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
• 负责人: Fabry, Frédéric
• 金额: $ 1.54万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2007
• 资助国家: 加拿大
• 起止时间: 2007-01-01 至 2008-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=362537
• 关键词: Improving; severe; storms; forecasts; better

In order to make better weather forecasts, it is essential to provide the computer with the best description of the current weather situation.  Because we cannot measure all atmospheric parameters such as temperature and humidity everywhere for the whole atmosphere from the surface up, we rely on a combination of observations and physical relationships between the different atmospheric parameters to get the best picture of the current weather situation.  This technique is referred to as data assimilation.Although data assimilation is done more and more often, it is not clear that we are doing it the best that we can.  Are the best combinations of observations used?  Should we use other instruments instead?  Are we using observations over too short or too long a period?In this work, we will simulate the process of data assimilation using a computer-based approach to investigate what types of observations do the best job at describing the atmosphere as completely as possible.  We will simulate how existing and planned instruments help in this process or not.  Finally, we will use the results of this simulation to demonstrate using real observations and weather forecasting that we have determined the best combinations of instruments to perform data assimilation.

为了作出更佳的天气预报，必须为电脑提供最佳的天气状况描述，因为我们不可能从地面向上测量整个大气层的所有大气参数，例如温度和湿度，我们依靠综合观测和不同大气参数之间的物理关系，来获得当前天气状况的最佳图像。这种技术被称为数据同化。虽然数据同化越来越频繁，但我们并不清楚我们是否尽了最大努力。是否使用了最佳观测组合？ 我们是否应该使用其他工具？ 我们使用的观测时间是太短还是太长？在这项工作中，我们将使用基于计算机的方法来模拟数据同化过程，以调查什么类型的观测在尽可能完整地描述大气方面做得最好。我们将模拟现有和计划中的仪器如何帮助这个过程。最后，我们将利用这次模拟的结果，用真实的观测和天气预报来证明我们已经确定了仪器的最佳组合，进行数据同化。