NASA ARSET North American Geostationary Trace Gas Data Products for Air Quality

Speaker 1: Welcome everyone to our RSET training series Geostationary Remote Sensing of Trace Gases for Air Quality Applications in North America. Today is part one of our training series North American Geostationary Trace Gas Data Products for Air Quality. My name is Christina Pistone. I am a research scientist at the Bay Area Environmental Research Institute and NASA AIMS in the California Bay Area, and I am the RSET lead for this training. Before we begin today's training, I'm just going to say a quick few words about the ARSET program.

Speaker 1: The NASA Applied Remote Sensing Training or ARSET program, provides cost free training on the use of remote sensing observations, analysis methods, and tools. We provide training in several thematic areas including agriculture, climate and resilience, disasters, ecological conservation, health and air quality, water resources, and wildland fires. Our SET provides trainings both online or in person. Our online trainings are delivered in two formats, live and instructor led like today's training, or asynchronous and self paced like NASA's free and Accessible data.

Speaker 1: All of our trainings are offered at no cost, and we only use no cost and open source software and data. We try to offer trainings in more than one language whenever we can, including a planned Spanish version of this training later this year. We offer our trainings at a range of levels, so you can find a training series that will fit your level of experience and need. Please visit us at our website to learn more. We'll start today by giving an overview of this training series. Geostationary a remote sensing of trace gases for air quality applications in North America.

Speaker 1: The Tropospheric Emissions Monitoring of Pollution or TEMPO mission represents a new capability of hourly monitoring of trace gases across North America. It is the first space base based hyperspectral instrument to continuously measure daytime air quality over North America from the Atlantic Ocean to the Pacific and from Central Canada to southern Mexico. These data are essential for understanding diurnal changes in air quality and monitoring real time movement of pollutant plumes such as wildfire and urban emissions.

Speaker 1: This training will provide an overview of the TEMPLE capabilities and available trace gas data products, and will illustrate how TEMPO data can be visualized using the NASA Worldview online tool. Temple's capabilities will be demonstrated through hands on case studies using the Temple data, and participants will learn how to interpret the TEMPO observations for understanding of the scope and potential air quality impact of some common scenarios. By the end of this training, attendees will be able to describe the basic characteristics of TEMPO and its benefits and limitations compared to other air quality relevant missions and instruments.

Speaker 1: Identify available trace gas data products from TEMPO and their associated characteristics. Visualize hourly Tempo trace gas data products using NASA Worldview for current and historical events. Evaluate Tempo trace gas products in Worldview to anticipate short term air quality risks such as high concentrations of ozone precursors. Distinguish uses for TEMPO trace gas data products given cloud and solar zenith angle thresholds, and determine the spatial patterns, temporal trends, and likely sources of trace gases related to wildfire, smoke, and urban area air pollution events using Tempo data in Worldview.

Speaker 1: As a perrequisite to this course, we've recommended that you take our Fundamentals of Remote Sensing training to familiarize yourself with some terminology and concepts that you might hear in today's training. Some examples that are important in today's training include what is meant by spatial and temporal resolution of a sensor, the differences between level one, level two, and level three of satellite data products, and the characteristics of satellites in geostationary orbit. This training will take place over two days, with Part one, which is today we are covering North American geostationary trace gas data products for air quality.

Speaker 1: It's offered in two sessions at two different times as you can see here. Part two will be two days from now, covering case studies and trace gas monitoring with North American geostationary sensors at the same times. On that day, after Part two, a homework assignment will be posted on the training webpage and a certificate of completion will be awarded to participants who attend all live sessions and complete the homework assignment before the given due date. And with that we're going to get into part one of this training.

Speaker 1: First, a brief introduction to who we are. As I said earlier, my name is doctor Christina Fistone. I am a research scientist at Barry and with me today is doctor Aaron Nager, the Tempo Mission Applications Lead at NASA Marshall Spaceplight Center.

Speaker 1: The objectives of Part one of this training. By the end of this part, attendees will be able to describe the basic characteristics of TEMPO and its benefits and limitation compared to other air quality relevant missions and instruments, Identify available trace gas data products from TEMPO and the associated characteristics, distinguish uses for Tempo trace gas data products given cloud and solar zenith angle thresholds, and be able to visualize hourly Tempo trace gas data products using NASA Worldview for current and historical events.

Speaker 1: So brief logistics for how to ask questions. Please feel free to put your questions in the questions box and we will address them at the end of the webinar. You can enter them as we go. We will try to get to all the questions that we can during the Q and A session, but the remainder of the questions will be answered in the Q and A document which we posted to the training website about a week after the training, and with that I will turn it over to Erin.

Speaker 2: All right, well, Thank you Christina for that introduction. Now we're in dig into an overview of the Temple Mission and the instrument and its capabilities. A few slides here the Tempo Mission. The major goal here is to provide high resolution observations or measurements on air pollutants across grittered North America every hour during the daytime, and some of the key objectives here are to deliver revolutionary air quality data to the community and the public, improve air quality forecast and alerts, better understand pollution sources, better inform policy and regulation, and enhance health studies from pollution exposure estimates.

Speaker 2: To the right, I'm sharing a few of the key products or observations from TEMPO, including nitrogen dioxide for modehyde and total ozone and those matters. Showing also the Tempo Field over guard as we call it as well there in red. And at the bottom left, I'm showing a really nice image of a nice picture of our Tempo instrument when it was completely integrated on our satellite IS forty E prior to launch a few quick facts on the Tempo Mission. It was NASA's first Earth Dventure instrument, selected in twenty twelve and it's a joint project with the Smithsonian Astrophysical Observatory.

Speaker 2: It is a hyper spectral ultraviolet visible spectrometer that is sensitive to policy relevant trace gases and aerosols. And do the bottom right here is a figure showing the observation area that TEMPO performs in. So it performs on two different detectors in the ultra violet and visible, so we're seeing these two ninety four ninety nanometer wavelength spectrum and five forty seven forty nanometer spectrum where Temple provides these very high spectral resolution observations to get at key information on these air pllutants shown here, and the Temple fieldver guard covers greater North America.

Speaker 2: The standard coverage of TEMPO is shown in the black area here, and however Tempo is able to go outside those bounds. There's a max field reguard coverage shown here in purple. We've done some test with moving Tempo outside the standard domain, but of course our standard normal observations were done within the black area shown there. Temple was launched into geostationary Earth orbit on a SpaceX rocket to its current longitude at ninety one degrees west on April seventh, twenty twenty three. Tempo's first light or Earth observations were done on August second, twenty twenty three, and we had a law fairly six month about commissioning phase, which was successful and then led into our nominal operations phase of the mission which we're currently in, and that occurred on October nineteenth, twenty twenty three, and we are currently Our mission is currently funded, funded and extended through September twenty twenty six.

Speaker 2: Of course, TEMPO can be up there ten plus years depending on funding down the road. So here's a nice picture too of the SpaceX launch the Tempo launch on the SpaceX rocket on April seventh, twenty twenty three. So during the pre launch phase of the mission, we definitely did discuss the apple core applications of TEMPO with our early adopter community of the mission, and this was a diagram that the science and the early adopter community kind of built during the pre launch phase of the mission. You can see that the core applications definitely center around air quality, air qual modeling and forecasting air pollution emissions and monitoring.

Speaker 3: We have a large.

