Finsterle W, Montillet J P, Schmutz W, Šikonja R, Kolar L, Treven L
Physikalisch-Meteorologisches Observatorium Davos/World Radiation Center (PMOD/WRC), Davos, Switzerland.
Department of Computer Science, Eidgenössische Technische Hochschule (ETH), Zurich, Switzerland.
Sci Rep. 2021 Apr 9;11(1):7835. doi: 10.1038/s41598-021-87108-y.
Various space missions have measured the total solar irradiance (TSI) since 1978. Among them the experiments Precision Monitoring of Solar Variability (PREMOS) on the PICARD satellite (2010-2014) and the Variability of Irradiance and Gravity Oscillations (VIRGO) on the mission Solar and Heliospheric Observatory, which started in 1996 and is still operational. Like most TSI experiments, they employ a dual-channel approach with different exposure rates to track and correct the inevitable degradation of their radiometers. Until now, the process of degradation correction has been mostly a manual process based on assumed knowledge of the sensor hardware. Here we present a new data-driven process to assess and correct instrument degradation using a machine-learning and data fusion algorithm, that does not require deep knowledge of the sensor hardware. We apply the algorithm to the TSI records of PREMOS and VIRGO and compare the results to the previously published results. The data fusion part of the algorithm can also be used to combine data from different instruments and missions into a composite time series. Based on the fusion of the degradation-corrected VIRGO/PMO6 and VIRGO/DIARAD time series, we find no significant change (i.e [Formula: see text] W/m[Formula: see text]) between the TSI levels during the two most recent solar minima in 2008/09 and 2019/20. The new algorithm can be applied to any TSI experiment that employs a multi-channel philosophy for degradation tracking. It does not require deep technical knowledge of the individual radiometers.
自1978年以来，各种太空任务都对太阳总辐照度（TSI）进行了测量。其中包括皮卡德卫星上的太阳变化精确监测（PREMOS）实验（2010 - 2014年）以及太阳和日球层天文台任务中的辐照度和重力振荡变化（VIRGO）实验，该任务始于1996年，目前仍在运行。与大多数TSI实验一样，它们采用双通道方法，通过不同的曝光率来跟踪和校正辐射计不可避免的性能下降。到目前为止，性能下降校正过程主要是基于对传感器硬件的假设知识的手动过程。在这里，我们提出了一种新的数据驱动过程，使用机器学习和数据融合算法来评估和校正仪器性能下降，该算法不需要对传感器硬件有深入了解。我们将该算法应用于PREMOS和VIRGO的TSI记录，并将结果与先前发表的结果进行比较。该算法的数据融合部分还可用于将来自不同仪器和任务的数据组合成一个复合时间序列。基于对经过性能下降校正的VIRGO/PMO6和VIRGO/DIARAD时间序列的融合，我们发现在2008/09和2019/20这两个最近的太阳活动极小期期间，TSI水平之间没有显著变化（即[公式：见正文]W/m²）。这种新算法可应用于任何采用多通道方法进行性能下降跟踪的TSI实验。它不需要对各个辐射计有深入的技术知识。
