This study compares two data assimilation (DA) methods, the Local Ensemble Transform Kalman Filter (LETKF) and three-dimensional variational analysis (3DVAR), in the assimilation of high-resolution three-dimensional remote sensing data. Different observation operators are applied to each DA method to reflect its specific characteristics and to provide best analysis for precipitation forecast over complex terrain. Since radial velocity has a linear relationship with wind components, it applies relatively easily to both DA methods. However, reflectivity has a nonlinear relationship with model state variables and LETKF applies direct DA, while 3DVAR uses indirect DA. A detailed analysis of two specific snowfall cases using ICE-POP 2018 observational data reveals significant differences in wind field changes. In 3DVAR, strong convergence on the windward side and the rapid growth of water vapor into hydrometeors during the forecast period lead to an overestimation of precipitation. In contrast, LETKF improves the simulation of airflow over mountains and enhances precipitation accuracy, attributed to the background error covariance matrix and observation operator. For accurate winter precipitation forecasts over complex terrain, high-resolution data and advanced DA techniques like LETKF are necessary, as they greatly improve snowfall prediction accuracy.

中文翻译：

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ICE-POP 2018 期间降雪情况的雷达数据同化系统相互比较


本研究比较了两种数据同化 （DA） 方法，即局部集成变换卡尔曼滤波 （LETKF） 和三维变分分析 （3DVAR），在高分辨率三维遥感数据的同化中。每种 DA 方法都应用了不同的观测运算符，以反映其特定特征，并为复杂地形上的降水预报提供最佳分析。由于径向速度与风分量呈线性关系，因此它相对容易地应用于两种 DA 方法。但是，反射率与模型状态变量具有非线性关系，LETKF 应用直接 DA，而 3DVAR 使用间接 DA。使用 ICE-POP 2018 观测数据对两种特定降雪情况进行详细分析，揭示了风场变化的显著差异。在 3DFAR 中，在预测期内，迎风侧的强烈收敛和水蒸气迅速增长为水汽凝结物，导致对降水的高估。相比之下，LETKF 改进了山上气流的模拟，并提高了降水精度，这归因于背景误差协方差矩阵和观测算子。为了在复杂地形上进行准确的冬季降水预报，高分辨率数据和先进的 DA 技术（如 LETKF）是必要的，因为它们大大提高了降雪预测的准确性。