All right, I'll get started. We're at the top of the hour. Greetings, everyone, and thank you for attending this month's science seminar presented by the NSF's National Ecological Observatory Network, which is operated by Patel. Our goal with this monthly series of talks is to build community among researchers at the intersection of ecology, environmental science, and neon. We are excited to have Manoj Hari here to present today. But before we turn it over to Manoj, a few logistics. So we have enabled optional automated closed captioning for today's talk. If you'd like to use it, please find the C button in your Zoom menu bar. The webinar will consist of a presentation followed by a Q&A session. As you think of questions throughout the talk, please do add them to the Q&A box. That would be preferred over the chat box, whereas in the chat you can share links, introduce yourself, kind of use that as more of a networking space, and put questions for the speaker in the Q&A if you can. We'll then facilitate a discussion at the end, and there will also be an opportunity to unmute and ask questions over audio if you would like. Neon welcomes contributions from everyone who shares our values of creativity, unity, collaboration, excellence, and appreciation. This can be found in our code of conduct, which is found on our science seminars webpage. So let me do a quick screen share of that. So here is our webpage showing all of the talks included in the Science Seminar series. And if you go all the way down to the bottom of this page, you can find our code of conduct. I'll drop the link to the webpage in the chat in a moment. And so obviously this applies to anyone, all the staff at Neon and anyone attending our event. So thank you so much for following the code of conduct. This talk will be recorded and made available for later viewing on this very Neon Science Seminars webpage. We can see all of the previous talks up here with links to the recorded presentations, and this last talk will be, the recording will be added soon, probably end of this week or early next. To complement our monthly Science Seminars, we host related data-skilled webinars on how to access and use Neon data. We are complete with our data-skilled webinars for this year, but there will be more webinars starting up after a summer break. So look for more of those in the fall. And of course, we do have a venue for nominating speakers for the seminar series. And so if you can think of someone who might give a great presentation, please don't be shy to nominate them here. Okay, now I will turn it over to Dave Durden to introduce today's talk and today's speaker. Thank you, Samantha. So, I should be sharing my screen now. So I just wanted to give a kind of brief introduction to the Incarnion system and how it's kind of bridging the ecological and climate sciences. The idea behind the Incarnion system was to kind of remove the barriers to enable integrating Neon ecological observations with Incarn or the National Center for Atmospheric Research Earth System model. And through this, we believe that we can enable exciting scientific discoveries such as predicting changes in terrestrial processes, understanding changes at and across continental scales, and disentangling complex ecological interactions between the climate and the land service. We recently submitted a paper describing this system to, and it's currently in review at EGU Sphere. So we hope that we will provide a link to this so people can look into that if they're interested, but to kind of just give a little overview of this is kind of a conceptual model describing the Incarnion framework where we're taking Neon observations, gap filling and partitioning those to enable those to be ingested into the Community Land Model or the CESM Lab, which was created as part of this project. It's a containerized version of CLM that can be ran anywhere, even on your laptop. And then, ultimately using the simulated flux data and comparing that against the observed Neon data to both help improve our data quality and to help us better understand the model processes and model prioritization. Lastly, I just wanted to mention that, now we have these kind of accessible framework for CLM simulation, and you can run the Community Land Model even on your laptop. There's a lot of great tutorials available that can show you how to use this containerized system to run a CLM simulation and how to visualize and evaluate the model data in a Python Jupyter Notebook kind of interface. You can see the link here at the bottom of the slide. So with that, I would like to introduce to you Manoj Hari, he is a PhD candidate at the National Institute of Technology Rorkela in Odisha, India, in the Department of Earth and Atmospheric Sciences. For the last 10 months, Manoj has been a visiting doctoral researcher at NCAR in the Climate and Global Dynamics Group, working with Dr. Sannika Lombardazi. Manoj worked with Dr. Lombardazi on outputs from the NCAR Neon framework in conjunction with satellite remote sensing data. The title of his talk today is Synergies Between the Community Land Model and Satellite Proxies in Capturing the Terrestrial Carbon Dynamics at Neon Sites. And with that, I will stop sharing and turn it over to Manoj. Thanks, Dave. That was a wonderful introduction. Hi everyone, myself, Manoj and I'm a doctoral candidate from India. And I've been working with Dr. Bhishma Thiyadee over here in the Department of Earth and Atmospheric Science. And most of my work has been like entangled to understand the background process of terrestrial carbon cycle at a regional state. But in terms of region, I just mostly focus over India. Okay, well, for the past 10 months, I've been working with Dr. Danika Lombardazi at National Center for Atmospheric Research with a full-grade project that I've been engaged over there where we were working on NCAR Neon framework where we tried to understand how carbon flux has been changing at different sites based upon different ecological and climatical background. So based on that one, we try to understand how satellite data has been used or infused within this model structure framework in order to understand the overall dynamics of carbon at each site level. So with that, I'll just jump into the research that we did. Next, please. So initially, I'll just give you a broad outline about the proxies like the satellite data that I've been using for this analysis. Satellite has a very broad facility in order to trace the carbon across the globe. The one facility that it promotes is like even though flux networks across the US or like globe has precise measurements of the flux of carbon and water flux, they are like site-confined. But when it comes to the satellite data, they do have synergies along with the flux networks, but it captures the whole spatial extent which the flux network cannot be done. So for this analysis, we try to understand a key factor for the carbon flux which is considered to be the gross primary productivity. Well, gross primary productivity is considered to be like the uptake of carbon by the plants and the key flux which is modulates these dynamics is considered to be the gross primary productivity. So when it comes to the satellite, there is no such satellite have a direct observations of GPV. So we do have like different proxies as a measurement of GPV. So we just like try to compare the observations along with different proxies. One such proxy is called NARV which is like a novelised, I mean, a reflectance for vegetation. But for this proxy, we derived all the characteristics from modus reflectance band. The spectral properties is like they try to matches with almost 68 percentage as of a GPV's observation. The spatial extent of this, sorry, the spatial resolution of this NARV is around 500 meter where we try to frame it on the footprint of the beach flux servers at different locations. Next please. The next advancement of proxy that we used is called KNDVA which is generalised, normalised differential vegetation index which is considered to be the advanced version of NARV where they have a sigma parameter which has dynamic infusion within the flux networks. I mean, sorry, within the reflectance product as they can able to trace the flux variations at like greater extent. Again, we try to extend this product with the same modus reflectance band and with the 500 meter resolution we try to confine it at like site level based analysis. The map over here, you can see that like, that's the global extent of KNDVA where we try to map on a global scale where you could see higher KNDVA values which is obviously unit list where you could see the higher KNDVA values on always on the tropics. Next please. So the other proxies which is considered to be much more closer to GPP is called SIF which is solar induced fluorescence where we try to extract this proxy from tropomy SIF which is having a higher resolution in order to trace SIF at site level. So the spatial extent for this SIF is about like 3.5 into 7.5 kilometers square like at the NADY level where they try to capture SIF at a daily scale. The advantage of having SIF is like they are considered to be the direct proxy compared to the earlier proxy that I told. Next please. So by using these three proxies we try to understand the variability at different neon sites all across over there in US. So we again used NCAR neon system in order to simulate site level simulations using the community land model which has a concept of biogeochemical concept and we try to simulate at a half hourly temporal scale at different sites. So the main theme around the tropics about this processes we need to compare how well CTSM I mean CLM the neon sites and the synergies I mean the proxies are capturing the carbon dynamics and how it has been varying at different timescale. Next please. So for this we framed structural analysis behind this theme. So the initial idea behind this analysis was to understand how satellite proxies has been like much more equivalent with CSM and in situ analysis in capturing the carbon dynamics. And we want to trace the pattern of GPP across different gradients based upon temporal based upon their vegetational background and their climatic background. And further we need to extrapolate this analysis from the point scale to the global scale. So based on this one we framed our workflow. Next please. We framed our workflow and different parameters structure. So initially we try to understand how well this proxies has been modulated by different global environmental parameters. And we classify these sites based upon different vegetational backgrounds which is called like PFTs. The PFTs is nothing but plan functional type categories where we just like lean towards the CLMs PFT types because in order to have an ambiguity across the global scale. And further in terms of climate. So we classify the sites based upon the carbon climatic classification. And further we are like for the whole data period we just temporarily build the data at different scales. Next please. So when it