student presentations from the different student projects that were done during the colloquium in the past three weeks. We would like to thank the student mentors again for all the projects for all the work that went into the preparation, especially the hard work put in by all the students for getting impressive results just in a short period of time. So, we had posted an order in terms of the presentation of the projects, the first group that will be the climate group would present up Pauline will you I'm getting an echo maybe if you reduce your speaker volume echo might reduce I think it's a bit better yeah let's try and okay do you see my screen yeah I can see your screen I'll give you a eight minute warning okay okay great I'm like we would be several to to go through the slides and we'll start with the screen okay perfect thank you very much. So our group was working on as to us verification using climp read, which is a Python package that was developed by our facilitator Aaron spring, and there were seven students participating in this total. There we go. So we divided ourselves into four subgroups and just a brief project overview for each of the groups. innocent and I were looking at certain stratospheric warming over the United States. I mean I was looking at state dependent predictability of two meter temperature depending on the nao phases. Jan and Matt, we're looking at state dependent predictability, particularly the influence of the MDF phase at initialization on European forecast. And Pauline and Shinja, we're looking at estimating prediction skills in precipitation hind casts. So the question that innocent and I asked were do SSWs lead to a better two meter temperature predictive skill over the United States. And so we looked at three different models, the WACCM CESM to the ECMWF models, each of these were initialized weekly. We looked at the United States and two meter temperature. The forecast time that we used was 1999 to 2020. And during this period, there were 16 SSW events. And the metric that we used to evaluate was the difference in root mean squared error between the SSW events and the non SSW winter time, which is December And so this difference in root mean squared error can be plotted as a function of lead time as shown where a positive value means that SSW is actually led to a worse prediction and a negative value means that SSW is led to a better prediction. And so going through each of these three models, the WACCM model for lead times zero to 10 actually had worse predictions during SSWs. So at lead times about 1530 days ahead of time had better predictions during SSWs. CESM to model, there was very little difference in predictions between SSW events and non SSW events, and the ECMWF, which is the green line for all lead times had better predictions during SSWs. And just to note that the bumpiness in these curves, we think is likely caused by there being relatively few events that were analyzed. And so general conclusion from our subgroup is that SSWs can lead to better prediction skill, but that's dependent upon lead time and also model. So I'm presenting the results from Irina, and she looked at the NAO and the impact on two meter temperature in two different models in one with normal top, the CSM2, and then one with a higher top WACCM. And she found that for, you can see here the top two, top four figures for lead time of 15 days, there's higher skill for forecast that are initialized in NAO positive for the region of Europe and also most of Asia. And she doesn't really see a difference between the two models so the high and low top doesn't seem to play a high role for this region. And she also looked more specifically for this Eurasia region, more at the temporal evolution of the forecast skill in the two models, and she can see that the forecast skill decays as you would expect fairly quickly. And that for forecast weeks three and four, she sees a higher skill for the Eurasia region using the higher top model. For me and Jan, we're looking into how the MGO phase affects forecast skill over Europe. So we decided to focus on phases two and three and six and seven. The standard patterns which are shown on the left of this slide, as these tend to give the strongest telecommunication pattern towards Europe. So the results of this were that after about two weeks when initiated in MGO phase six and seven, forecast seemed to become much worse than climatology. But this isn't seen in any other phases. I guess to find out how why this happened, we, on the next slide, looked at the geospatial pattern of Ruben Square. So on these maps, the red regions are forecast better when the MGO is in phase six and seven and blue regions are forecast worse. We can see, especially in the weeks three and four, that in the North Pacific, there is a much better forecast skill, but this better skill doesn't make it across Europe. And I guess following up on that, we've been wondering how this relates to the increased bias of the ECMWF model over Iceland in the same MGO phase. So estimating prediction skill of precipitation and after it is corrected by quantum mapping. And as you know that post-processing is necessary before a model forecast can be practically applied. So quantum mapping is a very useful tool for post-processing ensemble model forecasts. And this method because precipitation is not normally distributed. And so simple bias correction is not very applicable. And the theory behind this quantum mapping is to match the CDF of raw forecast forecast and to the CDF of observations. So we applied the quantum mapping in same thread and to the hand cuts of precipitation in different countries from ECMWF model, and we use several different metrics to estimate the prediction skill. And here we take the continuous ranked probability score as an example. And you can see that after the correction by quantum mapping, the skew and improved obviously, and because the lower value of the CRPS, the better skew of the model gives. And we also extended the verification by differentiating it's about into different phases of different processes. So you can see the underlying correlation coefficient that depending on whether we are in different phases of for example NaO or XO or NaO. And so far we didn't see that much difference in skew between the general layers. So we did this just with the first analysis and we hope maybe to find more results with other temporal accumulations of precipitation for example, or by focusing on a substantial intensity of precipitation for example in streams. I hope you'll hear me properly. It's echo, I can hear an echo falling but it's fine, yeah, yeah, and yeah you have one more minute. I'll be quick on the tech messages. So, I'm going to show that generally the presence of certain ones can be to a better model, sorry, and I'm giving a highlighted that there is a better skew for to mature temperature during the position in NaO phase. So I'm going to show that there is an increase in focus error if the focus is initiated in phase C, C7, and of energy. So we investigated, we went through before processing with the help of our own with the development of the Pantanamati in Indian CREB and we analyzed the improvement of the petitions here. And with this quick analysis we didn't notice any difference in focus error depending on different phases. So we thought about some positive elaboration between the groups, for example in San Juan and the Union are working on two meter temperatures and maybe some investigation on compound effect of this C7, which is very long and also a compound effect on two meter temperature prediction can be an option. Also to further the analysis of the impact of energy, BMI, or precipitation, so we were working, and also more generally the analysis of the precipitation can be applied to every method that are in the other groups. So we'd like to thank you very much for your attention. Thank you very much for your time. Great. Thanks Colleen and thanks all for great results and thanks to think about. So we'll move to our next group presentation we'll come back to question answers and discussion, either after all the four discussions but we also have more time this afternoon for longer discussions and presenting more research so thanks. Our next group will be the PiWR, PiCPT. My name is Kyle Lessinger and I'm here to briefly introduce you to our current research into weather typing using the Python package PiWR, developed by our leads and Helen Munoz and Andrew Robertson. Additional members of our research group include Danny Do, Pedro Herrera, Kyle, or me, Kyle Lessinger, Kelsey Malloy and Kyle Nardi. So in mid July 2021, there was large scale flooding that occurred over Western Europe, due to heavy sustained precipitation, and then the flooding had a large impact on both human lives and damage to property. With this event in mind, and our selection of the PiWR research group, our research goal was to understand specific weather types over the North Atlantic that cause extreme events. Ultimately, we'd like to increase forecast scale on the S2S time scale for these extremes. So here we see a time series on the left of 500 hexascals you have potential height between July 1 and July 22 over Western Europe. We see these low height fields and they're pervasive for several days and then they transition out. And then also on the right, we see the same pattern on the July 14. And so this is, and these aren't in the exact coordinate boxes, so they're not meant to be exactly the same, but we can still see that same pattern. And these are the kind of the types of weather types that we're looking to understand a little bit more so we can find out if they have any sources of predictability. So previous research has already identified that weather typing