Regional and Mesoscale Meteorology Branch

00:00:00:01 – 00:00:28:01
Speaker 1
Few minutes. So welcome to today’s visit Satellite Chat. But we’re going to look at today’s a little bit of current weather and Scotland’s storm. We’ll look at a recent event from, earlier in the month. So let’s begin by looking at, a short loop here of, water vapor imagery. And we have a pretty significant trough over Southeast Cana, Canada, extending into the Great Lakes and across the northeast region here.

00:00:28:04 – 00:00:52:28
Speaker 1
And then out west, we have, ridge. So in between, we have fairly strong flow, aloft, as you might suspect, here going across the northern and northern plains here. And, so you look at the 850 millibar temperatures, cold temperatures 850 across the Great Lakes. If this were December, you would say, well, kind of marginal for Lake effect snow.

00:00:52:28 – 00:01:15:26
Speaker 1
Maybe we’d like to see a little colder. But since it’s October, this is actually, quite substantial because the lake surface temperatures are still, very warm. And when you have this, cold air going over the, relatively warm water, we have some lake effect snow, rain occurring over the, Great Lakes. So look at that in some, detail.

00:01:15:28 – 00:01:43:25
Speaker 1
So here’s a loop of the visible imagery across, Lake Superior, Huron and most of Michigan here. So go ahead and let this loop slowly here. And just keeping in mind the very cold temperatures, aloft here, flowing over, the Great Lakes and, and you can see these, bands that are set up here as well as, cells.

00:01:43:25 – 00:02:05:20
Speaker 1
So you see, some, some areas where you see cells, some areas where you see bands that have set up and underneath those you have either snow or rain, depending on, where you are as you go further to the south and east, the temperatures at the surface are, warmer. So this is kind of at the pocket of the coldest air that’s over the, western Great Lakes that we’re looking at.

00:02:05:22 – 00:02:27:29
Speaker 1
And, with the very cold temperatures aloft, you can even see over the land, such as Michigan and Wisconsin, where, you quickly get these, cellular looking, convective clouds that develop here. And, and, I saw very cold temperatures, at the, the various soundings there. I believe it was around -30 at, 500 Millibar.

00:02:27:29 – 00:02:58:22
Speaker 1
So very steep lapse rates. So you get a little bit of sun and, you get this convection that develops even over the land here. So, just make this go a little bit faster. And when you when you see this in speed here, you can see some interesting, features, especially when you get more clouds later in the loop here you can see, a bit of, what might be a circulation here, or at least maybe, oh, differences.

00:02:58:24 – 00:03:21:01
Speaker 1
With the way the clouds are looking here, I guess you would say across, Michigan and just moving offshore here. So you see a lot of interesting, things across the scene here. Earlier in the day, you saw this, nice single band that was across, Lake Huron. Maybe it was, getting enhanced by the upstream moisture and higher, boundary layer height.

00:03:21:01 – 00:03:44:29
Speaker 1
That’s, starting across, Lake Superior here and then had a nice single band earlier. It looks like it’s been disrupted, as we got later in the day. And you get this more cellular look to the, convection as it’s going across here. So, that’s all I wanted to say about that. That in terms of the, Great Lakes, are there any questions or comments about that before we turn it over to, Scott.

00:03:45:01 – 00:04:04:28
Speaker 2
I’ll just say that the 850 temperatures, for example, green Bay this morning was minus eight. It’s -10 to -12 at Saint Marine International Falls. So the Delta TS with the lake surface temperatures are probably still, 50 ish. That’s a pretty substantial delta T. So.

00:04:05:01 – 00:04:27:08
Speaker 1
Yes. Yes. So, yeah. Very impressive. Lapse rates that we would have there. Starting over the lakes and, okay. I think I’ll, I’ll, change it to Scott Lindstrom. Here is our presenter. Who is it seems that, University of Wisconsin.

00:04:27:10 – 00:04:51:20
Speaker 2
And I’ll talk a little bit about fog. There was an interesting fog event over. The West coast. This is a day night band. We’re just a little bit past full moon. I think full moon was last Friday. So this day night imagery in the last week has been really spectacular. I hope you’ve been able to see some of it.

00:04:51:22 – 00:05:10:16
Speaker 2
This is an example. So you can see San Francisco in the center of the image. I’m kind of focusing on the Salinas Valley just to the east of Monterey, extending. I hope you can see where I’m moving my cursor, and you can kind of make out some fog there extending down the Salinas Valley, but there’s also cirrus on top of it.

