Scientific Investigations Using IDA4D and EMPIRE

Bust, G. S.; Datta‐Barua, Seebany

doi:10.1002/9781118704417.ch23

Cited by 29 publications

(37 citation statements)

References 24 publications

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“…To obtain analysis electron density in equation , the model and data errors are assumed to take on Gaussian statistics, although ionospheric electron densities are better approximated by a lognormal distribution (Bust & Datta‐Barua, ; Garner et al, ). In addition, equation does not have a nonnegativity constraint that is mandatory of ionospheric electron densities.…”

Section: Data Assimilation Scheme Of Ida4dmentioning

confidence: 99%

“…However, expressing

\overset{x}{true}

in such a form renders the forward operator

\overset{H}{true}

nonlinear. To solve such a nonlinear system, for an observed datum, i, and n iterations to optimization, the forward operator is linearized to a Jacobian matrix (Bust & Datta‐Barua, )

{{normalH}^{normali} ({\overset{true\to}{normalx}}_{n}) \approx {normalH}^{normali} ({\overset{true\to}{normalx}}_{normaln - 1}) + \sum_{normalk} (\frac{\partial H^{i}}{\partial x^{k}})|}_{{normalx}_{n - 1}^{normalk}} ((), x_{n}^{k} - x_{normaln - 1}^{k}),

{{\overset{H}{true}}_{ik}^{normaln} \equiv (\frac{\partial {\overset{true~}{normalH}}^{i}}{\partial x^{k}})|}_{{normalx}_{n - 1}^{normalk}}

and analyzed electron density at n th iteration estimated as

{\overset{true\to}{normalx}}_{a}^{n} = {\overset{true\to}{normalx}}_{f} + {\overset{true~}{normalP}}_{f} {({\overset{true~}{normalH}}^{n})}^{T} [{\overset{H}{true}}^{normaln} {\overset{P}{true}}_{normalf} {((), {\overset{H}{true}}^{normaln})}^{normalT} +]

…”

Section: Data Assimilation Scheme Of Ida4dmentioning

confidence: 99%

“…The assimilation process takes advantage of having 6 ionosondes and 27 GPS stations in the region, and occasional data from Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC). The multiple data types are assimilated into the well‐known IRI model by using IDA4D algorithm developed by Bust and Datta‐Barua ( ). Results are three‐dimensional ionosphere electron densities estimated in a temporal resolution of 15 min, a latitudinal resolution of 1°, a longitudinal resolution of 2°, and a vertical resolution of 10, 25, and 52.4 km between 80–150, 150–500, and 500–1,336 km, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Assimilation of Multiple Data Types to a Regional Ionosphere Model With a 3D‐Var Algorithm (IDA4D)

et al. 2019

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For the purpose of building a regional (bound 20–60°N in latitude and 110–160°E in longitude) ionospheric nowcast model, we investigated the performance of IDA4D (Ionospheric Data Assimilation Four‐Dimension) technique considering International Reference Ionosphere model as the background. The data utilized in assimilation were slant total electron content (STEC) from 27 ground GPS (Global Positioning System) receiver stations and NmF2 (ionospheric F2 peak density) from five ionosondes and COSMIC (Constellation Observing System for Meteorology, Ionosphere, and Climate) Data Analysis Archive Center. The period analyzed covered both geomagnetic quiet and disturbed days (15–18 March 2015). Assimilations were run under the following data combinations (cases): (1) GPS‐STEC's only; (2) GPS‐STEC's and NmF2's from five ionosondes; (3) only NmF2's from five ionosondes; and (4) GPS‐STEC's and NmF2's from both five ionosondes and COSMIC. Results showed that under case 1 the root‐mean‐square error (RMSE) in STEC reduced by 44% over the background International Reference Ionosphere values and on averaged over all ionosonde stations in the analysis RMSE values of foF2 (F2 layer critical frequency) reduced by 21%. Furthermore, foF2 RMSE values under Case 2 were 36% smaller than those under Case 1. Under Case 4, IDA4D performance improved even further in areas not covered by GPS and ionosonde measurements. Therefore, IDA4D is a potential candidate for regional ionosphere modeling that exhibits improved performance with assimilation of different data types.

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Section: Data Assimilation Scheme Of Ida4dmentioning

confidence: 99%

“…However, expressing

\overset{x}{true}

in such a form renders the forward operator

\overset{H}{true}

nonlinear. To solve such a nonlinear system, for an observed datum, i, and n iterations to optimization, the forward operator is linearized to a Jacobian matrix (Bust & Datta‐Barua, )

{{normalH}^{normali} ({\overset{true\to}{normalx}}_{n}) \approx {normalH}^{normali} ({\overset{true\to}{normalx}}_{normaln - 1}) + \sum_{normalk} (\frac{\partial H^{i}}{\partial x^{k}})|}_{{normalx}_{n - 1}^{normalk}} ((), x_{n}^{k} - x_{normaln - 1}^{k}),

