattan and Topeka.

Preliminary severe weather reports from NOAA’s Storm Prediction Center.

The ProbSevere v3 models track developing thunderstorms and use use radar, satellite, lightning data, and near-term near-storm environment data from the HRRR model to predict next-hour severe weather probabilities of hail, wind, and tornado. This guidance improves confidence for forecasters in their warning-decision making and helps provide enhanced situational awareness of how storms are evolving. Scientists are exploring methods to exploit polarimetric radar data and the important spatial aspects of radar and satellite data, to further improve the models’ accuracy.

Severe-hail-producing storms with ProbSevere probabilities (contours), MRMS MergedReflectivity, and NWS severe weather warnings (yellow polygons) for storms near the Kansas City metro area.

This method of data fusion and probabilistic guidance is especially useful in busy situations with numerous rapidly evolving storms with multiple threats (see below, in eastern Kansas). The quick-look probabilities, cursor read-out, and time series functionality help forecasters make more efficient and effective warning decisions.

Multiple severe storms with ProbSevere probabilities (contours), MRMS MergedReflectivity, and NWS severe weather warnings (yellow and red polygons) for storms in eastern Kansas along I-70.
A storm dropping large hail in the Kansas City metro area. The inner contour is colored by the probability of any severe hazard (hail, wind, or tornado), while the outer probability contour is colored by the probability of tornado. Probability and predictor readout appears when a user hovers over a ProbSevere. Users can also double-click for a time series of how the probabilities have evolved in the latest hour.

Another machine-learning model developed at CIMSS is the IntenseStormNet, which uses GOES-R ABI and GLM images to detect “intense” convection, as viewed from a geostationary satellite perspective. This model excels at isolating the most intense parts of storms in developing and mature convection using a computer-vision approach, which holistically uses features like strong overshooting tops, above-anvil cirrus plumes, cold-U signatures, and strong anvil-edge gradients to predict probabilities.

IntenseStormNet probabilities (contours), GOES-16 Ch02+Ch13 “sandwich” imagery, and severe weather reports for rapidly developing convection over Oklahoma.

IntenseStormNet is particularly valuable when radar coverage is diminished, but still adds value to ProbSevere v3 models even in the presence of good radar coverage. The final movie shows a zoomed-out view of IntenseStormNet applied to all of the convection in the central U.S. on March 14.

IntenseStormNet probabilities (contours), GOES-16 Ch02+Ch13 “sandwich” imagery, and severe weather reports for widespread convection over the central U.S.

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