00:05:10:16 – 00:05:38:08
Speaker 2
And that makes it kind of difficult for different algorithms to detect the fog, because the satellite, detection is compromised by the presence of the ice clouds in the cirrus. And if you look at the street brightness, temperature difference, product from viewers for the same time, I’ll just toggle between the two of these just once. And you see the nice indication of at the top of the Salinas Valley.

00:05:38:08 – 00:06:00:27
Speaker 2
But then you see this, cirrus cloud that’s making it difficult to see what’s going on the southern part of the Salinas Valley. It’s also suggesting maybe there’s something going on in the big valley of California, the Central Valley, although you don’t see any, real obstructions to visibility in the observations that have shown here. So, this is the one kilometer data.

00:06:00:29 – 00:06:33:06
Speaker 2
If you look at the ghost data, which at this latitude has a resolution maybe closer to five, 5.5km. Again, you can also see this characteristic signature of cirrus over the Salinas Valley, where you have cirrus clouds. Typically, instead of using the satellite data, because our IFR probability field is going to use, model data. But a difficulty with the model in the Salinas Valley is resolving that particular valley.

00:06:33:09 – 00:07:02:02
Speaker 2
It’s not very narrow. And the 13 kilometer resolution of the rapid refresh, which is used with the ghost data, is having trouble seeing it. So the the fairest data, the cirrus clouds are obscuring the satellite view, and the limitations of the model are preventing any real information from the model going into the IFR probability field. I also have the Motus motus data from earlier in the day.

00:07:02:02 – 00:07:32:00
Speaker 2
This is the IFR. This is a brightness temperature difference from Motus, and it also shows, a little bit more if we can compare these two. So this is at 623 instead. And here’s the 1045 V from, from the from Sumi NPP. Let me line them up. So when I toggle between the two, it doesn’t see topography isn’t jumping around and it’s still jumping around for some reason.

00:07:32:02 – 00:08:01:28
Speaker 2
Okay, this should work. Well, maybe, but you can see that the the modest data from six V, that’s those cirrus clouds that were on that are over most over the half of the valley at Tennessee. Haven’t quite shown up there. So if you look at the IFR probability from six V, you do see a region of higher probability of extending down the, extending down the valley.

00:08:02:05 – 00:08:22:18
Speaker 2
And you also see that with the Gos probabilities as well. So there’s no cirrus here obscuring it. So the so the satellite data can be used to influence the IFR probability fields. So when you’re looking at these IFR probability fields you really need to and you really need to know what’s going on. That might be affecting the view.

00:08:22:18 – 00:08:49:01
Speaker 2
And in this case some obscuring cirrus. Not very much of it is showing up. And that’s that in combination with the small scale feature in the Salinas Valley is so it’s making it look like, well, maybe the, probabilities are decreasing with time when in reality what’s happening is you’re just having more, more cirrus clouds, changing what’s going into the IFR probability computation.

00:08:49:04 – 00:09:14:14
Speaker 2
Now, if you look at the Motus fields, Bernie noticed that this morning you’ll see some interesting looking artifacts out over the Pacific Ocean. This is actually a different a processing area. So I talked to the people here and you’re seeing the boundary between one of the rapid refresh and the GFS. So they’re they were grabbing the wrong model when computing the Motus IFR probability out over the Pacific Ocean.

00:09:14:16 – 00:09:43:16
Speaker 2
So that has been fixed. So this kind of blotchy field should not show up in the in subsequent motus IFR probabilities. So that’s are there any other questions on these fog features. And again I’m, I want to urge you to look at the Motus, the virus data from Sydney MPP, because it’s really spectacular lately. Because we’re, because of our position in the lunar cycle.

00:09:43:18 – 00:10:17:17
Speaker 2
Any questions on this? If not, I can go on to the, recent, blizzard in the northern Plains. And hearing nothing, I’m going to jump to this particular event. So there was a big snow storm in the plains and that was accompanied by a severe weather outbreak with some fairly substantial tornadoes. So 18 tornadoes on the forth, including an F4 tornado, I believe it was near Webster.

00:10:17:20 – 00:10:45:10
Speaker 2
We have a loop here that Scott Buck put together of the water vapor showing, the structure of the storm that produced, upwards of four feet of snow in early October in the Black Hills. So this was a devastating snow storm for lots of trees in and around Rapid City. And there was also, you know, as you might expect with this kind of system, power outages galore.

