Thanks. So our next lecture will be Paul Dirmayer. Paul is a professor at the Department of Atmospheric and Oceanic and Earth Sciences at the George Mason University. Paul is also a senior research scientist at the Center for Ocean Land Atmosphere Studies. Paul works on studying the role of the land surface in the climate system. This includes the development and application of land surface models, studies of impact of land surface variability on the predictability of climate, interactions between terrestrial and atmospheric branches of the hydrologic cycle, and impacts of the land use change on regional and global climate. Paul is also a fellow of the American Geo Physical Union as well as the American Meteorological Society. Paul was also a senior Leonardo lecturer at the EGU European Geophysical Union. Thank you again Paul for accepting our invite. Look forward to your lecture. Thank you very much Anish and Judith for inviting me and giving me this opportunity. And thanks to Andy for a great presentation covering the hydrology side of the problem as well as the applications and operational hydrologic forecasting. I'm going to go more into you might say the theory and processes that go into our ideas, our current understanding, conventional wisdom about the role of the land surface in sub-seasonal to seasonal prediction. And so there is a sort of preamble, I would say that what we're really looking at or trying to discern is when and where is the land state important to the atmosphere, to weather and to climate? And it just so happens as I'll show that sort of the peak of this impact happens to be in sub-seasonal time scales. So the analogy is very much like with ENSO. We pay a lot of attention to the equatorial Pacific sea surface temperatures because we know that's a strong driver for aspects of weather and climate around the globe. And if you live in the US you're particularly paying attention during the winter because that's the season when there's the strongest impact. So there's similar geographical and temporal variations in how the land can affect the atmosphere as well and that's what we're really trying to get at. So for that let's see let's move along a little bit of history to start off with. Back in the 19th century during the westward expansion of European settlers into North America manifest destiny there was a phrase that was bandied about a lot saying that rain follows the plow. People would look at the great plains western US and say that's just a big desert. I mean people who grew up on the east coast or who come from Europe very different kind of climate. We can't farm there and you know the real estate speculators would say ah don't worry about it. If you plant your crops the rains will come and of course this was really just a marketing scam for real estate. In fact the same argument was used in the early 20th century to settle the interior of Australia and they sort of benefited from the fact that you could that they had a wet period a very unusually wet periods during this settlement when people moved out. Oh yeah look it actually is raining as I'm planting crops. Of course eventually comes a drought and then everyone starts to question well maybe they were lying to us. Maybe there's really isn't this land surface feedback on the atmosphere which mind you was not a scientific hypothesis it was an economic marketing hypothesis. So we might say well obviously the droughts proved that this was wrong or did they trying to build up a little little suspense here as we go forward make it more interesting. Okay into the modern era Jerome Namias we consider the father of long-range forecasting especially in an operational sense. He was not a modeler but he was really not much of a statistician either to some extent he was but really just a very clever and observant person working in what was called the Weather Bureau back then now the National Weather Service and he showed observational evidence and even proposed mechanisms that the land surface could be a source of memory from season to season that what happens in the previous season has a bearing on future weather that there's predictability here in terms of forecasting. He found that persistence of springtime conditions into summer existed over parts of the United States mostly in the central US not much on the east coast not much on the west coast but the central part he found that this was the case that heat and dryness were correlated a hot dry spring tended to be followed by a hot dry summer for instance and that these temperatures of the surrounding oceans which is kind of our conventional climate view of forcing really conditioned the atmospheric circulation over the continents. So the little contingency table here on the right is actually from one of his publications back in 1960 before even I was born and it showed that cold summers tended to follow cold springs over sort of the western Great Plains the high plains between sort of the front range of the Rockies on into say Kansas, Nebraska, Oklahoma and this area has the most continental climate in North America in other words least influenced by oceanic air masses and what you see here outline kind of the yellow bars show the temperature relationship spring temperatures on the the left column and then the summer temperature on sort of across the top that two and a half times more likely that a cool summer would follow a cool spring than a hot summer following a cold spring for instance and then furthermore warm summers tended to follow warm springs but not quite as high a probability but when you start to factor in precipitation which is sort of the other parts of the column it's another predictor hot summers followed hot dry springs and cool summers tend to follow cool wet springs in fact it's five times more likely that a hot summer will follow a warm dry spring than a cool summer would follow a hot dry spring you might say that's kind of intuitive but you know these were the first numbers to really put behind it that was observational on the modeling side evidence goes way back shukla and mince in 82 provided one of the first atmospheric climate modeling studies to demonstrate the impact of land moisture anomalies on precipitation it's kind of a hit it with a big stick experiment tremendous extremes in one case two runs one case all the continents are prescribed prescribed to be perpetually wet they're just swamps everywhere the other one perpetually dry you just pave the continents over turn them into parking lots and then you see how the boreal summer climate responds and they found positive feedbacks almost everywhere over land when the ground was wet you would get more rainfall they would keep the ground wet when the ground was dry you would get less rainfall the one exception curiously was over India which had negative feedbacks and that had to do with the thermal driving of the elevated heat source over Asia drawing in a stronger circulation actually changing the flow and bringing a lot more moisture from the Indian Ocean up over the continent kind of the classical monsoon driver but when you have a numerical model you can you can answer all kinds of questions you can do sensitivity experiments how large is the impact of a particular land anomaly on the atmosphere what are the relative roles of ocean versus land and the chaotic nature of atmospheric dynamics in determining climate over continents and weather predictability and predictions and models are great tools you can make changes and you can see the results in ways that you can't or maybe shouldn't do with the real world we're kind of doing that right now with climate change it's a real-world experiment but again what we really want to know is how do these land atmosphere feedbacks work in nature it's one thing to play with models are the models right and it gets to to David's question to Andy earlier about do we have the data to validate and to understand these models so the processes that link land and atmosphere it's the surface fluxes okay the physical link is there through the energy balance you have the radiative fluxes shortwave and longwave downward and upward what's class classically called the four components of radiation the thermal and the visible solar part and the sum of those is the net radiation that's available the net energy available at the land surface they can then be partitioned into sensible heat fluxes basically warming of the air by contact against a hot surface or the arrow going the other way if the ground is colder than the air then the essential heat flux will be from atmosphere to land latent heat flux which is the energy that it takes to evaporate water and that can latently be realized elsewhere when that water vapor somewhere else in the atmosphere condenses on the cloud that heat is then released and then ground heat which I list here is a residual and all the little terms there show the the variables the state variables and the parameters that affect these particular fluxes and then the ground heat fluxes the residual it's kind of like the the the budget the sum in your bank account of all the withdrawals and deposits ends up changing your balance in the end and that change in balance is what goes into additional heat storage or taking heat out of the soil likewise there's fluxes in the water cycle as well there's a water balance precipitation being the big input evapotranspiration is the same as the latent heat they're the same the same process except in one we're quantifying it in terms of a massive water flux and the other we're talking about an energy what's the energy it takes to evaporate that water and then runoff drainage which is basically you know much of andy's work andy wood's work is in that realm and then changes in storage soil moisture is a change in storage so is the ground water so is snowpack so is water and reservoirs and lakes these are all parts of storage change even water stored in the bodies of the plants themselves a vegetation is a variation in storage so each of these arrows that we show they're either sparsely measured poorly measured or basically unmeasured unfortunately again getting to to david's question about observations to validate these things and this has been a big issue and it's why progress in this sort of land atmosphere interaction realm has been slow things are finally improving significantly over the last 10 years or so both in terms of in situ measurements