Welcome to the NPTEL course on remote sensing and GIS for rural development. This is week 11, lecture 5. In this week, we have been looking at synergized mapping using crowd sourcing data, along with remote sensing and GIS platforms to check the data and update the data, more on rural infrastructure mapping, etc. we have seen. We have also noted that using buffer as a tool helps a lot in making sure that we have an area of coverage and creating more infrastructure that is needed for hamlets and villages. As promised, we will be looking at in this week. We have already seen OSM use in this week with remote sensing and GIS of the attributes. We will be looking at crops and water bodies in this today's lecture. Crops is very important because as we have seen in LULC mapping, we need to go there and find data for ground routing. However, if we do not have that data, then you use unsupervised classification. So to use supervised classification, this OSM tool can come very handy and you can also go and put in your data and request. So let us get into today's lecture on mapping of crops and water bodies. But also I would like to add a school layer if possible. And here we are. I will be sharing my screen. So let me first share the school that I've added. We noted that this is my profile. I have this profile. And I have added the school at a government high secondary school. You'd have noticed that in Google Earth, that school was not captured problemately and OSM does not have it. So initially this layer was not there and I've added that layer. So I've added all these layers here. And now with due course of time, in OSM it will start reflecting as a school. So these parcels have been identified and anyone can start using the OSM-3F cost and actually add, you can see here just a minute ago I've added the school. So here is where you could add the school, export the data if you need and then also look at other regions that you would like to use. So if you look here, I've added this plot and it does go through a checking and then it gets updated. So anyone who wants to use it, you can actually go through multiple layers and then say you want to edit, add a layer and then you can start mapping it. So for example, if you just start clicking, clicking, you can add a layer. So here you can see this is the layer that I've added in OSM for that race school. You can edit this if needed for a different attribute. I've added this and you can edit. Only features I created, I can edit, not the other features. So for example, this has changed name or some of the data of it has changed. We can go here definitely and edit it. So I can edit it from the type that we have. So you can click it and here you have the type. I can add the address, the grades. Grades is the class numbers, 0 to 6. Initially, as I said, when my father was studying, it was 0 to 9. And then now it has become full 0 to 12. 0 means 1, 1 to 12. So you can find that 1 to 10 was also there and then it gets startedly updated. There's no 0 because in the case of kindergarten, it's not available in villages. You can see it's a similar village. A lot of land is in agriculture and I'd be happy to showcase that all of this land is in agriculture. Very, very small village and then this part of the village has increased. So like this, I would like you to map your own villages and just think about how much data can be put on an open source if a lot of people participate. The mapathon idea is also part like this. We invite you to participate. So I've added this. So now it is reflected in the OSM database. And I am now going to go to our slides again. Okay. And we are going to open our QGIS screen so that we will be now doing as the slide indicates, we'll be doing crops and water bodies. Let me share the first screen. So before that, I would like to show some exercises that I have run out of curiosity. I ran a Maharashtra state health care system and OSM and look at the coverage. Look at how many points are there, beautifully covering all the places, all the locations. You can see that more hospitals are in the Mumbai region, the urban regions of Pune and other things are there. There's a big gap. So this is the gap we need to address. Either it's a data gap or there is no health facilities. So think about having people travel so far. Let's say how far it is. Let's say if, for example, if there is no hospitals in this region, then people would have to travel, let's say kilometers and then here. So 50 kilometers in between, there's no hospitals. So that's a lot. Think about villages where snake bites is still common. Insect bites, lead, fever, all these are very, very common. So in this whole parcel, you do not have any hospitals, which is a big concern. Either, as I said, either there is no data that the hospital is there or not on OSM. So it has to be populated or it is not there. So we can quickly check. So what I'm going to do is I'm going to open this layer. And first let me see what district that is. So let me add the district layer. So when we add the district layer, we will be finding more of the districts that are in this picture. But as I said, let me first export this into our Google Earth. Okay, I'm going to bring my Google Earth Pro here. So Google Earth Pro is now here and first we have to zoom out to Maharashtra before we input the data inside. So this is the Maharashtra region and I'm going to go near and see which region. Ahmed Nagar is really not having much hospitals as per this location. But if you would like to see that more of. So this region would be very, very underrepresented with the number of hospitals. I'm going to remove the places. I'm going to remove the roads. So this region, Janna and Sillal region have very, very less hospitals, which we will be seeing pretty soon when we download this. So I'm just going to keep it zoomed at this angle. So because when I'm going to open my Maharashtra health care shapefile, what happens is it last there's so many points we cannot do it. It has more than 2,500 features. It cannot be populated. Do you want to import sample, which means only 2,500 of the first 2,000 final features will be taken and then subsequent. So there's more than 2,500 it has to stop. Okay, or you can restrict to my view. So as I said, I don't care about Mumbai side hospital locations because those will be more accurately located. All the Uber, Ola, everything is being mapping that location. Zomato, Swiggy, Uber, Ola, everything has because there needs transportation. But if you go to villages, there's no transportation. There is no Ola car there, right? So what we're going to see is we're going to first say restrict view. If I say important, it will take a lot of time. I'm just going to restrict to this view the features. It says 2,000 features were important. Okay. Do you want the style of the features to be ingested? I'll say no. I just want the buttons, whatever default size is okay. And as I said, we are going to do a reset date and this location. So this location has very, very limited number of hospitals. Okay. So from Bath to Parthur, there's very, very less number of hospitals. And if you zoom in, you will definitely see either locations of houses. All these are agricultural land, right? You can see a lot of agricultural land. And you can see there is a dairy and stores. There's a small village kind of a thing. There's no hospital. There's no hospital located as per the OSM data set. But for sure, there's no hospital here in terms of number of houses and stuff. But if you can search for it and map it, then you are creating better access to rural health and stuff. So it starts like this. If there's no mapping of hospitals, if there's no proper mapping done, then how do you know how much people are being catered to this hospital? And especially during COVID, how many vaccines to be transported? If we don't know the location, we don't know the distance. We don't know how many people are around it. For example, there is a hospital here or a primary health care center, PHC, we call them, and 30, 40 houses that depend on it. So this is what is needed. The houses look big. So maybe it is a progressive area, but still it needs a health care center somewhere nearby. So these are smaller. These are, again, kind of bigger villages. But if you go here, you'll have very, very small houses, a number of houses. And there's a hotel. I think it's a small restaurant, not a place to stay. And the distance is also long. So this is where we could get some help from the, let me just go to double click. So we can see all the points here. And only part of the points have been added because it is actually taking the view. It's restricting to the view. And then you can go to the properties to understand where the location is. As I said, if you can move now. So for example, the data is here, but it is accidentally placed there like in the schools and other database. You can move it. And then this can be used as an updated shapefile in your GIS for making connections, for making raster out of it, interpolations, and also most importantly, for making access maps, vulnerability maps, risk maps, which are very, very important for the rural entities. You will not see the name injected because we said we don't want the style to be injected. But I'll do that also for this location just as a case study. So when you say escaped, it just goes out. It doesn't bother putting down the equation. I'm just going to remove this for now. Delete contents. And then let's zoom in and then re-add the layer, but only the particular layer we want to re-add. And do you want to reload the file and lose any elements you've made? Yes, I will say. I want only the restricted view. And I want the, I'd say, OK, 1000. Yes, I want the ingested styles so that these fields can come. You can say OK. And then you want to store it. I don't want to store it. I don't want to say what we're going to say. OK. So now we have all these files. Again, the same files are there. You can change the colors, styles, everything here, some part of it, not all. And then sometimes the names also don't get populated if it is not there. So now if you click the properties, all the names have been ingested. And as I said, it is given the name Jainan district. And you can see that it is in the Jainan district. So let's see how the hospital is located. This also looks like a village, a small village with very, very small coverage. And then let's see what it is. It is a health center and health sub-centers as a sub-center deviating one, which is good. So we now can map it. So just to showcase that you also have roads and labels in Google Earth Pro, right? But these are more like roads based on the color of the image. So automatically you could see that they are extracting because I am saying this is, there is no name. There's no name for the road and the road cuts through. So but the road goes here. So all these are very important to give access. But more importantly, the hospital is missing. So there is a positive in using OSM plus Google Earth Pro remote sensing data plus your GIS data. So for example, this is the hospital, but it is placed here. All you have to do is go to properties. It just clicks. The name is already there in the properties. You just move it here and say, okay. And now if I click again, you will see all the data. But now in a moved building. Okay. Because I have moved it from here to there. So you can just move it again by saying properties and taking it back to this place. Normally if you go to health centers, you'll see a tree around it. People for people to sit and rest. And that is part and parcel of the networks. Okay. So we do have a good understanding now of how to use it in a big state, zoom into the location you want. Only that location take out because we do too much in Google Earth. Also it will suffocate and drag a lot of your memory and internet speed. So make sure you do that. Google also needs internet. So just make sure you just use what just truncate your work to what you want. Okay. So good. We have stopped here with the health care centers and stuff. Now we'll go to crops and agriculture fields. In again, we'll do Tamil Nadu or Maharashtra. Maharashtra is too big. So let us go down back to another area. Okay. So Punjab has been using a lot of Haryana. Punjab has been using a lot of groundwater. And we need, we can actually see what crops they're growing by extracting that. So let's do that. We will have this one selected. Okay. As usual, it does let you select one path. Let's see how to go to properties and then open attribute table. You can select one. And select all. Okay. Now it will allow you to select. Yeah. For some reason it does that. It's fine. So let's say this, this part is where the groundwater is typically extracted. We'll do, yeah, we'll do this one. And then I'm going to extract now on the India full states, the OSM for crops. So I'm going to first put inject my layer. So I'm going to say layer India full states and only the selected layer. So only this layer will come up because I've selected it. And then now I will say agriculture. I like to actually use this data rather than the preset. So let me go back here and say, I agree. And you will see in the agriculture key, how many are there, there's designated, non-designated, et cetera, et cetera. We'll just use everything. Okay. I'm going to say yes, no designated, permissive, private, official. We'll just say yes. So that will add it. And then we can say crops. This is the beauty now. We can actually map the different types of crops.  