Speaker 2: Number of stakeholders in the air quality management community and also a pretty long list of early adopters involved in public health applications of TEMPO. Additional application areas include vegetation and ocean monitoring and weather analysis and forecasting. All Right, this slide here is showing the tempo's footprint size across the Temple field guard. The top figure here is showing the east to west pixel size and kilometers and the north south pixel size in kilometers. And as we're seeing here, we have very fine tempo footprints or high spatial resolution from Tempo across much of the field of our guard.

Speaker 2: We lose some of that information content as we go farther north in the field of our guard, especially in the northwest and northeast corners, but altogether very high spatial resolution. This table to the right shows different locations across the field regard and the Tempo footprint size tied to those locations. So key take home message here is again when you go further north in the Temple field reguard, like Juneo, Alaska Canadian oil sands, you have larger footprint sizes. If you go farther south, for example, over in Mexico City, we have fine scale pixel sizes.

Speaker 2: So there are some variations throughout the TEMPO field re GUARD. And here's a map zoomed in showing the TEMPO footprints in by these red boxes here red polygons, and showing the spatial coverage of TEMPO across the LA area. So you can see we're getting a lot of information content spatial information on air pollutants across the LA area by having this type of spatial resolution or footprint size across these urban areas. Now zoom into New York City or the New York broader New York area and again getting a lot of different Temple footprints that can retrieve information on air pollutants across the New York area and showing how Temple can observe the urban interurban air pollutant gradients.

Speaker 2: And same picture here over a similar picture here over in Mexico City. Of course, we have even higher spatial resolution across Mexico City. So we're really zoomed in here and showing the large number of Temple footprints that we're able to that we have over this region of interest to the south of the field for GUARD.

Speaker 3: All right, now we're.

Speaker 2: Going to dig into some actually showcase some TEMPO data here and look at our how temple performed it scans across the field for guard. This is an example on July twenty six, twenty four, where we had our nominal operations performing, and this is Tempo tropospheric. You know two data shown in this animation where we're seeing how these nominal operations consist of both these standard hourly daytime scans where we're gathering data across an entire field reguard and also these optimized shorter scans that we're seeing here later in the afternoon across the west.

Speaker 2: So we see the same picture here across the east in the morning where we have only daylight in that region, so we're performing the tempo operations in that region the optimized scans. Then we moved to these standard operations and then back to the optimized operations in the west.

Speaker 3: In the afternoon.

Speaker 2: So that's kind of the day in the life of Tempo in terms of making us observations.

Speaker 3: And we also had.

Speaker 2: A special component of Tempo where we can zoom in to areas of interest and perform Tempo special operations. So this was a four day time period from January sixteenth and nineteenth, twenty twenty five where we reformed these very high resolution time stands across the west coast of the US and then went back and forth between our nominal hourly operations and our special operations throughout the day, and this for a four day time period, so this is another really unique component of tempo as well.

Speaker 2: This was done right after the large La wildfires that occurred in January twenty twenty five, So if we zoom in to that La Basin area and this is kind of when the wild wildfires kind of died down, but they're still smoldering from the wildfires. And this animation to the left is showing though the propostrict two that was collected during those special operations and the high variability in two columns that were preserved across the La Basin region. And when taking a look at the time series information here for two days from January through seventeenth and taking a location of interest within the location of the Palisades wildfire, we're seeing how during this moldering time period of the wildfire event, we're capturing this two diurnal variability which is not quite captured by the standard normal observations shown here in black.

Speaker 2: So these the blue line here blue dots are showing the addition of the special operations over the region compared to the normal standard observations. Now down here to the bottom bright we're showing an area in downtown LA where again we observe the stronger variability and two variability in downtown Los Angeles during the morning of January seventeenth, which was not captured by our Tempo standard scans. So again the addition of the special scans helped collect more information on two. And you can actually go to our Tempo operations log through this QR code here and find the day is when these special operations occurred.

Speaker 2: And here's a screen capture of part of that operations log, and where the special operations that were done for the LA region from January sixteenth through nineteenth we're done. They can kind of find that in the operations log along with the other special operations that we've done to date. All Right, So now we're going to discuss a little more detail on the Tempo trace gas products in the following slides,

Speaker 2: and first showcase how Tempo observes these trace gas plutants that we provide from the mission. And the key variable of the key input here is the altra violet to visible radiation from the sun. TEMPO requires sunlight to make observations of these trace gas plllutants and which then we observe these top of atmosphere radiance shown here in this diagram and along with our retrieved slant calmn density information, and we combine that with sun and satellite viewing condition information to report or retrieve information on vertical calm densities, which really the VCDs here provide information on the target trace gas of interest within a standard verdized vertical column of the atmosphere.

Speaker 2: So we're saying this is our key output and our key parameter that we provide in our tempo trace gas products.

Speaker 3: And how we do.

Speaker 2: That going from our slant column to our vertical column information or data in the tempo retrieval process is done by first driving our slant column densities from our measured top of atmosphere radiances that were shown in that previous diagram, and then we calculate vertical calm densities using air mass factors. And these air mass factors are calculated offline using a ray of transfer model and input from a global three D atmospheric composition model and Tempo in particular uses the Gotter Earth Observing System composition forecasting system to put.

Speaker 3: Together these air mass factors.

Speaker 2: And this equation shown to the left is showing the equation where we have slant colmn densities in the numerator and this air mass factor variable which actually combines a large number of information in the denominator which then derives or retrieves information on our vertical calm densities. And shown at the bottom of the slide here are the in O two slant colmn densities zoomed into the Pacific northwest here and our INNO two air mass factors in the middle and our total n O two Erkele Colm densities on the right.

Speaker 2: The key message here is that you can see how our slant colmn densities from off in O two are higher than our total n O two VCDs, and that shows how this process from going from our slant Colm density to our verical calm density leads to lower VCDs. As we're looking at the vircal column directly above the temple footprint compared to the the slant column information that's shown here. And we also go a bit further with our temple retrieval to derive propospheric n O two VCDs. So here is a comparison between our tropospheric n O two rcle calm densities and our total n O two And as expected, our tripospheric n O two VCDs are lower than our total column in O two the VCDs.

Speaker 2: And there was a wildfire that occurred during this time period, so it's large and O two plume in this map is tied to a large wildfire smoke event that occurred in North California. Now we go a bit to do this whole operation where we derive tropospheric n O twovcds. We apply a stratosphere troposphere separation technique in our temple retrieval process, and this method estimates the stratospheric n O two BCDs based on the slant colmn densities, the air mass factors, and then O two columns from the GEOCF forecast.

Speaker 2: The stratospheric n O two BCDs are then subtracted from the total column in two VCDs to arrive at our tropospheric n O two. So, and note the different scale on these figures here but to the left here again I'm sharing the total N O two BCD map. Here's our stratospheric O two BCDs can lower scale here much lower two in the stratosphere in general. And then to the right, I'm showing the tropospheric you know to BCD, so you can see here how we have these lower tropospheric colum amounts compared to her total, and how we go from our total to our tropospheric.

Speaker 2: And there's also quality assurance variables within the data files of TEMPO, which I'll go over here in a minute to where we can filter lower.

Speaker 3: Quality or higher uncertain data.

Speaker 2: And here is where I applied a filter where we only kept data with the cloud fraction less than fifty percent and a Slursian thing go less than eighty degrees, and going back and forth, you can see how we end up having these areas where clouds are removed, but also areas where even the smoke plume were removed, which I'll go into a bit more detail of another use case of when that happens. All right, So for the tip, this is a table showing the TEMPO data products the baseline, the near real time and also the upcome NOAH aerosol products.

