Solar Cycle Prediction at NOAA's Space Weather Prediction Center
被引:0
作者:
Miesch, Mark S.
论文数: 0引用数: 0
h-index: 0
机构:
Univ Colorado Boulder, Cooperat Inst Res Environm Sci CIRES, Boulder, CO 80309 USA
NOAA, Space Weather Predict Ctr, Boulder, CO 80305 USAUniv Colorado Boulder, Cooperat Inst Res Environm Sci CIRES, Boulder, CO 80309 USA
Miesch, Mark S.
[1
,2
]
机构:
[1] Univ Colorado Boulder, Cooperat Inst Res Environm Sci CIRES, Boulder, CO 80309 USA
[2] NOAA, Space Weather Predict Ctr, Boulder, CO 80305 USA
来源:
SPACE WEATHER-THE INTERNATIONAL JOURNAL OF RESEARCH AND APPLICATIONS
|
2025年
/
23卷
/
06期
关键词:
solar cycle;
space weather;
sun;
solar activity;
forecasting;
SUNSPOT NUMBER;
D O I:
10.1029/2025SW004444
中图分类号:
P1 [天文学];
学科分类号:
0704 ;
摘要:
Predicting the level of solar activity years or even decades into the future has been both one of the most pressing and one of the most formidable challenges of space weather forecasting since its scientific origins over a century ago. The main operational goal is to provide actionable information to space weather stakeholders. To achieve this goal, predictions must not only be accurate but also robust and transparent. Reliable uncertainty quantification and assimilation of all available data is essential. In this paper we describe how the Space Weather Prediction Center (SWPC), as a division of the National Oceanic and Atmospheric Administration (NOAA), currently addresses solar cycle prediction from an operational perspective. This includes a series of international solar cycle prediction panels and a new product, introduced in 2023, to continually update the 2019 Panel prediction as new data becomes available. The new product is based on nonlinear curve fits to the international sunspot number and F10.7 cm radio flux, with uncertainties quantified by applying the same method to previous cycles at the same time in each cycle. We also present a preliminary operational forecast for Cycle 26. The methods presented here can serve as a robust empirical benchmark for progressively improving solar cycle predictions through validation, data assimilation, and ensemble modeling.
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