NASA ARSET Case Studies in Trace Gas Monitoring with North American Geostationary Sensors

0:01 Speaker 1: Welcome back everyone to our RSEET training series Geostationary Remote

0:05 Speaker 1: Sensing of Trace Gases for Air Quality Applications in North America.

0:10 Speaker 1: Today is part two of our training series, Case Studies

0:13 Speaker 1: in Trace Gas Monitoring with North American Geostationary Sensors. My

0:17 Speaker 1: name is Christina Pistone and i am a research scientist

0:20 Speaker 1: with the Bay Area Environmental Research Institute and NASA AMES

0:23 Speaker 1: Research Center in the San Francisco Bay Area, and i'm

0:26 Speaker 1: the r set lead for this training. To remind everyone

0:31 Speaker 1: of our schedule, today, we are on Part two and

0:33 Speaker 1: we are going to focus on case studies in trace

0:36 Speaker 1: gas monitoring with North American geostationary sensors. There will be

0:40 Speaker 1: one homework assignment after this training. It will be available

0:43 Speaker 1: today after Part two is complete, and it will be

0:45 Speaker 1: due two weeks from now on February fifth. A certificate

0:48 Speaker 1: of completion will be awarded to those who attend all

0:51 Speaker 1: live sessions and complete the homework assignment before the given

0:54 Speaker 1: due date. Once again, my name is doctor Christina Pistone.

0:58 Speaker 1: I'm a research scientist at Bay NASA AIMS and I'm

1:01 Speaker 1: happy to be joined today by doctor Aaron Nager, who

1:04 Speaker 1: is the TEMPO Mission's application lead at NASA Marshall Space

1:07 Speaker 1: Flight Center to recap what we presented in Part one.

1:12 Speaker 1: TEMPO is a geostationary hyperspectral instrument with coverage over North America.

1:16 Speaker 1: TEMPO provides data on atmospheric trace gas is relevant to

1:19 Speaker 1: air quality, including nitrogen dioxide, formaldehyde, ozone as a total

1:24 Speaker 1: column product and as a beta ozone profile product, and

1:29 Speaker 1: the TEMPO Level two in Level three data can both

1:32 Speaker 1: be visualized in the NASA Worldview tool. The quality assurance

1:37 Speaker 1: settings that are applied in Worldview are suitable for qualitative applications.

1:46 Speaker 1: The objectives for today's parts. By the end of today,

1:49 Speaker 1: attendees should be able to evaluate TEMPO trace gas products

1:52 Speaker 1: in Worldview to anticipate short term air quality risks such

1:55 Speaker 1: as high concentrations of ozone precursors, and should be able

1:59 Speaker 1: to determine in the facial patterns, temporal trends, and likely

2:02 Speaker 1: sources of trace gases related to wildfire, smoke and urban

2:06 Speaker 1: area air pollution events using the TEMPO data available in

2:09 Speaker 1: Worldview and today, we're going to present two case studies,

2:12 Speaker 1: one on fires in the Colorado Front Range from July

2:16 Speaker 1: of twenty twenty four and one on urban air quality

2:19 Speaker 1: conditions in Eastern Texas. Some bookkeeping for how to ask questions.

2:27 Speaker 1: Please feel free to put your questions in the question

2:29 Speaker 1: box and we will try to address them at the

2:31 Speaker 1: end of the webinar. You can put them in the

2:33 Speaker 1: Q and A box within WebEx at any time during

2:36 Speaker 1: the session and at the end of the session. We

2:39 Speaker 1: will try to address as many questions as we can

2:42 Speaker 1: and we will post these questions and written answers to

2:45 Speaker 1: any remaining questions on the training page within a week

2:48 Speaker 1: after the training And now I will turn it over

2:54 Speaker 1: to aerin.

2:58 Speaker 2: Thank you Christina.

2:59 Speaker 3: Now I'll be sharing the ozone case event that occurred

3:03 Speaker 3: over the Coloroffront Range in July of twenty twenty four.

3:07 Speaker 2: So to kick off this use case.

3:08 Speaker 3: Analysis, we're going to look at some true color imagery.

3:11 Speaker 3: Here to the left we have a true color image

3:15 Speaker 3: from Modus Saqua. This is from Worldview and we're zoomed

3:19 Speaker 3: in on Denver. Here they were centered on Denver and

3:22 Speaker 3: this is the first day on July twenty nine, twenty

3:25 Speaker 3: twenty four, when the initial wildfire erupted across the front range.

3:30 Speaker 3: You can see the grayish color intensity here tied to

3:33 Speaker 3: that wildfire smoke plume. Then on July thirty, the following day,

3:38 Speaker 3: this is again a true color of modus image and

3:42 Speaker 3: the fires were becoming more active. On July thirty, we

3:46 Speaker 3: are seeing a larger area here of grayish color intensities

3:50 Speaker 3: tied to that smoke plume. And this is a nice

3:53 Speaker 3: picture taken by Dan Welsh at the colorad Department of

3:56 Speaker 3: Public Health and Environment during his drive to work, where

4:00 Speaker 3: he had this nice view of the Stone Canyon fire

4:03 Speaker 3: and the Alexander Mountain fire.

4:05 Speaker 2: During this event and overall.

4:07 Speaker 3: There were numerous ozone exceedences that were observed across the

4:11 Speaker 3: Front Range during this month. During the first part, in

4:16 Speaker 3: the middle part of the month, those remote mainly the

4:18 Speaker 3: ozone issues were mainly from wildfires across the US West

4:22 Speaker 3: region from regional sources. Then by the end of July

4:26 Speaker 3: twenty twenty four, those local lies wildfires started to pop

4:30 Speaker 3: up across the Front Range, which impacted air quality in

4:33 Speaker 3: the area. So looking at that time period at the

4:38 Speaker 3: very end of July twenty twenty four, when those localies

4:42 Speaker 3: wildfires started to erupt, we are looking at the ozone

4:46 Speaker 3: the air now ozone monitor maps here, there were numerous

4:49 Speaker 3: monitors that did measure unhealthy ozone levels during this time period.

4:55 Speaker 3: July twenty eight was the day prior before the day

4:59 Speaker 3: before the localized wild wildfires started to erupt July twenty nine.

5:04 Speaker 3: Now we're seeing these healthy for a sensitive group ozone

5:08 Speaker 3: levels being observed being measured across the area, and then

5:12 Speaker 3: by July thirty one, we are actually seeing unhealthy for

5:16 Speaker 3: all groups ozone levels that were unhealthy for all groups

5:20 Speaker 3: and those were nearby the Denver Boulder area there. So

5:25 Speaker 3: what happened here, Well, let's take a look at the

5:27 Speaker 3: tempo data. This is two different animations here, with the

5:32 Speaker 3: one on the left is a troposphere two animation the

5:36 Speaker 3: hourly IN two data from morning to evening across the area.

5:41 Speaker 2: Zoomed in on the front range.

5:42 Speaker 3: Here, and we're seeing is this large n O two

5:45 Speaker 3: plume on July twenty nine that was associated with that

5:50 Speaker 3: wildfire smoke event, which you can also see the transport

5:54 Speaker 3: of that No two plume to the east during the

5:58 Speaker 3: afternoon and also kind of your typical urban two tied

6:02 Speaker 3: to the urban area of Denver Boulder in the morning

6:05 Speaker 3: as well. But that in O two plume from the

6:07 Speaker 3: wildfire was really distinct in the region. And looking over

6:11 Speaker 3: to the right at the total column from Adehyde, we

6:15 Speaker 3: also see a large form Adehyde plume that was tied

6:19 Speaker 3: to that wildfire smoke event. So we're going to see

6:22 Speaker 3: that here in a minute that started to pop up

6:25 Speaker 3: and then being transported to the east as during the

6:28 Speaker 3: later afternoon hours. So we definitely saw large increases in

6:32 Speaker 3: both n O two and from Autohyde due to the

6:35 Speaker 3: Alexander Mountain wildfire. Now looking at tempo total calm ozone

6:43 Speaker 3: same day on July twenty nine, and we it was

6:47 Speaker 3: really interesting that we saw this ozone production within the

6:52 Speaker 3: wildfire smoke plume in the total calumn ozone animation here

6:56 Speaker 3: to the top right. Those play this again here and

7:00 Speaker 3: you're going to see in the later afternoon hours this

7:03 Speaker 3: increase in ozone that occurs within the vicinity of that

7:08 Speaker 3: smoke plume as is being transported down wind during the afternoon,

7:13 Speaker 3: and the ozone monitors in the region did measure ozone

7:16 Speaker 3: concentrations of seventy five and seventy one ppb across the area.

7:24 Speaker 3: We're kind of zooming in on those monitors right here,

7:28 Speaker 3: and a zoomed in map, and this is also a

7:30 Speaker 3: zoom in to the modus aqua true color where we

7:33 Speaker 3: can clearly see the grayish color intensities tied to that

7:37 Speaker 3: smoke plume location that were impacted the ozone levels vanilla

7:43 Speaker 3: neat product that can be useful for tracking wildfire smoke

7:48 Speaker 3: is a tempo UV aerosol index. We did mention this

7:52 Speaker 3: in Day one, the Day one training, and now we're

7:56 Speaker 3: showing it for this wildfire smoke event based across the

7:59 Speaker 3: CEO four and range, and the temple UV aerosol index

8:03 Speaker 3: helps to distinguish and track absorbing aerosol particles in the

8:07 Speaker 3: atmosphere like smoke and dust for example, which are generally

8:11 Speaker 3: tied or associated with positive UV aerosol index values.

8:16 Speaker 2: And you can.

8:17 Speaker 3: Clearly see those high UV aerosol index values in this

8:22 Speaker 3: animation here on July twenty nine. Again, we'll see this

8:26 Speaker 3: pop up here in the midday and the later afternoon

8:30 Speaker 3: hours where we have these high values of UV aerosol

8:33 Speaker 3: index tied to that wildifier smoke plume and also the

8:37 Speaker 3: transport that we see down wind during the later afternoon hours.

8:44 Speaker 3: Another tool that can be useful when looking at Temple

8:48 Speaker 3: data is to do averaging a tempo data throughout the

8:50 Speaker 3: day that we did here, we did a daily average

8:55 Speaker 3: of the troposphere in two data from Tempo for July

8:59 Speaker 3: twenty eight, so for all the morning to evening scans

9:02 Speaker 3: on July twenty eight, we average these onto a point

9:06 Speaker 3: zero two degree grid and then same kind of operation

9:10 Speaker 3: on July twenty nine and July thirty. So now we're

9:13 Speaker 3: clearly seeing a few things here. One, this increase or

9:18 Speaker 3: this two column increase tied to the oil and gas

9:21 Speaker 3: well region to the northeast of the Denver Boulderate region.