Speaker 2: And we start with the level one data, the level one radiance data which is at this tempo footprint size of two by four point seventy five kilometer squared, which is the tempo footprint size at the center of the field regard. But as we saw earlier that footprint size varies throughout the tempo field regard. Then we drive or retrieve these various products shown here, including cloud products, ozone total column, nitrogen dioxide, fermadehyde, and ozone profile. Some of these are also near real time products, so we provide we are currently providing near real time products with data latency of less than two hours, and that includes our cloud nitrogen dioxide and FRAMATOHYDE data.

Speaker 2: And a key point with our ozone profile is the ozone profile provides information on triposphereic ozone column amount, so we get at information in the troposphere and lower troposphere, and we can actually track ozone concentrations really well in the troposphere through the ozone profile. Upcoming products in the near future are going to be our aerosol products including aerosol optical depth and aerosol layer height, and an aerosol detection product. Those level two products shown here. The baseline products also have a Level three product tied to them, which are pretty much very similar to our level two except they are put on a regular standard point zero two degree grid across the Tempo field regard, so those those grid point sizes don't change like the footprint sizes do.

Speaker 3: Up here.

Speaker 2: I do want to note that the Ozone profile Level three is a big courser at point zero four degrees compared to the other Level three products, and the reason for that is, as you can see here the resolution of our Ozone profile for level two courser because there's some co adding or averaging done of pixels to make that product of higher accuracy precision for the community, which is required. And following we have level four products which will include our surface PM two point five product with an hourly PM to point five estimation.

Speaker 2: Okay, now we're going to do a little more discussion on level two versus Level three products from Tempo. Picking off here is the general methodology where we go from our tempo level two products to a level three and the level three products are essentially created from re gridding level two data onto a regular grid using an area weighted averaging approach. So we do that at the point zero two degree grid for n O two from Autohyde and total ozone and at that point zero four degree grid for ozone profile.

Speaker 3: The equation that.

Speaker 2: Is used here is shown on the slide, and it's really just geomemetric regridding where we take contributions from nearby grid points that overlap that level two pixel a footprint and then apply this equation to get at our level three data for each grid point. And here's an example of the level two verst level three data zoomed into the Elliot Basin area on June eighteenth, twenty twenty five, zoomed in on Eli Basin and showing in the comparison between level two and level three. So we're seeing those kind of polygon shapes when we look at our level two which was shown on that map earlier, the Temple footprints, and now we have this like standard regular grid that we're seeing this Temple data appear in the level three product.

Speaker 2: So this is in the morning hours where we have High two likely tied to the traffic in the morning across the La Basin area.

Speaker 3: Before our level two data.

Speaker 2: We provide these in different data granules across the tempo field regard. There's nine different Level two data granules that compose each full tempo field regard scan during our hourly standard operations. So we're seeing us for one, this is simply an animation showing one hour of those nine different six of sevenment data granules that are provided across the temple field regard and notice did change before starting September twenty ninth, twenty twenty three, we went from ten to nine, so during the commissioning phase we had a little larger number before September twenty ninth, all right, So then we take those nine different tempo data granules for each full tempo field regard scan and compose a level three rescan data file.

Speaker 2: This is similar to the previous or same time frame as a previous animation, but just taking all those level two data granules, stitching them together and doing that remapping regrinding process to put that two data on a point zero two degree grid across a temple field reguard. This is how those level three scan data files are provided for tempo and zooming in Here to the LA basin again looking at the level two verst level three in two comparison for a morning, early afternoon and late afternoon evening time frame during this one day on June eighteenth, twenty twenty five, and we're seeing similar information as before in terms of you can really see the polygon shapes where the two column data are shown and then kind of the regular grid shown here.

Speaker 2: Uh. Kind of one key take home here is in general level three O two maps or data will have a smoother appearance compared to the level two n O two maps after doing that re guitting averaging process, and this can also lead to some significant differences in the like fine scale in O two hot spots and large in O two gradients when you're comparing the level two in level three data and maps. This is the same kind of comparison here except for for modehyde, so we're going from level two to level three for modehide comparison, Moldehyde is a noisier, more difficult retrieval compared to n O two, So in general that just makes this comparison a bit harder in terms of seeing where all these gradients or differences are occurring.

Speaker 2: But again similar thing here where we have a smoother appearance to our level three from moldehyde map due to that remap being recruiting process compared to our level two.

Speaker 3: All right, so.

Speaker 2: Now we want to talk a little bit more in detail about the quality assurance and data filtering methods which are really needed by the user community. And our recommendations here are based on the latest recommendations for the version four product release. It's an important to note here that we had a we went from version three to version four data. Version four data started being produced on September seventeenth, twenty twenty five. We haven't reprocessed the data prior to September seventeenth, twenty twenty five yet, but for now all our version four data are available starting September seventeenth, twenty twenty five.

Speaker 2: So these recommendations here are focused on that version four data and this table is showing the key quality assurance parameters that are recommended for use and this includes effective cloud fraction, solar ZMP angle and our main data quality flag.

Speaker 3: This quality flag is a bit.

Speaker 2: More complicated in terms of how you know what these flags refer to in the product in the variable and for example, zero of flag of zero means high quality temple retrieval.

Speaker 3: A flag of one.

Speaker 2: Means the suspect retrieval due to air mass factor or viewing geometry issues, and a flag of two means outlier retrieval or there is no successful air mass factor calculation. So for looking at the different ranges of these variables, we have the effective cloud fraction ranging from zero to one, solar zap angle as you'd expect from zero to ninety degrees, and our main data quality flag going from zero to two currently in Worldview for all the available Version three data that are available to view.

Speaker 2: These are the qualitative use thresholds that are applied, so they apply a cloud fraction lesson point five sols, the youth angle less than eighty, and a mandated quality flag less than two or quantitative use and research use. There are different recommendations, and our Tempo user guides that they recommend a much lower cloud fraction threshold of less than point one to retain high quality data that can kind of vary, you know, point two point one five depending on your application. Of course, solar nf angle is less than seven degrees for quantitative use and they recommend only retaining the high quality tempo retrievals or quantitative.

Speaker 3: Research use as well.

Speaker 2: All right, so this is an example of comparing those quality a currance thresholds and the different maps that can be shown based on those different assurance thresholds. So this is for one day on September twentieth, twenty twenty five, zoomed into the Pacific northwest here of tempo troposphrict n O two, and we're seeing here at the bottom are our most stricter quality assurance recommendations shown on the previous table. These are the thresholds at the top here are those currently used for the worldview in two visualizations.

Speaker 2: And the key message here is that there were some cloud cover during this time period and we're seeing more white masked out areas in the lower panels here due to cloud cover. But also we're seeing a wildfire event that occurred during this time period, so there is a pretty large wildfire and we're seeing very high n O two plume tied to that smoke. But when we apply that cloud fraction, the strict cloud fraction quality assurance threshold, we remove that smoke plume, the n O two plume from the map and analysis.

Speaker 2: So you know, carriit needs we take in when applying these cloud fraction thresholds based on your application and needs and looking at the similar thing here. But looking at solar zeth angle quality assurance, and this is using the strict cloud fraction of ten percent, but then looking at the solar z youngth angle of eighty degrees versus seventy degrees.

Speaker 3: So when we apply that.

