On the Considerations of Using Near Real Time Data for Space Weather Hazard Forecasting
On the Considerations of Using Near Real Time Data for Space Weather Hazard Forecasting
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DOI：
10.1029/2022sw003098
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发表时间：
2022-07
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通讯作者：
A. W. Smith;C. Forsyth;I. J. Rae;T. M. Garton;C. M. Jackman;M. Bakrania;R. M. Shore;G. Richardson;C. Beggan;M. J. Heyns;J. P. Eastwood;A. Thomson;J. M. Johnson
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作者：
A. W. Smith;C. Forsyth;I. J. Rae;T. M. Garton;C. M. Jackman;M. Bakrania;R. M. Shore;G. Richardson;C. Beggan;M. J. Heyns;J. P. Eastwood;A. Thomson;J. M. Johnson
空间天气对地面基础设施、卫星和通信构成严重威胁。准确预测此类威胁可能发生的时间（例如，当我们可能看到大的感应电流时）将有助于减轻社会和财务成本。近年来，已经创建了可以预测危险间隔的计算模型，但它们通常使用来自地球上游的后处理“科学”太阳风数据。在这项工作中，我们调查的质量和连续性的数据，可在近真实的时间（NRT）从高级成分探测器和深空气候观测站（DSCOVR）航天器。一般而言，NRT中可用的数据与后处理数据一致，但有三个主要关注领域：NRT数据的短期变异性较大，偶尔出现异常值和频繁出现数据缺口。如果这些问题也存在于用于拟合（或训练）模型的数据中，则一些空间气象模型能够弥补这些问题，而另一些空间气象模型则需要进行额外的检查，以生成高质量的预报。我们发现DSCOVR NRT数据通常更连续，尽管它们在太阳周期的一小部分时间内可用，因此DSCOVR经历了有限范围的太阳风条件。我们发现短间隙是最常见的，并且在等离子体数据中最常见。为了最大限度地提高预测的可用性，我们建议在可能的情况下实施有限的插值，例如，5分钟或更少的间隙，这可能会大大增加有效输入数据的比例。
Space weather represents a severe threat to ground‐based infrastructure, satellites and communications. Accurately forecasting when such threats are likely (e.g., when we may see large induced currents) will help to mitigate the societal and financial costs. In recent years computational models have been created that can forecast hazardous intervals, however they generally use post‐processed “science” solar wind data from upstream of the Earth. In this work we investigate the quality and continuity of the data that are available in Near‐Real‐Time (NRT) from the Advanced Composition Explorer and Deep Space Climate Observatory (DSCOVR) spacecraft. In general, the data available in NRT corresponds well with post‐processed data, however there are three main areas of concern: greater short‐term variability in the NRT data, occasional anomalous values and frequent data gaps. Some space weather models are able to compensate for these issues if they are also present in the data used to fit (or train) the model, while others will require extra checks to be implemented in order to produce high quality forecasts. We find that the DSCOVR NRT data are generally more continuous, though they have been available for small fraction of a solar cycle and therefore DSCOVR has experienced a limited range of solar wind conditions. We find that short gaps are the most common, and are most frequently found in the plasma data. To maximize forecast availability we suggest the implementation of limited interpolation if possible, for example, for gaps of 5 min or less, which could increase the fraction of valid input data considerably.