Advances in data assimilation methods for weather and environmental forecasts and climate simulations

天气和环境预报及气候模拟数据同化方法的进展

• 批准号: RGPIN-2014-04997
• 负责人: Gauthier, Pierre
• 金额: $ 2.19万
• 依托单位: Université du Québec à Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=633653
• 关键词: Advances; data; assimilation; methods; weather

In atmospheric and oceanic sciences, models and observations are combined in modern data assimilation systems to produce analyses which are our best estimate of the state of the atmosphere at any given time. Historically, the analyses were needed to provide initial conditions to numerical weather prediction (NWP) models as the error made on weather forecasts were attributed to error in the initial conditions. It was soon realized that comparing model simulations to observations offers information to pinpoint weaknesses in the model which helped to improve the NWP model. Much of the advances made in recent years in data assimilation methods aimed at being able to use the vast amount of data now obtained from several satellite-based instruments on top of those from ground-based instrments. Millions of data are used every 24-h to produce analyses because of the significant advances made in data assimilation methods. The author conducted the R&D which led to the implementation of a 4D assimilation system, the so-called "4D-Var", which is capable to extract information from observations sampling the atmosphere in both time and space. Wind information can be inferred from observations of atmospheric constituents. Precursors to the development of meteorological weather events are better resolved which resulted in a reduction of missed forecasts of significant severe weather systems.For twenty years, the author worked on the development and implementation of new advanced variational methods which have been part of the operational analysis and forecast suite of Environment Canada first in 1997, for the first stage of the project which was completed in 2005 with the implementation of 4D-Var which is still used to this day. Building on the success obtained in NWP, the same data assimilation systems were used to redo the analyses of the recent past, the so-called reanalyses which are now a key component of the validation of climate models. My own view is that climate models should be used to produce short-term forecasts used as an a priori to do the subsequent analysis. This cycle is repeated and this is through this constant comparison to observations that information can be obtained and used to better understand physical processes and their interactions and validate this against observations.The research presented in this proposal is concerned with specific aspects of data assimilation methods which could impact the quality of the analyses. The results will help to make an assessment of the impact this may have on the end result: improving weather forecasts and analyses. This supports the long term view of improving the observation and modelling of the Earth system to better understand its evolution, improve high impact weather forecasts and assess climate changes through modelling consistent with observations. This can now be tested with a quasi-operational data assimilation and forecast system which is now running on Compute Canada platforms, an effort led by the author for the last six years. This opens a wide range of possibilities for research projects in universities which were only possible within operational NWP research centres like that of Environment Canada, Météo-France or the European Centre for Medium-range Weather Forecasts with which the author collaborates. The specific questions will be addressed concerns the ability of ensemble approaches to capture correctly the temporal dimension that was responsible for the success of 4D-Var. As forecasting the development of significant weather events is extremely important, the observability of precursors to atmospheric instability will be examined with a view of finding out how to design an observation strategy that could detect them and improve the forecast of such events.

在大气科学和海洋科学中，模式和观测在现代数据同化系统中结合起来，产生的分析是我们在任何给定时间对大气状况的最佳估计。过去，数值天气预报（NWP）模式需要分析提供初始条件，因为天气预报的误差归因于初始条件的误差。人们很快意识到，将模式模拟与观测相比较可以提供信息，以查明模式的弱点，从而有助于改进NWP模式。近年来在数据同化方法方面取得的许多进展都是为了能够利用目前从若干卫星仪器获得的大量数据，再加上从地面仪器获得的大量数据。由于数据同化方法取得了重大进展，每24小时使用数百万个数据进行分析。作者进行了研发，最终实现了4D同化系统，即所谓的“4D- var”，该系统能够从时间和空间上采样大气的观测中提取信息。风的信息可以从对大气成分的观测中推断出来。气象天气事件发展的前兆得到更好的解决，从而减少了对重要恶劣天气系统的预报失误。二十年来，作者致力于开发和实施新的先进变分方法，这些方法于1997年首次成为加拿大环境部业务分析和预测套件的一部分，用于2005年完成的项目第一阶段，并实施至今仍在使用的4D-Var。在NWP取得成功的基础上，同样的数据同化系统被用来重新分析最近的过去，所谓的再分析现在是验证气候模式的关键组成部分。我个人的观点是，气候模型应该被用来进行短期预测，作为后续分析的先验依据。这个循环是重复的，这是通过与观察的不断比较，可以获得信息，并用于更好地理解物理过程及其相互作用，并根据观察来验证这一点。本提案中提出的研究涉及可能影响分析质量的数据同化方法的具体方面。这些结果将有助于评估这可能对最终结果产生的影响：改进天气预报和分析。这支持了改进地球系统观测和建模的长期观点，以便更好地了解其演变，改进高影响天气预报，并通过与观测一致的建模来评估气候变化。现在可以用一个准操作数据同化和预测系统进行测试，该系统现在运行在Compute Canada平台上，这是作者在过去六年中领导的一项工作。这为大学的研究项目开辟了广泛的可能性，而这些研究项目只有在加拿大环境部、msamtsamo - france或与作者合作的欧洲中期天气预报中心等NWP研究中心才能实现。将讨论的具体问题涉及集成方法正确捕获导致4D-Var成功的时间维度的能力。由于预报重大天气事件的发展极为重要，我们将研究大气不稳定前兆的可观测性，以期找出如何设计一种观测策略，能够探测到这些前兆，并改善对这些事件的预报。