电离层预报外文文献综述
时间: 2023-11-26 12:04:55 浏览: 43
1. "Ionospheric Forecasting: Recent Developments and Future Prospects" by A. J. Mannucci, W. J. Schreiner, and B. D. Wilson (2013)
This paper provides an overview of recent developments in ionospheric forecasting, including advances in data assimilation techniques, modeling approaches, and observational capabilities. The authors highlight the importance of accurate ionospheric forecasts for a range of applications, from GPS navigation to space weather monitoring, and discuss the challenges that remain in improving forecast accuracy and reliability.
2. "Real-time Ionospheric Forecasting using Machine Learning Techniques" by S. Sridharan, S. D. Pryse, and D. J. Knipp (2019)
This study explores the use of machine learning techniques for real-time ionospheric forecasting. The authors use a neural network approach to predict ionospheric parameters based on a combination of ground-based and satellite observations. Their results demonstrate the potential of machine learning for improving ionospheric forecasting accuracy and reducing computational costs.
3. "Ionospheric Forecasting for Radio Communication Systems" by R. G. Burrell and A. J. Kavanagh (2015)
This paper focuses on the importance of ionospheric forecasting for radio communication systems, particularly those operating at high frequencies. The authors review current forecasting techniques and highlight the need for improved accuracy and reliability, as well as the development of new prediction models that can account for the complex interactions between the ionosphere and the Earth's magnetic field.
4. "Forecasting the Ionospheric Response to Solar Flares using Data Assimilation Techniques" by J. K. Hargreaves, P. E. Sandholt, and K. Oksavik (2018)
This study investigates the use of data assimilation techniques for forecasting the ionospheric response to solar flares. The authors use a combination of ground-based and satellite observations to develop a prediction model based on a Kalman filter approach. Their results demonstrate the potential of data assimilation for improving ionospheric forecasting accuracy, particularly for predicting the effects of space weather events.
5. "Ionospheric Forecasting for Global Navigation Satellite Systems" by A. J. Mannucci, B. D. Wilson, and W. J. Schreiner (2011)
This paper provides an overview of ionospheric forecasting techniques for global navigation satellite systems, such as GPS. The authors discuss the challenges of ionospheric variability and the importance of accurate forecasting for maintaining reliable navigation services. They review current modeling approaches and data assimilation techniques, as well as the potential for using ground-based and satellite observations to improve forecast accuracy.