9:26 Speaker 2: That's pretty distinct on July twenty eight.

9:29 Speaker 3: But then as those wildfires erupted on July twenty nine,

9:33 Speaker 3: we're now seeing this strong n O two signal tied

9:36 Speaker 3: to that wildfire, the Alexander Mountain wildfire near Fort Collins.

9:40 Speaker 3: And then by July thirty there was another wildfire, the

9:44 Speaker 3: Stone Canyon one became more active to the south, and

9:47 Speaker 3: now we're seeing that n O two hot spot tied

9:50 Speaker 3: to the Stone Canyon fire and along with the two

9:55 Speaker 3: associated with the you know, the urban emissions from Denver Boulder.

10:02 Speaker 3: So now looking at formaldehyde, same kind of operation for No. Two,

10:07 Speaker 3: but we applied it to formata Hide doing the daily

10:10 Speaker 3: averaging of fromata Hyde for July twenty eight, July twenty nine,

10:15 Speaker 3: and July thirty, and we're not seeing much much increase

10:22 Speaker 3: in from alle Hide on the pre fire day here

10:27 Speaker 3: maybe a little bit across the oil oil and gaswell region.

10:30 Speaker 3: And then on July twenty nine, July thirty, when those

10:33 Speaker 3: wildfires became active, now we're seeing those large wild those

10:37 Speaker 3: large from aunto Hide plumes that are very distinct in

10:40 Speaker 3: the from auto Hyde maps with the Alexander Mountain wildfire

10:43 Speaker 3: and also a from all to Hyde hot frode Hyde

10:46 Speaker 3: hotspot tied to the Stone Canyon wildfire location. Again, I

10:51 Speaker 3: would make note here that these maps that I showed

10:54 Speaker 3: here we're using a cloud fraction filter less than fifty

10:58 Speaker 3: percent and silver thing angle lesson eighty.

11:03 Speaker 2: Okay, now I'm sharing.

11:04 Speaker 3: Here just the full picture in terms of the two

11:07 Speaker 3: maps at the top and the fromto Hyde maps at

11:10 Speaker 3: the bottom, again using that same cloud for action filter

11:14 Speaker 3: that we applied on the previous slides.

11:16 Speaker 2: Now if we tagle back and.

11:17 Speaker 3: Forth here between a using a cloud fraction of twenty

11:22 Speaker 3: percent on this slide here versus fifty percent on the

11:26 Speaker 3: prior slide. Now we're seeing how using this stricter cloud

11:32 Speaker 3: for action filtering of twenty percent and actually obscure the

11:36 Speaker 3: fire the wildfire smoke plume.

11:38 Speaker 2: So looking around going tiling them back and forth here.

11:42 Speaker 3: Between those two these two slides, we're seeing much of

11:47 Speaker 3: that two plume associated with the Alaxander Mountain wildfire disappear

11:52 Speaker 3: when using the stricter cloud fraction and also less of

11:56 Speaker 3: a signal from the Stone Canyon fire when applying that

12:00 Speaker 3: stricter cloud fraction threshold. And similar depiction there at the

12:04 Speaker 3: bottom with framadehyde. So we tackle back and forth, we're

12:09 Speaker 3: seeing how that from auto hyde plume becomes much less

12:13 Speaker 3: apparent when using that stricter cloud fraction filtering.

12:17 Speaker 2: And the main point here.

12:18 Speaker 3: Is to caution users that a certain cloud fraction filtering

12:24 Speaker 3: may not apply at all kinds of scenarios, so the

12:27 Speaker 3: application is important when applying a cloud fraction filter.

12:33 Speaker 2: Okay, now we're in.

12:33 Speaker 3: Look at the ratio of fromadohide to INDO two, which

12:38 Speaker 3: can be a tool to indicate the likelihood of near

12:42 Speaker 3: surface ozone production. And you can see the two different

12:46 Speaker 3: maps here during the pre wildfire period from July twenty

12:51 Speaker 3: seven to July twenty eight, and then the active fire

12:55 Speaker 3: period from July twenty nine to July thirty. So are

12:58 Speaker 3: two day average maps of mona high to two ratio here,

13:03 Speaker 3: and this color bar here is showing how the scale

13:07 Speaker 3: goes from these nock saturated conditions to NOx sensitive conditions,

13:11 Speaker 3: which are indicative of the ratio from an high to

13:15 Speaker 3: n O two. And we see much different regimes pre

13:19 Speaker 3: fire to active wildfire. So the wildfire smoke in general

13:23 Speaker 3: promoted more efficient ozone production in the region during the

13:27 Speaker 3: active fire period. And we're seeing here our large two

13:32 Speaker 3: increases in the NOx sensitive regime near the wildfire sources.

13:37 Speaker 3: So we have where this Stone Canyon and Alexander Mountain

13:41 Speaker 3: fire locations were here, we're seeing that being converted over

13:46 Speaker 3: to a nock saturate condition in the active fire period

13:50 Speaker 3: due to the large increase in two. But then farther

13:53 Speaker 3: downwind where we had that large transport of Forrmadehyde, we

13:58 Speaker 3: see this oil and gas will region turning more not

14:01 Speaker 3: sensitive with that transport of from aunt of high downwind

14:04 Speaker 3: in that region, but less.

14:05 Speaker 2: N two overall.

14:08 Speaker 3: Also we see that the transport of smoke also likely

14:11 Speaker 3: led to additional increases in ozone production in this NOx

14:15 Speaker 3: saturated regime in the Denver urban area by July thirty one.

14:20 Speaker 3: So looking at our July thirty one ozone modern map

14:23 Speaker 3: again to the right, this is also when those unhealthy

14:27 Speaker 3: ozone levels for all groups were measured and around the

14:30 Speaker 3: Denver Boulder area, as this smoke was transported across this

14:35 Speaker 3: region by July thirty one. All right, so quick validation

14:40 Speaker 3: check in here Tempo versus Pandora two.

14:44 Speaker 2: This is for the Boulder.

14:45 Speaker 3: InCAR site, and I's want to quickly mention here how

14:49 Speaker 3: there's overall good agreement in terms of the monthly and

14:53 Speaker 3: seasonal pattern of No. Two between Pandora and Tempo, and

14:57 Speaker 3: also a general low bias into two compared to this

15:02 Speaker 3: Pandora Boulder in Car site. So overall Tempo seems to

15:06 Speaker 3: perform fairly well. This is all based on the version

15:09 Speaker 3: three data, of course, so okay, now look in more

15:13 Speaker 3: detail here to the monthly correlations between Tempo and Pandora No.

15:19 Speaker 3: Two to the right here, and we see strong correlations

15:24 Speaker 3: throughout much of the year. However, there are lower correlations

15:28 Speaker 3: in the late spring and mid summer time period here

15:31 Speaker 3: have shown in this figure to the right. Again, this

15:34 Speaker 3: is based on the version three n O two data.

15:37 Speaker 3: This is an active area and we have actual validation

15:41 Speaker 3: with our current version four n O two data, and

15:44 Speaker 3: we believe much of this will improve when we do

15:48 Speaker 3: the validation for our version four data, So stay tuned

15:52 Speaker 3: for those results in validation and I'll just then pass

15:56 Speaker 3: it over to Christina for our case one exercise work.

16:03 Speaker 1: Thanks Erin. So now we are going to explore this

16:07 Speaker 1: case in a little more detail in Worldview. So you

16:10 Speaker 1: will remember from the part one, we're just going to

16:13 Speaker 1: go to Worldview dot Earth data dot NASA dot gov.

16:16 Speaker 1: I've already preloaded this here at the bottom left you

16:20 Speaker 1: can see this is the date that Aaron was just describing.

16:24 Speaker 1: So we have twenty twenty four July twenty ninth, And

16:27 Speaker 1: because we already have the tempo data loaded in this bar,

16:32 Speaker 1: we have hour and minutes which is two pm UTC,

16:35 Speaker 1: which is eight am mountain daylight time in this area.

16:41 Speaker 1: So in contrast to the case from yesterday we were

16:45 Speaker 1: little on the coast, we are going to be sure

16:47 Speaker 1: to turn on the reference layers of Worldview. So we're

16:50 Speaker 1: going to turn on some place labels and then the coastlines,

16:55 Speaker 1: borders and roads here. So this gives us a little

16:58 Speaker 1: bit more of an orientation and to where we are

17:02 Speaker 1: in the middle of the rocky mountains, so we can

17:05 Speaker 1: situate ourselves a little bit better. Now we can see

17:08 Speaker 1: that the labels are coming up. We have Denver and

17:10 Speaker 1: Boulder right there in the middle, and once again we

17:16 Speaker 1: are looking at some basse layers from Tarra Modus is

17:21 Speaker 1: the default Terra is a morning overpassed, so it's a

17:24 Speaker 1: time when we haven't yet had a the fire has

17:29 Speaker 1: not yet started. We can toggle off the terror layer,

17:33 Speaker 1: turn on the Aquamotus layer, and now we're starting to see.

17:39 Speaker 1: Just to orient ourselves, we can see it in the

17:41 Speaker 1: true color imagery that here is one of the wildfires

17:44 Speaker 1: that we're going to be exploring in more detail. We

17:47 Speaker 1: can see that by the afternoon overpast time, we're seeing

17:52 Speaker 1: the visible smoke from this event. So we'll just leave

17:56 Speaker 1: that on to orient ourselves. So as yesterday we are

18:02 Speaker 1: going to start with nitrogen dioxide. We are going to

18:06 Speaker 1: do level three vertical column and tropospheric values. So when

18:10 Speaker 1: we turn on this layer two pm UTC is again

18:14 Speaker 1: eight am local time, and we're looking for the event

18:19 Speaker 1: sort of in this area. Nothing really shows up. Again,

18:22 Speaker 1: We've set the increment to be one hourly, so when

18:26 Speaker 1: I do this little increment date button, we're seeing that

18:29 Speaker 1: we're stepping through the Tempo two data one hour at

18:33 Speaker 1: a time. And as we do that, we see some

18:36 Speaker 1: sort of signal from the cities until we get to

18:40 Speaker 1: eighteen UTC, which corresponds to about noon local time, and

18:46 Speaker 1: now we're seeing the beginnings of this wildfire event that's

18:51 Speaker 1: happening in this area. And again as I mouse over it,

18:55 Speaker 1: we can see at the bar on the left, we're

18:57 Speaker 1: seeing what the values are as corresponding to where my

19:02 Speaker 1: cursor is here compared to something a little bit more

19:05 Speaker 1: background in other areas, and we're also seeing some missing

19:10 Speaker 1: data here. These are likely clouds. So the fire event

19:16 Speaker 1: is first being seen by Tempo at eighteen hundred UTC,

19:25 Speaker 1: and as we step through a little bit we can

19:27 Speaker 1: see the evolution of this event through time, so it's

19:31 Speaker 1: not totally consistent throughout the day. It varies. As we

19:36 Speaker 1: get later in the day, we can see an interesting

19:39 Speaker 1: feature which goes to what Aaron was talking about in

19:43 Speaker 1: terms of the data filtering, particularly right here, which is

19:49 Speaker 1: at midnight UTC, which corresponds to six pm local. The

19:55 Speaker 1: plume seems to be quite strong here based on the

19:58 Speaker 1: values sort of at the edge of the plume, and

20:01 Speaker 1: again in scenes with very heavy smoke, we may be

20:06 Speaker 1: seeing those scenes be misidentified as cloud and it is

20:13 Speaker 1: reasonable to infer that that's what's happening right here because

20:17 Speaker 1: we have an area where there are no two values

20:22 Speaker 1: visible but on the edge, and based on the previous

20:26 Speaker 1: time series as we go back in time, we know

20:29 Speaker 1: that there were significant no two values associated with this event.

