1:45 PM - 3:15 PM
[O08-P73] The relationship between the twinkle of stars and aerology
Keywords:The Twinkle of Stars, Aerology, Wind
1.Purpose
Quantify the twinkle of stars and analyse the effect of the atmosphere on the twinkle.
2. Methods
(1) Observations: At the Arakawa river bank from 21:10-21:40, when a aerological observation were made by the Japan Meteorological Agency. An SLR camera and an astronomical telescope (aperture 80 mm, focal length 900 mm) were connected and stars were filmed as moving images for 30 seconds.
(2) Video analysis: software ,which was made by KTE, does.
1. Break down the video into frame images (720 images in total).
2. Extract the average luminance of the pixel on which the star appears in each image.
(3)Quantify: Take the coefficient of variation from the luminance data of the frame images.The average of the coefficient of variation of the 10 stars is defined as the twinkle deviation as an indicator of the twinkle.
*Coefficient of variation ... the standard deviation divided by the mean (Fig.1).
3. the twinkle trifecta - 'star magnitude, altitude and meteorological factors'
Preliminary observations (summer 2022) show that the three variables star magnitude, altitude and meteorological factor are correlated with the blink deviation. For the control experiment with meteorological elements, the two variables of magnitude and altitude need to be eliminated.
From separate observations, it was found that
(1) by adjusting the camera settings, the twinkle deviation values are consistent even for stars of different brightness
(2) if the altitude is above 65, the twinkle deviation values remain the same.
By taking these factors into account in the observations, it was possible to ignore the two variables of star brightness and altitude, allowing a contrasting comparison of twinkle deviations and meteorological factors.
4. twinkle deviations and meteorological factors
A total of 23 days of observations from 29 October 2023 to 15 January 2024 were used.
The light path changes when air masses with different air refractive indices are moved by the 'wind' (Fig. 7). Therefore, the meteorological factors that influence the twinkle can be divided into two main categories: the wind, which moves the air masses, and the factors that affect the refractive index.
The sum of the corrected wind speeds for each layer of the upper layers (summed wind speed) was calculated by defining a corrected wind speed that also takes into account the influence of air density. The summed wind speed and the twinkle deviation were strongly positively correlated(Fig8).
<"Factors affecting refractive index" and twinkle deviation>
Multiple regression analysis was used to examine the influence of "factors influencing refractive index" (Fig. 9), and as both the P-value and significant F were smaller than 0.01, it can be said that significant results were obtained. The corrected coefficient of determination was also large at 0.935, which gives a good model.
*Higher altitudes
The stronger the wind, the greater the variation in air density above the ground and the less water vapour, the better the twinkling was found.
*Ground
It was found that the smaller the air refractive index, the greater the refractive index at 20 o'clock than at 21 o'clock, and the weaker the wind, the better the twinkling.
5.Conclusion
It was found that the most significant factor in the twinkling of stars is the wind. The results of multiple regression analysis also showed the influence of other weather factors on the air refractive index. Using these results and weather forecasts, we are currently working on the prediction of the blink.
6.Acknowledgements.
We would like to take this opportunity to thank KTE for creating the software and Deze of the National Research Institute for Earth Science and Disaster Prevention for their advice in carrying out this research.
7.References
1) The Astronomical Society of Japan, "Quantification of stellar twinkle and luminosity measurement of variable objects - Kokugakuin University Tochigi High School."
https://www.asj.or.jp/jsession/old/2014haru/yokou2014/05.pdf
2) OpenCV-array operation http://opencv.jp/opencv-2.1/cpp/operations_on_arrays.html#cv-mean
Quantify the twinkle of stars and analyse the effect of the atmosphere on the twinkle.
2. Methods
(1) Observations: At the Arakawa river bank from 21:10-21:40, when a aerological observation were made by the Japan Meteorological Agency. An SLR camera and an astronomical telescope (aperture 80 mm, focal length 900 mm) were connected and stars were filmed as moving images for 30 seconds.
(2) Video analysis: software ,which was made by KTE, does.
1. Break down the video into frame images (720 images in total).
2. Extract the average luminance of the pixel on which the star appears in each image.
(3)Quantify: Take the coefficient of variation from the luminance data of the frame images.The average of the coefficient of variation of the 10 stars is defined as the twinkle deviation as an indicator of the twinkle.
*Coefficient of variation ... the standard deviation divided by the mean (Fig.1).
3. the twinkle trifecta - 'star magnitude, altitude and meteorological factors'
Preliminary observations (summer 2022) show that the three variables star magnitude, altitude and meteorological factor are correlated with the blink deviation. For the control experiment with meteorological elements, the two variables of magnitude and altitude need to be eliminated.
From separate observations, it was found that
(1) by adjusting the camera settings, the twinkle deviation values are consistent even for stars of different brightness
(2) if the altitude is above 65, the twinkle deviation values remain the same.
By taking these factors into account in the observations, it was possible to ignore the two variables of star brightness and altitude, allowing a contrasting comparison of twinkle deviations and meteorological factors.
4. twinkle deviations and meteorological factors
A total of 23 days of observations from 29 October 2023 to 15 January 2024 were used.
The light path changes when air masses with different air refractive indices are moved by the 'wind' (Fig. 7). Therefore, the meteorological factors that influence the twinkle can be divided into two main categories: the wind, which moves the air masses, and the factors that affect the refractive index.
The sum of the corrected wind speeds for each layer of the upper layers (summed wind speed) was calculated by defining a corrected wind speed that also takes into account the influence of air density. The summed wind speed and the twinkle deviation were strongly positively correlated(Fig8).
<"Factors affecting refractive index" and twinkle deviation>
Multiple regression analysis was used to examine the influence of "factors influencing refractive index" (Fig. 9), and as both the P-value and significant F were smaller than 0.01, it can be said that significant results were obtained. The corrected coefficient of determination was also large at 0.935, which gives a good model.
*Higher altitudes
The stronger the wind, the greater the variation in air density above the ground and the less water vapour, the better the twinkling was found.
*Ground
It was found that the smaller the air refractive index, the greater the refractive index at 20 o'clock than at 21 o'clock, and the weaker the wind, the better the twinkling.
5.Conclusion
It was found that the most significant factor in the twinkling of stars is the wind. The results of multiple regression analysis also showed the influence of other weather factors on the air refractive index. Using these results and weather forecasts, we are currently working on the prediction of the blink.
6.Acknowledgements.
We would like to take this opportunity to thank KTE for creating the software and Deze of the National Research Institute for Earth Science and Disaster Prevention for their advice in carrying out this research.
7.References
1) The Astronomical Society of Japan, "Quantification of stellar twinkle and luminosity measurement of variable objects - Kokugakuin University Tochigi High School."
https://www.asj.or.jp/jsession/old/2014haru/yokou2014/05.pdf
2) OpenCV-array operation http://opencv.jp/opencv-2.1/cpp/operations_on_arrays.html#cv-mean