We assess the value of machine learning as an accelerator for the parameterization schemes of operational weather forecasting systems, specifically the parameterization of nonorographic gravity wave drag. Emulators of this scheme can be trained to produce stable and accurate results up to seasonal forecasting timescales. Generally, networks that are more complex produce emulators that are more accurate. By training on an increased complexity version of the existing parameterization scheme, we build emulators that produce more accurate forecasts. For medium range forecasting, we have found evidence that our emulators are more accurate than the version of the parametrization scheme that is used for operational predictions. Using the current operational CPU hardware, our emulators have a similar computational cost to the existing scheme, but are heavily limited by data movement. On GPU hardware, our emulators perform 10 times faster than the existing scheme on a CPU.

中文翻译：

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数值天气预报中重力波阻力的机器学习仿真


我们评估了机器学习作为业务天气预报系统参数化方案加速器的价值，特别是非地形重力波阻力的参数化。该方案的模拟器可以经过训练以在季节性预测时间范围内产生稳定且准确的结果。一般来说，更复杂的网络会产生更准确的模拟器。通过对现有参数化方案的复杂性版本进行训练，我们构建了能够产生更准确预测的模拟器。对于中期预测，我们发现有证据表明我们的模拟器比用于操作预测的参数化方案版本更准确。使用当前运行的 CPU 硬件，我们的模拟器具有与现有方案类似的计算成本，但受到数据移动的严重限制。在 GPU 硬件上，我们的模拟器的执行速度比 CPU 上的现有方案快 10 倍。