and being able to utilize satellites to do a lot of a lot of the observation on a large scale but you know that's kind of in progress in process right now and again these are all the things that are happening just right at the surface at that interface between land and atmosphere and there are process chains that link land and state anomalies back to the atmospheric components like clouds and radiation and I show this here all these little abbreviations basically we're going from the earth surface on the left on up to the free atmosphere on the right surface and subsurface soil moisture net radiation surface temperature are all things that can then feed into these surface fluxes the latent heat flux and the sensible heat flux e f you'll see a lot in my talk evaporative fraction that's basically the fraction of the net radiation that's going into evaporation that's going into late heat flux and those fluxes affect the near surface humidity q and the temperature t and those then start to affect the lower atmosphere boundary layer processes and quantities characteristics like the depth of the boundary layer how much entrainment at the top of a rapidly growing boundary layer the lifted condensation level from your thermodynamics class parcel theory how far do you have to lift a parcel before it will cool to the dew point and form a cloud and the moist static energy or moist enthalpy in the boundary layer all of these things go towards determining if and how strongly you create clouds you generate precipitation okay now these are all arrows going in one direction of course there are arrows that go the other way obviously rainfall directly affects soil moisture when it rains you get mud right so we're kind of taking that as given an obvious here the key is to look at the more subtle feedbacks from land back to atmosphere that they create this linkage now these pathways involve the water cycle or the energy cycle or both the blue lines are basically purely water cycle pathways links in the chain the red lines are purely energy cycle and then there's sort of a rainbow pattern of degrees of water versus energy they were showing these different links and also when we model this they fall into the purview of different components of a of an earth system model the first two columns are really the land state and the surface fluxes are really calculated by land surface models like Andy mentioned earlier and then the near surface temperature humidity and all the atmospheric components are part of a GCM a general circulation model and so these components are coupled and they have to talk to each other every time step and pass this information these fluxes back and forth to operate correctly and then the arrows also make it seem like it's this sequential linkage things kind of moving along and in fact when we have computer models to simulate this climate models they necessarily represent these processes in a sequence of subroutine calls but in reality everything's happening all at the same time which makes it very challenging to untangle especially in observations in a model you can go in and pick one of these arrows and sort of change the code to to remove a link and then you rerun the model and you see how things change and say ah this link is important and it causes this response you can't really do that very easily or very wisely in the real world so understanding these links and process chains in the real world in nature and observations is tricky to suss out but we do have statistical techniques and process based techniques and metrics to do this but that's a whole other topic in a whole other 45 minute lecture which we're not going to get into here. Now if we sort of turn this the right way around on its side so that the lands at the bottom and the atmospheres at the top oh I was going to also mention sorry this is a very local couple view a one-dimensional view through the atmosphere neglecting circulation this is just saying what's happening between the land surface and the atmosphere directly above it and then we'll get into the how the circulation comes into play in a moment but if we turn it on its side and we look at this as kind of a pipe diagram of flows of water and energy we can actually quantify this and using data from flux towers and from in situ measurements and soundings and so forth we can quantify over time a kind of climatology and see how does that compare observations versus models here a particular location in Arizona we're looking at where we have flux measurements and meteorological measurements and rainfall rain gauges and so forth we can see these linkages and here the the arrows the colors indicate the correlation between the starting variable and its downstream variable so if a is the forcing or a is the driver the land surface driving the atmosphere b is a response variable the colors from blue to red tell you the correlation between a and b the green boxes tell you the variance the standard deviation of that particular variable over Arizona specific humidity varies a lot and the moist enthalpy varies a lot and there's a strong connection of humidity determining the moist enthalpy in the lower atmosphere it turns out say in the era 5 reanalysis they under represent this variability and the thickness of the arrow is the coupling strength it shows a very weak coupling oh we've got a problem in the in the ECMWF model it's not representing this process well if we look in the UFS the newly developed forecast model it's under development for the weather service we see it does that pretty well but it's like way overdoing some of the land surface to atmosphere coupling in this desert regime so this is becoming a very handy diagnostic tool so this is all thermodynamics now not the dynamics this is sort of just looking in the in the column now as soon as the sun comes up we have land surface impacts on the atmosphere here we're looking at location in Kansas and showing the difference in the daytime daily evolution of various quantities in the atmosphere and at the land surface between having wet soil versus dry soil so here's our evaporator fraction in the upper left higher values mean we have more latent heat less sensible heat when the soil is wet we have a very high evaporator fraction throughout the day when the soil is dry it dips down to very low values around one two o'clock in the afternoon and then rebounds again towards sunset net radiation is pretty similar between the two and the ground heat flux is a little stronger when we have dry soils and we have wet soils but we see a big difference in the depth of the boundary layer a daytime boundary layer grows much deeper when the air is dry than when it's wet because when the air is wet you don't have to lift it as far before it saturates and makes the cloud kind of makes sense right from dynamics the lower right is what's called a mixing diagram and this is actually an extremely useful tool and metric for understanding what's going on the x-axis shows humidity in this case it's specific humidity in grams per kilogram the y-axis is the potential temperature two meter potential temperature in kelvin and each dot is an hour throughout the day from sunrise to sunset so we see when the when the soil is wet we start off relatively cool and moist atmosphere and it just gets moisture warmer but especially wetter and wetter throughout the day until sometime around four o'clock in the afternoon we hit the peak specific humidity and then it kind of levels out drops back a bit and our temperature stays at a certain level in a dry soil case on the days with dry soil we start with a much lower atmosphere humidity the atmosphere only gets wetter from evaporation until about 10 o'clock and then the rest of the day it just dries out and gets really hot and we hit a much higher temperature a much drier situation you could take this cue at the bottom multiply it by the late heat of evaporation and take this theta and multiply it by ccp and then you would have energy units you would have joules per kilogram on each axis and then you could actually connect this to your sensible heat flux input your latent heat flux input entrainment of energy from the top of the atmosphere and really understand the components of what's going on and also when you do that then the slope of one of these lines actually starts to correspond to the bone ratio so it encapsulates a lot of information to tell you how the land in the atmosphere are connected and interact okay so to give it sort of the very descriptive empirical right side of your brain picture of land atmosphere feedback so you can think of it as being like a recipe that needs to have three ingredients there has to be this sensitivity by which I mean when and where is there an active coupling from land to atmosphere if soil moisture is changing if the vegetation is evolving if there's snow or no snow and the atmosphere doesn't respond to it doesn't change then who cares right the land is not important there are parts of the world where this occurs like places where you have very strong onshore flow most of the time the coast of Oregon land surface state doesn't affect things because it's the pacific ocean it's really determining the climate there variability so a coupling results in a significant impact only when the land surface anomalies are large enough when they change you can have a lot of sensitivity but if the land surface doesn't change it's never realized the Sahara Desert is a great example there's really strong sensitivity of evaporation to soil moisture you add water to the soil there's so much heat there's so much sunlight it evaporates readily but there's just never any soil moisture because it never rains so there's a lot of sensitivity but no variability every day is as dry as the last and the last one is memory so if the coupling and the anomalies are not persistent if they don't last very long then the effect on the atmosphere will be short lived and the impact will be minimal for instance when you have very sandy soil over over a karst terrain a lot of underlying chalk or limestone and yes geology matters and all of this too you could have pouring down rain for three days and then the water will just drain out of the soil very quickly and it will go back to a dry state within a couple of days there's not much memory in a situation like that okay so sensitivity this is kind of what it looks like so here's a plot soil