Speaker 2: Stricter solarcanth angle threshold during the morning and late afternoon here we can see that we lose information content on two based on that updated or strict solars youngth angle. In these maps here and to the right, we're looking at the whole entire temple field reguard here applying the less strict cloud fraction. But the main change here was looking at a flag of equal to zero versus a flag of less than two. And by using a flag equal to zero we lose this triposphere you know two information in the far northwest and far northeast due to the viewing angle restriction flag within the product.

Speaker 2: So it's another thing to keep in mind when using the flag variables within the tempo n O two and from auto hyde data. And I wanted to share another table here for the quality assurance of recommendations for tempo total calm ozone slightly different but similar good degree we have quality flag parameters, effective cloud fraction, solar xenath angle viewing zenith angle recommendations. There's a lot more information with contained within the quality flag here compared to two and from Adehyde, including sunglint contamination and so two being present for you can kind of see the different ranges, much higher range with this quality flag or quality use.

Speaker 2: They recommend less than ten twenty four for the quality flag and for quantitative use for total column ozone. For best highest quality data they simply apply the flag of equal zero and if you want to retain more adequate data, you can also keep those flags equal to one, two and five effective cloud fraction. There's no filter currently being used for qualitative use in Worldview. However, for quantitative use and research they recommend a uneffective cloud fraction of less than point five. And you're seeing similar solar interviewing zenith angle quantitative use restrictions for our total ozone column as shown for.

Speaker 3: Two and from all hide.

Speaker 2: And now we're comparing the different quality assurance threshold shown here same day on September twentieth, twenty twenty five, as shown for two, but this is for total calm ozone and applying the qualitative thresholds that were shown in the previous slide and the more quantitative research thresholds, so as we would expect when applying a cloud for action. On the bottom panels, here we're seeing more areas removed due to cloud. Also there are there is some influence from applying that quality flag equal to zero as well.

Speaker 2: Some of these areas around here were also removed due to that quality flag. So again taking account kind of what happens going from our different qualitative to quantitative thresholding for this product. All right, so now we're zooming out and look at the tempo field reguard for total calumn ozone and the top here we are looking at the quantitative more research thresholds versus the qualitative thresholds that were shown on the previous slide. And as you can see, when we apply that stricter cloud fraction, we remove the clouds from the scene.

Speaker 2: But also there's some influence from that quality flag equal to zero. You can especially to see that quality flag equal zero and the sunlin contermination removed from the far south extent here. This is sun glint that's removed when we apply that quality flag equal to zero versus retaining that sun glint area and the bottom map, and there's also some influence from applying that quality flag in other areas of the field reguard as well. Okay, we're going to actually show some examples of our level to trace gas products and for the example, we're going to be focused on that version for data time period.

Speaker 2: This is zoomed into the South US US on October fifteenth, twenty twenty five. To the left here where's showing a beer's true color image and on the right showing the animation of tempo troposphere No. Two erical column densities on this one day from morning to evening across the domain, and what we're seeing here are high n O two BCD columns over Dallas Houston. We're seeing some elevated n O two across the major traffic corridors and over the premium basin as well. So there's a large oil and gas infrastructure and across the premium basin and we're seeing higher some higher n O two columns tied to that premium basin, along with some small hot spots of n O two tied to power plant locations, and also some fires that were occurring across the domain.

Speaker 2: Across east here especially, we had some fires that were recurring across Louisiana, for example, and some of these two hot spots were tied to those spires. And then this is another animation for total calm from adehyde the same day, October fifteenth, twenty twenty five morning to evening from adehyde information across the South ust.

Speaker 3: And one of the areas of higher from.

Speaker 2: Moto hyde concentrations if you look at the animation closely occurs over the Houston area, So that is a typical urban area tied to high from Otahyde concentration. So we see that in our from Autohyde animation here, along with other areas of elevated from ata hyde even over the Permium basin tied to the O two in that region and the oil and gas industry. And this is animation for total calm ozone for the same day. And again it's this total calm, So this takes an account built the proposphere and the stratosphere over you know, ninety percent on average of the ozone is contained within the stratosphere.

Speaker 2: That being said, we're still seeing some variations in total calum ozone in this animation with some high higher concentrations across the various regions, including in around the Houston area.

Speaker 2: Of course, now we have our tempo ozone profile data which is shown here. This is the tempo tropospheric ozone column data for that same day, and now we're seeing some stronger, more apparent distinct variations in our tropospheric ozone calum amounts as we retrieve this information only in the troposphere, and I'll highlight again one of the areas of interest over the Houston area as these higher ozone concentrations that occur in the later afternoon area during that animation, we can even get down to the lower troposphere with their tempo ozone profile product.

Speaker 2: So same day, but looking at the zero two kilometer ozone column information, and this is really neat what we see here. We'll replay the animation again from morning to evening and as you will see in a later afternoon evening hour, we see a definitely distinct high values that are occurring in and around the Houston area, mainly to the southwest where we actually where the ground monitors observed higher ozone columns, higher ozone concentrations on this day as well.

Speaker 3: So really neat that we're.

Speaker 2: Picking out some of the elevated lower tropospheric ozone in the area where the ground monitors measured higher ozone concentrations and unhealthy ozone levels.

Speaker 3: So we're changing day.

Speaker 2: We're changing days here to September twenty five because we had a large wildfire event occur on this day. So again to the left, I'm sharing the veer's true color image here on the in the Pacific Northwest, and we're seeing these large grayish color intensities here tied to wildfire smoke on this day. And to right, we're looking at the Tempo ultra violet aerosol index and this variable is contained within the tempo futal ozone product. So it's really neat about this as we see these higher UV aerosol index values tied to these wildfire smoke plumes, and that's due to the absorbing the absorbing nature of smoke particles which leads to the high UV aerosol index value.

Speaker 2: So this parameter can be very useful in terms of monitoring and tracking smoke plumes throughout the day. Okay, So I don't want to share some comparisons between TEMPO and our current low earth observing satellite instruments, the NASA Ozone Monitoring Instrument and TRIPOMI. All these results here are based on our version three Tempo products. So this is the comparison between our low earth orbiting Ozone Monitoring Instrument versus Tempo. Again, OMI is a low worth orbiting so it only provides the midday observations of air pllutants across the globe.

Speaker 2: And it's spatial resolution it is much coarser at thirteen by twenty four kilometers squared compared to what we have with Tempo.

Speaker 3: And this is showing just as zoomed.

Speaker 2: Out you know, over the west region of the US OMI triposophic two versus Tempo, and it is you can see the really incredible information content now we get from Tempo n O two compared to TRIPOM. And if we zoom in look at the zoomed in images over the La basin area versus Tempo versus OMI, we can see that Tempo in O two tropospheric vertical column densities are larger than OMI in urban areas and this shows over the LA area over MEXICALI two. And this is a combination of both the much higher spatial resolution of Tempo that's able to capture these areas are high n O two, but also the higher instrument sensitivity of Tempo as well, So this is really incredible.

Speaker 2: Now we're seeing with Tempo compared to our previous legacy instruments like OMI. Okay, now we're looking at comparison between Tempo and Tropomy. So Tropomy, like OMI, is a low worth urbaning satellite instrument, was launched in twenty seventeen and provides higher spatial resolution.

Speaker 3: Information than OMY.

Speaker 2: So now we're TROPOMI is providing two observations at five point five by three point five kilometers squared. So we can definitely see right away that the comparison between Tropomy and OMI is much closer than OMI and Tempo number one. There are differences for sure tied to the different instrument retrieval techniques, but also the spatial resolution as well. So if we can kind of see that if we zoom in closer to the Tempo versus TRIPOMI comparison across the La Basin area here where we are seeing in general higher two tropospheret N two from Tempo across the La Basin.