is an established method for identifying recurrent circulation patterns. And our methodology includes first applying a multivariable EOS field to both 500 hexascals you have potential height and integrated water vapor. Next, we ran a canning clustering loop for four different weather types to identify recurrent circulation patterns. We finally analyzed various sources of predictability from local sources and teleconnections. And our data include in separate analysis for both global geopotential height and integrated water vapor and CPC precipitation, the global unified gauge based daily precipitation. So we use this to identify the land based extremes. The first set of images for weather typing using a coupled EOF for the first row is geopotential height. The second row is integrated water vapor. And then the third row is precipitation. So we coupled the geopotential height and the water vapor. And this represents about 80 to 90% of the explained barrier. So we see these four weather types in the summer over the North Atlantic. We have an EO, Scandinavian blocking and even a ridge like pattern. And without coupling of the geopotential height and integrated water vapor. Then we wouldn't have seen a strong signal with weather type three, which is the Scandinavian blocking with the low pressure and so this is kind of the weather type that we're looking for for extreme patterns that kind of affected Europe. Kind of similar pattern. And so we're going to explore a little bit more about this specific weather type. And then you can see at the very bottom precipitation, it is a composite of precipitation for each day's weather type. And so that we do see some are more likely to cause more or less precipitation over time. So with all these patterns in mind, we can start asking questions about weather type and their prevalence and predictability. So when we just saw summer and now this is the winter EOF, coupled EOF, and it's pretty much a different picture, but we do see a stronger signal for the patterns. And so that's pretty much the only mention for this point. So we then explored extreme wet and dry events that were associated with those specific weather types to identify when excessively wet or dry conditions occurred.  And you can ask us that later if you'd like, but for this one we want to look at summer, the top part, we look at weather type three. So across Western Europe, it does display more extreme wet days and less extreme dry days. So then for winter, we want to look at weather type one. And then this is the NAO positive, and it shows the highest percentage of wet days across the Western Europe, and as opposed to a lower persistence of dry days. And then I will move it on to Kyle. Alright, thanks Kyle. My name is Kyle Nardi. I will present the second half of this talk. So the next step is to explore some of the sources of predictability for these weather types. So first we explore sources of summertime predictability and one potential source is the East Asian monsoon. So here we show anomalous probability based on a specific East Asian monsoon state. We separate into weak, neutral and strong. And we show anomalous probability of a particular weather type in the days leading up to and the days following one of the particular East Asian monsoon phases. So what's noteworthy here is we have several areas where there are anomalously low or anomalously high probabilities of a particular weather type occurring in the days after either the week or the strong East Asian monsoon phase. Indicating that this could be a potential source of predictability for these weather types during the summer. So a potential avenue of future research is to understand why this is occurring physically. So these are just some preliminary plots showing the geopotential height pattern and the geopotential anomaly patterns for the specific weather types over the hemispheric domain. And our goal in the future is to understand whether or not there's some sort of dynamical response due to the East Asian monsoon in the summertime. So now we explore a predictability during the winter and one established source of predictability is the MJO. So this is a plot of anomalous frequency of occurrence for different NaO phases, NaO positive and NaO negative in the days before and the days after a particular MJO phase. Any MJO phase shown here is high amplitude and the RMM amplitude greater than one and then we have a phase zero here which indicates a low amplitude MJO phase of any of the one day phases. So what's noteworthy here is that we have an area of a propagating MJO signal especially in NaO negative. And this is something that has been shown actually in other studies. This was a figure that was shown a few days ago in a talk. This is from Casue et al 2008. And it indicates that the phase of the MJO can modulate the phase of the NaO in the week days and weeks after the phase occurs. So in comparison to what we found, we found very similar results namely that essentially five to 15 days after MJO phases three and four we get an anomalously high probability of occurrence for NaO positive. And then in the five to 15 days after phases six through eight, we get an anomalously high probability of occurrence for NaO negative. So again, the MJO produces a potential source of predictability for the NaO during the winter time. Another piece of the puzzle is exploring how some of these weather types have changed in frequency over time. So this is analysis from 1871 to 2008 from the NOAA 20th century reanalysis. And it's exploring how the probability of occurrence for these four weather types has changed over time in both the winter and summer. So this plot here shows the relative trend magnitude of each weather type over this time period for both the winter and the summer. And any area here shown in color has a statistically significant relative trend magnitude and 90% confidence. So what's noteworthy here in the winter is that weather type three, which we found to be a wetter weather type, increases in frequency over time during this time period, whereas in the summertime, weather type three actually is less frequent in terms of probability of occurrence over this time period. So the opposite occurs for weather type four, which we find to be the drier weather type. During the winter weather type four becomes less frequent, and weather type four during the summer becomes more frequent, indicating that these weather types are changing in frequency over time and as a result, the probability of these extreme precipitation events could also be changing over time. All right, thank you. So our conclusions are the main goal of the study was to connect extreme wet and dry events over Europe to certain weather types over the North Atlantic, and we showed that extreme precipitation events are favored under certain North Atlantic weather types. And we also are interested in exploring the predictability of these weather types and we found that specific phases of the East Asian monsoon during the summer and the MGO during the winter could contribute to windows of opportunity for forecasting some of these patterns. And we also found that regimes are changing frequency over time, and that potentially is going to lead to changes in frequency of these extreme weather events. Our future work, we feel there are many avenues of future research from this topic. We're interested in better understanding the physical mechanisms driving these extreme events under certain weather types. For example, we've done analysis of atmospheric rivers and we found that atmospheric rivers are more frequent under certain weather types, so that could be one source of precipitation from these weather types. We're also interested in evaluating model depictions of these weather types and seeing if the subseasonal seasonal teleconnection patterns from the East Asian monsoon and the MGO also are prevalent within the S2S models, and we're interested in exploring some of the relationships governing the weather types, the occurrence of these weather types and the modes of climate variability, whether it be NAO, MGO as we showed, or PNA potentially even lower frequency modes of climate variability like QBO and so on. So, thank you for your attention. This was our group. It was a great group to work with. We definitely want to thank Andy Robertson and Anhil Munoz who wasn't able to be here today for their guidance. And if anyone has any questions, we'd be happy to discuss during the longer form discussion. Thanks. Great. Thanks, Kailin. Thanks all again. Impressive work. Thank you. So our next group will be the hydrology group. I know you're ready to share screen and start. Can you see my screen now? Yeah. Okay, cool. So, our group looked at hydrology as to us focused for hydrology applications. And we were led by John and Andy, and our group members were Eric, Kahang, Funing and myself. And we actually started off with something different. So I'll present the first part of our work. And then Eric will take over for the second investigation. So we initially started with looking at an, basically an optimization problem for a dam in Japan. And we were using runoff from era five and optimizing for power generation. G here using this differential equation where I is like river runoff. W is flow rate through the turbines and R is the relief flow. And the two particular constraints, depending on the level of the water level and flow rate w. So the constraints were on W and head height H. And, right, so if you focus on the top left here, if you look at this dark orange box, the smaller box, that's the grid size for the era five data, whereas the lighter yellow box is the S to S database grid size. So we already see some issues that we would face here. And if you look at the time series, the