Welcome to the 34th meeting in 2023 of the Delegated Powers and Law Reform Committee. First, I'd like to remind everyone present to please switch off their mobile phones and other electronic devices or put them to silent. The next item of business is to decide whether to take items 5, 6 and 7 in private. Is the committee content to take these items in private? Under agenda item number 2, we're considering an instrument that's subject to the affirmative procedure. No points have been raised on the draft welfare of armed animals at Scotland, amendment regulations 2024. Is the committee content with this instrument? Under agenda item number 3, we're considering two instruments not subject to any parliamentary procedure. No points have been raised on SSIs 2023, 361 and 362. Is the committee content with these instruments? Under agenda item number 4, we're considering a document subject to parliamentary control. No points have been raised on the code of practice for the welfare of pigs, revocation Scotland, notice 2023, sg 2023, 304. Is the committee content with this document? Thank you. I'll move the committee into private.


So, with the 2.1 update patch for Cyberpunk 2077, CDPR included many new secrets for us players to discover. In this video guys, I bring you 20 plus of the best I have found in this game. How's it going guys? My name's DPJ and if you're doing enjoyed the video, leaving a like really helps out and if you like what you see and want to see more, be sure to subscribe. So not only have we seen the inclusion of new motorcycles, cars, weapons, there are also new events, easter eggs, references and much, much more. Today we check out the ones I have found and if I missed something, please do let us know down below. So let's start with the 3 new events. First up we have one which takes place within that cyber matrix factory premises, the exact location where this encounter takes place is Northside Watson near the Kabuki Market at the Pinewood Junction. So fast travel to the Kabuki Market fast travel point and follow the path I take on screen now. When you get here guys, there will be a spot where to see this dude with a sword sticking out of him can be found. Upon you scanning him you will notice he has been wiped, they actually know the collection of who this dude is, although he is wearing an alesaka outfit. Upon further inspection you can find somebody else who is also on death's door. Now if you try and speak to him, he just tells you to leave him alone and it results in you if you walk away from there and come back he will die. Upon him doing this guys, you can then loot his body which takes his conversation a little deeper. These two are actually best friends and they will test to take each other out. Pretty grim. Next up guys we have the trauma team versus the type of claws event. So this takes place from right here on the map. You want to come from this point to this newly built road and follow it around to your right as I do on screen now. You will find this event taking place now it isn't all the time but for me I mean just reloading my save or reloading my load even would actually make this event appear. So yeah it could be down to time today I'm not sure. Now here you actually can take a side if you're careful or you can just take them both out as I did. But a side with the trauma team keep this in mind would equate to something else going on slightly later in the game with a message you should receive. Now the reason of this fight between these two groups is found via shards showcasing the type of claws. Now dude Dan with the trauma team are trying to help out. This dude has sensitive info yes we've got to take that she's out. Now there are a few shards here you can actually find and read which takes the story a little deeper but I will leave you to learn this for yourself guys if you want to come to this event and experience it on your own. So if you find anything else cool though do let us all know. Next up with a third event of 2.1 an old name some will remember and that is Sandra Dorsi. She is still causing health that night cop. So coming to this point within the Badlands you come across this event taking place. Now as far as I'm aware you have to take everybody else if you want to learn more about what's going down here so do as I do. Now diving a little deeper into what can be found here ties to an alternate reality game which I don't know for an absolute fact 70% of the side points player base right now won't have a clue about this. But upon doing a bit of research on the recent ARGs with Cyberpunk 2077 we will learn a lot. Now for now I won't bore the people who ain't interested in that but upon you looking around within this spot you'll find loads of information referring to this ARG. Real life photos coordinates and much much more as well as information on Sandra Dorsi which who as we all know has definitely left her stamp within Night City. Okay so moving on now most people won't be aware of this but as we all know now Gang Chases with the last patch not 2.1 I believe it was 2.0 were added into the game or was it 2.0 1 I can't remember but either or Gang Chases have been added into the game. While this is something I have noticed upon when you're being chased by gangs if you stop to fight them upon you looting those shards from their bodies you can actually find hits on you and get information about them is pretty cool guys as complete instances you'll see it on screen now but I'm probably sure there's probably a little more to this too and if I find anything I'll be sure to let you guys know. Okay so depending on who you romanced CDPR have added in many new interactions with those romances Panam, Judy, Kerri and Uriva. These for the most part take place within your Night City apartments. I personally romanced Panam a dozen times but I know folk out there prefer one of the others. Now I do believe that the apartments you can invite your romances to will have different interactions. I believe you can sit, snuggle, dance and sleep and possibly shower at each but there are different things you can do within different apartments. For instance with Panam within that Galen apartment you can have an interaction with her near that coffin machine where you will snuggle up with her then after you've had that sleep over coming to this spot within this apartment you'll find a gift left from her to you. Now this may be the case for other romances though but this I cannot confirm. Now upon a few romances with your chosen partner you should receive a message in which you can send them a gift so I think it's from their delivery with transport and logistics. You take part in a promotion and you can purchase a miniature car model and send it to your romance. Another pretty cool feature. One I will say didn't notice anything else come from this but I mean something may come later and I'm not sure. Okay so moving on from those romances and another thing I've found hidden around Night City are new cats. Now these cats you can feed you can chill with and the other one can be found upon the metro but please don't get excited. Unfortunately we cannot take them back to any of our apartments which would have been cool. Now sticking with the metro you can also encounter these sad Keanu Reeves mean easter egg portrayed by none other than himself. A pretty cool addition by CDPR. Okay so there are 10 new weapons that have been added with a 2.1 update. Six of these weapons can all be obtained from the airdrops across Dogtown. These six weapons were previously Amazon Prime subscriber weapons but they are now in the game for us all to obtain. Now the