20:33 Speaker 1: So that's just something to keep in mind as we're

20:36 Speaker 1: looking at these data. So that was nitrogen dioxide. We

20:43 Speaker 1: can also toggle off the nitrogen dioxide. We can look

20:47 Speaker 1: at the formaldehyde values here. So we're just going to

20:51 Speaker 1: go turn on formaldehyde level three vertical column and let's

20:57 Speaker 1: return to our beginning of the day and again earlier

21:09 Speaker 1: in the day, there is no event to be seen.

21:11 Speaker 1: As we step through, we're again seeing in the formaldehyde

21:21 Speaker 1: as we did in the nitrogen dioxide. The plume starts

21:25 Speaker 1: to be visible around eighteen hundred UTC nineteen UTC twenty

21:35 Speaker 1: The plume is more visible later in the day, and

21:38 Speaker 1: again we have some filtering here which is indicative that

21:42 Speaker 1: likely the thick smoke is being flagged as cloud in

21:46 Speaker 1: the routine. But nonetheless we're still able to mouse over

21:49 Speaker 1: and see that their elevated values. Again, formaldehyde is a

21:53 Speaker 1: more noisy product, but we're able to get a sense

21:56 Speaker 1: for what the values are in the downwind plume of

22:01 Speaker 1: formaldehyde associated with this fire, So that is formaldehyde. We

22:09 Speaker 1: can also again look at the ozone product, and so

22:15 Speaker 1: we are going to look at ozone level three and

22:20 Speaker 1: calumn amount right here. Let's turn on this layer. Let's

22:24 Speaker 1: again go to the earlier part of the day. And

22:30 Speaker 1: as you'll remember from the case study that we showed yesterday,

22:33 Speaker 1: the ozone values tend to be much more uniform. They're

22:37 Speaker 1: dominated by the stratospheric component. But we can do some

22:41 Speaker 1: things to get a better sense that the ozone production

22:44 Speaker 1: that Aaron was talking about. So as we did in

22:48 Speaker 1: session one, we're going to go into the view options

22:51 Speaker 1: and play with it a little bit. So here for thresholds,

22:55 Speaker 1: this is showing basically the whole range of the ozone

22:58 Speaker 1: products or the ozone values that we have for this product.

23:02 Speaker 1: And in order to better be able to visualize the

23:07 Speaker 1: ozone signal that might be associated with this particular event,

23:10 Speaker 1: what we're going to do is take the thresholds and

23:16 Speaker 1: we're going to limit the values that it's viewing. That

23:23 Speaker 1: limit the values that are being displayed. Basically, so we're

23:26 Speaker 1: going to sort of exclude the lower values. Let's go

23:30 Speaker 1: to about two eighty five Dobson units. It can be

23:36 Speaker 1: a little fiddly, but I don't think we got it.

23:40 Speaker 1: And then let's also take the upper limit and squish

23:50 Speaker 1: it down a little bit further, you like, right there,

24:07 Speaker 1: and then we're going to tick this squash palette box.

24:14 Speaker 1: So now we've taken the ozone values, we've constrained it

24:19 Speaker 1: to only be showing a small range of the values

24:22 Speaker 1: to sort of be better able to look for features

24:27 Speaker 1: that we suspect might be happening in other parts of

24:30 Speaker 1: the atmosphere. Even though want to emphasize the ozone product

24:34 Speaker 1: that is in worldview is a total calm value, so

24:36 Speaker 1: it's going to be dominated by the stratosphere of component.

24:39 Speaker 1: Since we've done this squash palette, we've constrained all of

24:43 Speaker 1: the colors to only be visualizing as a smaller range

24:49 Speaker 1: of the values, so it allows us to see if

24:51 Speaker 1: we look in the map, we can see a little

24:55 Speaker 1: bit more of the variation which we were not able

24:57 Speaker 1: to see when we were looking at the full range

25:00 Speaker 1: of total colum ozone values. As we saw previously at

25:08 Speaker 1: two pm UTC, their fire had not happened yet, so

25:12 Speaker 1: we're going to just go forward a little bit. We

25:15 Speaker 1: can see that there's some variation that's happening of these values,

25:19 Speaker 1: and by the time we get to eighteen hundred, this

25:22 Speaker 1: is again where we saw the onset of the fire

25:25 Speaker 1: in the two and the formaldehyde data and the true

25:28 Speaker 1: color imagery for that matter. As we step through the

25:34 Speaker 1: times after that, we are able to see some variation

25:38 Speaker 1: in the values here, which because of the context and

25:42 Speaker 1: what we know, and again this is a qualitative analysis

25:45 Speaker 1: of the scene, but because of what we know, because

25:48 Speaker 1: of what we saw from the two data and the

25:50 Speaker 1: formaldehyde and the production that we expect to be happening

25:54 Speaker 1: photochemically in the trumposphere, we may be able to infer

26:04 Speaker 1: that perhaps at this time, in this location, there is

26:08 Speaker 1: enhanced ozone production happening in the troposphere, although again to

26:14 Speaker 1: emphasize this is the colon value. But as we step

26:17 Speaker 1: forward we're at twenty one UTC now twenty two. We

26:21 Speaker 1: are seeing that there is some values here which are

26:24 Speaker 1: elevated and which correspond to where we know which correspond

26:35 Speaker 1: to where we know the smoke plume associated with that

26:39 Speaker 1: fire was happening. So this is a more qualitative analysis,

26:42 Speaker 1: but it can be used to diagnose areas where we

26:46 Speaker 1: might want to look into more detail, more quantitative, either

26:51 Speaker 1: from ground sensors or from other products. So that is ozone.

27:00 Speaker 1: Let's toggle off that layer, and the final thing that

27:03 Speaker 1: we want to look at for this particular event, we

27:06 Speaker 1: are going to look at the aerosol index. Actually before

27:11 Speaker 1: we do aerosol index, because we mentioned the nitrogen dioxide

27:21 Speaker 1: patterns here later in the day where we saw very

27:25 Speaker 1: strong smoke plumes, particularly here around midnight UTC. We're seeing

27:38 Speaker 1: what we expect is as a very thick smoke plume

27:42 Speaker 1: which is masking out the No two data. Another tool

27:45 Speaker 1: that we can use in order to be able to

27:48 Speaker 1: further contextualize these data. There is also a cloud fraction

27:53 Speaker 1: layer which we haven't really looked at in detail previously,

27:57 Speaker 1: but we can turn on this cloud layer and just

28:03 Speaker 1: for clarity. Let's turn off nitrogen dioxide for a minute.

28:09 Speaker 1: The cloud layer again as similar to the other products.

28:14 Speaker 1: As you mouse over it, you can see here these

28:16 Speaker 1: dark values are cloud free conditions, and the whiter yellow

28:20 Speaker 1: white values are high cloud fraction, and above fifty percent

28:25 Speaker 1: is where we've cut the cloud fraction threshold for cloudy

28:28 Speaker 1: versus not cloudy, similar to what we did for ozone.

28:31 Speaker 1: Just now we can go into view options. We can

28:34 Speaker 1: make it a little bit more easy for our human

28:37 Speaker 1: brains to process. We're going to take these thresholds and

28:40 Speaker 1: we're just going to mask out everything about fifty percent.

28:59 Speaker 1: So now we've done is we've masked out all of

29:02 Speaker 1: the cloud free conditions essentially, so the colors that remained

29:06 Speaker 1: here more the yellows and the whites. This gives us

29:10 Speaker 1: a sense of where clouds have been identified as greater

29:14 Speaker 1: than fifty percent, which is our threshold. So if we

29:17 Speaker 1: then turn the nitrogen dioxide layer back on, we're able

29:21 Speaker 1: to see that this corresponds pretty closely well. This corresponds

29:28 Speaker 1: to where the missing data in No. Two are As

29:33 Speaker 1: we talkle this on and off, we can see that

29:35 Speaker 1: the missing values here, most of them are due to

29:39 Speaker 1: the cloud mask masking out values which have been flagged

29:44 Speaker 1: as clouds. Even though for this particular area right here

29:48 Speaker 1: with the plume, this is from our context from what

29:51 Speaker 1: we know of the scene, we can infer that this

29:56 Speaker 1: is quite likely very thick smoke instead of clouds. So

30:01 Speaker 1: one way again to better deal with this these types

30:08 Speaker 1: of situations where the retrievals might be challenging. Let's turn

30:13 Speaker 1: off n O two and now we're going to go

30:15 Speaker 1: to the aerosol index. So we're going to do aerosol

30:20 Speaker 1: index ozone level three. And here we are at the

30:26 Speaker 1: end of the day, midnight u GC, and we can

30:29 Speaker 1: see at this late time in the day it's really

30:32 Speaker 1: a quite significant plume that has been identified as absorbing

30:39 Speaker 1: aerosol in the atmosphere. So this is has been identified

30:42 Speaker 1: as smoke. So looking at this aerosol index in conjunction

30:48 Speaker 1: with the nitrogen dioxide products, this can give us more

30:52 Speaker 1: context of the scene to be able to determine that

30:57 Speaker 1: this is likely absorbing aerosols rather than clouds for this

31:01 Speaker 1: particular scene. Just for completeness of observing the scene, let's

31:06 Speaker 1: go back to the beginning of the day after the

31:11 Speaker 1: fire onset. So if we go before where we know

31:14 Speaker 1: that it, if we go before the onset of the fire,

31:22 Speaker 1: if we're at fifteen hundred zulu nothing eighteen, nineteen twenty,

31:31 Speaker 1: we can get a better extent of the We can

31:36 Speaker 1: get a better sense of the extent of the fire

31:40 Speaker 1: and the extent of the smoke distribution associated with this

31:45 Speaker 1: event here as it progresses through the day, in addition

31:48 Speaker 1: to being able to identify this as smoke as opposed

31:53 Speaker 1: to a thick cloud for this particular event.