Speaker 2: Again, this likely has to do with both the different instrument retrieval techniques, but also the in general, the higher spatial resolute of tempo is contributing to these different inn O two differences. But it is important to note that we're still capturing similar in O two gradients tempo versus tropomi.

Speaker 3: There's differences in the values here.

Speaker 2: Okay, Now we're going to dig into a little more quantitative comparison between tempo and tropo ME two at these selected areas across the field. Regard shown here in the map in the top right you take home points here is that tempo is higher than tropo ME at these locations of interest. In general, tempo tends to be a little bit lower than tripo ME in non attainment areas. Tropomi tends to have a high bias in these low in two conditions. And if we do more of a detailed comparison between tempo, tropospheric O two and tripomi at the Boulder and the LA South Coast locations here, we're seeing that we're getting better correlations between tempo and tripomi across the Boulder area compared to the LA South Coast area.

Speaker 2: So we grapping correlations of point seventy three in Boulder in correlations of point three six across the LA South coast. So the main take home here is that Tempo and Tropomei they generally are an agreement, but they can also be their comparisons can vary at different locations across the field for guard as well. Okay, so now we're gonna do a little more in detail with the validation of Tempo products. This is again a validation comparison done for our version three Tempo products, and this is a comparison Tempo validation done against our Pandora network.

Speaker 2: So our Pandora is our main ground based validation network for Tempo and Pandora provides two and from autohyde observations or measurements like Tempo, but from the ground based perspective. And here we're showing the correlations and mean biases between Tempo and two and Pandora and Tempo from Adehyde and Pandora from Audehyde. And the key take home notes are that Tempo N two in general reproduces the spatial variability of Pandora. It correlates well at most of the Pandora sites, and for Tempo from Adehide we have similar outcomes here with barely high correlation at the Pandora site level, there is some reasonable bias with some of the individual sites that are requiring further investigation which we're currently doing.

Speaker 2: This validation work is ongoing with our version four data as well, so more to come in terms of our Tempo validation versus Pandora for our version four data. In general, our Tempo version three data showed good comparisons between are with our tempo with our Pandora observations of n O two and from Atohyde.

Speaker 2: And if we go into looking at Tempo versus Pandora, taking account all those sites that we just showed in the previous slide and look at the per month comparisons and variability, this is showing Tempo versus Pandora in O two at the top and from Autohyde at the bottom, And for both of these comparisons and figures, we are showing that Tempo two and from Autohyde captures the seasonal variation as well with pretty good bias as well low bias. So this is showing again that Tempo is able to capture these seasonal variations as shown by Pandora, and we can get into more detail in terms of the variations per day of the day they met before.

Speaker 2: But looking at tempo versus pandora in O two at the top and from at a hyde at the bottom for different time periods throughout the day for all those sites that we saw. So the key one of the key messages here is that the n O two for tempo during the time periods in the morning in the evening with higher solar as vienap angles, we see a lower correlation, but still good correlation between tempo and pandora.

Speaker 3: This is expected.

Speaker 2: We know that our tempo retrievals have lower accuracy during the morning and the evening.

Speaker 3: It's not as apparent in the framato hyde.

Speaker 2: When you're looking at the formati hyde comparison between tempo and pandora throughout the day, our correlations are in general lower than n O two, and that's to be expected because the formato hyde retrieval it's more difficult from both the tempo and pandora perspective, so great that we're seeing these overall good correlations in general with from audehyde as well. Okay, now we're going to go into some tempo data visualization and worldview and Christina will take it over from here.

Speaker 1: Thanks Arin for a great overview of tempo. Now we're going to get into some of the data exploration that you can do with the Tempo that are available on the NASA Worldview. So we are just going to go to NASA Worldview from a normal web browser. You can type it in it's worldview dot earth data dot NASA dot gov. You can see I've typed that and it's already populated. So when you go to Worldview, this is the landing page that you'll see. Worldview has some great stories to peruse the different data sets that they have here.

Speaker 1: I encourage you to check these out at a later date. Many of them are relevant to air quality topics, but we're not doing that today. So we're just going to x out this welcome page. And I should note you are welcome to follow along with what we're doing here. But this is also a recording, so you can go back. You can follow it through later as well at your own pace, pose it and everything. Some of the homework questions are going to involve similar examples, so that will be another opportunity to go through these tools on your own.

Speaker 1: So we are going to go to explore in more detail. The wildfires of January twenty twenty five in the LA area. Note to orient you a little bit to worldview. Down here at the bottom left, we can see it has defaulted to the current date twenty twenty six, January twelfth. Since we're going to go into the LA area, I'm just going to use my cursor scroll wheel to scroll into roughly the California area for those who may not be most familiar, We're just going to put on some placeland for orientation. And because we're going to January.

Speaker 1: Oh, that's the wrong way, because we are going to January twenty twenty five. I'm just going to click this arrow down. So now it's twenty twenty six to twenty twenty five, and we are going to go to January ninth of twenty twenty five, and again for some quick orientation. Here this is the LA area. We can see that the fires were already in process. The images that show up now are true color imagers and they're represented as base layers. So it has defaulted to terror modus and that's what we're seeing here, so it has more of a morning overpass.

Speaker 1: We can turn off this layer. We can turn on the aqua modis. We can see that it had a little bit of a different view later in the day, or if we want to do a Noah twenty veers, we can see that this was a little bit more in the middle of the of the We can see it was a little bit more in the middle of the satellite swath for this particular time. But what we're interested in today is the tempo data. So we are going to go to this red button down here the plus ad layers. We're going to click on it and it brings up this page which has again a number of topics.

Speaker 1: Feel free to peruse them at your own leisure. We have some air quality topics, some specific air quality topics like fires, dust, ash, et cetera. But since we are going to tempo, what we're going to go is we're going to go to this top search bar and type in tempo and as we do that, we can see that a number of different products show up. So these are all of the products for Tempo which are currently available in Worldview. So we can click on formaldehyde and it'll give you an idea the temporal coverage, the a little bit of the metadata.

Speaker 1: We want to explore all of these, so We're just going to click the little checkbox here, and you can see behind this panel that I'm in right now, these data sets are slowly

Speaker 1: populating underneath here. You can see it's a little bit above this formaldehyde here, So if I we're to turn off the formaldehyde, we can see that the nitrogen dioxide is adding itself to the list of variables the layers that we're going to have available for this particular data. And as you can see, sometimes it can be a little bit tedious to do all of these things to check all of these boxes. So we are just going to go to a window where I've already had all of these loadeds, so we're just gonna pull the cake out of the oven.

Speaker 1: Already done. One thing again to note, so now we have all of the tempo layers loaded and they are hidden, so we will go through and look at them one at a time. So I've hidden all of those. And another thing to note for when we first opened Worldview, we had year, month, day. You'll remember, you'll notice since we included the tempo layers, we're now having an hour and minute and this is in time of UTC. And there's also this little increment feature here it is defaults to six minutes because the tempo revisit time is hourly.

Speaker 1: We're just going to click on this. When we click on these arrows, it's going to be an hourly timestep. So first here, what we're going to do is look at the nitrogen dioxide since Aaron showed some great examples there. So we're just going to click on this level three layer. And what are we seeing here? This may be something that will happen as you're exploring worldview. You can see that the values here are fairly uniform. As I'm mousing my cursor over the map, you can see on the left hand panel there's a little triangle in line that's giving you an idea what the numerical values are.