four other weapons you can get that I found just waiting for you around that Dogtown area. First up guys we have the BFC 9000. Yes a name referenced to Doom. So this weapon is a sex toy I can't show a gameplay of it on YouTube. I'm in fear of YouTube slapping me but it is a sex toy weapon tied to a new event which can be found near that badlands dam. Now this is an event that I think may be within a certain time of the day as when I originally came here the event wasn't here. I came back after doing a couple of non-story related things and the event was here. Now it consists of a crashed car where Doom patrolling if you take him out loot his body. Bam you can swing into action with this new new weapon. So from this point on the screen guys follow where I go do as I do and get this thing hopefully it's there for you. Next up guys we have a new X mod 2 iconic axe called Declar. So this weapon is found at the top of the Heavy Hearts Club. It's quite easy to grab but following the path I take will make this super super easy for you. So from this fast travel point follow and do what I do and you'll be all good. Next up guys we have the Achilles X mod 2 iconic tech precision rifle. So this is a weapon you can just come and grab it anytime you want it's that simple. So follow the path I take from this fast travel point and go get this amazing weapon people. And lastly from the four new weapons added into the game which you can just go and grab we have the MA 70HB X mod 2 iconic power like machine gun. So this weapon is found within a crashed helicopter and requires either a 10 in a body stat or those grueler arms to move a metal plate which is blocking your way into that said helicopter but once you are in you can just simply grab this weapon and there you have it guys pretty cool. Okay so moving on to new cyberware and with 2.1 CDPR added in two new cyberware for you to buy at random ripperdocks across Dugtown and both Night City. These cyberwares are the Fiend X and Kajito Lattice seen on screen now. CDPR people also added in six new vehicles all which can be purchased from the auto fixer website from your apartment. Now there are five awesome new motorcycles and a brand new Porsche. Now the Porsche however does have a small quest tied to it which is pretty cool which you get your car at the end of this quest I won't ruin it for you but it is a decent addition it's short but sweet. But yes guys there we have it 20 plus new things added with the latest update of 2.1. Guys if you enjoyed the video leave a like really helps and if you like what you see and want to see more be sure to subscribe and hopefully guys I will see you on that next one


Good morning, everyone. Thank you for coming here so early. I'm Steve Erlanger from The New York Times. Unfortunately, for you, I'm not Ali Aslan. There was a sudden change of moderator or animator. And I've been picked. So what we have today, I think, is an extremely important topic, which is the influence of Donald Trump, who's a very different kind of American president, to say the least, on world trade, on world politics. And I'm hoping, with this excellent panel, we can also get into something of Mr. Trump's character and how he's changed politics also. The one thing I know, I met Trump more than 30 years ago when he was a businessman. And even then, as he and Ivana and his wife were showing me around a yacht he bought and had Trumpified. It was a yacht. Actually, first owned by Jamal Khashoggi's uncle Adnan, who was the great Saudi arms dealer. And he put it up as collateral for a big loan from the Sultan of Brunei, who sold it to Trump. And so he had a Saudi yacht Trumpified, which was quite extraordinary to see. Lots of suede, lots of gold. But even then, when being president was far from everyone's mind, Donald Trump, I remember, and I wrote this at the time, was obsessed with the sense that trade had been unfair to the United States, that the Japanese then and the Germans were taking advantage of the United States. And even then, he was angry at the number of German Japanese cars on American roads. So some things don't change. But let's hear what our great experts have to say. And I thought we would begin with Michael Fuller. You can look at your books and figure out who everybody is. It's too time consuming for me to introduce them all. Michael, up to you. Thanks.


Do you know how to turn it on? No. That's probably good. Hello. Good morning. First, I would like to thank Fred and Glenn for giving me this opportunity to attend the workshop. And I would like to talk to you about sub-seasonal predictability of precipitation. So the outline that I'm going to follow is, first I would like to give an overview of the sub-seasonal to seasonal project. Maybe some of you are not familiar with it. Then I'm going to discuss the predictive skill of precipitation in the sub-seasonal range. And the community is defining sub-seasonal range as lead times between two, from two weeks to eight weeks. And this problem I have been studying. First, one of the questions that I want to investigate is how forecast skill varies with precipitation intensity. And first I'm going to show you results for the winter season in the United States. And later I'm going to show you some results for South America. And then conclusions. So if we think about when numerical weather prediction started in the 50s, there has been tremendous progress in the forecast skill in the short to medium range. So from one day up to 10 days or one week. So this graph here, actually this paper by Bauer et al. summarizes very well the progress that has been made in numerical weather prediction in the medium range. This figure here shows here's year and here's some measure of forecast skill. And we have several curves. Each of these curves are for some lead time like the blue is for three days, red for five, seven and ten days. The top of the curve is for the scale in the northern hemisphere and the bottom for the southern hemisphere. So what we see is that over time forecast skill has been increasing significantly. And also the difference between the northern and the southern hemisphere has become smaller and smaller. That means we have better observations in the southern hemisphere and also models have been improving in the southern hemisphere. So this is on one end of the spectrum like from short to medium range on longer time scales like seasonal time scales. One thing that I wanted to point out here is that the predictability on short lead times up to medium range, they are heavily dependent on the initial conditions. Now, on longer time scales like seasonal time scales, we have seen seasonal forecast become operational. This example here from the IRI, the Institute for International Research Institute, we see that now several centers around the world they are producing seasonal forecast. The predictability on these long timescales comes primarily from boundary conditions and ANSO is one of the main sources of potential predictability on these timescales. So between short and medium range and seasonal timescales, there is a big gap in what we know about predictability in the subsesional timescale. And that's what I wanted to point out here. So in the subsesional time range between 10 days and up to a few months, what we call subsesional predictability, we don't know if we can explore predictability on these timescales. And these timescales, they are very important because if we can develop accurate and reliable forecasts on these subsesional scales, there can be huge benefits for society because management decisions and also emergency preparedness, they can really