Despite the promising capability of multimodal foundation models, their application to the generation of meteorological products and services remains nascent. To accelerate aspiration and adoption, we explore the novel use of a vision language model for writing the iconic Shipping Forecast text directly from video-encoded gridded weather data. These early results demonstrate promising scalable technological opportunities for enhancing production efficiency and service innovation within the weather enterprise and beyond.

Due to rapid population growth globally, digitally-enabled agricultural sectors are crucial for sustainable food production and making informed decisions about resource management for farmers and various stakeholders. The deployment of Internet of Things (IoT) technologies that collect real-time observations of various environmental (e.g., temperature, humidity, etc.) and operational factors (e.g., irrigation) influencing production is often seen as a critical step to enable additional novel downstream tasks, such as AI-based yield forecasting. However, since AI models require large amounts of data, this creates practical challenges in a real-world dynamic farm setting where IoT observations would need to be collected over a number of seasons. In this study, we deployed IoT sensors in strawberry production polytunnels for two growing seasons to collect environmental data, including water usage, external and internal temperature, external and internal humidity, soil moisture, soil temperature, and photosynthetically active radiation. The sensor observations were combined with manually provided yield records spanning a period of four seasons. To bridge the gap of missing IoT observations for two additional seasons, we propose an AI-based backcasting approach to generate synthetic sensor observations using historical weather data from a nearby weather station and the existing polytunnel observations. We built an AI-based yield forecasting model to evaluate our approach using the combination of real and synthetic observations. Our results demonstrated that incorporating synthetic data improved yield forecasting accuracy, with models incorporating synthetic data outperforming those trained only on historical yield, weather records, and real sensor data.

Getting caught out in the rain might soon be a thing of the past thanks to a powerful new AI weather forecaster. Google DeepMind has unveiled an AI-powered weather model called GenCast which it claims is faster and more accurate than traditional forecasts. Compared to the top-performing supercomputer Google's GenCast model was more accurate across 99.8 per cent of predictions up to 15 days in advance. According to Google, this will not only help commuters decide whether to bring an umbrella but also spot natural disasters like Typhoons before it is too late. Normally, weather agencies like the Met Office predict the weather by using huge supercomputers to crunch the complex maths which simulates the climate.

It is good news for anyone who likes to sunbathe close to home. Bosses at the Met Office say weather forecasts could soon become'hyper local' - even predicting the temperature in your back garden. By using artificial intelligence and data collected by amateur forecasters, the new model was able to predict precisely how hot it will get down to the level of an individual street. The Met Office's standard forecasting model divides the UK into grid squares of 1.5km. By using AI techniques, the new method is able to predict the weather within 100 metre squares'showing the potential for hyper-local forecasts for temperature, even within the same street,' the Met Office said.

Several times a day, Francesca Simon, the author of the Horrid Henry children's books, gets out her phone to check the weather – not just for where she is, but where friends and family live, where she has been on holiday, where she was brought up. I find them quite magical," she said. With about 10 locations logged, her friends make fun of her "weather porn" habit. This week, Simon discovered she shared a weather app fixation with Queen Camilla when the pair discussed a miserable summer's day at a charity event. "[Camilla] said everybody teases her … so we were laughing at our mutual obsession," Simon said. It is an obsession shared by millions. If you are going on holiday, planning a summer barbecue, worrying about your garden or suffering from hay fever, you are likely to check an app at least daily for the latest forecast. The apps give much more localised and detailed information than traditional weather forecasts, including wind speeds and the percentage chance of rain, in ...

British researchers say they have created an artificial intelligence (AI) model that is highly effective at predicting rainfall within the next 90 minutes. The model was built by scientists at Google-owned research company DeepMind in London. The team says tests of the system showed it produced more accurate predictions, or forecasts, for short-term rainfall than other existing systems. The paper recently appeared in the publication Nature. The scientists centered on a kind of weather prediction known as "precipitation nowcasting."

Weather plays an important role in our everyday lives. Among other weather conditions, rain influences our day-to-day decisions significantly. Weather forecasting has always been important to our communities and countries throughout history. Machine learning has found applications in almost every field, and weather forecasting is no longer an exception. DeepMind's recent study presents a cutting-edge model that forecasts rain (and other precipitation phenomena) within the next 1-2 hours.

Computer scientists at DeepMind and the University of Exeter in England teamed up with meteorologists from the Met Office to build an AI model capable of predicting whether it will rain up to 90 minutes beforehand. Traditional forecasting methods rely on solving complex equations that take into account various weather conditions, such as air pressure, moisture, and the temperature of Earth's atmosphere. The trouble is, at least in Blighty, these systems tend to predict what lies in store for us whole days or weeks ahead. Deep-learning models are better suited for making more near-term forecasts – such as within the next couple of hours – according to a paper published by the aforementioned boffins in Nature on Wednesday. There are advantages to using AI algorithms; they don't have to solve thermodynamic equations and are less computationally intensive than other predictive techniques.

We've all been there: rushing out of the house without an umbrella only to be caught in an unexpected rain shower. But now experts at Google DeepMind have developed an artificial intelligence-based'now-casting' system which they claim is more accurate at predicting the chances of rain within the next 90 minutes than existing models. It uses high-resolution radar data from the past 20 minutes to estimate whether medium to heavy rain is likely to fall up to two hours ahead. This graphic shows how Google DeepMind's system uses high-resolution radar data from the previous 20 minutes to produce accurate predictions on rainfall to come Experts at Google DeepMind have developed an artificial intelligence-based'now-casting' system which they claim is more accurate at predicting the chances of rain within the next 90 minutes than existing models The now-casting system developed by Google's London-based tech company DeepMind relies on high-resolution radar data. The radar repeatedly fires a beam into the lower atmosphere which then tracks the amount of moisture in the air.

Forecasting rain, especially heavy rain, is crucial for a lot of industries, from outdoor events to aviation to emergency services. But doing it well is hard. Figuring out how much water is in the sky, and when and where it's going to fall, depends on a number of weather processes, such as changes in temperature, cloud formation, and wind. All these factors are complex enough by themselves, but they're even more complex when taken together. The best existing forecasting techniques use massive computer simulations of atmospheric physics.