Statistical Approach to Observe the Atmospheric Density Variations Using Swarm Satellite Data

Atmosphere

Jing-Jia Luo¹  Md Wahiduzzaman¹  Alea Yeasmin²  Md.Arfan Ali³  Muhammad Bilal³  Zhongfeng Qiu³ 

[1] Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC–FEMD)/Institute for Climate and Application Research (ICAR), Nanjing University of Information Science & Technology, Nanjing 210044, China;SPACE Research Centre, RMIT University, Melbourne 3001, Victoria, Australia;School of Marine Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China;

关键词: atmospheric density;    temporal variation;    Swarm mission;    correlation coefficients;    solar and geomagnetic indices;   

DOI  :  10.3390/atmos11090897

来源: DOAJ

【 摘 要 】

Over time, the initial algorithms to derive atmospheric density from accelerometers have been significantly enhanced. In this study, we discussed one of the accurate accelerometers—the Earth’s Magnetic Field and Environment Explorers, more commonly known as the Swarm satellites. Swarm satellite–C level 2 (measurements from the Swam accelerometers) density, solar index (F_10.7), and geomagnetic index (Kp) data have been used for a year (mid 2014–2015), and the different types of temporal (the diurnal, multi–day, solar–rotational, semi–annual, and annual) atmospheric density variations have been investigated using the statistical approaches of correlation coefficient and wavelet transform. The result shows the density varies due to the recurrent geomagnetic force at multi–day, solar irradiance during the day, appearance and disappearance of the Sun’s active region, Sun–Earth distance, large scale circulation, and the formation of an aurora. Additionally, a correlation coefficient was used to observe whether F_10.7 or Kp contributes strongly or weakly to annual density, and the result found a strong (medium) correlation with F_10.7 (Kp). Accurate density measurement can help to reduce the model’s bias correction, and monitoring the physical mechanisms for the density variations can lead to improvements in the atmospheric density models.

【 授权许可】

Unknown