32:00 Speaker 3: Thank you for that demo, Christina. Now we're going to

32:03 Speaker 3: dig into another use case looking at urban pollution and

32:06 Speaker 3: emission sources in the East Texas area and for two

32:11 Speaker 3: different time periods June twenty twenty four looking at the

32:14 Speaker 3: version three tempo data and another event in October twenty

32:19 Speaker 3: twenty five after the release of the version four tempo data.

32:24 Speaker 3: So zooming in here on the East Texas do main

32:29 Speaker 3: to the left. Here, I'm showing the Global power Plant database,

32:33 Speaker 3: and we are indicating or denoting these locations of the

32:38 Speaker 3: black circles here, which are locations of coal fired power

32:42 Speaker 3: plants in the region. And the larger the circle, the

32:46 Speaker 3: larger the capacity of the power plant. They looking at

32:50 Speaker 3: our Tempo tropospheric two animation here for this one day

32:54 Speaker 3: on June six, twenty twenty four, from morning to evening.

32:59 Speaker 3: One thing we noticed right away is a lot of

33:02 Speaker 3: n O two data, so this is a very cloud

33:05 Speaker 3: free day, a great day for TEMPO to gather data

33:09 Speaker 3: across the region. But also looking at the high n

33:13 Speaker 3: O two column data and evolution of the in two

33:17 Speaker 3: columns across the Dallas and Houston area as we normally see,

33:23 Speaker 3: and also some higher in two columns in the locations

33:26 Speaker 3: of the traffic corridors in the region. However, going back

33:31 Speaker 3: to the coal fired power plant locations, we are are

33:34 Speaker 3: also denoting those locations on the n O two map.

33:37 Speaker 3: Here we are seeing that very large capacity power plant

33:42 Speaker 3: to the southwest of Houston, and we're seeing a large

33:46 Speaker 3: inn O two plume in the vicinity of that power plant,

33:50 Speaker 3: which is you know, we see that being transport that

33:53 Speaker 3: plume being transported south in the later afternoon hours. In

33:57 Speaker 3: addition to that, we are seeing other areas is of

34:00 Speaker 3: n O two hot spots tied to those coal fired

34:03 Speaker 3: power plant locations. One in particular right here to the

34:08 Speaker 3: west of that large coal fire power plant we're seeing

34:11 Speaker 3: this evolving n O two hotspot tied to that power plant.

34:16 Speaker 3: So it's kind of showing the ability for TEMPO to monitor,

34:19 Speaker 3: to capture and monitor in two hot spots related to

34:23 Speaker 3: coal and also gas fired power plants in certain locations.

34:29 Speaker 3: So doing more of a quantitative look at the actual

34:34 Speaker 3: two column values tied to these power plants on this

34:37 Speaker 3: slide here.

34:39 Speaker 2: But first off, I'll note that.

34:43 Speaker 3: You know two column values and diurnal evolution of those

34:47 Speaker 3: n O two column values in and Dowtown urban location

34:51 Speaker 3: outside of the power power plant, and we're seeing here

34:55 Speaker 3: is also we have Pandora total n O two on

34:59 Speaker 3: this plot as well in black and the Tempo two

35:02 Speaker 3: total two data and red. The main point here I

35:05 Speaker 3: wanted to touch on is the increase in total two

35:10 Speaker 3: column in the morning late morning time period and also

35:14 Speaker 3: this peak that we see in the later afternoon time period,

35:18 Speaker 3: and that's kind of when we often see traffic starting

35:21 Speaker 3: to peak in the area. So those two peaks could

35:25 Speaker 3: very likely be tied to the increase in traffic and

35:27 Speaker 3: the increase in emissions related traffic. And looking at the

35:32 Speaker 3: coal fired power plant locations, though we'll start with the

35:36 Speaker 3: one and here to the north, and the evolving trice

35:39 Speaker 3: was the smaller capacity power plant, and we're seeing this

35:44 Speaker 3: diurnal evolution of O two, a peak happening here in

35:48 Speaker 3: the midday time period on June sixth, twenty point four,

35:52 Speaker 3: and then kind of a decreasing trend in the later after

35:54 Speaker 3: and afternoon. A much different look at the location of

35:59 Speaker 3: the lar the largest coal fire power plant in the region,

36:03 Speaker 3: where we're seeing these relatively low values throughout the morning

36:07 Speaker 3: and mid morning, but then a large increase around mid

36:10 Speaker 3: day and that continues to increase in the later afternoon

36:14 Speaker 3: hours as we kind of saw in the animation on

36:17 Speaker 3: the previous slide as well, so really showing the ability

36:20 Speaker 3: for Tempo to track these inn O two columns and

36:23 Speaker 3: associate with both the urban areas but also these hot

36:27 Speaker 3: spot areas of N two like coal fired power plants.

36:32 Speaker 3: This is for another day after the release of the

36:37 Speaker 3: version four Tempo data on September seventeenth, twenty twenty five.

36:42 Speaker 3: This particular day is October fifteenth, twenty twenty five. Name

36:46 Speaker 3: zoom in across East Texas, and once again we see

36:50 Speaker 3: this as a day where there is a lot of

36:53 Speaker 3: cloud free the very cloud free day temple is able

36:56 Speaker 3: to perform its retrievals and more kind of picture as

37:01 Speaker 3: we saw on the previous animation where we have these

37:05 Speaker 3: large n O two column values tied to the urban

37:09 Speaker 3: areas of Dallas and Houston, and that evolving trend of

37:13 Speaker 3: two throughout the day across those large urban areas, but

37:16 Speaker 3: also this large n O two plume that is once

37:20 Speaker 3: again in the vicinity of that very large coal fire

37:24 Speaker 3: power plant to the southwest of Houston, and we see

37:28 Speaker 3: that being transported to the southwest in the later afternoon

37:32 Speaker 3: hours there so, and also we see hot spots tied

37:35 Speaker 3: to those other areas of coal fire power plants in

37:37 Speaker 3: this domain as well. Now taking a look at formaldehyde.

37:44 Speaker 3: Now we have a total column from Audehyde on October fifteenth,

37:48 Speaker 3: twenty twenty five to the left here and the same

37:51 Speaker 3: tempo tripospric you know two map to the right as

37:54 Speaker 3: we saw on the previous slide. And the main take

37:57 Speaker 3: home point here on this slide is that we see

38:01 Speaker 3: these increases in from at a Hyde in and around

38:04 Speaker 3: the urban Houston area and in the vicinity of that

38:09 Speaker 3: large coal fire power plant that we saw in the

38:12 Speaker 3: two data. So this is kind of a common theme

38:16 Speaker 3: that we see in this very industrialized area of Houston

38:20 Speaker 3: where we have large, you know, enhancements in from aute

38:23 Speaker 3: hyde in and around that city. So one of the

38:28 Speaker 3: really big products that were released as part of our

38:33 Speaker 3: version four data product release was our ozone profile. So

38:38 Speaker 3: this is the same day on October fifteenth, twenty twenty five.

38:41 Speaker 3: But looking at the un animation of our ozone profile

38:45 Speaker 3: data to the left, I'm just sharing a screen capture

38:49 Speaker 3: of the air now ozone monitors, So the ozone concentrations

38:53 Speaker 3: observed by our ground monitors on this day, and what

38:58 Speaker 3: we're seeing here is on a healthy level of ozone

39:00 Speaker 3: being measured at the at the ground level and the

39:04 Speaker 3: area of interest here that we're going to be looking

39:06 Speaker 3: at more closely here to the southwest of Houston, where

39:10 Speaker 3: we're seeing these uh you know, numerous monitors measuring unhealthy,

39:13 Speaker 3: unhealthy ozone levels. Now looking at our tropospheric ozone animation

39:19 Speaker 3: here in the middle and our zero two kilometer ozone

39:24 Speaker 3: map here to the right, we're seeing here is now

39:27 Speaker 3: the ability of TEMPO to track and monitor propospheric ozone

39:32 Speaker 3: levels that do correspond to what we're seeing at the

39:36 Speaker 3: ground level. So one area of interests in that vicinity

39:40 Speaker 3: of the in and around Houston and nearby that coal

39:44 Speaker 3: fired power plant, where we are seeing these increases in

39:48 Speaker 3: tropospheric ozone column values and also an even more distinct

39:53 Speaker 3: increase in our zero two kilometer ozone column values. So

39:59 Speaker 3: and these really do correlate well with what we're seeing

40:01 Speaker 3: at the ground level. So the main take home here

40:04 Speaker 3: is that tempo, spatial and temporal information on ozone can

40:09 Speaker 3: help supplement the ground based monitors in these type of regions.

40:16 Speaker 3: And we'll move on to our case exercise too, and

40:19 Speaker 3: now I hand it off to Christina for that.

40:25 Speaker 1: Okay, So now we're going to explore some of the

40:28 Speaker 1: visualizations of the tempo data that we can do in

40:31 Speaker 1: worldview of these urban air quality conditions that Aaron just

40:37 Speaker 1: talked about. So here we have loaded a view on

40:43 Speaker 1: southeastern Texas for June sixth, twenty twenty four, which was

40:50 Speaker 1: a Thursday. We have our place labels on. We can

40:54 Speaker 1: see Dallas up here, Houston, San Antonio. Some things to

41:01 Speaker 1: note in the visible in a little bit of a

41:04 Speaker 1: contrast to the conditions that we were looking at in

41:07 Speaker 1: the first two case studies for these cases, when we

41:11 Speaker 1: look at the true color imagery, we're not necessarily seeing

41:14 Speaker 1: any any visible signals in that true color imagery as

41:19 Speaker 1: we would associated with wildfires. But we're here to look

41:23 Speaker 1: at tempo, so we are going to get into the

41:25 Speaker 1: tempo data. We're going to again focus on the nitrogen dioxide.

41:31 Speaker 1: We have level three data vertical column troposphere. So we'll

41:36 Speaker 1: just turn on this layer and what we can see

41:42 Speaker 1: because we're at twelve o'clock UTC, that is seven am

41:49 Speaker 1: local time at this time of year, so we're seeing

41:53 Speaker 1: just the beginning of the day. This is where the

41:58 Speaker 1: beginning of the sunlight extends. So we can increment at

42:03 Speaker 1: thirteen UTC. We can start to see the full scene again.