comes to the neon sites obviously neon has a lot of sites all across the US but our focus is mostly confined to the corners I mean the contiguous US where it has neon has around 47 sites. But 39 is considered to be the most prominent which is in corners and eight are in Alaska, Hawaii and the other islands. Next please. The background image over here is just like high resolution of CTSM simulations. The average annual period for the whole analysis period. So for this analysis we are just selecting 28 sites which is highlighted here which has a consistent data flow and has CTSM simulations enabled sites. So next please. Further these sites has been like categorized based upon their PFT based upon their climatic background. For example, if you could see the top left corner the square where it indicates the site which is having a background of warm summer Mediterranean climate. When it comes to the circle obviously it belongs to humid subtropical climate where the neon has 12 sites which is which comes under that category. The site that has direct marks is represented where the site doesn't have a proper data flow where we had an in situ data gaps in this site. So we just wanna mark this site. Further, next please. Further we again classify these sites based upon their plan functional types which is obviously based upon the vegetational characteristics. The buffer, the highlighted buffer over here each color represents a different climatic or sorry the plan functional types. For example, the blue one represents the crop land. The yellow represents the deciduous broadleaf forest the red one evergreen needle forest and the violet it's grassland and the green is open shrubland. So again, if you just take out the top left side that site represents a warm summer Mediterranean climate. Again, that sites where the PFD is being considered to be evergreen needle forest. So that's how we classify each site based upon their climatic background as well as on the PFD background. Next please. So in order to consider in order to give the results just for this presentation sake we just picked up very few sites in order to represent our overall analysis. So we selected the sites in such a way each site represents each of the PFDs and all the climatic backgrounds has been included in our analysis so that you could see the diverse nature of fluxes across different sites. Next please. So initially we started our analysis by understanding the diagonal flux across different sites. And you could see the green bins that has been represented here are like half hourly fluxes flux variations across that has been mean for the whole analyzing period where the red solid line represents the CTSM simulations where the blue solid line represents the neon observation mean. So the gray shaded region represents the day period and obviously the white one represents the night period. So in this way, like we structurally frame in order to see the diagonal flux variations across different climatic as well as the PFD background. Next please. If you could see the highlighted box over here these two sites represents the deciduous broadleaf characteristics of vegetation. However, they do have a different climatic background irrespective of the climatic background just like because of the PFDs you could see compared to all the other sites the CTSM simulations over these two deciduous broadleaf sites has been underrepresented than the neon observations. Next please. In case on the other hand, if you could see the evergreen needle forest which is highlighted in red or the other irrespective of the evergreen needle background these sites has different climatic background and each climatic background has a different flux variations at diagonal scale. This shows both PFDs as well as climate has a distinct variable influence in modulating the flux across different sites. Next please. So in order to understand the variability of environmental factors, we try to understand how the atmospheric dryness or like the BPD which is vapor pressure deficit is modulating the energy partitioning across these sites. So for this, if you could see the plot over here the solid black line represents the neon observations sorry, the BPD neon observations whereas the red one indicates the CTSM simulations where you could see each site has distinct representation of like having a different response in terms of BPD whereas next slide please. Whereas if you overlay the neon observation sites which is like highlighted black where there is a lot of mismatch between the simulations as well as the observation. Next please. If you could particularly see the highlighted plot irrespective of their PFD background you could see a lot of mismatch between the simulations simulated Latin heat and the observation Latin heat at different sites. This means that like the CTSM like the CLM has a background of algorithms in such a way that like it has normalized all the PFD backgrounds. So irrespective of the site's genetic nature. Next please. So in order to consider the influence of different environmental variables we try to understand like to understand the correlation between different environmental variables such as temperature, precipitation, Latin heat and BPD. So for this we are spatially just like correlated each site's GPP observations along with the observed temperature. The sites over here has been like has been highlighted over here depending upon the correlation that has been correlated with GPP neon as well as the temperature. Next please. The pie chart over here it represents the first pie graph represents PFD's ratio but at the second one, second pie graph represents climate backgrounds ratio where the divisions in the pie chart represents the ratio between each PFDs. That means for example, if you consider the yellow in the first pie chart all the deciduous broadleaf sites has been cumulative ratio in such a way that like it represents the sites has been represented in a single frame. So if you could see the ratio between other sites considerably GPP neon and having a very good relation especially in the deciduous broadleaf site factors. Next please. In such a way, we are again correlated with precipitation, BPD and Latin heat but unfortunately we couldn't able to establish proper relation with precipitation especially with the observed precipitation except for grassland where you could see a higher ratio of like correlation just for precipitation but other than that irrespective of the sites we couldn't able to establish a very good relation but when it comes to Latin heat except for the shrub land sites which is marked on the green it has a very good relation especially for the grassland as well as for the deciduous broadleaf site. So this shows each environmental factors is playing differently at different sites based upon their climatic and PFT factors. Next please. So further we just want to check whether the simulation is going along with the observation. So that's why we just like a simulator the observations as well as we simulated the GPP as well as the temperature and we just like correlated with along with different PFTs, sorry across sites. It seems that like again precipitation doesn't give a very good correlation at these sites but compared to the observations here the simulator of GPP and BPD has a very good relation compared to the Latin heat which we saw in the earlier slide. Next please. So in this way again CTSM is having a very differential opinion in compared to the observations. So now we are just like I'm just shifting my focus towards the satellite based observations. The plot here represents the daily bin of GPP observations which is obviously the x-axis whereas the y-axis represents the CTSM simulations where we normalized the bins in order to fit all the other proxies in the analysis. Where you could see the ever been needed part which is the first bar where you could see a very good relation between the neon observations and the CTSM simulations. The vertical lines over there in the bins that represents the standard error within the simulation. So that's I need to mention it over here. But next one please. But when we overlay the other proxy which is KNDVA where it has a dynamic relation at different PFTs. Again, we do have a mismatch in data gaps because like the data swap that has been provided by modus is around like eight days. So again, we do have data gaps in each site. For example, if you could see the panels in the middle where you could see where you couldn't able to establish a proper relation because of data gaps. But for the sites on the middle panel on the left side where you could see for the grassland it has a very good relation similarly over on the top right where you could see a very good relation with observations as well as the CTSM. Next slide please. Similarly, we again overlaid with CIF which is in blue bins, sorry green bins but compared to the KNDVA again, CIF is having like outrunning all the other proxies. They try to irrespective of their PFT background and the climate background. They try to always try to go along with CTSM at like in most of the sites but when they go with observations just for like a higher canopy architecture such as deciduous broadly for every green sites. Next please. So what we need, what we plan to do is like we try to annually like we just like average it in order to see the annual dynamics as well as the seasonal dynamics and how this fluxes has been changing at different sites and how it has been modulated at different proxy levels. So here we just like for the full analyzing period we just annually made the annually average in order to see the climatology and the black solid line again it represents the neon observations and the bar graph represents the seasonal dynamics of different seasons from winter, spring, summer and fall. Next slide please. You could see over here just over these three panels where we couldn't able to establish the proper annual or the seasonal dynamics. That's because again, we do these are the sites where we had the data gaps. So we couldn't able to establish a proper relation in terms of annual dynamics or seasonal dynamics. Next please. But when we overlay it with CTSN simulations like the simulations, we had a difference of like contrasting analyzing opinions especially for higher canopy structure as well as for the deciduous broadly see. Again, if you could see the middle top, the middle panel which represents a deciduous broadly for us where it has been underrepresented, under simulated compared to the neon observation. Similarly, the last panel in the middle sector where again, it is a deciduous broadly for us but compared to all the other sector where you could see the CTSN simulations overestimated compared to the neon observations. And similarly, this way again, the seasonality has been changed depending upon mostly over in the summer season. Next slide please. Surprisingly, when we overlay the SIF observations over these particular sites, we could see that like SIF has is going with different is in favor of