三维云辐射效应的机器学习仿真
Machine Learning Emulation of 3D Cloud Radiative Effects
摘要：
在数值天气和气候模式中，云结构的处理往往被大大简化，以使它们在计算上负担得起。在这里，我们建议使用计算成本低的神经网络来校正欧洲中期天气预报中心的1D辐射方案ecRad的3D云效应。3D云效果是ecRad的快速1D Tripleclouds求解器（忽略它们）和3D SPARTACUS（SPeedy Algorithm for Radiative Transfer through Cloud Sides）求解器（包括它们，但计算成本约为五倍）之间的差异。由于3D信号的典型误差在20%到30%之间，神经网络提高了Tripleclouds的精度，运行时间增加了约1%。因此，我们没有模拟整个斯巴达克斯，而是在大气中无云部分保持三重云不变，并在其他地方进行3D校正。如果我们假设两者的信噪比相似，则将重点放在非常小的3D校正而不是整个信号上，可以显着提高预测。
英文摘要：
The treatment of cloud structure in numerical weather and climate models is often greatly simplified to make them computationally affordable. Here we propose to correct the European Centre for Medium-Range Weather Forecasts 1D radiation scheme ecRad for 3D cloud effects using computationally cheap neural networks. 3D cloud effects are learned as the difference between ecRad's fast 1D Tripleclouds solver that neglects them and its 3D SPARTACUS (SPeedy Algorithm for Radiative TrAnsfer through CloUd Sides) solver that includes them but is about five times more computationally expensive. With typical errors between 20 % and 30 % of the 3D signal, neural networks improve Tripleclouds' accuracy for about 1 % increase in runtime. Thus, rather than emulating the whole of SPARTACUS, we keep Tripleclouds unchanged for cloud-free parts of the atmosphere and 3D-correct it elsewhere. The focus on the comparably small 3D correction instead of the entire signal allows us to improve predictions significantly if we assume a similar signal-to-noise ratio for both.
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