00:10:45:12 – 00:11:12:03
Speaker 2
I love this image because at the very end, you see just the persistent northeasterly flow that’s bringing moisture from, farther northeast and wrapping it around the system back down into the Black Hills right at the very end there. Probably consistent with a trough of warm air aloft or a trial that’s developed in this feature that’s linked to the, deeper moisture off to the east.

00:11:12:06 – 00:11:42:27
Speaker 2
There’s also that interesting feature of dry air punching out across the plains. You see that dry punch developing as the, in the middle of this animatic. And so there’s just a very interesting dry punch starting about right now, pushing out over the plains. So probably a little vorticity. Max and jet goes out over the plains, forces that convection that helps to develop the, system that had the tornado near Webster.

00:11:42:27 – 00:12:13:01
Speaker 2
So it’s just a really fascinating water vapor animation to look at. After this particular storm, whoops. I thought I had a picture of NPP data loaded in one of my tabs, but I guess I didn’t. So I’m just going to go back to this page and scroll down to there we go. So we have a,

00:12:13:04 – 00:12:43:27
Speaker 2
Visible image. And then an RGB that includes, the 1.61 micron reflectivity. Maybe that’s not our JB Scott. I know Scott might be on the line with us. But with it just showing you the large extent of the snow on October 6th. And if you look at the I won’t show this image, but if you go to that blog post, you’ll also see from the Motus true color, you can see exactly what the tornado, was on the ground in Webster.

00:12:43:27 – 00:13:00:06
Speaker 2
So you can see the damage path from modus. Always an interesting view. When that happens in Mount Hood. So are there any questions or comments on this?

00:13:00:09 – 00:13:33:18
Speaker 2
So it is, Scott Meyer in the light line. And is this an RGB using the, like the the point six views and then the 1.6 that is, that is correct. So it’s actually. Our GPA that uses those two different channels and so that’s so yeah, we are.

00:13:33:20 – 00:14:07:09
Speaker 2
Able to see, you know, if those are clouds or if there’s snow on the ground and if they, if it is if it has a reddish enhancement, then it’s still on the ground. And I was kind of intrigued in this instance here there are a couple areas that also feed Black Hills that don’t have have any snow on the ground at all.

00:14:07:12 – 00:14:24:20
Speaker 2
And so these were areas that were downwind. So the that area had some, a strong component of downslope.

00:14:24:22 – 00:14:48:12
Speaker 2
Yeah, I was surprised. I just I was surprised they didn’t get snow as the system was approaching. But I think it just shows you that it, it was I think it was initially a rainstorm that it all changed over to snow. So. I was.

00:14:48:15 – 00:15:02:12
Speaker 1
Okay. Let’s turn it over to Bernie next to, look at some snowfall RGB. And then we’ll turn it over to Scott Brockmire. After that. So.

00:15:02:14 – 00:15:33:23
Speaker 2
Okay, let’s see there. And everybody is everybody viewing my stream since we just saw the RGB and the snow season is coming upon us, I thought I’d, take just a couple minutes and show a few of the RGB BS that are available from either Sierra or, sport, or maybe even Sims, or for the Sims ones might not be available until you get they.

00:15:33:23 – 00:16:11:25
Speaker 2
What’s true. And but just to point out to you that, this is the one that was developed actually initially at NRL, Steve Miller and others. And since Steve is now at Sierra, we’re putting it out. It uses five different channels as shown here, to give you snow and cloud layers. The snow is white. When we combine all these and the yellow is a low cloud, the, orange is a mid-level cloud and the pink is high level and green is background surface.

00:16:11:25 – 00:16:33:21
Speaker 2
And so what we see from this is that in, when we’re looking at reflective channels, the we see it in the point six, in a 1.3, it’s sort of like a visible water vapor channel, sometimes often referred to as a serious channel. There’s water vapor absorption. So we don’t see to the surface most of the time.

00:16:33:27 – 00:17:05:16
Speaker 2
There are a few times we might, but we’re mostly seeing the upper level clouds. And in the 1.6, the snow is actually less reflective. So it’s, showing up this dark down here. Okay. Now, if we move into another one, which is sport one and this is kind of similar to what Scott was showing in that we have one of the more reflective bands being use points for six, along with two non-reflective ones for the snow.

00:17:05:19 – 00:17:43:08
Speaker 2
And I say respect to the 1.6 and then 2.13. And so the snow tends to be more reddish and the background tends to be more cyan, and like clouds will be, this dusty rose colored and lower clouds will be, whitish. And then the third one that we tend to see is this, false color snow. Europeans tend to use a variation on this, using, point six and point eight in visible and near air, and then the 2.13 in the near here as well.

