Ionospheric data assimilation and forecasting during storms
Ionospheric data assimilation and forecasting during storms
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DOI：
10.1002/2014ja020799
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发表时间：
2016-01
期刊：
影响因子：
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通讯作者：
A. Chartier;T. Matsuo;Jeffrey L. Anderson;N. Collins;T. Hoar;G. Lu;C. Mitchell;A. Coster;L. Paxton;G. Bust
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文献类型：
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作者：
A. Chartier;T. Matsuo;Jeffrey L. Anderson;N. Collins;T. Hoar;G. Lu;C. Mitchell;A. Coster;L. Paxton;G. Bust
电离层暴会对无线电通信和导航系统产生重要影响。电离层暴时的预测有可能成为针对这些问题的有效缓解策略的一部分。电离层暴是由太阳风的强烈作用引起的。电子密度的增强是由穿透电场以及热层 - 电离层行为驱动的，包括行进式大气扰动和行进式电离层扰动以及中性成分的变化。本研究评估了在一组固定的太阳和高纬度驱动因素下，使用总电子含量（TEC）观测来确定初始电离层和热层条件对1小时预测的影响。预测性能是根据TEC观测、非相干散射雷达和原位电子密度观测来评估的。共转TEC数据提供了预测准确性的基准。主要的案例研究是2005年9月10日的风暴，而2005年1月21日的异常风暴提供了次要的比较。该研究使用了一个由数据同化研究试验台和热层电离层电动力学通用环流模型构建的集合卡尔曼滤波器。经过预处理的垂直化GPS TEC地图被同化，同时使用电离层电动力学同化映射的高纬度参数和太阳极紫外实验的太阳通量观测来驱动模型。滤波器调整电离层和热层参数，利用随时间演变的协方差估计。该方法在纠正模型偏差方面是有效的，但没有捕捉到风暴的所有行为。特别是，没有预测到美国大陆上空的脊状增强，这表明预测风暴时电场行为对电离层预报问题的重要性。
Ionospheric storms can have important effects on radio communications and navigation systems. Storm time ionospheric predictions have the potential to form part of effective mitigation strategies to these problems. Ionospheric storms are caused by strong forcing from the solar wind. Electron density enhancements are driven by penetration electric fields, as well as by thermosphere‐ionosphere behavior including Traveling Atmospheric Disturbances and Traveling Ionospheric Disturbances and changes to the neutral composition. This study assesses the effect on 1 h predictions of specifying initial ionospheric and thermospheric conditions using total electron content (TEC) observations under a fixed set of solar and high‐latitude drivers. Prediction performance is assessed against TEC observations, incoherent scatter radar, and in situ electron density observations. Corotated TEC data provide a benchmark of forecast accuracy. The primary case study is the storm of 10 September 2005, while the anomalous storm of 21 January 2005 provides a secondary comparison. The study uses an ensemble Kalman filter constructed with the Data Assimilation Research Testbed and the Thermosphere Ionosphere Electrodynamics General Circulation Model. Maps of preprocessed, verticalized GPS TEC are assimilated, while high‐latitude specifications from the Assimilative Mapping of Ionospheric Electrodynamics and solar flux observations from the Solar Extreme Ultraviolet Experiment are used to drive the model. The filter adjusts ionospheric and thermospheric parameters, making use of time‐evolving covariance estimates. The approach is effective in correcting model biases but does not capture all the behavior of the storms. In particular, a ridge‐like enhancement over the continental USA is not predicted, indicating the importance of predicting storm time electric field behavior to the problem of ionospheric forecasting.