wetness on the x-axis going from zero is completely dry to one is saturated and then the y-axis is the evaporation rate in this case it's shown in in watts per meter squared and so there'll be some very low range of soil wetness where there's basically no evaporation the water molecules are locked into the soil they can't really get out and then you hit the wilting point and then suddenly evaporation increases very rapidly as soil moisture increases this red area is an area of very strong sensitivity a range of soil wetness then when you get above a certain point a wilting point then the soil moisture is no longer increasing with i mean the evaporation is not increasing with increasing soil wetness there's a lot of spread so clearly there's other things that are affecting evaporation but it ain't soil wetness so this is where you would have a range of sensitivity and again in that high range what's actually happening here is that things like wind speed and how dry or warm the air is is affecting the evaporation rate if there's clouds or no clouds that's really what's controlling it the atmosphere is controlling it in this range you're energy limited in this range as opposed to here where moisture limitations are controlling the evaporation rate and so that slope is a measure of the sensitivity and how tightly those dots are grouped along that line the correlation is also a very strong measure of this coupling strength the sensitivity another way to portray it here this is the mean evaporation rate and then the y-axis is the sensitivity of rainfall to a change in the local ET and it shows there's sort of a medium evaporation rate probably somewhere in the middle of this red line this slope where the rainfall is most responsive to changes in evaporation so the the green plot is that land surface component land connecting to the surface fluxes and then down here is that surface flux is connecting to the atmosphere links in the chain going all the way up so variability is whether a location is moving around if from day to day you're moving back and forth within this range then you have the variability to for the land surface to be driving changes in the weather if you're up in this wet range moving to higher lower soil wetness in this rather wet range isn't resulting in anything at all this is what the variability looks like this is a map of the standard deviation of daily soil moisture the left column is the surface soil moisture top 10 centimeters uh subsurface is on the right and this is going from April May June in the middle July and August so you see global patterns dark colors are a lot of day to day variability what you see this this strong band that kind of migrates north from month to month is basically the snow melt front uh whether the snow melts earlier later in a particular year when is it's melting how much is it melting melting snow increases the soil moisture so you see that in that region the largest variability is there also in transition areas between arid and humid zones like the Sahel of Africa between the wet tropics and the dry deserts or the us great planes where it's humid in the eastern us dry in the western us these are places where you tend to have a lot of this variability and here's a map of soil moisture memory for instance persistence of anomalies again surface soil moisture on the left deep soil moisture on the right there's more the deeper you go in the soil the the the more memory you have the longer the time scales but again we see these spatial patterns very long memories where it's arid that's deserts that's also non-desert areas that have a wet and dry season when you're in the dry season a lot of the southern hemisphere subtropics is in the dry season during june july august and so those areas will have a lot of memory if you had a if the wet season was really wet that wet anomaly will carry through the entire dry season as it's drying down whereas if you had a a failed monsoon a dry anomaly then that you'll have a dry soil moisture but they will stay separated those two years will always stay different from month to month to month very long memory long memory where the ground is frozen the ground's frozen the water can't move so the soil moisture stays the same when it's covered by snow it's sort of isolated insulated from the atmosphere short memories where it's humid where it rains a lot short memory in forests forests are deep rooted they can kind of moderate the soil moisture and in fact there's even a process called hydraulic redistribution by which plants will take soil moisture from meters deep in the soil and bring it up and redeposit it in the shallow soil and it tends to moderate and prevent soil moisture anomalies from lasting very long okay so quick recap of the theory pathways by which the land can affect the atmosphere energy balance or energy cycle water cycle we sort of have these two legs these two main links in the chain the land states affecting surface fluxes and then the surface fluxes propagating up into the atmosphere a series of chains going up to clouds and precipitation and then our three ingredients sensitivity variability and memory all the same mechanisms yes sorry to interrupt with three or four minutes oh okay i got to get moving here so predictability and prediction s2s you've probably seen this diagram um two to four weeks sub seasonal range is a very hot topic and it turns out that's right where the land tends to have most of its impacts so you need those ingredients you need good models you need accurate analyses and by the way this diagram you've seen it a few times already it's meant to be representative of a mid latitude location sort of mid continental where an ocean anomaly takes a while from to propagate from the tropics into uh into the mid continental areas um so i mean this is kind of reiterating i'm going to skip that our positive feedbacks they occur when a change in soil moisture an increase in soil moisture results in an increase in evaporation the plus areas positive correlation that means we're in an energy limited regime and soil moisture is driving evaporation that happens when we're moisture limited in an energy limited regime it's the opposite evaporation goes up when there's more energy and that draws down soil moisture they become anti-correlated so the blue shaded areas there's not a land surface feedback on the atmosphere the red colored areas there are and so that's kind of a reversal we've put a diode the wrong way in the circuit and now the current is not flowing and snow cover tends to cut off the connection and we become uncorrelated we get zero correlations and that kind of the situation now these now we were talking about where these couplings happening it is mostly in these transitions between arid and humid regimes and the glossay experiment showed this with a dozen models simulating climate in a way that we could quantify by playing with the soil moisture states we could see the atmospheric response and it turns out that these hot spots tend to be this is for June July August in these transitional regimes the great planes of the US between humid and arid the Sahel in Africa between humid and arid northwest India Pakistan between the humid and the arid it doesn't show up so well in this original experiment but many will quantify a band that kind of goes across the Eurasian steppes from Mongolia across to Ukraine is another band if you look into December January February you see areas in the southern hemisphere that light up in Australia and South Africa and South America so why is it there I already show that that soil moisture to surface flux region shows up in arid to semi-arid regions where you have a moisture limitation but the atmosphere has to play along too and it's in the humid areas it's sort of the eastern and especially the southeast US is where you have high convective available potential energy and where the atmosphere is sort of primed to respond an increase in moisture to the atmosphere will make it just that much more unstable and that much more moisture that can be condensed out into clouds and turn into convective rainfall and where these two regions overlap happens to be right along the central US kind of in that great planes area and so it turns out in a prediction experiment glossay to which was a hind cast experiment kind of like the s2s or sub-expo we did this about a dozen years ago we find that if you look at the wettest half of all of the thousand we did I think a thousand different forecast cases across many years ensemble members and forecasts and the wettest half of the initial soil moisture anomalies had more contribution in the dry areas and the driest half of the initial soil moisture anomalies impacted just on the wet side of that hot spot which kind of makes sense because if you're in a dry location and the soil is anomalously wet oh you've moved into the sensitive range now we've got some predictability from the land surface if you're in a humid region and the soils are kind of dry now you've moved down into that sensitive range and ah now we have some predictability from soil moisture and in fact what we found was and this is showing the temperature skill across 12 models the top is with realistic land initialization at various lead times out to from two weeks out to two months the middle rose when we randomized the initial states of the land surface but the atmosphere and the ocean states are the same so we see very dark in the short range that's initializing the atmosphere that gives us a lot of predictability um we have these patterns that show up in both that have to do with sst this large scale forcing changes in circulation if you take a difference map what you see the red areas is where the land surface initialization is contributing some extra predictability some extra skill into the forecasts interestingly it's not really in the hot spot it's in the western us and kind of across the northern tier and that seems to have to do with the fact that the memory is actually weak in this great plains hot spot the memory is stronger in the western us and early on in the forecast it's the quality of the soil moisture initialization that gives you skill in your forecast but as time goes on it's areas that have a lot of memory that began to dominate after three four five six weeks because if you don't have memory how are you going to communicate skill into a forecast six weeks out and so this