00:17:43:10 – 00:18:06:29
Speaker 2
So a combination of ones where the snow is reflective versus where it’s not reflective. So in this case the snow is cyan. The background is more reddish and like clouds are more whitish. Some where you see the ice clouds, they might have a similar roof like reflectivity to the snow. So they might also appear sandy.

00:18:07:01 – 00:18:36:06
Speaker 2
And the last one I saw, a quick for light, is one that, Dan Hilder here put together using just ghost based channels. So it uses a point, the visible 3.9 and the 11, and snow in this case is, this British pink here. The high clouds are not so pink and background surfaces are, brownish sort of thing.

00:18:36:09 – 00:18:58:19
Speaker 2
So there’s quick examples in reference to that snow example we have. Here’s this. It comes in a little bit better resolution. They whips and I didn’t get to grab it at the time. So here the with Steve Miller’s product. The snow is white and the background is green and the water is blue. Low clouds are yellowish, high clouds are, pinkish.

00:18:58:19 – 00:19:24:10
Speaker 2
And then we see a few in between the orange. Here again is the, the false color with the cyan snow. And you can see some of the green or vegetation features or the background, drier surface and some lower clouds that are white, and then higher ice clouds that are cyan as well. And let’s see that one.

00:19:24:10 – 00:19:48:04
Speaker 2
I wanted to let me see if I had a real quick go back to this, because then this just points out that, this is from this lamb, I mean, the Motus Rapid response page. And here the snow is a really bright red and the background is plants with sort of opposite to the one we were just looking at.

00:19:48:07 – 00:20:17:12
Speaker 2
And and we’re, they’re essentially doing the same things but putting it on different colored channels, the reflective snow surface versus the non reflective. But depending upon how you apply the brightness or the filtering of the image, you can get a real deep red or not. And then just to compare again with what Scott showed for Scott, Mock, Myers and Tree on the web page.

00:20:17:14 – 00:20:45:09
Speaker 2
And finally, this is just a group of five images to see. With those you can see where the snow is and this area and the movement of clouds. So helping to, locate the snow feature as well. Okay. Does anybody have any comments or questions?

00:20:45:11 – 00:20:50:03
Speaker 2
Okay. Then I guess you can hand it off to Scott Black man okay.

00:20:50:03 – 00:20:59:01
Speaker 1
We’ll go to Scott back Merritt Simms in Wisconsin next.

00:20:59:04 – 00:21:09:15
Speaker 2
Okay. Show my screen. So hopefully you should see my day to day. Care. Correct.

00:21:09:18 – 00:21:10:06
Speaker 1
Yeah I see it.

00:21:10:13 – 00:21:49:22
Speaker 2
You’re going to have it. All right. So I just thought I’d do, since we’re talking a little bit about, taking advantage of the higher spatial resolution of polar orbiters to do a quick comparison of the water vapor imagery from a motors instrument, which is available at a one kilometer resolution, and compare to what we get on the current, vehicles, which is that, nominally about a four, kilometer.

00:21:49:22 – 00:22:28:03
Speaker 2
So we have, if there’s a ridge, off over, this particular area. But, but there’s also a, a cut off, which we can see pretty obvious here on the goes watch vapor imagery. But if we take a look at, the water vapor imagery from the the motors instrument at one kilometer.

00:22:28:05 – 00:23:00:04
Speaker 2
There you go. You can see a lot more detail in the actual gradients. If I sort of toggle between the two here. The enhancements took a little bit for us because, the, the, the channels are not as broad. They’re more system. And so you won’t get the exact range of temperatures, but, you know, the actual gradients, and you can see just, obviously a lot more detail.

00:23:00:07 – 00:23:28:02
Speaker 2
And let me see if this works here. If I try and zoom out so I can look over at eastern corners and then move ahead and time, there’s a pretty good case this over the Appalachians this morning, if I can get to that. Yes. And then if I. So man. We can see a good case of Mount Smyth’s over the Appalachians.

00:23:28:05 – 00:24:06:02
Speaker 2
So here again we’re looking at, This is how it appears at one kilometer from Steve Morris instrument. And here’s what it looks like on the goes, which again, it has a hard time trying to pick up the smallest scale. So it it’s not obvious hardly at all on a on because imagery. But again, it, it really jumps out at you on the water vapor imagery at one kilometer.