But I'm just going to say in there we have wheat. And yes, we have to map the wheat data. And then let's see how much of these are wheat. So see how interesting it could be just to make questions out of this. We can have advance. We can have nodes, polylines. We can have polygons also. Polygons is good. Run query. No, some object is selected. So, okay. Just say nodes and points, polygons. So always nodes has to be selected. We'll just run this now. Successful query, but no result. So for some reason, the data has not been mapped. And these are the regions where there's tons of data that are needed to be mapped. So if that is the case, if it doesn't go by the area, I'll show you the other way around in and around Punjab. Okay. We do know Punjab has a lot of agriculture and crops. And then we can see it's now done the same query again. If we are lucky, we'll find a lot of crop area. Okay. So it says again, which means that not much data has been given for that region, which is fine. We can use, let me double check this one. And what we'll do now is we will clear this aspect. And then we will now go to Tamil Nadu to take crop data. So let us first zoom into this layer. And then we can take Tamil Nadu as a selected area. And then we'll go to quick OSM. We would like to see what data they have. And then I'll also, I've done already some crops for Maharashtra. I'll show you how different they are. So this is where we could use this data for cross-checking the data for Maharashtra and Tamil Nadu for ground roofing, et cetera. So we can say agriculture, you have crops and agriculture as precepts. You can say crops, there's no precept for crops. So the best way is to put it here as crop data. And then in this, you can say that what do you want in terms of Tamil Nadu? I know they grow rice a lot. So there is no paddy. There is rice here. And let me add it and say, or you cannot have paddy and both paddy and rice and banana because it grows in different different seasons. So we can say canvas, layer extent, or we can say Tamil Nadu. I'm just going to say Tamil Nadu here just to see if we can have a different in aspects. I'm just going to run everything so that if we have polygons also we will get it. So the query is running now for rice and bananas. We know that, especially in some regions, there's a lot of rice growing there and bananas, especially the chips that they make grow there. A lot of export is being done to other places. And you can see that mostly it is a polygon that has been exported. Beautifully the data has come up as polygons. And we will just minimize this and close this and then go back here, remove the selections. Then we just see zoom to layer. And you can see that small, small particles are there but we will not see it until and unless we zoom into this part in terms of properties. It is very small. So what we do is OSM type is way as a path. So sometimes we do have, we can zoom into a particular region and then see if, for example, this region may or may not have crops into the dataset. But first let's export this as a safe feature as PN crops. And then say, okay, save to the file. And then we go here, say open PN crops. And say we don't want the style for now. Okay. So for some, okay. So you could see that you could select a particular crop out. And then I keep it in crops and bananas we have sent. The only issue here is are the boundaries the same? We can establish that by first double clicking here. And then we'll save borders on the bottom. And yeah, so it is, you cannot see that small in GIS. That is why we exported as a shape file and then created a link. So let me see, for example, they have a particular crop. And it says rice. We can see this. And then I'm going to open the properties, make it opaque, style color, go to opacity is 100%. Just going to add 50% so that we can see the land. It still is not visible. Style color is 50%. Area color can be 50% also. Yeah. So now you could see that they claim, they claim that all of this is rice, but technically it is coconut and some other things are there. So here's where the data issues happens. But in some regions, like a big belt of crops, like in Maharashtra, the sugarcane, et cetera, we are able to see a good link between the data sets. So first let's look at this and maybe it was when the data was taken, it was a rice. We don't know. So that is what we're going to see here in 2006. Yes. So all of this has been rice. So this is a very, very important finding we have here. I'll tell you why. So rice requires a lot of human time in terms of management, water irrigation, and harvesting. There's a lot of labor that is needed. Not always you can have a tractor that can come in because look at the small roads. It is very, very hard to bring a tractor into these fields for changing the landscape and stuff. So what normally happens is slowly, slowly like look here within four years. So I'm looking at 2006 where everything is the same land. There is no bifurcations. But then after that, what happens is there is bifurcations in the land and you have some growing coconuts and other things. So coconut does not require that much management like rice. There's no every year tilling. There's no every year fertilizer application and intense labor for sowing and stuff. Only one time you would sow for that. So look at this. They have divided the land now and slowly some part has been done for horticulture. These are like fruits or vegetables or flowers. And then you have coconut and then you have other other aspects. So everything is beautifully done here. And then this, this is how the power of remote sensing and satellites is with ground sourced data. You can actually map and take the data out for your particular area of interest. So in QGIS, as I said, you could not see it very, very small because the size is very small. But if you go to the attribute table, you did notice that, you know, it is, it is small, but the area is there. So for example here, the Jerusalem form is going to click it. And then we can, when you click it on to this map and then say zoom to layer, zoom to selection. There you can see the, see the parcel. So the parcel was not visible in the huge frame when Tamil Nadu, the entire Tamil Nadu was shown. You see, it's very, very small, very, very small. But still there's a lot of attributes like this. So the best way to see maybe the coloring also can help. And then you can see all of them to be zoomed out. Right. So here is a big farm here. There's a small farm there. And around Prichi, my area where a lot of rice is grown. We know that there is a lot of rice grown. Okay. So I'm just going to close this part, go back to Google Earth and then show that this is a quick way of assessing. So as I said, if you go back to this village at right on the banks of the Kaveri water allocations, you will see, so these are, these are, this is the waterway. As I said, a lot of agriculture that happens around it, right? All these are water, agriculture and very, very fertile land, rice, plantations, bananas, et cetera, very, very highly grown. However, when, when there is less water and because this is basically the Kaveri water release also is, is being shared. And if this water doesn't come, all these lands are dry. So there's groundwater being explored. And that is one of the reasons why these regions have migrated from rice to coconuts and plantations which are short grown and they don't have to put as much as effort and time as rice. Okay. So for some reason, we do not have much data here. And so, which is the need of the hour they have to have data. Let's look at another area. So entire terminal has been taken. I didn't take just to cheat. Another area for rice is here. Right in the city area, it looks like again, we can have this properties. We can see style and colors and everything is individual. So we cannot, we have to go every style and then look at it. So we have clicked on this one. Right. So this is going to have properties. I think there's one more we'll do for time. We'll just make it 50%. And then there you can see right in a village area and a seed area, you can have a lot of agricultural stress still happening. You'll do the same exercise for Bangalore. You will get a lot more data that is very, very accurate. Why I chose Tamil Nadu is because of my field work experience. I for sure know that some areas I can relate from my field experience and remote sensing and where some data, if it makes sense or not. So this is being claimed as rice, which may and may not be true. But then as I said, it depends on where, which season, which timeframe they're looking at. This is also rice. It says again, initially maybe it was rice.  Yes. So this is there. I can remove this part and then put a time frame on it to show you that it has always been under agriculture. This can be really, really a big rice field. One thing you can definitely check is if you can check an agricultural university, you'll see a lot of these croppings and patterns that they use. Okay. So this is about your crops. I would also like to showcase the Marashra one that I've truncated. It's a big data set. So I'll just truncate a part of it and then show for the class, which, which crops that we could use. But again, as I said, it does, it does rely more on the use of the data in terms of if you want to do cropping for sugarcane, et cetera. So since we know that sugarcane is a very important subject, let us quickly do one for sugarcane in the Maharashtra region. Because that is where a lot of water has been consumed. So we will go back here and go to the India full states zoom the layer. And this is where we want. So we'll try to see if it selects. It does select this time. Sometimes your QGIS does have some issues. And you can see here your crops and crops. And I know for sure that the plus banana not and or bananas and I'm just going to say their extent Marashra or India full states only the select features. And then I'm just going to close this part and then keep all of them on all of them on just for sake of continuity. Because the updated version sometimes does not run for specific calls. It does require it always. If you do more ways and stuff, what happens is it does neglect some data. So initially when we ran, it did not run the whole thing. But now it has run it. And you see that crops sugarcane and rice, which we ran are now sugarcane and bananas. If I can. So it says crop sugarcane crop bananas. In Tamil Nadu's crop rice sugarcane and bananas. So now I am going to export this as a safe feature as and then let us put it in the image. Sugarcane plus banana. This is a very interesting find. Why? Because I'll show you that normally you do not have again the step is first do it in QGIS then go to open file. And then open the shape file in. They want the same files. We need the data. You can say, okay, don't need to save it. Only when you like it, you can save it. You