 I want to just share with you what it's like to be a meteorologist and just the data and the information and just the sheer load of questions and insights that we deal with every single day. So I coined the term drowning the data in Thursday for information and I'll share some insights about how much data we absorb every single day and actually how we then filter that data, the volumes of data to provide information. And I echo with a lot of the panel discussions here about how do you operationalise research? How do you deliver value and impact to customers so they can make the right decision? I also echoed the conversation earlier about the quality of the information which is used in the data. And we have to deal with this every single day, every hour, every minute in the Met Office. So the first I want to do is show you, this is real time and this is a snapshot of the observations which are going into our unified model. It starts at nine o'clock and it finishes at about three. Anything which is yellow is observations which are collected from aircraft, it's global. So every minute we have observations flooding into our supercomputers from aircraft, from satellites, you see the sway of the satellites and also you'll notice blue points which are surface observations as well. And when I run it, it starts when it's still light in the United States and you'll notice at the end of the sequence people are waking up in Australia and Japan. So this is in real time and just gives you the sense of what it's like. In terms of the amount of data that is accessible to not just us but also our global community and our global partners. And you can see that all that information is every single second, every minute is flooding into the Met Office. And the difficulty bit we have is when we add all that up and I asked my colleagues yesterday for the global model how many observations did we ingest before it got assimilated? 130 million in one day, 130 million in one day. And we're able to filter that down to 8 million through quality assurance, through ensuring that the data fits with the background state and also there's also some other duplication as well. So that's the endeavour that we as my organisation, I know others in the planet are also having to deal with every single day. So that's kind of a snapshot there. The other thing we're dealing with is novel observations, opportunities observations, drones, autonomous vehicles, you name it. There are so much information out there is that we're also having an endeavour. How do we then plug that in, integrate that into the 130 million observations that we see every day? So that's a massive endeavour which we are embracing at the moment and embarking. And then you need the machine. You need something to crunch all that observations into a forecast. And we have access to supercomputers. So again in the Met Office, the current supercomputer that we're running, as you can read there, deals with about 14,000 trillion arithmetic operations per second. So that's about 2 million calculations per second for every person on the planet. I mean that's a huge investment. And that puts us in a situation where we can now predict globally. So this is a forecast from yesterday for the globe. From the 130 million observations that I've been taking, using the physics and the algorithms and the deep science that underpins that and assimilates the observations and then projects the forecast 10 days out. And that is a massive scientific achievement. And it's not just, you know, this is Met Office as a vehicle for that, but there's also a huge amount of investment and science that's gone for decades. And it's that investment in science, research and the application of it that puts us into this position that we have now in the UK. And if we don't stop there, we are being blessed and awarded with a contract with Microsoft of the order of 1.2 billion that we're now embarking on a next upgrade to our supercomputers through cloud technology, which would be renewable energy that's feeding into it. So that little box down to the bottom there, that green box, is what we have now. By 2028, 2029, we're up to, you can see that, you can see that yourself, 18 times the power we have now. This is ours. This is the UK investment. This prints us into a different league. Once this is implemented, we will be number one on this planet for weather and climate. You want digital twins, you know, you can swim in this stuff, right? It's incredible achievement. And if you look up now, this is Magadasca, this is a tropical cyclone. So this is our current capability now, the future. It's incredible. From a meteorologist, my draw drops of all the different types of meteorological kind of features that emerge from that tropical cyclone and the data that goes with that. If we integrate as a digital twin all the stuff that we have collectively into this system, then we're in a position where we will be world leading but also not only serving the UK's interests but our global interests too. And so that's why I've been working very closely with the Environment Agency because, you know, so earlier, the first stages of it was the observe and also some of the modelling and forecasting and that's how through other projects that we are starting to look at integrating. How do we merge this collective endeavour? So not just dealing with weather but we're dealing with impact and all the other responses that go with that. And I have to leave the last message as well, this collaboration. It's getting painfully obvious that, you know, we've got so much richness of data, it's having access to it to avoid some of the misinformation that goes with that access so we need to ensure the quality assurance of that data but doing it in a way that when we start to build an endeavour of the same thing we just seen that we do it together so we co-design together, it's nothing worse. It's to come in and say we've built something, let's go and use it. We need to be doing it from the beginning. And so collaboration and co-design process I think is ultimately a really, really way to go and I'll just cite this example here from selfhood university, ThinkLab where they're starting to use digital twins and other technologies so that we can start bringing some of that capability together and X to university, there's others that are starting to endeavour on that and other universities as well. So it's a generally a public-private partnership, public transport with academia, the business and also the public investment working together to co-design so we can really make best use of that value. So that was my presentation. Just want to give you what it's like to be a meteorologist, the amount of information that we've got, the capabilities we have and the key thing is then how do we invest that to research into applications so we actually, people can stay safe and thrive. Thank you very much.