different sites. For example, if you could see the middle, I mean the first panel in the middle, where you could see which represents a grassland where it goes with the neon, sorry, it goes with the CTSN simulations. So again, similarly, the sites that has lower canopy architectures such as Disney on the top right where you could see it goes along with the neon, sorry, CTSN simulations, both in terms of annual dynamics as well as in the seasonal dynamics. But apart from those, for the Evergreen and the deciduous broadly for us, the surprisingly, SIF tries to go capture the dynamics. It goes along with the neon observation but it doesn't go with the CTSN. So it means that like a SIF has an capability to capture in terms of higher canopy architecture. Whereas when it comes to the lower bottom panel, like none of this is matching with either with the observations or with simulations. So we couldn't able to establish a proper compatibility along with these different proxies. Next please. So these are all the Evergreen middle for us and the deciduous broadly for us where you could see that there has been like the SIF tries to go along with the neon observations. Next please. Over here, these are all the sites which is again grassland and cropland where it goes along with the CTSN simulations. Next please. So further, in order to find like how well it has been going with the different temporal bins, we classified our data from daily scale to inter-annual bins based upon like different temporal bins. So for this, like over here, we like normalized all the data in order to fit everything in the graph where the green bar represents the neon observations and the horizontal green line represents the mean neon observations. Where the red bar represents the CTSN simulations where especially for the DB of deciduous broadly for us, it's been underrepresented. Similarly, all the other proxies is overrepresented. Next please. Likewise, we try to do the analysis for daily, weekly, monthly, seasonal to inter-annual. We just bring the data at different temporal bins in order to see the analysis. Irrespective of the temporal bins, it seems that like CTSN is underrepresented at different bins, whereas there has been a lot of fluctuations in terms of proxies. As you could see, compared to daily bins, for the monthly bins of KNDVA, which is like the black bin, where you could see that there's a lot of fluctuations and it seems like it's trying to match with the observations. Next like this. Likewise, we try to capture for different PFTs, the crop land, evergreen, native grass, grassland and shrubland. Next please. So each has distinct representation of sites, but then our focus has been limited to three distinct PFTs, next one please, which is deciduous, evergreen and grassland. That's because you could see the red, you can concentrate just on the red bar where you could see there's been a lot of changes, especially from daily scale to inter-annual scale, from even if you could just take grassland irrespective of evergreen or deciduous broadleaf forest. This way we again find a pattern where there's been a lot of fluctuations happened, especially if you just go along with the temporal scale as well. Next please. Likewise, we compare the analysis based upon the climatic background where the major of this climatic fluctuations has been found in the DFB, which is Heart Summers Humid Climate. Next please. So we try to again restrict our focus just on CFA and DFB in order to see the analysis. But comparing the others, other climate background, these two sites has a major dominance of evergreen native forest and the deciduous broadleaf forest. And that's why you could see the CTSM has been underrepresented in DVF climate background. Next please. So again, likewise, we just like compare CTSM with different proxies at different temporal scales in order to have how structurally there's been diversified at different sites. So here you could see it has been diversified across different sites when we compare the CTSM simulations with the observation. Next one please. Similarly, we just did the analysis for daily bins just for KNDVA in ARD and CTSM at different proxies. Unfortunately, we couldn't properly establish a very good relation with different proxies, except for CTSM. So try to capture a very good, try to replicate along with the neon. But unfortunately, neither KNDVA nor NRV could able to, again, could able to replicate the observations. And they do have a site which has a negative correlation aspect. Next one please. But it has been marked here. But this is the particular site where it has a background of deciduous broadening site. And still we are puzzled then why we couldn't able to have a very good relation just for this site. And we are trying to analyze the environmental background for these sites as well. Next one please. Similarly, again, another deciduous broadening site has comparatively higher correlation compared to the observations as well. They do have a very good relation with CTSM observations and the proxies. So this is just for the daily temporal bins. Next one. Similarly, we did the analysis for seasonal and you could see obviously there is a lot of better correlations at different proxy level, especially for SIFT and KNDVA. Next one please. But when we go, like when we again increase the temporal bins, you could see it almost caught the neon's correlation. It seems that like it almost replicate both the neon and the CTSM in terms of SIFT as well as KNDVA. Next please. So they do unfortunately, we couldn't able to have a very good relation just for these every native for a site. But apart from that one, we have a very prominent results. Next one please. Just all across the BF sites and the problem sites where they had the maximum correlations compared to the neon observations. Next please. So again, in order to see how these proxies has been modulating at different sites, we've been at like annual level. So we've just been the sites that has having the same PFD background. So here we are representing the Desiree's broadleaf site for us, where you could see the first panel represents the neon mean observations. And the next one is CTSM and KNDVA and SIFT. We neglected NARV because it doesn't give proximal value corresponding the other proxies. So again, like you could see, SIFT, the last panel, it tries to replicate the neon observations. And like still we are like, we are wondering how it is possible for a KNDVA in order to have a proper replication of CTSM. We're just having again a steep towards April and May, but then again, it tries to capture the same seasonality. Next one please. Similarly, we just build different PFDs and we try to find the annual dynamics. Next please. So this is for prop land and this is for evergreen where we could see the seasonality mix them please. And this is for grassland and this one is for next please. This is for grassland, sorry, shrubland. Unfortunately, again, because of data constraints and there is a lot of noises between in the reflectance product, we couldn't able to properly establish a relation with the neon observations. Neither proxy nor the model could able to have a very good relation just for this shrubland. Next one please. Likewise, we've been based upon different climatic background and again we found that like a CSB which is a humid climate and DFB which is hot summer Mediterranean or humid climate has again, it dominated the whole ship where CTSM and KNDVA again, it matches and SIF it goes along with the neon. It shows that like SIF is trying to go capture the footprint of neon luxus whereas KNDVA is trying to go with the CTSM. Next one please. So we just like try to go on an overall correlation at different levels in order to see how each PFDs has been performing at different proxies. Without doubt, obviously this is probably first which is having a higher canopy architecture. It has a very good relation irrespective of the proxies especially for KNDVA and SIF just over neon and CTSM. Similarly, grasslanders have again grasslanders, grassland and cropland they do have a very good relation with SIF as well as with KNDVA irrespective of the proxies but unfortunately we couldn't able to have establish a proper relation with CTSM and KNDVA for the shrubland background PFD. Next please. Similarly, we did the same for climatic background we found that like CSE and BFC we couldn't able to have a better relation that because CSE is having sites that have data gaps and both represent every native forest and again BFC has been represented by another every native forest but we couldn't able to establish a proper relation but DFB which is a humans of tropical climate background where most of the sites has a background of deciduous broadly forest again has dominated the whole the proxies as well as with the observation. Next one please. So here over here we are trying to go like again we are bringing back the temporal points in order to go with different time scales where you could see the parallel over here we compare the correlation between CTSM and KNDVA and we observed that like a different temporal points like each bins represents different temporal points for example the gold and the blue represents the annual and the inter-annual things and that seems having the higher correlation especially when we are comparing proxies. Likewise we did the same next one please. We did the same for like different PFDs irrespective of the different proxies where you could see higher the proxies like the irrespective of the proxies the higher the temporal points the higher the correlations between these model simulations as well as with the satellite proxies. This shows that like irrespective of the proxies the satellite goes along with the model simulations and they go along well with the different PFDs as well next please. So you could see that like except for ever being needle for us which is in the middle panel all the other side has a very good relation especially for annual and inter-annual bits but for ever being needle for us we couldn't able to have a proper higher correlation that's because again we do have data gaps in the sites that we simulated but then again we do find not environmental noises over the back just for the site and we are trying to expand this analysis in order to understand how will this has been globally represented at different sites. Next please. Likewise again we classify these sites based upon the climatic background and again irrespective of the climate or the PFDs temporal points when you increase at higher temporal points it seems higher correlation with proxies observations and the satellites. Next please. So we like we try to expand our analysis on a global scale. So we just like simulated a global CTSM for the same period from 2018 to 2022 and similarly we collected the data for KNDVA, NARV and so on irrespective of sites we do have a lot of variable animal dynamics just in the tropics but then if you could look in the temporary region just over in Europe the different proxies has a difference