In this study, the investigation is made to reveal the impact of multi-strategically assimilating Global Precipitation Measurement (GPM) precipitation and Himawari-8/Advanced Himawari Imager (AHI) water vapor radiances (WVR) on forecasting a heavy snowfall event in the Eastern Qinghai-Tibet Plateau (EQTP) employing the Weather Research and Forecast model (WRF) and the Four-Dimensional Variational (4DVar) assimilation system (WRF-4DVar). The multiple data assimilation (DA) strategies include control tests (CON), the individual assimilation of AHI and GPM tests (DA_AHI and DA_GPM) and the joint assimilation of GPM and AHI (DA_G&A), with different initial times. The results indicate that GPM precipitation effectively captures mesoscale atmospheric details, but its scope is confined to a limited area. AHI WVR is sensitive to upper-middle atmospheric humidity and furnishes extensive-scale environmental parameters such as water vapor transport characteristics. The joint assimilation of the two not only yields multi-dimensional atmospheric insights but also addresses the limitations of individual assimilation. Assimilation GPM and AHI are respective sensitivity to the lower layers (about 800hpa) and upper layers (about 400hpa) of model. The individual assimilation GPM has the greatest effect on near-surface humidity field, and AHI plays a dominant role in the joint assimilation. By assimilating different remote sensing products at different initial times of NWPs, the thermodynamic and dynamic structures are variously reconstructed, leading to the different snowfall scenes. In addition, we further compare the 12-hourly cumulative snowfall with in-situ meteorological station observations. The predictions of snowfall from DA_G&A perform much better with the correlation coefficient (CC) and root-mean-square error (RMSE) 0.36 and 3.14 mm, respectively. As for different initial times of NWPs, the best snowfall forecast is 0600 UTC on October 28, 2022, and the CC is 0.4. Nevertheless, accurately predicting precipitation areas, intensity, and temporal variations remains challenging, particularly for solid precipitation like snowfall. Thus, meticulous consideration of weather process characteristics, observation attributes, and relevant parameter configurations during DA are imperative to enhance the efficiency of observation data utilization.

中文翻译：

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GPM/IMGER 降水与 Himawari-8/AHI 水汽辐射联合同化对 WRF 模型和 4Dvar 系统降雪预报的影响


在本研究中，本研究旨在揭示多策略同化全球降水测量 （GPM） 降水和向日葵 8/高级向日葵成像仪 （AHI） 水汽辐射 （WVR） 对采用天气研究和预报模型 （WRF） 和四维变分 （4DVar） 同化系统 （WRF-4DVar） 预报青藏高原 （EQTP） 强降雪事件的影响。多重数据同化 （DA） 策略包括对照测试 （CON）、AHI 和 GPM 测试的单独同化 （DA_AHI 和 DA_GPM） 以及 GPM 和 AHI 的联合同化 （DA_G&A），初始时间不同。结果表明，GPM 降水有效地捕获了中尺度大气细节，但其范围仅限于有限的区域。AHI WVR 对中上大气湿度敏感，并提供广泛的环境参数，例如水蒸气传输特性。两者的联合同化不仅产生了多维大气的洞察力，而且解决了个体同化的局限性。同化 GPM 和 AHI 分别对模型的下层 （约 800hpa） 和上层 （约 400hpa） 敏感。个体同化 GPM 对近地表湿度场的影响最大，AHI 在联合同化中起主导作用。通过同化 NWP 不同初始时间的不同遥感产品，以不同的方式重建热力学和动力学结构，从而产生不同的降雪场景。此外，我们还将 12 小时累积降雪量与原位气象站观测结果进行了比较。 DA_G&A 对降雪的预测表现要好得多，相关系数 （CC） 和均方根误差 （RMSE） 分别为 0.36 和 3.14 mm。至于 NWP 的不同初始时间，最佳降雪预报为 2022 年 10 月 28 日 0600 UTC，CC 为 0.4。然而，准确预测降水面积、强度和时间变化仍然具有挑战性，特别是对于降雪等固体降水。因此，在 DA 期间仔细考虑天气过程特征、观测属性和相关参数配置对于提高观测数据利用效率至关重要。