Data assimilation with an adaptive Kalman filter for short-term forecasts of the F30 and F10.7 cm radio flux
Abstract
Solar activity indices such as the F30 and F10.7 cm radio flux are required by most models characterizing the state of the upper Earth atmosphere, such as the thermosphere and ionosphere, in order to specify satellite orbits, re-entry services, collision avoidance maneuvers and modeling of space debris evolution. With the aim of advancing current forecasting capabilities, we develop a novel prediction method of the F30 and F10.7 solar indices 1 to 3 day-ahead using an adaptive Kalman filter. Traditional approaches to the solar radio flux forecasting are based on linear regression models. However, one of the major concerns with such data assimilation scheme is that the evolution of the solar radio flux is a strongly non-stationary process, and thus the use of constant regression coefficients cannot be an optimal selection. The Kalman filter technique removes this disadvantage by adjusting the regression coefficients in real-time during the observation period, thereby increasing the forecasting accuracy.
Testing the developed prediction technique over the period 2004-2016, we obtain a correlation coefficient between the predicted and observed values of about 0.99 (1 day) and 0.98 (3 day ahead) for the F30 index and 0.99 (1-day ahead) and 0.96 (3-day ahead) for the F10.7 index. We compared our forecasts with the results provided by the few currently operating models for various periods of the solar cycle (minimum, ascending, maximum and declining phases). The RMS errors of the predictions are reduced by 3-14% in comparison with the predictions of F30 index based on neural networks (CLS) and the F10.7 index provided by the SIDC manual analysis. Additionally, the developed technique produces non-biased predictions, which is a strong advantage compared to the CLS forecast (overestimation) and SIDC forecast (underestimation). Thus, the proposed adaptive Kalman filter method significantly improves the quality of the F30 and F10.7 cm solar radio flux predictions and can be recommended for space weather applications.- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFMSH34B..03P
- Keywords:
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- 7536 Solar activity cycle;
- SOLAR PHYSICS;
- ASTROPHYSICS;
- AND ASTRONOMY;
- 7537 Solar and stellar variability;
- SOLAR PHYSICS;
- ASTROPHYSICS;
- AND ASTRONOMY;
- 7544 Stellar interiors and dynamo theory;
- SOLAR PHYSICS;
- ASTROPHYSICS;
- AND ASTRONOMY;
- 7594 Instruments and techniques;
- SOLAR PHYSICS;
- ASTROPHYSICS;
- AND ASTRONOMY