{"id":44913,"date":"2022-03-02T18:11:04","date_gmt":"2022-03-02T18:11:04","guid":{"rendered":"https:\/\/cimss.ssec.wisc.edu\/satellite-blog\/?p=44913"},"modified":"2022-03-07T16:21:30","modified_gmt":"2022-03-07T16:21:30","slug":"turbulence-probability-and-aviation","status":"publish","type":"post","link":"https:\/\/cimss.ssec.wisc.edu\/satellite-blog\/archives\/44913","title":{"rendered":"Turbulence Probability and Aviation"},"content":{"rendered":"\n
\"\"<\/a>
MOG Turbulence Probability for the 30-41 kFt layer, 1455 UTC on 2 March 2022, along with pilot reports (Click to enlarge)<\/figcaption><\/figure>\n\n\n\n

Turbulence probability is an aviation tool created (using machine learning techniques) at CIMSS<\/a> to diagnose the likelihood of M<\/span>oderate O<\/span>r G<\/span>reater (MOG<\/span>) turbulence at least once during a 10-minute period. The image above shows the 1455 UTC MOG Turbulence Probability along with pilot reports (PIREPS) of turbulence, taken from this website<\/a>. Turbulence Probability fields are also in AWIPS, and the 1500 UTC image is shown below.<\/p>\n\n\n\n

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MOG Turbulence Probability, 1500 UTC on 2 March 2022 (Click to enlarge)<\/figcaption><\/figure>\n\n\n\n

There are frequently features in the water vapor imagery (or in visible, or near-infrared, imagery during the day) that have a known association with turbulence. The turbulence probability field is toggled below with the 6.19 \u00b5m upper-level water vapor infrared imagery<\/a>, and beneath that with the 1.38 \u00b5m “cirrus band” near-infrared imagery<\/a>. Striated features that are apparent in, for example, this event from 28 February<\/a>, are not obvious.<\/p>\n\n\n\n

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GOES-16 MOG Turbulence Probability and Band 8 (6.19 \u00b5m, Upper level water vapor) infrared imagery, 1500 UTC on 2 March 2022 (click to enlarge)<\/figcaption><\/figure>\n\n\n\n
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GOES-16 MOG Turbulence Probability and Band 4 (1.38 \u00b5m, “Cirrus Channel”) near-infrared imagery, 1500 UTC on 2 March 2022 (click to enlarge)<\/figcaption><\/figure>\n\n\n\n

MOG Turbulence Probability also includes information from the GFS: temperature, height, and winds from 850 mb to 50 mb! (Such GFS fields are especially important when using GOES-17 data during times when the Loop Heat Pipe malfunctions leads to data loss over the Pacific.) The toggles below show the relationship between the Turbulence Probability and upper-tropospheric stability (as diagnosed by the 400-300 mb lapse rate<\/a>), and the also the pressure on the 1.5 PVU surface<\/a>. Turbulence is occurring just Equatorward of a slope in the tropopause, in a region of weak stability.<\/p>\n\n\n\n

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MOG Turbulence Probability and GFS 400-300mb Lapse Rate, 2 March 2022 (Click to enlarge)<\/figcaption><\/figure>\n\n\n\n
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MOG Turbulence and pressure on the 1.5 PVU Surface, 1500 UTC on 2 March 2022 (Click to enlarge)<\/figcaption><\/figure>\n\n\n\n

GOES Data can also be used to diagnose wind speeds in the upper-troposphere. Peak winds in the 350-450 mb layer<\/a>, shown in a toggle below with the MOG Turbulence Probability, are 100-120 knots from southeastern North Dakota into northwestern Wisconsin.<\/p>\n\n\n\n

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MOG turbulence and derived motions winds between 350-450 mb, 1500 UTC on 2 March 2022 (click to enlarge)<\/figcaption><\/figure>\n\n\n\n

How did this potential for turbulence (and strong winds) affect air traffic? Consider the path of one late-morning flight, Delta Flight 867 from Minneapolis to Seattle. Its path is shown below for February 28th, and for March 2nd, courtesy of FlightAware<\/a>. The 2 March flight was diverted south to avoid the strong winds and potential for turbulence.<\/p>\n\n\n\n

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Delta Flight 867, KMSP to KSEA, on 28 February 2022 (left) and 2 March 2022 (right) (Click to enlarge)<\/figcaption><\/figure>\n\n\n\n
\n\n\n\n

You might ask: Why are the upper-tropospheric lapse rates shown? The reason is because they align the use of the 6.19 \u00b5m imagery. The (new and improved!!) CIMSS Weighting Function<\/a> page shows that at 1200 UTC, much of the signal in the upper-level water vapor imagery (in brown in the plot below) around Minneapolis was coming from the 300-400 mb layer. A plot using GFS data at 45o<\/sup> N, 95o<\/sup> W<\/a>, shows a similar distribution.<\/p>\n\n\n\n

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Computed (clear-sky) Weighting Functions from KMPX, 1200 UTC on 2 March 2022 (click to enlarge)<\/figcaption><\/figure>\n\n\n\n
\n\n\n\n

AWIPS imagery in this blog post was created using the NOAA\/NESDIS TOWR-S AWIPS Cloud Instance.<\/p>\n","protected":false},"excerpt":{"rendered":"

Turbulence probability is an aviation tool created (using machine learning techniques) at CIMSS to diagnose the likelihood of Moderate Or Greater (MOG) turbulence at least once during a 10-minute period. The image above shows the 1455 UTC MOG Turbulence Probability along with pilot reports (PIREPS) of turbulence, taken from this website. Turbulence Probability fields are […]<\/p>\n","protected":false},"author":19,"featured_media":44915,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[21,74,25],"tags":[],"class_list":["post-44913","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-aviation","category-goes-16","category-satellite-winds"],"acf":[],"_links":{"self":[{"href":"https:\/\/cimss.ssec.wisc.edu\/satellite-blog\/wp-json\/wp\/v2\/posts\/44913","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cimss.ssec.wisc.edu\/satellite-blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/cimss.ssec.wisc.edu\/satellite-blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/cimss.ssec.wisc.edu\/satellite-blog\/wp-json\/wp\/v2\/users\/19"}],"replies":[{"embeddable":true,"href":"https:\/\/cimss.ssec.wisc.edu\/satellite-blog\/wp-json\/wp\/v2\/comments?post=44913"}],"version-history":[{"count":7,"href":"https:\/\/cimss.ssec.wisc.edu\/satellite-blog\/wp-json\/wp\/v2\/posts\/44913\/revisions"}],"predecessor-version":[{"id":45065,"href":"https:\/\/cimss.ssec.wisc.edu\/satellite-blog\/wp-json\/wp\/v2\/posts\/44913\/revisions\/45065"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/cimss.ssec.wisc.edu\/satellite-blog\/wp-json\/wp\/v2\/media\/44915"}],"wp:attachment":[{"href":"https:\/\/cimss.ssec.wisc.edu\/satellite-blog\/wp-json\/wp\/v2\/media?parent=44913"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/cimss.ssec.wisc.edu\/satellite-blog\/wp-json\/wp\/v2\/categories?post=44913"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/cimss.ssec.wisc.edu\/satellite-blog\/wp-json\/wp\/v2\/tags?post=44913"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}