Abstract
The Cassini Orbiter Ultraviolet Imaging Spectrograph (UVIS) obtained interplanetary hydrogen Ly
α
observations from 1999 to 2017, with mid-2004 to 2017 observations obtained from Saturn ...orbit. During its Saturn orbital phase, the spacecraft moved from mostly downwind and sidewind in the heliosphere to upwind. We analyze the full set of observations with our existing hot hydrogen density model with a solar illumination model most recently used to study Solar and Heliospheric Observatory Solar Wind Anisotropy Experiment data and selected Cassini UVIS observations from 2003 to 2004. We find general agreement between data and model, but with evidence for a decline in UVIS Ly
α
sensitivity, with a significant decline in 2002 June during a starburn event and an overall roughly linear decline in sensitivity. While earlier work by Pryor et al. fit the UVIS Ly
α
data from 2003 to 2004 with a hydrogen density in the outer heliosphere (but after filtration at outer heliospheric boundaries) of 0.085 cm
−3
using the UVIS laboratory sensitivity calibration, including the sensitivity decline found here leads to a revised hydrogen density estimate of
n
H
= 0.14 ± 0.03 cm
−3
. This density estimate is consistent with a recent neutral hydrogen density estimate near the termination shock of 0.127 ± 0.015 cm
−3
based on models of observations of pick-up hydrogen ions from the New Horizons spacecraft.
The Solar EUV Experiment (SEE) is one of four scientific instruments on the NASA Thermosphere Ionosphere Mesosphere Energetics Dynamics (TIMED) spacecraft, which has been simultaneously observing the ...Sun and Earth's upper atmosphere since January 2002. The SEE instrument measures the irradiance of the highly variable, solar extreme ultraviolet (EUV) radiation, one of the major energy sources for the upper atmosphere. The primary SEE data product is the solar spectral irradiances from 0.1 to 194 nm in 1 nm intervals that are fundamental for the TIMED mission's investigation of the energetics in the tenuous, but highly variable, layers of the Earth's atmosphere above 60 km. The TIMED mission began normal operations on 22 January 2002, a time when the Sun displayed maximum levels of activity for solar cycle 23, and has provided daily measurements as solar activity has declined to moderate levels. Solar irradiance variability observed by SEE during the 2 years of the TIMED prime mission includes a variety of moderate and large flares over periods of seconds to hours and dozens of solar rotational cycles over a typical period of 27 days. The SEE flare measurements provide important, new results because of the simultaneous spectral coverage from 0.1 to 194 nm, albeit limited temporal coverage due to its 3% duty cycle. In addition, the SEE measurements reveal important, new results concerning phase shifts of 2–7 days in the intermediate‐term variations between different UV wavelengths that appear to be related to their different center‐to‐limb variations. The new solar EUV irradiance time series from SEE are also important in filling the “EUV Hole,” which is the gap in irradiance measurements in the EUV spectrum since the 1980s. The solar irradiances measured by SEE (Version 7, released July 2004) are compared with other measurements and predictions from models of the solar EUV irradiance. While the measurement comparisons show reasonable agreement, there are significant differences between SEE and some of the models in the EUV range. The data processing algorithms and calibrations are also discussed.
