Why did I short silver? I'm just gonna go over why I decided to short silver It's been ages since I've done a quick technical analysis video. So I'm gonna break this down for you Everything you need to know in terms of the tools I'm using Beetoro trading two on two of the platforms. I'm gonna go back and forth between technical indicators are the EMA SMA VWAP RSI MACD That's everything so The reason I initially got invested and interested in shorting silver is it's trading at some all-time highs So if we just have a look at that on it work, it's a little bit easier So this is the price chart for silver on it or and if we put it on one day You can see if we scroll out of it that it's trading near the highs that it was at in August and And Obviously it held that that was a resistance and then it dropped down And then if we go even further than that and we go on the weekly you can see that silver hasn't even remotely traded anything this high since 2013 2012 so the the likelihood is that it's gonna pull back from it. It doesn't have to But it's likely if you look at previous patterns, right? So you can see it was at this level in 2013 it dropped and it's not reached anything that high. It's here again It's likely that it's gonna drop in my opinion The reason I went for the short yesterday is we jump back on train two on two So the reason I went for the short or decided to open the short yesterday is when I'm trying commodities I like to use a short time frame because I prefer to be in and out You can see that silver's been pretty flat the SMA Hasn't really taken any kind of direction or a size been pretty neutral kind of throughout And then we had this massive spike again, like I said to the levels where previously it's It's pulled back. I know that's not part of like the recent trend, but commodities Always tend to kind of trade between these between like set levels in my opinion And then what's happened is it's consolidated at the top here once it's peaked So you can see here it's accelerating except accelerating. There's no consolidation here It stayed at the same level for a little while and then it started to pull back, right? and then once it's broken below the VWOP and The EMA it basically tells you that the momentum is pulling back. You can see that it's Overbought on the RSI and the MACD was pulling down. So all of that combined so the Support and resistance levels that I was seeing would indicate to me that it was overbought anyway all the indicators are showing me that it's overbought and The momentum Of the trade is showing me that it's lost a bit of steam and it's pulling back So you can see that since yesterday it has pulled back the entire time And now it's getting a little bit of support at the SMA so you can see here It's consolidating at the SMA so what I'm likely going to do is Move we can see I've been moving my stop loss down But I'll probably set my stop loss to something just above the S the EMA and the VWOP Because it does look like it's consolidating so it's even going to consolidate and get some momentum and push back up or It's if it can't break so you can see its support at the SMA and then the resistance at the EMA So basically whatever way it breaks out is probably where it's going to surge through in my opinion It's probably it might push back up Which is why I want to bring my stop loss tight and just kind of cash in those profits And the MACD is to turn into a buyer that RSI says it's oversold so I'm gonna pull the stop loss down, but yeah, that's just a quick little breakdown of Why shorted silver and just a short little trade less than 24 hours, I think we'll be in and out Hopefully I mean I'm gonna hold on to it and as I said this stop loss and obviously if it continues to pull down I just keep moving the stop loss down or set a trail in stop loss if you've got any questions or you disagree or Have anything to say let me know in the comments as I haven't done a technical analysis video in a little while So I kind of want to get back into doing these and Trading actively Etor I use for like my safe trades and trade two on two is kind of my Not YOLO, but just like I'm a bit more reckless because I don't want I don't have to worry about people copying me or anything like that so Yeah, so that's it. I hope that was helpful if it was thumbs up subscribe There's plenty more videos like this and money-making content and all that sort of stuff And I'll catch you guys in the next one


Okay, so welcome to the next session, which is going to be done by Jaka from Ljubljana. Jaka, as I have been told, is a guitar player and programmer and long-time Nix contributor, working on systems stuff, and that's what he's going to talk about today. So welcome everybody, Jaka. Welcome to my talk about running Kubernetes and Nix iOS. I'm Jaka and we'll talk about why Nix and Kubernetes are a great combination. Okay, so something about me, as it says, I'm full stack engineering, JavaScript, Python C and different other languages with experience in provisioning and some better devices and security. Lately I'm mainly backend JavaScript developer, but I'm interested in what spectrum of IT fields. One day I'm fixing bugs of incompetent front-end developers and other days I'm doing some low-level hardware programming. So the project I'm currently working is actually the first one is a startup called Gate Hub and it's a new FinTech platform for multi-currency payment trading and exchange. It's based in Ripple. We already got the CIME lesson and it's currently we are just releasing our new product that is mostly for enterprise users. And the other product I'm making is actually data-driven distributed task automation and aggregation work using graph databases and Docker containers and also Nix. And this project is actually making my free time and I also want to make a startup from it. And so when I started working on Gate Hub, we decided that we want to split our infrastructure in many multiple microservices. So we started to think how can we deploy scalable infrastructure. Our developers wanted to use their Docker for development and deployment. But I still wanted to use Nix.js and Nix packages for our deployment. So the questions I had were how to deploy scalable Nix.js systems, how to deploy scalable apps on top of Nix.js, how to have reliable distributed storage and scalable monitoring. So I decided we need something like a cluster process manager, secure distributed, overlay networking, load balancer, distributed and replicated storage, a schedule for different resources like power, networking, storage. And this all should be managed by some cluster manager and there should be some monitoring system to actually see what's going on. So to do everything, we have to start with... Let's make an overview of what we have. So the first thing is the decision of which container service manager we will use. So in that case, we have different choices. One of the first is usage of system. It's not like container manager, but the main real difference is for container managers they actually run processes in containers and for... And for example, system also has this command called systemD and spawn that also runs processes in containers. And the other you will probably know is Docker. So main advantage of Docker is that it's user friendly and it provides hub of pre-made Docker app containers. It has two simple format for building containers. And the idea behind Docker is that you run one app inside container, not the whole OS. Because if for comparison, for example, with Alex T, container manager where you usually run the whole OS in container, whole distro base image. Of course, you can also do this with Docker and with other container managers, but that's not how it should be done. The rocket, I don't know much about rockets. It's a new thing developed by CoreOS. It actually, they define some specification, declarative specification format for building images. And it's a bit different from Docker, but in a sense that you can... It's a bit different from Docker that it has more... You can specify more things, because Docker has pretty much simple format for building images, and as I said, it's sometimes too simple. Okay, and then we... Let's go to the Overline Networking. While we need Overline Networking, it's actually to connect all these services together across different machines we are running because we don't want to... To statically type in all the IPs, we just want to spawn these services anywhere in the cluster, and they should connect to all other services they need. So in that case, we have a few choices. We have Open-WiSwitch. As we will see later, we use Open-WiSwitch with IPsec networking or across our cluster. Then we have also some other choices that are more Docker-related. For example, this Wave that simply enables to connect multiple machines, and I think they also use now Open-WiSwitch as a back-end. QuoraS Flanel... No, Flanel. Flanel is actually... It's not as optimized as Open-WiSwitch because Open-WiSwitch has a kernel support. So it's actually a really low latency. With Flanel, there is a bit more latency because it's running in the user space. So in that sense, it's lower, but it connects to ETCD. ETCD is a distributed configuration