take advantage of forecasts on these timescales. And so many applications can be made having these forecasts in mind. So based on these facts, several organizations like the World Weather Research Program, the World Climate Research Program and Thorpex, they implemented this project called a subsesional to seasonal prediction research project or S2S. And one of the goals is to improve forecast scale and understanding on subsesional to seasonal timescales, especially with emphasis on high-impact weather, so extreme weather events, and also to promote exploitation of applications from the forecast on these timescales. So the focus of this project, the S2S, is from two weeks up to a season lead times. Now, together with this idea for this project, the S2S research committee has realized that there are some sources of potential predictability on these timescales. One of them is the Madden-Julian Oscillation, the MGO. Also, stratospheric initial conditions are very important to exploit subsesional forecast. Land eyes and snow initial conditions, it seems that they play an important role in the subsesional forecast and also sea surface temperatures. Together with this S2S project, there are several, a few sub-projects. They all focus in some specific goals. There is one subsesional S2S sub-project devoted to understand more teleconnections and how they impact subsesional, S2S predictability. The MGO is also another sub-project. One focusing on the monsoons, one for Africa, extremes, and verification and products. One of the sub-projects that I'm participating is on the extremes. This is led by Frederic Vitard from ECMWF. In this S2S project, we are interested in assessing the predictability of extreme events such as heat waves, cold waves, floods, extreme heavy precipitation, etc. Also, subsesional predictions of tropical storms and we are doing this on case studies and things like that. So now, if you're not familiar with the S2S project, I would like to discuss a few points here because I think they are important in how we can investigate S2S predictability. So if you go to the ECMWF website in the S2S project, you can find this table. Let me explain a few things here. There are currently 11 models participating in this S2S project. One thing that is a little bit complicated in this project is that each center is developing forecasts in a different framework. So some centers, they forecast up to 60 days, other centers up to 30 days, and things like that. For example, in ECMWF, the forecasts run out to 46 days lead time. They have different resolutions, but in the S2S database, they all have been standardized to one degree latitude-longitude resolution. Now, in these columns, we are seeing here is for near real-time, as the S2S forecasts become available, they are in the database. On these columns here, they are for re-forecast. The near real-time data also varies quite significantly. Some centers, they produce forecasts every day. They initialize the model and run out to 30 days or 60 days, 40 days. Other models, they have less frequent initializations. The ECMWF runs two forecasts. They initialize twice a week, Mondays and Thursdays, and they run out for up to 46 days. For ECMWF, in near real-time, there are 51 members in the ensemble. And you can see that other centers, they have very small ensemble size. Some, they have only four members, 25, so it's quite variable. Now, one thing that we need to do also is calibration in the forecast and also have large samples. So each center developed re-forecasts. And for the re-forecasts, it's the same thing. So they are quite variable the way they do. For the ECMWF, the re-forecasts have 11 members in the ensemble size. Other centers, like the ANSEP, they have only four members in the re-forecasts. So this poses some issues of how you can investigate some problems. So what I'm interested is in extreme or heavy precipitation. And precipitation is very different from the other fields, like the circulation or even temperature. So precipitation is highly variable in space and time. So ideally we want forecasts with a very large ensemble, so that ensemble members, so that we can spend all the possible outcomes. The other thing is that we need, for precipitation, in order to validate the forecast and understand more about sub-seasonal predictability, we need very large sample sizes. So working with the near real-time is quite difficult because especially if we don't have initializations every day, if you have only twice a week, the sample size becomes very small. So here in this talk, I'm going to show you results based on the re-forecast. For the ECMWF, we have 20 years, twice initializations each week. They run out for 46 days, and we have 11 members in the re-forecasts. I have also looked at the ANSEP model. It seems that ECMWF, at least for precipitation in North America, in the United States, ECMWF is better than the ANSEP. Not much, but it's better. Now working with the other models to understand sub-seasonal predictability of precipitation becomes challenging because of these small sample sizes. So why understand more about predictability of extreme events? This is quite obvious. In this graph here, it shows the number of weather fatalities in 2016. And if you look at the red bars, so here in the bottom is the type of weather events. So if you look, for example, at floods and winter storms, many fatalities occur in the United States every year. So this is just to motivate that we can have tremendous benefits if we can explore sub-seasonal predictability of extreme precipitation. So the questions that I want to investigate are listed here. So first of all, I think it's important if we determine if there is predictive skill or what is the current predictive skill of precipitation in the sub-seasonal range. And because we are dealing with extreme events and also very long lead times, the forecasts have to be probabilistic in nature. Then the second question is how does the predictive skill of precipitation vary as a function of precipitation intensity? One outcome of this could be to determine if heavy precipitation is more or less predictable than light to moderate precipitation. And I'm going to show you results for the United States. But also what is important is to understand how predictability of heavy precipitation varies on different climatic regimes. And I'll show some results later for this. So in the first part of this talk, I'm going to show, as I said, exam this S2S probabilistic forecast skill of precipitation in the Contiguous United States during winter time. So we're going to look at forecasts from November 1st up to March 31st. And this plot here shows the climatology. So during the winter season, precipitation is very heavy in the western United States, especially along the coast or on the western states, California, Oregon, and Washington. Then you have a minimum here in the central part. And then in the eastern part of the US, precipitation is very heavy. So first I'm going to show you several steps in the methodologies so that we can understand more about the results. So I'm going to use only the ECMWF model. The forecast range goes out to 46 days. The spatial resolution that we are using is a one-degree latitude launch too. There are 11 members, one control and 10 perturbations, two initializations per week, always on Mondays and Thursdays. The period is November 1st to March 31st from 1995 to 2015. I'm using the re-forecast. For verification, I'm using the CPC, the Climate Prediction Center, Unified Precipitation Graded Data. And then there are a couple of intermediate steps. So first I