is a very interesting and useful tool as well to understand when and where is a land surface state important for forecasting i'm going to skip this slide about the contributions and focus on this land impact on prediction skill so this was actually trying to quantify this with the cfs model looking at april may and june initialize forecasts on the top for temperature humidity depth of the boundary layer and rainfall so there's three curves here there's a red curve which is well let me start yeah red curve is forecasts where the land surface is not initialized realistically the green curve is where it is realistically initialized uh from reanalysis initial conditions and blue is where we specified we cheat we specify the soil moisture states throughout the entire forecast and in this case these are all leads that are going out from zero days out to two months 60 days and so you see skill of course drops off with time the difference in the curves are the shaded areas the tan region is the difference between the red curve and the green curve the effective land surface initialization the cyan shaded area is the difference between the green and the blue curve what happens when you include when you specify perfect forecast as it were of the land surface states and what you see is there's an immediate impact on day one but the peak depending on the variable you look at is always between one and two weeks is when the land surface has the greatest impact but that impact carries out for many weeks into the future all the way out the exception being precipitation it does have a peak in about two weeks but in this model precipitation was very insensitive to land surface states unrealistically so this is something we're looking at correcting in ufs so in fact okay so i'll go ahead and wrap up here uh let me skip ahead then and oops to um to get just to the point of application so oh here we go um forecasting applications a lot of talk about uh about droughts uh by andy to talk about the sort of the other side of the coin and heat waves um oh yeah right here the 2020 heat wave in the western us we saw very warm anomalies very dry dew point depressions it was very much connected with very dry soil moisture and you asked the question did a hot dry atmosphere dry the soil or did a dry soil heat the atmosphere and the the answer is there's a feedback it's both dry soils can greatly amplify a heat wave you can have warm air invoked into a region desiccates the soil that can feedback from day to day creating a warmer and warmer boundary layer storing energy in the atmosphere leads to an extended heat wave and our current understanding is that this is what happens and it was very prevalent in the 2018 european heat wave where we were able to show then in fact the land surface states the very dry soils impact fed back on the atmosphere and created these very warm conditions and in fact there is a critical soil moisture that when you cross below that threshold value suddenly you get hypersensitivity of temperatures to soil moisture drying conditions getting that break point right and forecasting that correctly in a model is critical for getting heat wave forecast which you need to do on a one to two week time frame so to quick summarize predictability of weather and climate from land surface states comes when there's feedbacks there's not always feedbacks there's two legs in this path land surface component and atmosphere component you need to have variability you need to have sensitivity you need to have memory and the effects start as soon as the sun comes up but the peak is around one to three weeks and can last for several months the impacts can be local but there's also i didn't get a chance to show the remote effects but there can also be downstream effects with teleconnections and rosby ways much like you have with sst feedbacks so i'll stop there sorry i went a little bit long no it was great fault thank you again there's really great explanation of these complex feedback so thanks


That's that's mistaken. Yes, I Just think that's an important point because I think You know you get you can perceive your body as As well It's not sick, but then still be perceiving sickness and someone else and there's really no difference That's what I hear you saying. Mm-hmm because the perception is mistaken and And the healing to correct that has to take place in the mind


So, so welcome all to the campaign office hour. Sorry if there were delays or challenges getting into the room. One of the links was wrong on the page. And we sincerely apologize if you got the wrong one. Hopefully a few more people be able to find us and join. And hopefully it'll all work out. So this is the campaign office hour. This is a space that we're creating to connect and have conversations about content campaigns and the media movement hosted by the Wikimedia Foundation team. This, we want this to be an open space so we have a theme this month is wiki for human rights. But we're also creating space for other kinds of conversations so if you have questions need mentoring or support. This is the space to do it. The theme for this round is wiki for human rights. We, I'll introduce that campaign in a few minutes. It's a partnership between you and human rights and our team, but we will have space towards the end for questions. We have a few people entering the room right now so we will give them just one more moment while that happens. We will be recording the first part of the meeting where I present and talk so if you don't want to be seen or hear your voice or whatever you can say that to the q amp a session which will not be recorded. I just feel comfortable asking questions or raising hands. Felix and Michelle and myself will be kind of manning the questions. I want to briefly introduce. So those of you have been around campaigns for a while probably know Felix and I working for the foundation on the campaigns team. I also wanted to introduce to contractors we have helping us this during the next five, six months on campaigns, Michelle, Bakhni and Venus Louie. Go ahead and say hi, if you would like, Michelle. Hello everyone, my name is Michelle. Where do we find you or what communities are you from? Okay. My name is Michelle I'm from originally from Syria. Now I'm living in France. I'm active in the Arabic Wikipedia and Wikidaza. I do a lot of things with Wikimedia and with all of the Arabic community groups. Thank you, Michelle. It's so great to have you on the team and we'll invite him to talk a bit more later as we ask questions and talk about wiki for human rights. And Venus, do you want to introduce yourself briefly. I'm Miss Venus originally from Hong Kong and I live in Germany. So in general I'm active on English Wikipedia and Chinese Wikipedia. At the same time I do a lot of outreach here for the Wikimedia movement so my, I'm very interested in education program and also like the GLAM project here so that's me. And Venus is going to be helping us with one level and ref the cycle. So if you have questions are interested in that she she's available. Cool. So since we folks have been showing up. I want to introduce you to wiki for human rights. So what is wiki for human rights. It is a broad opportunity for knowledge about human rights in a public. And so the Wikimedia Foundation partnered with the UN human rights, a few years ago, through a former memorandum of understanding to partner on sharing information about human rights in a global context. The first campaign, we ran about a year and a half ago with the help of Louisiana and many other community groups around the world Louisiana from Wikipedia Argentina that included 12 languages and 210 editors working on about 700 and some articles. As you all know, these aren't always precise. And we think that there's probably more that happened, but it had a pretty good reach and the month following the campaign we had about 9 million eyes on the Wikipedia articles that were edited so this is quite important. And we are kind of relaunching this this collaboration. We didn't do it last year for human rights day because of timing and COVID and complexity. But we decided to do a series of small or not small focus on wiki for human rights during 2021. We had a small challenges part of one level and ref as an experiment that we are still evaluating and learning from the Wikipedia challenge again this year is going to have some sponsorship from UN human rights. And so if you're interested in women human rights defenders we invite you to join the wiki gap challenge this year. And then we saw an opportunity to grow something around Earth Day to match the theme of the Wikipedia is human communication theme that the foundation is doing this year for the 20th birthday around impacts of environmental issues. And so we we reached out knowing the success and knowing how many communities engaged in this topic. We both on wiki activities about 14 languages last time, and at least eight off wiki activities. In addition to following on activities from Wikipedia Brazil and subsequent months and other communities. We were kind of excited to relaunch around a theme UN human rights is interested in the topic right to a healthy environment.  