storage. So whenever a new machine comes to a network, it automatically connects to this distributed networking. And Docker is now working on LIP network, and it's not as developed as other alternatives. So to be actually able to run our services across different machines, we need to have some kind of storage that's remotely available or available on-demand on a specific server. So in that sense, we have a choice of different distributed file systems, like SAV, GlasterFS, and ExtremeFS, and also in cloud solutions like Amazon and Elasticbox Storage. In NICSYS currently, there is a model for ExtremeFS that Matei Siwa is working on, and I don't know how well it does it work. We still need... I would really like to have NICSYS model for SAP for GlasterFS that's better supported by cluster managers like Kubernetes and also Docker now. And then let's move to cluster managers that we can choose from. The first I will talk more about later is Google Kubernetes and others. I'll describe now. The second one is Quora as Fleet. So Fleet is actually a cluster manager that's managing system services across a cluster. It was actually, I think, developed before Docker was so popular. But it's not so much developed now. So it's really nice because you can spawn system services across a cluster and also probably NICSYS could use it. The third one is Docker swarm. So Docker is usually running as a single service that manages your containers on a single host. But if you use Docker swarm, it actually distributes containers to many different nodes that are connected to it. So it actually exposes the same API as Docker, but it can communicate with many machines. And then you have... Actually, Docker is not here, a cluster manager. It's... But I still put it on the list. It's actually like an open-sourced Heroku written in Bash. And it's really nice if you want to deploy some simple application. And then the last we have Rancher. Rancher is a solution on its own. It's actually also using Docker. And it's actually providing its own overlay networking. They have pretty nice UI, but I actually spoke with the guys from Rancher and asked if... Because their solution could only be start from Docker, and then they provision everything. And I asked them if they could, like, provide an instructions how to deploy it separately on the machines, and they don't have this in-plan right now. But it would be really nice to see Rancher. What? Mesos, yeah. Maybe I forgot about Mesos here, yeah, also. Yeah, Mesos actually... But Mesos is actually also a cluster manager that can work with not only containers, but processes among the cluster. But it doesn't have built-in support for networking and storage and stuff like that. But it's a really nice distributed scheduler. And, for example, what would be nice to see is, for example, for Hydra tasks, for example, use Mesos, that's one nice case, because all other cluster managers are more for running applications, but, for example, Mesos is more for running, like, I would say tasks, but it can be used for different things. But you have many more available here on this link. It's actually, if you Google how to run, how to scale Docker, container production, you'll find a long list of these solutions. Okay, so now let's talk, let's say something about Docker and Nix. So Docker is primary use for running application, not a whole distros. And, of course, running Nix inside Docker is easy. A few benefits here we have compared to other Docker images is that you can pick the exact version of Nix and exact versions of packages, because, for example, if you use some other images, like, I don't know, Debian or Ubuntu, you can just select which revision of distro use, and here you can actually pick the commit you want, like, of course, you can add a channel and then install whatever you want. And, yeah, it's really simple. I actually just pushed a new Nix image for Nix 110, and so you can pretty much easily make a Docker image and run it. So you can try this yourself. I will not run this now. Okay, this slide. So what about running Nix.js in Docker? As I said, actually, you can run Nix.js inside Docker containers using privilege mode, but you actually don't want to do that, because Docker was not meant for that, and that's actually more for if you want to, like, test or develop on Nix.js, you wouldn't do this in production. Now, working... I was working on service abstraction layer that would actually abstract the services and provide a way to run... that you would be able to make, like, an Nix.js config and then build... and then make a separate container for every service and run it on cluster, but currently I have different things to do, so it's not my priority list, but I think it would be a nice thing to do to actually separate services from Nix.js, because Nix.js is currently quite a monolithic system. So now let's go to Kubernetes. It's actually open sourced and announced by Google in 2014 and influenced by Google's Borg system. It's written in Go, like, actually most of these things I showed you before is they are, like, written in Go, so mainly because Go allows to actually pretty easily build static binaries and distribute them anywhere, so that's also a Google's approach currently, how they're deploying stuff. So Kubernetes uses Docker as primary process manager but also has support for Rocket, so you can make a Rocket image and run it in SystemD and I think Garbus was also working on making... also wrote an article how to build a Rocket image, so maybe we could try and run it with Kubernetes. So Kubernetes has a lot of commits, I would say a lot of contributors, mainly because it's supported by Google and... it's currently for a pretty long time in stable release from the summer somewhere. So what does Kubernetes provide? It provides replication, so it actually manages Docker containers across multiple machines. It provides load balancing, so it has built-in load balancer that balances traffic across all the replicated services. It integrates with distributed storage just like EBS, SAF, GlastroFS, NFS, now they added support for this fiber file system, and there are also some other storage just like Git and secret storage so you can put secrets and stuff like that. It has supports for resource quota, so you can say, okay, I only allow this process to take this much of RAM and this much of processing power. It has built-in support for logging and monitoring, and it has declarative configuration for actually for everything. So Kubernetes consists of many components and these are separated in microservices and here is the description of them. The first one is API server. It's HTTP API service on which you connect and control Kubernetes, and the changes the made are applied by a control manager, a Kubernetes scheduler for resources like servers, so it decides where to put some containers and it also now has support for allocating storage, so if you have a pool of storage it can allocate it, but currently this only works in Google Cloud. Then it has proxy service, it's like load balancing proxy across for all the services we expose, and it of course has this QBlad that's actually running the containers themselves, so it manages containers and reports on containers. Okay, so here's the schema. So usually run one master with which you have applied API server scheduler control manager. Okay, we have also here hipster. It's actually monitoring service for all these things. And then you have many, they called Minions, so many worker nodes where you have this Qproxy for load balancing, QBlad for running services, and also a DNS service, so they also integrate with this DNS called Sky DNS that provides DNS service for all the applications you run on Kubernetes. Okay, now about some terms they use. So namespace is a separate group of application control services. So you can have, for example, development and production namespace. It actually doesn't provide a physical separation between containers themselves. It only provides logical ones. So for example, one container can still communicate with other container, but you can actually write your own firewall plugin that would prevent this. Then there is a Minion that's a worker node, a pot. In a Docker world, you usually run one container and then you can attach multiple containers together. Here, actually, you can run a set of containers on a single host, but they actually share the same networking namespace. So that's pretty nice and they actually can share the file system between these containers. Then you have replication controller. Replication controller is a controller for group of pots. You can say, okay, I want to deploy these pots and I want to make these many replicas. You define this in replication control. We will see an example later. Then a service is a load balancer for a group of pots. It's running on Kubernetes. Okay, so maybe let's make a demo. So what we will do here is actually we will deploy on... It doesn't show everything. Yeah, okay, it's okay. So we will deploy... Here we have our replication controller. So what we will make here is... We will deploy two NGINX containers that... Okay, so let's do this. Okay, so I actually already have this deployed, but I can... Okay, now first let's