interpolate the model forecast to the same grid of the verification. Then I estimate the daily mean model bias. The model bias is going to be a function of space, latitude launch too, and also the lead time. And I remove the model bias from the forecast for each day, each grid point, et cetera, and each lead time. Now, because we are in this sub-seasonal project, we're interested in exploiting predictability. And after, well, precipitation is always very variable in space and time. So here, instead of looking at a daily precipitation, I'm averaging into a weekly mean precipitation. And that helps to reduce noise in the data. Then for each grid point, I'm going to fit a gamma probability distribution function. And I do that for the validation data set and also for the forecast. Now, for the forecast, since we don't have a free run from the ECMWF, we don't have a climatological run from the ECMWF, I use the one-week lead to fit the gamma PDF. So here is a schematic of the PDF for precipitation in this time period from November to March. Instead of just looking at one precipitation intensity, I'm actually computing the percentiles in the PDF from the 10th, 20th, 30th, et cetera, up to the 90th percentile. So I have nine different categories of precipitation. In this first set of categories of precipitation, I look for the probability forecast of precipitation being less than a given percentile. For example, less than the 40th percentile, 30th, et cetera, up to the 10th percentile. That's what is in orange. And then the other set of probability forecasts, when the probability forecast is greater than, for example, the median or the 50th percentile, greater than the 60th, 70th, et cetera, up to 90th percentile. So we go from moderate precipitation up to the tails of the PDF, to very heavy precipitation. Now, for the forecast, the probability forecast is computed as the number of members forecasting that given event divided by the total number of members. So 11 members here. The validation, as I said, is from November to March 1995 to 2015. Each season, from November to March, there are 44 initializations. Each initialization, the forecast run out to one to six weeks lead time. So that's the lead range that I'm going to show results. So in total, for each grid point and each lead time, there are 880 forecasts that I'm going to validate. And here you can have an idea if we have a very short, a very small sample size in the reforecast or if we have very small ensemble members. So that poses some complications. Because in the end, we end up with a very small sample size to verify the forecast. Now, there are many ways to verify the forecast. For probabilistic forecast, I'm using this methodology here. So if we think about a given precipitation category, which depends on the percentile that I showed earlier, and lead time, we're going to have pairs of forecast probabilities here indicated by y. So the probabilities, of course, are between 0 and 1. And for validation, we have the outcome. So this OK is 0 if the event doesn't occur, and 1 if it occurs. Then to validate this probabilistic forecast, we use the bryer score. The bryer score is the mean squared difference between the forecast probability and the outcome. So we want bryer score to be as small as possible, like an RMS error. Now, the bryer skew score is computed as a function of the bryer score here normalized by the reference. In the reference, we usually use the climatological probability. So we compute this from the observations with the CPC unified grid data set. So the climatological forecast, for that given day, you always use the climatological probability. So the first question is, what is the current probabilistic forecast skew in the sub-seasonal range, at least from the ECMWF model? So here is the bryer skew score, and the formula is there. First, looking at the bryer skew score, for forecasts of precipitation exceeding the 50th percentile or above the median. So each panel here, it goes from one week lead time, two weeks, three and four weeks lead time. And here is the bryer skew score. So as expected, for up to one week lead time, the skew is quite reasonable. So the colors here, they represent improvement from the climatological forecast. So most of, over the United States, we see improvements ranging from 30 to 40 percent better than climatology. In some places, it's less, so the improvement is only 10 to 20 or so percent. As the lead time increases to two weeks, three, four, the skew drops. Now if we look at another precipitation category, like when forecast, when the precipitation exceeds the 70th percentile, we see some interesting changes here. First, for one week lead time, actually the skew is better in this MWF model. So you see, in many places in this category, you see lots of yellows and light greens. For 70th percentile, you start to see improvements on the order of 50 to 60 percent over climatology. And also, not only for one week, but also in two weeks, you see, you still have a actually very reasonable skew into these weekly times. And if we increase the percentile or for this precipitation category, probably forecast when the precipitation exceeds the 90th percentile, for one week, you can also see that in many places here, the skew improves to 60, 70, in some places even higher. And the same thing for two, three, four weeks lead time. Another way of looking at these results is aggregating over different sectors in the U.S. So I split the contiguous United States into three sectors, Western, Central, and Eastern. And for each sector, I computed the percentage of that sector with bias skew score greater than a given threshold. And that's the type of curve that we get. So let me explain this. So this is when the precipitation is above the median value, the 50th percentile. Here in the bottom is the lead time from one week up to six weeks. The color represents the percentage of that sector that had bias skew score greater than the threshold. The threshold is here in the vertical. So for example, in the Eastern U.S., for one week lead time and bias skew score greater than 10 percent, almost from 80 to 100 percent of that sector had skew better than that threshold. So if you increase the threshold from 10 percent to 20 or 30, the percentage of that sector that had that given threshold, bias skew score, drops. So you see that it goes to only 10 or 20 or 30 percent of that sector. And as the lead time increases, the percentage of that sector with that bias skew score also drops. And the same for the other sectors in the Western and Central U.S. So it's interesting to see that in the Eastern U.S., the predictive skew of precipitation is not that great after two or three weeks lead time for precipitation exceeding the median value. Now if we increase the precipitation threshold, or in other words, when precipitation exceeds the 70th percentile, we get a very different curve. Now, for example, again in the Eastern U.S., the percentage of that sector that has a bias skew score greater than a given threshold increases to longer lead times. For example, up to three weeks, let's say three weeks, for a bias skew score between 10 and 15 or 20 percent, 20 to 30 percent of that sector has that given bias skew score. So these curves, they increase upwards and also to the right. Now if we increase the precipitation threshold to 90th percentile, we can see that there's still some skew on lead times from three to six weeks, which is encouraging. So that's good news. But the bias skew score is just one single measure that can give you an idea about the skew in the forecast. There are other measures to