42:08 Speaker 1: As we mouse over the Dallas area, the urban area

42:13 Speaker 1: around Dallas Fort Worth, we can see the higher values

42:16 Speaker 1: associated with the urban emissions, the same thing with Houston,

42:21 Speaker 1: and then some other signals throughout. So we can just

42:27 Speaker 1: step through the day, step through the tempo observations which

42:31 Speaker 1: are available for this day. The time step has been

42:34 Speaker 1: set to one hour, so if I do increment date,

42:37 Speaker 1: we're seeing that it's stepping one hour at a time.

42:40 Speaker 1: And as we do that, we can start to see

42:43 Speaker 1: some of the diurnal, some of the time varying signals

42:47 Speaker 1: that are happening during this particular day. Again, this was

42:51 Speaker 1: a Thursday, around fifteen hundred three pm UTC is around

42:58 Speaker 1: ten am local. This is about rush hour. We can

43:03 Speaker 1: sort of see some elevated nitrogen dioxide between San Antonio

43:10 Speaker 1: and Austin. If we zoom in a little bit, we

43:15 Speaker 1: can see that this corresponds to the highways that go

43:20 Speaker 1: between the two metropolitan areas. So it's quite possible that

43:26 Speaker 1: this is a signal associated with some of the traffic

43:31 Speaker 1: emissions during a typical weekday. We can step through. We're

43:39 Speaker 1: seeing that there's some variability, there's a pretty consistent signal

43:45 Speaker 1: of nitrogen dioxide associated with the urban areas some of

43:50 Speaker 1: the traffic emissions. And if we go back to the

43:56 Speaker 1: beginning of the day, we can also look at the

43:59 Speaker 1: feature look for the features that Aaron had highlighted, specifically

44:04 Speaker 1: those cold fire power plants do that again, So if

44:16 Speaker 1: we go back to the beginning of the day, we

44:20 Speaker 1: can change the view a little bit to draw out

44:23 Speaker 1: some of the more strong features of higher nitrogen dioxide.

44:30 Speaker 1: So similar to what we did, we can go to

44:32 Speaker 1: this view options little slider similar to what we did

44:36 Speaker 1: for ozone previously. We're going to take the thresholds and

44:41 Speaker 1: we're just going to essentially mask out the lower values.

44:49 Speaker 2: Of NO.

44:49 Speaker 1: Two. So we've just masked out the lower values to

44:59 Speaker 1: be a to better highlight the areas of higher nitrogen

45:05 Speaker 1: dioxide in this scene. So if we step through the

45:08 Speaker 1: day again, we can see the highlights of the traffic

45:16 Speaker 1: that we noted before. It stands out a little bit

45:19 Speaker 1: better when we're only focused on the elevated values. And

45:25 Speaker 1: one other thing to note, as Aaron had pointed out

45:28 Speaker 1: in his slides, there are some coal fired power plants

45:31 Speaker 1: in the area. If you keep an eye on this

45:35 Speaker 1: area of High N two right here, and there was

45:38 Speaker 1: another one just to the southwest of Houston right there.

45:43 Speaker 1: Keep an eye on those. You can see that they

45:46 Speaker 1: are associated with elevated two in a way that the

45:52 Speaker 1: surrounding areas are not necessarily showing. So you can pull

45:59 Speaker 1: out some areas of focus for potential sources in this way.

46:10 Speaker 1: So that's the day of one example of a weekday.

46:16 Speaker 1: If we go back to the beginning, now, we're going

46:20 Speaker 1: to do some comparisons with across multiple days. So we're

46:24 Speaker 1: going to go to the comparison feature in Worldview. So

46:27 Speaker 1: this start comparison button at the bottom left. When the

46:36 Speaker 1: comparison panel first opens, it has the layer that we

46:41 Speaker 1: were just on, which is the June sixth example. It's

46:44 Speaker 1: added a different layer for a week previous. That's just

46:46 Speaker 1: the default that it chooses, and we can see that

46:49 Speaker 1: this is a particularly cloudy case, so not necessarily great

46:52 Speaker 1: for looking at tempo data. Anyway, We're just going to

46:55 Speaker 1: go a couple days later. We're going to go to

46:59 Speaker 1: June eighth at the same time, and the time incrementor

47:08 Speaker 1: has defaulted back to six minutes. Let's just change it

47:11 Speaker 1: back to an hour to be associated with the tempo

47:14 Speaker 1: resolution there. So now we have two days. We have

47:20 Speaker 1: on the left June sixth, twenty twenty four, on the

47:23 Speaker 1: right we have June eighth, twenty twenty four, and we

47:26 Speaker 1: can move this slider back and forth to see the

47:28 Speaker 1: differences between the two. And so this is a weekend day,

47:34 Speaker 1: a weekend day, and a weekday day, and some things

47:41 Speaker 1: jump out. Let's step forward a little bit. Unfortunately, we

47:46 Speaker 1: have to do it one step at a time, each

47:51 Speaker 1: one individually. But let's compare some apples to apples. So

47:59 Speaker 1: if we go to fifteen UTC, that's ten am local

48:05 Speaker 1: time on June sixth, we see that there's a pretty

48:10 Speaker 1: visible association with commuter traffic. Likely that's not necessarily the case,

48:17 Speaker 1: or that's not the case for the two days later

48:20 Speaker 1: June eighth. And of course this is a sample size

48:23 Speaker 1: of two. This is a qualitative examination of the conditions

48:29 Speaker 1: over two separate days. But this allows us to identify

48:35 Speaker 1: locations and times and potential areas of interest for further

48:40 Speaker 1: quantitative analysis, hopefully using some of the other tools that

48:43 Speaker 1: are available to an analyzed tempo data. So let's go

48:51 Speaker 1: a little bit later in the day just to check

48:56 Speaker 1: a different timestep. This is going to nine pm UTC,

49:09 Speaker 1: which should be four pm local time. Okay, here we

49:15 Speaker 1: are at nine pm UTC, four pm local time. We

49:21 Speaker 1: can again see traffic is present at this time, that's

49:27 Speaker 1: not necessarily present, and we don't see the stronger signal

49:32 Speaker 1: from these power plants that were identified either on the

49:36 Speaker 1: weekend versus the weekend or the weekday versus the weekend days.

49:42 Speaker 1: So we can explore the spatial patterns a little bit

49:45 Speaker 1: and we can identify locations and places for further examination

49:51 Speaker 1: or further analysis. One more example, just from these snapshots

49:58 Speaker 1: of two days, Let's go to twenty three UTC about

50:01 Speaker 1: six pm.

50:09 Speaker 4: Let's go to the same time on June eighth.

50:17 Speaker 1: This is an interesting example because it also highlights the

50:24 Speaker 1: differences in spatial distribution of urban air pollutions. So if

50:31 Speaker 1: you look at Houston on June sixth here and June

50:36 Speaker 1: eighth here, Admittedly the magnitudes are different, but it seems

50:39 Speaker 1: quite likely that there's something meteorologically going on different because

50:44 Speaker 1: the highest concentrations tend to be in the south, and

50:48 Speaker 1: then they tend to be in the north for both

50:51 Speaker 1: the Dallas and the Houston metropolitan areas for these particular dates.

50:56 Speaker 1: So again a tool to examine, a tool to identify

51:02 Speaker 1: areas and times for further examination. Okay, so another thing

51:11 Speaker 1: that Aaron showed was a second snapshot in time from

51:17 Speaker 1: October of twenty twenty five. Unfortunately, the version four data

51:21 Speaker 1: are not available in Worldview at the time of this taping,

51:24 Speaker 1: but we are going to go and explore some of

51:27 Speaker 1: the seasonal differences by going to October of twenty twenty four.

51:35 Speaker 1: October tenth was also a Thursday, as was June sixth,

51:45 Speaker 1: twenty twenty four, and the differences between the two. So

51:50 Speaker 1: now we have June sixth on the left and October

51:53 Speaker 1: tenth on the right, and the differences in magnitude of

51:59 Speaker 1: two values that we can see when the scales are

52:02 Speaker 1: the same are really quite significant here. So there's much

52:11 Speaker 1: higher concentrations of No. Two in this particular day several

52:17 Speaker 1: months later compared to the signals which are in June.

52:22 Speaker 1: So here we have October and let's just step through

52:29 Speaker 1: to see what the daily cycles look like. Remember last

52:33 Speaker 1: time we started at twelve UTC, which was seven am,

52:38 Speaker 1: it's actually six am because of the time change that

52:42 Speaker 1: happens in the US, So if we go to twelve

52:45 Speaker 1: o'clock at six am, there's no data because there hasn't

52:49 Speaker 1: been sunlight yet in this particular time of year. So

52:54 Speaker 1: let's just go a little bit later, and here we're

52:57 Speaker 1: starting to see at thirteen hundred we're starting to see

53:00 Speaker 1: some of the data to the east. By the time

53:04 Speaker 1: we get to fourteen hundred, we can see that there's

53:07 Speaker 1: a pretty significant we're seeing a view of the Houston

53:13 Speaker 1: and Dallas areas, which are within Tempo's view at this

53:18 Speaker 1: time of day, and so fourteen hundred is eight am

53:22 Speaker 1: local time at this time of year. So if we

53:26 Speaker 1: go and look at the nitrogen dioxide, we can see

53:29 Speaker 1: that the values are much higher than they were for

53:32 Speaker 1: a similar day in June. If we move a slider

53:36 Speaker 1: back so we can look at it a little bit better,

53:41 Speaker 1: expand that let's go to the same time of day

53:48 Speaker 1: at fourteen hundred if we'll remember what that looked like.

53:58 Speaker 1: So here is October and here is June. So these

54:06 Speaker 1: data really allow us to examine potential seasonal patterns as well.

54:12 Speaker 1: And of course, as we know, these are only snapshots

54:15 Speaker 1: in time. This is one particular day, one particular time

54:19 Speaker 1: of day for this location. It lets us identify a

54:24 Speaker 1: place to start for a more quantitative analysis looking at

54:27 Speaker 1: different patterns, either between seasons or at different parts of

54:34 Speaker 1: at different times throughout the day. But we can quite

54:39 Speaker 1: easily see the magnitude of these values that are happening

54:44 Speaker 1: in October, so a late fall. Let's go so we

54:48 Speaker 1: can see the scale right here. We can see that

54:51 Speaker 1: these the maximum values here are much higher in Dallas

54:56 Speaker 1: and across Houston compared to what they were in June,

55:02 Speaker 1: where we had lower values overall, and masking out some

55:08 Speaker 1: of the lower values can sort of highlight that to

55:11 Speaker 1: the human eye a little bit better. So we can

55:14 Speaker 1: see that the values are significant much higher at this time.