don't need to save all the data sets. And apparently it is very, very less compared to the number of data sets that are there. Okay. Double click. Okay. Very, very less number of monitoring. Only two for a state that runs on sugarcane. So which is interesting, but it's good. At least we have a style and color. Let's say we have 50%. And this is. Properties. You can see all the properties. Yeah, this is sugarcane. It says, so if you go close. Okay. This is a sugarcane plot. And in time frame, we can also see that yes, it is a lot of sugarcane. Some windows have been created by a lot of sugarcane. So now this, this plot, this plot, if we use in the university classification, we have data from OSM that this is a sugarcane. So I'm going to show you how this could be useful. So if I, if I'm going to take this out, and I know that this is sugarcane from a ground proof entity, then all the, all the pixels and all the colors that I take from here will reflect sugarcane in my supervised classification. So we just supervised classification. We're going to say all these green color and the growing grid. I'm going to show you how the leaves change color during the sugarcane period. So you can see here, slowly the big, big leaves are, are coming up in terms of the sugarcane growth. Yeah. See the rows are being planted and now the sugarcane comes up and then within two months and another six months, the crops are growing along the side. Also they will grow the same things. Okay. So almost every region will have the same cultivation. Maybe one or two farms will grow slower or later, depending on the budgets they have, but approximately all of them grow the same thing. Yes. So this is how you could take a data set for sugarcane and other things. And then let's see this one. I'm just going to go zoom in. This is also saying sugarcane, there's no bananas. So there is a conversion of bananas of the, of the area to bananas. And you can see here, okay. All this is sugarcane as per the data set. And I'm going to zoom in and zoom out to see how they grow. And now I have a spectral signature from a particular area, which says it is growing sugarcane. So all these is sugarcane aspects. So with this, I'm going to stop here for the crop part. We'll quickly do again, we'll just clear the selections and then see if Permanado can be selected again, or we can say water bodies, right? So we'll do the water bodies in the vector with OSM. Yep. And then say water. Waterway is there. We don't want that. We can say agriculture and then water. So to find this use, you can have different ways. There's no water, amenity. You can say amenity. And then water, what a basket disposal transfer, water point watering is there. And then government. There's no water for government. Let's say water. We have facilities, animal watering, drinking water, water park, man made water towers, water tap, and then utilities, shops, water. Let's say just water. There's a lot of waters. So there is or, or, or, or. So now I just click water as a preset. Do I need a river stream title, a waterfall dam? Let's do a dam in a particular waterfall. I don't need spring. I don't need, it depends on where you are. If you're in the Himalayan region, yes, there is a lot of need for that part. Fish pass, no breakwater, pressurized pipeline water basin reservoir. Reservoir is good. We can keep that covered water park, and river land, wetland. All these are not important for us for now. Okay, let's keep these two. And then let's do it for a particular state. Pune, Pune is known for a lot of waterways. And I'm just going to look. It depends on if the data is there. I know for sure Pune has a lot of dams, but we need to see if the data is there. So it has mapped, it has mapped as waterways and reservoirs in Pune. What I'm going to do is, again, we'll export this as a feature. Go here, put it in my folder as Pune, water, say okay, go to Google Earth, open Pune waterways, open. You want to apply the same style, you say yes. All the ways are there. Okay, fine. When it starts, it is empty. So just see that it goes to the space, but it's empty. So don't get scared that why it's not showing up. Just click this button and that's it. So you'll have all the waterways. In Pune water, there's three bodies only that has been mapped, very, very less that has been mapped, which is a concern also, one, two, and three. So all within the same region. Maybe they didn't mark Pune into it, which is fine. So you can see here that I'm just going to remove this and say that this is the water body, but for some reason they could not map it in terms of that. So there's a small reservoir type of thing on the side. That is what has been mapped, which is good. And the main water is there, and this is also been mapped. So you can see here how a water body can also be mapped and that spectral signature can be used later for making maps on water bodies and access to water bodies. So this has been also made. And you also have this one, which says it's a water body. We can quickly see if it is a water body. Okay, remove the time frame. It is not a water body, but it has been accidentally marked. Maybe they converted the land from a water body to a thing. Let us see in 1985, we don't have data, much data. 2004, no, it's always been a land. So maybe a land associated with that has been created, which is fine. So here also rivers and streams are there, but I don't expect rivers and streams to be mapped that easily because you need to go through this and do a path. It's not like a road. A road you can go through, drive through, cycle, and then take a point and then put it in OSM. How do you do that for a river? It is not going to be easy. So it's good that we have this exercise of picking and choosing data. Sometimes if your query doesn't work, do not get disheartened. Just make sure that you try different combinations that we did. Initially, when we also did, I also wanted to show that it is getting stuck so that how do you come out of it and still get data for a particular region is by using different query systems. And Punjab and Haryana, the crop mapping was not there, but definitely you can use other query sessions for it. Same thing, sugarcane builds in Maharashtra we can do. But more importantly, leaving the agriculture because we have NDVI, a lot of remote sensing satellite data that we could use. More than that, we were very successful in targeting rural infrastructures such as roads, schools, healthcare. Now just think other things that can be done for rural things like ration shops and how they call it may be different because they will have a different naming scheme. But just you can update your data on it and then say if the ration system is there. So there is different rationing systems here, but it solves shops, homes, and different aspects. But you can just definitely say government. It all depends on how they store it. Resistor, they will store it as ration, but it's not there. So just keep on searching. Food is there. What is there? It's not food. Yeah, food is there. And then ration shop is not there. I don't know what in food. What are the subclasses will be there? There's only less subclasses. Okay. So food could be a restaurant, food could be a ration shop, pre-processed food, et cetera. So this I think I've given some good indications of how to use this for mapping your infrastructures when there is no data, creating your own database for rural development, which is very, very important. Now I will conclude week 11 and look forward to week 12, which is the last week of this course. As you are, I'm also excited to see how your exam goes. And then in the week 12, we'll show a lot of applications, which will make you think on using these remote sensing data for greater lengths. This I would like to conclude here. And thank you.


going on, everyone. Sam here, late in sports guard. Drippin' 22, 23, P.D. Donner's Passable Hobby, five box break, number five. Pick your team style and we have a list of our names and our teams. Alex S. Heat, Alexandros, Rockets, Cavaliers, Bob H. with the Magic, Brian C. Spurs, Bucks. David H. Jazz, Dawson C. Thunder, Ed B. Wizards, Emily P. with the Clippers, Evan M. Kings, H. Wang with the Timberwolves, Sixers, Hill and T., Pistons, Hung Wai with the Suns, Jason W. Bowles, Jessica G. Raptors, Joshua B. Lakers, Marcy S. Nicks, Mark S. with the Nets, Marvin N. Nuggets, Pogen K. with the Mavs, Shelly L. Pelicans, Thomas D. Warriors, Grizzlies, Hornets, Somao C. Hawks, Vaughn with the Pacers and Celtics, and Wanzi with the Blazers. And I do agree that Dennis Smith was not given a fair shot. All right, there is a lot on the line. A Tatum Animators and Pawnbys and Aaron Judge, Jersey. That would be hilarious. And what, Night Moves LeBron? Night Moves LeBron, or maybe a Derek Jeter, Jersey on that one, too. It's foambolf, one box, foambolf. So we'll kinda see how this goes. All base, till we get to the rookies, still base. Pressproof, the Anthony Melton. That is, I'll open the upside down. Oops. Luca Craftsman, nice one there. That's cool. Red, what is that? Red Velocity, is that a 99? Terry Rozier. There's Dominique Wilkins, throwback. Steph Curry, Marvels, nice one there. Net Marvels, Steph Curry. We've got Unleashed, Giannis. Jabari Smith rookie, Wendell Moore. Luke Travers, Christian Braun, and Tyrese Martin. Bring up the checklist. I forget what that's called. 2022, Don Russ, I don't think it's Velocity. No, Don Russ. Basketball, geez. Hollow Laser, that's what it is, Hollow Laser. My bad, forgot what it was called. Let's pull it next day, dude. Let's pull like a huge next day. Callow next day would be massive. Base, base, base, base, base, base. Pressproof, KCP. Got Crunchtime, Devin Booker. Crunchtime, Zion. Matt Johnson, throwback. That is production line of Nikolay Yolkic, Craftsman, Chris Paul. A.J. Steele of the Draft, A.J. Griffin. For the Hawks, E.J. Liddell. Osemane Zhang, Max Christie, Shaded Sharp. Nice one there for the Blazers. McClung, that'd be sick. McClung Auto would be huge right now, still probably. Base, base, base, base, base, base. Wendell Carter, Jr. Pressproof. Max Struths on the Gold Hollow Laser. 10 of 10, nice one there for the Heat. Max Struths. Jason Tatum, Net Marbles. Grant Hill, throwback for the Pistons. Unleashed Cade, second year. God, that's weird. Kawhi Leonard, Crunchtime. Peyton Watson, Shep. Nice one there for the Thunder. Lake Wesley, Kendall Brown, Malachi Branum. For the Spurs, which time do we have on these? Yeah, that's probably about right. And all your base will ship, so won't miss anything on these. Pressproof, CP3. Got Josh Minot on your Basketball Parallel to 75. That's kinda cool, the Basketball Parallel. That's pretty cool. Nice one there for the, Net Marbles, D-Book. Jay Kidd. Craftsman, Jalen Green on Leashed Trey. Rookies, Nicola Jovic, Jabari Walker, Walker Kessler for the Jazz, nice. Ryan Rollins, and Terry Eason. You got a lot on you, I'm sorry. Yeah, not at all good. At least he's always proof. Guess we got a double header for Basketball. Eric Gordon, Pressproof. The Rockets, there's a Cade Cunningham on the Pressproof to 199. Net Marbles, Yolkich, Ray Allen, throwback. Craftsman, Jaw, Unleashed, Dame, Gowens, Mark Williams, Diabite, Jeremy Sochan for the Spurs, Christian Coloco, Pressproof. Jalen Williams, Rookie, nice for the Thunder. De-loading, that is gonna be Pressproof to 199. Production line, Lebron, Patrick Ewing, throwback. Jalen Green, Net Marbles. Crunchtime, Dame, these are cool, I like those. You've got Trevor Keels, Jalen Williams, nice one there for the Thunder. Jaden Hardy, Dyson Daniels, and Andrew Nemhardt for the Spurs. Andre Trumman was on the bulls, good lord. There's Oceag Bajie for the Jazz. You've got Lamello Ball, Net Marbles, nice one there on the Hollow Laser. Production line, Tatum, Chris Weber. Anthony Edwards on the Net Marbles. Crunchtime, Jimmy Buckets, Benedict Matheran, Ty Ty Washington, Palo, there you go. Magic, Marchion Bochamp, and Vince Williams, Jr. What's up, Mooc? How you doing, buddy? Me drop you guys the link to choice again. We are down to 13 to go on it now. Ah, not bad. I mean, pretty standard from what Don Russ usually is, I would say, Mooc. Take the Jazz and any PYCs. Well, Jazz