Speaker 1: They're fairly uniform values. What has happened here and you might encounter this. We have perhaps inadvertently put on the stratosphere level three layer. So this is nitrogen dioxide level three vertical column stratosphere, which is not maybe necessarily what we want to do if we are looking at the air quality issues. So now we are looking at nitrogen dioxide level three vertical column, tropospheric and sub daily. This is a little more what we would expect as we're looking at nitrogen dioxide near surface values, and again we can use the cursor to mouse over the areas of very high concentrations.

Speaker 1: You can see the values here almost three times ten at the sixteenth molecules per centimeter squared. If we move the cursor out towards the ocean, where it's a little bit more pristine or uncontaminated, we can see that the values get much lower. We can see again some other values from general other sources of pollution in the San Francisco Bay area and the central value of California, so we're seeing different sources in addition to the very strong smoke plume that we're seeing in the LA area. So now we're going to use these arrows that we set up to increment hourly, and as we do that, we're seeing again the bar down at the bottom.

Speaker 1: You can see it's stepping by an hour at a time, and we can see that the values are also changing. So we're able to see the different time steps that are going on throughout the course of this day. Looking at the scene and looking at the values that we're observing here, what has happened here? We have gone to January tenth, at twelve forty five am UTC. This corresponds to four forty five pm local California time, which is after sunset at this time. So you'll remember tempo is only available during daylight hours, and it's a little bit difficult to see, but if you look on this course, if you look on the time bar here, there's a little bit of a different shading from the nighttime values here, so it's a little bit darker, and then there's a change where this is showing where the values are the data are available over the course of the daytime.

Speaker 1: So that can give you a sense of where data are available from this instrument for this particular time. So we can step backwards in time again. So now we've reset to sort of the beginning of the day. Okay, so that is nitrogen dioxide level three data. We can also look at the two level two data, and again we're picking the tropospheric values. As we turn off this layer the tropospheric level three data and turn on the level two data, you can see this is how that looks. As Aaron mentioned, this is the level two data are presented in this way.

Speaker 1: One thing that you can do there's a little tool here called view options. If we click on these little slider options, we're going to increase the granule count here from its default of one to nine, because again there's nine granules in the Tempo overpass. And so now we have the full US one snapshot of Tempo is visible at a time, and we can see if we mouse over these, it's giving us a little bit more specific timestep for this swath sixteen forty five, sixteen thirty eight, sixteen thirty one. So these are in approximately six minute intervals, going from starting at the earliest time fifteen fifty two as it progresses from east to west across the US So we can see that the Level two data are visualized a little bit differently.

Speaker 1: Here, again we can step through and see the evolution of the scene over the course of the day to give a sense of the differences between Level three and Level two data. This is just a comparison mode. We're going to talk about this more on day two. Here we're comparing the level three data on the left and the Level two data on the right. And if we drag the slider, this is the level two data and the Level three data and You'll remember, as Aaron described, the native grid of tempo in the level two data are a little bit more of an angled grid, whereas the level three data are averaged over point zero two degree grids, so it's a little bit more regular.

Speaker 1: This is a way to sort of visualize a little bit the differences between the two between level two versus level three. So that's the No two level two data. We can also look at the ozone column values, so we can only look at the ozone total column. But let's see what we have. This is the ozone level three column values. Again, if we mouse into different places of the map, we can see that the values on the color bar to the left are changing with the different values to giving you a sense of the numerical values that we're looking at here.

Speaker 1: The main takeaway here with the ozone total column is it's very difficult to see any signal from the wildfire event that we know is happening in the troposphere in the column values. So that's ozone. We can again hide that layer and now let's look at for aldehyde. And looking at for aldehyde, it is similar to what we saw in the nitrogen dioxide. We're seeing a clear plume a very distinct plume associated with the wildfire smoke that we know is happening as it goes from beginning of the day. We can sort of step through hour by hour and see the changes the evolution in the scene and the values that we're seeing informaldehyde over the course of the day and as it disperses off the coast, and again we can mouse over and get a sense of quality the quantitative values that are within this plume as well.

Speaker 1: And finally, what we're going to look at, We're going to briefly look at the aerosol index Aaron mentioned as well. It's retrieved in conjunction with the ozone product here and higher aerosol index indicates the presence of absorbing aerosols, so such as dark smoke in this scene, which we certainly can tell is happening here. So going to the beginning of the day, we can see that detection of absorbing particles is present in this plume as it was observed by Tempo as it evolves over the course of the day.

Speaker 1: So that's the aerosol index as well. So one final thing that we can look at for this event. As Aaron mentioned, there were special operations special every ten minute observations that were happening a few days later during this same event. And so here is the custom interval selector one thing we're going to do. Because they were every ten minutes currently we're set out. We've had it set to be every hour. We're going to go to Custom and do intervals of every ten minutes, and so now our increment is going to be every ten minutes.

Speaker 1: The special operations happened on January sixteenth and seventeenth, so we're just going to navigate to the sixteenth, and let's start at eight o'clock Zulu, and then we are going to look at the too level two tropospheric values here, so we'll turn on that layer. And then because we're looking at special operations, we are going to go back into our tools and reduce the granule counts so we're only looking at let's say two at a time. So now we're only looking at two granules at a time, so we can x this out.

Speaker 1: And so now we are looking at January sixteenth, eight o'clock Zulu, which is noon Pacific standard time, and we are seeing there are two overpasses here or two swaths here. Nineteen fifty four, nineteen fifty nine. If we increment another ten minutes, now we're seeing twenty oh four twenty oh nine. As we step through the time incrementor of every ten minutes, we can see how the scene evolves over the course of this day. At this ten minute resolution, where we have the high high revisit time, high temple observations during this particular hour.

Speaker 1: So I encourage you to go to the Tempo log and see if there were any other special operations that may have happened at scenes of interest. So that concludes our visualization exercise in Worldview for today. We will be back in Worldview on day two to explore a couple more case studies that we can observe with these data that are available. There are also some other tools that you may want to explore for visualization of tempo data. The slides are of it will be available on the website after the after the training is done.

Speaker 1: So here are some other tools that you may be interested in. So this is the conc illusion of our part one Tempo Geostationary trace gas training to summarize what we went over today. Tempo's hyperspectral capability enables us to retrieve criteria trace gas pollutants in the troposphere at unprecedented spatial and temporal resolution across Greater North America. It allows us to observe small scale emission sources and fine scale pollutant gradients that have not been adequately resolved by prior satellite missions.

Speaker 1: The geostationary nature of its observations allow for monitoring of rapidly evolving pollutants which support air quality analysis and forecasting. The nitrogen dioxide and formaldehyde products show overall good agreement to the Pandora Validation Network and tropomeat satellite data. The ozone Profile product, which is currently in beta, will offer enhanced capabilities to monitor and characterize ozone concentrations within the troposphere. And that's a worldview applies tempo quality assurance, which is suitable for qualitative visualization.

Speaker 1: For more quantitative analysis, users will need to apply quality assurance variables to both level two and Level three products, which remove the tempo data with larger errors or uncertainties from that analysis, with the caution that using strict quality assurance tends to remove large nitrogen dioxide columns from wildfire smoke. To look ahead to our Part two, which again will happen in two days. In Part two, we will evaluate the tempo trace gas products in Worldview to anticipate short term air quality risks such as high concentrations of ozone precursors.