55:21 Speaker 1: So we're going to step through the time the diurnal

55:27 Speaker 1: cycle for this particular date in October. So starting two

55:33 Speaker 1: pm UTC is eight am Central Standard time the local time.

55:39 Speaker 1: Here we can step through similar to what we did

55:45 Speaker 1: in June. We can see there is variation throughout the

55:58 Speaker 1: course of the day, still seeing some of the sources

56:03 Speaker 1: associated with transport likely but the No. Two values very

56:17 Speaker 1: early in the day and towards the end of the day,

56:25 Speaker 1: so it was twenty three twenty three hundred would be

56:35 Speaker 1: five pm local. There's still some significant differences that are

57:04 Speaker 1: visible at each of these timesteps. Comparing these two particular cases,

57:12 Speaker 1: so that can give you a sense of how to

57:15 Speaker 1: use this world view tool to identify times and places

57:20 Speaker 1: further analysis to compare particular cases particular days where you

57:25 Speaker 1: may be interested. We can also let's exit comparison now,

57:36 Speaker 1: so we can also set up animations to visualize the

57:40 Speaker 1: full daily cycle for example of a particular parameter. Notice

57:46 Speaker 1: I've slimmed down the variables that we're just that we're

57:51 Speaker 1: visualizing right now, just because this can be a little

57:56 Speaker 1: computationally intensive depending on your network connection and your computer system.

58:02 Speaker 1: But we go down to set up animation in this

58:06 Speaker 1: little video icon, and it'll bring up a tool here,

58:11 Speaker 1: and we have our start time here June sixth at

58:19 Speaker 1: one pm UTC, two June sixth at eleven pm UTC.

58:23 Speaker 1: So let's yeah, let's do a twelve hour window. It's

58:27 Speaker 1: going to animate the map in one hour increments. You could,

58:30 Speaker 1: for example, pick one the same timestep every day, and

58:34 Speaker 1: then you can also alter the speed of the animation

58:39 Speaker 1: so how quickly it displays them. But three is three

58:43 Speaker 1: is a decent speed. And we've turned on the animation loop.

58:48 Speaker 1: We can enable or disable the animation loop, which just

58:50 Speaker 1: means it's going to play it over and over again.

58:55 Speaker 1: As we do that, we can see down at the bottom.

58:58 Speaker 1: It's giving us an indication of what timestep is being

59:03 Speaker 1: displayed at a given time, and then we can just

59:06 Speaker 1: sort of watch the daily evolution of the two patterns

59:12 Speaker 1: here based on how we've chosen to visualize it. If

59:16 Speaker 1: we want to save this for later viewing, it's easy

59:20 Speaker 1: to create an animated GIF, click on this little save

59:26 Speaker 1: icon with a video on it. It may give you

59:29 Speaker 1: a notification based on whichever layers you've had. It won't

59:32 Speaker 1: save the place labels, but you can just say okay,

59:38 Speaker 1: and then it will give you oh and it also

59:41 Speaker 1: will only it won't preserve those thresholdings that we've done

59:49 Speaker 1: to only display the higher values of n O two

59:56 Speaker 1: if we want to export it as a variable, but

59:59 Speaker 1: we can are exported as an animation mother. But we

1:00:02 Speaker 1: can drag this box to encompass the area that we'd

1:00:07 Speaker 1: like to save. There's some parameters here based on the file.

1:00:11 Speaker 1: You can change the resolution. It'll give you a sense

1:00:14 Speaker 1: of how big it is. And including date stamps is

1:00:18 Speaker 1: nice so you know exactly what time and place you're

1:00:21 Speaker 1: looking at. We can say create gift again. This may

1:00:28 Speaker 1: think about it for a little bit, and then we

1:00:33 Speaker 1: can see that it is saved this animation over the

1:00:38 Speaker 1: daily time cycle for this June sixth, twenty four day.

1:00:44 Speaker 1: We can click download and we can see that that

1:00:47 Speaker 1: just has saved this to our computer and make a

1:00:54 Speaker 1: bigger view it in whichever of the gift visualization rules

1:01:00 Speaker 1: that you would like. So let's keep that off to

1:01:03 Speaker 1: the side for a little bit longer. Another way to

1:01:09 Speaker 1: compare different cases, we can go back to our October case,

1:01:16 Speaker 1: and we were on the tenth Thursday. Let's go to

1:01:22 Speaker 1: the beginning of the daytimes. It needs to be manually

1:01:32 Speaker 1: be done. It's mad in the red there because the

1:01:36 Speaker 1: ending time is earlier than the starting time, but we

1:01:41 Speaker 1: will fix that October tenth. October tenth. There we go,

1:01:53 Speaker 1: and we can see that the blue here is showing

1:01:58 Speaker 1: where we're going to display it, which is going to

1:02:00 Speaker 1: twelve hour period. Some of these, as we can remember,

1:02:04 Speaker 1: will not be showing data because of the lack of

1:02:08 Speaker 1: sunlight and as we get into the winter, the Northern

1:02:12 Speaker 1: Hemisphere winter season. So now we can see that it

1:02:15 Speaker 1: is animating as we hope it will over the course

1:02:23 Speaker 1: of this particular day. If we want to save this animation,

1:02:30 Speaker 1: and it doesn't want to deal with the change thresholds,

1:02:33 Speaker 1: but that's okay, and it doesn't want to have the

1:02:36 Speaker 1: place labels, but that's okay. This is about the same

1:02:43 Speaker 1: area that we had viewed last time. So if we

1:02:51 Speaker 1: create our gift, you can see that there are a

1:02:55 Speaker 1: couple of time steps of missing data that will incorporate

1:02:59 Speaker 1: when you support it in this way. But overall, it

1:03:03 Speaker 1: can give you a sense of what the diurnal cycle

1:03:06 Speaker 1: is for a particular case or a particular time open

1:03:10 Speaker 1: that perhaps pare the two to get a sense for

1:03:35 Speaker 1: how the diurnal cycles may compare and how the magnitude

1:03:40 Speaker 1: of the values may compare one to the other. So

1:03:43 Speaker 1: it's again a qualitative visualization, but it can be useful

1:03:48 Speaker 1: for some purposes to be able to visualize the evolution

1:03:52 Speaker 1: in that particular way. So one final thing that we

1:04:00 Speaker 1: haven't yet looked at is other parameters aside from nitrogen dioxide.

1:04:05 Speaker 1: So if we go back to June sixth, beginning of

1:04:08 Speaker 1: the day again, let's see if we can do a

1:04:12 Speaker 1: comparison back to our case in the fall, so summer

1:04:19 Speaker 1: versus fall. Let's go back here and change our eight

1:04:35 Speaker 1: Let's speak.

1:04:40 Speaker 4: Sure we're looking at hourly, and let's turn off our

1:04:46 Speaker 4: nitrogen dioxide layer. Let's not do fifteen hundred, let's do sixteen,

1:04:54 Speaker 4: and let's do from aldehyde here. So similar to what

1:05:26 Speaker 4: we saw with nitrogen dioxide, formaldehyde as a parameter tends

1:05:31 Speaker 4: to be, as we know, more noisy. But we can

1:05:35 Speaker 4: set up a comparison between two different days or two

1:05:40 Speaker 4: different timesteps within a day to see how those variables

1:05:44 Speaker 4: compare with one another. So similar to what we saw

1:05:48 Speaker 4: in the nitrogen dioxide, there are higher values here in

1:05:54 Speaker 4: this is October on the right hand side, So the

1:05:57 Speaker 4: main part of the figure right now compared with the

1:06:02 Speaker 4: values that we see on the day in June, and

1:06:06 Speaker 4: there is some variability, so we can see that that

1:06:09 Speaker 4: variation as well. So this is again another thing to

1:06:13 Speaker 4: explore the different variables. Hopefully the tools that we've shown

1:06:18 Speaker 4: here can give you a sense of how to look.

1:06:23 Speaker 1: At events that you may be interested in or that

1:06:26 Speaker 1: you want to learn more about or see how they

1:06:28 Speaker 1: evolve at a broader spatial area, and give a starting

1:06:33 Speaker 1: place for where you can get into maybe more quantitative

1:06:37 Speaker 1: analysis or otherwise look at the effects of these kinds

1:06:43 Speaker 1: of air quality events in your in your area of interest. Okay,

1:06:56 Speaker 1: and with that, we're coming to the end of the

1:07:00 Speaker 1: presentation portion of this training to summarize what we've looked

1:07:05 Speaker 1: at today. Over two case studies, we've examined the spatial patterns,

1:07:09 Speaker 1: temporal evolution, and likely sources of trace gases related to

1:07:14 Speaker 1: cases of wildfire, smoke and urban air pollution events using

1:07:18 Speaker 1: the Tempo data visualization via Worldview. The Temple trace gas

1:07:23 Speaker 1: products in Worldview can be used to anticipate short term

1:07:25 Speaker 1: air quality risks such as eye concentrations of ozone precursors.

1:07:30 Speaker 1: The hourly tempo data allows for tracking of diurnal cycles

1:07:33 Speaker 1: of pollutants and identifying time varying emission sources. The Temple

1:07:38 Speaker 1: products is good correlation with ground based column measurements, indicating

1:07:41 Speaker 1: they may be useful to complement ground based air quality

1:07:44 Speaker 1: monitoring to improve the situational awareness of air quality risks.

1:07:48 Speaker 1: And finally, the new temp ozone profile products will add

1:07:52 Speaker 1: more insight to the air quality impacts of near surface

1:07:55 Speaker 1: ozone and those products should be within should be available

1:07:59 Speaker 1: within world View shortly, so keep an eye out for those. Now,

1:08:06 Speaker 1: I just want to present a few other options for

1:08:09 Speaker 1: accessing the Tempo data for more quantitative purposes. NASA data

1:08:15 Speaker 1: are stored at NASA's Distributed Active Archives Centers or dacks,

1:08:19 Speaker 1: and the Tempo data live at the Atmospheric Science Data

1:08:23 Speaker 1: Center highlighted here in yellow. And here are some links

1:08:30 Speaker 1: which will be provided on the slides associated with this

1:08:33 Speaker 1: training for some further tools and services which allow for

1:08:38 Speaker 1: TEMPO data distribution and access and understanding. And here's a

1:08:45 Speaker 1: link to the TEMPO Operations page, which provides a record

1:08:53 Speaker 1: of the nominal operations the scans some particular special ops

1:09:00 Speaker 1: such as we saw with the California wildfires or any

1:09:04 Speaker 1: unplanned outages. You can follow that link and check for

1:09:08 Speaker 1: particular cases. We also have the Earth Data Forum, which

1:09:16 Speaker 1: is a place where scientists and data users can connect

1:09:22 Speaker 1: to answer any questions that may come up regarding utilization

1:09:27 Speaker 1: of the TEMPO data. And that's a resource available to

1:09:32 Speaker 1: the community. And now we're at the ending of our training,

1:09:39 Speaker 1: so to summarize what we've gone over over the past

1:09:43 Speaker 1: two sessions. Tempo's hyperspectrol capability across ultraviolet to visible wavelengths

1:09:49 Speaker 1: enables retrieval of criteria trace gas pollutants in the troposphere

1:09:52 Speaker 1: at unprecedented spatial and temporal resolution across Greater North America.

