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Median Filtering Algorithms for Multichannel Detectors and their Influence on Thunderstorm Peaks in Cosmic Ray Data

This study investigates the use of median filtering algorithms in multichannel detectors to reduce errors and improve the accuracy of cosmic ray data during thunderstorms. The algorithms are described and their application is demonstrated using data from the Aragats Neutron Monitor. Different types of errors, such as spikes and slow drifts, are identified and corrected using the moving median filter algorithm. The effectiveness of the algorithms is evaluated and compared. The study also discusses the impact of the algorithms on data from the Nor-Amberd Station and provides insights for future enhancements in thunderstorm detection.

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Median Filtering Algorithms for Multichannel Detectors and their Influence on Thunderstorm Peaks in Cosmic Ray Data

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  1. Median Filtering Algorithms for Multichannel Detectors and their influence on Thunderstorm Peaks in Cosmic Ray Data Armen Hovhannisyan Alikhanyan Physics Institute, Armenia

  2. ASEC Monitors

  3. Aragats Neutron Monitor Data

  4. Different Kinds of Errors 1 Spike 2 Slow Drift 3 Abrupt changeof mean

  5. Algorithm 1 – Moving Median Filter(MMF) Moving window width – L; L=2I+1, where I is number of detections to the left and to the right of the filtering (smoothing) measurement; Maximal possible value – Pmax Minimal possible value – Pmin Maximal value of window width – Lmax Algorithm Description 1 Select time series from database with N elements; 2 Start smoothing from the (I+1)th measurement of time time series – Vi , i=I+1; 3 Calculate the median value Mi,L; 4 Validate the median value: Mi,LÎ(Pmin;Pmax) if not, enlarge L by 2, and after checking L<=Lmax go to 2; 5 ELSE go to STOP and report operator about data failure; 6 Substitute selected measurment by median value Mi,L Î Vi; 7 Move to next i+1 element of time series; 9 If i+1<=N THEN GO TO 2 10 ELSE STOP, ask operator where to write smoothed time series.

  6. Algorithm 2(3) – Median filter for multichannel measurements Let's suppose that we have M channels of one monitor, and several of them have been down for some period. , where ni are mean values of each channel, Sum is the sum of means of all channels (1) ,where Med is median of all working channels at same minute, (2) Fi are coefficients of each channel. These coefficients are the relation of total median value of all channels for same minute and the i-th channel mean. For each minute of corrupted period of corrupted channel we calculate Med value(which is median of working channels), and then calculate value for that minute using equation (2).

  7. Combination of 2 Algorithms We have some date to start the smoothing, for that beginning we calculate and coefficients and write them to a file Then we take 1 day data and start smoothing all channels with constant and not big I.e. (10 minute) window. If some channels have been not corrected by 1 algorithm, second one turns on, it reads the means and algorithms from files we have created After correcting with 2 algorithm , if everithing is ok, we calculate again means and coefficients for corrected data and write them to a the same file. If second algorithm didn't corrected the data (which means that all or nearly all channels have been corrupted) send an e-mail to operator. Take next Period and do 2-5 again.

  8. Atmospheric Pressure, Nor-Amberd Station, Period – 6 Months

  9. Atmospheric Pressure - Corrected

  10. Aragats Neutron Monitor, MAY 2008 – after filtering

  11. Correction of the AMMM time series, >5 Gev Muons

  12. Correction of the ARNM time series

  13. Simulation of Data For NANM

  14. Simulation of Spoiled Timeserie

  15. The Same Timeserie After Correction

  16. CR Intensity Modulation during 23th solar cycle, Measured by NANM

  17. Comparison of Corrected Data of NANM with Alma-Ata Data

  18. Day-to-day changes of the channel coefficients of Aragats Neutron Monitor

  19. Neutron Monitors used in the “coherence” test

  20. Pressure Corrected Data from NMDB

  21. Pressure Corrected and Median Smoothed Data from NMDB

  22. Equalizing coefficients of the NMDB facilities

  23. Enhancements associated with thunderstorms

  24. Types of Algorithms • Algorithm1 – Spike Removal • Algorithm2 – Correction of EACH channel by coefficients • Algorithm3 – Getting WEGHTED TimeSerie using data of all channels and coefficients

  25. Filtering with Algorithm 1 and 2Window=60 minutes

  26. Filtering with Algorithm 1 and 2Window=20 minutes

  27. Filtering with Algorithm 3

  28. Filtering with Algorithm 1 and 2Window=60 minutes

  29. Filtering with Algorithm 3

  30. Conclusions We use this method for online and offline filtering of ASEC data. The program is online filtering the data of Nor-Amberd and Aragats Neutron Monitors, and soon it’ll be implemented it to other monitors. Also the corrected data is being transferred to NMDB. Changing Parameters of different algorithms it is possible not to oversmooth enhancements associated with lightning events. Besides the filtering, this programme also provides an automatic alerting system in case of malfunctioning of some of 280 detecting channels.

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