基于超快速数据同化的无需模型重新积分的集合预报更新：以一次强降雨个例的理想化试验

中文摘要 AI 辅助

随着近期技术的进步，高频观测数据变得极为丰富。超快速数据同化（URDA）被提出以利用这些数据，在不重新积分预报模型的情况下，以较低成本频繁更新集合预报。在本研究中，我们考察了URDA在真实数值天气预报（NWP）中的适用性。具体而言，我们针对2021年8月的强降雨事件，使用区域大气模型“可扩展计算高级库与环境”（SCALE-RM）进行了理想化试验。在试验中，模拟了模拟气象数据采集系统（AMeDAS）的伪观测数据，并假设其每10分钟可用一次。结果表明，通过基于集合的误差协方差，与预先存在的基线预报相比，经URDA更新的预报在逐小时降水威胁评分上普遍有所提高。此外，对于海平面气压、温度、相对湿度和风，随着通过同化额外观测对预报进行连续更新，URDA更新预报的均方根误差相对于基线预报有所减小。这些结果表明URDA在真实NWP模型中有效运行的潜力。

英文摘要

Observations at high frequency have become dramatically more abundant with recent technological advances. Ultra-rapid data assimilation (URDA) has been proposed to exploit them, frequently updating ensemble forecasts at lower cost without re-integrating the forecast model. In this study, we examine the applicability of URDA to a realistic numerical weather prediction (NWP). Specifically, we conducted idealized experiments for the heavy rainfall event of August 2021, using the regional atmospheric model Scalable Computing for Advanced Library and Environment (SCALE-RM). In the experiments, pseudo-observations emulating the Automated Meteorological Data Acquisition System (AMeDAS) were assumed to become available every 10 min. The results show that the threat score for hourly precipitation is generally improved in the forecasts updated by URDA relative to the pre-existing baseline forecasts, through the ensemble-based error covariance. Furthermore, for sea level pressure, temperature, relative humidity, and winds, the root mean square error of the URDA-updated forecasts is reduced relative to that of the baseline forecasts as the forecasts are successively updated by assimilating additional observations. These results indicate the potential of URDA to operate effectively with a realistic NWP model.

发表机构

• Chiba University（千叶大学）

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