1:09:57 Speaker 1: TEMPO observes small scale emission sources and fine scale pollutantingradients

1:10:01 Speaker 1: that have not been adequately resolved by previous satellite missions,

1:10:05 Speaker 1: and its geostationary hourly and daylight observations and special observation

1:10:10 Speaker 1: modes allow for monitoring of rapidly evolving pollutants supporting air

1:10:14 Speaker 1: quality analysis and forecasting. The newly available ozone Profile product

1:10:19 Speaker 1: which we talked about, offers enhanced capabilities to monitor and

1:10:23 Speaker 1: characterize ozone concentrations within the troposphere, and as a Worldview

1:10:27 Speaker 1: provides visualization capabilities of the hourly Level two and Level

1:10:31 Speaker 1: three trace gas data products with basic quality assurance screenings.

1:10:36 Speaker 1: This can be used to determine spatial patterns, temporal trends,

1:10:39 Speaker 1: and likely sources of trace gases related to wildfire, smoke

1:10:43 Speaker 1: and urban air pollution events, so Worldview can be useful

1:10:50 Speaker 1: in qualitative analysis. For more quantitative analysis, you just will

1:10:53 Speaker 1: need to apply quality assurance variables to remove tempo data

1:10:58 Speaker 1: associated with larger errors or uncertainty for their analysis. And

1:11:01 Speaker 1: hopefully we've given some resources to help those interested in

1:11:08 Speaker 1: that next step. Regarding homework and certificates, there will be

1:11:12 Speaker 1: one homework assignment after this training available later today. You

1:11:16 Speaker 1: can access it from the training web page. Answers will

1:11:19 Speaker 1: be submitted via Google Forms and it will be due

1:11:21 Speaker 1: two weeks from today on February fifth, twenty twenty six.

1:11:25 Speaker 1: A certificate of completion will be awarded to those who

1:11:28 Speaker 1: attend all live sessions and complete the homework assignment before

1:11:31 Speaker 1: the given due date. You will receive a certificate via

1:11:34 Speaker 1: email approximately two months after the completion of the course.

1:11:38 Speaker 1: Thank you everyone who's attended today. And now we're going

1:11:42 Speaker 1: to transition to our question and answer session. Okay, we

1:11:48 Speaker 1: have a number of questions in the Q and A

1:11:52 Speaker 1: that are coming in. Any other questions, please feel free

1:11:55 Speaker 1: to keep typing those in the Q and A box.

1:11:58 Speaker 1: It should be at the bottom right of your web

1:12:00 Speaker 1: x screen. There's a little Q and A right there. Okay,

1:12:05 Speaker 1: So yeah, so question one was it came in a

1:12:09 Speaker 1: little earlier. It was asking about how to tell the

1:12:12 Speaker 1: cloud fraction threshold that is used in Worldview hopefully a

1:12:17 Speaker 1: little bit later in the demo we showed how to

1:12:19 Speaker 1: do that. By toggling the thresholds, tuggling on the cloud

1:12:24 Speaker 1: layer and then changing the threshold, you can isolate particular

1:12:29 Speaker 1: values of cloud fraction to better be able to identify

1:12:34 Speaker 1: where it is. So everything in Worldview is masked out

1:12:37 Speaker 1: at greater than fifty percent, and by toggling those thresholds

1:12:41 Speaker 1: you can have a better sense of what the cloud

1:12:44 Speaker 1: fraction is for different values. There, So and Aaron Please

1:12:49 Speaker 1: feel free to chime in for anything, but I'll just

1:12:52 Speaker 1: go through as many of these as I can. So

1:12:58 Speaker 1: question two, Aaron, do you want to speak to this one?

1:13:03 Speaker 1: This is asking about recommended cloud fraction and solar zenithangle

1:13:08 Speaker 1: quality control recommendations. Given that we showed in the case

1:13:15 Speaker 1: study that there are clouds smoke smoke plumes being identified

1:13:20 Speaker 1: as clouds. Uh, do you have any recommendations for how

1:13:24 Speaker 1: we can address that or how people can address that.

1:13:27 Speaker 3: Yeah, So, as was mentioned here or is mentioned here,

1:13:32 Speaker 3: the kind of stricter cloud fraction thrusholds that are within

1:13:38 Speaker 3: the tempo user guides, for example, are trying to kind

1:13:43 Speaker 3: of remove those those higher uncertain areas where tempo might

1:13:48 Speaker 3: be you know, not performing as well. So that is

1:13:52 Speaker 3: a complication, you know, when you're looking at the wildfire smoke.

1:13:56 Speaker 3: What can be a complication when looking at wildfire smoke plumes.

1:14:00 Speaker 3: We don't have a specific quality ish quality control recommendations

1:14:05 Speaker 3: for using tempo data in those smoke wildfire scenes. As

1:14:10 Speaker 3: we kind of highlighted it in the presentation material, you

1:14:12 Speaker 3: know that a cloud fraction of less than fifty percent

1:14:16 Speaker 3: appears to retain the majority of the tempo data within

1:14:19 Speaker 3: those smoke looms, but in terms of the understanding of

1:14:23 Speaker 3: the actual uncertainties and errors within those scenes have not

1:14:27 Speaker 3: been fully evaluated yet. So I think it's an area

1:14:31 Speaker 3: of ongoing work right now that we have, you know,

1:14:34 Speaker 3: to better understand kind of the best cloud fraction and

1:14:38 Speaker 3: also the uncertainty within those smoke looms.

1:14:43 Speaker 1: And retrieval of trace gases and aerosols where they coincide

1:14:47 Speaker 1: as always a that's a scientific challenge, I think beyond

1:14:50 Speaker 1: the scope before we're.

1:14:51 Speaker 2: Talking about exactly.

1:14:52 Speaker 1: Yeah, question three, if I wanted to graph the pollution

1:14:58 Speaker 1: the pollutant changes over period of time, can you download

1:15:01 Speaker 1: that data in Worldview? We didn't show it in the demo.

1:15:05 Speaker 1: Worldview does have a charting feature that is in beta.

1:15:08 Speaker 1: It's available for some of the layers, but it's not

1:15:12 Speaker 1: available for Tempo and it again is in beta. But

1:15:18 Speaker 1: if someone is interested in graphing the changes over a

1:15:22 Speaker 1: period of time on some of the ending slides, specifically

1:15:27 Speaker 1: tool specifically slide thirty five, there were a number of

1:15:32 Speaker 1: links that you can explore to download the data and

1:15:36 Speaker 1: to do a more quantitative analysis of changes over time.

1:15:40 Speaker 1: And hopefully we did show later after this question came in.

1:15:45 Speaker 1: We showed how to export an animation as a gift

1:15:48 Speaker 1: of a particular scene. So depending on how quantitative versus

1:15:53 Speaker 1: qualitative you want the changes over time the visualization to be,

1:15:59 Speaker 1: that might also sovere purposes. Question for.

1:16:07 Speaker 3: Aaron, did you have some of the ned Oh no,

1:16:10 Speaker 3: that was great, okay.

1:16:13 Speaker 1: Question for the aerosol product didn't overlap the true color

1:16:18 Speaker 1: smoke plume neatly? Yes, that is a that is due

1:16:27 Speaker 1: to the true color imagery being again a snapshot from

1:16:31 Speaker 1: either Modus or Veers. I believe this one may have

1:16:34 Speaker 1: been Veers, so those have a single overpass. They are

1:16:38 Speaker 1: polar orbiting satellites, so it only captured one snapshot at

1:16:42 Speaker 1: one particular time of day. So that is the base layer.

1:16:46 Speaker 1: For some scenes in Worldview, you can get the Goes imagery.

1:16:54 Speaker 1: I believe it only goes back about ninety days, and

1:16:56 Speaker 1: then they only save the Goes imagery for particular scenes

1:17:05 Speaker 1: that have been already flagged in advance. So the short

1:17:08 Speaker 1: answer is the true color imagery is showing a single

1:17:12 Speaker 1: overpast time, and one of the advantages to tempo is

1:17:15 Speaker 1: it allows us to see the evolution as opposed to

1:17:18 Speaker 1: just having the one snapshot that we have previously gotten

1:17:21 Speaker 1: from polar Orders. So question five came in as the

1:17:30 Speaker 1: demo was whereas we were going through the demo, it

1:17:36 Speaker 1: wasn't totally clear what we meant by the resolution. The

1:17:39 Speaker 1: demo focused on the level three products, so those are

1:17:42 Speaker 1: at point oh two by point oh two degree resolution

1:17:46 Speaker 1: throughout the field of regard. But there is a link.

1:17:50 Speaker 1: There's a link here in the Q and A documents

1:17:54 Speaker 1: and it's also on the slides in slide in the

1:17:58 Speaker 1: slide deck on slide twenty one one. So if you

1:18:01 Speaker 1: have any particular questions regarding the resolution or particular visualizations,

1:18:08 Speaker 1: you can go and explore that yourself and hopefully answer

1:18:13 Speaker 1: all of your burning questions there. And if anything else

1:18:17 Speaker 1: comes up, please feel free to add it into the

1:18:18 Speaker 1: Q and A box while we're still here. Question six

1:18:22 Speaker 1: is regarding converting molecules per unit area to concentration units. Arin,

1:18:26 Speaker 1: do you want to speak.

