【摘要】 极光扰动期间的红外辐射增强对航空航天领域的通信与光学监测技术具有重要影响,但当前在4.3μm波段的研究相对较少。本文利用搭载于TIMED (Thermosphere-Ionosphere-Mesosphere Energetics and Dynamics)卫星上的SABER (Sounding of the Atmosphere Using Broadband Emission Radiometry)红外辐射计第7通道的辐射数据,分析了2003—2007年期间,高纬度地区平静和极光扰动两种大气条件下夜间4.3μm辐亮度的年均变化,发现极光扰动对120～170 km高度范围内的夜间4.3μm辐射的影响显著高于60～110 km。进一步重点分析了两次强极光扰动事件,对事件中的地磁扰动指数(Dst指数)和极光电流指数(AE指数)的逐小时变化进行了分析,发现极光期间的辐亮度与这两个参数之间的综合相关性系数(R~2)在200 km处可达0.97。通过多次强扰动事件对拟合式进行验证,发现在120～180 km高度区域,平均相对误差可低至15%,在180 km和200 km处误差取极小值。该研究结果可为极光扰动下的临近空间天气预测提供重要参考。更多还原

【Abstract】 Objective Enhanced infrared radiation during auroral disturbances significantly impacts aerospace communication systems and optical monitoring technologies.However,research focusing on the 4.3 μm infrared band under such conditions remains limited,particularly regarding systematic quantitative analyses of its variability and correlation with geomagnetic parameters.This study addresses this gap by systematically analyzing the annual variability and enhancement characteristics of nighttime 4.3 μm radiance during auroral disturbances and establishing quantitative relationships with geomagnetic disturbance indices.The results are critical for advancing near-space weather forecasting and optimizing space-based communication systems,especially in high-latitude regions frequently affected by auroral activity.Methods We utilized nighttime radiance data from Channel 7（4.3 μm） of the sounding of the atmosphere using broadband emission radiometry（SABER） instrument aboard the thermosphere-ionosphere-mesosphere energetics and dynamics（TIMED） satellite.The dataset covered the period from 2003 to 2007,focusing on a high-latitude region at approximately 65°N,0°W.Radiance measurements were classified into quiet（K_p <3） and disturbed（Kp> 3） atmospheric conditions.Geomagnetic disturbance indices,including the Dst（geomagnetic storm index） and AE（auroral electrojet index）,were sourced from the NASA/NOAA OMNI database.The analysis involved calculating annual mean radiance across three altitude layers—50-90 km,100-150 km,and 160-200 km—and conducting detailed case studies of two intense auroral events:Event 1（October 27-November 3,2003） and Event 2（November 6-13,2002）.Results and Discussions Auroral disturbances significantly enhance nighttime 4.3 μm radiance,particularly between 120-170 km,where the maximum radiance under disturbed conditions exceeded the quiet baseline by approximately an order of magnitude.In the50-90 km layer,variations were minor and within the range of background atmospheric processes,whereas the middle（100-150 km）and upper（160-200 km） layers exhibited sharp,irregular,and pronounced increases,especially during intense auroral events.For instance,during Event 1,the average nighttime radiance increased by approximately 1.81 times relative to quiet conditions,reaching1.61 × 10^-4 W/（m²·sr）,while Event 2 peaked at 1.81 × 10^-4 W/（m²·sr）—an enhancement of 1.86 times over the baseline（Fig.5）.Hourly time series revealed that peak radiance coincided closely with the main phase of geomagnetic storms,with rapid increases of up to 80% within two hours,followed by a gradual decline during the recovery phase.Vertical profiles（Figs.6-7） further indicated that above 140 km,radiance during disturbances was consistently 10-20 times higher than during quiet periods,underscoring strong coupling between high-altitude energy deposition and infrared emission processes.Statistical analysis confirmed that the 91-160 km altitude range exhibited the greatest radiance variability,with standard deviations exceeding 5 × 10^-5 W/（m²·sr） under disturbed conditions.Correlation analysis revealed strong relationships between radiance and both AE and Dst indices.The combined use of these indices yielded the highest correlation（R²=0.97） at 200 km altitude（Fig.8）.This strong correlation persisted above 140 km but weakened below 100 km,reflecting the altitude-dependent nature of auroral energy deposition.These findings highlight the complementary roles of AE（indicating substorm activity） and Dst（representing ring current intensity） in capturing auroral dynamics,with their joint use offering superior predictive capability for radiance variability compared to either index alone.Based on these correlations,we derived empirical altitude-dependent equations to predict nighttime 4.3 μm radiance using AE and Dst values（Table 2）.Validation with independent cases of strong auroral disturbances（Dst <-100 nT,AE> 1000 nT） confirmed high predictive accuracy.In the 120-180 km range,the average relative error was approximately 15%,with minimal errors at 180 km（0.20%） and 200 km（1.28%）（Figs.9-10）.For example,during a November 2004 validation event,predicted and observed peak radiance values at 160 km differed by less than 5%,demonstrating model robustness under strong disturbance conditions.However,predictive accuracy decreased by over 25% in weaker disturbance cases,indicating that the model is primarily applicable to pronounced geomagnetic storms.Conclusions This study provides a comprehensive characterization of nighttime 4.3 μm radiance responses to auroral disturbances,supported by multi-year SABER observations and detailed case analyses.The empirical models developed here enable effective prediction of radiance enhancement as a function of altitude and geomagnetic activity,offering quantitative assessments of auroral impacts on the near-space infrared environment.These models can be directly applied to improve near-space weather forecasting and optimize the performance and resilience of aerospace communication and monitoring systems in high-latitude regions.By clarifying the altitude-dependent nature of radiance enhancements and their strong correlation with combined AE and Dst indices,this work provides essential tools for anticipating operational impacts during space weather events.Future research should extend the dataset to include more recent auroral events,incorporate multi-platform and ground-based observations,and integrate numerical modeling to better resolve the underlying physical and chemical processes,thereby further enhancing predictive capability and operational relevance.更多还原