top time series on the, on the right, we see when we incorporate the ensemble forecasts on around 120, we see there's some ensembles, which are negative. So there was some issues there. And another particular reason for this start day was we had an extreme event extreme precipitation we wanted to see how that would affect the optimization. And so we tried to apply some bias correction, and we use the CDF matching, but we matched the S to S CDF to the era five CDF. And then when we applied that bias corrected ensembles we get this bottom bias corrected time series full runoff. And these, this is a time series for runoff. And then we use the optimization to obtain a time series for a few values so we had inflow a head height in orange green was the flow rate. The head is the relief flow and the purple line is for power generation which we optimizing for. And so the first, the top figure is showing just one ensemble member before bias correction was applied. Around, actually when we start to be incorporating the ensemble number forecast, and around a 120 approximately. We see that we get some negative values for generation so we see that this ensemble is causing some issues with the optimization model. And at the bottom is the bias corrected ensembles so these ensemble means for these particular parameters. And we see we get some relatively useful values. One thing, since our direction of a project change we don't show these the RMSE and comparison with the climatology and the resistance. So this is as far as we've got for the first part of the project. And I will let Eric, I need to go to Eric. Thanks as you know can you keep going along the slides for me. Thank you. So our second project involved using as to us data to inform ensemble stream flow prediction. And I'll get into all without that all means in just a second, next slide please. Thank you. So, the idea is, if you want to predict stream flow in a basin, depending on the base and you're in you can actually get a lot of information just from the initial land surface moisture conditions. And what they found out is if you can use, if you use historical weather and force it through a hydrologic model and take the ensemble mean of that, as long as you have the initial land surface and moisture conditions, then you'll have a pretty accurate representation of the flow into the future, as long as you're, you know, in the right time of time of the year.  And so that that idea is called the ensemble stream flow prediction. But what what it's lacking is any actual information of future weather and climate. So what we wanted to do is to use future weather and climate data to weigh different ensemble members and provide an alternate weighting of ensemble matters, instead of just the mean, we'd have we put more stress on ones where we that's where we thought the future would more likely be. So in this study we look at the Buffalo Bill Reservoir in Wyoming. Next slide please. And so what we to do this project we had needed four pieces of the puzzle first we had observed stream flow into Buffalo Bill Reservoir about 40 years of it. We observed meteorology from era five focusing on two meter temperature and precipitation, along with S to S forecast of two meter precipitation temperature. And what we do is we create a rank table and what a rank table is, is you have your historical record of temperature and precipitation, you know, two weeks ahead. And you compare that value for every year so you know, if we're starting on April 1, and we have our S to S data for two weeks ahead that would be, you know, April 1, April 15, once the average temperature, how does that compare year to year. So the lowest temperature would have a low rank and the highest have a high rank. So if 2016 had a temperature of 25 degrees Celsius 2000 had a temperature of 20 degrees Celsius, then 2016 would be ranked higher on there. They're kind of ranked between zero and one. And so we do that for the S to S forecast as well as the observe meteorological values we have over the same time periods. And we build rank tables for both those and we also have these S to S watershed flow forecast, where we take a hydrologic model forced through with historical weather data like I described earlier. And then we have these ensemble members that say okay I have 40 different years of weather data forced through this this hydrologic model based on this initialization date. So typical, typical waiting. One more minute. Okay, I'll go quicker. Yeah. And basically we just wanted to check you know can we use S to S data to better inform our waiting. So next slide please. Next slide. Yeah, right here. So, first thing we wanted to check is which S to S data set would work best and we found that they all had similar spread so anything would be appropriate future work will be used to actually select the best set but for now and you and you're fine. Next slide. So that's a different waiting function so the idea is which ensemble members to weigh more and by how much it we found that the less emphasis we gave to any single member the better accuracy is we didn't want anything too wild because if you miss a peak, you know completely then you're going to have a worse performance and just a straight line. That's the next slide. And so between selecting our S to S data between understanding our waiting function we also introduced climate oscillation indices from and so and the Pacific North American oscillation to see if those helped at all and what we found is, if we took. If we took the S to S forecast of precipitation temperature and gave that half half the weight in value and the other half we gave it to the end so and P and a states at the time, then overall, if you look at the bottom summary stats overall we can just barely outperform the ensemble me. So graphically we're showing a very good case in 2016 where we beat the ensemble mean by almost point one in the modified clean looped efficiency and, and by almost point two national cliff efficiency so much better prediction than just the ensemble mean. However, overall we still have a lot of work to do before we, you know, can really claim we're better. So next slide please. The idea is as to us forecast and climate oscillation states do help with future forecasting. We, we have a lot of potential to do, and we have some future steps, including looking at what works and what doesn't different days we can start at, and what different and using climate oscillation transitions rather than their states. So we're looking forward to new directions and we're open to anyone's comments or suggestions thanks so much for going over. Great. Thanks. Thanks all really good work. Two different projects. So our next group is the US West Coast precipitation prediction group. I'm going to be presenting. Full screen. Yeah. Great. So hi everyone, my name is Deanna Nash, and I will be presenting on behalf of US West Coast tutorial group. The other student members of this group are genoc, Nico's Alex and bill. So today I'll be telling you about our project regarding S to S prediction of a series of atmospheric river or AR events that made landfall in the western US during the winter in 2017 and led to several extreme rainfall events, which damaged the mains bill way of the February 2017, which is pictured on the far left on the plot in the middle we see that the AR landfall frequency during the 2017 water year was higher than average at most latitudes. And the plot on the right from white out is showing the precipitation during 2017 water year was 300% of normal at the Oregon dam which is indicated by the black cross on the map. So the science goal of this project is to provide a hind cast skill assessment for AR activity integrated water vapor transport or IVT and precipitation prior to the Orville dam incident in February 2017. So to complete this, our group explored the hind cast skill through a variety of facets, including MJO connections, synoptic conditions, IVT and precipitation forecasts and AR probability forecasts. So first let's look at the MJO connections. The plot on the left is showing the RM index values for November 2016 to March 2017. So we focused on a particular AR event, which happened on February 5 as indicated by the yellow star. And this was the event right before the Orville dam incident. MJO was in phase five and six during this AR event and had a really high amplitude above to RM index value. And this really aligns with what we know from previous research. And so our plot on the right is showing the five day rolling mean of daily anomalies of AR landfall frequency in California broken down by the active MJO phase and different lags from zero to the 35 days. So if we look at the 20 to 25 lead before our AR event, the MJO was roughly in phase one. And if we reference the plot on the right, we would expect to see below AR landfall frequency in California, which doesn't really align with what was happening. So, or what happened 20 to 25 days later. So for a 20 to 25 day lead time. This particular event is not in accordance with our long term statistics, though other work has shown a really strong connection with AR and MJO. So next we'll explore the synoptic characteristics leading up to this AR event. On the left are showing the weekly 500 hectopascal geopotential height anomalies where the forecast values are shaded and the observed values are the contour lines. A progressive pattern characterized by an equator word shifted an extended jet, along with cyclogenesis in the central Pacific was observed leading up to the Orville dam incident. The surges off East Asia, by the week of the event increased the tropospheric meridional temperature gradient, thereby enhancing and retracting the north Pacific jet that led to enhance cyclogenesis in the central