of opinions in terms of compared to CTSM similarly over there in the boreal forest this shows that like again sites in boreal forest do have like difference in variability which we are trying to expand our analysis in the future research. Next please. When we try to correlate it with the CTSM observations in a global scale we tried a lot of negative correlations has been observed just for the tropics that because like again these are the regions where you have tropical coral with like higher canopy architecture wherever you have higher canopy architecture the region seems to have a lot of variabilities that has been observed throughout this whole presentation but again it seems that like except NARV SIP seems to have a good correlation with CTSM over boreal and the tropical and the temporary regions over there in Europe but NARV is the only proxy where we couldn't have a proper having a negative correlation just over there in the boreal forest. Next please. So with that I'll just like summarize the work that we did. So initially we tried to understand how environmental factors have been playing their role at different structure. We consider that Latin heat and temperature has been driving GPP at like mostly over this year's broadleaf site and grassland but as there's been a lot of biases between Latin heat the observed Latin heat and the simulation one because just because they are having a higher canopy architecture and they have larger bias but when it comes to precipitation it just goes having a good correlation for the grassland site but other than that we don't have established a proper significant correlation for the climate exhaust but other than that temperature and VPD have a very good agreement for neon observations whereas Latin heat having a very good observation with the CTSM simulations. Similarly see grassland is only a site which is having a stronger relation irrespective of different proxies even though K and D we have a lesser representation for higher canopy grassland is a particular site where it had a stronger consistent correlation for different proxies but when it comes to PFT gradients you could see that like CTSM has been like always outperformed by neon but CTSM is underrepresented almost like just for the DVF sites. So irrespective of the climatic background except for DVF the CTSM has been like overrepresented whereas they are having a good relation with CTSM and the very good proxy that has been observed over this has been considered to be SIP. So they have a stronger seasonal correlation as well as annual relation except for grassland in the propland and when it comes to the shrubland neither model nor proxy could be able to capture a proper dynamics neither seasonal nor the annual dynamics just for the shrubland sites. When it comes to the climate gradient the CFA type and the DFB which is subtropical from the climate have exhibited a significant correlation irrespective of the proxies across different temporal gradients and similarly CSA and DFB pins has having a stronger disagreements with different proxies. When it comes to the temporal gradient aspect we again find that like the lower the temporal range it seems like we can be able to establish a proper significant correlations between proxies and observations but when we increase the temporal range it seems that like correlation seems to having an upscale especially on the higher temporal range and with that I would like to conclude my talk I'll take up some questions. Thank you, Manos. Everyone feel free to add your question to the question and answer box and zoom interface. I can go ahead and start with one question though. So how do you think the plant functional type impacts the modeled outputs? Do you feel that using something like FATES and where you can provide percentages of plant functional types across the site to kind of better characterize the site could improve this? No, again, since CLM is using the consistent data I mean PFT types which is again along 78 PFT types it is hard for us to again go with the percentage. Again, we could differentiate each site based upon their ecological background. Again, NEON has its own vegetational characteristics for each site but then we didn't go based upon the NEON. We just thought in order to go with on an ambiguity scale so we just try to stick with the CLM based PFT types but then based upon the ground variations again these results may change and obviously it will change because again we do obviously found some changes within the site level with a different PFT background as well. I mentioned it in the chat but just if anyone wants to make a comment or ask a question over audio there is a raise hand button in your Zoom menu and so if you do that we can unmute you and allow you to join the conversation. I can ask another question. So kind of following up on the importance of vegetation because that was interesting that you were pointing out that certain satellite proxies were better at matching the observations depending on whether there was essentially like short stature like not structurally complex vegetation versus tall structure structurally complex like forests and I wondered if you could speak more like why do you think that is? Why does NDVI, I think it was better in grasslands with SIF or vice versa in forests like what is going on there that interplay between what the satellite is picking up and the structure of the vegetation? Yeah, the characteristics of SIF is just like they are capturing just the particular wavelength of the reflected band that has been entered from plants. So they could able to capture even though if they have a sparse or like structurally composed vegetational characteristics but when it comes to modus which is like we just having a multi optical band. They don't have a proper algorithm in order to differentiate between the background or the vegetation or structure over the particular region. And that's why there has been a lot of noises especially for shrubland varieties of types where neither of the satellites like even though they do have a different background neither could able to have a proper relation for especially for the shrubland type but when it comes to shrublands SIF and NDVI tries to match with the same but then they do have different personalities because the reflectance where SIF captures are only 750 to 800 micrometer whereas the KNDV is just like an indices that we develop. So again, we do expect a lot of similarities between these two proxies. I'll also add that the different spatial resolution of the SIF related to the NIRV could potentially have a player role as well. I was wondering if we were able to get a better kind of spatial resolution cut out around the neon tower it might improve that relationship, right? If we were able to, yeah. I mean, you mentioned shrubland so we could talk a little more about that because I thought that's really interesting that drylands that tend to be shrubby were very poorly simulated. It seemed across the board like neither the proxies nor the models seemed like they were doing a great job. Could you elaborate more on what you think is going on there? Like were you just sort of alluding to the fact that there's not as much leaf biomass? It's like a lot of woody biomass or why are we having such trouble and trouble and what do you think we could do about that? Still shrubland is a mystery for us because like even neon would be able to establish a proper annual or seasonal dynamics. Again, we do have a lot of data gaps especially for shrubland side. So like they are mostly kind of like sub tropical climate. So again, we do consider like climate might influence a larger background on the perspective but then since like we do have a lot of data gaps in neon we couldn't establish a very good significant results in order to portray what's happening in reality. But then if you could compare SIF, if you just take SIF, they do capture a double hump seasonality like in the early spring like in spring and in summer. So it might capture, it may capture the structure unless until we do have a proper observation of shrublands we won't be able to like validate it with SIF. So still we are in a mystery phase. I will point out that there are some issues with precipitation data products at grasslands sites in particular. And it's partly due to the fact that there's not replicate data streams. So a lot of the gap filling was performed using a replicate data stream structure and relationships between these replicate data streams. And so at these grasslands sites we have a known issue that we're working on. And one potential answer is being worked on by Keegan King at NCAR. So she's working on developing some data using Prism. Yeah, to be able to initialize the model using the Prism data as well. Yeah, Prism will do show a strong correlation with these sites, but then still again there's been a lot of new observations as well. Any more questions from the crowd? Quiet crew today or any comments from Manoj? I think he's still working on writing this up, so. These are just like the preliminary results we are still working on. And we are just like we were confused how to portray our results because it's just like we need to represent each site and have to represent all the results. So just like against finding an effective way to represent the results. We're finding the story. Yep. Lot of interesting work though. It's an exciting way to see the application of neon data and neon combined with model simulations. I think it's really cool. Dave, did you have anything else? Otherwise maybe we'll end a few minutes early. No, I think it's really interesting work Manoj. And I'm curious too if maybe we could adapt some of the work you've done to work with AOP data or AOP remote sensing proxies at some point. I think that would be very interesting as well. That would be great. Sure. AOP is the airborne observation platform. It's our neon airborne remote sensing payload. We definitely don't cover the whole globe. They give us a very fine scale hyperspectral remote sensing at the neon site. So some cool avenues for future research there. Absolutely. Yeah, we do plan to use the neon drone observation that we tried to, but then none of them could be able to, like we couldn't be able to properly match temporarily the observations that we manage. So we thought of excluding the analysis over there. Well, great. Thank you, Manoj, for taking the time to give this great presentation. And thank you, Samantha, for organizing. I guess with that, we'll wrap up a little bit early. Give people back some time. Yeah, this was our last science seminar for kind of this academic year, if you will. We'll be taking the summer off and then we're gonna start back up in September with another series of hopefully very interesting and stimulating neon science talks. It'll be same time and a monthly schedule. And we're also gonna ramp up another, the paired series of data skills webinars. So look for some more communications on that. Thank you all so much and have a great rest of your day.


