The operational and research DTM-2020 thermosphere models

IF 3.4 2区 物理与天体物理 Q2 ASTRONOMY & ASTROPHYSICS
S. Bruinsma, C. Boniface
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引用次数: 17

Abstract

Aims: The semi-empirical Drag Temperature Models (DTM) predict the Earth’s thermosphere’s temperature, density, and composition, especially for orbit computation purposes. Two new models were developed in the framework of the H2020 Space Weather Atmosphere Models and Indices (SWAMI) project. The operational model is driven by the trusted and established F10.7 and Kp indices for solar and geomagnetic activity. The so-called research model is more accurate, but it uses the indices F30 and the hourly Hpo, which are not yet accredited operationally.Methods: The DTM2020 models’ backbone comprises GOCE, CHAMP, and Swarm A densities, processed by TU Delft, and Stella processed in-house. They constitute the standards for absolute densities, and they are 20–30% smaller than the datasets used in the fit of DTM2013. Also, the global daily mean TLE densities at 250 km, spanning four solar cycles, were now used to improve solar cycle variations. The operational model employs the same algorithm as DTM2013, which was obtained through fitting all data in our database from 1967 to 2019. Because of the Hpo index, which is not available before 1995, the coefficients linked to the geomagnetic activity of the research model are fitted to data from 2000 to 2019. The algorithm was updated to take advantage of the higher cadence of Hpo. Both models are assessed with independent data and compared with the COSPAR International Reference Atmosphere models NRLMSISE-00, JB2008, and DTM2013. The bias and precision of the models are assessed through comparison with observations according to published metrics on several time scales. Secondly, binning of the density ratios are used to detect specific model errors. Results: The DTM2020 densities are on average 20–30% smaller than those of DTM2013, NRLMSISE-00, and JB2008. The assessment shows that the research DTM2020 is the least biased and most precise model compared to assimilated data. It is a significant improvement over DTM2013 under all conditions and at all altitudes. This is confirmed by the comparison with independent SET HASDM density data. The operational DTM2020 is always less accurate than the research model except at 800 km altitude. It has comparable or slightly higher precision than DTM2013, despite using F10.7 instead of F30 as solar activity driver. DTM, and semi-empirical models in general, can still be significantly improved on the condition of setting up a more complete and consistent total density, composition, and temperature database than available at this time by means of a well-conceived observing system.
业务和研究DTM-2020热层模型
目的:半经验阻力温度模型(DTM)预测地球热层的温度、密度和组成,特别是用于轨道计算目的。在H2020空间天气大气模式和指数(SWAMI)项目框架内开发了两个新模式。运行模型是由可靠的和已建立的太阳和地磁活动的F10.7和Kp指数驱动的。所谓的研究模型更为准确,但它使用的是F30指数和每小时Hpo指数,这两种指数尚未获得操作上的认可。方法:DTM2020模型的主干包括GOCE、CHAMP和Swarm A密度,由TU Delft处理,Stella内部处理。它们构成了绝对密度的标准,它们比DTM2013拟合中使用的数据集小20-30%。此外,在250公里的全球日平均TLE密度,跨越四个太阳周期,现在被用来改善太阳周期的变化。操作模型采用与DTM2013相同的算法,该算法是通过拟合我们数据库1967 - 2019年的所有数据得到的。由于Hpo指数在1995年之前是不可用的,因此研究模型中与地磁活动相关的系数是根据2000年至2019年的数据拟合的。对算法进行了更新,以利用Hpo的更高节奏。用独立数据对两种模式进行了评估,并与COSPAR国际参考大气模式NRLMSISE-00、JB2008和DTM2013进行了比较。通过与在几个时间尺度上发表的指标的观测结果进行比较,评估了模型的偏差和精度。其次,利用密度比的分组来检测特定的模型误差。结果:DTM2020密度比DTM2013、NRLMSISE-00和JB2008平均低20 ~ 30%;评估结果表明,与同化数据相比,研究DTM2020是偏差最小、精度最高的模型。与DTM2013相比,它在所有条件和所有高度下都有显著改进。与独立的SET HASDM密度数据的比较证实了这一点。除800公里高度外,作战DTM2020的精度总是低于研究模型。尽管使用F10.7而不是F30作为太阳活动驱动,但它的精度与DTM2013相当或略高。在建立更完整和一致的总密度、成分和温度数据库的条件下,通过精心设计的观测系统,DTM和一般的半经验模型仍然可以得到显著的改进。
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来源期刊
Journal of Space Weather and Space Climate
Journal of Space Weather and Space Climate ASTRONOMY & ASTROPHYSICS-GEOCHEMISTRY & GEOPHYSICS
CiteScore
6.90
自引率
6.10%
发文量
40
审稿时长
8 weeks
期刊介绍: The Journal of Space Weather and Space Climate (SWSC) is an international multi-disciplinary and interdisciplinary peer-reviewed open access journal which publishes papers on all aspects of space weather and space climate from a broad range of scientific and technical fields including solar physics, space plasma physics, aeronomy, planetology, radio science, geophysics, biology, medicine, astronautics, aeronautics, electrical engineering, meteorology, climatology, mathematics, economy, informatics.
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