A statistical study of the observed and modeled global thermosphere response to magnetic activity at middle and low latitudes

International audience[1] From one year (2004) of thermosphere total density data inferred from CHAMP/ STAR accelerometer measurements, we calculate the global thermosphere response to auroral magnetic activity forcing at middle and low latitudes using a method based on a singular value decomposition of the satellite data. This method allows separating the large-scale spatial variations in the density, mostly related to altitude/latitude variations and captured by the first singular component, from the time variations, down to timescales on the order of the orbital period, which are captured by the associated projection coefficient. This projection coefficient is used to define a disturbance coefficient that characterizes the global thermospheric density response to auroral forcing. For quiet to moderate magnetic activity levels (Kp < 6), we show that the disturbance coefficient is better correlated with the magnetic am indices than with the magnetic ap indices. The latter index is used in all empirical thermosphere models to quantify the auroral forcing. It is found that the NRLMSISE-00 model correctly estimates the main features of the thermosphere density response to geomagnetic activity, i.e., the morphology of Universal Time variations and the larger relative increase during nighttime than during daytime. However, it statistically underestimates the amplitude of the thermosphere density response by about 50%. This underestimation reaches 200% for specific disturbed periods. It is also found that the difference between daytime and nighttime responses to auroral forcing can statistically be explained by local differences in magnetic activity as described by the longitude sector magnetic indices. Citation: Lathuillère, C., M. Menvielle, A. Marchaudon, and S. Bruinsma (2008), A statistical study of the observed and modeled global thermosphere response to magnetic activity at middle and low latitudes

Atmospheric gravity waves due to the Tohoku-Oki tsunami observed in the thermosphere by GOCE

International audienceOceanic tsunami waves couple with atmospheric gravity waves, as previously observedthrough ionospheric and airglow perturbations. Aerodynamic velocities and density variations are computed from Gravity Field and Steady-State Ocean Circulation Explorer (GOCE) accelerometer and thruster data during Tohoku-Oki tsunami propagation. High-frequency perturbations of these parameters are observed during three expected crossings of the tsunami-generated gravity waves by the GOCE satellite. From theoretical relations between air density and vertical and horizontal velocities inside the gravity wave, we demonstrate that the measured perturbations are consistent with a gravity wave generated by the tsunami and provide a way to estimate the propagation azimuth of the gravity wave. Moreover, because GOCE measurements can constrain the wave polarization, a marker (noted C 3 ) of any gravity wave crossing by the GOCE satellite is constructed from correlation coefficients between the observed atmospheric state parameters. These observations validate a new observation tool of thermospheric gravity waves generated by tsunamis above the open ocean

Synoptic solar radio observations as proxies for upper atmosphere modelling

The specification of the upper atmosphere strongly relies on solar proxies that can properly reproduce the solar energetic input in the UV. Whilst the microwave flux at 10.7 cm (also called F10.7 index) has been routinely used as a solar proxy, we show that the radio flux at other wavelengths provides valuable complementary information that enhances their value for upper atmospheric modelling. We merged daily observations from various observatories into a single homogeneous data set of fluxes at wavelengths of 30, 15, 10.7, 8 and 3.2 cm, spanning from 1957 to today. Using blind source separation (BSS), we show that their rotational modulation contains three contributions, which can be interpreted in terms of thermal bremsstrahlung and gyro-resonance emissions. The latter account for 90% of the rotational variability in the F10.7 index. Most solar proxies, such as the MgII index, are remarkably well reconstructed by simple linear combination of radio fluxes at various wavelengths. The flux at 30 cm stands out as an excellent proxy and is better suited than the F10.7 index for the modelling the thermosphere-ionosphere system, most probably because it receives a stronger contribution from thermal bremsstrahlung. This better performance is illustrated here through comparison between the observed thermospheric density, and reconstructions by the Drag Temperature Model.Comment: 13 page

Medium-scale gravity wave activity in the thermosphere inferred from GOCE data

This study is focused on the effect of solar flux conditions on the dynamics of gravity waves (GWs) in the thermosphere. Air density and crosswind in situ estimates from the Gravity Field and Steady-State Ocean Circulation Explorer (GOCE) accelerometers are analyzed for the whole mission duration. The analysis is performed in the Fourier spectral domain averaging spectral results over periods of 2 months close to solstices. A new GW marker (called C3f) is introduced here to characterize GWs activity under low, medium, and high solar flux conditions, showing a clear solar damping effect on GW activity. Most GW signal is found in a spectral range above 8 mHz in GOCE data, meaning a maximum horizontal wavelength of around 1000 km. The level of GW activity at GOCE altitude is strongly decreasing with increasing solar flux. Furthermore, a shift in the dominant frequency with solar flux conditions has been noted, leading to larger horizontal wavelengths (from 200 to 500 km) during high solar flux conditions. The correlation between air density variability and GW marker allows to identify most of the large-amplitude perturbations below 67∘ latitudes as due to GWs. The influence of correlated error sources, between air density and crosswinds, is discussed. Consistency of the spectral domain results is verified in the time domain with a global mapping of high-frequency air density perturbations along the GOCE orbit. This analysis shows a clear dependence with geomagnetic latitude with strong perturbations at magnetic poles and an extension to lower latitudes favored by low solar activity conditions. These results are consistent with previous Challenging Minisatellite Payload (CHAMP) data analysis and with general circulation models