get replication controller. So we already can see that we have replication controller with NGINX with two replicas, but we can delete this. Okay, so it now deleted our replication controller. We can see that now. And now we made create with this file we have on the top. NGINX controller. Okay, so now we can see it's running the replication controller. In this replication controller it's running two pots. One has already started, the other is almost started. So now both are running. Actually, maybe if I just make curl it would be just fine. So it... Because I said that these are exposed by DNS, so I can just make a curl like NGINX. Oh yeah, I forget something. We also have a service. We also have to expose this. So I already have a service available, but I can delete it of course and NGINX service. And I can actually go to the next slide that shows the service. So here is... Maybe just describe this. So here we have a service that selects all the pots that have label app NGINX and it exposes port 8000. And target ports on the container is port 80. So let's do this. Now we have service available. It has its IP dedicated. So every machine has to have IP space for these services and for pots again. Now let's do this curl. NGINX service. Just a second. Probably I have DNS problems. I actually usually have different DNS server. Let's see if now works. You're not working. Oh yeah. Oh yeah, the different port. It's a wrong port. It's 8080. Thanks. 80,000. Oh yeah. So here is NGINX responding. And it's loud balancing between these two pots that are running. If you would have multiple machines, it would run these pots across multiple machines. So it provisioned them on different machines. That's what scheduler does. Of course, here we have some additional commands. We can also get the logs of containers running in our classroom and access some command inside container and also have a shell to this container. Okay, now about running Kubernetes on XIS. I created an XIS model. And it's a deployment in production for a longer period of time. I actually been testing this for quite a long time. I think from version 0.6. And now we have version 1.0. So actually not the latest stable release, which is 1.1. That's also what most other distros have. And it's actually to actually deploy it. It's actually just a few lines of NIC-size config. So what you have to do, it's like make a bridge interface.  And to actually just define the docker where on which interface that it should use this interface. So in that case, CBR-0 interface. So you here don't see enable. That's because with these roles, you already defined that it kind of should be enabled. But I will probably change this. So you will have to explicitly enable it. You just define the rows of services. You can of course also enable the different components separately. This is just a shortcut with these dot rows where you define master and nodes. But that's running only on a single node. For production environments, we need a list cluster of three machines. And one of which has to be a master and also a minion node, also a worker. And that's because usually in production, Kubernetes depends on ETCD. ETCD is this configuration storage. And if you want to run it reliably, we have to have a quorum of to be at least two of three machines running. So that three machines kind of make a cluster that you can run in production. And you also of course need to have an overlaid networking. So these machines have to be connected somehow and have rootable subnets. Every machine gets its own subnet. So services that are deployed on different machines can talk together and have different IPs that are rootable to different servers based on IPs. So in our case on Gate Hub, we do it on Amazon AVS instances. And we have virtual private cloud. And on top of that virtual private cloud, we are running open-v-switch connected with IPsec because we don't just run instances on Amazon but also on Hetzner. And with Hetzner, because we are communicating over the Internet, we need some kind of secure communication. And that's because that's why we have IPsec links. Yeah, then the deployment is with KnicksOps, elastic box storage. And then we have separated production and development namespace. Here is just a high-level overview of how it looks like. So a couple of minions connected. And then we have Amazon load balancer that's load balancing the traffic to these servers and open-v-switch overlay networking connecting all these instances. Maybe just a bit about monitoring. We use collector for matrix aggregation, influxable for matrix storage, Grafana as dashboard for matrix visualization, Bossoon for alerting. Yeah, as it says. It's actually we are currently using Bossoon for alerting because there is, I haven't found any better component, but Grafana will get in an extra least support for alerting, so this would be really nice because then you would just have to run influxable, Grafana and some service like collector that collects the locks and that would be it. Configuration, actually I will not show how to deploy a multi, how to deploy cluster now, but actually I have available set of profiles. You can include this into your NICSIS configuration and I have something like profiles.kubernetes.enableTrue and for example profiles.open-v-switch-enableTrue and it will, with a bit of options, you pretty simple configurable system. Why I don't want to use bare NICSIS config because I reuse this configuration in different deployment. I don't want to duplicate my code, so I have developed a set of profiles that I can reuse in different deployments, but of course it's still a better documentation. That's about it. I think it's already time for questions. Thank you. At which point when you deploy to, when you build images, docker images, right? When you build docker images, you include the whole closure inside the image so they can be moved around. Yes, currently. But we are working on having distributed NIC storage. Which is mounted across? Yes, that's the idea, but we are not there yet. But yes, that's the idea. This would be really awesome. For me, you really need a big scale to benefit from all this big setup. You need to really have a good problem to solve this, to have this big setup. I mean, for us, it's easier because what's the size? We are running around 10 Amazon instances, around 20 microservices replicated. And then we also have support services like GitLab, like Sentry, and Grafana, Elasticsearch, a lot of stuff. And actually, if I would have to deploy all this by... I actually, for example, GitLab is just deployed with image available on docker hub because this was much easier, quicker way. So this setup actually enables you to run NIC services and also the other, and it enables us to later scale or replace it with a full NIC setup. I was just curious on that topic with copying the closures into the other containers. How big are you seeing your containers being, like for your Nginx container, for example? Oh, Nginx? I don't know how big is it. It's an exposure size plus a few 10 megabytes, maybe. So it's not a big container. The bigger problems are other services. For example, we have a lot of Node.js packages that could be really big in size. If they have a lot of dependencies, this becomes... Has that ever been a problem in actuality, or is it just kind of a... Actually, we had problems with storage because when we updated it, if we updated it, and for example, especially for Node packages, if you updated the Docker file, Docker description file, and you update the version, it will rebuild the whole image with all the dependencies, and it will be a different hash. And then if you have a lot of updates, it will just fill the disk space. So now we actually have a garbage collector that runs every day and cleans the old Docker images. So it is a problem, and distributed storage with Onyx would be a really nice solution here. Can you tell a little bit more about how the VPN works, or the OpenV switch? OpenV switch. Between Amazon and Hatsner and stuff. So OpenV switch is a virtualized switch that's integrated in Linux kernel. So what it actually provides is to simply create a virtualized networking, so we could say it's after-defined networking between machines. We are not using all the features of it, but it provided us with actually not so easy setup of encrypted tunnels. We could also probably use just GRE tunnels, so it's like an IPsec integrated directly in Linux, but I actually decided to use OpenV switch because it has better support for routing. We could do firewalling in some sense with it, so we can actually limit communication between different containers in a cluster. So in that sense, OpenV switch... It integrates IPsec, or... I mean, IPsec is used in Linux kernel, it's part of kernel. Okay, and the configuration between the different servers, is that manual? Yeah, currently we have... We deploy the configuration, its static is generated by NixOps, so when I deploy it, and if I add another server, actually it configures all the other servers to provide this mesh networking between machines. Okay, cool. So we need a better solution to actually be able to spin up servers just without NixOps because... Okay, I'll play more, thanks.