If it's possible in your coven context and we encourage you to use the grant teams event evaluation for coven. But we're also going to be running a wiki gap challenge like challenge that will be internationally focused and will not require a local organizing. So this campaign focus on human rights, and we want to remind everyone that human rights topics are complicated at the very least in all contexts, and in some context, certain human rights topics are particularly challenging. If you want to kind of think about the risk profile of this topic in your context, please pay attention to that we don't want to encourage you to be working on the topics that does not make sense for your community. And if you are recruiting new editors or working with existing editors you need to be thoughtful about what you focus on, because we don't want to to endanger anyone. So being said, we think appropriate topics for this broad theme of right to a healthy environment could be topics like environmental defenders so people who are advocating for the rights of various communities, or the right to a healthy environment more generally. So legal systems treaties or political actions that are responsive to the environment or other human rights topics but are connected. For example, the right to water is a healthy environment, or the right to food are both healthy environment related topics, but are, but could be also treated communities advocating for the protection of their environment. So if you've been following the climate movement recently environmental justice and climate justice topics are very central to how the movement is forming. There are a lot of opportunities to cover who is advocating for what for what underrepresented communities, and then also the health and social consequences of communities, not having access to a healthy environment. This, we are hopeful that this topic gives a really inviting space for communities to organize different sub topics that's appropriate. For example, the health or social consequences of communities denied access might allow you to say, expand articles about cities with unhealthy environments, or might allow you to expand articles about mining or deforestation, or whatever kind of environmental issues are appropriate in your context. So emerging decentralized events. And what what do we mean by events. Most of the movement organizes edathons and workshops, and we, we are supportive of that. And if you need help. There's a really good guide on climate change and edathons that CD can produced recently. We've, we've seen a lot of these kinds of events happening in the last six to 12 months, including this wiki for climate edathon that happened in Ghana, a Philippines translation event, which actually is a really good example of a local community focusing on an environmental topic in an appropriate way. They, they really focused on the impacts of hurricanes on communities and how that is disruptive. And this is a monsoon wiki specific drives. So last time during wiki for human rights, we saw wiki media, the community in Macedonia, customize the campaign to cover basic human rights topics, not the broader theme about right to health are youth standing up their rights because they realize they had huge gaps on basic human rights topics, but also French Wiktionary created a content drive for language and topics unique to them. We've also been seeing an emerging number of new kinds of editing events because of going online because of coven like this week and a half long wiki for climate event. And these models are not the edathon where everyone sits down in one place, but they actually spend a lot more time doing multiple meetings, multiple rounds of training and space for kind of different types of competition, and then expert conversations. So, both in the climate space and in the human rights space, we've been seeing more communities reach out to experts to do a careful reading of Wikipedia articles or topic areas on Wikipedia so in the bottom right as the wiki droids humans program wiki media Switzerland ran with communities in southern France that invited immigrant rights experts to talk about what kinds of knowledge that migrants needed to defend their rights. Similarly on the left scan who's in the call did a call with an expert to talk about the environmental rights considerations or environmental issues related to a river. The environmental social issues related to a river in Argentina, and by recording the call got a careful reading of those issues, and was able to improve the article quite a bit on topics that would not have otherwise been there. And then we ran a pilot activity at National Academies Edathon in Washington DC last year, where we pulled experts aside out of the main room to do careful readings of high impact topics like extreme weather and carbon capture. In both of those, we were able to see kind of public knowledge problems on those articles that you would not otherwise be aware of. So, kind of an expert conversation and recording it might be a lightweight way for you to organize in, especially in the coven context. So, I encourage you to think about that. Roughly this is the timeline for the next two to three months. And Z, we're going to have the conversation in a moment. This only about 10 more minutes or two more minutes of me talking. And so, yeah, this is what we're looking at. We're encouraging communities to organize from now through June, but the main challenge is going to be running April 15 to May 15. We are working with the rapper grants team at the foundation to make sure rapper grants are available to you during the next about month and a half for these events. And also, wiki loves earth is exploring supporting this theme. So there might be opportunities there if you're more interested in photography. So this is where we want to start conversation. Do you need help? Our team can help you apply for rapper grants kind of think about identifying topics or conversations that might be appropriate for your community and we can also provide mentoring and support. Also, I want to point you at this great handbook we community Argentina did for talking about human rights topics and your local context and planning for it.


Oh, yeah, ah, yeah, ah, ah I said I said it, yes See, she's pretty less Hey, you know she got tank fashion over here in Instagram Everything on clearance, it's a sale on sale Yeah, we gon' spit it again She got the clear heels, he got them jeans from City Trends Rankin' on fashion over here in Instagram devant, frankin' on fashion over here in Instagram Rankin' on fashion over here in Instagram Rankin' on fashion on CO Don't forget about the wet seal He said the wet seal Who be shovin' at the wet seal, gettin' shovin' at the wet seal She bought them leggas at the gas station, she bought them leggas at the gas station, she bought them leggas at the gas station, the gas station, the gas station, fast, say rainbow fashion.   We gotta do a video on our day, we gotta be like, rainbow city trends, rain.   Fly, one more time, what's the move? Change to the move. First guy bloody. I said rainbow, I said rainbow, I said rainbow.


Himachal Pradesh, jisnu De Bhumi bhi kya jaan da hai, isthaate kai itihasik ate Tarbik mandar vadi ginti vech maju dhaan. Lekin aaj asitaan u jis mandir de darshan didar kroun dele le ke jaarayaan, uthe Bhagwan Shri Krishna di ek aji hi moorti hai jo Rajasthan to chal ke Himachal de isha Noorpur ila ke ve chai. Aakhir e moorti ke we isthaan tiai issaab jaan na dele asitaan u le ke jaarayaan usha Noorpur Brajraj Swamide mandar. Yaha pe ye jo vigre Krishan ji ka iski puja kia krti thi Meera bhai Chitotkar bhai. To yaha ke jo Raja thi Raja Jagasin, wo gaisi Chitotkar ke Raja ke mandaran par 1619 se 1623 ke madhi ma. Jab wo gaisi Chitotkar ke Raja ke mandaran par to baha pe jo onko reine ke liye kamara diya gaya tha, uske saath main mandir ka. Raat main mandir me gunguron ke awajiya aur bhajan kirtan ke dunjodhi wo suniti Raja ji. Jab unha ne mandir ka Darbaza kula to andar ek aurat jo hai bhajan kirtan karte hui naajte hui dikhaini hui. Phear jo Prahit to waha pe usne bataya ki ye jo bhajan kirtan kar riye naajte hi ye Meera bhai. Aur ye jo vigre ye Meera bhai aur Krishan ji ka sabru paye. Jab yaha se vapis jaana hai to upahar me yehi Raja ji se maag liya tha. To unha ne upahar ke torpe yehi vigre jo tha wo maag liya tha. Aur la ke phir unha ne apne hi Darbawar me yaha pe kapit piya tha. Yaha Darbawar ek khas tha Raja ji tha. Yaha iske saath ek Mulsri ka peda hai tha. Jo abhi soogya hai. Lekin uske hi grafting karke 2 nai laga diye bhai. Inke charno me jo hai wo padam chakkar hai. Padam jo thi wo mani thi paung ki niche. Jahaan pe Bheel ne Shikari ne Jotir mara tha jis than pe laga tha. Wo sthaan sir phi isi vigre me haan aur kaya hi vigre. Kyi ye Meera ji dwara hi puja hua hai to. Meera ji hi isi ki puja kar thi thi. To inke saath jo hai wo Raja ji ne Meera ji ki hi ashtradha to. Vigre jo tha wo banu hai tha. Aur saath me sthapis tha tha. Bhahti acha yaha pe shanti si milti hai. We have come here for the third time. We came here 2-3 years back. So after going home, we felt that we should go again. So we have not made a routine to come here every year. It feels so good to come here. It feels like Thakur ji will offer himself. We have come here once or twice before. And it feels so good to come here. And we feel that we should come here once every year. Because we get a lot of spiritual peace here. It feels so attached that like Thakur ji is here alone. And Meera ji is here. He is a devotee. So we cannot complain about him. It feels so good to see Thakur ji here. When I heard about Thakur ji. That these are Meera ji. And these were the kings of Noorpur. They came here with him. It was the best to see him in my mind. So every year I feel so good. So these days I come here. This is our third visit. And it feels so good to come here. This is the temple of Brijra swami ji. And this is a very historic temple. In every temple there is a statue of Radha with Krishan ji. But this is such a temple. With which Meera bhai is the resident. This temple is very famous of this temple. From very far away, from everywhere. Here there is Haryana, Punjab, Jammu, Kashmir, Himachal Pradesh. And from outside the state. And from outside, like America, German. People come here to see God. They are connected to this temple. And here there is a trust in the temple. It was a trust 4-5 years ago. After the trust, there is a lot of help in the temple. Trust people have done it. And this is the sign of the temple. They have done it very well. And it has been done very well. Every effort is made by the temple trust. Because the temple trust has decided. It feels good to come from far away. It has been done for them. It feels very good. And here in the morning and in the evening. There is a feast here. People from the city and from outside. People also come to the feast here. The committee of our temple. It was made in August 2018. After that, the work of the temple. It is being done in the view of the committee. And in this, the test is made. There is a holy council. In our nationality. It is a state level nationality. And it is very well known. And there are a lot of people here. People come here. And every day, there is a festival here. As it is a festival. It is a festival. We also celebrate it very well. And every time, there is a festival here. It is being done by the committee. So, this is the bridge. It was made in the view of the committee. It was made in the view of the committee. It was made in the view of the committee. Nourpur toh. Jatinsharma. News 18 network.


