Improving high impact weather forecasts via an international comparison of ObServation error Correlations in data Assimilation (OSCA)

英国约有400万处房产面临着地表水泛滥的风险，当暴雨淹没了当地的排水能力时，地表水泛滥就会发生。未来，由于气候变化，风暴和洪水等恶劣天气事件的频率和强度可能会增加。对恶劣天气的准确预报带来了巨大的好处，使家庭和企业能够采取缓解行动，并提供紧急服务来调动资源。数值天气预报是通过使用求解描述大气运动和其他物理过程的方程的计算技术向前演变来获得的。目前的大气状态是通过一种被称为数据同化的复杂数学技术来估计的。数据同化将以前的预报与新的大气观测结合在一起，根据它们所尊重的不确定性进行加权。观测中的不确定性没有得到很好的理解，目前高达80%的观测没有用于同化，因为这些不确定性不能被适当地量化和解释。与英国气象局合作，我们最近在NERC法郎：使用新的数据同化技术和新的对流观测项目(NE/K008900/1)预测降雨中表明，现在可以估计观测不确定性的空间统计数据。我们以前在理想化系统中的工作表明，更好地解释同化中的这些错误有望提供显着的预报改进。在业务预测中实现这些好处之前，仍有一些基本问题需要解决。这项对NERC国际机遇基金的提议将通过支持获取国际观测数据、数值天气预报模型和同化系统，为以瑞士法郎开展的工作增加价值。我们将与德国气象局(Deutscher Wetterdienst)建立新的合作关系，并将Deutscher Wetterdienst的多普勒雷达风场数据的观测误差统计数据与英国气象局的观测误差统计数据进行比较。通过考虑业务预报系统之间的异同，并将其归因于观测误差统计中的特征，我们将获得关于误差源的详细知识。通过进行理论和理想化的研究，并将其结果与业务系统的统计数据进行比较，我们将了解同化系统的差异对用于估计观测误差统计的诊断数据的影响。反过来，这应该会减少观测误差，从而更多地利用而不是丢弃昂贵获得的观测。最终，了解观测的不确定性将导致对恶劣天气事件的更好预报。

Approximately 4 million properties in the UK are at risk from surface-water flooding which occurs when heavy rainfall overwhelms the drainage capacity of the local area. In the future, as a result of climate change, the frequency and intensity of severe weather events, such as storms and floods, is likely to increase. Accurate forecasts of severe weather provide significant benefit, allowing households and businesses to take mitigating action and emergency services to mobilize resources. Numerical weather forecasts are obtained by evolving forward the current atmospheric state using computational techniques that solve equations describing atmospheric motions and other physical processes. The current atmospheric state is estimated by a sophisticated mathematical technique known as data assimilation. Data assimilation blends previous forecasts with new atmospheric observations, weighted by their respected uncertainties. The uncertainty in the observations is not well understood, and currently up to 80% of observations are not used in the assimilation because these uncertainties cannnot be properly quantified and accounted for. Working in partnership with the UK Met Office, we have recently demonstrated in the NERC FRANC: Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection project (NE/K008900/1), that it is now feasible to estimate spatial statistics for observation uncertainty. Our previous work in idealized systems has shown that better accounting for these errors in the assimilation is expected to provide significant forecast improvement. There are still a number of fundamental questions to address before the benefits can be realized in operational forecasts. This proposal to the NERC International Opportunities fund will add value to the work carried out in FRANC, by supporting access to international observation data, numerical weather prediction models and assimilation systems. We will build a new collaboration with the Deutscher Wetterdienst (German Weather Service), and compare observation error statistics for Doppler radar wind data from Deutscher Wetterdienst with those from the UK Met Office. By considering the similarities and differences between the operational forecasting systems, and attributing these to features in the observation error statistics, we will obtain a detailed knowledge of the error sources. By carrying out theoretical and idealized studies and comparing their results with the statistics from the operational systems, we will gain understanding of the impact of differences in the assimilation systems on the diagnostic used to estimate the observation error statistics. In turn, this should allow the observation errors to be reduced, and therefore more of the expensively acquired observations to be utilised, rather than discarded. Ultimately, understanding the observation uncertainty will result in improved forecasts of severe weather events.