Statistical Approach to Observe the Atmospheric Density Variations Using Swarm Satellite Data
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 le...
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| Format: | Article |
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MDPI AG
2020-08-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/11/9/897 |
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| author | Md Wahiduzzaman Alea Yeasmin Jing-Jia Luo Md. Arfan Ali Muhammad Bilal Zhongfeng Qiu |
| author_facet | Md Wahiduzzaman Alea Yeasmin Jing-Jia Luo Md. Arfan Ali Muhammad Bilal Zhongfeng Qiu |
| author_sort | Md Wahiduzzaman |
| collection | DOAJ |
| description | 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<sub>10.7</sub>), 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<sub>10.7</sub> or Kp contributes strongly or weakly to annual density, and the result found a strong (medium) correlation with F<sub>10.7</sub> (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. |
| first_indexed | 2024-03-10T16:53:16Z |
| format | Article |
| id | doaj.art-c23f06a5ef08462ba12f123a51fd3bb8 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-10T16:53:16Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-c23f06a5ef08462ba12f123a51fd3bb82023-11-20T11:14:04ZengMDPI AGAtmosphere2073-44332020-08-0111989710.3390/atmos11090897Statistical Approach to Observe the Atmospheric Density Variations Using Swarm Satellite DataMd Wahiduzzaman0Alea Yeasmin1Jing-Jia Luo2Md. Arfan Ali3Muhammad Bilal4Zhongfeng Qiu5Key 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, ChinaSPACE Research Centre, RMIT University, Melbourne 3001, Victoria, AustraliaKey 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, ChinaSchool of Marine Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaSchool of Marine Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaSchool of Marine Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaOver 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<sub>10.7</sub>), 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<sub>10.7</sub> or Kp contributes strongly or weakly to annual density, and the result found a strong (medium) correlation with F<sub>10.7</sub> (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.https://www.mdpi.com/2073-4433/11/9/897atmospheric densitytemporal variationSwarm missioncorrelation coefficientssolar and geomagnetic indices |
| spellingShingle | Md Wahiduzzaman Alea Yeasmin Jing-Jia Luo Md. Arfan Ali Muhammad Bilal Zhongfeng Qiu Statistical Approach to Observe the Atmospheric Density Variations Using Swarm Satellite Data Atmosphere atmospheric density temporal variation Swarm mission correlation coefficients solar and geomagnetic indices |
| title | Statistical Approach to Observe the Atmospheric Density Variations Using Swarm Satellite Data |
| title_full | Statistical Approach to Observe the Atmospheric Density Variations Using Swarm Satellite Data |
| title_fullStr | Statistical Approach to Observe the Atmospheric Density Variations Using Swarm Satellite Data |
| title_full_unstemmed | Statistical Approach to Observe the Atmospheric Density Variations Using Swarm Satellite Data |
| title_short | Statistical Approach to Observe the Atmospheric Density Variations Using Swarm Satellite Data |
| title_sort | statistical approach to observe the atmospheric density variations using swarm satellite data |
| topic | atmospheric density temporal variation Swarm mission correlation coefficients solar and geomagnetic indices |
| url | https://www.mdpi.com/2073-4433/11/9/897 |
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