1:18:27 Speaker 3: To this a little bit, yeah, you know, this maybe

1:18:32 Speaker 3: a longer response here, but there are techniques or methods

1:18:36 Speaker 3: to as from the standard molecule for unit area, you

1:18:45 Speaker 3: know unit that's measured by tempo. Of course, this would

1:18:49 Speaker 3: involve inclusion of auxiliary data to convert those units, which

1:18:53 Speaker 3: can lead to additional uncertainties as well even after that,

1:18:59 Speaker 3: but even after that can version. Just keep in mind

1:19:02 Speaker 3: that the n O two ppm will still be valid

1:19:06 Speaker 3: for that column rather than the purpose level of values

1:19:10 Speaker 3: that you at those AQS data are providing. So there's

1:19:15 Speaker 3: not a direct, you know, comparison in terms of your

1:19:18 Speaker 3: AQS data versus the converted even if you did the

1:19:22 Speaker 3: conversion to n O two ppm. So, but there's some

1:19:27 Speaker 3: more information here though that people can check out. There's

1:19:30 Speaker 3: a Jupiter notebook that is shown here where folks can

1:19:34 Speaker 3: go through there and estimate surface concentrations from tempo data

1:19:38 Speaker 3: using the GEOCF model as additional source of information. So yeah,

1:19:44 Speaker 3: feel free to go check that link out there if

1:19:46 Speaker 3: you're intrigued by driving or if you are interested in

1:19:50 Speaker 3: driving a surface level concentration from tempo data.

1:19:56 Speaker 1: And I'll also note in in the slides there's the

1:20:00 Speaker 1: Earth Data forums, So any particular questions I think there,

1:20:05 Speaker 1: I think there's an active community on there for if

1:20:08 Speaker 1: anybody has any particular questions you can ask ask the community.

1:20:12 Speaker 1: Internet forums can sometimes be useful. So question seven, how

1:20:20 Speaker 1: do you create those figures in Houston. I think this

1:20:23 Speaker 1: was regarding talking to Aaron slides hopefully some of the

1:20:28 Speaker 1: figure in terms of the gift that was exported from Worldview.

1:20:33 Speaker 1: This is recorded. You can go back and you can

1:20:35 Speaker 1: follow through for that, Aaron, Do you want to talk

1:20:39 Speaker 1: about how you did it? It's python code, is the

1:20:42 Speaker 1: short answer.

1:20:43 Speaker 3: Yeah, Python code is a short answer. Again, there's a

1:20:46 Speaker 3: good link here that you can check out for the

1:20:51 Speaker 3: data access scripts that's provided by the ASDC center. So

1:20:56 Speaker 3: can I recommend clicking on that link there or using

1:20:59 Speaker 3: that link there to check out more information in regards

1:21:02 Speaker 3: of that, But yeah, those are just you know, what

1:21:04 Speaker 3: we did there was download the TEMPO data from the

1:21:07 Speaker 3: Atmospheric Science Data Center and then run Python code on

1:21:13 Speaker 3: our system here to produce those maps. So it's kind

1:21:17 Speaker 3: of the long or the short answer there.

1:21:24 Speaker 1: Our question eight is the question about improving temporal and

1:21:30 Speaker 1: spatial resolution using combined satellite products and other tools. Is

1:21:34 Speaker 1: it possible to check trace gas variability over small spatial

1:21:37 Speaker 1: skills in regions without ground based stations such as Pandora.

1:21:41 Speaker 1: I think that is exactly the advantage of TEMPO is

1:21:46 Speaker 1: that it has a very high spatial resolution that it's

1:21:50 Speaker 1: described in this answer here two kilometers by four point

1:21:54 Speaker 1: seventy five kilometers at the center of the field of regard.

1:21:57 Speaker 1: So it does have a lot higher spatial ReSm compared

1:22:01 Speaker 1: to some other sensors. Aaron, I don't know if you

1:22:03 Speaker 1: want to answer and add anything.

1:22:06 Speaker 2: To that or I think that's great.

1:22:09 Speaker 3: I was a little bit confused by using the using

1:22:11 Speaker 3: combined satellite products or other tools. But yeah, essentially TEMPO

1:22:14 Speaker 3: will provide this directly right because of a high spatial

1:22:18 Speaker 3: resolution alongside the high temporal resolution, so we can resolve

1:22:23 Speaker 3: those kind of variability and trace gas plutants in those

1:22:26 Speaker 3: you know, and those highly complex urban areas where you

1:22:30 Speaker 3: might expect those to occur.

1:22:34 Speaker 1: Sounds good. Question nine is asking when whether Tempo will

1:22:40 Speaker 1: be added to the Noah Aerosol Watch site. Yes, that

1:22:44 Speaker 1: is something that has come up that we've all been

1:22:47 Speaker 1: eagerly waiting for. The We can see that the answer

1:22:52 Speaker 1: is being written right now. As far as this is

1:22:57 Speaker 1: in line with my understanding as well, there are plans

1:22:59 Speaker 1: to include the aerosol products into the Noah Aerosol Watch site.

1:23:07 Speaker 1: We are hoping mid twenty twenty six, and we are

1:23:10 Speaker 1: planning a aerosol specific training. So this is the tempo

1:23:15 Speaker 1: trace gas specific training, and we're planning a tempo aerosol

1:23:18 Speaker 1: training for after those data are available, so that we

1:23:22 Speaker 1: can fully demonstrate all the capabilities there. And yeah, there's

1:23:28 Speaker 1: a link to the in person training that is mentioned

1:23:31 Speaker 1: in this question as well. It was a much longer

1:23:35 Speaker 1: version of what everyone here has gotten as sort of

1:23:39 Speaker 1: a crash course of about three hours. It was instead

1:23:42 Speaker 1: three days. So the materials are available on the website

1:23:45 Speaker 1: and feel free to look into those a little bit

1:23:47 Speaker 1: more as well. Anything else, Aaron, That was great. Yeah,

1:23:54 Speaker 1: you always seem to have the insider infota when the

1:23:59 Speaker 1: temple airsol.

1:24:01 Speaker 3: Oh, looking forward, I know there is a discussion. Yeah,

1:24:04 Speaker 3: and then mid twenty twenty six timeframe may or thereafter,

1:24:08 Speaker 3: so hopefully by may's kind of the UH as early

1:24:13 Speaker 3: as May of twenty twenty six, I would say, in

1:24:16 Speaker 3: terms of the timeline, but it could be.

1:24:17 Speaker 1: After of course, but we're hoping I think we're hoping

1:24:21 Speaker 1: for around yeah, mid mid twenty twenty six. UH. Question

1:24:25 Speaker 1: ten is Question six was so how to convert molecules

1:24:30 Speaker 1: per unit area to concentration units? But for ground level

1:24:34 Speaker 1: ozone an Aaron, I see your I know you can't

1:24:38 Speaker 1: write and talk at the same time.

1:24:40 Speaker 3: Do you want to speak to that and I'll just

1:24:43 Speaker 3: speak to it as I just saw this come across

1:24:46 Speaker 3: right now. But yeah, So again the tempo we showed

1:24:49 Speaker 3: tempo ozone data, right, we have two different types of

1:24:53 Speaker 3: tempo ozone products. We have the Tempo Total column ozone

1:24:58 Speaker 3: and then the Tempo ozone pro file, which provides that

1:25:01 Speaker 3: additional information on ozone concentration in the troposphere. So that

1:25:07 Speaker 3: being said, that product does not provide information on ozone

1:25:12 Speaker 3: concentrations at the ground.

1:25:14 Speaker 2: Or the surface level.

1:25:15 Speaker 3: So we do provide that lower tropospheric ozone concentration which

1:25:21 Speaker 3: was kind of referred to as a zero f two

1:25:23 Speaker 3: kilometer ozone concentration that was shown in the presentation, but

1:25:28 Speaker 3: there is not an actual surface level ozone that one

1:25:33 Speaker 3: can get at from that product. That would require additional

1:25:38 Speaker 3: additional techniques to derive a tempo surface ozone value that

1:25:44 Speaker 3: would be directly comparable to the AQS minors.

1:26:01 Speaker 1: Question eleven is regarding formaldehyde available on Google Earth Engine.

1:26:10 Speaker 1: It seems like there might I haven't got into that

1:26:13 Speaker 1: personally myself, but there's some links here in this answer,

1:26:19 Speaker 1: and again this document will be posted on the website

1:26:23 Speaker 1: probably in the next couple of days, if not sooner.

1:26:28 Speaker 1: So yeah, formaldehyde, I guess should be available on Google

1:26:32 Speaker 1: earth Engine.

1:26:34 Speaker 3: They should what you're saying, they should already be available

1:26:37 Speaker 3: right now. I thought you right this one, but yes,

1:26:44 Speaker 3: let me.

1:26:45 Speaker 2: I can verify that.

1:26:46 Speaker 3: I'm pretty sure they're supposed to have from out of

1:26:48 Speaker 3: hyde data already available in Google earth Engine.

1:26:51 Speaker 2: But I can verify.

1:26:56 Speaker 1: And our emails are also in these slides as well,

1:27:00 Speaker 1: So if there are any particular questions that can't be

1:27:05 Speaker 1: answered on again, peruse the earth Data forum as well

1:27:08 Speaker 1: to see maybe somebody else has the same question. That's

1:27:11 Speaker 1: always a good place to start for quick answers. But

1:27:14 Speaker 1: you also have our emails for anything particular.

1:27:19 Speaker 3: Yeah, I'll just say that when I'm kind of googled

1:27:22 Speaker 3: here when on Earth Engine data catalog for Tempo, and

1:27:27 Speaker 3: I'm looking at five different products in here right now,

1:27:31 Speaker 3: including from Autohyde, the O two and also the Ozona

1:27:36 Speaker 3: total column should be available within Google earth Engine.

1:27:41 Speaker 2: I'm seeing on their main page here.

1:27:44 Speaker 1: Sounds good, And the link to Tempo and Google earth

1:27:47 Speaker 1: Engine is also in the slides on slide thirty five

1:27:50 Speaker 1: on the link of Tempo Data Distribution Tools, so you

1:27:55 Speaker 1: don't need to have I think exactly that link. That

1:27:58 Speaker 1: might be the same link that are in the slides,

1:28:01 Speaker 1: but again this will be posted as well. That might

1:28:06 Speaker 1: be the end of our questions. Give it. I mean, we're, we're.

1:28:16 Speaker 1: We got two minutes left, so I think we That

1:28:20 Speaker 1: was actually quite on time, wasn't It filled the time

1:28:25 Speaker 1: with the questions and hopefully we answered everyone what everybody

1:28:29 Speaker 1: was wondering about. Yeah, I think we can just end

1:28:38 Speaker 1: it there. Thank you everyone who joined. Thanks again for

1:28:42 Speaker 1: having so many good questions for us. We hope this

1:28:46 Speaker 1: was useful and have a good rest of your day

1:28:50 Speaker 1: and thanks for joining us.

1:28:53 Speaker 2: Thank you every much.