First of all, I'm Brian Gallagher. I'm the CEO of United Way Worldwide, a non-government organization headquartered in Washington, DC, with 1,800 locations in 41 countries around the world. Just like L'Oreal was described this morning, we're centralized in terms of our strategy and consistent in our governance. But we're local in our operation, and you'll hear that from Brazil and from India and from Ghana today.  And in fact, without being facetious, one of the things that I'll suggest is that part of the solution going forward to a lot of the issues we've been talking about all day is to formally put people in the center of the problem to be solved. Immigration is about people. Economic success is about people. I've worked my whole life. I'll introduce our panelists here in a second. I've worked my whole life working with corporations, for-profit corporations, and human service nonprofits and communities all over the world to try to put those two things together. And the best thing a person can have is a livelihood. But the marketplace doesn't always work for everybody in every instance, so it's the need to put those things together. There's no long-term economic success without human success. It's just not possible. And the last thing I would say as we get into this conversation, just to put it back into an economic and political frame, if that's easier for us to think about it, and I would say even maybe provocative for us to think about it this way, is think about human beings as infrastructure. We talk a lot about inputs into the marketplace. We talk about the digital revolution. We talk about knowledge-based economies. And yet we don't seem to talk a lot about people as inputs, their success. So we're going to talk a bit about what is the measure of success in our current environment. We've touched a bit on a few of these things, but I would say for our conversation, four very disruptive trends. Globalization of the economy, digital technology, evolution. We just talked about the migration of people. There are more people living outside their country of birth today in the world than any time in the history of the world. And demographic shift. As an American citizen, actually an American and Irish citizen, I would suggest one of the things we don't talk a lot about in the US that's causing most of the political turmoil is that economically, we've lost economic mobility in the country. So if you're born into poverty, you're much more likely to stay there in the United States than you would have been historically, like my father. And the second is we're fast becoming a non-white majority country. And even though we don't talk about it a lot, that's freaking a lot of people out. So we've lost mobility. The white majority, especially working class without a college education, not doing as well economically. And we're becoming less white. And it's caused a great deal of tension. It doesn't have to, but it has. So we're going to get into how to get young people ready for this new reality, this new economic reality. And we're going to start where you get the most return on your investment in the development of young people. And that's at the very beginning. The more you invest in a young person early, if it's a positive input, the more positive return. If it's a negative input, the more negative the return. We've got three folks to help us do that today. First, to my far left is Dr. Juliet Tuakli. Juliet is the CEO, Chief Medical Officer for Child Acra in Ghana. She is a medical doctor, a longtime professional health leader and administrator. And she's on the United Way Worldwide Board of Trustees. Next to Juliet is Eduardo de Campos-Keros. He has just recently left as the CEO of the Maria Cecilia Soto Vigigal Foundation in Sao Paulo, Brazil. He had a, I'm sorry. I went to my immediate left is Eduardo. Eduardo has got a career in banking and then early childhood education and private foundation in Brazil. And in the center is Chitaranjan Kaur from Pune, India. He runs a center for learning resources in Pune, deeply interested in not just early childhood education, but learning and learning styles and systems generally. Let me just share a couple of statistics and thoughts and then we'll get right into our discussion. 85% to 90% of our brains are developed in the first three years of life. So every child born in the world is going to develop their brain most substantially within the first few years of life. And if you think of one of the things that develop as your brains develop is personality. So if we'd like to make a positive intervention early in a child's life, seems to be the time to do it. As we get ready, we'll also explore the idea of hard skills and soft skills. I once listened at a panel in East Asia, the Minister of Education from Korea, lament to the audience that they were training too many engineers in Korea. They have too many engineers. And they weren't teaching people soft skills, collaborative skills, creativity, and so forth. So we're gonna touch on both of those. We're gonna talk about the need to upskill. We've all seen the reports, whether it's the World Economic Forum's estimate that in the next four years 75 million jobs will be lost worldwide to automation and to machine learning. Whether that's right or not right, whether you see it as a pro or a con, clearly machine learning and artificial intelligence is something that we have to deal with. Last two, the risk of losing an entire generation. According to the OECD, right now 13.5% of all young people in the world, 15 to 29 years of age, are neither in school or have a job. If you look inside the United States specifically, there are five million young people in the United States who are not in school and do not have a job. We run the risk of losing an entire generation of young people. And finally, one of the things that will explore a great deal on the panel is the need to do this together. That I forget who it was in the last panel is that made the point that our generation, I think is faced with having to create brand new systems, economic systems, health care systems, education systems, because our systems were built for a different economy, a different time. And we saw the idea of governments and nonprofits and businesses working together to create collective systems, I think is what's in front of us.


I'm going to introduce five key practices that I believe are critical for IDAD which is independent development and deployment. So first one is automated testing like that's pretty straightforward everyone understands if I want to deploy very frequently I need to have automated tests. Automated CICD pipelines again no problem there that's well understood. Feature toggles is an idea that's not very well understood but again I think most people know what it means and why that is important if you want to independently deploy things you need the ability to be able to manage what is visible and what is not visible of course the ideal thing is to design everything so it's backward compatible and you ever don't need any toggles but that's not always the case and so when it's not possible you look for a toggling solution and these days there are fantastic platforms that are available for you to be able to do this trunk based development again feature toggles trunk based development generally go very much hand in hand again the idea here is that you instead of using something like Gitflow or whatever you essentially work off one branch all developers work off one branch some organizations like the process of a pull request and they do it through PR but you bring it back into the main and that's the branch that you're always you know taking forward right and so trunk based development again becomes very important the fifth which I believe is the most important but the most neglected one which is contract driven development and what does it even mean because I don't think there's enough talked about this particular topic I would say these five are absolutely necessary but more practices can help you like story slicing BDD, TDD, config S code, infra S code some of these practices are absolutely helpful but not like the bare minimum you need


What's up everybody? Ricky Krooth here. So this just in Redfin is publicly displaying buyers agents commissions. This is very, very interesting. They're only going to do this with Redfin's listing. So they're only going to do this with their own listings. They say that they're doing this because they want to be more transparent to the public. And also they did a survey and they realized that over half of the general public does not know how a buyer's agent is paid. We'd love to hear your opinion on this and smash that like button while you're at it. And also right here in the article it talks about how Redfin argues in its statement that adding transparency regarding commission should stimulate conversations between consumers and their agents about what is fair and ultimately lead to more competition and lower fees for consumers. This is a very interesting statement by Redfin because it's literally saying that Redfin is not looking out for real estate agents, which is fine. They could argue that they're looking more at the consumer and trying to protect the consumer in these situations. I believe that this is just another attempt of Redfin's to try to squeeze out the real estate agent. I think it's extremely unfair and here's why. Not because I'm against transparency but if we're going to be transparent here why don't we talk about all the fees that real estate agents have to pay E&O insurance to the local boards etc etc etc.  How many hours goes into that deal? How about any credits that a lot of real estate agents give towards inspection and repair items? Let's go ahead and be transparent about everything if we're going to be transparent about just the commission. So that's what I think is unfair about this is that the consumer is going to see the buyer's agent's fee and just think oh that's a lot of money you know we need to negotiate this fee when they're not seeing the whole picture they're just seeing one side of it. I think this is definitely a low blow by Redfin. Not worried about it whatsoever. I've said this in videos before they have very low end service to the consumer that's why they have two out of five star ratings and they continue to lose money every year every quarter and they're scrambling around to try to figure out a way to make money and disrupt the industry and it's just not happening for them. They just keep banging their head against the wall doing things like this to try to disrupt the industry and all it's doing is making them look bad.  I want Redfin to succeed. I want them to flourish. I want them to do very well. I just think that the manner that they're trying to do this is absolutely backwards and is giving off the wrong impression. It's creating a very bad reputation for them as a company and overall I think in the long run it's not going to work out for them. But we'll see how this all works out in the end. We'll be watching it. I'll be reporting anything that I find again. Please leave me a comment and let me know what you think about this. Let me know what in the world I can do for you and let's go.


