 Right, okay, so our virtual laboratory, this data enhanced virtual laboratory is about building information infrastructure in support of the National Ocean Modelling System. I'm giving the presentation but two of the guys who are listed below, Angus on the right there and Hugo on the left and other people involved in my group here in Imos are Guillaume Gallibera and Sebastian Mancini and the other members of the project are folk in the Bureau of Meteorology, CSIRO, NCI, Claire's involved a bit, South Australia Research Development Institute, Tasmanian Partnership for Advanced Computing, University of New South Wales, University of Western Australia and in this case, Met Ocean, New Zealand. So what are we actually trying to do here? So our project aim here is to further develop the Marine Virtual Laboratory to support the long-term goal of the ocean research community, namely developing the Australian National Shell Seas Reanalysis or ANSA as we call it. So what is a real analysis? This is a technical term for a product which combines the optimum combination of model simulation and observations maximizing the value of the observational set through data assimilation. Now this project that we're doing isn't going to do the reanalysis. Here the emphasis is on building on our capabilities to assemble the necessary observational data and to construct services to enable model ingestion of these data into the data assimilation schemes to be used in the analysis. The actual project to do the reanalysis is probably about a three to four year project to actually complete it. So given that we've only got money till December, it's not the realistic goal we can think about doing the reanalysis at this stage. So why do we want to do this? So the growth of the Blue Economy is predicted to be large by 2025 and it's predicted to grow three times faster than the Australian GDP. So there is a burgeoning interest in the marine environment and arguments for this national modeling service system have been around for quite some time and in fact it's a recommendation of the National Marine Science Plan 2015 to 2025. Earlier this year the Bureau of Meteorology commenced a plan for operational coastal modeling service and to enable that to really get going then certain groundwork has to be done to provide for such an operational system and this is really what this project is about. It's doing the groundwork to provide the observations and the service to enable reanalysis program to be done. So reanalysis is quite simple in concept. It combines combining observations in a model state through the simulation process to produce an updated model state which essentially will bring the model, generally speaking, closer to the observations and thereby providing a far more realistic simulation than sometimes do come out of model simulations. So there are various ways you can do data assimilation and there are various methods that you can use. There are ensemble methods such as optimal interpolation and using a Kalman filter. There are variational methods either in three dimensions or in four dimensions, I think three dimensions in time, but each method actually requires a way of reducing the observations that are available in the community down to a number that are manageable and this manageable number of observations are called superobs and you want to do this in such a way that you don't lose the information content of the larger data collection and then when you actually want to do the analysis there are various ways in which you can do it and on the right here you can see four different approaches to doing assimilation combining the observations into an analysis running a model and you can see you get step changes. This is the top version. You can do it sequentially with continuous assimilation and again you've got a continuous solution but there are step changes in this and then there is non-sequential intermittent assimilation when you put information in when you actually have it available or the bottom one which is what we really would like to aim for which is the non-sequential continuous assimilation to end up with a smooth solution from the results of the assimilation process. So in setting up coastal models and this is what the Marine Virtual Laboratory actually does. The Marine Virtual Laboratory as it stands at the moment is a tool for accelerating the development of coastal modeling studies. So in the Marine Virtual Laboratory you can go through a step through a menu which enables you to select different community open-source models. You can choose from a menu of those. Excuse me, I've got a bit of a cold still. And you can choose that. You can define your grid and region of interest from a map. You can then select and you can play around with that to get resolution right and things like this that you can then select from another menu to choose from data sets for atmospheric forcing from initial conditions for ocean boundary conditions. And you can tune some of the parameters and at the end of the process you can either submit your configuration into the cloud to do a simulation with a particular model or you can actually bundle data sets that you've collected, constructed from the particular model of interest and take them away and use them on a computer somewhere else. But in all those processes there are certain things that you need to do and you need some, you need information about the geography about coastlines, you need information about the sea depths, the bathymetry, you need an initial condition, you need boundary condition, boundary forcing for the ocean and you need surface forcing of meteorology to drive the ocean circulation in the process. And you also need some other information particularly in coastal areas. In Marvel at the moment, once you've chosen your region of interest in your time period then a web processing service delivers observations that you can use for validation of the simulation. And what we're moving on to now is thinking about how you use the observations available in data assimilation processes. Sorry? Three minutes. Okay, plenty of time. So what you actually want in a coastal ocean reanalysis is a variable grid. And this example here, which I've chosen as the Syro compass model for Storm Bay, which is just down here in Hobart, shows the kind of resolution that realistically you want to do if you're going to do a sensible reanalysis of coastal processes. So here you've got grid sizes that range between five kilometers down to 100 meters. So our project here intends to acquire and assemble the necessary data to do this reanalysis between 1992 and 2016. A compiling observational atmospheric forcing, ocean forcing. Part of the project is providing a coastal discharge set, which doesn't exist for Australia at the moment to enable us to do this because fresh water inflow into coastal regions is an extremely important forcing function. And we need to assemble the necessary bathymetry and information about tides because tides in coastal areas have to be taken account of when you're doing data assimilation. So the second part of this is to develop the services to prepare these model ready observations, the super ops, for use in the intended schemes and the schemes that we're going to use, assemble stuff for ensemble Kalman filter, ensemble optimal interpolation and 4D bar. And there's remote sensing data, satellite information, the long track data. There's in situ data, combination of profile data, mooring data and glider data. And the intention is to assemble all of this information on NCI and to run a service to process those observations to produce the super ops outputs for the necessary inputs to assimilation guns. So we're in the process of looking at designing a GUI, a user interface, which will take a limited number of parameters. It will take the grid that you want your observations gridded to, take the time period of interest and the data cycle time that you want to adopt. You'll then choose a particular assimilation scheme. You'll choose observation parameters from a list that we have. And then the observations will then be processed and that's our job, that's the Australian Ocean Data Network, using either web feature services or web processing services. We're still in the process of deciding how the best way approach to go. Then all this then runs on NCI using a code that's been developed by Australian data assimilation scientists. One code for ensembles, one code for 4D var. And then your output from the end is these super ops formatted for the chosen scheme that you will then use to run your particular model. We're aiming for this service to be open and this is something we're negotiating with NCI at the moment as to how this can actually work because a lot of these data sets are under different projects in NCI. We want to bring them together and make them available to the community. Some of the key questions we're still to resolve because of the involvement of New Zealand in this space then we're contemplating we have to extend this region of interest over to New Zealand. We're still as I said mentioned we're looking at the best way of delivering the observations in terms of which sort of service would be appropriate and in general looking at how efficient can we make this production service for the users. And then the latter parts of the project say two things really one is about demonstrating the utility of these services through test cases which our modeling teams are in the process of defining at the moment. So it may be they will run different assimilation scheme to the one they currently use in their work to look at the comparison between the results but also look at how efficient it is to construct these super ops compared to the way they do it manually at the moment. And then when we've piled all those data sets for the 25-year simulation then we'll look at how we add these data sets into Marvel as it stands at the moment which will make the utility of this service much more attractive to our users. And I think that's it. Yes. Okay thank you Roger.