IRI的实时同化foF2地图

B. Reinisch, Xueqin Huang, I. Galkin, D. Bilitza
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引用次数: 1

摘要

电离层模型大多不能正确预测空间天气事件对电离层的影响。这对于国际参考电离层(IRI)来说尤其如此,IRI设计为每月中值(气候)模式(Bilitza et al., J. Geodesy, 85, 909-920, 2011)。IRI电子密度分布严重依赖于F2层峰高和密度,hmF2和NmF2(或foF2)的正确值。对于电离层特征,IRI使用基于CCIR/URSI系数的预测(W. B. Jones和R. M. Gallet, ITU电信局)。J. 29(5), 129-149, 1962),由每小时电离层探空仪测量的月平均值得出。例如,傅立叶级数foF2(T,φ,λ,χ)=a0(φ,λ,χ) + Σn=16 (an(φ,λ,χ)cos nT + bn(φ,λ,χ)sin nT)表示foF2特征的日变化,其中T为世界时(小时),φ,λ,χ分别为地理纬度、经度和修正倾角纬度。系数an和bn依次展开为函数φm λ, χ得到一组24个全球图,每个图有988个系数,一个代表一年中的每个月,代表两个太阳活动水平,R12=10和100,其中R12是每月太阳黑子数Rm的12个月运行平均值(2*12*988 =总共23,712个系数)(ITU-R,电离层制图信息文件,2011年10月)。来自Digisonde GIRO网络的实时数据(B. W. Reinisch和I. A. Galkin, Earth, Planets and Space, 63(4), 377-381, 2011)可用于调整系数以生成更精确的foF2地图。仅用42个测量的foF2值调整988个系数是一个完全不确定的问题,需要特殊技术(例如,Galkin等人,无线电科学。, 47, r007, 10页,2012)。我们应用了线性优化的数学工具来确定新的988个系数集,这些系数将模型预测与实测值的总体偏差降低了2倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Real time assimilative foF2 maps for IRI
Ionospheric models are mostly unable to correctly predict the effects of space weather events on the ionosphere. This is especially true for the International Reference Ionosphere (IRI) which by design is a monthly median (climatologic) model (Bilitza et al., J. Geodesy, 85, 909-920, 2011). The IRI electron density profile is critically dependent on the correct values of the F2 layer peak height and density, hmF2 and NmF2 (or foF2). For the ionospheric characteristics IRI uses predictions based on CCIR/URSI coefficients (W. B. Jones and R. M. Gallet, ITU Telecomm. J. 29(5), 129-149, 1962) that were derived from the monthly median values of hourly ionosonde measurements. The diurnal variation of the foF2 characteristic, for example, is presented by the Fourier series foF2(T,φ,λ,χ)=a0(φ,λ,χ) + Σn=16 (an(φ,λ,χ)cos nT + bn(φ,λ,χ)sin nT), where T is Universal Time in hours, and φ, λ, χ are the geographic latitude, longitude, and modified dip latitude, respectively. The coefficients an and bn are in turn expanded as functions φm λ, χ resulting in a set of 24 global maps of 988 coefficients each, one for each month of the year and for two levels of solar activity, R12=10 and 100, where R12 is the 12-month running-mean of the monthly sunspot number Rm (2*12*988 = 23,712 coefficients in all) (ITU-R, Information Document on Ionospheric Mapping, Oct. 2011). Real time data from the Digisonde GIRO network (B. W. Reinisch and I. A. Galkin, Earth, Planets and Space, 63(4), 377-381, 2011) can be used to adjust the coefficients to produce more accurate foF2 maps. Adjusting 988 coefficients with merely 42 measured foF2 values is a completely underdetermined problem requiring special techniques (e.g., Galkin et al., Radio Sci., 47, RS0L07, 10 PP, 2012). We have applied the mathematical tool of linear optimization to determine new sets of 988 coefficients that reduce the global deviation of the model prediction from the measured values by a factor of 2.
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