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.

本文介绍了 MetNet-3，即一个基于气象观测预测降雨、风、温度和露点的神经网络模型，该模型引入了一种重要的数据密集化技术，并通过性能比较，证明了 MetNet-3 与最先进的概率性数值天气预报模型相比，在 24 小时内具有更好的预测表现。