Mariangel Fedrizzi

Global Joule heating index derived from thermospheric density physics‐based modeling and observations

The primary operational impact of upper atmospheric neutral density variability is on satellite drag. Drag is the most difficult force to model mainly because of the complexity of neutral atmosphere variations driven by solar UV and EUV radiation power, magnetospheric energy input, and the propagation from below of lower atmosphere waves. Taking into account the self-consistent interactions between neutral winds, composition, ion drifts, and ionization densities, first-principles models are able to provide a more realistic representation of neutral density than empirical models in the upper atmosphere. Their largest sources of uncertainty, however, are the semiannual variations in neutral density and the magnitude, spatial distribution, and temporal evolution of the magnetospheric energy input. In this study, results from the physics-based coupled thermosphere-ionosphere-plasmasphere electrodynamics (CTIPe) model and measurements from the CHAMP satellite are compared and used to improve the modeled thermospheric neutral density estimates. The good agreement between modeled and observed densities over an uninterrupted yearlong period of variable conditions gives confidence that the thermosphere-ionosphere system energy influx from solar radiation and magnetospheric sources is reasonable and that Joule heating, the dominant source during geomagnetically disturbed conditions, is appropriately estimated. On the basis of the correlation between neutral density and energy injection, a global time-dependent Joule heating index (JHI) is derived from the relationship between Joule heating computed by the CTIPe model and neutral density measured by the CHAMP satellite. Preliminary results show an improvement in density estimates using CTIPe JHI, demonstrating its potential for neutral density modeling applied to atmospheric drag determination.

A real‐time run of the Coupled Thermosphere Ionosphere Plasmasphere Electrodynamics (CTIPe) model

[1] The availability of unprecedented amounts of real-time data from Global Navigation Satellite Systems and ionosondes coupled with new and more stringent requirements for specification and forecast of the neutral and electron densities in the thermosphere-ionosphere system are driving a new wave of development in data assimilation schemes for the thermosphere and ionosphere. However, such schemes require accurate knowledge of any biases affecting the state-propagating models, and characterizing such biases involves significant effort. A first step in the estimation of the model biases, a steady state neutral temperature comparison with the empirical Mass Spectrometer Incoherent Scatter model, was published in Space Weather in 2008. Here we present another step in the validation of the Coupled Thermosphere Ionosphere Plasmasphere Electrodynamics (CTIPe) general circulation model in preparation for its future inclusion in a data assimilation scheme. We describe an implementation of the model at the Space Weather Prediction Center (SWPC) and present real-time comparisons between CTIPe and GPS total electron content and F2 layer ionosonde measurements. The CTIPe results are generated automatically about 20 min ahead of real time. The model inputs are based on NASA’s Advanced Composition Explorer and F10.7 data available in the SWPC database. The results and the comparison with measurements for the current 2-week period are available at http://helios.swpc.noaa.gov/ctipe/. The results are quite encouraging and offer hope that physics-based models can compete with empirical models during quiet times and have tremendous potential to provide more reliable forecasts during periods of geomagnetic disturbance.

FIVE YEARS of SYNTHESIS of SOLAR SPECTRAL IRRADIANCE from SDID/SISA and SDO/AIA IMAGES

In this paper we describe the synthetic solar spectral irradiance (SSI) calculated from 2010 to 2015 using data from the Atmospheric Imaging Assembly (AIA) instrument, on board the Solar Dynamics Observatory spacecraft. We used the algorithms for solar disk image decomposition (SDID) and the spectral irradiance synthesis algorithm (SISA) that we had developed over several years. The SDID algorithm decomposes the images of the solar disk into areas occupied by nine types of chromospheric and 5 types of coronal physical structures. With this decomposition and a set of pre-computed angle-dependent spectra for each of the features, the SISA algorithm is used to calculate the SSI. We discuss the application of the basic SDID/SISA algorithm to a subset of the AIA images and the observed variation occurring in the 2010–2015 period of the relative areas of the solar disk covered by the various solar surface features. Our results consist of the SSI and total solar irradiance variations over the 2010–2015 period. The SSI results include soft X-ray, ultraviolet, visible, infrared, and far-infrared observations and can be used for studies of the solar radiative forcing of the Earths atmosphere. These SSI estimates were used to drive a thermosphere–ionosphere physical simulation model. Predictions of neutral mass density at low Earth orbit altitudes in the thermosphere and peak plasma densities at mid-latitudes are in reasonable agreement with the observations. The correlation between the simulation results and the observations was consistently better when fluxes computed by SDID/SISA procedures were used.

CEDAR-GEM Challenge for Systematic Assessment of Ionosphere/Thermosphere Models in Predicting TEC During the 2006 December Storm Event

In order to assess current modeling capability of reproducing storm impacts on TEC, we considered quantities such as TEC, TEC changes compared to quiet time values, and the maximum value of the TEC and TEC changes during a storm. We compared the quantities obtained from ionospheric models against ground-based GPS TEC measurements during the 2006 AGU storm event (14-15 Dec., 2006) in the selected eight longitude sectors. We used 15 simulations obtained from eight ionospheric models, including empirical, physics-based, coupled ionosphere-thermosphere and data assimilation models. To quantitatively evaluate performance of the models in TEC prediction during the storm, we calculated skill scores such as RMS error, Normalized RMS error (NRMSE), ratio of the modeled to observed maximum increase (Yield), and the difference between the modeled peak time and observed peak time. Furthermore, to investigate latitudinal dependence of the performance of the models, the skill scores were calculated for five latitude regions. Our study shows that RMSE of TEC and TEC changes of the model simulations range from about 3 TECU (in high latitudes) to about 13 TECU (in low latitudes), which is larger than latitudinal average GPS TEC error of about 2 TECU. Most model simulations predict TEC better than TEC changes in terms of NRMSE and the difference in peak time, while the opposite holds true in terms of Yield. Model performance strongly depends on the quantities considered, the type of metrics used, and the latitude considered.