TOLEOS: Thermosphere Observations from Low-Earth Orbiting Satellites

The objective of the TOLEOS project is to process the CHAMP, GRACE, and GRACE-FO accelerometer measurements with improved processing standards to obtain thermosphere density and crosswind data products. These new data products will cover the entirety of the accelerometer missions and complement the existing ESA databases for Swarm and GOCE. The improvements in the processing focus on the radiation pressure modelling, which is expected to have a significant effect on the density and crosswind data, in particular at altitudes above 450 km during solar minimum conditions. Substantial validation activities are performed since the project’s start in June 2021 and will continue until the end of the project in July 2022

European Gravity Service for Improved Emergency Management

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Preliminary Validation of Thermosphere Observations from the TOLEOS Project

OBSERVATIONS of upper atmospheric neutral mass density (NMD) and wind are critical to understand the coupling mechanisms between Earth’s ionosphere, thermosphere, and magnetosphere. The ongoing Swarm DISC (data, innovation, and science cluster) project TOLEOS (thermosphere observations from low-Earth orbiting satellites) aims to provide better calibrated NMD and crosswind data from CHAMP, GRACE, and GRACE-FO (follow-on) satellite missions. The project uses state-of-the-art models, calibration techniques, and processing standards to improve the accuracy of these data products and ensure inter-mission consistency. Here, we present preliminary results of the quality of the data in comparison to the high accuracy drag temperature model DTM2020, and physics-based TIE-GCM (thermosphere ionosphere electrodynamics general circulation model) and CTIPe (coupled thermosphere ionosphere plasmasphere electrodynamics) models

Model Evaluation Guidelines for Geomagnetic Index Predictions

Geomagnetic indices are convenient quantities that distill the complicated physics of some region or aspect of near‐Earth space into a single parameter. Most of the best‐known indices are calculated from ground‐based magnetometer data sets, such as Dst, SYM‐H, Kp, AE, AL, and PC. Many models have been created that predict the values of these indices, often using solar wind measurements upstream from Earth as the input variables to the calculation. This document reviews the current state of models that predict geomagnetic indices and the methods used to assess their ability to reproduce the target index time series. These existing methods are synthesized into a baseline collection of metrics for benchmarking a new or updated geomagnetic index prediction model. These methods fall into two categories: (1) fit performance metrics such as root‐mean‐square error and mean absolute error that are applied to a time series comparison of model output and observations and (2) event detection performance metrics such as Heidke Skill Score and probability of detection that are derived from a contingency table that compares model and observation values exceeding (or not) a threshold value. A few examples of codes being used with this set of metrics are presented, and other aspects of metrics assessment best practices, limitations, and uncertainties are discussed, including several caveats to consider when using geomagnetic indices.Plain Language SummaryOne aspect of space weather is a magnetic signature across the surface of the Earth. The creation of this signal involves nonlinear interactions of electromagnetic forces on charged particles and can therefore be difficult to predict. The perturbations that space storms and other activity causes in some observation sets, however, are fairly regular in their pattern. Some of these measurements have been compiled together into a single value, a geomagnetic index. Several such indices exist, providing a global estimate of the activity in different parts of geospace. Models have been developed to predict the time series of these indices, and various statistical methods are used to assess their performance at reproducing the original index. Existing studies of geomagnetic indices, however, use different approaches to quantify the performance of the model. This document defines a standardized set of statistical analyses as a baseline set of comparison tools that are recommended to assess geomagnetic index prediction models. It also discusses best practices, limitations, uncertainties, and caveats to consider when conducting a model assessment.Key PointsWe review existing practices for assessing geomagnetic index prediction models and recommend a “standard set” of metricsAlong with fit performance metrics that use all data‐model pairs in their formulas, event detection performance metrics are recommendedOther aspects of metrics assessment best practices, limitations, uncertainties, and geomagnetic index caveats are also discussedPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147764/1/swe20790_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147764/2/swe20790.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147764/3/swe20790-sup-0001-2018SW002067-SI.pd