are expensive, though, with Kessler and a Bajie, and they have one other guy, too. They only have three rookies in this. Evan Mobley at Press Proof. Throwback, Larry Legend, Lamello Craftsman, a Legend One throwback, LeBron, Net Marbles, a unleashed Jason Tatum, Jake Leravia, Jalen Williams' rookie, Oceag Bajie, Jaden Ivy, nice one there for the Pistons, and Patrick Baldwin, Jr. Still looking for our auto here. Gonna be, ooh, Andrew Nemhard, what a great way to start, going to the Pacers, congrats on that. Nice one there, congrats, Indiana, got Donovan Mitchell Press Proof, Crunchtime, Jason Tatum, Howell Laser Foil, Devin Booker Crunchtime, Larry Bird, production line AD, a Craftsman Trey, we've got Kenneth Lofton, David Roddy, Josh Minott, Dale and Terry, Isaiah Mobley, Press Proof, Deandre Hunter, unleashed is a Kevin Durant Press Proof, nice. Net Marbles, Scotty Barnes, Clyde Drexler, unleashed Luca, Crunchtime KD, Jalen Durin, Wendy Chandler, Johnny Davis, Caleb Houston, and a Keegan Murray for the Kings, nice one there. On to box number two, what's up Mark? I don't know if I said what's up, but what's up Mark? Bad, that's awesome Mark. Omar Viscale, eh, it's possible. He had some, he wasn't, he had some like, some sort of tarnishes to him though. He was a great defender, but he also was kind of like a stuck around for too long. Yeah, I know last year they were literally the cheapest. Yeah, true, true. There's Malik Monk, Press Proof, D'Angelo Russell, Press Proof 2.199, KD unleashed, Crunchtime Devin Booker, Larry Bird, Anthony Davis, EJ Liddell, Oseman Zhang, Max Christie, Shaden Sharp, and Peyton Watson. Yeah, I see what you're saying. Wendell Moore, Jr. Press Proof, Rookie for the Timberwolves, Scary Terry to 1.49, Trey Unleashed, LeBron Production Line, Happy Ewing, Jalen Green, Happy Washington, Paolo Rookie, nice one there. Bochamp, Vince Williams and Jake LaRavia, for the Grizzlies. Grizzlies have like four guys in this too. He's got so many. Perry Press Proof, Gary Trent Jr. KD, nice one there. Hollow Weezer to 49, Crunchtime Devin, Craftsman Luca, Dominique, Steph Curry, Netmarvels, Jalen Williams, Bajie, Jaden Ivy Rookie, Baldwin, and Kenneth Lawson. Yeah, I like the Retros. The Retros are cool in this, like the throwback ones. Those are good. Press Proof, Brandon Clark, Malik Monk to 1.49, for the Kings, Crunchtime Step, the Green Netmarvels, KG Throwback, Kairi Jabari Walker, Kessler Rookie, Ryan Rollins, Terry Eason, Trevor Keels. Yeah, I know, it's like weird. It's like a little off-center, it's kind of strange. Whoa, there we go. Now we're good. No, no, I think you're right. I think it's a little off-center. Jimmy Buckets, we've got Netmarvels, Devin Booker, Trey Craftsman, Scotty Barnes, Netmarvels, Luca Unleashed. See the pretty hands, oh yeah, Chet Holm Crinn, Blake Wesley, Malachi Branham Rookie, and Bryce McGowan's. Jabari Smith Press Proof, nice one there for the Rockets. Got a Press Proof Jaw, Jaw Unleashed, Yanis Production Line, Steve Nash Throwback, Jaw Nemhard, and Jabari Smith on the back as well. We've got nice, Walker Kessler, Rated Rookie Auto, congrats Utah. Heck yeah, dude, great two first autos here. Really good first two autos. That's awesome, you can mag this one. Fantastic, congrats on that one there. Utah, Zaire Press Proof, Steel of the Draft, A.J. Griffin on the basketball at 75 for the Hawks. Kwi, Jaw, Shaq, Steph Production Line. Moop came in and brought the Jalen Thurin. The Jazzlock, yeah. Yeah, he's a stud, man. So who's above him? Obviously Paolo, Paolo's gonna win it, I think we can all kind of agree on that. Is Matherin still above him, technically? Potentially, you're probably not wrong about that.  You just got jingled. Yannis Press Proof there, Booker, Jalen Green. Matherin, yeah, that's what I thought. Bless you. Ceremi So-Chan, Rookie. Shinkaloko, Benedict Matherin, seat down. Yeah, I thought it was Jalen too, Vaughn. It could be Matherin, you know. Jalen Williams is a stud. Yeah, he's a starter, I don't think Matherin's even gonna start. No, Matherin's not a starter, I know that for a fact, yeah. He's a six man. Yeah, he's kind of gotten better as the seasons went on. There's Evan Fournier. There's a Red Devante Grim to 99. He looked really bad in the beginning of the year, but he stepped it up for sure. KD, that's Wendell Morris, Williams at two, gotcha. Travers, Braun, Martin and AJ Griffin. So is that mean, is Nemhard's been a stud, dude. Nemhard's really good. I really like it, Nemhard.  There's Bobby Portis, Pressproof, Josh Giddy. That is Pressproof to 199. Craftsman, LeBron, Dane, Tim Duncan, Chandler, Davis, Houston, there's a Keegan Murray again. Nicolette Yovitch. Tony taking a spot in each of the two choice breaks. Thank you, Tony. Down to 11 to go now, guys. 11 to go. Breaking relatively soon. 11 to go. And guys, just a five box break of choice, and it is a random team style, and it is $63 a spot, so not too bad. Relatively affordable break for sure. Then we've got, I'm acteered after as well, tiered basketball, that is. I'm acteered basketball. We're pray to God there's a logo, man. Or else Dylan has to do 50 push-ups if we don't pull a logo, man. You hear, everybody else heard him say that? I heard him say that. I didn't say it. This is Daniel Quickly, Pressproof. Nice, Yovitch. Basketball is 75, 42 of 75. Zion, A.J. Griffin-Rookie. E.J. Liddell, Jang, Christie, Shaded Sharp as well. I love A.J. Griffin. A.J. Griffin's my guy, dude. G is a, see, Lee said it. Lee's heard it. There's Johnny Davis, Pressproof. Nice one there for the wizards. There you go, Worm. John Collins to $199. Jason Tatum, production line. No, I like Matherin a lot. Ant-Man, Netmarvels. Matherin, speaking of, there he is. Hi, Ty. Hello, nice pack there, geez. Marchion Bochamp and Vince Williams. Yeah, Griffin's like what we need, dude. Because again, when you have a turn style on defense, that is Trey Young. You need good defending wings. And Griffin's been pretty solid on defense. There's Tobias Harris, Pressproof. Spencer Dinwitty, that is Red, Howell Laser to $99 for the Mab still. Craftsman is Lamello. Keem, Lebron, Netmarvels. Jason Tatum unleashed. LaRavia, Jalen Williams, Jaden Ivy. And, where's the Jalen Williams? There it is. And Patrick Baldwin. Yeah, he's got really efficient. Barrett, nice. Malachi Branham now on the auto. Congrats, Spurs, great break for rookie autos so far. Very nice, congrats on that to San Antonio. League Beasley, Pressproof. Harrison Barnes to $149 for the Kings. Yeah, So-Chan and not Isaiah Moeb. You're talking about So-Chan and Branham? They're both really good. Both really good. So-Chan played at Baylor. He was nasty at Baylor too. With So-Chan there when they won the championship, he must have been, right? Justin Holliday, there's Bradley Beal at Netmarvels. Hollow Foil, Jason Tatum.  They're doing all this without Chet. Like they're in the West, Chet Holmgren. And Shay's been out a lot. Yeah, and Shay had, exactly. Kendall Brown, Malachi Branham. Right, right. What's up, Heather? Yeah, he had to have been there when they won. He had to have been there. What's up, Bray? Why do I keep going off here? Don't mess around my camera, dude. Killian Hayes. Tyrese Halliburton, Sue at 99. Scotty Barnes. Drexler, Unleashed, Luca. KD, Jalen Duren. Kennedy Chandler, Johnny Davis, Caleb Houston. Keegan Murray. So what's like the top 10 right now for the Kia rookie ladder? Does anybody have that information off the top of their heads? I'm very interested to what that is. Pressproof, D-Book. Production line, Lebron Pressproof. Yolkich, Ray Allen. Williams, DB Atte, Jeremy So-Chan, Rookie, Christian Coloco. I'm not memorized. Can everybody go look it up? I'm not asking if you haven't memorized. Obviously, nobody has it memorized. It's a legal guy. The top 10, no less. Traymon Green. D'Angelo Russell. Another D'Angelo Russell numbered card, a 199. Yeah, what's up, Heather? Jalen Green. Shrey Yum. Nicolay Jovic. Duren. Williams. Walker Kessler, Jalen Duren. Ivy, Murray. Walker Kessler. Bro, where is AJ Griffin? I'm sick. Yeah, I don't think he's really up there. What do you know? I don't think he's up there. You don't know anything. He might be like, 30s, 40s. Ah, ha, ha, ha.  There's Al Horford. Lonzo to 149. Luca. Steph Curry. Giannis. Jabari Smith-Rookie. Lundalmore. Christian Braun and a Tyrese Martin. You just earned yourself 10 more top-loaded cards per pack. So that little situation there, pal. Yeah, we're pretty interactive, Heather. There's a Kennedy Chandler. Shrey, Lebron production line. Who's the auto in this one? Oh, it was Brandon. Never mind. Jalen Green, Damien Lillard. Kiehl's, Jalen Williams. Okay, there is no shot that Mark Williams is the head of AJ Griffin right now. That's sickening. Andrew Nemhard. No way. Nope, nope, don't believe it. I'm anti. I'm anti, the Kia Rookie ladder now. What's Jabari beating and how are you doing? Yeah, I know. He's just gonna have, he's gonna be inflated because he was as highly picked as he was. So he doesn't have to do as much to get up as high. Another choice spot. Another tiered spot. Thank you guys. Another Bob, taking a tiered random team spot. Where is AJ Griffin at, dude? Like the channel live, thank you. I didn't appreciate that. Ridiculous. Really is, this anti-hawk agenda, that's what it is. Lew Dort, they wanna see us succeed. Another D'Angelo Russell, dude. There's like five of these so far for the Timberwolves. Kawhi Unleashed, Jah, Shaq, Curry production line. Got to EJ Liddell, Jan Christie, Shaden Sharpe, Peyton Watson. The AJ Griffin, press-proof steel of the draft. That is definitely higher than 12th next to Jalen Johnson. Joshua Primo, basketball to 75. Okay, that's not bad. That's actually not bad, 17 and 13. Yana, Steve Nash, Netmarvel's Jah, Ty Ty, there's a Paolo. No, I like Dyson Daniels, Marjon Bochamp, Laravia, no, Dyson Daniels is legit. AJ Griffin at the end of the house. AJ Griffin gets like meaningful playing time now. Especially when everybody gets hurt, inevitably. Rudy Gobert, it's a 199. Crunchtime, Anthony Edwards, Craftsman, Harden. The Rant, Jalen Williams. Bajie, Jaden Ivy, aren't there two Jalen Williams? They have a J, yeah, J-Y and J-L-E-N. That's gonna be making group-breaking difficult. Cause at least I think they're both on the Thunder though, right? Yeah, there's a third one too, it's not. Is there actually? Yeah. There's Luca, press-proof, nice. Luca again, this time on the Unleashed, Holofoil, J-Dubbs, J-Will, yeah, I remember that. I was watching, when I was on drip, I was watching one of their games like before I went live and they were talking about that. Walker Kessler, Jaden Hardy and Jaden Ivy. Oh yeah, Trevor Keels. Yeah, let's make everything as difficult as possible. And Young, press-proof, Bradley Beal. The Wizards. Trey Lyles, he's still in the league, I did not know that. For the Kings, the 149. Kentucky legend, he's traded for Donovan Mitchell. Check, Ongren Rookie. No, both of them are good, yeah. Blake Wesley, Kendall, Brandon Rookie. Bryce McGowan, McGowan's is kind of legit to Nebraska. The Hornets. Porter, there's a Tyrese Martin for the Hawks. Press-proof, Craftsman, James Harden, press-proof. It was Trey Lyles, that marvelous kid, Gunningham. Nah, I appreciate that there, Timmy. Jalen Williams, there's the good, the really good Jalen Williams. Jaden Hardy, Tyson, there's a Nemhard Rookie. And Jabari Smith as well. Gary Payton, the Mitten.  I love that, dude. Tony Parker, press-proof throwback. French Times, Steph, net marvels, Levine. Gee, Mark Williams. So Diabita, Jeremy So-Chan Rookie, Baylor legend. Christian Coloco, and Benedict Matherin. Where'd Matherin go again? He was Gonzaga, right? Is that right? Gonzaga, Matherin? I feel like it has to be right. Oh, Arizona, Arizona, yeah, yeah, yeah, yeah. Yep, yep, yep. My bad, my bad, my bad. Useless. There's Shaden, this has been a great break. Shaden Sharp now on the auto for the Timberwolves. Our Todd for the Trailblazers. Nice one there. Benedict Matherin screwing me up now. Thanks a lot, Benedict. Nice one there, Portland. Trey Murphy. There's an Andrew Nemhard, press-proof to 199. Mobley, there's a Jalen Dern, as well. I'll drop you guys a link again to number nine. 11 to go. Yeah, Nemhard went there for sure. And his brother goes to Creighton. The other Nemhard goes to Creighton. Again, guys, 11 to go on the first choice breaks. Thank you guys for getting that knocked down a bit. Good break for autos, I have to say. Kelly Ubrej Jr. Another Tarot as you have. Million Tarot as your number two to 149. Craftsman Steph Harden, Netmarvels. A Chandler, Davis, Houston, Keegan Murray. And Nicolay Jovic. Adam Morrison, I did not like Adam Morrison. It's an OG and none of me. My dad cannot stand Gonzaga. There's Marvin Bagley to 99. It's so funny too.  