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 Summary
One 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 Points
We review existing practices for assessing geomagnetic index prediction models and recommend a “standard set” of metrics
Along with fit performance metrics that use all data‐model pairs in their formulas, event detection performance metrics are recommended
Other aspects of metrics assessment best practices, limitations, uncertainties, and geomagnetic index caveats are also discussed
The Community Coordinated Modeling Center has been leading communitywide space science and space weather model validation projects for many years. These efforts have been broadened and extended via ...the newly launched International Forum for Space Weather Modeling Capabilities Assessment (https://ccmc.gsfc.nasa.gov/assessment/). Its objective is to track space weather models' progress and performance over time, a capability that is critically needed in space weather operations and different user communities in general. The Space Radiation and Plasma Effects Working Team of the afore mentioned International Forum works on one of the many focused evaluation topics and deals with five different subtopics (https://ccmc.gsfc.nasa.gov/assessment/topics/radiationall.php) and varieties of particle populations: Surface Charging from tens of eV to 50keV electrons and internal charging due to energetic electrons from hundreds keV to several MeVs. Single event effects from solar energetic particles and galactic cosmic rays (several MeV to TeV), total dose due to accumulation of doses from electrons (>100 keV) and protons (>1 MeV) in a broad energy range, and radiation effects from solar energetic particles and galactic cosmic rays at aviation altitudes. A unique aspect of the Space Radiation and Plasma Effects focus area is that it bridges the space environments, engineering, and user communities. The intent of the paper is to provide an overview of the current status and to suggest a guide for how to best validate space environment models for operational/engineering use, which includes selection of essential space environment and effect quantities and appropriate metrics.
The JB2006 empirical thermospheric density model Bowman, Bruce R.; Kent Tobiska, W.; Marcos, Frank A. ...
Journal of atmospheric and solar-terrestrial physics,
03/2008, Letnik:
70, Številka:
5
Journal Article
Recenzirano
A new empirical atmospheric density model is developed using the CIRA72 (Jacchia 71) model as the basis for the diffusion equations. New solar indices based on orbit-based sensor data are used for ...the solar irradiances in the extreme and far ultraviolet wavelengths. New exospheric temperature and semiannual density equations are employed to represent the major thermospheric density variations. Temperature correction equations are also developed for diurnal and latitudinal effects, and finally density correction factors are used for model corrections required at high altitude (1500–4000
km). The new model, Jacchia–Bowman 2006, is validated through comparisons of accurate daily density drag data previously computed for numerous satellites. For 400
km altitude the standard deviation of 16% for the standard Jacchia model is reduced to 10% for the new JB2006 model for periods of low geomagnetic storm activity.
New solar extreme-ultraviolet (EUV) irradiance observations from the NASA Solar Dynamics Observatory (SDO) EUV Variability Experiment provide full coverage in the EUV range from 0.1 to 106 nm and ...continuously at a cadence of 10 s for spectra at 0.1 nm resolution and even faster, 0.25 s, for six EUV bands. These observations can be decomposed into four distinct characteristics during flares. First, the emissions that dominate during the flare's impulsive phase are the transition region emissions, such as the He II 30.4 nm. Second, the hot coronal emissions above 5 MK dominate during the gradual phase and are highly correlated with the GOES X-ray. A third flare characteristic in the EUV is coronal dimming, seen best in the cool corona, such as the Fe IX 17.1 nm. As the post-flare loops reconnect and cool, many of the EUV coronal emissions peak a few minutes after the GOES X-ray peak. One interesting variation of the post-eruptive loop reconnection is that warm coronal emissions (e.g., Fe XVI 33.5 nm) sometimes exhibit a second large peak separated from the primary flare event by many minutes to hours, with EUV emission originating not from the original flare site and its immediate vicinity, but rather from a volume of higher loops. We refer to this second peak as the EUV late phase. The characterization of many flares during the SDO mission is provided, including quantification of the spectral irradiance from the EUV late phase that cannot be inferred from GOES X-ray diagnostics.
New solar indices have been developed to improve thermospheric density modeling for research and operational purposes. Out of 11 new and 4 legacy indices and proxies, we have selected 3 (
F
10.7,
S
...10.7, and
M
10.7) for use in the new JB2006 empirical thermospheric density model. In this work, we report on the development of these solar irradiance indices. The rationale for their use, their definitions, and their characteristics, including the IS 21348:2007 spectral category and sub-category, wavelength range, solar source temperature region, solar source feature, altitude region of terrestrial atmosphere absorption at unit optical depth, and terrestrial atmosphere thermal processes in the region of maximum energy absorption, are described. We also summarize for each solar index the facility and instrument(s) used to observe the solar emission, the time frame over which the data exist, the measurement cadence, the data latency, and the research as well as operational availability. The new solar indices are provided in forecast as well as real time and historical time frames (
http://SpaceWx.com JB2006 Quicklink). We describe the forecast methodology, compare results with actual data for active and quiet solar conditions, and compare improvements in
F
10.7 forecasting with legacy HASDM and NOAA SWPC forecasts.
