Assimilation of shipborne precipitable water vapour by Global Navigation Satellite Systems for extreme precipitation events
Assimilation of shipborne precipitable water vapour by Global Navigation Satellite Systems for extreme precipitation events
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全球导航卫星系统同化极端降水事件中的船载可降水水汽
DOI：
10.1002/qj.4192
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发表时间：
2021
影响因子：
8.9
通讯作者：
Yoshinori Shoji
中科院分区：
文献类型：
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作者：
Yasutaka Ikuta;Hiromu Seko;Yoshinori Shoji
来自海洋的水蒸气平流导致日本的特大降雨。因此，在预报模式的初始条件下，准确地描述海上水汽分布将提高暴雨事件的预报精度。因此，我们将船上全球导航卫星系统（GNSS）观测到的船载可降水量（PWV）同化，以显示其对2020年7月一次强降雨事件的影响。我们在海上进行的连续水蒸气观测活动期间获得了全球导航卫星系统的观测结果，这是世界上为数不多的观测活动之一。在本研究中，我们将船载PWV应用于四维变分资料同化方法，并在暴雨的上风侧进行同化-预报循环。虽然船载PWV是一个点观测，但由于进行GNSS观测的船只环绕日本航行，因此可以将其视为涵盖空间和时间的观测数据。此外，在同化-预报周期中，同化的影响在预报区域上传播得很广。虽然船的运动影响船载观测，我们发现，PWV同化的影响是可以忽略不计的实际应用。对于船载PWV同化槽的时间细化，间隔30 min比间隔1 hr更有效。由于在2020年7月灾害性强降雨的上风侧同化了这一船载PWV，提高了降雨量特别是降雨量的预报精度。我们还发现，统计上的改善，可以得到从水汽廓线和风速场在低层大气。结果表明，同化船载PWV观测资料可以提高暴雨预报的精度。
Water vapour advection from the sea causes extremely heavy rainfall in Japan. Therefore, accurately describing the water vapour distribution over the sea in a forecast model's initial conditions should improve the prediction accuracy of heavy rainfall events. Thus, we assimilated the shipborne precipitable water vapour (PWV) observed by the Global Navigation Satellite Systems (GNSS) onboard ships to show its impact on a heavy rainfall event in July 2020. We obtained the GNSS observations during a continuous water vapour observation campaign conducted at sea, one of the few in the world. In this study, we applied the shipborne PWV, on the upwind side of the heavy rainfall, to the four‐dimensional variational data assimilation method and conducted assimilation–forecast cycles. Although the shipborne PWV is a point observation, it can be assimilated as observation data covering space and time because the ships conducting the GNSS observation sailed around Japan. In addition, in the assimilation–forecast cycle, the effect of assimilation spread widely over the forecast area. Although the ship motion affects the shipborne observations, we found that the impact on PWV assimilation is negligible for practical use. For the temporal thinning of the data assimilation slot for the shipborne PWV, an interval of 30 min is more effective than an interval of 1 hr. As a result of assimilating this shipborne PWV on the upwind side of the disastrous heavy rainfall of July 2020, the forecast accuracy of rainfall, especially rainfall amount, was improved. We also found that statistical improvements could be obtained from the water vapour profiles and wind velocity field in the lower atmosphere. We demonstrate that the assimilation of shipborne PWV observations can improve the prediction accuracy of heavy rainfall events.