measure the quality of the forecast. One of them is called the reliability diagram. So in the reliability diagram, we have to compute some other statistic parameters. One of them is called the conditional average observation. So we are looking at the probability of the outcome or that event occurring for a given probability forecast. So you have to compute this statistical measure. But basically this tells you how well each forecast is calibrated. Now the other parameter is called the refinement distribution and you have to compute this n sub i is the number of times each forecast is used in this data set. So the refinement distribution tells you if the model can discern different outcomes. And here are some schematic possibilities of what can happen. So if the model is well calibrated, ideally this conditional average observation is going to follow along this one to one line. Now if you have over forecasting or wet bias in the model, they deviate from the one to one line. And you can have many different possibilities in the conditional average from the model. And the refinement distribution, if you have this type of distribution, indicates that the model has low confidence. And if you have this other type, it says that it has intermediate confidence and this type of the model is highly confident in the probabilities. This is what we get for the reliability diagrams. Now one complication is how to visualize this because for each grid point in the US I compute the reliability diagram. So in order to get an idea how the model, what is the reliability in the ECMWF forecast model, I average all the reliability diagrams over the US where the bright skill score was positive. So just to get an idea how the reliability diagram looks like in grid points where there was some skill. So here we are looking at the reliability diagram for one week lead time, two, three and four weeks lead time. So for one week lead time we see that the model, the conditional frequency doesn't fall along the one to one line. So the model, actually the conditional frequency, it is above the one to one line for probability forecast from zero to point two. Now for probability forecast greater than that then it deviates from the one to one, but below the one to one line. So it indicates the model has some conditional bias in the forecast. Now as the lead time increases you can see that this line gets even further and further from the one to one line. By three weeks the model starts to show very poor resolution in the forecast. So that's for precipitation exceeding 50th percentile. Now for 70th percentile it kind of looks like the same with some small difference, but the main point is that as the lead time increases, so the bias skew score can be large like 20 percent or so average over the grid points where there is skew. The model doesn't have a resolution, so some calibration has to be performed. So one of the conclusions from this is that is there skew in probability forecast of precipitation in the sub-seasonal range? So probably yes, but we need to do additional calibrations in the forecast. So the next question is how does the predictive skew of precipitation vary as a function of precipitation intensity? Since I computed this probabilistic forecast for nine categories, we have nine different percentiles, I can actually plot the bias skew score, sorry, I can plot this forecast validation matrix as a function of the percentiles of the precipitation. So here I'm looking at how the bias score varies as a function of the precipitation intensity or the precipitation percentiles. So here in the bottom is the percentile of precipitation and here's the briar score. And I separate the curves for one week lead time and then in this other plot it shows the bias skew score for two to six weeks. Now there are a few points here that I want to highlight. So first of all you see that I mentioned that you want the bias score as small as possible because it's kind of like an iron mass metric. So we can see that the bias score for one week is less than the bias score for two to six weeks. So this curve is below those curves. The other point is that if we see that it has this negative slope, so if we think about, for example, forecasts above the 50th percentile, like here in the middle, as we increase the percentile, the probabilities become smaller and smaller in the forecast and also in the outcome. So it's natural that it has to decrease because you're summing smaller and smaller numbers. So it has this negative, it has this kind of slope, the same for one week and two weeks. The other thing that is interesting is that if you look at two to six weeks, it's almost linear. The bias score varies as a function of precipitation percentile almost linearly. Now, for one week, it's also decreasing but it has this slight change for some percentiles, for 20 percentile. Now, when we compute the bias skew score, which again is given by this formula, so you're going to normalize or divide by the bias skew score, the bias score from the reference. The reference is the climatological probability. So it turns out that for one week, we have this kind of shape in the bias skew score, which is quite different than the bias skew score for two to six weeks, lead times. So for bias skew score, for one week lead time, it's going to maximize precipitation percentiles between 70 and 80 percent here and it goes to a minimum for low percentiles. Now, for two to six weeks, it has this kind of a parabola shape. So the bias skew score is smaller for precipitation percentiles for case of precipitation above the 50 percentile. Now, as we increase the threshold for the precipitation percentile, the bias skew score is actually higher. Now, these results, they were for the United States. Now, what I showed you is for the wintertime, for the wintertime and over the U.S., right? So these results may vary over a different climatic regime. So one thing that I did is compute the same thing for South America. So now, we are looking at, I'm going to show you results also from November to March, but it's the southern hemisphere in summertime. So we go from mid-latitudes, exotropics, mid-latitudes in the U.S., different precipitation regimes to South America, where it's highly convective in the summer monsoon. So here is the bias skew score for precipitation exceeding the 50th percentile, 70th percentile in this column, and 90th percentile. So for one week lead time, we see, in some places, we see high scores, good skew in the forecast for 50th percentile and 70th percentile. In some places here, especially in the eastern part of South America, in Brazil, Argentina, Uruguay, the score, the bias skew score, is between 30 and 40 percent better than climatology. And as the lead time increases to two to three and four weeks, it starts to decrease. Now, let me see. I also computed the reliability diagrams of South America for each grid point. And one thing that is very challenging is that the reliability in the forecast is quite variable in space. So you may pick one grid point and look at the reliability diagram, and it's quite reasonable. Then you look at the grid point, just the next grid point, and you have a very poor calibration in the forecast. So it indicates that it's quite challenging. We have a lot of work to explore these probabilistic forecasts in the sub-seasonal range. Now, just to compare the same results, here is the bias score. Again, the same plot. Here is the presentation percentile and the bias score for one week lead time, two to six weeks lead time. It behaves almost similarly. There are some differences. This slight tilt for