Pacific induced by deep western north Pacific dropping over the February 1 through seventh week period. So this is a cyclone activity aided in the development of downstream ridging over the Western US and subsequently over the Bering Sea via poleward latent heat transport. So this ridging or blocking or Omega regime conditions were conducive to multiple AR events in California. On the right are showing the forecasted daily geopotential heights with a three to 21 day lead, and we see that this observed Omega block in the contour lines is only accurately forecasted up to seven days in advance. So here we have the forecast skill exploration of IVT and precipitation. So the top row plots show the forecasted precipitation for February 7 at four different lead times. And the correlation values, which are a little small in these plots tells that precipitation in general is poorly forecasted, even with a one day lead. So we can actually use IVT as a proxy for ARs and precipitation, which is shown in the bottom row of those plots, where the forecast is shaded and the observed data is shown in black contours. We have a weak pattern correlation of 0.39 for the first forecast that's 12 days out. But as you get closer to the actual event or the lead time decreases the forecast becomes much more similar. We analyze the skill of the forecast at different lead times for the period January 1 through May 12. And the top panel on the right is showing the mean of each ensemble members spatial anomaly correlation in black and the anomaly correlation of the ensemble mean in blue. It shows that there's a bit more skill for the ensemble mean but both drop below 0.4 within 10 days. The bottom panel is showing the root mean square error of the ensemble mean compared to the spread of forecast. Spread follows very similar to the root mean square error and increases rapidly with lead time prior to 10 days, and then it levels off. So if we focus in on the forecasted IVT in the grid cells or the area around Orville Dam, which is indicated on the map as the black dots, we can see that the model skill is dependent on initialization. And the colored lines in the figure on the left represent the ensemble means for various initialization times, and the black line is the observation. So for example, the orange line is the ensemble mean for the model initialized on February 2, and the forecast skill around the peak in IVT on February 21 is greatly decreased. So that particular initialization time doesn't do well in that second peak. But if we compare this to the cyan line, which was initialized a little bit later on February 16, it does a better job representing that peak in IVT on the 21st of February. If we look at ensemble spread, the figure on the right is just the same as the figure on the left, but the shaded regions represent the each ensemble spread. And this is just telling us that certain initializations have more spread than others. And then last we looked at the number of ensembles that detected an AR for a two to three week leap. And the shaded contours and the figures on the left are showing the odds that an AR was detected at that grid cell for that time, and then the black contours indicate an AR was detected by the observation data. So for day to day probabilities, the model has some skill at predicting spatial location of an AR up to a 10 day leap. If we look at the number of ensembles that forecasted a detected AR within 17 to 23 day lead, roughly a three week lead, the model predicted that the AR frequency was going to be much higher than the climatological average, which is quite accurate. It doesn't necessarily catch the spatial scale of the AR, but the general frequency for that week was well done. So in conclusion, we did make some progress on answering our question on S to S model skill for AR events leading up to the Orville Dan crisis. However, like any good research project worth pursuing we discovered that our one question led to several other questions. For example, can we improve our results with a bias correction or what kind of skill do we have in predicting multiple AR events in one season or high intensity AR events compared to lower intensity AR events. Can we leverage our knowledge of MJO, QBO and and so to improve our prediction skill. And so in all, we only really saw skill and predictions of this AR event, at least less than two weeks. And for more discussion, please come to our extended session this afternoon. Thank you. Thanks Deanna and thanks all in the group for a really, again, an impressive amount of work in such a short period. So, thank you. So we have set us, we had set aside 15 minutes, which originally was a break for student feedback, we still have three to five minutes and then we'll take a 10 minute break. Before the next plenary talk. So, does anyone have questions for any of the student groups. I don't see any right now in the chat but I had one for, oh, Zane, did you have a question. I see you're unmuted. I'm sorry, I just don't believe myself by accident. Yeah, should go ahead. Hello, hi. Can you hear me. Yeah. I have a question regarding this I'm Jill influence on the downstream weather both in Europe and atmosphere river. It seemed to be that everyone indicating the face, six and seven seem to have issues right so, in fact, face six and seven is when I'm Jill, and I'm index cannot tell you where the heating is. So, a lot of downstream weather impact depend on the convective heating precipitation. For instance, the connection large scale connection generated the recipe wave trend is in northern hemisphere. So if the collective heating of MGO in southern hemisphere, you would have very different impact. So, have you all thought about actually using the actual precipitation tracking, like we presented in the database is available to everyone is called large scale precipitation tracking that tells you exactly when, and where the collective heating is. So, I'll just speak for the, I'll just speak for the PWR side that's a really good suggestion we did not look at that but I think that would actually be really fascinating and it might, you know, increase the skill and maybe increase the anomalous probabilities if we can sort of fine tune it a little bit so that's a really good suggestion that will definitely take into account. Thank you. I guess the question also is, yeah, to the other groups. Yeah, I can go for the for the clip at your group. We did not consider to look into that but we also would find it really interesting to look at the different publications of the MGO and, and yeah, it's definitely something that is very exciting and interesting to look at. Thanks, Jan. We should go on getting that into Clifford. As part of the state dependence. Yeah. Yeah. The question is also to the US West Coast group that was looking at ARS and MGO teleconnection. Does anyone from that group want to respond. Yeah, maybe I can say so indeed we also didn't do that but definitely something worth looking at so hopefully yeah we're going to do it by time. Great. Yeah, the US West Coast is in particular because MGO heating if it is in northern northern hemisphere that influence your synoptic pattern, much more direct. And this is a place where the heating split really strongly either the northern hemisphere or southern hemisphere rarity on the equator. Great. Thanks. Thanks for the insights will definitely have a look at it. Thanks. Yeah. Thanks for your suggestion. So I had a question regarding the monsoon index that was used. And I'll be when you were presenting the slide. Did you use the seasonal mean monsoon? So maybe Kelsey, it was your analysis or was it the interseasonal component of the monsoon that you're looking at. Yeah, I can take this question. That's a really good question. Because we were focused on mostly I would say the sub seasonal scale. So we used two different methods as kind of preliminary ways to look at the monsoon and they're actually a lot of different indices you can use. I tested to one being the definition using u850 circulation. So we have daily data that's run with a five day running mean for u850. I calculated that circulation and then looked at the relationship between or I should say we looked at the lead lag relationship between what that indices was based on that circulation. And then I also looked at the circulation based on u200. So I looked at like upper and lower level ways to identify the monsoon phases. But I am interested in kind of taking that to the next step by looking at the boreal summer interseasonal oscillation or BSISO because that looks at it in terms of phases like the MGO. So I think that that could be also really interesting to look at since we did that, since we found stuff with like the MGO lead lie relationships, maybe same with the monsoon. That sounds great. Yeah, that was going to be my other comment and it's also related to Shuie's comment about a lot of these teleconnections are related to convective heating and less so to the dynamical forcing and some of it is dynamical forcing but a lot more to the convective heating right and just the wind indices may not capture that convective heating aspect of it. So, great. Yeah, very good point. Yeah, definitely. Okay, so I don't see other questions right now we'll come back to a larger discussion in the afternoon. Thanks again to all four groups who presented. Thanks, I thank you because I'm part of it is all the data crunching before a tutorial started and we use different databases but I want to say thanks to Abbey Jay who has prepared a lot of the data that went, especially into the materials, Unish downloaded, especially for the hydro and US West Coast. And so, thanks so much for all the background work in the background and in data pre processing and thanks to the tutorials.