Speaker 1: And we will determine the spatial patterns, temporal trends, and likely sources of trace gases related to wildfire, smoke and urban air pollution events using tempo data in Worldview, and we will look at two case studies, one on some fires in the Colorado Front Range and then urban air quality conditions and pollution sources over East Texas. As a quick reminder, this training series will have one homework assignment. You will be able to access the homework assignment from the training web page starting January twenty second after Part two, and answers should be submitted via Google Forms within two weeks so by February fifth.

Speaker 1: Certificates of completion will be issued to participants who attend all of the live trainings and submit the homework before that deadline. The certificates will be issued via email about two months after the training. Here we've included the contact information for the speakers today, as well as links to the our set website and YouTube channel. We encourage you to sign up for our mailing list. Here are some additional links and resources. Thank you for attending today. I'm going to transition to the Q and A portion of the session.

Speaker 1: Okay, we've been getting some Q and A already coming into the WebEx. Anybody who has questions there should be a Q and A box to the bottom in the bottom right of the WebEx. Feel free to put your questions in that chat and we will try to get to them. So question one, I guess we'll start there. Can you explain why Tempo is using a continuous UV visible spectrum instead of discrete bands like other satellites, Aaron, do you want to elaborate a little bit on that?

Speaker 2: Yeah, hopefully can hear me? Okay, yep, sound great to me, all right, great, Yeah, So Tempo is using a spectrometer that very fine spectral resolution across that UV visible spectrum. So that's very critical in terms of detecting that very fine absorption spectrum and the differences in the absorption between those critical trace gases that Tempo was designed to provide to the community. So that's really the key part, and that really goes even further with the ozone concentrations because that the uv VIZ spectrometer does also enable improve sensitivity to ozone in the troposphere layer so compared to using distrect discrete bands like other satellites.

Speaker 2: So in general and overall, the uv VIZ spectrometer provides that higher precision and improves sensitivity to trace gases compared to using a satellite with distrect with discrete bands for example.

Speaker 3: Hopefully that clears things up.

Speaker 1: Thanks. Yeah, And to remind everyone, this Q and a document will be posted on the training web page after the fact as well, so you'll be able to go back over these. So question two is regarding the special scans. So basically I think is what happens when the when the special scans are happening. Is there a special instrument or or how does how do the special scans affect the standard scans that are going on?

Speaker 3: Do you mean to go for that one?

Speaker 1: Sure?

Speaker 2: Okay, yeah, so I think you kind of answer it there on the document here. But yeah, so we're always there's no special no, no separate special instrument. It's all the tempo spectrometer that does the special versus the standard scans, right, so we just transition the tempo instrument from performing the standard normal hourly daylight operations to a special scan mode during a day, right. So you saw that example in the training where we did that same kind of special scan operation for a four day time period.

Speaker 2: So you know we can do that too, where we set the special scan mode to operate for four continuous days and then have those ten minute you know, for example, ten minute scans over a certain portion of the field of our guard, you know, happening for that four day time period during the daylight.

Speaker 1: Yeah, And in the slides, I'll also mentioned in the slides there's a link to the Tempo Operations LUG which can give a record of when special scans were happening. And so if if there's data missing, it could in a particular time, it could be because there was a special scan happening at a different in a different time and place, or in a different place during the same time. Rather, we're getting a number of questions, I'll just combine them all together. There's a number of questions about the stratospheric tropospheric division and how that's, how that is, how that process is done, and how that distinction is made.

Speaker 1: Aaron, do you want to talk a little bit more about that in sort of a general term. I think I'm combining a bunch of these questions, but they're all similarly themed.

Speaker 2: Yeah, I did notice there's a handful of questions related to that. So what we really do is we actually use the total column, right, and we subtract out the tropospheric column. So really we're not retrieving information directly on the stratospheric portion of that. We have a you know, TEMPO provides a total column and information directly we use in a retrieval process to get at the tropospheric colum amount and that and after that we're able to at least get an idea on the stratospheric portion.

Speaker 2: That's how it separate out in the Tempo data prodcasts well, for n O two, for example, we have you can get information on the total column, the triposphere column, and the stratospheric colum portion within the Tempo data product for two. For formalde high, there's a question later on regarding formaldehyde, I believe, and for formalde high, the reason why we don't do the stratosphere troposphere separation is that the formatohyde concentrations in the stratosphere are very low, so there's not really a need to do that stratosphere triposphere separation for the formaldehyde product for example.

Speaker 2: And I think there's a question also relayed to ozone and the separation of that.

Speaker 3: Yeah, so the ozone is similar to No.

Speaker 2: Two in regards that we can get at information or we do use a separation technique right to get at the stratospheric and the tropospheric concentrations for ozone, which are within the data product itself. Ozone is special in regards to we're able to retrieve additional information in the lower triposphere layer, so we actually have a you know, you can get at the tropospheric or the lower tripospheric column amounts for or ozone within the ozone profile product. I think that might clear up those three or four questions related to troposphere for stretosphere I'm misstanding.

Speaker 1: Let me know, Thanks, and yeah, feel free if anybody has any follow up questions, you can again put them in that Q and A box in the WebEx. Let's see, so we covered those, there's question number five which is just about the orbital period of the satellite. Again, this isn't we have that in the chat this is a geostationary satellite, so it is constantly the orbital period is one day. It's constantly staring at north of And for more detail on that, we have some fundamentals of remote sensing self paced training.

Speaker 1: If you would like to get some more detail about the different warbal types I see numbers. Question six is about the lower signal to noise ratio with formaldehyde. Aaron, do you want to do that one now or should we leave it for it?

Speaker 2: Kind of skip over that one and let me let's go back to that one. I can go to the following question.

Speaker 1: Let's see how about question seven? There there was some striping striping feature on the trope Sphirit profile that you showed. Do you want to talk a little bit about what causes that?

Speaker 2: Yeah, So the ozone profile, I wouldn't make it clear that we are still in the validation phase of that product, like a like you mentioned in the training, it is a beta product right now. So we're undergoing pretty intense validation on that ozone profile product currently with the hope that we can have a provisional ozone profile by the end of this year. So, but you know, during our early validation results and one of our main mechanisms for validating our tempo ozone profile product is the whull net stations, which I'm not sure people know about tollnet, but it's a ground based validation network for our tempo ozone profile.

Speaker 2: Our early validation results have shown that this horizontal striping is kind of similar to what we see with Tropomi and only data, for example, in terms of insufficient calibration across a tempo wavelength spectrum, especially those wavelengths that are used for retrieving of ozone information. So it's kind of so much to like the row not now homies row phenomenomalies that you see in the trop Amy data that are also tied to calibration.

Speaker 3: Calidate calibration problems.

Speaker 2: And again we hope to have much more information regarding this and also additional improvements in the product once we have that. Once we have the provisional product available later on in twenty twenty.

Speaker 1: Six, sounds good. I think we're all looking forward to further validation and further improving of this product. But it's a really cool product. So question eight, how you can sort of reconstruct information about atmosphere processes or pollutant emissions during periods where the satellite is not making observations. The written response I think covers this. I don't know, Aaron, if you have anything to chime in, but essentially this is something that I don't think Temple can't do it by it else.

Speaker 1: This is a case where you would bring in some other perhaps modeled data or other observations to be able to get the fuller picture there. And there's links to some other trainings which may be relevant to people interested in pursuing that question. I don't know if you have anything else to add, Aaron.

Speaker 2: Nothing from my end, I think that response right there tells it all.

