Assimilation of simulated Doppler radar observations with an ensemble Kalman filter
Assimilation of simulated Doppler radar observations with an ensemble Kalman filter
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DOI：
10.1175//2555.1
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发表时间：
2003-08-01
影响因子：
3.2
通讯作者：
Zhang, FQ
中科院分区：
文献类型：
--
作者：
Snyder, C;Zhang, FQ
将多普勒雷达数据同化到云模型中是对对流规模运动进行常规数值天气预报的一个重要障碍；困难在于仅根据雷达的径向速度和反射率观测来初始化风、温度、湿度和凝结水场。本文研究了集合卡尔曼滤波器（EnKF）的潜力，它通过预测集合来估计观测变量和状态之间的协方差，以同化对流尺度的雷达观测结果。在基本实验中，从分裂超胞的参考模拟中提取模拟观测结果，并使用 EnKF 和产生参考模拟的相同数值模型进行同化。经过大约 30 分钟（或六次扫描）的径向速度观测后，EnKF 可以生成准确的分析，包括未观测到的变量。通过集合均值分析进行预测的其他实验表明，在这个简单的系统中，预测误差显着增加，因此 EnKF 跟踪参考解的能力不仅仅是因为稳定的系统动力学。还发现径向速度与温度、湿度和凝结水之间的协方差对于分析的质量很重要，就像在同化第一次观测之前为系综成员选择的初始化一样。这些结果是有希望的，特别是考虑到 EnKF 的实施很容易。然而，仍然存在许多重要问题，包括首次观测之前系综的初始化、环境探测中不确定性的处理、预测模型中误差的作用（特别是微物理参数化）以及横向边界条件的处理。
Assimilation of Doppler radar data into cloud models is an important obstacle to routine numerical weather prediction for convective-scale motions; the difficulty lies in initializing fields of wind, temperature, moisture, and condensate given only observations of radial velocity and reflectivity from the radar. This paper investigates the potential of the ensemble Kalman filter (EnKF), which estimates the covariances between observed variables and the state through an ensemble of forecasts, to assimilate radar observations at convective scales. In the basic experiment, simulated observations are extracted from a reference simulation of a splitting supercell and assimilated using the EnKF and the same numerical model that produced the reference simulation. The EnKF produces accurate analyses, including the unobserved variables, after roughly 30 min ( or six scans) of radial velocity observations. Additional experiments, in which forecasts are made from the ensemble-mean analysis, reveal that forecast errors grow significantly in this simple system, so that the ability of the EnKF to track the reference solution is not simply because of stable system dynamics. It is also found that the covariances between radial velocity and temperature, moisture, and condensate are important to the quality of the analyses, as is the initialization chosen for the ensemble members prior to assimilating the first observations. These results are promising, especially given the ease of implementing the EnKF. A number of important issues remain, however, including the initialization of the ensemble prior to the first observation, the treatment of uncertainty in the environmental sounding, the role of error in the forecast model ( particularly the microphysical parameterizations), and the treatment of lateral boundary conditions.