My dad hates Gonzaga, because he was like at a restaurant or something. And Gonzaga was playing Yukon, Ron, Martin, AJ Griffin. And my dad went to Yukon. So he's like a big Yukon basketball fan, men and women. And there was like two people sitting next to him who were like huge Gonzaga fans and they kept like complaining about it. And he just has hated Gonzaga since then. So I just, I don't like Gonzaga because of that. I can't like Gonzaga. They're like trashing Yukon or whatever. And he's like, oh, that's the end of Gonzaga. But to be fair, like Gonzaga, like he always says like they don't play anybody in their conference. It's kind of true. Like St. Mary's hadn't really been good up until this. I mean, they've been good here and there, but really up until this year, they haven't been like super, super impressive. There's more PYTs coming in. Yeah, no, they just never win in the turn either. Nice, thank you. First PYT is sold out for iMac. Appreciate you guys, we're down to eight to go as well.  Dale and Terry, they're not somebody you ever want to play in the tournament. That is Yanis. Dude, their alumni is crazy. That's come out of there, Jason Tatum. There's Palo, gonna be behind Ty Ty, Bochamp, Vince Williams, and Laravia. Like just the guys in the NBA right now from Yukon, dude. Like you have Rudy Gay, Andre Drummond, Kemba, Jeremy Lam, James Book Knight. Like, thanks for the follow their center mass. Yeah, no one cares, illegal. There's, you know, it's Valencianus, George Mason. There's D'Angelo Russell, the one night in. My dad also does not like George Mason because of that. Steph Curry, Mark Williams, Jeremy Sochen. Oh, Kemba, Cardiak Kemba when he was there, dude. Benedict Matheran. Like I said, dude, there is, I would say during like when college, when Kemba got really big, during like college basketball season, I would say every day I was probably in middle school at that point still. There was at least one person wearing Kemba Walker, Yukon jersey. Kemba Walker is like God in central Connecticut for like kids my age. There's Trey Young, press proof. Damian Lillard, Mecca Okafor, Cron Butler, Ray Allen. Jalen Williams, not Jalen Williams, but. Baji, Jaden Ivey, Patrick Baldwin, and kind of the Shabazz run was insane, dude. The Shabazz run was insane. I remember there is this, like one of my buddies. No, he went to Illinois, I'm pretty sure, Darren Williams. He was, it was like him and TJ Ford were there. I'm pretty sure at the same time. There was that really, really good. Jeez, dude, Jeremy Sochan to 99 now. Nice one there, Brogdon press proof. Crunchtime is Zion on the Holofoil. Yeah, D-Will was a problem at Jalen Duren. Yeah, it was what, D-Brown, TJ Ford, and Darren Williams were all there at one time. Yeah, the beat was really good. Well, he was good in college, he wasn't good in the pros, but. Yeah, Mecca Okafor was another one, Ben Gordon, Crom Butler, Charlie Villanueva. Oh yeah. Yeah, that's when Yukon really like jumped on the map too. Mark Elfoltz, Terry Rozier to 149, Jason Tatum, Moore, Travers, Braun, Martin, AJ Griff. Hey, thanks, Dad. Appreciate you, buddy. Taking a spot in that five pack choice.  They're women's alumni.   Johnny Davis, Caleb Houston, Keegan Murray-Rookie, Yelena Deladon, yep, Nikolay Jovic. Tarazzi, no, Tarazzi went there too. Tina Charles was really good on those Maya Moore teams. Sue Bird, Sue Bird went there obviously too. Yeah, dude, it's just, that's unstoppable program. No, yeah, Deladon went to Delaware. It's, I think you're thinking of Sue Bird. Sue Bird is the one who went to Yukon. There's Pokashevsky, Charles Barkley, and Craftsman Jochich, Liddell, Jane, Christie, Shaden Sharp, Rookie. Yeah, they're a powerhouse, they're a legitimate powerhouse. What's up, Mike? Big Mike D, Dorian Finnesmith, Mike D in the place to be. That is Joe Harris, 2.99. That's actually a Seanism right there, Mike D in the place to be. Sounds like it. Yep, Damien Lillard. I mean, that was gonna say, I was gonna say, I was surprised you at all. Walker Kessler, Rookie. Rollins, Eson, Trevor Keels. Might as a small hockey crowd, true. Mike, I'm so pumped for baseball. Dude, we watched the World Baseball Classic last night. Darius Garland, Ant-Man. Basketball to 75, for the Timberwolves. Luke Gunnett-Marvels. Shet, Rookie. Wesley, Brown, Branham.  We don't have any, like if the whalers were still there, we'd probably have the big whalers, fan base. I know I would be the biggest Hartford whalers fan in the world. That is Josh Menats, press proof. Got a Trevor Keels to 199, production line Tatum. Weber, Ant-Man, Butler. The good, really good Jalen Williams. Hardy, Dyson Daniels, Andrew Nemhard, and Jabari Smith. Yeah, right, exactly. Yep, let's do a quick recap here, guys. We had a Shaden Sharp, Andrew, or no, Malachi Branham, Andrew Nemhard, Jeremy Sochan to 99, and a Walker Kessler that will do it for the... Great, thank you guys again for the film. Next up, we have the next five bucks.


How's it going everybody? Dahtonoi here, and we are once again back with the brand new Demon DLC for Demon Slayer. We got Rui and Akka's the last time, and this time we got Yahaba, which we did in the last video, and in this video we're going to be playing Sasamaru. Now both of these characters kind of have a more zoner-ish feel to me, but I still think they're both pretty fun, especially this character because she is definitely the most aggressive character in the game. Both her supers and specials are both very satisfying to land. With that said, if you guys are excited for more Demon Slayer videos and want to see more like them on the channel, make sure to head down below. Let me know in either the comments or by leaving a like. And with that said, let's go ahead and get into the matches. I hate that it warns me every time that the frame rate stops reminding me. I'm going to change my frame rate back. Why they said there was a 60fps patch coming that I could only enjoy in the menu screen?  It makes no sense. Alright, come here, come here though. You don't skate really? Yeah, bro. I'm not going to throw these dodgeballs at you.  Okay, okay, here we go. Yeah, yeah, you better hold that. Hold that. Okay. I ain't going to lie. I'm a little nervous. This is the first time I've played one of these characters that gets a rush down, bro. I don't know. I'm nervous, bro. Look at you. You guys are just not watching this. I know this is Dotto's video, but we already recorded where we were both the zoning demons. And this is our first attempt to see how the game is a zone or against a, I guess, more standard character. Yeah, so this is our first, like, our first go-around.  I don't know why I did, but I, damn! Damage, I'm not used to seeing damage, bro. I'll be honest. Not damage from normal combos. Bro, why do I look like a spider? I move like a goddamn spider, bro.  What do you mean? It's just, it's bothering me, dude. I'm not used to it. I told you last time, the way they were like, I don't know how you're supposed to run with six arms, but I don't know if that's the move. Bro, I've been kicking these balls, though. Hold on. Yeah, you do. There's no appeal.  I told you this character is the most aggressive character in the game. Bro, if I was hunched a little over, like hunched over a little more, if I could say that sentence for a fourth time, I'd love to, but I'd be crawling, bro. I'm going to let you do your thing. I'm going to let you do your damage. I'm going to let you enjoy yourself, but then I got to stand up for myself. Okay. Go ahead. Stand up for yourself. What?      I'm having a good time. I pressed the wrong block button. Look at you, G. Bro, I didn't mean to say that. He's the block button I tagged him in. Are you me struggling? Wait, oh, I'm curious. I'm curious.  Wait, hold on. Do it again? Well, you melted it. Okay, it doesn't do anything. Cool. You wanted to see if you could counter it. I did see you, bro. Because those are her standard basic attacks. And if you can't counter it, you can't do anything. Damn, bro, you're getting good.  I'm going to say it respectfully, but this character is great. I picked the matchup nightmare, bro. That's what I did.  I'm just a demon, bro. I'm just a demon of the night. Are you good? You got this. Hey, what you trying to get into?  I ain't going to care. That's not that, bro. Damn. Okay. Okay, great. All right, my turn. Oh, not armored. I got to remember that. Whoa, whoa, it's level three.  I hope I don't, you know, you did a combo. It should be a skill a little bit, you know? Yeah, it's going to be skilled. You're alive. I hope so. Regretfully. Why are you living it? Shabburah! Shabburah! Shabburah! Shabburah! Good man. Whoa! No, no, no, no. Yeah, yeah, yeah, yeah. What a... Dude, I'm fast as... Get a cue!  I'm fast. I'm fast. I don't care. Whoa! Whoa! Yeah, I'm crazy. I'll just do it. I'll just I'll just throw these ball. I don't care.   When are you gonna accept that? Cannot end at round one All I had to do is block and click square I'll take it up. I'll take it serving medium.  It's respectfully I won't kick that. I won't kick this. We use your supplies though.    Yeah, I play too much. I also care too much Oh You don't got to do this man, you don't got to do You know, this is just petty I can literally heal this I'll do it.  She's right there. Let me drop shot.  All right I noticed you switched up your strategy so that you know skates in the back But uh can I ask you how that's going to change the fact that I'm gonna throw these balls that you broke is like I don't really see all right fair play.  I'm floating. I'm gonna float past all these balls now. Whoa Nothing gets past me bro Click a button on your control Up with you froze up with fear, bro Oh and that character just has a great armor move ladies and gentlemen that was all me My eyes are over here. I didn't want that Yeah, you know, I'd be kicking the stuff What are you doing Fortune I can't afford to lose I Wish I could do but I can't and because I can't that's the same reason.  Oh, you're good at the game.  You know No, I'm running out of time to make your poison.  I got enough for both of you. Come here. I'll give you the full piece Well, six piece no wings. Oh, yeah I Like to get carried away, dude. It's a personality flaw.  Gotta love me for it, right?      I was joking out. It was a Josh. It was a Josh I'm dead One more squee coming right up Shout out to moon I Thought I lived I didn't get cooked Need more damage, that's what I need.  I could just hear the dog's balls out of it I decided to fix it I ain't getting three oh dude, I'm getting three one. Let's go I gotta be realistic I am getting cooked right now I can't believe I got hit. I Cannot believe it again What in the upper-cut street fighter happen? Regal No, we're dude back off What are you doing trying to see what you're doing bro? I'm trying to see what you're doing, bro Shut up, dude. That was corny. Come on. That was corny That's why I beat some patient.   I got to start doing the cake first I'm throwing it away I can't I really can't get hit there. I got a block and punished. I got a block and punished.  No, no way too much You often play too much yeah, you know, it's gonna get out of here, man. I'm doing stuff I Love the aggressive Oh, man, I just missed the rage so much man. Can you like Jesus 17?  I might be dead Man, look on the brighter side, dude I forgot about the finishing cut that's what I was waiting to see I tried to counter your ultimate with my ultimate I'll take it dude, you know, I had to let you win Yeah, I appreciate that dude.   I'm still my favorite even at the game.  That move is so good. I Reflect oh damn, bro. Stop bro. Let me Zody. I'm not blocking. I'm not blocking Were you throwing them balls? All right, take some of this That was so good.  I got Yeah, I could zone to oh You actually went Yeah, I did it all There we go now now we do it again, bro. Now he's zoning. Now we zone it Okay, if one of your air palms hit me that's kind of Understandable have a nice day.  Oh No way, dude No way you trying to get into it.  Do I just take the win I'm just gonna take the way I'm just gonna take the round, dude. I'm not trying to play with Just get him out of my face. They just I'll take the round clap. Give me the ball spin it Forest and come back To see you I'll take it dude not bad not bad at all. I Just realized we drew a very unfortunate shape in the dirt here, but that is fine I just had to change up the strategy shut up All right, I'll be over here. Okay, I'll be in the air.   I blocked that.  I blocked that Well, I almost threw one Damn no, please dude stop being me. This can't be the end. I dropped it.  Oh, so this is how I die Who would another dig Rock is a talking like I'm not a demon already, bro. I'm still alive. All right, that's gonna do it for this video Hopefully you enjoyed us check it out the new characters I might come back around with more of these guys online ranked see how they're holding up after a you know Some new tech is discovered by the community But for now that's going to be all like I said before go down below if you're excited for more demons layer DLC and content Like button subscribe check out the link to the on channel and check some other videos out that should be on your screen I've been dotted away. I'll see you next one. Goodbye. Pokemon win