one week lead time is not as apparent as over the US. So you get kind of a different shape. So some conclusions. Probabilistic forecast of precipitation over the US. So the ECMWF shows a high skew up to one week, which is kind of expected. The bright skew score can be up to 40% better than climatology. For precipitation exceeding the 50th percentile. But if we increase the percentiles, the bright skew score is much higher, actually. It shows a skew out to two to four weeks higher for heavier precipitation, but also it shows conditional biases and indicates that we have to do better calibration in forecast. There is a distinct behavior in forecast skew versus precipitation intensity. And as I showed, you have these different behavior in the bright skew score and the bright score. Now, just a few words about how I'm planning to use the open IFS. In another paper, we did some predictability studies using Worf. And one of the results from that study is that, as I mentioned before, the MJO is considered a very important potential source of predictability in the subsystem range. Now, one thing that I did in this study is to look at one case study when the MJO was occurring and look at how forecast errors grow within the MJO. And one of the conclusions is that the forecast errors on scales not directly associated with the MJO, they grow very, very fast and they can actually propagate to the exotropics and impact the forecast skew in the exotropics, for example, in precipitation forecast in the U.S. Now, Worf is a regional model and all the results that I showed here, for the ECMWF, they may be model-dependent, so we need to look at other models. So, in this predictability study of the MJO, I'm planning to use the open IFS to conduct more investigation of how forecast errors, when the MJO is occurring, how they grow in time. So that's what I'm planning to do with open IFS. So thanks for your attention. It's nice overview.


So I figure it's about time we do the second part of the college experiences miniseries. This one on sort of the bubbling I noticed in college. Now you look enough into sociology research, you definitely can notice that this is a common phenomenon anywhere, anywhere at all. It was just sort of interesting noting the specifics that I would, it came across in my college experience. And I'll talk a little bit on just sort of this, because it's closely related, the reactionary attitude some professors had become more obvious what I mean about that in a bit. So is the best place to start with this was would be a professor I had an addiction. The class was on addiction. She taught sort of drug related stuff in general, whether it was what the drugs did to your body or why people went to certain drugs or whatever, but this class was specifically on addiction or at least supposed to be. Now if you're not super invested in academia or you managed to slip into one of these bubbles, you might not be aware that there's a lot of different schools of thought for basically anything and there's evidence for almost all of it. The world we live in is not so nice and simple as this is why that happened. At least for complicated systems, really simple things, it's pretty straightforward. You trip and fall, your knee hurts because it hit the ground hard. But for more complicated things like why somebody gets addicted to a certain thing, it's complicated. There's not only evidence for one thing, there's evidence for a lot of different things. So well that mind her big thing was a sociological basis of addiction and this is not to discredit her or that theory by any means because there does seem to be a bit of a sociological basis for this. You can absolutely see higher rates of drug use, especially certain kinds of drugs in horror impoverished areas. You can also see on the other side of that generally less drug use but certain drugs get used considerably more in wealthier areas. The great example of that is because they're basically the same drug otherwise, crack, poor, cocaine, rich. So there's definitely evidence for a sociological basis of this but it's quite a bit more complicated than that because we've been noticing that there are also genetic predispositions. Now I want to be very careful with that one because these are predispositions. They make it considerably more likely to see it in that population but they do not decide the trait. Most genes are just predispositions and not specific traits. Sort of like with general system complexity, you really only see genes decide specific traits when it's simple straightforward stuff like eye color or hair color, skin color. But behavior is just predispositions. You can see something like predisposition causes 40% more likelihood of being addicted and so in populations with that gene you see higher rates of addiction but it's not a deciding factor. I'm actually a great example of that most of my biological family are cocads and I've never touched the stuff. That predisposition is there. I'm definitely more likely to develop that. There's a good chance that's going to get passed on to the kids so that's something I'm going to need to pay attention to later on if I ever wind up having kids. These things are complicated. The other part of that, her approach to drug effects was largely that of take the observational approach. That is, you have a set of people who are under the influence of the drug and you're observing what is happening to them. Now that is definitely useful and should be done for certain types of things. For example, observation is one of the best ways of determining whether a drug makes somebody more agitated, less anxious or hyperactive or any of those things that are observable traits and not controversially observable. We'll get into that in just a moment. But that's not the only theory for why drugs do, well that's not the only approach to determining what the different ways drugs behave and depending on the approach you use you can actually get quite different results. As another theory, approach to this sort of broken into two different schools, pharmacodynamics and pharmacokinetics. Pharmacokinetics is medical field on how long it takes drugs to break down into the body, so like half life of the drug and what it breaks down into, so it's metabolites. Pharmacodynamics on the other hand is what receptors the drug binds to and then also looking into when something is bound to that receptor in either the agonist or antagonist fashion, what does that express? Now I'm of the stance that both of these are definitely very important and should be understood. Both of these in that the pharmacology subfields and the observational approach, both of these have their purposes, both of these should be done. Her attitude was largely that only the observational thing is correct and that's the end of it.  