Better very short term forecasts for old and new needs

虽然天气预报总体上稳步发展，但在过去二十年中，夏季恶劣天气预警的及时性和质量并没有明显改善。鉴于目前加拿大雷达基础设施的重大更新以及天气预报模型和卫星图像等其他数据的更好可用性，我们希望探索如何改进夏季恶劣天气威胁的预警，并在此过程中更好地了解天气现象。预警可以根据雷达和其他观测发现的与当前或未来恶劣天气有关的特征发出，也可以根据天气预报模式的预测发出。我们过去在后者方面所做的努力显示出，我们目前和未来有效利用雷达数据来帮助确定当前大气状况的能力存在根本性障碍，而这是任何预报的先决条件。因此，我们的重点将放在如何改进测量信息的使用，以更好地探测和预测恶劣天气威胁。我们还寻求将这种方法扩展到风和云的预测，以用于新的应用，如风能和太阳能生产的短期预测。具体来说，我们希望利用来自雷达、卫星、天气预报模型和风暴破坏事件的日益庞大的档案，发现与现有或近期的恶劣天气威胁（如冰雹、强风、暴雨和龙卷风）相关的特征的新组合，并了解它们的基础。对这些新特征的搜索将得到植根于人工智能的技术的帮助，尽管我们最终寻求的是学会识别威胁并了解这种检测的基础，而不是简单地训练一个算法，让它在没有很好地了解它学会了寻找什么的情况下完成这项工作。我们不仅要识别当前的恶劣天气，而且要识别与不久的将来的恶劣天气相关的模式，因为掌握这些知识将加快发布天气警报。我们首先打算开发更好的模式识别技术，使用不同的数据源，同时考虑到图像的质量，以更好地识别我们已经熟悉的严重风暴模式。然后，我们希望使用人工智能来发现其他模式，这些模式也与不同情况下当前或未来的恶劣天气威胁有关。这项工作的结果将被加拿大环境和气候变化的气象学家和研究人员用于开发更好的算法来检测恶劣天气威胁，并提供更准确和及时的警报，以提高加拿大人的安全。

While weather forecasting in general is showing steady progress, the timeliness and quality of warnings for severe summer weather has not markedly improved in the past two decades. Given the current major renewal of Canada's radar infrastructure and the better availability of forecasts from weather prediction models and of other data such as satellite imagery, we want to explore how to improve warnings of summer severe weather threats and understand weather phenomena better in the process. Warnings can be issued either based on the detection of signatures associated with present or future severe weather by radar and other observations or based on predictions by a weather forecasting model. Our past efforts working on the latter have revealed fundamental roadblocks in our current and future ability to use radar data effectively to help determine the current conditions of the atmosphere, a prerequisite to any forecast. Our focus will hence be on how to improve the use of information from measurements to better detect and predict severe weather threats. We also seek to expand this approach to predictions of wind and cloudiness for new applications such as the short-term prediction of wind and solar energy production. Specifically, we want to exploit the increasingly large archives from radar, satellite, weather prediction models, and storm damage occurrence to discover new combinations of signatures that are associated with existing or near-future severe weather threats such as hail, violent winds, torrential rain, and tornadoes, and understand their bases. The search for these new signatures will be helped by techniques rooted in artificial intelligence, though we ultimately seek to learn to recognize threats and understand the bases of that detection, not simply train an algorithm to do the work without having a good idea of what it learned to look for. We not only want to recognize current severe weather, but also patterns associated with severe weather in the near future, as having such knowledge will speed up the issuance of weather warnings. We first intend to develop better pattern recognition techniques using different data sources that also take the quality of the imagery into account to better recognize severe storm patterns we are already familiar with. We then want to use artificial intelligence to find other patterns that are also associated with current or future severe weather threats in different situations. The results of this work will be used by meteorologists and researchers at Environment and Climate Change Canada to develop better algorithms to detect severe weather threats and provide more accurate and timely warnings to improve the safety of Canadians.