Humberto C. Godinez

Data Assimilation in the ADAPT Photospheric Flux Transport Model

Global maps of the solar photospheric magnetic flux are fundamental drivers for simulations of the corona and solar wind and therefore are important predictors of geoeffective events. However, observations of the solar photosphere are only made intermittently over approximately half of the solar surface. The Air Force Data Assimilative Photospheric Flux Transport (ADAPT) model uses localized ensemble Kalman filtering techniques to adjust a set of photospheric simulations to agree with the available observations. At the same time, this information is propagated to areas of the simulation that have not been observed. ADAPT implements a local ensemble transform Kalman filter (LETKF) to accomplish data assimilation, allowing the covariance structure of the flux-transport model to influence assimilation of photosphere observations while eliminating spurious correlations between ensemble members arising from a limited ensemble size. We give a detailed account of the implementation of the LETKF into ADAPT. Advantages of the LETKF scheme over previously implemented assimilation methods are highlighted.

Modeling the corona and solar wind using ADAPT maps that include far-side observations

As the primary input to nearly all coronal and solar wind models, global estimates of the solar photospheric magnetic field distribution are critical for reliable modeling of the corona and heliosphere. Over the last several years the Air Force Research Laboratory (AFRL), in collaboration with Los Alamos National Laboratory (LANL) and the National Solar Observatory (NSO), has developed a model that produces more realistic estimates of the instantaneous global photospheric magnetic field distribution than those provided by traditional photospheric field synoptic maps. The Air Force Data Assimilative Photospheric flux Transport (ADAPT) model is a photospheric flux transport model, originally developed at NSO, that makes use of data assimilation methodologies developed at LANL. The flux transport model evolves the observed solar magnetic flux using relatively well understood transport processes when measurements are not available and then updates the modeled flux with new observations using data assimilation methods that rigorously take into account model and observational uncertainties. ADAPT originally only made use of Earth-side magnetograms, but the code has now been modified to assimilate helioseismic far-side active region data such as those available from the Global Oscillation Network Group. As a preliminary test, a helioseismically detected active region that first emerged on the far-side of the Sun in early July 2010 is incorporated into maps produced by ADAPT and then used in the Wang-Sheeley-Arge (WSA) model to simulate the corona and solar wind. The WSA model results, with and without far-side data included in the ADAPT global maps, are compared here with coronal EUV and in situ solar wind observations available from STEREO. We find that the observed and modeled values are in better agreement when including the far-side detection.

Localized adaptive inflation in ensemble data assimilation for a radiation belt model

[1] In this work a one-dimensional radial diffusion model for phase space density, together with observational satellite data, is used in an ensemble data assimilation with the purpose of accurately estimating Earth’s radiation belt particle distribution. A particular concern in data assimilation for radiation belt models are model deficiencies, which can adversely impact the solution of the assimilation. To adequately address these deficiencies, a localized adaptive covariance inflation technique is implemented in the data assimilation to account for model uncertainty. Numerical results from identical-twin experiments, where data is generated from the same model, as well as the assimilation of real observational data, are presented. The results show improvement in the predictive skill of the model solution due to the proper inclusion of model errors in the data assimilation. Citation: Godinez, H. C., and J. Koller (2012), Localized adaptive inflation in ensemble data assimilation for a radiation belt model, Space Weather, 10, S08001, doi:10.1029/2012SW000767.

Modes of high-latitude auroral conductance variability derived from DMSP energetic electron precipitation observations: Empirical orthogonal function analysis

We provide the first ever characterization of the primary modes of ionospheric Hall and Pedersen conductance variability as empirical orthogonal functions (EOFs). These are derived from six satellite years of Defense Meteorological Satellite Program (DMSP) particle data acquired during the rise of solar cycles 22 and 24. The 60 million DMSP spectra were each processed through the Global Airlglow Model. Ours is the first large-scale analysis of ionospheric conductances completely free of assumption of the incident electron energy spectra. We show that the mean patterns and first four EOFs capture ∼50.1 and 52.9% of the total Pedersen and Hall conductance variabilities, respectively. The mean patterns and first EOFs are consistent with typical diffuse auroral oval structures and quiet time strengthening/weakening of the mean pattern. The second and third EOFs show major disturbance features of magnetosphere-ionosphere (MI) interactions: geomagnetically induced auroral zone expansion in EOF2 and the auroral substorm current wedge in EOF3. The fourth EOFs suggest diminished conductance associated with ionospheric substorm recovery mode. We identify the most important modes of ionospheric conductance variability. Our results will allow improved modeling of the background error covariance needed for ionospheric assimilative procedures and improved understanding of MI coupling processes.

New density estimates derived using accelerometers on board the CHAMP and GRACE satellites

Atmospheric mass density estimates derived from accelerometers onboard satellites such as CHAllenging Minisatellite Payload (CHAMP) and Gravity Recovery and Climate Experiment (GRACE) are crucial in gaining insight into open science questions about the dynamic coupling between space weather events and the upper atmosphere. Recent advances in physics-based satellite drag coefficient modeling allow derivation of new density data sets. This paper uses physics-based satellite drag coefficient models for CHAMP and GRACE to derive new estimates for the neutral atmospheric density. Results show an average difference of 14–18% for CHAMP and 10–24% for GRACE between the new and existing data sets depending on the space weather conditions (i.e., solar and geomagnetic activity levels). The newly derived densities are also compared with existing models, and results are presented. These densities are expected to be useful to the wider scientific community for validating the development of physics-based models and helping to answer open scientific questions regarding our understanding of upper atmosphere dynamics such as the sensitivity of temporal and global density variations to solar and geomagnetic forcing.

