Delayed response of the ionosphere to solar EUV variability (DRIVAR)

在太阳自转过程中，电离层等离子体对太阳极紫外光和紫外光的变化起反应，时间延迟为1-2天。这种延迟被认为是由于从低电离层到F区的传输过程，但到目前为止只进行了有限的模拟来证明这一假设。在DRIVAR中，将通过全面的数据分析和建模来研究造成电离层延迟的过程。将使用不同类型的太阳代理以及来自卫星观测的光谱极光和紫外线通量，并结合全球定位系统无线电掩星和电离层探测仪的电离层参数进行分析，并提供全球定位系统总电子含量图和高分辨率覆盖图。特别是，电离层延迟将在不同的时间尺度(短期、太阳自转、季节时间尺度)进行分析，并分析延迟的区域相关性。由于热层和电离层中众多相互作用过程的复杂性，需要数值模拟来物理地了解造成延迟的基本过程。我们将使用耦合的热层电离层等离子体电动力学(CTIPe)模型来模拟延迟，并通过灵敏度实验详细地调查和分析导致观测延迟的过程。将利用高层大气模式(UAM)进行额外的模式实验，以供比较。DRIVAR结果将提供更多关于电离层过程的一般信息，对于电离层在预报电离层天气方面的应用，例如用于全球导航卫星系统误差分析和预报，将是最有价值的。

The ionospheric plasma reacts on solar EUV and UV variations in the course of the solar rotation with a time delay of 1-2 days. This delay is assumed to be owing to transport processes from the lower ionosphere to the F region, but only limited modeling has been performed so far to prove this hypothesis.Within DRIVAR, the processes responsible for the ionospheric delay will be investigated through comprehensive data analyses and modeling. Different kinds of solar proxies as well as spectral EUV and UV fluxes from satellite observations will be used and analyzed together with ionospheric parameters from GPS radio occultations and ionosondes, and, providing both global and high-resolution coverage, GPS total electron content maps. In particular, the ionospheric delay will be analyzed at different time scales (short-term, solar rotation, seasonal time scale), and the regional dependence of the delay will be analyzed.Due to the complex character of numerous interacting processes in the thermosphere and ionosphere numerical modeling is required to physically understand the basic processes contributing to the delay. We shall use the Coupled Thermosphere Ionosphere Plasmasphere Electrodynamics (CTIPe) model to simulate the delay and, through sensitivity experiments, investigate and analyze in detail the processes that lead to the observed delay. Additional model experiments will be performed using the Upper Atmosphere Model (UAM) for comparison.The DRIVAR results will provide more insight into ionospheric processes in general, and will be most valuable for ionospheric applications in forecasting ionospheric weather, e.g. for the use in GNSS error analyses and predictions.