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In five minutes Zenit will be great and your presentation please. Okay thank you very much. I will start sharing the screen now. Let's hope this works. You should see the screen and if we go to full screen. So you should be seeing the full screen right now, I hope. Yes. Okay, welcome everyone and as Alec mentioned today I'm going to give you an overview of data simulation and inverse modeling of atmospheric trace constituents. And since I know this is an area with which many of you are not familiar I'm going to spend a little bit of time at the beginning introducing the subject matter and then go through some examples of the use of data simulation inverse modeling in this area of research. As many of you are aware I'm sure that you know much of the motivation for this work is due to the fact that human activity has profoundly changed the composition of the atmosphere and that has implications for climate as well as for air quality. In an air quality context it is now well established that air pollution is a major cause of human mortality globally. The images here from the Cohen et al study shows this unfortunate reality. The top panel shows deaths in 2015 due to fine particulate matter exposure. These are PM 2.5, these are small particulates that are less than 2.5 microns in diameter. This is showing the percentage of total deaths in each of the countries in 2015 due to PM 2.5 exposure. And just to give you know if you can't see the numbers clearly this is on the order from you know three all the way up to more than nine percent of total deaths due to particulate matter exposure. In 2015 they estimated there were about four million deaths globally associated with that particulate exposure. For ozone the picture is less grim but equally unfortunate. The numbers here the scales are different. The numbers here are the percentage of deaths per hundred thousand people and Cohen et al estimated that in 2015 there were about a quarter of a million deaths globally due to ozone exposure. And so from an air quality perspective to develop effective air quality regulation strategies we need reliable estimates of emissions of pollutants on policy relevant scales. In addition there's also a need to understand how emission of pollutants will impact the global atmosphere and how those changes in the global atmosphere will in turn influence local air quality. And this information is needed everywhere because every country is impacted by this problem. In North America for example we've seen significant improvements in air quality as a result of air quality regulations but despite those improvements there are still large numbers of people who are living in regions where the ozone abundances for example exceed the air quality standard. The plot on the top left here shows those measurements of NO2. NO2 is a compound that is precursor gas to ozone so it leads to the production of ozone in the atmosphere. It's not NO2 is not emitted but it's created from NO nitric oxide which is emitted from high temperature combustion and NO very quickly forms NO2 which very quickly forms ozone. So if you have NO2 you will make ozone. This shows satellite observations from the OME instrument of the vertically integrated column abundance of NO2 over North America in 2005 and in 2013 and you can see the red color showing high abundances of this ozone precursor gas decreasing significantly when we get to 2013. So very dramatic reduction in the abundance of NO2 in the atmosphere of the North America as a result of air quality regulations over the past two decades. Associated with this reduction in NO2 since NO2 is a precursor gas we see reductions in ozone. This plot is showing urban trends in surface ozone. The arrows indicate the direction of the trend and the magnitude of the trend. Downward pointing arrows indicate a negative trend and so you can see ozone is decreasing pretty much most of the urban areas across the United States and Southern Canada as well. However according to the US EPA in 2019 there were over 74 million Americans who were living in regions where ozone abundances exceeded the air quality standard. In Canada it's about eight million people a comparable fraction of our population still living in areas where ozone is an issue. So despite the improvements there's still a long way to go and there's critical need for top-down as we call inverse modeling estimates of changing emissions of pollutants over you know how the pollutants are changing in time so we can better target the various pollutants that are important to to improve the health situation. And much of the talk that I'm going to give today will be focused on inverse modeling of these pollutants. Another key issue that we face is a global society is the dramatic rise in greenhouse gases in the atmosphere the fact that human activity is significantly perturbing the global carbon cycle which of course is leading to dramatic changes in the climate system. The figure here on the left shows it's a sort of the canonical picture if you will of the CO2 record from Mauna Loa in Hawaii. The red curve shows the monthly mean CO2 concentration as measured at Mauna Loa and you can see the strong seasonal cycle due to the fact that in summer across much of the northern hemisphere in boreal summer we have significant vegetation growth the leaves come out on the trees they're strong uptake of CO2 by due to the photosynthetic activity that leads to this drop in CO2 that we see in the atmosphere. In winter photosynthesis is suppressed and we have net respiration net emission of CO2 to the atmosphere and that drives this seasonal cycle which effectively you know looks like we're watching the biosphere breathe as we go through the seasonal cycles. The black line is the de-seasonalized curve showing the very constant increase in CO2 over time due to human activity so this increase that you see here is really being driven by fossil fuel emissions to the atmosphere. Now however as bad as this is we are somewhat fortunate in that only about 50 percent of the CO2 that we emit to the atmosphere remain in the atmosphere the rest of it is taken up by the oceans and the biosphere and how that how that partitioning is changing is shown here on the right the top part of the panel shows the emissions as a function of time from the 1800s to the present and you can see this significant increase over time due to fossil fuel emissions there's also land use change which contributes to CO2 emissions to the atmosphere. As I said not all of that CO2 remains in the atmosphere this sum of it is taken up by the ocean so the blue colors here indicate the ocean uptake and then there's a fracture that's taken up by the land and then the residual that remains in the atmosphere is indicated by the light blue. If you look at the land sink you can see there's significant variability of the time so large seasonal variations are due to the biosphere but also interannual variations are also due to the planned biosphere the drys variations in the atmospheric CO2 so if we want to make reliable projections as to how atmospheric CO2 concentrations will evolve in the future it's really critical that we able to model how the biosphere component will change with time because this biosphere signal component is is highly variable and it's sensitive to changes in climate El Nino and La Nina variations will lead to changes in the biosphere long-term changes in climate will affect the biosphere sink which in turn will then feed back on atmospheric CO2 levels and as a result global warming so it's critical that we model how the biosphere changes in time and unfortunately at the moment our terrestrial biosphere models do not do a very good job of simulating these biosphere fluxes this figure shows five CO2 fluxes in summer boreal summer from five different terrestrial biosphere models ignore the units I didn't plot the units here and the color bar doesn't have any relations the main point here is for you to focus on the spatial patterns and the intensity of the colors it's during the summer boreal summer when we have significant photosynthetic uptake across the northern hemisphere due to the vegetation growth and so you see blue colors indicating a sink of CO2 across much of the high latitude northern hemisphere but as you look across the five different models you see that there's a fairly large spread in the fluxes you have sip three versus the C 10 crew and set model for example this is suggesting a much weaker uptake of CO2 than the crew and set model spatially the models all seem to be consistent which is great for the magnitude of the uptake varies dramatically and so a key objective of the inverse modeling community is to use atmospheric CO2 observations to try to obtain robust estimates of regional sources and sinks of CO2 that can be used by process based models like these to try to improve our understanding of the terrestrial biosphere the biosphere processes and hopefully in doing so then be able to make more reliable projections of how atmospheric CO2 levels may evolve in the future to also go after anthropogenic emissions because there's a growing interest in using atmospheric CO2 measurements in an inverse modeling context to quantify anthropogenic emissions from fossil fuel combustion for example we need to be able to reliably estimate what the biosphere is doing so that we can tease up what the human signal is so both of those issues you know are driving this this inverse modeling work that we're doing as part of the community now a challenge that we face in doing this is to develop an effective observing system that will provide the information needed to allow us to constrain these fluxes on regional scales to get robust fluxes on regional scales and to give you a sense of the challenge of developing this observing network I have this animation here this is a model simulation of CO2 this is from the environment and climate change carbon assimilation system there's no simulation in this particular animation this is just the model simulation of CO2 the white color represents high levels of CO2 the dark colors lower levels of CO2 and in spring coming out of winter you can see the CO2 being exported from the continental regions in Asia and North America and being transported around the world you see the strong synoptic signatures effectively you're looking at CO2 weather here as the emissions are dispersed globally as we move into summer you're starting to see some dark colors emerging this is the uptake signal from the biosphere as the biosphere starts to draw down CO2 in summer and that signal also gets exported globally now in the context of the inversion it's the gradients that you see here the transport patterns acting on the sources and syncs of CO2 that provides the information that we need for our inversion analysis and as you can see the system here is incredibly variable so the challenge that we face is to develop an observing system that's able to capture this incredibly varying system so we need to be able to have sufficient spatial and temporal