Thank you, Mr. Speaker. Mr. Speaker, I rise to make a very brief intervention in support of the motion to exempt import of certain items in barrels, and there are two periods that have been cited in the motion from the first of November to the 29th of February, and that applies to barrels that are shipped by loved ones overseas to their counterparts in Lusia. And the other part of the motion, Mr. Speaker, speaks specifically to members of parliament who are desirous of importing toys, food items and care products for distribution and use in their respective constituencies, and that period is from the first of November to the 31st of January. Mr. Speaker, I believe that this is progressive, notwithstanding that it has been in the making for some time, and when the Prime Minister presented, he did allude to the fact that this was the brainchild of his predecessor Labour Party Prime Minister and member for the Fourth South. But, Mr. Speaker, it would be remiss of me to express support for the motion and not reference the misguided, desperate and incoherent presentation that came from the former Prime Minister and Minister and member for Mikutsa. Mr. Speaker, what we saw here this morning was a brilliant exhibition of showmanship, a flawless display of parliamentary hypocrisy, and we saw a portrait of political desperation. And for those persons who were in the chamber for the first time and would never have seen him before in this chamber, you would believe that here is a man who means well, here is a man with policy. But this is the same individual. Today, hypocritically, posturing as a champion of poor people. Today, he has ascribed to himself the title of advocate of poor people. But what did he do when he was the Prime Minister, Mr. Speaker? When he was in a position to formulate policies that would have enabled poor people to rise out of poverty and improve their laws. Mr. Speaker, he does not like poor people. And history is repleted examples of things he did when he was Prime Minister, things that were at variance with policies that he found in government that were meant to improve the situation of poor people. Mr. Speaker, on Mother's Day, when poor people who could not find employment in the machinery of government decided to resort to trades as vendors to sell on the streets of castries and in the market on Mother's Day. Single mothers whose trades were packed on the sidewalk, Mr. Speaker. Under his Prime Ministerial Watch, he sent the authorities to collect the trades and to dump them in a place where they were not accessible by single mothers. And today the same hypocrite stands here and wants to speak on behalf of poor people, Mr. Speaker. Mr. Speaker, in recognition of the fact that poor people, they will not be able to build or construct homes to the tune of $200,000 and $300,000, but they can sacrifice, they can save and buy a few sheets of tidal note and have shelter and comfort for themselves and their children. Notwithstanding the amenities may not be the greatest, but as a pride that lives or resides in those plywood structures. And we know on many occasions, Mr. Speaker, when we listen to the news, you hear of a house having been destroyed by fire and they didn't have insurance. Some of them had not even finished the small loans, pay off the small loans for the homes that they had constructed. And in his wisdom, the former Prime Minister and member for the South, he introduced as a line item in the budget the distress fund. So at those persons when the houses were destroyed, they would have gotten a lifeline compliments the government. As a line item, the distress fund under the member for me could south was withdrawn from the budget, Mr. Speaker. But today, who comes there and postures as an advocate of poor people. We did not withdraw the fund. The fund is a humongous fund and we broaden the amount of money that we spent. So the member going to go in and you're in government so you can go and check the facts with yourself. We were spending upwards of six million dollars a year on helping people with medical issues, housing issues and distress. And all the facts today for them to the system. That's where it came from. Mr. Speaker, I will repeat verbatim what I said. I said as a line item in the budget the distress fund disappeared. And I challenged the member for me could south to take copies of the estimates during the period he was Prime Minister and show me a line item in it where it says distress fund. That is what I said Mr. Speaker. It disappeared. But today Mr. Speaker, he comes and he postures as a champion of the poor. Mr. Speaker, we as an administration between 2011 and 2016. We recognized that there was a need for the children of St. Lucia to have been online and under the member for view for itself again as Prime Minister we introduced the one laptop to child program. Upon entry into government under his watch, this self-proclaimed champion of the poor, he stopped the one laptop to child program. And when Mr. Speaker, the pressure began to mount and I know you were there in the cabinet at the time. They quickly tried to bring in a program that they believed would have survived. Mr. Speaker, this champion of the poor, a self-proclaimed champion of the poor. When he is not well or members of his family are faced with certain medical challenges, he has the means to be able to fly them overseas. Upon entering this government as Prime Minister, he found that a hospital that had been damaged by fire was being repaired. Mr. Speaker, that started during the reign of the member for castries north. And after the United Workers Party was voted out of office and the St. Lucia Labour Party entered in 2011, the member for view for itself for our Prime Minister, he continued with the plans he made there to give the people of the south a hospital. But came then came in 2016 the member for Mikudself, self-proclaimed champion of the poor. What did he do? He stopped construction on the hospital. Not for two days or two weeks, you know, for two years. And whilst construction on the hospital had halted Mr. Speaker, less than 100 yards away, he was busy facilitating construction of facilities for horses to come and gallop. You did not remember at the time that the poor people on whose behalf you want to speak today needed healthcare facilities when you are busy preparing for horses.   And Mr. Speaker, let me tell you what he did. This champion of the poor, let me tell you what he did Mr. Speaker. When he came into government Mr. Speaker, he found a program in government known as the constituency development program, the CDP. And when the CDP was established as a program in government, every single one of the 17 members of parliament would have received resources to execute projects in their constituencies, whether you were opposition or in government. He came in Mr. Speaker and he found the program entrenched. What did he do? There were six of us in opposition and we had poor people in our constituencies as we still do today. We used to come knocking at our constituency doors asking for help with basic things Mr. Speaker. But as a deliberate policy ploy from him, you know what he did? He starved us of resources. And after the five years were up for elections, he dragged us into a six year.  But today out of convenience he stands here and wants to lecture us about poor people. Mr. Speaker, there is a shamelessness that characterizes the politics of this particular member and he needs to stop. So don't come in posture as an advocate of poor people. You don't like poor people. All he did prior to election was to take selfies and photos with poor people so it can resonate on social media. You don't like poor people. You don't dump the trays of poor people and come into the amplitude and pretend you are poor people. You don't starve opposition parliamentarians of resources to help poor people. And as it is convenient, I know this like to be in opposition, you know. I start across there and you just halfway through the term and you have more days in opposition to come. So don't come and bump those people as if you are champion of the poor. I believe this intervention by the prime minister is progressive, it is necessary. And for constituents like those in January north that have had the pleasure of representing after free general elections, I know Mr. Speaker this will touch lives because they rely heavily on remittances throughout the year. And when Mr. Speaker, when in December to help spread the Christmas chair, they look forward to receiving barrels and other items from overseas. Mr. Speaker, what's up with the whole day? When Mr. Speaker was nēpōkle ala nēya. Mē nū tūk sāv, Mr. Speaker. Lēk āveni pli pui pūnwēl, tēle nū kāen pli bārel, ām kāsotilot pii. Mū le pui lū kāziopū o sī di jāsēt li sī, that we have to use the concession responsibly. And as the Prime Minister would have indicated in present, during his presentation of the motion, let us ensure that all the items in the bārels are items that do not contribute in the laws of solution. Leave the illegal items out. There should be no guns, no weapons in those bārels, Mr. Speaker. The explanation is clear. It is simple in terms of what we expect in those bārels. And so I want to say, Mr. Speaker, that I only equivocally support this particular intervention on behalf of the government. And I need to say to those of you in the chamber, notwithstanding what you would have heard, do your research, and you will know who the real champions of poor people are in this country. You will know which government has made it possible for poor people who cannot even access healthcare. We know who Mr. Speaker has made it law in this country for them not to pay the ambulance fees. We know which government is responsible for that. So we will come here today and think that you can speak on behalf of poor people and that will be your path to victory.  Poor people are more discerning than you believe. And from a poor family, and several of us are. So be careful, Mr. Speaker, with who wants to come and lecture us on poverty. And when they were given an opportunity, a very solid mandate to come and stand up for poor people. Almost every policy initiative that they, Mr. Speaker, put forward in this country was inimical to the plight of poor people, the well-being of poor people. And today, Mr. Speaker, I want to support, as I said on it vocally, this motion by the Prime Minister to waive certain duties on barrels to help spread the Christmas chair and to give our people, Mr. Speaker, a merry Christmas and a prosperous start to the end of the year.