You may know him from such shows as Mamma Mia. You may have seen him not ice skating in Canada, or you've seen him in the Nocilic Asteroids chat rooms, but we are now talking to Shy Yamini on behalf of PAI Technologies. How you doing today, Shy? I'm great, thank you, Alison, so good to see you. I will not ask you to break into, oh, what a beautiful morning, but sometimes that happens when we talk. Yeah, no singing today. All right. So what are we looking at here at PAI Technologies today? Well, by technology, we've got a brand new product. It's called PAI Bots, and it's an augmented reality interactive construction kit. Okay. So basically what it means is we're creating robots that children can interact with and learn how to code. Oh, I like that. I like where you're going. Yeah, so basically what we start off with is we build the robot first. So we've got the build mode right here. Yeah. So let's say we're gonna build a little robot friend here called Nova. And so all this comes in the kit. We've got six different robots that we actually build in the game. So for the audio listeners, he's got a little robot on a platform here, but he's got one similar. He's building on an iPad. Yes, so it shows in sort of a virtual world all the pieces that are required to build Nova. And then once it's done, it asks me to actually look at her and scan it, and it says, yep, I've built it correctly. Okay, so he built up the pieces rapidly, but imagine you were putting it together in the actual robot, and then he pointed the screen of the iPad at the robot and said, okay, you built it correctly. Exactly. So now that I've built it, I can play? Now you've built it, now you can play. And the play is in augmented reality, and it creates this beautiful big world, as you can see in the TV behind me. And as you go through the gameplay, it teaches you different coding skills. So let me show you what the coding skills we've got available. And each robot actually has a different skill set. Oh, okay. That it does. Sorry, so it's just connecting. So it's scanning, so he's got to connect it up to her again. Let me just connect this. Got to love live... Live demos with lots of interference. The best kind of demo. So this is actually being done via Bluetooth. So we've got quite a few Bluetooth devices right now. There we go. This is the most challenging place on Earth to do a live demo. Seriously. Oh, looks like we're connected. So now we've got this interface on the screen where we can do building blocks. So it sort of looks like the way Scratch is designed, where it's got little blocks that you can connect together. Exactly. So let me say I want to use the motion sensor that's in the robot, and then say, when you see motion, I want you to spin to the left, go forward and back, and let's activate it. So... Just hit play.  And then it went forward. And back. Oh, wow. And so we've got all the different type of things that we can control the lights. We can control movement. We can... Make the eyes blink. The eyes don't blink, though, unfortunately. It's actually the body. The whole body. Oh, okay. Well, we'll go with the whole body blinking. But as I said, each robot has a different skill set. So some will play drums, some will move their tail. All depends on that particular robot that you've built in the storyline of the game. What I like about this is it's combining a lot of different things. They're actually tactile building in a Lego-like environment. Exactly. So they're building things they're having to use your imagination. But then they can use your imagination, again, in a tablet environment where the kids really want to be playing on a tablet. Exactly. But then you're tricking them into learning programming. Exactly. And one good thing is, because you've got all the pieces, you've got 130 pieces here to build the robots, but you don't have to build just those robots. You can build whatever you want and then go into the RV mode where I can actually just control it just like a remote control vehicle. Oh, okay.  Exactly. Okay, but it won't know what skill sets it has because it's not one of the standard robots, right? It's not, but you know how to control the movements. You know how to control the lights. And so once you've know those skill sets, you can make your own codes. Oh, you can. Even with a monster robot you've built. Exactly. Oh, that is so cool. So I was calling it PAI, but it's PAI Technologies, but it's spelled PAI, so I don't know how to spell it. Where would people go to find more? You can do PAI.technology, and that's the website you'd better go there. And they've got quite a few different other educational games and building construction kits that they can use. So are these available today? This one is available in spring this year and the other games and building construction kits are available already. Oh, great, and what's the price point going to be on this? This will retail for $99.99. OK, that's actually not bad when you compare it to some of the other high-tech stuff that's out there. Very cool. Yeah, exactly. Thank you, Shai. My pleasure. So good to see you.


Well, hello friends and welcome back to my channel. It's been a hot minute since I've done any kind of planning video. To be honest, I've just been a little uninspired with my like regular planner. I still like it, but you know, I get in these ruts sometimes and I know a lot of you do too, but today I wanted to share this planner right here, which I've actually really been liking and I am gonna be doing, since I do back planning, I'm gonna be sharing my month of March. So this is the Aaron Condring 2019 monthly planner. I believe these are called the petite planners. They're just like a, I don't know, like a bound flexible planner. Oh, whoops, I already put the washi I want to use in here. But yeah, it's just it's a lot smaller than the regular life planner as you can see. And I don't know, I just really like these and I think it's nice because you could like throw it in your bag or put it in your like drawer at work or just even like leave it on your desk at work or whatever.  And to be honest, I haven't been using the other things at all, but I really like this. So this is what I did in January, you know, not a ton, but some stuff. And then in February, I really slacked. But anyways, in March, I haven't done anything yet, but I have my other calendar off to the side here. So we're going to fill some things in. So let me see if I can kind of get this to lay a little flatter. I know we're kind of in the beginning. And then anyways, all the way through, so it has the whole year and then it has like a ton of notes pages that are lined, which is nice.  So anyway, let's get to planning, y'all. I haven't pulled out any stickers. I just pulled out some washi because there's something that I need to put in for the washi situation. So the first thing I'm going to put in is on the same day, I had two different kind of food dates with people. So I got to find some appropriate stickers for that. And I need to find myself some like smaller event stickers. I think I have some here in my Chrissy and Designs binder, not really sure, but we are gonna see what I have because these boxes are way smaller than like this, which is like, you know, normally fits like an Aaron Condon vertical. But I think I have some smaller ones in here. I've got way too much Chrissy and Designs stuff in here. But I guess you can never, you can never have enough, right guys? Okay, where are those little things? Maybe I don't have any. I have used them up. This, yeah. Ah, this is what I'm looking for right here. These guys right there. And if y'all are interested, it's the KAD 00232. So all right, I'm going to pull this one out and use up what a can of that. And I got my pens, as always. All right, so the first thing was I had lunch with my friend Heather. And we just went to Panda Express. We both kind of work by each other. So she actually works from home, but that week she didn't. So we made a lunch date. And this month I'm not going to be like really going with any specific theme at all. I have these little guys. I think this sticker company, I couldn't find a while ago. I don't know. I mean, I'll link everything down below, whatever I can find. So, but yeah, I got these a long time ago, like when I first started planning and I still love them. And then also that same evening, I did a little Go Wild thing with Jodi and Ryan. And I don't know if I've posted this yet by the time that this goes up, but these are like all my Go Wild stickers that I have purchased for the trip. So I figured I would use maybe one of these, some of these, something like that. I don't know. And let's see. Let's see what I want to use in here. Maybe I'll use one of these that has like a little Go Wild on here. This is from Planetopia. I only have so much room to write. All right. I'm going to have to cut this one for sure. But we met up for some dinner that evening, which was delicious. Then the next day, these are from Birds Fly Studios. I actually bought our pajamas because we talked all about it. So I'm just going to put here by pajamas. Whoops. I'm going to put this up just a little bit. Super cute. All right. And that Wednesday, I also had a QC meeting. So I'm going to put that in. Lots of quality control at work. Yay. So I have that. And then I actually had a doctor's appointment on Thursday. Met with Dr. Blady. So much fun, guys. So much fun. Not really, but you know, whatever, right? All right. So I had that doctor's appointment that day. And I've got these cute little doctor peoples. I put that in. And since we're on the doctor theme, because of that doctor's appointment, I had an ultrasound. Everything's fine. I'm fine. Don't worry. I had an ultrasound the next week. So we're going to just put that in as well. I'll probably get questions. No, I am not pregnant because ultrasounds don't necessarily mean that you're pregnant. Okay. Another doctor appointment sticker right here. And do it like that. I'll put in another little doctor person. I guess it wasn't really a doctor's appointment. It was just more of a, like a tech appointment kind of a thing. But either way, putting it in anyways. All right. That was it for doctor stuff, I think. Yes. It was, it was. And then moving on to this week. I know I kind of jumped around. Sorry. I was sick. So I had a migraine, which really, really stinks. So I chose not to work from home that day because migraine. And I didn't want to look at a screen at all. All right. And my Christian design spender. I'm going to use this.  