A parametric study of the source rate for outer radiation belt electrons using a Kalman filter

m = 2083[MeV/G] and K = 0.03[G 1/2 RE] respectively, from a five satellite data set (three LANL-GEO, one GPS, and Polar), and 2) a one-dimensional radial diffusion model with loss and source terms included. We augment the Kalman filter to include the intensity of local acceleration in the state vector. The output is an estimate of PSD for the radial range of the outer radiation belt and the time-dependent amplitude parameter of a Gaussian shaped source rate term for given location and width. To further constrain the source rate parameters, a root mean square (RMS) analysis of the observation residual vector (a.k.a. innovation vector) is performed in a parameter space of source location and width. We analyze five storm periods spanning from July 30th to October 24th of 2002, and each period’s unique solution in the location-width parameter space is assimilated with the Kalman filter for a continuous reanalysis of the full 87 day period. The source amplitude parameter is analyzed for insight into time periods of enhanced local heating, suppressed loss, or, as the parameter can take negative values, additional loss. The source is found to peak in the recovery phases of the storms where the rate is sufficient to repopulate the radiation belt in approximately one day, suggesting that local heating is a major contributor to the electron radiation belts during the recovery phase.

Determining Key Model Parameters of Rapidly Intensifying Hurricane Guillermo (1997) Using the Ensemble Kalman Filter

AbstractIn this work the authors determine key model parameters for rapidly intensifying Hurricane Guillermo (1997) using the ensemble Kalman filter (EnKF). The approach is to utilize the EnKF as a tool only to estimate the parameter values of the model for a particular dataset. The assimilation is performed using dual-Doppler radar observations obtained during the period of rapid intensification of Hurricane Guillermo. A unique aspect of Guillermo was that during the period of radar observations strong convective bursts, attributable to wind shear, formed primarily within the eastern semicircle of the eyewall. To reproduce this observed structure within a hurricane model, background wind shear of some magnitude must be specified and turbulence and surface parameters appropriately specified so that the impact of the shear on the simulated hurricane vortex can be realized. To identify the complex nonlinear interactions induced by changes in these parameters, an ensemble of model simulations have been condu...

An efficient matrix-free algorithm for the ensemble Kalman filter

In this work, we present an efficient matrix-free ensemble Kalman filter (EnKF) algorithm for the assimilation of large data sets. The EnKF has increasingly become an essential tool for data assimilation of numerical models. It is an attractive assimilation method because it can evolve the model covariance matrix for a non-linear model, through the use of an ensemble of model states, and it is easy to implement for any numerical model. Nevertheless, the computational cost of the EnKF can increase significantly for cases involving the assimilation of large data sets. As more data become available for assimilation, a potential bottleneck in most EnKF algorithms involves the operation of the Kalman gain matrix. To reduce the complexity and cost of assimilating large data sets, a matrix-free EnKF algorithm is proposed. The algorithm uses an efficient matrix-free linear solver, based on the Sherman–Morrison formulas, to solve the implicit linear system within the Kalman gain matrix and compute the analysis. Numerical experiments with a two-dimensional shallow water model on the sphere are presented, where results show the matrix-free implementation outperforming an singular value decomposition-based implementation in computational time.

Simultaneous event-specific estimates of transport, loss, and source rates for relativistic outer radiation belt electrons: Event-Specific 1-D Modeling

The most significant unknown regarding relativistic electrons in Earths outer Van Allen radiation belt is the relative contribution of loss, transport, and acceleration processes within the inner magnetosphere. Detangling each individual process is critical to improve the understanding of radiation belt dynamics, but determining a single component is challenging due to sparse measurements in diverse spatial and temporal regimes. However, there are currently an unprecedented number of spacecraft taking measurements that sample different regions of the inner magnetosphere. With the increasing number of varied observational platforms, system dynamics can begin to be unraveled. In this work, we employ in situ measurements during the 13–14 January 2013 enhancement event to isolate transport, loss, and source dynamics in a one-dimensional radial diffusion model. We then validate the results by comparing them to Van Allen Probes and Time History of Events and Macroscale Interactions during Substorms observations, indicating that the three terms have been accurately and individually quantified for the event. Finally, a direct comparison is performed between the model containing event-specific terms and various models containing terms parameterized by geomagnetic index. Models using a simple 3/Kp loss time scale show deviation from the event-specific model of nearly 2 orders of magnitude within 72 h of the enhancement event. However, models using alternative loss time scales closely resemble the event-specific model.

Improved forecasting of thermospheric densities using multi-model ensembles

This paper presents the first known application of multi-model ensembles to the forecasting of the thermosphere. A multi-model ensemble (MME) is a method for combining different, independent, models. The main advantage of using an MME is to reduce the effect of model errors and bias, since it is expected that the model errors will, at least partly, cancel. The MME, with its reduced uncertain5 ties, can then be used as the initial conditions in a physics-based thermosphere model for forecasting. This should increase the forecast skill since a reduction in the errors of the initial conditions of a model generally increases model skill. In this paper the Thermosphere-Ionosphere-Electrodynamic General Circulation Model (TIE-GCM), the US Naval Research Laboratory Mass Spectrometer and Incoherent Scatter radar Exosphere 2000 (NRLMSISE-00) and Global Ionosphere Thermosphere 10 Model (GITM) have been used to construct the MME. TIE-GCM has been used for forecasting. It has been shown that thermospheric forecasts of up to 6 hours, that have been initialised using the MME, have a reduction in the root mean square error (compared to ‘standard’ runs of the model) of greater than 60%. The paper also highlights differences in model performance between times of solar minimum and maximum. 15