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.

翻译：深度神经网络为天气状况建模提供了另一种范式。神经模型能在获取数据后不到一秒内做出预测，且具备极高的时空分辨率，同时能够直接从大气观测中学习，这些都是这类模型的独特优势。利用大气观测（最高保真度和最低延迟数据）训练的神经模型，与最先进的概率性数值天气预报模型相比，迄今为止仅在超前时间不超过12小时且仅针对单一变量（降水）时表现出良好性能。本文提出的MetNet-3显著扩展了基于观测的神经模型能够良好预测的超前时间范围和变量类型。MetNet-3从密集与稀疏数据传感器中学习，可对降水、风、温度和露点做出长达24小时的预测。MetNet-3引入了一项关键的密集化技术，该技术隐式捕捉数据同化过程，尽管网络训练基于极其稀疏的目标数据，仍能生成空间密集的预测。MetNet-3具有高达2分钟和1公里的高时空分辨率，且操作延迟低。我们发现，MetNet-3在美国本土地区长达24小时的超前预报中，能够优于HRRR和ENS等最佳单成员和多成员NWP模型，为基于观测的神经模型树立了新的性能里程碑。MetNet-3已投入业务运行，其预报结果与其他模型一同在谷歌搜索中提供服务。