In December 2019, the International Association of Geomagnetism and Aeronomy (IAGA) Division V Working Group (V-MOD) adopted the thirteenth generation of the International Geomagnetic Reference Field ...(IGRF). This IGRF updates the previous generation with a definitive main field model for epoch 2015.0, a main field model for epoch 2020.0, and a predictive linear secular variation for 2020.0 to 2025.0. This letter provides the equations defining the IGRF, the spherical harmonic coefficients for this thirteenth generation model, maps of magnetic declination, inclination and total field intensity for the epoch 2020.0, and maps of their predicted rate of change for the 2020.0 to 2025.0 time period.
In December 2019, the 13th revision of the International Geomagnetic Reference Field (IGRF) was released by the International Association of Geomagnetism and Aeronomy (IAGA) Division V Working Group ...V-MOD. This revision comprises two new spherical harmonic main field models for epochs 2015.0 (DGRF-2015) and 2020.0 (IGRF-2020) and a model of the predicted secular variation for the interval 2020.0 to 2025.0 (SV-2020-2025). The models were produced from candidates submitted by fifteen international teams. These teams were led by the British Geological Survey (UK), China Earthquake Administration (China), Universidad Complutense de Madrid (Spain), University of Colorado Boulder (USA), Technical University of Denmark (Denmark), GFZ German Research Centre for Geosciences (Germany), Institut de physique du globe de Paris (France), Institut des Sciences de la Terre (France), Pushkov Institute of Terrestrial Magnetism, Ionosphere and Radio Wave Propagation (Russia), Kyoto University (Japan), University of Leeds (UK), Max Planck Institute for Solar System Research (Germany), NASA Goddard Space Flight Center (USA), University of Potsdam (Germany), and Université de Strasbourg (France). The candidate models were evaluated individually and compared to all other candidates as well to the mean, median and a robust Huber-weighted model of all candidates. These analyses were used to identify, for example, the variation between the Gauss coefficients or the geographical regions where the candidate models strongly differed. The majority of candidates were sufficiently close that the differences can be explained primarily by individual modeling methodologies and data selection strategies. None of the candidates were so different as to warrant their exclusion from the final IGRF-13. The IAGA V-MOD task force thus voted for two approaches: the median of the Gauss coefficients of the candidates for the DGRF-2015 and IGRF-2020 models and the robust Huber-weighted model for the predictive SV-2020-2025. In this paper, we document the evaluation of the candidate models and provide details of the approach used to derive the final IGRF-13 products. We also perform a retrospective analysis of the IGRF-12 SV candidates over their performance period (2015–2020). Our findings suggest that forecasting secular variation can benefit from combining physics-based core modeling with satellite observations.
Measurements of the Earth’s magnetic field collected by low-Earth-orbit satellites such as
Swarm
and CHAMP, as well as at ground observatories, are dominated by sources in the Earth’s interior. ...However these measurements also contain significant contributions from more rapidly-varying current systems in the ionosphere and magnetosphere. In order to fully exploit magnetic data to probe the physical properties and dynamics of the Earth’s interior, field models with suitable treatments of external sources, and their associated induced signals, are essential. Here we review the methods presently used to construct models of the internal field, focusing on techniques to handle magnetospheric and ionospheric signals. Shortcomings of these techniques often limit the quality, as well as spatial and temporal resolution, of internal field models. We document difficulties in using track-by-track analysis to characterize magnetospheric field fluctuations, differences in internal field models that result from alternative treatments of the quiet-time ionospheric field, and challenges associated with rapidly changing, but spatially correlated, magnetic signatures of polar cap current systems. Possible strategies for improving internal field models are discussed, many of which are described in more detail elsewhere in this volume.
Using observatory data, we report the detection of a geomagnetic jerk in 2007, which we relate to a jump in the second derivative of the geomagnetic field previously noted in satellite data. Although ...not of worldwide extent, this jerk is very intense in the South Atlantic region. Using the CHAOS‐2 model, we show that both this jerk and the previous 2003 jerk are caused by a single core field acceleration pulse reaching its maximum power near 2006.0. This pulse seems to be simultaneously occurring in several regions of the core surface where it corresponds to dominant n = 5 and 6 spherical harmonic modes. Geometrical attenuation explains why the 2003 and 2007 jerks are local and not fully synchronized at the Earth's surface. Our results suggest that this core field acceleration pulse is the relevant phenomenon to be investigated from the point of view of core dynamics, rather than the jerks themselves.
Purpose
To describe the burden, and characteristics, of influenza-like illness (ILI) associated with non-influenza respiratory viruses (NIRV).
Methods
We performed a prospective, multicenter, ...observational study of adults admitted with ILI during three influenza seasons (2012–2015). Patients were screened for picornavirus, respiratory syncytial virus (RSV), coronavirus, human metapneumovirus, adenovirus, bocavirus, parainfluenza virus, and influenza, by PCR on nasopharyngeal samples. We excluded patients coinfected with NIRV and influenza.