I did some rather interesting things like say NMDA receptor antagonists, so you want to look that up and methyl deaspartate. Ketamine is one of them, they're others, that group of drugs is generally known as dissociatives, but it's like so many other things, it's a bit more complicated than just that. It's possible to be a dissociative without necessarily involving yourself in NMDA receptor antagonization. So but that is the most widely recognized group of dissociators. This actually does a bit of that. So alcohol does a bit of NMDA antagonization. If you look at it from a purely pharmacodynamics standpoint, you would come to the conclusion that an NMDA receptor antagonist will decrease cognition, but increase memory retention, this gets a bit complicated because you have the decreased cognition sort of interferes with that. If you can't process what's going on, it's not going to be committed to memory, it's just that the stuff that gets processed gets committed to memory better. So obviously under really high amounts of this, your processing of things would be so bad that nothing gets committed to memory. So under an observational approach to this, you can very easily come to the conclusion that while this decreases cognition and memory, you see why this is a bit of a problem depending on the different school, you see different effects. Now I'm one for, and you can see this regularly in videos that I do, you can see this in this video. I'm one for sort of just expressing all the different theories in a teaching setting instead of this is the way it is because we still don't have a definitive answer. Now I have the thing that I think is most likely and I bet you can't tell. You might think you know at least the things I mentioned here but I try to keep my beliefs as separate from the information as possible. Everybody's biased is seep through. It's going to happen but in an academic setting you should definitely, in my opinion, be trying to just express the different theories and how they explain different phenomena. That's it. I would be okay with expressing your specific opinion as long as you express the general attitudes as well, arguably that's a little bit better because then you're making your biases known while still also presenting the necessary stuff people to formulate their own opinions. There was a lot of that though. She was just sort of the most standout one because there were a lot of being quite a few times where her beliefs didn't seem to fit with any major theory that I could find on the subject and whether it was me or another student asked about that or even just asked for more information, not necessarily calling her out on making shit up or whatever but asking for more information on the subject maybe you just want to read up more about it because find it interesting. She pretty much go off on us, really didn't like being questioned so she definitely stood out for that. Another one and I'll try to refrain from what I think her political beliefs were but it should kind of seep through regardless, you should probably, you'll probably be able to tell just from me explaining this little interaction. Nutrition, we were all assigned a macronutrient that we needed to research and research, look up stuff about, I don't really call that research but learn stuff about it. Diet was iron and over the course of it wound up, obviously you need to look into what the general diet is like in your region, in my case that would be the United States so look up what the iron consumption is for the mean and median for the United States diet but also what the upper and lower quartiles are and upper and lower extremes are and get an idea of what is rich with that macronutrient, what happens when you have an excess of that macronutrient versus a deficiency in that macronutrient, what should seem like pretty obvious stuff and I was able to get all of that, did a presentation on it, during the presentation made a comment just essentially part of the presentation really that I did not understand given the information about the American diet I could find, why such a large percentage of Americans had an iron deficiency, now it's not crazy high numbers by any means but it also seemed way higher than could be explained through some of the obvious ways that iron deficiency occurs, I think most people would interpret that as here's a student, somebody at a learning institution expressing that he doesn't know something, that's an opportunity to teach him right, she interpreted that as me being sexist towards women, the comment of I don't understand how why given the American diet there's this high prevalence of iron deficiency, nothing about sex at all, nothing about any subgroup at all as sexist which is particularly interesting because I even found figures earlier about the prevalence of iron deficiency caused by menstruation, those numbers were extremely low which is part of why I was surprised the actual prevalence of iron deficiency was so high in this country, again so high with respect to the diet we have in general, the overall levels of iron deficiency in this country are extremely low, so it was already with recognition that this is not why this is happening, this seems to be caused by some sort of dietary anomaly or something, she basically went off on me for being sexist in her opinion to which I just responded I don't understand this, why are you accusing me of something instead of explaining why to which she shut up immediately after, I think she realized she got a bit triggered, I have no other way of explaining that, I'm not trying to be mean toward women in any way, I didn't understand something, I wasn't accusing anybody of anything, I did not understand something, why does that make me a bad guy, I'm at college to learn things, clearly I don't understand things, this is why I'm here, but that was interesting, that was interesting, I would eventually, it's just a few weeks after that, still trying to find research explaining why and not being able to find anything, I would eventually go ask another professor at that college who even though this should be irrelevant also happened to be a woman, asked her the same thing and she just explained to me, simple as that, I got my answer, that's all I wanted, it's the freaking answer, yeah she explained it to me nicely and I would come to find out that a few years later that professor who went off of me got fired for sexism, she was grading papers, she got caught grading papers differently based on the individual sex, was rather unfortunate how they caught that because it was because of students cheating, so they would have the same answers the whole way down and she'd grade them differently despite the same exact answers, yeah that's unfortunate, that shouldn't happen regardless of the individual sex, man or woman you shouldn't be advantaged based on your sex, it's a fucking test, answer the questions right, that's the only thing that matters on the test, but I'm sure in this day and age somebody's gonna get pissed off about that, anything else about bubbling, not really, like I said this is actually something that happens throughout all of society, we all do this, I do this, you do this, everyone does this, we like to hear ideas that fit our own pre-existing beliefs, it makes us feel good, this is why you will have a discussion with a friend who says the same fucking things back to you, you accomplish absolutely nothing, but we all do this and it feels good, all of us, it was just something I noticed, now I look at the age demographics and plenty of other statistics about the people who view this on YouTube, they're viewing this in bitchute, they have no analytics for me to look at, so I don't know who you guys are, but over on YouTube I have a good idea of who you've used my channel on, it's a lot of young people, so this just sort of a maybe this doesn't startle you as much as the experiences startled me, because it was quite an eye-opener, you're gonna get bubbling and you're gonna get bubbling for the rest of your life, those clicks that you saw in high school that for some freaking reason people said you would stop seeing afterwards, yeah that's bullshit, you will see that for the rest of your life, and you're a part of that, I'm a part of that, we're all a part of that, it's okay, that's how we work as a people, but just be aware that that's what people are like, that's what we do, have a good one.