resolution to capture a lot of the structure that you see here and much of the early inverse modeling work that was done starting in the late 80s and early 90s relied on a surface observing network that was fairly sparse and couldn't really provide the kinds of constraints that were needed to allow us to estimate these fluxes on on regional scales so this is what's driven the investment in new satellite technology over the last 20 years space agencies around the world have invested significant resources in developing space space observations to allow us to more densely sample this this state and hopefully provide the information that's needed so that we can get estimates of these fluxes on policy relevant scales for both you know carbon cycle applications as well as air quality so here's a picture of the NASA Earth fleet this is a very NASA-centric view of the world unfortunately but we have similar investments being made across other space agencies in the world these satellites that are listed here are not all just for atmospheric composition of course yeah it's for all of earth science there's a SMAP mission here for example which Fabio talked about yesterday this is the soil moisture active passive mission that was launched in 2015 and the the Landsat missions I'm going to focus on a couple of the the missions here I'm going to talk a bit about the Terra mission in particular one instrument on Terra there's the ORA mission this also had a number of composition instruments on board and there's still many of those instruments are still operational as you move into the future we have instruments like tempo which is a geostationary instrument that's that will be launched next year as well as geocarbon that's another geostationary instrument and I'll also talk a little bit about the orbiting carbon observatory OCO2 that was launched in 2014 so how do we make these measurements of these trace gases from space clearly from space we cannot make in situ measurements we can only rely on the radiation field that's emanating from the atmosphere and so to walk through how we do these this inverse analysis to estimate the trace gas concentrations given the measure of radiation field I'm going to use Mopit as an example Mopit is the measurement of pollution in the troposphere and it was launched on the Terra spacecraft but I just pointed out in December of 1999 it was supposed to be a five-year mission and it's still operating well this December it will be 22 years in orbit the instrument actually was designed here at the University of Toronto and Jim Drummond is the PI of the instrument and this was the first instrument designed specifically to measure pollution in the troposphere and the troposphere is that lower 10 kilometer of the atmosphere where you have convective overturning that drives a lot of the weather that we're familiar with. To measure carbon monoxide CO Mopit measures radiation in two parts of the spectrum in the near infrared part of the spectrum around 2.3 microns in which the satellite looks at the absorption signature of CO2 in reflected solar radiation and in the thermal infrared part of the spectrum around 4.6 microns in which we are looking at thermal emission of CO from the atmosphere and the way the retrieval works it's based on a Bayesian approach inverse modeling approach in which we have a set of measurements Y which are the measured radiances so these would be the level one data that Fabio talked about yesterday and given those measured radiances in the different parts of the spectrum we want to infer the atmospheric concentration of CO that would have produced the measured radiances and so X in this case are the retrieved profiles and this these retrieved profiles are referred to as level two products so we try to go from a level one product to a level two product when we do the inversion and as I said for the Mopit retrievals and for many of the retrievals today from these atmospheric composition instruments we take a Bayesian approach to do the optimization and going back to Collins lecture on Tuesday if we consider a linear model that relates our observations to our state of interest so X here is the state that we're trying to quantify given knowledge of the of the system based on the observations Y and we have an observation operator here that maps the state into the measurement space so if we assume we have a linear model and further let's assume we have Gaussian errors which Colin talked about on Tuesday the maximum a posteriori estimate which is the state you actually mentioned the maximum a posteriori estimator is an approach that tries to maximize the conditional PDF that to give us a statement that maximizes the conditional PDF and so the map maximum a posteriori estimator is obtained by minimizing this cost function assuming that we have Gaussian error statistics and what you're looking at here is an innovation which is the observations minus the projection of our multistate into the observation space and yes recalling the nomenclature that Colin introduced on Tuesday so we have the information coming from the observations here constrained by our prior knowledge of the system with XB this the background or a priori estimate of the state with an observation error with a background error covariance B and an error covariance for the observations are so if we minimize this cost function we get a solution for the state so our analysis given by this expression here and I've listed it in two different forms because what you decide to use for your analysis really will depend on the particular problem that you're working on so if you have a situation in which for example your state space is much smaller than your observation space so n is much smaller than m then this would be the preferred form to use because this matrix would be much smaller than the matrix here this form is used very often in the satellite inverse modeling community because our state space typically consists of maybe 100 levels in the atmosphere for the trace gas of interest and the measurement space can be tens of thousands of elements in spectral space for the atmospheric inversion problem which I will talk about as well this is often the preferred form and you may be more familiar with this form of the expression of the analytical solution to this inverse problem assuming Gaussian errors and linear model because often for our atmospheric models the state space is usually is usually much larger than the observation space and then of course the analysis error covariance matrix is given by this expression so what do the retrievals actually look like i'm going to show you an example of this approach applied to the retrieval of mothid data starting with the level one gradients is y and ending with the profile of carbon monoxide x so here's an example from a paper by Jim Crawford et al from 2004 remember mothid was launched in 1999 and so we're seeing the first set of observations coming from mothid being used by the community and in this paper they were looking at mothid data and comparing those data to aircraft profiles over the north pacific as part of a nasa aircraft campaign called trace b so this was an aircraft mission that nasa had over the north pacific in spring of 2001 to measure the export of pollution from asia over the north pacific to north america and they took advantage of the fact that mothid was in orbit at the time to isolate particular overpasses of the satellite that coincided with aircraft measurements so that they could compare the measured carbon monoxide from mothid with the measured carbon monoxide from the aircraft flying over the north pacific so these profiles shown here are for coincidence coincidences over the north pacific around 20 degrees north near the data plot so on the plot here we have as a function altitude in pressure units the mid troposphere about five kilometers is around the five hundred hectopascal level showing carbon monoxide as a function of altitude if we focus on the aircraft profiles there are two different aircraft profiles here the p3b and the dc8 we see as we move up from the surface a significant increase in carbon monoxide the units here are parts per billion and then decreased back to more background conditions what you're seeing here is a pollution plume that's been lifted off of asia in spring you tend to have cold fronts that come down from Siberia that lifts the pollution from asia into the mid troposphere and over the north pacific so you can see this fairly localized pollution plume over the north pacific here and both aircraft measurements are picking up that signal very nicely the thin black line here is showing the average of the aircraft profiles and you can see very nicely here this pollution plume that's fairly localized in in in altitude the moped measurement is the thick black line and you know when the first set of moped data became available there was a lot of confusion as to what are we actually seeing with moped because at first glance when you look at this moped profile the thick black line it doesn't match what the aircraft is seeing at all and we expected that the moped instrument would be would have smoothed the atmosphere would not be able to provide measurements at fairly high vertical resolution but if you were to take the aircraft profile and smooth that you would get something that looks like the thick dashed line here which still does not look like the moped profile the thick black line so there's a lot of confusion initially in the community as to how do we make sense of these remote sensing observations keep in mind this moped was the first instrument that you know took this sort of a Bayesian inverse modeling approach to provide this kind of space-based observations of pollution in the lower part of the atmosphere so to make sense of why moped looks like this compared to why to the aircraft profile that clearly shows this pollution plume signature we need to think about the smoothing influence of the retrieval of the inverse of the inversion that we're conducting here so if we go back to our analytical expression for the analysis this is our background guess plus the increment from the inversion going back to the nomenclature from Colin we have our innovation this is our gain matrix which gives us you know we multiply the gain matrix which we can write as k and the innovation we get the increment that's added back to our background to provide our estimate of the analysis but we do have we can take this a little bit further we have an estimate of a model that relates the observations y to the true state and that is our linear model that takes this true state and maps it into the observation space but of course our instrument has errors and so we have an observation error epsilon that we have to account for so we can substitute y into our expression here and in doing so we have an expression for the analysis in which we now substitute our observation model for y that is of this form and we're going to take the product of k times h so the gain matrix times the observation operator and we'll call that a now in this