We present a new high‐resolution empirical model for the ionospheric total electron content (TEC). TEC data are obtained from the global navigation satellite system (GNSS) receivers with a 1° × 1° ...spatial resolution and 5‐min temporal resolution. The linear regression model is developed at 45°N, 0°E for the years 2000–2019 with 30‐min temporal resolution, unprecedented for typical empirical ionospheric models. The model describes dependency of TEC on solar flux, season, geomagnetic activity, and local time. Parameters describing solar and geomagnetic activity are evaluated. In particular, several options for solar flux input to the model are compared, including the 10.7 cm solar radio flux (F10.7), the Mg II core‐to‐wing ratio, and formulations of the solar extreme ultraviolet flux (EUV). Ultimately, the extreme ultraviolet flux presented by the Flare Irradiance Spectral Model, integrated from 0.05 to 105.05 nm, best represents the solar flux input to the model. TEC time delays to this solar parameter on the order of several days as well as seasonal modulation of the solar flux terms are included. The Ap3 index and its history are used to reflect the influence of geomagnetic activity. The root mean squared error of the model (relative to the mean TEC observed in the 30‐min window) is 1.9539 TECu. A validation of this model for the first 3 months of 2020 shows excellent agreement with data. The new model shows significant improvement over the International Reference Ionosphere 2016 (IRI‐2016) when the two are compared during 2008 and 2012.
Key Points
The study introduces a formulation of new high‐resolution empirical model for total electron content
The impact of several solar flux proxies on the modeling of total electron content is examined
The FISM2 EUV flux is found to perform the best, closely followed by S10.7 index and the Mg II index
Air safety is tied to the phenomenon of ionizing radiation from space weather, primarily from galactic cosmic rays but also from solar energetic particles. A global framework for addressing radiation ...issues in this environment has been constructed, but more must be done at international and national levels. Health consequences from atmospheric radiation exposure are likely to exist. In addition, severe solar radiation events may cause economic consequences in the international aviation community due to exposure limits being reached by some crew members. Impacts from a radiation environment upon avionics from high‐energy particles and low‐energy, thermalized neutrons are now recognized as an area of active interest. A broad community recognizes that there are a number of mitigation paths that can be taken relative to the human tissue and avionics exposure risks. These include developing active monitoring and measurement programs as well as improving scientific modeling capabilities that can eventually be turned into operations. A number of roadblocks to risk mitigation still exist, such as effective pilot training programs as well as monitoring, measuring, and regulatory measures. An active international effort toward observing the weather of atmospheric radiation must occur to make progress in mitigating radiation exposure risks. Stakeholders in this process include standard‐making bodies, scientific organizations, regulatory organizations, air traffic management systems, aircraft owners and operators, pilots and crew, and even the public.
The goal of this study was to characterize the thermospheric semiannual density response to solar heating during the last 35 years. Historical radar observational data have been processed with ...special orbit perturbations on 28 satellites with perigee heights ranging from 200 to 1100
km. Approximately 225,000 very accurate average daily density values at perigee have been obtained for all satellites using orbit energy dissipation rates. The semiannual variation has been found to be extremely variable from year to year. The magnitude of the maximum yearly difference, from the July minimum to the October maximum, is used to characterize the yearly semiannual variability. It has been found that this maximum difference can vary by as much as 100% from one year to the next. A high correlation has been found between this maximum difference and solar EUV data. The semiannual variation for each year has been characterized based on analyses of annual and semiannual cycles, using Fourier analysis, and equations have been developed to characterize this yearly variability. The use of new solar indices in the EUV and FUV wavelengths is shown to very accurately describe the semiannual July minimum phase shifting and the variations in the observed yearly semiannual amplitude.
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