This is, these are like the Cindy lettering stickers, which are so cute. And I also have these once more with love headache stickers. So I'm going to kind of put these together somehow, some way. I've never used one of these clearly. So let's use a pink one. And that was on this day right here.   I'll use one of these little dudes. Maybe I'll put it, oh yeah, yeah. Here, I'll put it right over the little tissues because I didn't have a cold. I had a migraine, which made no fun, no fun guys, no fun. All right. And then on Tuesday I had an oil change and I just found these stickers. I cut them all up because I've stored my stickers in like a bajillion different ways. So these are actually from Adriana Piper, which are super cute. And it's like a whole, I don't know if she still has the sticker sheet. Maybe she does. I'm not really sure. I bought these a long time ago, like years ago. But it's like a big kind of like car maintenance, like auto maintenance sheet. So yeah. But yeah, I did that on the 12th and I'm just going to write in that it was at 5pm. All right, perfect. And then I don't really have anything notable for the rest of the week. And then now I need some washi. And this washi I got from Michaels and it was part of the first Christian designs launch at Michaels. I know there's like a second season right now. I don't know if it's a season. I'm probably calling it the wrong thing. Sorry, Kristen, if you're watching, I'm not really sure. But there was like a, you know, like new stuff, which was kind of cool. I haven't, you know, to be honest, I haven't been to a Michaels in such a long time. But I did get some of the Christian design stuff for Michaels. This is part of the washi that I had purchased. And it doesn't come on a bobbin like this. I just rolled it onto a bobbin because that's how I store my washi. I, you know, don't prefer it in a full roll. That's just me. And then let's see, I should do this as well. I'll mark down what these are in just a minute. All right, so at work, my coworker that works remotely was here during this time. And then I had to do some training with her remotely because she was back in her home then and she works from out of state. So we did lots of training together this week. All right, her name is Sarah. I'm going to use one of these little cute stickers from, let's see here, plan about it. Come here. There we go. These are the, these are the tiniest desks that I have. Cute, cute, cute. All right, so we did that. And then sadly, my cell phone that I had, it was like a Apple seven plus, I think it was. Overnight that night, my alarm didn't go off that morning because my phone just completely like a blacked out. Didn't like, you know, like I couldn't start it again. I couldn't do anything. So anyways, I had to go get a new phone at the Apple store. But first I like made an appointment at the Apple store and did all of that stuff. So I had to leave work just a little bit earlier than I normally would that day just to take care of that. And then I ended up getting the iPhone XR. Yeah, iPhone XR. That's what it is, like the newest phone. So anyway, I'm just going to like put XR on here and, you know, go for it. Looks good to me. I don't really need more explanation than that. All right, so that's what happened that day. How fun. Spending money I didn't want to spend. Woo, and then I need sports stuff. Probably the first time I've said that in a video. And then Wednesday, I went to one of our sports stadiums. What is it? US Bank Stadium here in the Twin Cities and the NCAA is going to be in town at some point. I don't know. My company, the company that I work for is providing some of the products to convert a football stadium into a basketball stadium. So anyway, I went there and got a tour of the progress and what they were doing on the 20th. And yeah, I don't know. It's kind of cool. It was really very, very interesting. So and then Sarah and I both went. So it was kind of cool that she was in town while that was happening. And then basketball, even though there was no basketball being played. There wasn't even a court up yet. So and then I needed another one. Look at, look at me and all my sports stuff this month. I actually did a lot of sports stuff this month. Cool. All right.  So then I'll put a little baseball because it was about a baseball themed thingamajiggy. So I'll put that there. Wow, there's so many sports. All right. And then here I'm going to write in what I was doing. I was shell training. Okay, my camera just cut off and I don't know what the last thing that I said was, but I'm going to put in here that I was shell training. So that's what I was doing that day or those days, I should say, did it for two days. But when Sarah was here, she was like teaching me how to properly do the shells. So yeah, it's kind of cool. I also put a sign on my desk that said I was closed. And if you needed any help to call Sarah, but some people respected the sign. Other people did not respect the sign. So it, you know, took me took me a lot longer than I thought. So that's all right. I got the job done. And yeah, I'll probably be doing more acoustical shells of very, very soon. So my work bestie, Jamie, we sit across from each other. She is obsessed with Jonathan Vaness from Queer Eye. And she has gotten me on the JVN train. So he's actually coming into, I don't know, it's like not a concert, but it's like, I don't know, he's coming and he's doing something. I don't know exactly what he's doing, but he's going to be at the Minnesota State Theater this fall. But the tickets went on sale this day. So we bought tickets and we are going and we are both very, very excited. So I have these cute little admit one tickets plan about it. JVN ticks. To say that like she's pumped is like a little bit of an understatement, but I'm pretty pumped too. I'm very excited. We're going to have fun, fun, fun. All right. And then on Thursday was the Minnesota Twins home opener. And that's what I had to talk to HR about because I was kind of like hosting it for my building. So they just needed some help with some things. So I don't have like a blue or a red, which is kind of what I want. Let me see what else I can find around here. All right. Here we go. There is red stickers right there. Yay. I just want to write lunch. So the company provided lunch for us this day, which was very nice of them. And then I have these from Paper and Clips. And they're like the Minnesota Twins logo. We'll just put that right there. And maybe we'll put a baseball because I have them out. Why not? There we go. Lunch. Awesome. Okay. Put that back. And then on Saturday, I had another Go Wild fun thing happen. I went over to Ryan's house from A Man with the Plans and we had like a little brunch and Jody was there too. And we just kind of like discussed Go Wild stuff. So it was kind of good. And Ryan made this like really yummy egg bake, which was delicious. And Ryan made this like super yummy like egg bake thing. It was delicious. Thanks Ryan. If you're watching, it was really, really good. And then I actually just got these in the mail not too long ago. I had purchased these from Aaron Condren. And this is like the Go Wild set. So maybe I'm going to use one of these. What shall I do? I don't know. Oh, that doesn't fit. Oh, you know what? I could put it right here. That's all right. I don't have like things to do. So maybe I want to use like some other Go Wild stickers that I have over here. All right. And I'm going to use this sticker right here that says Go Wild Las Vegas from Planetopia. Put that right. Oh, gosh. I just like, I can't do anything. Okay. I got a whole new plan now. Oh my gosh. I'm losing it. See, this is like real life of when you really, you know, mess up some things and you didn't think about it before. So you're done messed up. I'm going to use a sticker now because messing up. That's all right though. Okay. So I'm just going to put this like a little bit above it. So yeah. Okay, cool. All right. I like that. That's good for me. Oh, and then I forgot to put I purchased art. So I bought pajamas on this day because we had like a conversation about it. And then I believe that Friday. Yeah, it was that Friday. I purchased our airline tickets, you know, so we could like all be in the same row or whatever. So that was good. So I'm going to use this from Planetopia. It's just called air travel. And let's see, which color should I use? I don't know. Let's use one of the pink ones. It's just the first one here. Why not? I'm just going to write Minneapolis to Las Vegas. Okay. I'm done now. Oh my goodness. All right. So that is it for my March monthly view in my 2019 monthly planner from the Petite planner line. I hope you guys enjoyed this video. I'm going to be using this for the next month as well. So I actually have something in here already here appointment 530. Yay. So yeah, I'm going to be using this for next month. And I'll probably be filming a video if you guys are interested in this. I know this is kind of like a very different thing for me to share because I really have never been in this before. But yeah, let me know if you guys are interested and I can, you know, make this a monthly thing if this is kind of what I'm going to stick to. So let me know down in the comments below. But anyway, I am going to jet. Thank you guys so much for hanging out with me today. I really appreciate it. If you haven't already, make sure to give the video video a thumbs up. I cannot talk today and hit the red subscribe button if you haven't already. And I will see y'all in the next video. Bye guys. Well, folks, it's time to kick it old school. So you can feel cool.


