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Existing weather forecasting models are based on physics and use supercomputers to evolve the atmosphere into the future. Better physics-based forecasts require improved atmospheric models, which can be difficult to discover and develop, or increasing the resolution underlying the simulation, which can be computationally prohibitive. An emerging class of weather models based on neural networks overcome these limitations by learning the required transformations from data instead of relying on hand-coded physics and by running efficiently in parallel. Here we present a neural network capable of predicting precipitation at a high resolution up to 12 h ahead. The model predicts raw precipitation targets and outperforms for up to 12 h of lead time state-of-the-art physics-based models currently operating in the Continental United States. The results represent a substantial step towards validating the new class of neural weather models.

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Purpose The purpose of this paper is to explore automobile fuel efficiency policies in the presence of two externalities: a global environmental problem and international innovation spillovers. Design/methodology/approach Using a simple model with two regions, the authors show that both a fuel tax and a tax on vehicles based on their fuel economy rating are needed to decentralize the first best. Findings If standards are used instead of taxes, the authors find that spillovers may alleviate free-riding. Under some conditions, a strict standard in one region may favor the adoption of a strict standard in the other one. Originality/value The authors also show that if policies are not coordinated between regions, the resulting gas taxes will be set too low and each region will use the tax on fuel rating to reduce the damage caused by foreign drivers.

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