MetNet-3: A state-of-the-art neural weather model by Google

Next, we need better climate models! The current ones are junk! 😊

"... Today we present a new weather model called MetNet-3, developed by Google Research and Google DeepMind. Building on the earlier MetNet and MetNet-2 models, MetNet-3 provides high resolution predictions up to 24 hours ahead for a larger set of core variables, including precipitation, surface temperature, wind speed and direction, and dew point. MetNet-3 creates a temporally smooth and highly granular forecast, with lead time intervals of 2 minutes and spatial resolutions of 1 to 4 kilometers. MetNet-3 achieves strong performance compared to traditional methods, outperforming the best single- and multi-member physics-based numerical weather prediction (NWP) models — such as High-Resolution Rapid Refresh (HRRR) and ensemble forecast suite (ENS) — for multiple regions up to 24 hours ahead. ..."

From the abstract:

"Deep neural networks offer an alternative paradigm for modeling weather conditions. The ability of neural models to make a prediction in less than a second once the data is available and to do so with very high temporal and spatial resolution, and the ability to learn directly from atmospheric observations, are just some of these models' unique advantages. Neural models trained using atmospheric observations, the highest fidelity and lowest latency data, have to date achieved good performance only up to twelve hours of lead time when compared with state-of-the-art probabilistic Numerical Weather Prediction models and only for the sole variable of precipitation. In this paper, we present MetNet-3 that extends significantly both the lead time range and the variables that an observation based neural model can predict well. MetNet-3 learns from both dense and sparse data sensors and makes predictions up to 24 hours ahead for precipitation, wind, temperature and dew point. MetNet-3 introduces a key densification technique that implicitly captures data assimilation and produces spatially dense forecasts in spite of the network training on extremely sparse targets. MetNet-3 has a high temporal and spatial resolution of, respectively, up to 2 minutes and 1 km as well as a low operational latency. We find that MetNet-3 is able to outperform the best single- and multi-member NWPs such as HRRR and ENS over the CONUS region for up to 24 hours ahead setting a new performance milestone for observation based neural models. MetNet-3 is operational and its forecasts are served in Google Search in conjunction with other models."

MetNet-3: A state-of-the-art neural weather model available in Google products – Google Research Blog

Deep Learning for Day Forecasts from Sparse Observations (that preprint dates back to June 2023)