Alright, so hello everyone and welcome to today's online workshop. Today's topic is how to validate content feedback for the WordPress training team. So, the WordPress training team looks after the Learn WordPress website. Let me open that website up. So this is the Learn WordPress website. And the Learn WordPress website is an educational resource for both learners and teachers of WordPress. So we have, for example, tutorials, tutorial videos, online courses and online workshops like this session we're having right now for learners. And then we also have lesson plans, which are teaching aids for teachers to go and teach WordPress. Now, I'm going to open one of these resources. So I'm just going to open tutorials. And most recent tutorial is about the WordPress 6.3 release. So that release came out yesterday, the day before. Alright, but anyway, so on each of these resource pages, if you scroll down a bit on the right, we have a section for learners or users to send us suggestions. So for example, if they find a typing mistake in the content or if there's a grammar mistake, or even an outdated screenshot, the sometimes WordPress update and the screenshot needs replacing, when people find a piece of content needs an update, they can let us know from this button here. So I'll press let us know. And this takes users to the report content feedback page. And this is what we're going to be talking about today. So the topic today is how to validate content feedback. Users send the training team content feedback. And then there are two steps. We first need to validate the feedback, and then we apply the changes if necessary. And the reason why we have to validate feedback is because sometimes the feedback people send us isn't about the content. So sometimes they are experiencing a browser issue on their device or a network issue. So sometimes it's not necessarily something we can fix. And so that's why there's first a step to validate the feedback, see if it is an issue that is still occurring. And then once it's validated, then the editors in the team can edit the content. So when people report content feedback, they come to this page. And if you look at the yellow box, it says, if you're familiar with GitHub, then feel free to submit your feedback directly to our GitHub repository. So users can send their feedback directly into the training team GitHub repository if they're familiar with GitHub. And then this automatically puts their feedback into our triad process. But underneath the yellow box, you'll also see, if you're not sure what GitHub is, or you're not comfortable submitting an issue there, that's okay. Please fill out the form below and our team will be notified of your report. So if people aren't familiar with GitHub, that's totally fine. They can fill out this form here, which will send the training team an email. And then the training team have administrators who copy the content of that email and create a GitHub issue for that user. So all feedback lands in GitHub. But depending on whether the user is familiar with GitHub, it might go directly there from the user, but might go via an email. So once the feedback lands in GitHub, we have a content feedback project board, which looks like this. And the project board has three columns and issues move from the left to the right. So the first column awaiting validation, all the issues first land here. And you'll see we have 28 issues awaiting validation. And this is the column we'll be talking about in today's online workshop. Anybody can come to the training team's GitHub project board and validate feedback for the team. Once feedback has been validated, it's either closed out if it's not feedback or sometimes we get spam, people submit spam. So things like that, people can close out. But if it has been validated, it then moves into the awaiting fixed column. So once it gets to this column, these changes need to be submitted to the learn.wordpress.org website. And to protect the content on the loan website, edit access is limited to faculty editors in the training team. So anybody can validate content feedback. And once it moves to the awaiting fixed column, it's then waiting for faculty editors to apply the updates to the content. And then once the updates have been applied, the issue moves to the done column. So that is an overview of how content feedback moves through the training team. We'll step into the actual details of validating feedback in just a moment. Are there any questions so far about the overview here? Anyone can reject? In a sense, yes. So anybody can look at an issue in the awaiting validation column and reject the issue. For example, it's not happening anymore. So it's something we don't even have to look at. Or maybe the user needs personal help. So you send help to the user and you can close the issue. How is that rejection reason captured or no reason required? That is a good point. And it will come up in the steps I'm about to show. So every time we either close an issue or move an issue forward, that person must leave a note about why that issue was closed or moved. And we'll see in a moment. The normal person is able to validate content feedback. But they don't actually have a close issue button. So they can leave their feedback and say this issue doesn't happen anymore. So we can close it out. But then they still need to wait for a faculty editor to actually press the close button for them. So it's sort of a mechanism to make sure we don't have just any contributor come through and close all the issues out. There's always a second person to double check to make sure issues are indeed worthy of being closed or worthy of being moved forward. But we'll get to those steps in just a moment. All right. So how do we validate content feedback? I'm going to drop a link here to the training team table. And so if you're interested in joining the training team and contributing to the training team, I highly recommend you bookmark this link. You see I bookmarked it in my bookmark as well. And so I open the handbook quite frequently. And down the left here you'll see we have a large list of table of contents. What we're going to be focusing on today is under the how to guides. And how we use GitHub. There's a page titled Validating and Applying Content Feedback. So let me share this direct link in Zoom chat as well. There we go. And so this handbook page will walk contributors through the steps of validating content feedback. That's what we're going to look at today and applying content feedback. Applying content feedback can only be performed by faculty editors because they have the edit access on WordPress. Validating can be done by anyone. All right. So let's jump into this. Validating content feedback issues. So no special access is needed to validate reports. Anyone in the training team can do this. So if you're interested, please join us in validating feedback. Now, when you validate a report, you check to see if the report is relevant. Sometimes a fix may have already been applied to the content or the report was just banned. In these cases, the report is no longer relevant and can be closed. And then we list here five steps in order to validate content feedback. So there's five steps here and we'll go through each one together. Number one, open the list of content feedback awaiting validation. So we click on this list. And this will give us a list of content feedback. And you can see under status, they're all still awaiting validation. So we have 28 issues here. So step one, done. Step two, open any issue. And in a new comment, copy and paste the following checklist. So we have a grade area here. I'm going to copy that text. And I'm going to switch over to this browser tab here to work on this. So let's see page with incorrect content. Let's click on that. All right. So let's have a look at this issue. Let's open the issue. And what we're going to do is we're going to paste the checklist I copied from the handbook into a new comment down here. So you'll see it says feedback validation checklist. First point, if this is reporting an issue, can you, that's me, confirm reproduced the issue? So we type here whether we can or can't reproduce the issue. And then the second point is what should happen next to apply the feedback? So we leave a note here about what needs to happen next. For example, this issue is no longer relevant. So the issue needs to be closed out. Or if we have validated the feedback, we can, for example, upload the screen, the renewed screenshots into GitHub and say, please replace the screenshot in this content with this new one I created. Or we can say this paragraph needs to be changed this way, what have you. So the validator can leave next steps to make the editor's job a bit easier. All right. So coming back to the handbook. Step one was opening the list of content feedback. We did that. Step two was open any issue and copy paste the following checklist. We did that. So now step three, if the feedback is reporting an issue in the content, confirm if you can still see reproduced the issue. So this is where we actually now have a look at the issue and see if we can validate it. If you can reproduce the issue, type yes. Next to the first item in the list, you just copy the cross and move to step four. If you can't reproduce the issue, type no. If it looks like the issue has been fixed already, leave a comment with your findings and move to step five. And finally, if the feedback is unclear, leave a comment explaining what you tried and ask the author for more information. You can do this by mentioning the author in your comment. And then it says you can skip steps four and five below. All right. So let's see if we can validate or reproduce the feedback that was provided here. Let's see. They've given us the content title, custom post types and capabilities. They've given us the URL. So let's open that up. So they've given us feedback about the tutorial here. And actually, let me share this GitHub issue link in the Zoom chat. So other people can open the same issue with me. All right. Let's have a look at their feedback. The page described above has mostly the same content as another page, developing with user roles and capabilities. I would expect this page to have completely different content, since it is a different topic. All right. So let's open this page, custom post types and capabilities, developing with user roles and capabilities. And they're saying the content is pretty similar. The learning outcomes are identical. Comprehension questions. And there's still the default user roles and capabilities. Well, comprehension questions are different. The learning outcomes are the same. And then the transcript. The transcript is identical. At least this first section I'm seeing on screen here. All right. So it does look like maybe this person