Weather forecasts need to capture the full range of possibilities – including the worst-case scenarios, so planning is essential.
Weatherharnext 2 can predict hundreds of weather conditions from one starting point. Each prediction takes less than a minute on a single TPU; It can take many hours using physics-based models.
Our model is also very efficient and capable of high resolution predictions, down to the hour. Overall, Weatherthertx 2 surpasses our previous WeathertherText model with 99.9% of variables (eg. Temperature, wind, humidity) and 0-15 times), enabling more useful and accurate forecasts.
This advanced functionality is enabled by a new AI Modeling method called functional network (FGN), which adds 'noise' directly to the model design so predictions are made more realistic and connected.
This method is especially useful in predicting which meteorologists refer to the 'mardadal' and 'joints. ' Certain Marginals, standalone weather features: specific temperature in a certain area, wind speed in a certain area or humidity. What is novel about our method is that the model is trained only on these mardals. However, from that discipline, learning to make 'joints' is 'big, difficult, interconnected' that depends on how all the individual pieces fit together. This 'joint' prediction is necessary for our most useful predictions, such as identifying all regions affected by high temperatures, or the expected energy from a wind farm.
