Deep Learning Atmospheric Features

气象数据中有趣的特征(气旋、大气河流、锋面)的检测有着越来越高的保真度的悠久历史，因为利用越来越多的计算机能力和更复杂的数学技术开发了更新的技术。最近，借用计算机视觉的新技术的加入，增加了工具的武器库。早期的工作表明，它们在特征检测方面的准确性可能比现有技术更高，但在深度学习的情况下，这带来了巨大的计算要求--至少在应用于原始数据时--以及对数据存储的巨大要求。未来，我们预计将运行大型气候模拟集成(即许多对特定场景下可能发生的情况的实现)。今天，我们将写出所有数据，然后研究有趣特征的输出数据。随着分辨率的提高，仅仅存储所有这些数据将变得困难，更不用说对存储的数据进行特征检测了。未来处理这一问题的一种可能方法是在模型运行时寻找集合成员中的每个成员的特征-并且只写出我们已经知道具有有趣特征的模拟。这个项目是关于测试和比较一系列探测技术的保真度，既有原始的高分辨率气候模型数据，也有相同数据的降低精度和降低分辨率的变体。目标是开发一种可在模型模拟期间使用的学习技术，并在不造成巨大计算成本的情况下识别这些特征。这很可能是通过使用简化的数据版本来完成的，而不是改变技术，但不同的技术在不同级别的简化数据上可能工作得更好。然而，如果我们确实需要改变和/或修改技术，我们就会问自己“哪些是健壮和多才多艺的学习最重要的基石？哪些是有效培训的关键内层和神经元？”

The detection of interesting features (cyclones, atmospheric rivers, fronts) in meteorological data has a long history of ever-increasing fidelity as newer techniques have been developed exploiting increasing computer power and more mathematical sophistication. Recently, the armoury of tools has been increased by the addition of newer techniques borrowed from Computer Vision. Early work suggests they may have higher levels of accuracy in feature detection than existing techniques, but in the case of Deep Learning this comes with enormous requirements on computing - at least when applied to the raw data - and with that enormous requirements on data storage.In the future we expect to run large ensembles of climate simulations (that is, many realisations of what might occur under a specific set of scenarios). Today we would write out all the data, and then investigate the output data for interesting features. As resolution increases, just storing all that data will be difficult, let alone doing the feature detection on the stored data. A possible way to handle this in the future would be to look for the features in each one of the ensemble members as the model runs - and only write out the simulations which we already know have interesting features.This project is about testing and comparing the fidelity of a range of detection techniques to both raw high- resolution climate model data and both reduced precision and reduced resolution variants of the same data. The goal will be to develop a learning technique that can be used during the model simulation and identify such features without itself being enormously computationally expensive. This will most likely be done by using reduced versions of the data, rather than changing the techniques, but different techniques may work better at different levels of reduced data. However, if we do need to change and/or modify techniques, we will be asking ourselves "Which are the most important building blocks for a robust and versatile learning? Which are the crucial inner layers and neurons for effective training?"