form this looks should look a little familiar to to many of you we can think of this as a linearization of the analysis around our background state where a is the sensitivity of the analysis with respect to the true state but as I said you know in this expression here a is just k times h so the gain matrix times our observation operator but we can just think of it as the sensitivity of the analysis to the true state we can now can substitute our expression for the gain matrix here back into our expression for a so we have a equation of this form and with a little bit of algebra we can write it as that a is equal to the identity matrix minus the analysis error covariance matrix times the inverse of our background error covariance matrix a priori error covariance matrix now if a is indeed the sensitivity of the analysis with respect to the true state ideally we would like to have an a matrix that is the identity matrix that suggests that we have perfect sensitivity to the the true state and if that were the case for a state vector x that consists of n components if we take the trace of a we would get n and that would be the total degrees of freedom for signal that we have it would suggest we have perfect sensitivity to each element of our state vector in our inversion analysis in reality that's often not the case and that's critical understanding that is critical to understanding why the moped retrievals look the way they do so let's go back to that example and look at the moped retrievals so what we have here are this is the same plot that I showed you before but now I've added the averaging kernels of the moped retrievals so actually let me go back and walk you through one more thing so this is the sensitivity of the analysis to the true state and so this matrix is going to be an n by n matrix but it's not going to be symmetric and each row of the matrix tells you the sensitivity of a given element in the retrieved state to all of the other elements components of the retrieved state so keep that in mind and what's plotted here are the is the averaging kernel matrix and in particular each line is one row of that averaging kernel matrix so if we look at the line with the open circles that's the averaging kernel for the surface level of the profile retrieval and it's telling us what is the sensitivity of the retrieved carbon monoxide at the surface to all of the other levels in the state vector x and there in this case this was a seven element state vector and you can see that at the surface the sensitivity of the retrieval to co at the surface is quite small so the averaging kernel is quite small there the surface carbon monoxide has greater sensitivity to co in the mid troposphere not at the surface if we look at the 850 hectopascal retrieval that's the open triangles that also has low sensitivity the surface low sensitivity also at 850 hectopascal with the sensitivity peaking around 500 hectopascal right in the middle troposphere around five kilometers and there are two things to keep in mind here what this means is that when you look at a market retrieval and you look at the profile of carbon monoxide the estimate that you're getting near the surface is not really a true reflection of the co at that surface because the instrument sensitivity to co there is quite low and so to a large degree you're seeing the background information being reflected in the version rather than actual information from the from the true state itself so the average problems provides a way of characterizing the the the smoothing influence of the sensitivity of the inversion to the the actual state and I should add actually add what make one more interesting point here which I'll I'll get to later on but I think it's worth mentioning here is that the reason the sensitivity is low here is because these retrievals are relying on radiance measurements in the infrared part of the spectrum around 4.6 microns and when you get near the surface it's very difficult to discriminate between thermal emission coming from the ground versus thermal emission coming from the atmosphere just above the ground so all the instances where you have significant thermal contrast between the ground and the air above it will you be able to discriminate thermal emissions from the atmosphere versus thermal radiation from the ground and so for these thermal infrared instruments sensitivity near the ground is very problematic and in this particular case we're looking at a retrieval over the north pacific the north the pacific ocean in early spring where conditions are quite cold and so there's very little sensitivity to carbon monoxide emissions near the surface. Given the smoothing influence of the of the retrieval and the fact that we can think of the retrieval as this linear expansion around the background state to properly compare the aircraft data to mop it we need to account for the smoothing influence of mop it and the biasing influence of the background information that went into the mop it inversions and so we can take the same expression that we have for this retrieval this linear relationship and substitute you know the true for which we don't really know but the retrieval is giving us an estimate of the true profile but now we can say well what if the aircraft profile were the true profile in the atmosphere well we can subtract the background state from that and apply the averaging kernel to get an increment that we add back to the background state and in doing so we're taking the aircraft profile and transforming it into the instrument space the mop it space so that we can make a proper comparison between mop it and the aircraft profile in the mop it measurement space so if you take the aircraft profile within black line and apply this expression here where we substitute the true state yeah yeah with the aircraft profile we get this process line that is this thick gray line so now this looks much more like what mop it actually saw this thick black line and this is telling us that mop it is indeed seeing this pollution plume but because of the smoothing influence of the retrieval the fact that we have low sensitivity near the surface we can't capture this signature fairly well but we can't we can't isolate the signature in space but the information is actually there in the retrieval nevertheless but in a very smooth representation and so you know we can now compare the two and these two look much more similar than this thick line and the thin black line from the aircraft the conclusion one of the conclusions in this Crawford et al paper was that these results provide insight on the sources of variability both real and artificial in satellite observations and that understanding these sources of variability is important if mop it observations are to be quantitatively useful and indeed the mop it has provided has provided the longest continuous measurements of carbon monoxide in space and the measure the measurements have been fantastically useful but there was a significant learning curve in the community initially to understand how to interpret these remote sensing measurements because of the significant smoothing influence of the inversion in going from the measured radiances to the concentration profiles of carbon monoxide and this applies for all the instruments as well all the trace gases as well for example here is an example of a retrieval of ozone from the tropospheric emission spectrometer tests troposphere commission spectrometer as an instrument on the aura spacecraft that was launched in July 2004 there were four instruments on board of which test was one test unfortunately is no longer operational it was decommissioned in 2018 however it was a five-year mission and the instrument worked wonderfully on 2004 until about 2018 the instrument itself is an infrared Fourier transform spectrometer that measures radiation in the thermal infrared part of the spectrum from three to 15 microns and ozone is estimated in the atmosphere from measuring emission ozone thermal emission around 9.6 microns where there's a strong ozone emission band now if you look at the average in kernel for the ozone retrieval you get something that looks like this this is one measurement on the eastern seaboard of north america at 30 degrees north 87 west on the 15th of august 2016 each line is one row of the average in kernel matrix the retrieval here was done on a 65 level grid so the average in kernel is a 65 by 65 matrix with each row of the average in kernel indicating the sensitivity of the retrieval on a given level to all to the ozone on all of the other 65 levels so if we plot the individual rows of the average in kernel matrix we get something that looks like this and in this paper mark parrington there's a postdoc in our group at the time color coded the various retrieval levels so all of the levels in the lower part of the troposphere between 1,500 hectopascals are color coded in red and as we move up in altitude the levels between 500 and 150 hectopascals are color coded in green and then the levels in the stratosphere from about 16 kilometers to about 25 kilometers are color coded in blue so the first thing to note as with the moped retrieval if we look at one of the lower tropospheric retrieval levels so one of these red lines we see that the sensitivity to ozone near the surface is quite small again it's a thermal infrared measurement and so it cannot really discriminate between variations in thermal emission coming from the sprung versus variation of thermal emissions coming from ozone just above the cloud we just don't have that sensitivity but we see that the sensitivity peaks around 800 hectopascals so around you know one to two kilometers is where we can we actually start to get sensitivity to variations in ozone and the instrument can now start to detect the changes in the radiation field associated changes in ozone at those levels but the fact that these red lines all look the same and essentially fall on top of each other and these red lines correspond to all of the retrieval levels from the surface to about five kilometers is saying that we can't really discriminate though between the change in the radiation field due to a change in ozone at two kilometers versus a change in the radiation field due to change in ozone at four kilometers so the measurement even though it were providing a profile on a 65 level grid the measurement itself is highly correlated and the average and curl captures that correlation structure to some extent so instead of getting 65 independent pieces of information for the 65 level retrieval for this particular retrieval we obtained about four pieces of information so four independent pieces of information from the retrieval despite the fact that it's a 65 level grid throughout the whole atmosphere if we look only at the lower part of the atmosphere the troposphere so the first 10 to 12 kilometers this particular retrieval provides about one independent piece of information and for this particular day if you look at the ozone retrievals across the the globe so this is showing the degrees of freedom for