{{\overset{H}{true}}_{ik}^{normaln} \equiv (\frac{\partial {\overset{true~}{normalH}}^{i}}{\partial x^{k}})|}_{{normalx}_{n - 1}^{normalk}}

and analyzed electron density at n th iteration estimated as

{\overset{true\to}{normalx}}_{a}^{n} = {\overset{true\to}{normalx}}_{f} + {\overset{true~}{normalP}}_{f} {({\overset{true~}{normalH}}^{n})}^{T} [{\overset{H}{true}}^{normaln} {\overset{P}{true}}_{normalf} {((), {\overset{H}{true}}^{normaln})}^{normalT} +]

…”

Section: Data Assimilation Scheme Of Ida4dmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assimilation of Multiple Data Types to a Regional Ionosphere Model With a 3D‐Var Algorithm (IDA4D)

et al. 2019

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“…[], Millward et al . [], and Bust and Datta‐Barua [] should be mentioned to name a few. One of the models, performing data assimilation technique for ionosphere nowcasting, was developed by Fusion Numerics LLC (Boulder, CO) and Central Aerological Observatory (Dolgoprudny, Russia).…”

Section: Introductionmentioning

confidence: 99%

Ionosphere data assimilation capabilities for representing the high‐latitude geomagnetic storm event in September 2011

et al. 2014

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Severe geomagnetic storms have a strong impact on space communication and satellite navigation systems. Forecasting the appearance of geomagnetically induced disturbances in the ionosphere is one of the urgent goals of the space weather community. The challenge is that the processes governing the distribution of the crucial ionospheric parameters have a rather poor quantitative description, and the models, built using the empirical parameterizations, have limited capabilities for operational purposes. On the other hand, data assimilation techniques are becoming more and more popular for nowcasting the state of the large-scale geophysical systems. We present an example of an ionospheric data assimilation system performance assessment during a strong geomagnetic event, which took place on 26 September 2011. The first-principle model has assimilated slant total electron content measurements from a dense network of ground stations, provided by the Norwegian Mapping Authority. The results have shown satisfactory agreement with independent data and demonstrate that the assimilation model is accurate to about 2-4 total electron content units and can be used for operational purposes in high-latitude regions. The operational system performance assessment is the subject of future work.

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“…In particular, IDA4D has been used to investigate the ionospheric response to major geomagnetic storms [Bust and Mitchell, 2008;Bust and Crowley, 2008], including the formation and evolution of SED. A complete mathematical derivation of the IDA4D algorithm can be found in Bust et al [2000Bust et al [ , 2004 and Bust and Datta-Barua [2012a]. One of the powerful features of IDA4D is that the inversion grid is user selectable and need not be regular.…”

Section: Ionospheric Data Assimilation Four-dimensionalmentioning

confidence: 99%

First storm‐time plasma velocity estimates from high‐resolution ionospheric data assimilation

2013

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This paper uses data assimilation to estimate ionospheric state during storm time at subdegree resolution. We use Ionospheric Data Assimilation Four‐Dimensional (IDA4D) to resolve the three‐dimensional time‐varying electron density gradients of the storm‐enhanced density poleward plume. By Estimating Model Parameters from Ionospheric Reverse Engineering (EMPIRE), we infer the three‐dimensional plasma velocity from the densities. EMPIRE estimates of ExB drift are made by correcting the Weimer 2000 electric potential model. This is the first time electron densities derived from GPS total electron content (TEC) data are being used to estimate field‐aligned and field‐perpendicular drifts at such high resolution, without reference to direct drift measurements. The time‐varying estimated electron densities are used to construct the ionospheric spatial decorrelation in vertical total electron content (TEC) on horizontal scales of less than 100 km. We compare slant TEC (STEC) estimates to actual STEC GPS observations, including independent unassimilated data. The IDA4D density model of the extreme ionospheric storm on 20 November 2003 shows STEC delays of up to 210 TEC units, comparable to the STEC of the GPS ground stations. Horizontal drifts from EMPIRE are predicted to be northwestward within the storm‐enhanced density plume and its boundary, turning northeast at high latitudes. These estimates compare favorably to independent Assimilative Mapping of Ionospheric Electrodynamics‐assimilated high‐latitude ExB drift estimates. Estimated and measured Defense Meteorological Satellite Program in situ drifts differ by a factor of 2–3 and in some cases have incorrect direction. This indicates that significant density rates of change and more accurate accounting for production and loss may be needed when other processes are not dominant.

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Scientific Investigations Using IDA4D and EMPIRE

Cited by 29 publications

References 24 publications

Assimilation of Multiple Data Types to a Regional Ionosphere Model With a 3D‐Var Algorithm (IDA4D)

Assimilation of Multiple Data Types to a Regional Ionosphere Model With a 3D‐Var Algorithm (IDA4D)

Ionosphere data assimilation capabilities for representing the high‐latitude geomagnetic storm event in September 2011

First storm‐time plasma velocity estimates from high‐resolution ionospheric data assimilation

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