Middle atmosphere density data and comparison with models

1990 ◽  
Vol 10 (6) ◽  
pp. 17-26 ◽  
Author(s):  
K.S.W. Champion
2019 ◽  
Author(s):  
Xuan Cheng ◽  
Junfeng Yang ◽  
Cunying Xiao ◽  
Xiong Hu

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.


Atmosphere ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 341
Author(s):  
Xuan Cheng ◽  
Junfeng Yang ◽  
Cunying Xiao ◽  
Xiong Hu

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.


2020 ◽  
Vol 125 (24) ◽  
Author(s):  
Clara Orbe ◽  
David Rind ◽  
Jeffrey Jonas ◽  
Larissa Nazarenko ◽  
Greg Faluvegi ◽  
...  

2009 ◽  
Vol 15 (1) ◽  
pp. 13-18 ◽  
Author(s):  
A.I. Maslova ◽  
◽  
A.V. Pirozhenko ◽  

Author(s):  
LAKSHMI PRANEETHA

Now-a-days data streams or information streams are gigantic and quick changing. The usage of information streams can fluctuate from basic logical, scientific applications to vital business and money related ones. The useful information is abstracted from the stream and represented in the form of micro-clusters in the online phase. In offline phase micro-clusters are merged to form the macro clusters. DBSTREAM technique captures the density between micro-clusters by means of a shared density graph in the online phase. The density data in this graph is then used in reclustering for improving the formation of clusters but DBSTREAM takes more time in handling the corrupted data points In this paper an early pruning algorithm is used before pre-processing of information and a bloom filter is used for recognizing the corrupted information. Our experiments on real time datasets shows that using this approach improves the efficiency of macro-clusters by 90% and increases the generation of more number of micro-clusters within in a short time.


1985 ◽  
Author(s):  
I. NOLT ◽  
J. RADOSTITZ ◽  
K.V. CHANCE ◽  
W. TRAUB ◽  
P. ADE

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