signal so the number of independent pieces of information that each retrieval provides on tropospheric on ozone as a function of latitude from the southern hemisphere to the northern hemisphere we see that for the whole profile for the whole 67 level profile typically we get between two and four independent pieces of information from the retrieval if we look only at the tropospheric component so the lowest 10 to 15 kilometers of the profile we see that in the northern hemisphere in boreal summer we're getting maybe one to one and a half pieces of information in the winter hemisphere where it's colder and in the southern hemisphere in particular where there's mostly ocean we're getting very little information so less than 0.5 degrees of freedom for signal so significant smoothing effects associated with the retrieval and it's important to keep that in mind this smoothing though really reflects the physics of the retrieval itself it's it's it's it's it's something that we cannot work around it's a fundamental aspect of the retrieval but there are ways to get more information on the vertical structure to improve this the inverse problem and get a better constraint on the overall state and one way of doing that is by combining information from different parts of the spectrum and this is something that has been pioneered with moped very nicely so if you look at the moped retrievals you know you can retrieve carbon monoxide from the thermal infrared part of the spectrum as I mentioned at 4.6 micron but I also said that moped measures around 2.3 microns as well in the near infrared part of the spectrum where it's looking at backscattered solar radiation so you can retrieve CO in this thermal infrared part of the spectrum as well as in the near infrared part of the spectrum and you get very different information you know that the smoothing effects are very different depending on where you are in the spectrum but you can also combine the TIR and thermal infrared information with the near infrared information and that also will change their performance of the retrieval and so let's take a look at what that how that works here in the five minutes or so that we have remaining so if we assimilate if we do the inversion using only the thermal infrared retrievals we get average in kernels that look like these and again each line here is a given row of the average kernel corresponding to different retrieval levels so if we look at the this cyan color here this is for the 700 hectobascale retrieval and you see that that peaks around 700 to 800 hectobascales there's no sensitivity near the surface and then falls off quickly with altitude the 300 hectobascal retrieval level peaks at around 300 hectobascales and then the sensitivity falls off with altitude as you go down toward the surface which is great for this particular retrieval the trace of this average in kernel suggests we get about one and a half pieces of information if we use just the near infrared channel this is looking at backscatter solar radiation we really can't say anything about the vertical structure of carbon and oxide because you're really looking at the absorption signature of the cl molecules in the atmosphere in this reflected solar radiation measured by the satellite and so when you look at the average in kernel it's they're all fairly uniform with altitude because there's very little vertical information that you can get from these retrievals you can see closer to the surface because you're looking at backscatter radiation near the ground but you can't really say anything about changes in carbon and oxide as a function of altitude you can really just capture the extinction signature of the sea molecules so the average kernels are fairly uniform and the degrees of freedom for signal here is about one so there's like one piece of information so you're getting essentially think of it as a column integrated estimate of the carbon molecules co molecules in the atmosphere so very different depending on where you are in the spectrum because it changes what the inversion provides you can now also combine the tir and the nir data as i mentioned and if you do that the average in kernels now look very different so if you compare the surface level retrieval which is in black here you see very little sensitivity near the surface with a black line some peak sensitivity near 700 hectobascals and then it falls off but very little sensitivity to the surface so the surface carbon oxide retrieval has essentially very little sensitivity to carbon oxide near the surface with the tir there's more sensitivity with the nir near ir but as i said you don't you can't discriminate between variations in co and the vertical however when you combine the two this is the surface level retrieval you can see it is quite the sensitivity is quite high so by bringing the two together you can use the near ir information to provide constraints on the co variations in the lower part of the atmosphere and the tir information to get some constraints on the mid to upper troposphere and in doing so you can now get close to two pieces of information you can discriminate variations near the surface as well as variations in co aloft at higher altitudes and the impact of this can be shown very not seen very nicely here in this comparison with moped and aircraft profiles this most these mosaic profiles are aircraft profiles over new deli there's a typo here my apologies for that so this is over new deli on the 3rd of september in 2004 so if we focus on the tir retrievals the aircraft profiles are shown here our aircraft profiles shown here as a red dotted line you can see high carbon monoxide levels near the surface this is reflecting the pollution near the ground which falls off with altitude and then increases aloft this c shape structure is typical of the influence of convection this time of year you tend to have a monsoon structure over over over south asia and you get lofting of this pollution from the surface and then convective outflow aloft and that drives this c shape structure when you look at the moped retrievals using just the tir data you get the black line so the aircraft measurements look nothing like the moped measurements and that's as i mentioned on the last two slides a reflection of the smoothing influence of the retrieval within near infrared only measurements there's even more smoothing of the retrieval so you get a fairly uniform profile over much of the lower to middle part of the atmosphere as opposed to some of the structures that you see here however when you combine the tir and near infrared you get the black line this is the moped retrieval combining the information from those two different parts of the spectrum and now you see the c shape structure showing up very nicely within the moped retrieval moped is actually picking up the surface pollution here with this multi spectral inversion compared to using only the tir or only the near infrared information and if you take the aircraft profile and transform it into the instrument space using the averaging kernels and the prior or background profile you get the red line here so not only is the moped looking very much like the aircraft profile by itself but also when you transform the aircraft profile into moped space they're very consistent so this is a beautiful example of how bringing additional information from the different parts of the spectrum together can give you more constraints on the atmospheric state and you can now start to constrain the vertical structure of carbon monoxide very effectively and you know in this paper by Helen Warden she showed you know just how transformative this multi spectral retrieval is in terms of the information it provides on surface level co so keep in mind the surface level averaging kernel the row of the average kernel for the surface level retrieval shows high sensitivity near the surface and these are the average kernels again but now if you look at the surface level carbon monoxide and this is a five-year average of co near the surface from the moped retrievals you see the benefits of this multi spectral retrieval on the left this is only using the thermal infrared data and on the right is this multi spectral retrieval you can see on the left there's some structure of the major pollution regions in asia but this stands out very nicely the high the red colors represent high levels of carbon monoxide you can see the endogangetic plane showing up very nicely here uh you know the pro river delta much of eastern china showing up very strong so there's significant information that we're able to get from the multi spectral retrieval on surface level co and so now with this kind of information we can take this what we call a level two moped data product so we went from the we did an inverse an inversion asked to go from the level one satellite radiances to level two carbon monoxide profiles and now we can take this level two profile and do another inversion using an atmospheric model to now infer the surface emissions of carbon monoxide to the atmosphere given the observations that we have available to us and i'm using moped here as an example of how we do this you know this is also done for co2 which i'll talk about methane and many other trace constituents so the next part of the talk will focus now on taking these satellite measurements and bringing them into the atmospheric models to try to do the inversion problem in the atmospheric models to infer these emissions on policy relevant scales so i think it is a good time to stop for the break alec okay yeah wonderful girl okay thank you very much here for the first part and now it's break let's continue at seventeen zero zero central european time and ten nine minutes thank you