Speaker 1: Yeah. So question nine, I think you touched on this already about how the data calibrated with ground based observation systems. You mentioned Pandora, and you mentioned tole nets, and again there was a whole training specifically to the ground networks that NASA uses to be able to get at air quality and climate applications. Let's see. So question ten, I think we already covered that. That's the trope tropospheric stratospheric division.

Speaker 1: The question eleven's a little bit different with converting the vertical column values into something that is like what is measured at the surface and PPB or PPM, and I think this again goes to, yeah, there's other information needs to be incorporated, something like models or something like ground based sensors. I don't know, Eron, if you have anything else to add to that.

Speaker 2: I think that is a great response right there. It just takes further work and further research to really get at that PPM or PPB value when you're looking at tempo data.

Speaker 1: Yeah, that's always the million dollar question is converting from the column values to something at the surface level. So question twelve, I don't know Eron if you want to elaborate a little bit more on this, but I think this is essentially talking about how can researchers best deal with places where there are a lot of clouds going on? And we talked about it with Quality Assurance. Temple can't see through clouds, so you need to sort of filter with that. Any anything else you want to add with how how to deal with areas which are very cloudy.

Speaker 2: Yeah, it's gonna be a problem, right, I mean, when you're looking at cloud covered areas that are you know, cloud fractions with the cloud cover is encompassing too much of the tempo footprint right where the tempo is trying to perform that retrieval, it's going to be very difficult to provide a high quality retrieval. So depending on what is meant by cloud noise, which I guess is just a tempo footprint that has a lot of cloud within it to really get you really can't get high quality information on from tempo in those areas where the cloud for action is high in the footprint.

Speaker 2: So, like we mentioned in the quality assurance discussion, even when doing a qualitative look at tempo data, we recommend using filtering out any tempo footprint or tempo data that's tied or associated with the cloud for action of a grade and fifty percent. So that's probably at the level where this cloud always become a problem.

Speaker 1: Yeah, one of the challenges in doing remote sensing observations is those darn clouds sometimes get in our way. So question thirteen is asking about whether the AOD product is going to be in worldview. It's not currently. We are planning to have a tempo aerosol product training later this year when those data are available, or more broadly, I don't know if we have any better timeline on that but I think I think later this year is what I think we can confidently say hopefully that the AOD product will be available.

Speaker 2: Yeah, okay, hopefully may ish, potentially after, but yeah, hopefully may ish. Yes, the question I should clarify here the level four AOD product, it's really a level four PM two point five product.

Speaker 3: The level the AOD is actually a level two product.

Speaker 1: Is there a level two and a level three AOD that will be No.

Speaker 2: So that is one difference between what we're generating for our aerosol products versus our versus our trace gas products.

Speaker 3: So for our as you saw.

Speaker 2: On the table earlier and some of the presentation material, we provide the trace gas products on both the standard tempo footprint but also on a standard grid level three standard regular grid, which is referred to our level three product. They are we are not doing that for the aerosol. So we'll have the aerosols as a level or AOD as a level two product, and then additional retrieval processes are used to drive that level four PM two point five product.

Speaker 1: Sounds good. Thanks for the clarification there. Question fourteen, Yeah, I think that's an interesting question, but a little bit beyond the scope of what we are doing right here in terms of how solar radiation modification will affect these products. That's I don't know that we have anything else to add beyond what's written on the screen right there, but it could be interesting. Question fifteen about the validation of tropospheric ozone products. I think we covered this in detail and in some of the in the presentation some of the other Q and A. I don't know if we have anything else specific to add there, Aaron, But that's done with toll net and done with Pandora.

Speaker 1: Yeah.

Speaker 2: Again, like our our Pandora network was really more for validing our total column of zone product.

Speaker 3: The toll net, the toll.

Speaker 2: Net program with the ozone profile retrievals from tollnet are really the main, the main validation mechanism for our tempo ozone profile. So yeah, we're more work to be done there and we'll have much more results and progress by late later on in twenty twenty six.

Speaker 1: Sounds good. Question sixteen talks just about the difference between geostationary orbit and polar orbiting. I think that was covered in the slides as well, and the fundamentals of remote sensing. I know we're at our ending time. But let's can we just perhaps Aaron, if you want to end on question seventeen, which is the when is the reprocessing for the version four going to be available? Do we have a timeline on that?

Speaker 3: Man, I would love to provide a timeline.

Speaker 2: Question with the Tempo team back and forth on this, and we don't really have a good timeline for when all that reprocessing will be done. But I will provide an email, and we will provide an email to all the Tempo folks and on our various email chains and email communication chains when that reprocessing is complete. But I hope I'm gonna throws a ballpark. We hope by the middle of us a year, it'll be all done and reprocessed. But I don't have a good timeline. Unfortunately, there's a lot of data to.

Speaker 1: Go back over and there's uh yeah, it's I want to be sure we're doing it right, and it can take some time to go back over for years.

Speaker 2: The key is will provide an email notification when that's done, so everybody will know if you're on our emails.

Speaker 1: So sign sign up for the emails exactly when that

Speaker 1: will happened, so I know we're at time. I think a couple.

Speaker 3: We can try to maybe address question six. That was a bit.

Speaker 2: I guess the main thing with from out of hide it's just a challenging trace gas. Do you know when you're trying to use a satellite to measure from aldehyde, it has a lower atmospheric composition compared to n O two.

Speaker 2: We have much weaker not much, but a weaker spectral signature compared to n O two. For example, we don't have that same sensitivity that we do with from Autohyde as n O two. But that being said, we can still we have shown that our from the hide to retrieval is good from tempo, just not as good as our n O two due to these challenging you know, these other challenging issues tied to from Autohyde in the in the proposphere.

Speaker 3: Hopefully that clears that up.

Speaker 1: Right, you know, there were a couple more questions that came in. I don't know if we want to stay on or regardless, these will all be answered in this document and posted on the website. So question eighteen is referring to question thirteen. Yeah, so PM two point five,

Speaker 1: I think the link I think we're asking for the Okay, I think the link to where the aerosol tempo training will happen. I think if you sign up for the our set mailing list, the announcement will go out when we have dates on that. We're we don't have set dates for when the Tempo Aerosol training will be happening at the moment because we want to make sure the products are available at that point. But I think sign up for the mailing list and you'll get you'll be the first to know when the registration opens for that that training.

Speaker 3: Do you see that question nineteen that came in there? If we want to address that or not?

Speaker 1: That one's all you do? You want to have anything to add to that?

Speaker 3: Well, people need to do that, No they don't.

Speaker 2: I mean, right, like we mentioned in the training, we use the GEOCF model for this CTM and conversion of in the in the generation of a MSS two for our products. So and the GEOCF is at a quarter degree a resolution or grid spacing.

Speaker 3: So I'll just leave it at that.

Speaker 2: I mean there are things that can be done with other models to bring in that may be higher resolution, right, but that another.

Speaker 3: More intense training.

Speaker 1: And I think for for purposes of this training, probably you all on the back end, on the on the tempo side of things have already incorporated that. So I think what I would think is people don't need to worry about that, and you can use the products as they are and don't need to worry about that. Yeah, okay,

Speaker 1: I think we might be at the end then. Thanks to everyone who attended, and hope to see everyone two days from now on on Thursday, same time or a few hours later. You can find the link on the website. There's there's two sessions each day. Thanks everyone for joining and I'll see you next time.

Speaker 2: Thank you as

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