00:24:06:02 – 00:24:37:07
Speaker 2
So, it is important if you’re trying to look at some of detail on the gradients or, or features, you really, it helps to, to capitalize on the higher spatial or resolution that we get from a lot of states. Okay, the polar orbiters. So that’s, basically the only items of interest that I had here. I’ll go ahead and pass it back to you.

00:24:37:09 – 00:25:07:28
Speaker 2
Okay. Scott, I have a quick question. So do you think because Goes-r is going to have two kilometer resolution in the water vapor, so did you degrade? Look, I guess you could could degrade this and see if you could still see those creatures in two kilometers. Yeah, yeah. And I imagine you you could probably, and obviously it won’t be as credible as it is here, but I’m hoping that, you know, we’ll be halfway in between what we have here and what we have, you know, here.

00:25:07:28 – 00:25:33:03
Speaker 2
So halfway in between these two hopefully means that, yes, we’ll we will indeed have a better indication that, there are indeed some waves over this particular area. So which, you know, that would be an early heads up of a, possibly a aircraft, hazard for turbulence.

00:25:33:05 – 00:25:43:12
Speaker 1
Okay. Any other questions? For for anybody?

00:25:43:15 – 00:25:47:04
Speaker 1
Okay, I see. No, you joined us now. Northwest.

00:25:47:06 – 00:25:56:09
Speaker 2
Yes I did, I’ve been having a little difficulty getting online with. There was just a local computer problem, but I did finally get it. Get it up and running.

00:25:56:12 – 00:26:06:06
Speaker 1
Okay, great. Great. Well, we’re we’re pretty much done here. Did you have any, specific questions or comments for us? No.

00:26:06:09 – 00:26:30:13
Speaker 2
I, I did have one. I just as I came on on the audio, someone was looking at the, daylight the day night, I suspect, from what was being said. Yes, I had. Would I be able to have you jump back and just, maybe the the the couple of images? Sure. Did you want to give me the.

00:26:30:16 – 00:26:55:10
Speaker 2
Yes. That’s one of the areas that I really want to try to use more with our fog situations that we get. So this image hasn’t showed up yet, but I’m sure it will momentarily. So I was I showed both the day night band and the, fog band, from the Earth. And you’ll notice in the Salinas Valley here that there were cirrus over the, over the central and southern part of the valley.

00:26:55:17 – 00:27:25:25
Speaker 2
Sure. So you couldn’t see, the, the full extent of the fog that was forming in the valley. And that was a problem with the goes as well. So you see the cirrus and the ghost imagery and that translated into the IFR probability field, because, you know, the rapid refresh really does not resolve the Salinas Valley if you don’t have if you have the cirrus clouds there for the so the satellite can’t see it, you’re kind of flying blind there.

00:27:25:25 – 00:27:55:20
Speaker 2
So the even though the earlier imagery this is from 60 VFR probably showed the fog starting to develop in the Salinas Valley, and the mode of fog products showed a nice extent of fog down. That, and the IFR probability kind of showed the same thing. Because that cirrus, that small patch of cirrus moved over, you lost the ability and the IFR probability field to see it, but sure.

00:27:55:23 – 00:28:25:09
Speaker 2
But yeah, the the day night band up for the last week was just around full moon. It’s just been spectacular. Are you getting that in your office? Yeah. This is the answer. I still don’t know how many were actually using it. Okay. Yeah. You just you really have to become in sync with the lunar cycle. And, and once you realize that you’re outside and the moon is rising just as the sun is going down, and you have to think, oh, yeah, got to look at that day night band because it’s going to look great.

00:28:25:12 – 00:28:43:19
Speaker 2
Yeah, well, we do have everybody, for other reasons. We’re looking at tides and that obviously is connected. So I think I can get them that way too. Yeah that’s true. All right. Well, thank you very much for that. I know I came on late. I apologize for that, but appreciate it. Anything else you want to hear about it or.

00:28:43:22 – 00:28:59:16
Speaker 2
No I, I just well I, I really can I didn’t realize it was connected with the flash products. That’s really great because we’ve been you know, we had run as a part of that test and, and now as we go into the winter, I’m going to be trying to get the guys to use it more for the inlet valley problem that we have, right?

00:28:59:16 – 00:29:16:27
Speaker 2
Where the Sumi NPP stuff is not tied in with the IFR probability field yet, but the Motus data is so you can get you can get IFR probability with test data, but not yet with sumi NPP data. Oh okay. That’s good to know.

00:29:16:29 – 00:29:20:16
Speaker 1
Are there any other questions before we, wrap up here?