Atmospheric Density Measurement in the Middle Atmosphere

Abstract. Hauchecorne and Chanin (1980) developed a robust method to calculate middle-atmosphere temperature profiles using measurements from Rayleigh-scatter lidars. This traditional method has been successfully used to greatly improve our understanding of middle-atmospheric dynamics, but the method has some shortcomings regarding the calculation of systematic uncertainties and the vertical resolution of the retrieval. Sica and Haefele (2015) have shown that the optimal estimation method (OEM) addresses these shortcomings and allows temperatures to be retrieved with confidence over a greater range of heights than the traditional method. We have calculated a temperature climatology from 519 nights of Purple Crow Lidar Rayleigh-scatter measurements using an OEM. Our OEM retrieval is a first-principle retrieval in which the forward model is the lidar equation and the measurements are the level-0 count returns. It includes a quantitative determination of the top altitude of the retrieved temperature profiles, the evaluation of nine systematic plus random uncertainties, and the vertical resolution of the retrieval on a profile-by-profile basis. Our OEM retrieval allows for the vertical resolution to vary with height, extending the retrieval in altitude 5 to 10 km higher than the traditional method. It also allows the comparison of the traditional method's sensitivity to two in-principle equivalent methods of specifying the seed pressure: using a model pressure seed versus using a model temperature combined with the lidar's density measurement to calculate the seed pressure. We found that the seed pressure method is superior to using a model temperature combined with the lidar-derived density. The increased altitude capability of our OEM retrievals allows for a comparison of the Rayleigh-scatter lidar temperatures throughout the entire altitude range of the sodium lidar temperature measurements. Our OEM-derived Rayleigh temperatures are shown to have improved agreement relative to our previous comparisons using the traditional method, and the agreement of the OEM-derived temperatures is the same as the agreement between existing sodium lidar temperature climatologies. This detailed study of the calculation of the new Purple Crow Lidar temperature climatology using the OEM establishes that it is both highly advantageous and practical to reprocess existing Rayleigh-scatter lidar measurements that cover long time periods, during which time the lidar may have undergone several significant equipment upgrades, while gaining an upper limit to useful temperature retrievals equivalent to an order of magnitude increase in power-aperture product due to the use of an OEM.

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A Simulation Experiment to Retrieve the Atmospheric Density and Three-Dimensional Wind Field by Double Falling Spheres

Atmosphere ◽

10.3390/atmos11121312 ◽

2020 ◽

Vol 11 (12) ◽

pp. 1312

Author(s):

Yue Wu ◽

Zheng Sheng ◽

Xinjie Zuo ◽

Minghao Yang

Keyword(s):

Wind Field ◽

Simulation Experiment ◽

Middle Atmosphere ◽

Three Dimensional ◽

Optimal Solution ◽

Vertical Wind ◽

Horizontal Wind ◽

Atmospheric Density ◽

Falling Sphere ◽

Falling Spheres

Falling-sphere sounding remains an important method for in situ determination in the middle atmosphere and is the only determination method within the altitude range of 60–100 km. Traditional single-falling-sphere sounding indicates only the atmospheric density and horizontal wind but not the vertical wind; the fundamental reason is that the equation set for retrieving atmospheric parameters is underdetermined. For tractability, previous studies assumed the vertical wind, which is much smaller than the horizontal wind, to be small or zero. Obtaining vertical wind profiles necessitates making the equations positive definite or overdetermined. An overdetermined equation set consisting of six equations, by which the optimal solution of density and three-dimensional wind can be obtained, can be established by the double-falling-sphere method. Hence, a simulation experiment is designed to retrieve the atmospheric density and three-dimensional wind field by double falling spheres. In the inversion results of the simulation experiment, the retrieved density is consistent with the constructed atmospheric density in magnitude; the density deviation rate does not generally exceed 20% (less than 5% below 60 km). The atmospheric density retrieved by the double-falling-sphere method is more accurate at low altitudes than the single-falling-sphere method. The vertical wind below 50 km and horizontal wind retrieved by double-falling-sphere method is highly consistent with the constructed average wind field. Additionally, the wind field deviation formula is deduced. These results establish the fact that the double-falling-sphere method is effective in detecting atmospheric density and three-dimensional wind.