Hi everyone and welcome back to JSA TV and JSA Podcasts where we're covering the latest stories, trends, and innovations from leaders in global connectivity, real estate, and the networks within. I'm Barb Mitchell joining us here today as Eric Boonstress, CEO of Kendlings. Thank you for joining us. Thank you Barb for having me. Thank you very much. Yeah and you know it's a busy, busy week here at PTC and I know you no doubt came from one thing and you're heading to another. How's the week been so far for you? It's a crazy week. So it's my first time here at Hawaii. I never wanted to go here because of the flight. Like it's insane why the hours so but it's a great event. So the whole world is here. Europeans, Asians, Americans, and you know it's a great event. I love it. Yeah we consistently hear that. I mean people travel for 20, 30 hours to get here but no one really complains about it once they're here. No then you're fine, right? Yeah so great event and also the shows you know in the night so it's yeah I love it. And now congratulations to you. You recently just before Christmas in November I believe you took the new role as CEO of Keplings. So congratulations. Thank you very much. Yeah tell us about what are your plans for your new role? Yeah so the reason why I wanted to have this role is that you know it was time to start something new again and you know build a company and you know I was talking for some time with Baro Leuches who was walking with his idea for quite some time and with McCorrie Capital who was backing it up right the whole company and I was you know exciting about starting something new and what we're going to do is we're going to build a new platform in Europe in the emerging markets so not FLAB as we call it right Frankfurt London Amsterdam Paris and we start in Brussels it's the first one next year opening in October 25 and we're looking at land in the southern Europe you know Austria Poland and so that's what we're going to do. That's great yeah and I mean we've been chatting with your team even even prior to you joining and have been watching some of the developments I know there's been a lot of big milestones that Keplings has hit notably the first financial close I believe. Yeah that's also very important one of course as you know building data centers comes with the used huge cost so we yeah we have McCorrie Capital you know where the financial close was last year and we also have PMV which is a Brussels Flemish Investment Company as main investors and its shareholders you know companies so that's that's was a huge thing yeah yeah so that helps us you know building the platform. Yeah it allows you all the your vision now can can move into action and you can start playing out some of I know the big plans that have been in store one of which I think is the the build out that's happening in Brussels can you talk about that? About the project itself yeah so we we have the land power the permits we contracted the all the suppliers and subcontractors okay and so we're ready to go and as I said open next year that's great everything's done yeah well you just have to build easy easy easy does it right yeah and so then with with all of those in mind what else would you say what are some of the key things when you when you look at what's next you know when I'm here with you this time next year you know what do you hope to be telling me? Now first of all of course that we're close to opening Brussels and and no delays right that would be great right to build something without delays yeah and we will have signed new land and power in Europe for a second month so we do things simultaneously and not waiting to build and open and then find the next one we're going we're going on and build simultaneously but the market is changing big time right so the the density is getting higher and higher and where we used to build for 5kw per rat now we're going up to 50 or 100kw so we need to work with flexible designs etc so things are changing a lot so a lot of work to be done yeah well it's a pleasure to have this time to to chat with you today and so I know that you have lots to still do I'm sure before the week ends but for our viewers or anyone here who may want to connect with you either during this week or after everyone's back home how can they connect with you they can connect with me the best thing is LinkedIn so you can find me Eric Boonstra CEO of Egg Keflings you know send me a note and happy to talk okay well thank you so much thank you very much thank you Eric for joining us and thank you viewers for tuning in to JSA TV happy networking


Okay, so I just spoke to an insurance agent in his first seven weeks. Guess how much money he made? $152. That's a joke and that's the number one reason why insurance agents fail is they struggle with prospecting and getting in front of people to make sales. My name is Cody Askins. I started in the business at 19 years old. My first eight months as a full-time insurance agent, I made $117,361.13 using three specifics prospecting strategies that I'm going to share with you on April 11th at five o'clock Central Standard Time. If you would like to know what those three prospecting strategies are and be a part of a private virtual live training that's specific to new and struggling insurance agents to help them make six figures immediately, then click the link below to go to CodyAskins.com to register for insurance agent prospecting 101 April 11th, five o'clock Central Standard Time. Hope to see you there.


Dominic team versus Daniel Medvedev is the final of the ATP finals for 2020, and it's been a battle for both players to get to the final. Team started his campaign in this first round Robin match against Definal SiddiPas. Getting through him in three sets, 7-6, 4-6, 6-3. His second round Robin match was against Rafa Nadal, and after an impressive performance, team got through in a couple of tie breaks, 7-6, 7-6. His third round Robin match, team looked a little out of sorts, having already qualified, he went down to Rublev, 2-6, 5-7. And in the semifinals, he faced world number one Novak Djokovic, and this was an instant classic. After winning the first set and having match points in the second set, eventually winning the match on his sixth match point, 7-5, 6-7, 7-6. Medvedev's had a relatively cruisey road to the final. His first round Robin match was against Alexander Zverev, getting through that in straight sets, 6-3, 6-4. His second round Robin match was against number one Novak Djokovic, and again an easy win for Medvedev, 6-3, 6-3. His third round Robin match, yet again a straight sets win over Diego Schwarzman, and again the scoreline being 6-3, 6-3. And then the semifinals, the biggest test so far for Medvedev, against Rafa Nadal, a man he's never beaten, and after winning the first set and serving for the match, Rafa eventually went down to Medvedev in three close sets, 3-6, 7-6, 6-3. These two have played four times before, with team leading the head-to-head 3-1. On a hard court, it's 2-1 in favour of team, and they've only ever played once on an indoor hard court, with team getting that win as well. And the last time they played was a couple of months ago, in the semifinals of the US Open, with team getting the win. If team's going to win this match, he's going to have to be aggressive. The backhand down the line is going to have to work, and he's going to have to do something other players haven't been able to do with Medvedev, and that's get the ball past him. Medvedev's going to have to do what he did against Rafa and Novak, and that's just a fend. Get everything back, and serve big when he needs it the most. I'm leaning towards team winning this final. Either a couple of tie breaks, like the US Open a couple of months ago, or a three setter, but it still could go either way. Let me know in the comments below who you think's going to win, and what the score's going to be.


A federal government has invited the Nigerian Labour Congress over the union's plan to go on indefinite strike, while the NOC is planning a nationwide shutdown owing to the removal of fuel subsidy and hardship in the country. Labour and Employment Minister Simon Lalung has invited them for discussions. He quoted the ministers, reiterating President Balatinova's government's commitment to always engage the organised Labour and respond to each consent after due consultation and negotiations. And this is in order to guarantee industrial harmony, which is critical to the attainment of the renewed hope agenda.