Our next speaker is Andrea Molod from NASA GMAO. Andrea is a research scientist at GMAO and also the lead of the S2S group. Andrea also gave a talk during the student colloquium. A couple of weeks back, thanks for that, Andrea. Looking forward to your talk. You're still muted, Andrea. I don't hear you, but I can see your screen. Sorry, I can't seem to unmute with the screen share. So let me go through it again. OK. Oh, and sorry, I missed your question to you, Andrea. Maybe you can come back to it. Yeah, that's OK. That's OK. So I think you guys should see my screen. We should be in full screen mode and you can hear me. Yeah. OK. So during the summer school, I spoke about some aspects of our geosubseasonal prediction data assimilation and model. And here I pointed out the issue, the use of the aerosol in there, but I'd like to focus a little bit today on what's going on with that. So these are our system characteristics for the new system that is running a new reanalysis now. And there are two aspects of this here that I'm going to focus this talk on today. One is the use of an interactive aerosol model and the aerosol cloud interaction that comes with the higher moment cloud microphysics. In the experiments that I'm going to talk to you about, they were not initialized like the lower one with the nudged optical depth. But that's to be looked at at another time. I'm going to focus on the use of the aerosol. So we haven't thoroughly analyzed how it behaves in our system. We're just starting to do that now. And I'm going to talk a little bit about some preliminary results. But why did we do this in the first place? There's a lot of evidence out there, first of all, that we can get useful subseasonal to seasonal prediction skill in the aerosol itself. Aerosol optical depth, PM, the particulate matter 2.5. That's relevant if air quality. There's also a lot of evidence out there that it including these kinds of effects increases the weather forecast skill, so-called forecasts of opportunity under certain conditions. That's been shown in the Geos system and in the European center by the forecast system as well. A recent paper shows that in the aftermath of a big volcanic event, and I'll show you another example of this later, that it can increase the seasonal skill, questions about increasing the subseasonal forecast skill itself. Benedetti and Vittart indicate that in a scorecard. Some local study also show that it seems to have enhanced the MJO activity, which for our model was a good thing. Decadal prediction, so there's a lot of reasons to think that you want the interactive aerosol model. And now why the second bit of it, which is the aerosol cloud interactions. And that has to do more with the physics of how the aerosols will affect the evolution of the clouds. One is the Tumi effect, which is basically you're going to increase the cloud albedo because you have more nuclei. The fan mechanism suppress collision and coalescence. And so you get longer cloud lifetime and higher frequency. The deep convection enhancement and more recently some issues related to ice clouds. In addition to that, there is a growing community activity in interest in at least the interactive aerosol model part, if not the aerosol cloud interaction part. And there's an intercomparison project going that are asking the models to run side by side suites of forecasts with and without the interactive aerosol and then presenting the presence of the indirect or the aerosol cloud interaction as an optional experiment to run. I'm going to show you some results from the GMAO's experiments with the so-called near real-time system that's running now. We had a couple of unique problems that had to be dealt with in order to do this. Because our default system has the interactive aerosol and aerosol cloud interaction, if you just replace one parameterization with the aerosol cloud with another, you're basically doing a different model and you can't look at the two of them side by side to attribute what is due to the presence of the aerosol cloud interaction. And so for a lot of different reasons, Dony von Barahona, who's our cloud two-moment microphysics expert at GMAO, decided that the best way to do this is this thing in red here. From our control runs, which have the aerosol cloud, he developed an climatology of the cloud drop numbers that the two-moment is predicting. And we use that to turn off the aerosol cloud interactions. And in the mean, it looks very similar to the control. And so that's going to be used for our aerosol climatology and for all of our no aerosol cloud interaction runs. And so the first bit of the result that I want to show you is the actual result of the AOD and of the cloud drop number. Because if we can't predict those, then looking at the impact of them on prediction skill for the temperatures and things like that is irrelevant. So here's just sort of a look back at global mean bias and RMS of the optical depth in two different seasons. I would argue based on this that at least it's credible for the aerosol optical depth, which is kind of the easiest thing to get. The bias in the aerosol optical depth, again, is it's there, but it's not substantial. On the right, we've got anomaly correlation for PM 2.5 over the US. This is one of the better regions in the world where we have this, but I don't have the scale here. This is anomaly correlations up in the 70s and 80s for one month, two month, and three month lead time. So I'm going to argue at least here that the aerosol prediction is credible. And now what about the cloud drop number? Here, upper left, we have some results from MODIS cloud drop number estimates. Upper right is the run with the aerosol cloud interaction. Lower left is no aerosol cloud interaction. And lower right is no aerosol cloud interaction, but no interactive aerosol model either. And the magnitude is off relative to MODIS, certainly. Again, patterns are credible. I can give you a better sense if you can take a regional look at this in these three different regions. We have numbers from Merit 2 for aerosol optical depth. I'll show you the cloud drop number in a moment. Merit 2 assimilates optical depth, and so we use that as our validation. We could just as well have used TAMS. The time series are credible. The red line is the ensemble mean, and the blue is Merit 2. So again, capturing trends is the easiest thing to do, but at least we can do that. Cloud drop number concentration also seasonally, this is the mean. Again, we argue here that it reasonably reproduces the mean cloud drop number concentration. The trend we're getting in the US, but East Asia trend we're not getting. And so it's a little bit more complex over there. So there's also one other issue to talk about here related to prediction of cloud drop number concentration, and that's so-called forecast of opportunity. And so the idea is that in the wake of an event like a big volcanic or a big kill way of degassing in this case, we expect the forecast skill to be better in the wake of an extreme event like this. And so the question is here are the time series, from MODIS and for the ensemble mean and the spread. And if you take a look in some detail on the left is the aerosoloptical depth anomaly itself. The middle is the number concentration on the right is the two meter temperature itself in that region. And so the idea is that there was this few year long kill away of degassing event in obviously not predictable in anybody's model. But we can see if you look at the dashed lines, you can see enhanced correlation with the observations in the wake of these degassing events. So once it happens, we are seeing increased prediction skill in the wake of this. And now the aerosol and the cloud drop number are within reason. And so now what is the impact on the meteorology, two meter temperature, for instance, here? And we're trying to also help sort out which of the bits are contributing to the prediction skill. So we're trying to separate out the aerosol-cloud interaction from the interactive aerosol, et cetera. So on the left is the two meter temperature difference from merit two. We could have used a better observational climatology for sure, but to point out the difference here. So the upper left is interactive aerosol and aerosol cloud. The middle one is interactive aerosol, but no aerosol-cloud interaction. And the bottom is the aerosol-climatology. And we see what's happening here. The control run is warm in the Northern Hemisphere and cold in the Southern Hemisphere. Removing the aerosol-cloud interaction fixes the warm bias in the Arctic and in the Northern Hemisphere, but makes it colder. It's made it colder everywhere. And if you look at the run with the aerosol-climatology, we see that that's very similar to the run without the aerosol with the interactive aerosol. So the point here is the aerosol-cloud interaction is what did the work. On the right, we just have some difference plots to help sort out what's going on on the left there. This is the no aerosol-cloud versus the aerosol-cloud. And this is the aerosol-climatology minus the aerosol-cloud. And again, aerosol-climatology is not what's making the difference here. It's the aerosol-cloud. And removing it basically just cools everywhere. Another little look at the two meter temperature basically is showing us the merit to at different lead days. This is North American average. And basically we're seeing that the track of it with the interactive and the direct and the indirect is closer to merit to than it would be without it for North America. Couple of other parameters going along with it to sort of help tell the story about what's going on here is what's going on with the cloud fraction. And so this cooling of the two meter temperature that we saw when we took away the aerosol-cloud interaction is associated in the Northern Hemisphere with an increase in low level cloud, but in the Southern Hemisphere it's a decrease. And so this confuses the issue a little bit. Also the interactive aerosol, the top and the bottom pictures are much more different from each other than what we saw for the two meter. In other words, the two meter temperature for the cloud fraction, the interactive aerosol is playing a role, but it's in the same sign. And so it increases the difference from the aerosol simulation. And now another player in this mix is the precip. So again, on the left is the difference due to the aerosol cloud. And on the bottom on the left is the difference due to both the interactive aerosol and the interactive cloud. So again, removing the aerosol cloud lowers the precipitation rates in the Northern Hemisphere in general, enhances the precipitation in the ITCZ. Neither one of those is a good thing. If you take a look at the lower right and the difference with GPCP in the control, we're seeing getting further. And so to sort of help tell the story in a little schematic with the aerosol-cloud interaction running over time, the depiction on the left and the one on the right are what happens over time. So you've got a lot of aerosol, it activates a lot of drops, makes a lot of drop numbers, rains more and scavenges more, so you lose the aerosol. So when you get later in time, there's less aerosol, less activation and less cloudiness. So over time, the scavenging and the inhibited activation that comes with it deplete both the aerosol and the cloud. If you turn off the aerosol-cloud interaction, the scavenging rates are never enhanced. And this may be part of why we're seeing an increased AOD and cloud fraction and reduced and more cooling and less precipitation when