Hi, I'm Chris Thompson for Investor Intel and today we are speaking with Dan Blondell, the CEO and founder of NanoOne Materials. Ticker symbol is NANO on the TSX. How are you today, Dan? Great, Chris. Thanks for bringing me on the show. So Dan, NanoOne focuses on a process for the production of cathode materials used in lithium-ion batteries and as people might know, cathode is a critical component of these lithium-ion batteries and a major expense in electric vehicles. Maybe you can just touch on exactly what NanoOne does. Yeah, so NanoOne is an industrial technology company and we're developing a process to make these cathode materials. The technology is a platform that allows us to make a whole range of different types of cathode materials. So we have a sort of strategic efforts in lithium-ion phosphate in which are iron-rich materials, manganese-rich materials for sort of fast charging applications, and deco-rich materials for energy-dense applications. The underlying technology is what we call a one-pot process and it makes coated single crystal powders for more durability in the battery. And it also enables us to make cathode materials directly from metal powders, a technology that we call M2CAM or metal to cathode active material. And that allows us to bypass the intermediate conversion of metals to sulfates. So we eliminate the use of sulfates all the associated waste, all the associated shipping costs, environmental footwork, and cost that comes with that. So this is pretty significant from, as you mentioned, from both a waste and a cost side of the equation. How are you finding it received in the marketplace? There's a tremendous amount of interest, particularly in the last six months to a year, as the auto-codes and the supply chain wakes up to how they have to green the supply chain and we have to further drive down costs. So it's about integrating the supply chain and making it better at the end of the day. And so it's resonating very well. And I guess if we're looking at the last couple of news releases, this is coming to fruition with a couple of agreements you've put in place, one with a mining company, one with an automotive company. Maybe you can just quickly walk through those items. Yeah, we have been developing more and more relationships way upstream with mining companies, both junior and fully integrated miners. And I think that because obviously there's an interest there in developing a supply chain that brings greater value to their product and helps eliminate some of the waste in the stream. The automotive companies as well, of course, are very interested in that. But they're also interested in integrating everything kind of downstream of mining to leverage the chemistry of the batteries that can be made from that chemistry and how it fits between the wheels and their vehicle. And we announced a deal recently with Euro manganese on the sort of junior mining thing related to manganese. And at the same time, we announced an MOU with a large global automotive manufacturer to develop a manganese-rich lithium-mine battery for fast charging type applications. So you're basically looking at that from both sides. You're looking at from helping the miners get their product into a battery and helping the automotive companies get locked down their supply chain. Yeah, essentially, we're working at the bookends of the supply chain to create demand for our technology. And we're finding we're getting a lot of traction and a lot of advancement with the miners and with the auto coast. Obviously, we have to fulfill that in the midstream between the two. And working with the chemical companies and looking at opportunities to put demonstration facilities in place in various jurisdictions around the world is an extremely sort of important part of our strategy as well. For instance, we announced a joint development agreement with Johnson Matthew earlier this year to develop a nickel-rich material that they call ELNO. It's intended for the high end of batteries for long-range luxury electric vehicle type applications. And how are you protecting your investment? Well, I think the key thing here is around IP and technology development. This is about building out a robust, resilient technology portfolio that can be used to license basically into the space as a wage for us to partner with the auto coast, the mining companies, and the midstream chemical producers to help build out that supply chain, green it up, clean it up, and make it better for the future. And did I see you got some new patents as well this summer? Yes, I think we're patent number 20 right now that has been issued to us. And those fall into jurisdictions, obviously Canada and the US and then in Asia, there's Japan, Korea, Taiwan, China. And we have about another 40 patents in the pipeline and a roughly about 20, 26, 27 different patent families that are currently being prosecuted in various states of pending. So how are you going to turn this into profits? So this is all obviously it's all about execution. It's all about getting to a point where we are licensing our technology either into a joint venture or into a pure royalty play and we're well along the way with a whole number of our partners. And what sort of news do you think investors should see over the next six months with the company? Well, I think investors can expect to see more news about partnerships and obviously more news about what our strategic efforts are going to be towards I guess partnering on these facilities, on these demonstration facilities. I think particularly they should look to us to be doing things very actively in North America, ramping up our program on lithium iron phosphate on the high nickel materials and on the manganese rich materials as well. Well, that's great. I thanks for your time today. I think this, you know, as most people are aware of the electric vehicle space is booming. A lot of commitments from the environmental conference that they're looking to, you know, move towards cars, towards EV cars, towards the mid 19th, sorry, 2035 timeframe. So I think over the next few years you've got a lot more interest in, you know, the material side to ensure that you can actually produce these batteries. Certainly find ourselves in a sweet spot and are very excited about the coming months, the coming decade as well.  Well, that's great. And we look forward to watching your progress and speaking with you again soon about it. Thanks. It was Dan Blundell. It was the founder and CEO of Nano One Materials. Thanks, Chris.


As we've been talking about, logic is a form of reasoning conducted according to a strict systematic set of principles. It is generally understood to be the systematic study of the form of arguments, the science that investigates the principles governing correct or reliable inferences. Logic arose from a concern with the correctness of argumentation and a desire to find a solid basis for the verification of an argument. The great logician Gottlob Brege described the function of logic as such. To discover truth is the task of all sciences. It falls to logic to discern the laws of truth. I assigned to logic the task of discovering the laws of truth, not of assertion or thought. How logic should be properly defined is a somewhat controversial matter. For some, it is interpreted in a narrow sense as simply dealing with deductive reasoning. Such a narrow conception controversially excludes much of what is called informal logic and processes of inductive reasoning. It is important to distinguish deductive validity and inductive validity. As we previously talked about, an inference is deductively valid if and only if there is no possible situation in which all the premises are true but the conclusion is false. An inference is inductively strong if and only if its premises give some degree of probability to its conclusion. Informal logic is a study of natural language arguments. Informal logic is associated with fallacies, critical thinking, general thinking skills, and the interdisciplinary inquiry known as argumentation theory. Informal logic may include both deductive and inductive reasoning. Formal logic involves deductive reasoning, typically using a formal language or formal system. Logic is generally considered formal when it is translated from natural language into a formal language based upon a well-defined set of rules. For example, the expression, all x's are y's, shows the underlining logical form common to the sentences, all men are mortals, all cats are carnivores, all Greeks are philosophers, and so on. Throughout history it has been recognised that in all reasoning there are certain common patterns, three of which have come to be known as the laws of thought. They are the law of identity, whatever is, is, or all a's are a's. The law of contradiction, a thing cannot both be and not be. And the law of excluded middle, a thing must either be or not be. Deductive thinking is largely reducible to a form such as all men are mortal, socrates is a man, therefore socrates is mortal. Or more exactly, if all men are mortal, and if socrates is a man, socrates must be mortal. Such a form is known as a syllogism. The investigation of deduction and the elaboration of the syllogism are the work of Aristotle. Aristotle defines the syllogism as, quote, a discourse in which certain, specific things, having been supposed, something different from the things supposed, results of necessity, because these things are so. The syllogism is the basis of deductive reasoning and Aristotelian logic, forming the backbone to logic for many centuries to come, building the rationale behind Euclidean geometry and scholastic philosophy. With the rise of the modern era, the inextricable development of science brought new systems of thought and reasoning. It was the English philosopher Francis Bacon that challenged the orthodox of Aristotelian thinking that placed priority on deductive reasoning, seeing inductive reasoning as a temporary solution. Modern science was built on the logic of induction as a method for understanding the world. The old logic of the syllogism was sound for clarifying what we already knew, but it was not useful for discovering new knowledge. Francis Bacon proposed the method of inductive reasoning as the foundations for modern science, whereby empirical data would be collected and analyzed to infer general conclusions. Francis Bacon described this empirical inductive process of acquiring knowledge as such, quote, Now my method, though hard to practice, is easy to explain, and it is this, I propose to establish progressive stages of certainty. The evidence of the sense helps and guarded by a certain process of correction I retain, but the mental operation which follows the act of sense I for the most part reject, and instead of it I open and lay out a new and certain path for the mind to proceed in, starting directly from the simple sensuous perception. In the 19th century arose a new logical field called logistic or symbolic logic. Its characteristic form is the application of mathematical symbols to logic and its substance is the analysis of relations. The fundamental inadequacy of Aristotelian logic, according to the logicians, arises in the use of language rather than symbols. In 1854, George Boo created the first algebra of logic to express natural language arguments in algebraic form. An example of how language can fail the logician is the alleged ambiguity of the copula. For example, in the statements a is b and all a are b, the is and are seem to express different relations. The application of mathematical symbols to logic not only removes any such possible ambiguity, but also greatly simplifies logical processes and admits the extension of their application far beyond the province of the Aristotelian logic. This classical form of logic discussed above is a form of bivalent or two-value logic that is that it most naturally divides propositions into true or false. Non-classical logics are those systems that reject various rules of classical logic, most notably the creation of this dichotomy. Classical logic only permits conclusions which are either true or false. However, there are also propositions with variable answers, such as one might find when asking a group of people to identify a colour. In such instances the truth appears as the result of reasoning from inexact or partial knowledge in which the sampled answers are mapped onto a spectrum of possibilities.