Results
Among 1421 patients enrolled, influenza virus was detected in 535 (38%), and NIRV in 215 (15%), mostly picornavirus (
n
= 61), RSV (
n
= 53), coronavirus 229E (
n
= 48), and human metapneumovirus (
n
= 40). In-hospital mortality was 5% (NIRV), 4% (influenza), and 5% (no respiratory virus). As compared to influenza, NIRV were associated with age (median, 73 years vs. 68,
P
= 0.026), chronic respiratory diseases (53% vs. 45%,
P
= 0.034), cancer (14% vs. 9%,
P
= 0.029), and immunosuppressive drugs (21% vs. 14%,
P
= 0.028), and inversely associated with diabetes (18% vs. 25%,
P
= 0.038). On multivariable analysis, only chronic respiratory diseases (OR 1.5 1.1–2.0,
P
= 0.008), and diabetes (OR 0.5 0.4–0.8,
P
= 0.01) were associated with NIRV detection.
Conclusions
NIRV are common in adults admitted with ILI during influenza seasons. Outcomes are similar in patients with NIRV, influenza, or no respiratory virus.
Inhaled corticosteroids (ICS) have been associated with increased risk of pneumonia. Their impact on respiratory virus infections is unclear. We performed a post-hoc analysis of the FLUVAC cohort, a ...multicenter prospective cohort study of adults hospitalized with influenza-like illness (ILI) during six consecutive influenza seasons (2012-2018). All patients were tested for respiratory virus infection by multiplex PCR on nasopharyngeal swabs and/or bronchoalveolar lavage. Risk factors were identified by logistic regression analysis. Among the 2658 patients included, 537 (20.2%) were treated with ICS before admission, of whom 282 (52.5%, 282/537) tested positive for at least one respiratory virus. Patients on ICS were more likely to test positive for non-influenza respiratory viruses (25.1% vs. 19.5%, P = 0.004), especially for adenovirus (aOR 2.36, 95% CI 1.18-4.58), and respiratory syncytial virus (aOR 2.08, 95% CI 1.39-3.09). Complications were reported in 55.9% of patients on ICS (300/537), primarily pneumonia (171/535, 32%). Among patients on chronic ICS who tested positive for respiratory virus, 14.2% (40/282) were admitted to intensive care unit, and in-hospital mortality rate was 2.8% (8/282). Chronic use of ICS is associated with an increased risk of adenovirus or RSV infections in patients admitted for ILI.
Detailed mapping of the Earth's magnetic field brings key constraints on the composition, dynamics, and history of the crust. Satellite and near‐surface measurements detect different length scales ...and are complementary. Here, we build a model, selecting and processing magnetic field measurements from the German CHAMP and ESA Swarm satellites, which we merge with near‐surface scalar anomaly data. We follow a regional approach for modeling the magnetic field measurements and next transform the series of regional models into a unique set of spherical harmonic (SH) Gauss coefficients. This produces the first global model to SH degree 1050 derived by inversion of all available measurements. The new model agrees with previous satellite‐based models at large wavelengths and fits the CHAMP and Swarm satellite data down to expected noise levels. Further assessment in the geographical and spectral domains shows the model to be stable when downward continued to the Earth's surface.
Plain Language Summary
The magnetic field of the Earth's results from the superposition of various internal and external fields. The field produced by magnetized rocks in the Earth's lithosphere brings essential constraints on the crust composition, dynamics, and history. Its description requires magnetic measurements in all regions of the world at different altitudes. Satellite measurements detect well the large Earth's magnetic field structures but are too far to detect the smaller ones that are weak at high altitudes. Historical marine and airplane data can be used to complement the satellite observations. Considering all these data makes it possible to build a global model that represents the magnetic field generated within the Earth's crust with a global 40 km horizontal spatial resolution.
Key Points
Model based on a joint inversion of Swarm and CHAMP satellites magnetic field measurements and near‐surface scalar anomaly gridded data
First global lithospheric vector magnetic field model at the Earth's mean radius to about 40 km resolution
The magnetic field of the Earth's lithosphere arises from rock magnetization contrasts that were shaped over geological times. The field can be described mathematically in spherical harmonics or with ...distributions of magnetization. We exploit this dual representation and assume that the lithospheric field is induced by spatially varying susceptibility values within a shell of constant thickness. By introducing a statistical assumption about the power spectrum of the susceptibility, we then derive a statistical expression for the spatial power spectrum of the crustal magnetic field for the spatial scales ranging from 60 to 2500 km. This expression depends on the mean induced magnetization, the thickness of the shell, and a power law exponent for the power spectrum of the susceptibility. We test the relevance of this form with a misfit analysis to the observational NGDC-720 lithospheric magnetic field model power spectrum. This allows us to estimate a mean global apparent induced magnetization value between 0.3 and 0.6 A m−1, a mean magnetic crustal thickness value between 23 and 30 km, and a root mean square for the field value between 190 and 205 nT at 95 per cent. These estimates are in good agreement with independent models of the crustal magnetization and of the seismic crustal thickness. We carry out the same analysis in the continental and oceanic domains separately. We complement the misfit analyses with a Kolmogorov–Smirnov goodness-of-fit test and we conclude that the observed power spectrum can be each time a sample of the statistical one.
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