A Comparison between the 4DVAR and the Ensemble Kalman Filter Techniques for Radar Data Assimilation
A Comparison between the 4DVAR and the Ensemble Kalman Filter Techniques for Radar Data Assimilation
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DOI：
10.1175/mwr3021.1
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发表时间：
2005-11
影响因子：
3.2
通讯作者：
A. Caya;Juanzhen Sun;C. Snyder
中科院分区：
文献类型：
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作者：
A. Caya;Juanzhen Sun;C. Snyder
摘要将四维变分资料同化（4DVAR）算法与集合卡尔曼滤波（EnKF）算法在对流尺度雷达资料同化中的应用进行了比较。使用云解析模式，模拟，不完善的雷达观测的超级单体风暴同化的假设下，一个完美的预报模式。总的来说，这两个同化方案表现良好，能够恢复超级单体具有可比的精度，径向速度和反射率的观测降雨。4DVAR产生一般更好的分析比EnKF给出的观察限制在10分钟（或三个体积扫描），特别是风分量。相比之下，EnKF通常产生更好的分析比4DVAR经过几个同化周期，特别是对于模型变量不功能相关的观测。EnKF在后期周期中的优势至少部分来自于这样一个事实，即这里实施的4DVAR方案不使用预测…
Abstract A four-dimensional variational data assimilation (4DVAR) algorithm is compared to an ensemble Kalman filter (EnKF) for the assimilation of radar data at the convective scale. Using a cloud-resolving model, simulated, imperfect radar observations of a supercell storm are assimilated under the assumption of a perfect forecast model. Overall, both assimilation schemes perform well and are able to recover the supercell with comparable accuracy, given radial-velocity and reflectivity observations where rain was present. 4DVAR produces generally better analyses than the EnKF given observations limited to a period of 10 min (or three volume scans), particularly for the wind components. In contrast, the EnKF typically produces better analyses than 4DVAR after several assimilation cycles, especially for model variables not functionally related to the observations. The advantages of the EnKF in later cycles arise at least in part from the fact that the 4DVAR scheme implemented here does not use a forecast ...