copied the transcript across. Let's read the rest of the feedback. The different elements are title, video comprehension questions. The same elements are learning outcomes and everything from introduction down. Okay. Learning outcomes and anything from introduction down. So introduction down is the same. And all right. That is wrong. So what we need to figure out. First of all, we have reproduced the issue. So can we reproduce the issue? The answer is yes. We can confirm what this person is saying. So then let's come back to the handbook. So if you can reproduce the issue, type yes, and move to step four. So let's move to step four. Leave a note about what needs to happen next and press comment. For example, if a screenshot needs to be updated and you are able to create the correct screenshot, you can upload the image into your comment too. In this case, it's not a screenshot that needs to happen. It's the actual content. So we can leave a bit of a note here about what needs to happen next and then we'll press comment. So what I want to confirm is, is this the correct content for this video? Or is this content correct for this video? I'm just going to play the first part of the video. Let's see if the transcript matches that much. You will learn how to add capabilities to an existing role and also how to create a new user role. All right. So it looks like the captions and audio in this video match the content. So let me come over here and check the same with this video. So talk about restricting access to custom post types. And when come down here, this is incorrect. All right. So we've confirmed developing with user roles is correct and the custom post types and capabilities post is incorrect. So let's clarify that in the comments down here. So the content of, let's see the content of the page and video match for this tutorial. And then we'll leave a link to the one that does correct, that does match. All right. And then the content of the page and video do not match for this tutorial. And then let's look at what the user said once more. Different elements have these. So these don't need to be updated. The same elements are learning outcomes and everything from introduction down. So please update learning outcomes and everything from the right introduction down to match the video content. Please update learning outcomes and everything from introduction down to match the video content. And they also left a note here. This article seems to have some titles and also notes cleaned up, which probably should have been applied to the other article instead. So there are probably notes and stuff in this one, which should be applied to this one. So let's also write that. The feedback submitter noticed there are some typos and notes which were tidied up on. Let's copy that link across, which should have been applied to that one. Let's summary, copy and apply the content from custom postage and capabilities to developing the user roles and capabilities. Copy and apply the content from this to this. And then step two, write the learning outcomes and the introduction on the custom post types to match the actual video. All right. So let's preview this before we submit it. So we have been able to reproduce the issue and what should happen next. So the content of the page and video match. Let me say that a bit differently. The content of the page, this makes sense. The content of the page and video match for this tutorial. The content of the page, the content and video on this tutorial match. The content and video on this tutorial do not match. That might be a bit easier to understand. On the video on this tutorial match, the content of the video on this tutorial do not match. Please update learning outcomes and everything from introduction down to match the video content. The feedback submitter noticed there are some typos which are tidied up on this one, which should have been applied to this one. For the summary, copy and apply the content from custom post types to working with user roles and capabilities. And then number two, rewrite the learning outcomes and introduction or content from introduction down on this one to match the actual video. So we've validated the feedback from the user and we've written out the next steps that have to happen. So we don't actually have an edit access on Lomar Press to fix this, but this will let the editors know what needs to happen next. So we press comment. And that was step four of validating content feedback. And so then finally, we come to step five. If the feedback has been validated and the next steps are clear, ping faculty editors in the training Slack channel, letting them know. They will be able to move the issue to the awaiting fixed status, apply the necessary changes to the content or close the issue for you. All right, so we don't have access to change the status of the issue, but we can ping faculty editors who do have that access and they can make the edits for us. So to do this, we open the training team Slack channel. So let me bring my Slack window over into the screen here. Do that. Do that. All right, so this is the Slack channel and you'll notice we are in the training team. And so it says, we have an example message you can copy and paste here. Hi faculty editors, I have validated this content feedback linked to GitHub issue. Please move this to the awaiting fixed status and or apply the necessary changes back. So let's just copy that across. So in the training channel, let me paste that message. And actually this section here, what should happen is when you type faculty editors, we'll go blue here and you'll notice we have different people's names now pop up above faculty editors. This act mentioned, what it does is it pings all the faculty editors in the training team at one to anybody who's logged in or has availability, they can pick up on this message and come and make the edits for you. So hi faculty editors, I have validated this content feedback please move this to the awaiting fixed status and or apply the necessary changes. Thanks. So we can close this preview here and we press enter. So you'll notice a GitHub preview pops up like this. We don't need that. So you can close that off. And this has now the previous content feedback that you can see here. So let's just copy that. So you can close that off. And this has notified all the faculty editors in the training team. So they can now come and apply the fix we suggested. And that is step five. So we just finished step five. Thanks for validating content feedback. GitHub issues. So M, before you talked about rejecting feedback and can anybody reject feedback? So what would happen is if the feedback needs to be rejected inside the GitHub issue here here it says if this is reporting issue can you confirm reproduced issue you would type no and then what should happen next you would leave a brief comment under this. For example, I've checked the same page but the issue doesn't happen anymore or it looks like the issue has been fixed already. We can close this out. Leave a comment like that and press comment like we did for this one and you would still come to the training team channel and ping the faculty editors. So you would say hi faculty editors. I have validated this content feedback. Leave the GitHub link. But the issue doesn't happen anymore and you please close the issue out for us. And so you let them know and that just makes sure there's a second field of eyes that checks the content for us. And so yeah editor would come over here they would look at the issue and they would go okay yeah this one doesn't need to be open anymore and they will close the issue for you. And that's it. That is how anyone can validate content feedback for the training team. Any questions? Will the editor who rejected have to add a note too? No. So they would just read your comment and as long as they agree with whatever the validator has written they would just press close issue and close that for you. They won't necessarily leave a comment. How do you search for a specific issue? So let's come back over here. So at the beginning of the check steps here. So it says open the list of content feedback awaiting validation. This is the important link in the process. So when you click on that link it opens the page of all the issues awaiting validation and most of them have a bit of an explanation in the title here. So broken linking course or not clear that a block theme is needed. So this would give you a rough idea of what the feedback is about. You can choose any of these feedback pieces of feedback and validate them. So really we need all 28 of these pieces of feedback validated. So you can choose any out of this list and work through the list for us. So if you don't know the title of the issue someone else created the issue but didn't tell you the name of the issue or provided the link so how do you search for that issue? So are you talking about a validator or are you talking about the editor? When an editor comes and updates an issue they do need the link. So you'll notice I gave the link in the Slack message here. So we really do need to share the link with each other. But does that answer your question? You'll notice while we've been talking here one of our editors has put done and they've been working on the issue. So that's good. Assuming that you created an issue about the problem I reported but you never provided the link or the title of the issue. Hmm. I think that the easiest thing would be to contact whoever you reported the issue to and maybe just directly ask them and say I gave this feedback I don't see the feedback in this list. Can you help me see and make sure my feedback was accepted? I guess what you can do is coming back to the main screen of content feedback here you'll see 46 pieces of feedback have been completed. You'll see 13 have been triaged and are still awaiting a fix. So what you could do is try searching here I guess and that will search all columns and that might give you an idea of where your feedback has moved to. For example let's say broken by type broken link you'll notice this has now filtered the issues so we see there are six issues related to broken links there's one issue still awaiting to be triaged three that are waiting to be fixed and two that have been fixed. So this is probably the closest thing to the search feature you're looking for. So that is in the content feedback project board under content feedback we have all the issues here so you can filter issues out by keyword and hopefully find the one you're looking for there. Otherwise thank you for joining Tracy were you saying something? Thank you Oh thank you for co-hosting and I look forward to having you and anybody watching this recording afterwards to join us in validating content feedback. So thank you for your time today thank you Tracy