When you see the narcissist after no contact when the narcissist sees you after no contact Their perception of you will depend on how much you have healed How much your life has improved since going no contact? And how long it has been since you have last seen them if they see you and you are still at the beginning of your healing and Your life has stayed the same Or you are still trying to repair the damage that they had caused They are still going to have a negative view of you If they see you and you have healed to some level and you have improved or changed your life They are likely to have a different view of you. They may even re-idealize you Even though they may be with someone else Or even if you are with someone else They don't care about you They don't care about improving your life in any way Remember it's all about them when a narcissist interacts with a person It's all about what you can do for them Or how you can make them feel Narcissists are self-absorbed and lack empathy They don't even see you as a real person in their minds You're just an object that exists to serve them Although you went no contact with a narcissist And it may have been some time since you last saw them in their minds. You still belong to them It doesn't matter what happened or how devastating it was towards the end of the relationship in the narcissist mind If they need you Or if they believe that you have something to offer them They will engage with you again as though nothing happened if you have healed to some level And you have improved or changed your life And you see the narcissist And you make eye contact with them or you both notice each other They will be trying to read into your emotions They will be trying to detect if you still have feelings for them Or if you are still interested in Engaging with them They will then either approach you and then interact with you as though nothing happened Or they might ignore you or treat you with contempt Due to their sense of pride and arrogance This is usually because they feel threatened by you And they always believe that you are superior to them So they act as though you're worthless or beneath their consideration They may also not interact with you because they have a fear of rejection They don't want to put themselves in a position where they could be humiliated if they do decide to interact with you It's only because they've re-idealized you and they believe that you have something to offer them It's not about you It's not about giving anything to you They only care about themselves and people are just objects they use to meet their needs If you do interact with them They might just use the conversation to emotionally abuse you and cause you stress and anxiety This could re-traumatize you and undo all of the weeks or months that you have spent on healing yourself With this in mind You should avoid looking at the narcissist Don't engage with them Avoid drawing attention to yourself Leave the room or event if you can if you are forced to talk to them Keep the conversation short if they ask a question Answer with two or three words and then tell them that you have to go or you can just walk away If that feels more comfortable You don't have to talk to them just avoid giving any emotional reactions They want to see you being upset Because you miss them or angry because of all of the things they did to you They want to see some kind of reaction from you even if you do miss them or if you are angry Do not react emotionally It will only feed the narcissist and give them satisfaction if you react emotionally It will give them narcissistic supply It will regulate their emotions and boost their self-esteem When you see the narcissist after no contact, it may re-traumatize you when this happens You need to view the situation from a logical standpoint And understand that you are reliving the initial trauma This can have a very negative effect on you if you feed into it If you allow yourself to entertain the thoughts and emotions That come along with seeing the narcissist again Have an awareness of your thoughts and emotions Be mindful But then let them go Don't feed into them But also don't resist them accept that they are there Understand that it is normal And do not judge them and they will go away on their own put your energy and focus into yourself Rather than feeding it to the narcissist When you do this you are giving your power away and you begin to feel weak anxious and stressed Don't feel bad for walking away or not talking to them. They don't respect you So why should you have any respect for them? They don't even see you as a real person in their minds people are just objects that exist to meet their needs They don't care about what you think They don't care about what you want Even when it seems as though they are doing something to help you It's still about them It's about them having control over the situation Don't waste your time or energy thinking about the narcissist Don't feel guilty for going no contact focus on yourself Focus on rebuilding your self-esteem Practice self-care practice self-love And everything will begin to improve Thank you for watching. I hope this video resonated with you Please like comment share and subscribe Click the bell icon to receive notifications for my future videos if you would like to donate My PayPal link is in the video description Coaching inquiries you can email me at nags from the coaching at gmail.com Check out the immersion dice in the nags from a store where you can purchase your own nags for the t-shirt tank top or mug The link is in the video description Thank you for watching and I'll talk to you soon