Arthur D. Richmond

Bio: Arthur D. Richmond is an academic researcher from National Center for Atmospheric Research. The author has contributed to research in topics: Thermosphere & Ionosphere. The author has an hindex of 67, co-authored 262 publications receiving 15605 citations. Previous affiliations of Arthur D. Richmond include University of California, Los Angeles & High Altitude Observatory.

Ionospheric conductivity dependence of electric fields and currents estimated from ground magnetic observations

Abstract: Recent efforts in estimating the global distribution of the electric potential as well as of ionospheric and field-aligned currents on the basis of ground magnetic records are reevaluated. For this purpose, we have repeated crucial tests for the algorithms by using a data base from the IMS Alaska chain of magnetic observatories along with completely different ionospheric conductivity models. It is found that the ionospheric current patterns are only weakly dependent on the choice of the conductivity, while the calculated field-aligned currents are somewhat more sensitive and the electric field is quite sensitive to the assumed conductivity, as expected. A test is also conducted by using the calculated field-aligned currents as inputs to an inverse calculation to attempt to reproduce the original ground magnetic perturbations. The results were quite satisfactory, indicating that the numerical accuracy of the algorithms is adequate. These tests increase our confidence that ground magnetic records from a close network can be used to study the extent to which the magnetosphere and ionosphere are electrically coupled.

Ionospheric control of the magnetospheric configuration: Thermospheric neutral winds

Abstract: [1] In this study we present the first results from the University of Michigan's coupled magnetosphere-ionosphere-thermosphere general circulation model. This code is a combination of the Michigan MHD model with the NCAR thermosphere-ionosphere-electrodynamics general circulation model (TIEGCM). The MHD code provides specification of the high-latitude ionospheric electric potential and the particle precipitation pattern, while the TIEGCM provides the divergence of the height-integrated neutral wind multiplied by the conductance. This can be easily incorporated into the electric potential solver in the MHD code. We show in this study that the neutral winds cause an approximately 6% increase in the cross polar cap potential when the IMF is strongly southward. This causes the magnetospheric field aligned currents to decrease by a small amount. In the magnetosphere, the flow speeds are increased by only a small amount while the IMF is strongly southward, but when it turns northward the differences become 10–20%. When the IMF is northward, the pressure on the dayside magnetosphere is reduced while the pressure on the nightside is increased by ∼10% of the total pressure.

Effects of high-latitude ionospheric electric field variability on global thermospheric Joule heating and mechanical energy transfer rate

Abstract: [1] Effects of high-latitude ionospheric electric field variability on the Joule heating and mechanical energy transfer rate are investigated by incorporating realistic spatial and temporal characteristics of electric field variability derived from observations into the forcing of a thermosphere ionosphere electrodynamic general circulation model. First, the characteristics of subgrid-scale variability are examined from a spectral analysis of Dynamic Explorer-2 (DE-2) plasma drift measurements. The analysis reveals that the subgrid-scale electric field varies with magnetic latitude, magnetic local time, interplanetary magnetic field (IMF), and season in a manner distinct from that of the resolved-scale electric field and of the climatological electric field. The subgrid-scale electric field varies strongly with season, and its magnitude averaged over the polar region does not depend on IMF. On the other hand, the resolved-scale electric field depends less on season but more on IMF. Second, the spatial-temporal structure of resolved-scale electric fields are characterized from various electromagnetic observations taken during the storm period of January 10–11, 1997, using a space-time covariance model derived from the DE-2 observations. Finally, the modeling results show that the amount of Joule heating and mechanical energy transfer rate in the thermosphere is significantly altered by taking into account the electric field variability and its space-time structure. Additional electromagnetic energy due to the electric field variability dissipates in the ionosphere almost exclusively as Joule heating if the variability has no spatial and temporal correlation. However, the spatially and temporally correlated electric field variability has seasonally dependent effects on the mechanical energy transfer rate.

Riemann manifold Langevin and Hamiltonian Monte Carlo methods

Abstract: The paper proposes Metropolis adjusted Langevin and Hamiltonian Monte Carlo sampling methods defined on the Riemann manifold to resolve the shortcomings of existing Monte Carlo algorithms when sampling from target densities that may be high dimensional and exhibit strong correlations. The methods provide fully automated adaptation mechanisms that circumvent the costly pilot runs that are required to tune proposal densities for Metropolis–Hastings or indeed Hamiltonian Monte Carlo and Metropolis adjusted Langevin algorithms. This allows for highly efficient sampling even in very high dimensions where different scalings may be required for the transient and stationary phases of the Markov chain. The methodology proposed exploits the Riemann geometry of the parameter space of statistical models and thus automatically adapts to the local structure when simulating paths across this manifold, providing highly efficient convergence and exploration of the target density. The performance of these Riemann manifold Monte Carlo methods is rigorously assessed by performing inference on logistic regression models, log-Gaussian Cox point processes, stochastic volatility models and Bayesian estimation of dynamic systems described by non-linear differential equations. Substantial improvements in the time-normalized effective sample size are reported when compared with alternative sampling approaches. MATLAB code that is available from http://www.ucl.ac.uk/statistics/research/rmhmc allows replication of all the results reported.