Hi everyone, welcome back to another episode of the KYC Modified. Freedom Family, we have made a video of every YouTube guideline there is. Of course, if you still do, you can always approach us by reaching out to our support team. The email for the support team is supportatfreedom.tm. Now for the rule of thumb, we will be diving into YouTube's additional policies. These policies are vulgar language and inactive account policies. This will be a short segment Freedom Family, so let's dive right in. You know you can't excessively swear on your videos right? Here's what YouTube has to say about it. Some language is not appropriate for young audiences. Use of sexual explicit language or excessive profanity in your video or associated metadata may lead to the age restriction of your video. There are some inactive channels out there in YouTube. Let's see if your channel is inactive. If an account is found to be overly inactive, the account may be reclaimed by YouTube without notice. Inactivity may be considered as not logging into the site for at least six months, never having uploaded video content, not actively partaking in watching or commenting on videos or channels. Let's break it down guys. Okay, this is pretty basic. Basically you're not allowed to swear excessively on your videos or to use any sexually explicit languages as well. If you violate this rule, your videos will be age restricted. Now for inactive accounts, these are the things that you need to consider. Number one is if you haven't logged into YouTube for at least six months. Number two is if you haven't uploaded anything. Number three is if you're not watching or commenting on YouTube. Basically anything that says, hey I'm still here. That's it for the rule of thumb today guys. Told you this will be a short segment. Let's head on over to the KYC, know the dish in. Our first partner is yourmoviesuck.org. Hey, how are you? I hope you're doing great. Let's take a look at your channel. It looks like your channel reviews a lot of movies. I really like movies and I dream to make my own movies soon. So let's take a look at your videos. So I just saw Jurassic World Fallen franchise and it was really bad. Now I didn't even particularly like the first Jurassic World movie. Everything felt fake and uninspired. In this video you are reviewing Jurassic World the Fallen Kingdom. I kinda agree with most of your premises but I really like Star Lord and I would say that Chris Pratt is a really good actor. But everything else I would agree with you. Let's take a look at another video.  As many of you already know I didn't quite care for the first film either. So I wasn't really expecting all that much from this one. I haven't watched this film yet but judging from your review I feel like this will be like the first movie. Thanks for your videos. Ok I have reviewed your channel and considering everything it seems that your channel is clean. Your videos do contain clips from popular movies but you can still invoke the legal doctrine of fair use. You did not just show these clips to your audiences just for the sake of showing it to them but you use these clips to transform your videos and supplement your argument and premises about the movie. Your thumbnail, your video descriptions and of course the about page is really good. You are very entertaining and I love it. The only thing I can impart to you now is a piece of advice. Try to swear less. I will have to rate your channel as nullified. Let's move on to another partner. The next partner is Azman. Hey how are you? I hope you're doing great. Let's take a look at your channel. There's a lot of clips here about different video games. Let's take a look at your videos. I enjoyed playing that game and it played a lot of hours on it. Let's take a look at another video. This video you are playing the game The Hunt Showdown which is a survival game out of paranormal experiences. It's a really good game and I really want to purchase this game soon. Hey Azman, I really like your channel and I have reviewed your channel. It seems like you have not yet violated any of YouTube's policies which is great. Keep up the good work. The only thing I can impart to you now is to look into your about page. In your about page there's only two sentences. You can improve on this by inserting a little bit about yourself, a little bit about your channel and of course the goals of your channel. All in all I would have to rate your channel as, Nullified. That's it Freedom Family. I hope you guys enjoyed and learned something from this episode. If you have any questions, suggestions, violent reactions, comment in the comment section below. And if you liked this video, click that like icon. And if you want the latest episodes from our channel, you can click that subscribe button. My name is Null Guys and you are watching KYC Nullified. See ya. Hi, have you checked out our freedom forums? If not, well you should because you will find me there alongside free channel assets like audios, banners, thumbnails and a lot more. If you want to hang out with me or with our freedom staff members, visit our discord page. See ya there.


I hope everybody's having a blessed day out there. As you can see, we have a brand new 2022 Ram 1500 Longhorn 4WD with a 5.7 Hemi and we are testing out the carnage performance muffler on it. We're going to get some awesome sound clips today of the new carnage muffler and just a quick rundown. This is a bone stock, bone bone bone stock Ram. It's not tuned. It don't have anything, no cold air intake, nothing. All it has is a muffler replacement. That is it. Took off the stock muffler and installed the performance carnage muffler in place of it and that is it. It still has the factory resonators, factory calip converters, factory piping all the way out to the factory tips out the rear bumper.  So stay tuned. We're going to get some awesome sound clips of it. Most of all, go have some fun in a Ram. 2022 Ram 1500 5.7 Hemi stock exhaust system, bone stock. All right, we'll be right back. We're going to be installing a carnage series one same muffler that we have on Frostbite on this stock Ram. It'll have stock exhaust manifold, stock calip converters. Only thing we're replacing is the muffler. Cutting off the muffler, we're going to be welding in this carnage muffler and it's going to still retain the factory exhaust tips and resonators out the back. So we're going to see what it sounds like. All right, stock Ram muffler is removed and that's the carnage muffler next to it. Massive size difference, that's for sure. Carnage muffler, very, very clean exhaust note, that's for sure. No raspy, no clapping, very deep, aggressive clean exhaust note. I absolutely love the way them things sound. All right, Mopar Fam, you just heard the new Ram with the new carnage muffler on the outside. We got some cool flybys. We got some cool little takeoffs. Now let's hear what it sounds like on the inside of the Ram.  So let's get driving. And there is zero, absolute zero drone on the inside of the truck and it's, I mean, it's pretty quiet. You hear the exhaust note, but it's very, very quiet. Definitely easy to have a conversation inside the truck, but I'm going to quit talking so you guys can listen. Now we're coming up to a stop sign here and then I'm going to do just a normal driving kind of takeoff so you guys can hear the exhaust as it goes through the gears. And now we're at pretty much highway speeds. We're about 60, 65 miles an hour right now. And in high gear at cruising speeds, you don't hear hardly any exhaust at all. Like it is absolutely quiet. Even with the MDS engage, which the Eco MDS is still activated right now. Gotta love that eight speed. This is the first time I've driven an eight speed and it's toasty. That's all I'm going to say, especially for a stock one. All right. So that's it. New carnage muffler on this brand new 22 Ram 1500. Bone stock, literally. The truck's still running right now and you can't even hardly hear the muffler on the inside of the truck at all. But when you get on it, you definitely can hear it on the outside and that's what matters. And it sounds amazing to me. Stay tuned. We're going to be doing a full exhaust system on project storm here very soon. It's going to be a little louder because as you guys seen, we got new long tubes from Texas speed to go on that truck. It's going to be X piped and it is going to be awesome. It will still have Cadillac converters, but it's going to be loud. It's going to sound good. And it's going to be awesome. So stay tuned. Don't forget, hit that subscribe button below, hit the thumbs up button and put a comment down there below what you think of the new carnage mufflers. Link will be in the description below. Click the link. It'll take you right to carnage mufflers. You can check out all their products. They got different sizes, different links, different tones. They got something for everybody. Definitely check them out. Can't beat the quality for the money. 100% USA made in their shop right here in the States. Got to love that. So until next time, we'll see you.


The president of an organization known as the Alternative Ivarian Citizen, ACI, with closed links to the opposition in Ivory Coast, Pochery Globet, has been released. Ms Globet was arrested in August 2020 and imprisoned at the Abidjan House of Arrest and Correctional Facility. She was charged with inciting revolt, calling for an insurrection, disturbing public order, violent and assault. Ballet's organization was among opposition and civil society groups that called and protested to march against a third temp beat for President Alasane Wattara. According to an official report, the protest degenerated into violence over three days. The report said the event led to six deaths, 100 injured, 1,500 internally displaced persons, 69 people arrested, as well as a great deal of material damage.