the aerosol-cloud interaction is turned off. So just in summary here, the first thing is dynamical predictions of the AOD, the PM 2.5 and the cloud drop number can be skillful. And the inclusion of the aerosol-cloud interaction had an effect on the surface temperature, the cloud fraction, the precipitation. We told a little story. The prescribed aerosol itself versus the interactive aerosol, the aerosol radiative interaction alone did not seem to have as big an impact. And we're getting a hint that the indirect seems to be a little too strong in geos leading to increased biases. This is all ongoing. We have a very nice suite of experiments to be looking into this. Donny von Barahona is leading the effort in GMAO to examine the impact of both the interactive aerosol and the aerosol cloud. Thank you very much for your attention. Thank you, Andrea. It was really interesting talk. We haven't had enough discussion about aerosols in this workshop. So thank you for this. It was really great. Any questions for Andrea? So I had one, Andrea. I think one of the regions that were highlighted with the interactive aerosol cloud effect was the Southern Ocean, right? And in the Southern Ocean, there's also like the mixed-face clouds that play a big role in like cloud radiative feedbacks where you have super cool liquid in the clouds. I was wondering if there were biases in both the representation of mixed-face clouds in the geos system and how does the ACI impact that? Yeah, so of course the answer is yes, like every other model out there. Mix-face clouds are really hard. Southern Ocean is hard enough. There may be a missing source of some kind of biogenic aerosol or something like that there, but the place where we're horrible at that is in the Arctic. The mixed-face clouds are, you have the ice over the liquid and things like that. And so none of that is captured well in these models. And so how much influence would they have on like the sub-seasonal timescale? Would they have a big impact on the sub-seasonal timescale because they're rapid or they would more come up on the longer-term seasonal and climate timescale in terms of scale? We've been looking a month out, okay? And that's sub-seasonal. And we're seeing the impact related to the lifetime of the clouds. Okay. Yeah. So it's a challenge for our S2S forecast systems, to represent these. Yeah. But I guess our indication at least, I haven't shown a lot of the other results. The indication at least is that it's worse without it. You know, the gap is still huge and we incrementally nudge our way closer to where we need to be by including the aerosol cloud interaction. Great. Any other questions for Andrea? So I don't, oh, Jana, yeah. Go ahead. Thank you for the great talk. So I have question on the dust to aerosol particularly. I assume that in GeoS S3S initialization for aerosols comes from Meridu. If I'm not correct, please correct me. So in Meridu, data assimilation for aerosol is only done based on AOD, aerosol optical depth. Correct. Yeah, not including like PM2.5 or PM10. So for the dust aerosol, does that undergo chemistry also? Like is the dust chemically active? I'm not sure. No, what do you mean chemically active? So that means like when dust is transported by winds, it undergoes many processes. Like is there heterogeneous chemistry, like chemically acting with other constituents? So we don't have an interactive chemistry model running here. It's just the aerosol. So yeah, that means like it's just like a tracer. Like, yeah, it only goes physical processes like deposition, wash out these things, but not chemistry. Okay, so the second question is, is vegetation interactive in GeoS S2S model? No, no. It's only prescribed, right? With some annual cycle?  And the vegetation fraction and things like that. Randy Koster, there is an interactive vegetation model that we have as an option. We've not run the seasonal forecast with it. There is a little nose of a hint that we get some increased predictability and prediction skill when we use it, but it wasn't enough to justify the computational cost, which is substantial. Just to be clear, like even for the hand gas, like there is set up hand gas, for example, for NMME, that GMAO provides to NMME. Correct. Even for those hand gas, so vegetation is not interactive, right? Correct. Thank you, thank you very much. Thanks, Jenna, thanks Andrea. Shui, you have a question. Hi Andrea, Shui Chen here, can you hear me? Yeah. Okay, I have a question about the sea salt aerosol. So that has been in GeoS and there seem to be a lot of time it gets transported really high. And I wonder if that's still an issue because we'll see hurricanes, you'll see the sea salt gets all the way to a stratosphere. So that's sort of one is a generation issue and the other is a transport issue. So has that been addressed or is there any change recently? Yeah, Shui, that's not happening anymore. Okay? Okay. Anton Darman fixed that for us by dealing with the settling and dealing with a few things. So this model doesn't have that in it anymore. This is a post-merit 2 model that doesn't have this issue anymore. Yeah, it had to get dealt with before we go running aerosol cloud interactions for sure, especially with sea salt. Yep. Great, thank you. Great. Thanks Shui, thanks Andrea. Thank you for your question. Thank you again. Very interesting talking. Also I see that you have answered your question on the chat. So yeah, thanks again to all the speakers from this morning session.


What's going on, everyone? Sam here with late in sports cards. We are ripping 2023 Top to Archive Signature Series Baseball, 20 box full case break number 15, random team style. We've got a look at our random results right there. As always, you can find those on the website under the results tab, as well as the link in chat. Yeah, Wahoos. We're doing the three case Bowman break at SGC's headquarters tomorrow. So they took the streaming setup from the main room, so I'm over in math room for tonight. That's why the overlay is different. That's why there's baseball breaks instead of LSE on it and all that stuff. That's going to look so funny, dude, when we see the video of the messy and it's late in baseball breaks on the side, ripping soccer. There we go. All right, back those up. Here we go. Yeah, so I'm in the drift room right now, technically. This is where Kyle always is, too. Starting off with the Rangers. Patrick Wisdom on a rookie. That's 68 out of 72. There's a Ranger in this, Patrick Wisdom. That is a 19 tops. Patrick Wisdom. It is the holy grounds that it is pouring out. We've got Zach Wheeler, 66 of 99 on a 22 tops for the Phillies. This one there, Philadelphia. One of 17 is Alex Kirolov for the twins on a, what is that, 2020 Heritage Miners. Nice one there. A little RKO me. I think he might hurt himself. I think even he'd admit he'd hurt himself if he tried to do that. Got a Matt Olson for the A's, six of nine. There you go, Ramars. Nice, dude. Six of nine and it's Matt Olson. Hey, are you right through the table? Yeah, through the baseball breaks table. Six of nine. Matt Olson on a top scrum. Still for the A's there. Let's go, Ramars. Speaking of hurting themselves, oh boy. Chris Sahel, what do you do? All right, we've got autograph signer Eddie Rosario. I know he's got a couple of different teams, so this will have a random at the end. Let me one Zach to look that up. Eddie Rosario, sorry, Eddie Rosario. We're going to have twins, braves and guardians, I want to say. I might have played somewhere else too. I know for sure the twins and the braves and the guardians. Yeah, twins, guardians and braves. So the way this works is if a player switches their team in 2023, we don't include the 2023 team, but Rosario's been in Atlanta since 21. So he played from the twins 15 to 20, guardians 21 and braves 21 to present. So that'll be a three-way random between the twins, the guardians and the braves for this Eddie Rosario. So we'll do a random once the break's over. Zero strikeouts, only enough fit, yeesh, that's tough. Next pack, we've got nice, Alec Thomas. 79 of 99 on a 2020 Bowman. 2020 Bowman for the Diamondback, Alec Thomas. That's actually pretty solid in this, really solid in this, with Galen and Thomas all over the place. There's probably like one or two other guys as well. Right, we've got a, ooh, Ryan Mountcastle, 14 of 29 on a Bowman first for the Orioles. Let's go Steve. That was Steve, right? I think you said you had the Orioles, or maybe not. Nice one there, Ryan Mountcastle, 2015. It was Eddie Rosario, Eddie Rosario, yeti. I know Steve said let's go O's earlier, but no, that's Edward P with the Orioles in this one. Edward P, Mountcastle first Bowman. We've got Reed Dettmers, five of 43 for the Angels on a Bowman first as well. 2020 Bowman first, paper for the Angels, nice one there. This is actually pretty sweet. Going to the Braves, two of 17, Christian Pachea on a 21 Bowman Chrome, I believe, right? Yes, 21 Bowman Chrome, nice one there, Atlanta. Nice, we've got 27 of 99, Gunnar Henderson on a 22 Bowman, or 21 Bowman, excuse me, for the Orioles. Orioles killing it right now, nice one there, Gunnar. Hopefully he starts to pick it up a little bit. Doesn't look super fantastic for his first few games, but always room to improve. It is his rookie year. Ooh, nice, going to the Blue Jays, Lattie Jr., 7 of 15 on a 22 tops. There we go, Toronto. Toronto is going to be Andrew R, let's go Andrew. Nice one there, buddy, Lattie Jr. It's a solid name. We've got Michael Brantley, 17 of 34 on a 22 opening day for the Astros. Michael Brantley, one of those guys you always want on your team. This one here for the Cardinals, Adolis Garcia, 10 out of 28, of course, this is before he got traded, so still a Cardinal in this. On a 2020 Bowman rookie. Stop, we've got Adam Frazier, 17 of 81 on a 18 tops. 19 tops, excuse me, for the Pirates. Nice one there, Adam Frazier, still a Pirate in this. Pre-trade, nice one there. Congrats, Pittsburgh. Got two cases of Dynasty, left side serial number coming up after this, everybody. Could be a fun one. There's Jose hunting up Berrios for the twins, 61 out of 70. A 2020 tops, right? Yeah, 2020 tops. Jose at Berrios. I'm going to soda. Tim Anderson, 22 tops, 60 out of 94 for the White Sox. Nice one there, Chi Town. Tim Anderson. There's a Randy Arroz Arena, 48 out of 92 for the Rays. On a Peter Starr's 21 tops. Randy Rakes. Cool one here, Brandon Woodruff for the Brewers, 13 out of 57 on a 22 tops heritage. Brandon Woodruff, one of the better pitchers in the MLB. He's on, he is on. That rotation is so deadly when it's good. Oh, pretty good. Most of the time, it's the rest of the team. Actually, they are pretty good this year though. Ooh, that's sick. One of one, George Springer for the Astros on a Bowman Platinum. That is a 2020 Bowman Platinum. George Springer, one of one, that's sick. Nice one there, congrats on that, Houston. Springer Platinum. And last box. Yeah, once Burns is gone, he will be the race. True, him and Peralta. Last is a Chris Taylor, 43 out of 84 on a 2020 tops heritage. There you go Dodgers. Let's do a quick random for the Eddie Rosario. Eddie Rosario, again it's going to be between the... Twins, Guardians and Braves. Twins, Guardians and Braves. No problem remarks, thank you buddy. Alright guys, we're going to do the random for the Eddie Rosario. Roll the dice, whatever team's on top, after 11 times we'll get the card. One, two, three, four, five, six, seven, eight, nine, ten, and eleven. And we'll do the twins, twins picking up the Eddie Rosario. Thank you guys again for the fill. Next up we've got Dynasty.