Hi, I'm Todd Baker, Corby Vice President of Engineering at Future Electronics. Thank you very much for attending our class today at ST's 2020 Virtual DevCon event. We're really excited about the event. We're excited about all the changes that are going on. And I think today we've got a really neat topic that's gonna help you with your next design and make you a little bit more efficient, get you to market a little bit faster with a great solution from ST and actually a number of great solutions for ST. Today, I'm gonna be talking to you about the Future Electronics Design Companion Board. This is an add-on board that we've built to work along with ST's own ST-WIN sensor tile platform which showcases their STM32L4 plus microcontroller series. Now, if you're not familiar with that microcontroller series, it's an excellent low-power Cortex-M4 design where they've really brought the power consumption down. They've added a ton of memory to the overall microcontroller itself for a lot of different applications. In addition to that, they've added, they've got their Chrome Art Mathematics Accelerator on it which is excellent for a lot of different applications and really speeding the ability of the processor to do more complex algorithms. They also on it have their own Chrome GRC graphic controller on board. So for the design in a low-power application like a wearable, you can use that microcontroller, maintain long battery life and still have a very sophisticated user interface with great graphics on it and allow the core to focus on the primary application you need it focused on, let it do the main method it might need to be doing, maybe keep it asleep so you can maintain the battery life in just a lot of those powerful aspects that can be very, very useful in many applications with a powerful microcontroller like the L4 Plus. Now, those graphics applications in low-power isn't where our companion board is really focused on though. What we really wanted to focus on with the L4 Plus was the performance capabilities of it. Even though it's a very low-power microcontroller, it's able to generate 150 drystone mips out of the Cortex-M4 plus all the math capability that it has. So with that, we wanted to see how do you get this connected very easily to the cloud? How do you get the L4 Plus to a point where it's collecting very sophisticated sensor data and do that kind of thing? And so by taking our companion board, adding it to the ST-1 sensor tile, with that you end up with a full system that now has a thermal imaging camera added on board. So a lot of impressive capabilities and ideas and applications for that these days. We've also added a CAT-1 cellular radio on board as well. And then a number of sensors like CO2 sensors, particulate matter sensors, and others. So that we're collecting a ton of data into the L4 Plus, getting it connected to the cloud, and still have a lot of horsepower left over to do many, many other things. So it's really a solution that we've built that's focused on applications for you, the design engineer. We believe it's something that you can take, use it as a launching pad for your next design. No matter what you're doing, what we've kind of designed it for today and what we'll be looking at are things like building monitoring, sophisticated air quality control systems that we've seen a lot of engineers working on these days, crowd monitoring, things along those lines. But we've also built the board to be flexible enough that you can modify it to your own application and start down that road. Do these kinds of things, easily be connected to the cloud, very, very quickly with the L4. Easily be able to select data from the sensors that we've selected for this board or whatever sensor you may need to put it on an I squared C bus or a spy bus or a UR or whatever else might be necessary there and then get your application going. In addition, because of all the memory that the L4 plus has, we believe down the road and some of the things that we'll be talking about a little bit and things that we're developing ourselves to help customers with is adding the ability to artificial intelligence and machine learning at the edge of the cloud. So you can take this little micro controller and start making decisions on the environmental data that you're getting without ever having to interface with the server, without ever having to have a human come into contact with your device, make intelligent decisions, maybe decide that you have a problem going on in the system. Maybe there's some kind of maintenance that needs to occur. Maybe we need to make a decision to send an email or a text or an alert that we have someone in the crowd that has a fever or maybe we've sent something in the air quality that's dangerous. Those kinds of things are capable with this system and we're really excited to show you all that we've got built today. So let's go ahead and get started. So as we've discussed, the future electronics companion add-on board is meant to be an add-on to the ST-WIN sensor tile for the STM32 L4 plus industrial platform. And that is an industrial platform that's already ready to go with a lot of features already built in. But what we wanted to do with that platform and by adding companion onto it was to turn it into a true industrial wireless multi-sensor edge node. So that we'd be able to easily connect up to the cloud, get customer and design embedded engineers connect to the cloud faster and be able to do a lot more sensing than what the current platform does. So let's talk about the current platform first, what's available on it and then we'll get into what companion offers in addition to it. So we take a look at the ST-WIN development kit. The sensor tile has a lot of features on it. You've already got a microphone, you've already got different environmental sensors that we'll talk about the different ones in a moment. We've got motion sensors accelerometers on board, you can communicate via USB. And then also you've got the ability to expand and add a Wi-Fi socket onto it. So there's a Wi-Fi add-on board that you can buy the ST-WIN WF-V1 that would allow you to have that connectivity. And then there's already on board an ST Bluetooth low energy module as well. So you've got a lot of features here that can really get you started. If we go into kind of the details on the exact sensors that are already there, we've got vibration sensors. We've got 3D acceleration on there from ST. We've got a five-axis IMU for magnetometer. We've got humidity sensors, temperature sensors, pressure sensors, a number of other products from ST, including also a MEMS microphone as well that's on board to me, an analog microphone, pardon me, that's there on the system. So you've got a lot of data that you can collect with the L4 already. You can then send that out over the Bluetooth radio, get an app that's available, and do a lot to have started to do that kind of control with the system. So it really is already pretty full-featured with a battery, with a case, and able to get you to a point where you can start prototyping with your own application and your own design. What our vision was to add, was to add the Campania on board to this, and you can see that the eval board already in its little case here. And so with the Campania board, what we come in and we add is we add first and foremost a cellular CAT-1 modem. So we end up with that that gives us a lot more flexibility to be connected anywhere, to not just be reliant on a phone or be reliant on a Wi-Fi module or some other network, we can connect ourselves to whatever cellular towers and range and be able to move the board anywhere that we go. We've also added a CO2 sensor. Well, big part of what we were trying to do is to have air quality control, air quality monitoring, so that CO2 sensor is on there, also a particulate matter sensor. We also entered an air quality sensor, the TVOC sensor from Censirion, which gives us a good idea if there's different aspects of the air quality, like methane and things along those lines that may be in there. There's also the grid eye. That's one of the key features of this, a very small part that gives you a 64-zone thermal imaging sensor.  And in addition to that, we added a number of ST parts that weren't on the original system. So we added a GPS radio on there so we can see the exact location of our industrial node if it's moving and where it might be. We also added an NFCE prompt. So we've got near field communication so we can easily reprogram the device. We can collect data off the device with our cell phone and transmit data very easily there with the ST25 DV04K. And then in addition with that, all those added sensors with a lot there that had to be added on, obviously we needed a lot of LDOs and ST does a great job with a lot of their power consumption or their power, not the power consumption, but the voltage regulation circuitry that we wanted to make sure that we did a lot of utilization of. So we ended up with a very full system and a lot of new features added on to the sensor tile with Campanio being added on. And Campanio, of course, being Italian for Campanio, we wanted to make sure that we represented our key relationship with ST. You can get a little better view of the Campanio board here and this is one of the renderings that we have of the board without the cellular module actually attached. So for our cellular module, we use a Nimble Link SkyLink cellular module that connects to many different cellular networks. We'll talk about that a little bit more in a moment. The board's a little bit larger than what you see in the sensor tile. So it does add a little bit of space because we wanted to be able to put a lot on there, but it adds a ton of functionality that really does help us get to what we wanted to accomplish in the system. And part of what we wanted to accomplish is get to a point where we're starting to think of things like industry 5.0 in the smart factory system architecture. Can we get to a point where we've got a node that can monitor many, many different things on something like a factory floor, monitor vibrations, monitor air flow, monitor air