You know what I'm saying? I know what he was trying to say. And he may have been well within his right, especially if you feel that there's a threat on him and his family. Any man's going to stand up for that. But I don't believe that just that little part probably wasn't the moment in the time. Because at the end of the day, because you already fighting against this narrative that you guys are bullies. You know what I mean? And that somehow or another you had something to do with that. OK, cool. The facts came out. You just proved that. Those of us who knew, I already knew that wasn't the case. Don't boss talk one on one. One on one. Here we go on talk. You know what? I see you posting Jay Prince a lot lately now. For sure.  And we respect him on everything that he says. Legacy is big. Like just the way that things are spinning out for him on the internet right now, it's crazy to me, bro. It is. It is. I'll speak on it in that way, you know? Meaning I don't know the situation. Don't care to know it. It's just I would people, for some reason, like I was telling even Silk, like it's the way we put ourselves out there, man. A lot of times, man, you can't take it back, bro. That's real. You can't take it back. Yeah. You know, man, shout out to old man. You know, I got a lot of love for Jay Prince.  You know, he been on my show. Yeah, I've seen that. That's family. But you know, I think that it's in my opinion, you know, and I'm going to say the truth or just what I think. I think that, you know, like you said, it's a murky situation. I think Jay is trying to find, in my opinion, I think he's trying to find his footing with this social media shit. I think he speaks very direct and very curt. And I think that he doesn't understand like how we understand, especially because we're in media, about controlling a narrative and understanding sound bites and understanding people changing the context and misunderstanding the context of what you're saying because he's from the old school, so he's going to say exactly what he means, you know. And I think some of it gets lost in context. Now, as it pertains to the million dollars worth of game, you know, shout out to Gilean Walo. I believe, you know, OG may have misspoke a bit. You know what I'm saying? I know what he was trying to say. And he may have been well within his right, especially if he felt that there's a threat on him and his family. Any man's going to stand up for that. But I don't believe that just that little part probably wasn't the moment in the time, you know. Because at the end of it, because you already fighting against this narrative that, you know, you guys are bullies. You know what I mean? And that somehow or another you had something to do with that. OK, cool. You know, the facts came out. You just proved that. Those of us who knew, I already knew that wasn't the case. But so you're trying to, you know, authoring your own narrative. But I think that that one little statement toward offset may have kind of undid it. And it kind of to some degree backs up what everybody's saying. You know what I'm saying? Now, I don't think this is a disreputable moment for him.  And this is what you're saying. That clip thing is something else. But no, I've seen another clip where this is the irresponsibility of social media, where somebody captioned that little part when he was like when Jay said, yeah, you know, we never been attacked like that.  Wow. He wasn't talking about takeoff. But they can take and try and spend the narrative. You see what I'm saying? So, you know, it's really, you know. It's murky like you just said. And if you don't take the time to do the research, just really find a full clip to figure it out. Exactly. So how you mitigate that, how you circumvent having to deal with all of that, is, you know, I probably just wouldn't say anything. You know what I'm saying? I'm gonna be honest with you, like it's certain things you can't take back, man. That's real.  They clip it. They clip it and take it wherever they wanna take it. That's why on here I try to stay as neutral as possible. Absolutely. And I try to stay as positive as I can. Exactly. Even though some things, you know, I don't agree with it. If I don't agree with it and it don't come in my way, I don't really speak on it. You know, I just seen that because certain things vibrate the whole internet when it comes out and that was one of those things that just shocked the whole world. Especially controversy. It goes crazy, man. Anything that's negative and controversy, then people are gonna push that, you know what I mean? And it drives the algorithm.  You know, I'm my own entity. That's kind of their business, but, and again, you know, I have love for QC. I have love for Migos and it was a loss and takeoff and all that, but, you know, it's a tragedy all the way around. But, you know, I just hope they figure it out and I hope they do. You know what I think will go? And this is the idea, because I get ideas all the time. Tell me if this will go or not.  That's the impossible show. But it would, a lot of people would flock to it because it'll be talking about, you know, truth and stuff like that. So, and it'll be showing a lot of people up. Yeah, and I think ultimately if people just really just go back to the basics of minding their business. Man. That's not gonna happen, man. You know what I'm saying?  Yeah, that's true. People is nosy. But yeah, and I know, you know, but I don't know, man, I'm different. I'm from the old school, you know, so where, you know, like even with trolls and people that get, you know, social media has given people egos and given people courage that they normally wouldn't have and they do not have in real life. That's right.  From a safe place and just disrespect you. Because you don't know where they are. Yeah, because you don't know where they are, you know, and the chances of y'all running into each other is not impossible, but it could be. Definitely not impossible. You know, yeah, definitely not impossible, you know. So, you know, social media is a gift and a curse. You know, you have to be very careful with it. Yeah, we on boss talk one on one. One on one.


And as fuel-sensitive by its harder-in-equity state president of the National Association of Nigeria's Students Nuns, Sunday Achefo has been speaking. He has won petroleum marketers and fuel station owners in the station to desist from selling above-approved pump-price and stop hoarding the product. The Nuns president moved from one fuel station to another in Aduakiti to enforce the government-approved pump-price of fuel. He explained that the warning became imperative after observing the persistent cues at fuel stations in the state. He appealed to all fueling stations to dispense the commodity, and the government regulated pump-price to reduce the long hours spent at fueling stations by customers. We want to be a burden on Nigerians. Catastrophe. And you know what's in the kitty here? Once a price goes up, it takes years before it goes down. We will regulate it by ourselves. Four is everywhere now. Four is everywhere now. Okay? We are not here. Our interest here, if you cannot sell 165, we want to run this game. And if you sell the Nun 165, you will see the other side of Nigeria's student. One of the commercial operators at a fuel station, where the task force enforcement of the Nuns president extended his tentacles to, lauded the efforts of the team at making life more bearable for them. Please, on behalf of my colleagues, the Okada riders, I want to appraise the Nuns, the President Asafon, for what he did for us. They are helping us to sell the petroleum at the normal price here at the top company station at Basiri. We really, really, really, really appraise him. Though our government, let me say they are busy to do this for us, but the President of Nuns and his colleagues, they are helping us. They are doing what our government is supposed to do for us, on behalf of the government. So for this, on behalf of my colleagues, the Okada riders, we really appraise him for what he is doing for us in Nekiti. Please do subscribe to our YouTube channel and don't forget to hit the notification button so you get notified about fresh news updates.


Alrighty, I have a really quick construction update. It wasn't meant to be so quick, but I filmed so little footage, so I gotta do this really quick. Tundra Twister, the entrance side boarding building is built. I believe that's the side that's gonna house the elevator, but I'm really confused because there's a lot of blue tarp over on the right, but I don't know. One of these sides is supposed to house an elevator. The exit platform is back there as well. The queue line's right there. I just burped. The queue line is right there. On top of it, they've just been fine tuning some electrical, I don't know if they work today due to the rain. Soobies Racing Railway has made so much progress. If you look here, the theming buildings are there. The photo booth building where you purchase your photos and get your photos is right there as well. The launch shed, the train, the midway that's flooded as you can see in this footage that leads up to the long walkway that goes to the entrance to the ride and the exit. So it's coming along well. I think that's a dip and dot stand, but I could stand corrected, but I believe that's dip and dots with the little red roof by the Whirly Twirly Birds ride. I think that's pretty much all I have to say about this. I will say this ride is a lot more ready, even though it doesn't look like it, but a lot more ready than Tundra Twister. From what I've been told, this ride is almost ready to go in terms of the actual ride. It is still missing the full op booth, the op panel, but the electrical, they've done a lot of work with it. They've had a lot more ability to do to the location and then the team, but I don't know guys, I can give me two more days and I'll make a prediction on these rides, okay? Two more days and I'll let you know if they're gonna be ready in time. I promise you, I'm gonna be flying tomorrow. I'll stake it out. I am hopefully going to have an announcement, not hopefully, I will have an announcement soon about something really cool involving Amusement Insiders in Canada's Wonderland, but until then, see you guys tomorrow. Bye.