Better very short term forecasts for old and new needs
对新旧需求更好的短期预测
基本信息
- 批准号:RGPIN-2022-03610
- 负责人:
- 金额:$ 2.19万
- 依托单位:
- 依托单位国家:加拿大
- 项目类别:Discovery Grants Program - Individual
- 财政年份:2022
- 资助国家:加拿大
- 起止时间:2022-01-01 至 2023-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
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.
虽然天气预报总体上显示出稳步进展,但在过去20年里,夏季恶劣天气警报的及时性和质量没有明显改善。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善夏季恶劣天气威胁的警报并在此过程中更好地了解天气现象。天气预报可以根据雷达和其他观测对当前或未来恶劣天气相关特征的检测或根据天气预报模型的预测发布。我们过去在后者方面的努力揭示了我们目前和未来有效利用雷达数据帮助确定大气当前状况的能力的根本障碍,这是任何预报的先决条件。因此,我们的重点将是如何改进对测量信息的使用,以更好地检测和预测恶劣天气威胁。我们还试图将这种方法扩展到预测风和云量的新应用,如短期预测风能和太阳能的生产。具体来说,我们希望利用来自雷达、卫星、天气预测模型和风暴破坏发生的越来越大的档案,发现与现有或不久的将来的恶劣天气威胁(如冰雹、狂风、暴雨和龙卷风)相关的新特征组合,并了解它们的基础。这些新特征的搜索将得到植根于人工智能的技术的帮助,尽管我们最终寻求学习识别威胁并理解检测的基础,而不是简单地训练算法来完成这项工作,而不知道它学会了寻找什么。我们不仅要识别当前的恶劣天气,还要识别与不久的将来的恶劣天气相关的模式,因为拥有这些知识将加快天气警报的发布。我们首先打算使用不同的数据源开发更好的模式识别技术,这些数据源还考虑到图像的质量,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寻找其他模式,这些模式也与当前或未来不同情况下的恶劣天气威胁有关。加拿大环境和气候变化部的气象学家和研究人员将利用这项工作的成果,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。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Fabry, Frédéric其他文献
P-type Processes and Predictability: The Winter Precipitation Type Research Multiscale Experiment (WINTRE-MIX)
P 型过程和可预测性:冬季降水类型研究多尺度实验 (WINTRE-MIX)
- DOI:
10.1175/bams-d-22-0095.1 - 发表时间:
2023 - 期刊:
- 影响因子:8
- 作者:
Minder, Justin R.;Bassill, Nick;Fabry, Frédéric;French, Jeffrey R.;Friedrich, Katja;Gultepe, Ismail;Gyakum, John;Kingsmill, David E.;Kosiba, Karen;Lachapelle, Mathieu - 通讯作者:
Lachapelle, Mathieu
Fabry, Frédéric的其他文献
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{{ truncateString('Fabry, Frédéric', 18)}}的其他基金
Bridging the gaps between data collection and prediction at thunderstorm scales
缩小雷暴规模数据收集和预测之间的差距
- 批准号:
RGPIN-2017-04475 - 财政年份:2021
- 资助金额:
$ 2.19万 - 项目类别:
Discovery Grants Program - Individual
Bridging the gaps between data collection and prediction at thunderstorm scales
缩小雷暴规模数据收集和预测之间的差距
- 批准号:
RGPIN-2017-04475 - 财政年份:2020
- 资助金额:
$ 2.19万 - 项目类别:
Discovery Grants Program - Individual
Bridging the gaps between data collection and prediction at thunderstorm scales
缩小雷暴规模数据收集和预测之间的差距
- 批准号:
RGPIN-2017-04475 - 财政年份:2019
- 资助金额:
$ 2.19万 - 项目类别:
Discovery Grants Program - Individual
Bridging the gaps between data collection and prediction at thunderstorm scales
缩小雷暴规模数据收集和预测之间的差距
- 批准号:
RGPIN-2017-04475 - 财政年份:2018
- 资助金额:
$ 2.19万 - 项目类别:
Discovery Grants Program - Individual
Bridging the gaps between data collection and prediction at thunderstorm scales
缩小雷暴规模数据收集和预测之间的差距
- 批准号:
RGPIN-2017-04475 - 财政年份:2017
- 资助金额:
$ 2.19万 - 项目类别:
Discovery Grants Program - Individual
Improving severe storms forecasts by better constraining initial conditions
通过更好地限制初始条件来改善强风暴预报
- 批准号:
203521-2007 - 财政年份:2010
- 资助金额:
$ 2.19万 - 项目类别:
Discovery Grants Program - Individual
Improving severe storms forecasts by better constraining initial conditions
通过更好地限制初始条件来改善强风暴预报
- 批准号:
203521-2007 - 财政年份:2009
- 资助金额:
$ 2.19万 - 项目类别:
Discovery Grants Program - Individual
Improving severe storms forecasts by better constraining initial conditions
通过更好地限制初始条件来改善强风暴预报
- 批准号:
203521-2007 - 财政年份:2008
- 资助金额:
$ 2.19万 - 项目类别:
Discovery Grants Program - Individual
Improving severe storms forecasts by better constraining initial conditions
通过更好地限制初始条件来改善强风暴预报
- 批准号:
203521-2007 - 财政年份:2007
- 资助金额:
$ 2.19万 - 项目类别:
Discovery Grants Program - Individual
Precipitation microphysics at sub-zero temperatures and the detection of icing
零下温度下的降水微物理和结冰检测
- 批准号:
203521-2002 - 财政年份:2006
- 资助金额:
$ 2.19万 - 项目类别:
Discovery Grants Program - Individual
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