quality, particulate matter, and then quickly send that information up to a cloud so that decision makers can decide maybe there's something that needs to be fixed in that system and make those decisions a little bit faster than they've been able to in the past. We also thought about things along the lines of smart buildings and HVAC maintenance systems. One of the things that we found from a lot of our customers and we hear a ton from engineers is that there's a tremendous amount of cost in waiting for a machine to truly fail. And it would be very nice if they could know the machine is about to fail, roll a truck before massive failure actually occurs, and there's a tremendous amount of cost savings that occur in that preventive maintenance. So the system is built for that. So that if in an HVAC system, maybe you have one of your motors within that system that begins to have a vibration pattern that you can recognize. And when you recognize that pattern, you may know that a bearing is going out or something else is starting to fail. So before it actually fails and makes everyone else in an office building in Texas in July burn up and it caused a lot of issues for the building owner, you can roll a truck and fix the actual HVAC system before the failure ever occurs. Those kinds of designs can be very, very powerful in this day and age. So before we go into further details on the overall system and how we were gonna make that look like an industrial node, let's talk a little bit about how we actually get things up and running and bring the board up a little bit. So the first thing we wanna use is the STM32Q program, which is a free tool offered by ST makes it very easy to upload or download a binary file onto your micro controller and program, everything. Unfortunately, I don't have enough time today in this class session to walk you through all the programs that we wrote, but just suffice it to say, all that's available for anyone that would wanna use that for your designs. We've basically used free RTOS, written a little bit of application code to get everything running. And then we've compiled that and now we have a binary file. The first thing you wanna do once you have that compiled program is connect via the ST link, which is a little dongle that comes with the sensor tile. We're gonna connect to that. We see everything come up. At that point, we wanna go and find our binary file and the open file here. We've got it right here, companion project binary is our latest software. And once we see that loaded up, when you go to the download button and at that point, we get everything going, it's in progress and now our code is ready to go. So once we've actually programmed the board, now we wanna go ahead and bring everything up. We wanna start interfacing with our free RTOS terminal, start executing some of the different functions to read sensors and get to a point where we can start connecting to the cloud. And that's when I'm gonna spend some time walking through with you here. So bear with me on this. So the first thing we wanna do is bring up some kind of a serial terminal application. I like Terra Term very well. It's free, always works well. And we're gonna set that to 115K bot. Get it all plugged in. We're gonna connect it to the ST win interface. So again, we're connected to that little dongle that's tied into our USB on the sensor tile itself. And then we just need to do a reset on the board. So if we go to, there's a little black button on the ST win itself, push that button. And at this point, we should start to see everything boot up. See the free RTOS come up and at this point, we've got a command prompt. So the command prompt, we can start to see, okay, what are the different things that we can actually do on this? If we type in help here. So at that point, we've got a long list of different commands that we can execute from the command prompt to read different sensors and do different things. I'm gonna walk you through some of that right now. I'm also gonna walk you through some of the engineering behind the different components on our system and then how we're reading them, what we're gonna do with them and then we're gonna connect to the cloud and show you exactly how you can do that as well. So before we start getting started reading the different values off of our board and the companion board, let's talk about some of the actual sensors that are on the Campania system. So some of the things we've added, first and foremost, a Sincere and CO2 sensor, their SCD30, this is a really good performance to price ratio module that we've seen on the market, great for applications where you need to be moderating CO2, but also has the capability of doing relative humidity and temperature sensing as well. It's perfect for the kind of application that we sort of had in mind when we started designing Campania, just from the standpoint of, being an HVAC system, indoor air quality, quickly being able to get a good sense of where things stand on that. Small form factor on it, it's about 35 millimeters by 23 millimeters, it's not directly on our board, it sits next to our board, we've got it wired over and it's got very accurate measurement, 30 parts per million plus or minus and then it's all, one of the nice things about it is it's already calibrated, it's already linearized, you don't have to do any of that in your own code, it's done for you. The interface can be UR or for us, we're using the I squared C bus for the majority of our sensors, just for ease of design. The next sensor that we've got is another Sinserian sensor, is their VOC sensor, so the SGP 40. This one is on our board, it's a very small sensor, it's able to detect a lot of different types of gas, MOX based gas sensors for indoor air quality. So you can be able to detect if the air quality is actually diminished for some reason, is there some matter that's gotten into the air system that shouldn't be there and that you don't want to have. So something that we've seen a lot of growth and need for just for the air quality and just a great interface with I squared C, already tied in, this is a very easy sensor to integrate into your systems and get going. And then lastly, we've added a Panasonic particulate matter sensor, their SNG CJ5.  And so I gotta say, when I first was starting this, what are the things I would use a particulate matter sensor for? Well, it's things like dust, ash and cooking situations. If you might have some kind of a smoke coming off of a cooking surface, maybe plant matter from fireplaces, furnaces, also construction materials in a construction site. If you've got a lot of drywall that possibly is flying around or in something like a demolition site, there may be a situation where there's too much particles in the air that's making it difficult for people around it to breathe and you don't want people walking into that area without some kind of a respirator on. Things along those lines are what we're seeing a lot of need for particulate matter sensors these days. It can detect any kind of particle down to something that's 0.3 micrometers in size, and then is outputting that information out to the STM3204 plus. So it's really a very, very nice system uses a laser diode internal to it to detect the different particulate matter that's there, and we'll kind of show you what it's seeing here in my home office, and hopefully everything is safe for me. So now that we've talked a little bit about the sensors that we have on the board, let's take a look at actually how we get the data out of them using our system. So first let's take a look at the CO2 sensor. So we've got it right here, the Syrian SCD-30. You can see the little bit of a heartbeat light on there, just letting you know that it is powered on, which is a nice feature on there. So in order to get that one's reading, we're going to type in CO2-READ, and then it's going to give us the information coming directly from that sensor. So nice and easy. Same thing for the VOC sensor. Take a look at that, that's just a small little sensor right there on our board. On that one we're going to have TVOC-READ. Again, we're going to get the basic data coming out of that sensor right now. And then lastly, for a particulate matter sensor right here, so a little bit larger box that gets black, my hand's probably in the way. It's going to be right there. So for that sensor, we're just going to very simply, the command that we've done for our function there, just PM-READ. And again, we're going to get the particulate matter information out of that. Fortunately, none of those are too terribly high right now, so I'm okay in here, but it does give you a good sense of some of the data. So the next sensor I want to talk to you really quickly about is the Panasonic Grid Eye Sensor. This thermal array gives you 64 different zones and a very low resolution temperature in thermal imaging camera, which is a really powerful thing we're seeing needed in a lot of new applications. From anything from room air conditioning where you want to be able to detect different temperatures across the room, people tracking, be able to sense bodies and how many bodies are in front of you, digital signage is one of the areas where we've seen that, where a digital sign will change when someone is standing in front of it, temperature detection inside microwave ovens. And then also one of the innovative things I've seen is a new way of doing human machine interface with gesture recognition. So you don't actually have to touch the machine or touch the surface, which is obviously important today, but then still be able to interact with the machine without having that touch. It's able to detect temperatures from zero degrees to 80 degrees C if you're willing to put it into high gain, a little bit more power consumption. If you go into a low gain, it's negative 20 to 100 degrees C. So very, very useful system, just have an I squared C interface, very small part in a lot of really cool applications that we're really happy to have on the Campania board. So let me show you exactly how that works. So the Panasonic Grid Eye, you can get raw data in the Territorm app and just see exactly