International Reference Ionosphere 2000

Abstract: The International Reference Ionosphere (IRI) is the international standard for the specification of ionospheric densities and temperatures. It was developed and is being improved-updated by a joint working group of the International Union of Radio Science (URSI) and the Committee on Space Research (COSPAR). A new version of IRI is scheduled for release in the year 2000. This paper describes the most important changes compared to the current version of IRI: (1) an improved representation of the electron density in the region from the F peak down to the E peak including a better description of the F1 layer occurrence statistics and a more realistic description of the low-latitude bottomside thickness, (2) inclusion of a model for storm-time conditions, (3) inclusion of an ion drift model, (4) two new options for the electron density in the D region, and (5) an improved model for the topside electron temperatures. The outcome of the most recent IRI Workshops (Kuhlungsborn, 1997, and Nagoya, 1998) will be reviewed, and the status of several ongoing task force activities (e.g., efforts to improve the representation of electron and ion densities in the topside ionosphere and the inclusion of a plasmaspheric extension) will be discussed. A few typical IRI applications will be highlighted in section 6.

DARN/SUPERDARN : A global view of the dynamics of high-latitude convection

Abstract: The Dual Auroral Radar Network (DARN) is a global-scale network of HF and VHF radars capable of sensing backscatter from ionospheric irregularities in the E and F-regions of the high-latitude ionosphere. Currently, the network consists of the STARE VHF radar system in northern Scandinavia, a northern-hemisphere, longitudinal chain of HF radars that is funded to extend from Saskatoon, Canada to central Finland, and a southern-hemisphere chain that is funded to include Halley Station, SANAE and Syowa Station in Antarctica. When all of the HF radars have been completed they will operate in pairs with common viewing areas so that the Doppler information contained in the backscattered signals may be combined to yield maps of high-latitude plasma convection and the convection electric field. In this paper, the evolution of DARN and particularly the development of its SuperDARN HF radar element is discussed. The DARN/SupperDARN network is particularly suited to studies of large-scale dynamical processes in the magnetosphere-ionosphere system, such as the evolution of the global configuration of the convection electric field under changing IMF conditions and the development and global extent of large-scale MHD waves in the magnetosphere-ionosphere cavity. A description of the HF radars within SuperDARN is given along with an overview of their existing and intended locations, intended start of operations, Principal Investigators, and sponsoring agencies. Finally, the operation of the DARN experiment within ISTP/GGS, the availability of data, and the form and availability of the Key Parameter files is discussed.

The ionospheric disturbance dynamo

Abstract: A numerical simulation study of the thermospheric winds produced by auroral heating during magnetic storms, and of their global dynamo effects, establishes the main features of the ionospheric disturbance dynamo. Driven by auroral heating, a Hadley cell is created with equatorward winds blowing above about 120 km at mid-latitudes. The transport of angular momentum by these winds produces a subrotation of the mid-latitude thermosphere or westward motion with respect to the earth. The westward winds in turn drive equatorward Pedersen currents which accumulate charge toward the equator, resulting in the generation of a poleward electric field, a westward E × B drift, and an eastward current. When realistic local time conductivity variations are simulated, the eastward mid-latitude current is found to close partly via lower latitudes, resulting in an ‘anti-Sq’ type of current vortex. Both electric field and current at low latitudes thus vary in opposition to their normal quiet-day behavior. This total pattern of disturbance winds, electric fields, and currents is superimposed upon the background quiet-day pattern. When the neutral winds are artificially confined on the nightside, the basic pattern of predominantly westward E × B plasma drifts still prevails on the nightside but no longer extends into the dayside. Considerable observational evidence exists, suggesting that the ionospheric disturbance dynamo has an appreciable influence on storm-time ionospheric electric fields at middle and low latitudes.

Riemann manifold Langevin and Hamiltonian Monte Carlo methods

Abstract: The paper proposes Metropolis adjusted Langevin and Hamiltonian Monte Carlo sampling methods defined on the Riemann manifold to resolve the shortcomings of existing Monte Carlo algorithms when sampling from target densities that may be high dimensional and exhibit strong correlations. The methods provide fully automated adaptation mechanisms that circumvent the costly pilot runs that are required to tune proposal densities for Metropolis-Hastings or indeed Hamiltonian Monte Carlo and Metropolis adjusted Langevin algorithms. This allows for highly efficient sampling even in very high dimensions where different scalings may be required for the transient and stationary phases of the Markov chain. The methodology proposed exploits the Riemann geometry of the parameter space of statistical models and thus automatically adapts to the local structure when simulating paths across this manifold, providing highly efficient convergence and exploration of the target density. The performance of these Riemann manifold Monte Carlo methods is rigorously assessed by performing inference on logistic regression models, log-Gaussian Cox point processes, stochastic volatility models and Bayesian estimation of dynamic systems described by non-linear differential equations. Substantial improvements in the time-normalized effective sample size are reported when compared with alternative sampling approaches. MATLAB code that is available from http://www.ucl.ac.uk/statistics/research/rmhmc allows replication of all the results reported.