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：深度神经网络为气象条件建模提供了替代范式。神经模型能够在数据可用后不到一秒内做出预测，且具备极高的时空分辨率，以及直接从大气观测中学习的能力，这些仅是此类模型的独特优势。利用大气观测（具有最高保真度和最低延迟的数据）训练的神经模型，在与最先进的概率数值天气预报模型进行比较时，迄今为止仅在长达十二小时的预测时间内取得了良好性能，且仅针对单一变量——降水。在本文中，我们提出MetNet-3，该模型显著扩展了基于观测的神经模型能够良好预测的预测时间范围和变量类型。MetNet-3从密集和稀疏数据传感器中学习，并能对降水、风、温度和露点进行长达24小时的预测。MetNet-3引入了一项关键的致密化技术，该技术隐式地捕获了数据同化过程，尽管网络训练基于极其稀疏的目标，却能产生空间密集的预测。MetNet-3分别具有高达2分钟和1公里的高时间与空间分辨率，以及低运行延迟。我们发现，MetNet-3能够在CONUS区域超越最佳的单成员和多成员NWP模型（如HRRR和ENS），实现长达24小时的预测性能，为基于观测的神经模型树立了新的性能里程碑。MetNet-3已投入运行，其预测结果与其他模型一起在Google搜索中提供服务。