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Global change induced trends in ion composition of the troposphere to the lower thermosphere

Annales Geophysicae ◽

10.5194/angeo-26-1181-2008 ◽

2008 ◽

Vol 26 (5) ◽

pp. 1181-1187 ◽

Cited By ~ 5

Author(s):

G. Beig

Keyword(s):

Middle Atmosphere ◽

Anthropogenic Activities ◽

Lower Thermosphere ◽

Cluster Ions ◽

Atmospheric Density ◽

Mesosphere And Lower Thermosphere ◽

Induced Changes ◽

Positive Ion ◽

Mlt Region

Abstract. In this paper a brief overview of the changes in atmospheric ion compositions driven by the human-induced changes in related neutral species, and temperature from the troposphere to lower thermosphere has been made. It is found that ionic compositions undergo significant variations. The variations calculated for the double-CO2 scenario are both long-term and permanent in nature. Major neutrals which take part in the lower and middle atmospheric ion chemical schemes and undergo significant changes due to anthropogenic activities are: O, O2, H2O, NO, acetonitrile, pyridinated compounds, acetone and aerosol. The concentration of positive ion/electron density does not change appreciably in the middle atmosphere but indicates a marginal decrease above about 75 km until about 85 km, above which the magnitude of negative trend decreases and becomes negligible at 93 km. Acetonitrile cluster ions in the upper stratosphere are likely to increase, whereas NO+ and NO+(H2O) in the mesosphere and lower thermosphere (MLT) region are expected to decrease for the double CO2 scenario. It is also found that the atmospheric density of pyridinated cluster ions is fast rising in the troposphere.

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Density correction of NRLMSISE-00 in the middle atmosphere (20–100 km) based on TIMED/SABER density data

10.5194/angeo-2019-93 ◽

2019 ◽

Author(s):

Xuan Cheng ◽

Junfeng Yang ◽

Cunying Xiao ◽

Xiong Hu

Keyword(s):

Correction Factor ◽

Middle Atmosphere ◽

Geomagnetic Storms ◽

Correction Function ◽

Quiet Period ◽

Atmospheric Density ◽

Density Data ◽

Density Correction ◽

Correction Effect ◽

Relative Errors

Abstract. This paper describes the density correction of the NRLMSISE-00 using more than 15 years (2002–2016) of TIMED/SABER satellite atmospheric density data from the middle atmosphere (20–100 km). A bias correction factor dataset is established based on the density differences between the TIMED/SABER data and NRLMSISE-00. Seven height nodes are set in the range 20–100 km. The different scale oscillations of the correction factor are separated at each height node, and the spherical harmonic function is used to fit the coefficients of the different timescale oscillations to obtain a spatiotemporal function at each height node. Cubic spline interpolation is used to obtain the correction factor at other heights. The spatiotemporal correction function proposed in this paper achieves a good correction effect on the atmospheric density of the NRLMSISE-00 model. The correction effect becomes more pronounced as the height increases. After correction, the relative error of the model decreased by 40–50 % in July, especially at ±40° N in the 80–100 km region. The atmospheric model corrected by the spatiotemporal function achieves higher accuracy for forecasting the atmospheric density during different geomagnetic activities. During geomagnetic storms, the relative errors in atmospheric density at 100 km, 72 km, and 32 km decrease from 41.21 %, 28.56 %, and 3.03 % to −9.65 %, 5.38 %, and 1.44 %, respectively, after correction. The relative errors in atmospheric density at 100 km, 72 km, and 32 km decrease from 68.95 %, 24.98 %, and 3.56 % to 3.49 %, 3.02 %, and 1.77 %, respectively, during geomagnetic quiet period. The correction effect during geomagnetic quiet period is better than that during geomagnetic storms at a height of 100 km. The subsequent effects of geomagnetic activity will be considered, and the atmospheric density during magnetic storms and quiet periods is corrected separately near 100 km. The ability of the model to characterize the mid-atmosphere (20–100 km) is significantly improved compared with the pre-correction performance. As a result, the corrected NRLMSISE-00 can provide more reliable atmospheric density data for scientific research and engineering fields such as data analysis, instrument design, and aerospace vehicles.