what it's showing for each one of the zones live. That's one way to do it. It's not nearly as cool as actually using the Panasonic app. So they've got a really great Grid Eye Evaluation Kit software app completely free on their website. We've got everything on the Campania board hooked up and ready to go with it. So we just need to send the command to the Campania board for grid eye dash software. Okay, and that puts us into a mode where we're just sending out data that the software wants to see. At that point, the board will continue to run. I need to close, if I can, go and close Territorm so it's not taking up the terminal. We want to connect to our same USB port. It's using that same USB port. We're going to connect to it. And now you can see that we've got the actual different zones and you can see the temperature in each one of the zones. You can actually set this up so it shows you the exact values for the zones as my hand moves across. You can kind of see the sensor is working. And it's just a really nice way to do many, many different things. And there's so many applications for these kinds of sensors coming out these days. So one of the first wireless features that we added was to add an STM NFC radio on board. So this is a nice little dynamic NFC tag. It's their ST25 DV with an I squared C interface on board. And we find that a lot of these NFC radios are used in many things. I know we've all gotten used to, to some extent, to the paying with our phone and the tapping of things along those lines. But we're seeing it also just for fast data transfer for MCU firmware upgrades, also just parameter upgrades of a system and any kind of just really quick, small data transfer that may be required to change a system or get data out of a system very fast with something that every maintenance person is going to have a cell phone, which has been very, very convenient. We've also seen it being used for key storage, for password protection, data protection, things along those lines. So there's a lot of great features in NFC. So let me show you how that works on our system. All right, so to do an NFC read, you really need to have some kind of a tool on your cell phone. For me, I use a tool on the iPhone called NFC tools, completely free. You can get it and it'll allow you to create whatever text you might want, then you want to send. So the text that I want to send over the NFC radio is something I think we can all agree on in a world where it doesn't seem like anyone can agree on anything anymore.  I think we can agree on that, right? So from there, I'm going to go ahead and set, put the phone into write mode. So I want to send that message to my NFC eProm. It's now ready to scan. At this point, I want to go ahead and put the actual device into the write mode so that it can see that. Sorry, I should have done that first. So the command I'm going to use for that is going to be NFC dash read dash texts. Sorry, I'm working on two screens here. It gets interesting. NFC dash read dash text. Okay, now it should be ready to look for an NFC to come nearby. So at that point, if I can get it. There we go, I found my antenna. Just took me a second to find my own antenna on my phone. And then you can see, from there, we should be able to see that the information got exchanged. Hit enter again, which should be the new thing. And there it is. Now we have the correct message that's inside the system. So before we start talking about our cloud connectivity, let's talk about some of the wireless features that we added to make that more valuable. And the first one is the STMs, GPS modules. That their Tiseo, LIV3F, GNSS modules are ones that we've now at Future Electronics have designed into a number of our systems. Makes adding GPS to an application very, very simple. Being a fully integrated device just takes a little bit of programming on the microcontroller, reading out, reading the GPS coordinates and then being able to upload this. And this makes it very simple. So in our system, we have the Tiseo module, we have an antenna. And as we've been doing the development with engineers in Montreal, Quebec, in Austin, Texas, with me being here. And then also some of our engineering team being in Mexico, it's made it very interesting for us to be able to see who's working and when because we always see the nodes come on at those times. The next one and probably the most important part, especially for the cloud connectivity is the addition of a CAT1 modem. And so we added a Nimbolink Skywire 4G radio onto this, which has been a real, very easy device to add on. It's able to work with a lot of different carriers, AT&T, Bell, Rogers, Telus, T-Mobile in the Americas. And then just add a simple antenna on a UFL port that's actually there on the board. So it makes it very, very easy. So again, this has been very simple just to send a couple of AT command sets to this, get up and going, get on the cellular network, get connected to the cloud. And that's what I want to show you how we did next. So next we want to take a look at how we actually get the Capagno board connected to the cloud. And we've got a Scripter interface and a Scripter IO server that we're running that we want to show you. And unfortunately it's a 30 minute class. There's a lot that goes on into getting that up and going. But I want to walk you through the processes of what we've done. And obviously it's all on free RTOS. And we developed a number of functions that are really just sending AT commands to the cellular radio and getting everything configured properly so that we can actually first get connected to the tower and then get connected to the cloud server. So the first thing we want to do obviously is to actually initialize the cell phone itself. So we'll go ahead and the first command that we send for the function that we called to get the right AT commands on to the Skywire radio is cell dash I-N-I-T, okay? And that's going to start getting everything configured. And that basically just initializes the cellular radio into a known state. So we know exactly where it is. And we can kind of start from scratch a little bit. Make sure that there are no issues. So one of the things, the next thing that we need to do is actually connect to a tower. So we've got a SIM card that's installed into our modem here. We use a SIM card from a company called Convergia. Convergia is a great company to look at if you're looking for CAT-1 services that absolutely can get you that low cost that you can't find from a lot of the other providers. We'd be happy to make introductions for that. So the first thing we're going to do is just see if we're online. And we may not be this first time because this cellular radio, yeah. Okay, so we're not actually attached to the tower yet. And the reason for that right now is this cellular radio's been in a number of different countries. And so we're looking for an AT&T tower. So we need to get everything set up to GPRS. So we now have the right configuration settings for that. So we'll go to cell dash is online. Hopefully this time we see a good IP address that we get. So we see this time we are in fact on attached to the tower <IP_ADDRESS>. So we are actually on there. So now at this point we're on the cellular tower. We need to figure out how do we actually get onto the cloud. So for that we need to basically go ahead and define a node address for ourselves so we know what we're going to do. So the command that we've built for that is L's dash companion dash address. In the node we're going to go with the default one is 443 08 1, let's take that quickly I did. Okay, so now we're set. We got a message back from the server saying it's good with that node address and it's now going to see our particular companion board as this address and then multiple boards could be connected to the same cloud site. So you could have a lot of different nodes all with different addresses. So now it's a matter of let's send our payload. So let's check our payload first see if it looks good. We hit test dash body message. So this is a payload that contains all the information from our GPS radio, all of our different sensors. This is what's getting fired up to the cloud now. And now we're going to go ahead and send it with the command cells dash script dash post. Okay, and we see that that's going through waiting for the okay, we got the okay, we got our information back. And so now this data that we just sent should now be live up on the server. So let's go ahead and take a look. So we go ahead and log into our site and this is the site that we've created. You see we've got the data for the location. If we go ahead and click on the board, information for the board, we now can see that this is again, simulating kind of an HVAC system. You have the information, they're live for all of the different components, all the different sensors inside of this HVAC system for air quality, for temperature, for humidity levels, particle sensing, everything along those lines. And then we can go and actually view some live data on it and take a look at this. Right now we're not streaming continual data, but you can actually see all the information from the sensors here on our dashboard. We can see what the IR sensor is reading as well. And we've got a very, very clean, nice user interface that many people can see and utilize. So I hope this time looking at the future electronics companion board has been useful for you. Again, this platform is meant to help our customer engineers get to market faster by showing you a very easy way to get connected to the cloud. It's tough to show all the details on this in 30 minutes. There's a lot more detail that our engineers can get into with you to help you faster get to market. We'd be happy to share our board with you, happy to share our files with you, our software, our hardware, so that you can take that and convert what we've created as a launching pad for your own eventual applications. For any of your hardware needs, any of your software needs with ST Microelectronics, Future Electronics is here to help. Please feel free to reach out to us and we look forward to speaking with you soon. Take care.