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Lidar measurement for atmospheric density and temperature in middle atmosphere over Hefei

Infrared and Laser Engineering ◽

10.3788/irla201746.0730003 ◽

2017 ◽

Vol 46 (7) ◽

pp. 730003

Author(s):

邓潘 Deng Pan ◽

张天舒 Zhang Tianshu ◽

陈卫 Chen Wei ◽

刘洋 Liu Yang

Keyword(s):

Middle Atmosphere ◽

Lidar Measurement ◽

Atmospheric Density

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Density Correction of NRLMSISE-00 in the Middle Atmosphere (20–100 km) Based on TIMED/SABER Density Data

Atmosphere ◽

10.3390/atmos11040341 ◽

2020 ◽

Vol 11 (4) ◽

pp. 341

Author(s):

Xuan Cheng ◽

Junfeng Yang ◽

Cunying Xiao ◽

Xiong Hu

Keyword(s):

Geomagnetic Activity ◽

Correction Factor ◽

Middle Atmosphere ◽

Geomagnetic Storms ◽

Correction Function ◽

Quiet Period ◽

Atmospheric Density ◽

Density Data ◽

Correction Effect ◽

Relative Errors

This paper describes the density correction of the NRLMSISE-00 using more than 15 years (2002–2016) of TIMED/SABER satellite atmospheric density data from the middle atmosphere (20–100 km). A bias correction factor dataset is established based on the density differences between the TIMED/SABER data and NRLMSISE-00. Seven height nodes are set in the range between 20 and 100 km. The different scale oscillations of the correction factor are separated at each height node, and the spherical harmonic function is used to fit the coefficients of the different timescale oscillations to obtain a spatiotemporal function at each height node. Cubic spline interpolation is used to obtain the correction factor at other non-node heights. The spatiotemporal correction function depends on six key parameters, including height, latitude, longitude, local time, day, and year. The evaluation results show that the spatiotemporal correction function proposed in this paper achieves a good correction effect on the atmospheric density of NRLMSISE-00. The correction effect becomes more pronounced as the height increases. After correction, the relative error of the model decreased by 40%–50% in July, especially at ±40° N in the 80–100 km region. The correction effect of the spatiotemporal correction function under different geomagnetic activity may have some potential relationships with geomagnetic activities. During geomagnetic storms, the relative errors in atmospheric density at 100, 70, and 32 km decrease from 41.21%, 22.09%, and 3.03% to −9.65%, 2.60%, and 1.44%, respectively, after correction. The relative errors in atmospheric density at 100, 70, and 32 km decrease from 68.95%, 21.02%, and 3.56% to 3.49%, 2.20%, and 1.77%, respectively, during the geomagnetic quiet period. The correction effect during the geomagnetic quiet period is better than that during geomagnetic storms at a height of 100 km. The subsequent effects of geomagnetic activity will be considered, and the atmospheric density during magnetic storms and quiet periods will be corrected separately near 100 km. The ability of the model to characterize the mid-atmosphere (20–100 km) is significantly improved compared with the pre-correction performance. As a result, the corrected NRLMSISE-00 can provide more reliable atmospheric density data for scientific researches and engineering fields, such as data analysis, instrument design, and aerospace vehicles.

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

Atmosphere ◽

10.3390/atmos11090897 ◽

2020 ◽

Vol 11 (9) ◽

pp. 897

Author(s):

Md Wahiduzzaman ◽

Alea Yeasmin ◽

Jing-Jia Luo ◽

Md. Arfan Ali ◽

Muhammad Bilal ◽

...

Keyword(s):

Correlation Coefficient ◽

Large Scale ◽

Density Measurement ◽

Atmospheric Density ◽

Physical Mechanisms ◽

Swarm Satellites ◽

Atmospheric Density Models ◽

Swarm Satellite ◽

Level 2 ◽

Medium Correlation

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 (F10.7), 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 F10.7 or Kp contributes strongly or weakly to annual density, and the result found a strong (medium) correlation with F10.7 (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.

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