Discrete inverse theory for 834‐Å ionospheric remote sensing

Picone, J. M.; Meier, R. R.; Kelley, Owen; Meléndez-Alvira, D. J.; Dymond, K. F.; McCoy, R. P.; Buonsanto, M. J.

doi:10.1029/97rs01028

Cited by 20 publications

(27 citation statements)

References 18 publications

(1 reference statement)

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“…These approximations are vital for constructing "forward models" of measurement processes. We adopt the simple, practical point of view that the forward model ideally provides a parameterized representation of the "true" signal that would be measured if statistical noise were not present, either in the a priori inputs to the forward model or in the measuring process [Picone et al, 1997]. Our key criterion of performance therefore is how closely the similarity transform method can approximate, or fit, exact or noiseless functions.…”

Section: Approachmentioning

confidence: 99%

“…testing, which usually simulates the extraction of information from data that contain both systematic errors induced by the observing system and statistical noise [Picone et al, 1997]. On the other hand, the key application of the similarity transform method is to the construction of forward models for DIT, and furthermore, we compute our measures of merit using unweighted nonlinear least squares fitting, a limiting-case DIT method.…”

Section: This Approach Contrasts With Discrete Inverse Theory (Dit)mentioning

confidence: 99%

“…Two specific examples are computerized ionospheric tomography [e.g., Fremouw et al, 1992] and remote sensing of thermospheric and ionospheric composition via ultraviolet limb scanning or limb imaging [Meier and Picone, 1994;Picone et al, 1997]. In the inversion process, one may parameterize the species altitude profile by one of several means, for example, (1) using an analytic function, (2) specifying model parameters to be the actual species concentration values on an altitude grid, or (3) expanding the profile in a set of basis functions (e.g., splines or empirical orthogonal functions), which is often truncated to increase computational This paper is not subject to U.S. copyright.…”

Section: Introductionmentioning

confidence: 99%

“…In discrete inverse theory (DIT) [e.g., Menke, 1989;Picone et al, 1997] the forward model includes a parametric representation of the variable to be retrieved; the data then provide the basis for computing optimal values of the model parameters. Herein we describe a new, highly robust, stable parametric representation of the function to be retrieved.…”

Section: Introductionmentioning

confidence: 99%

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Similarity transformations for fitting of geophysical properties: Application to altitude profiles of upper atmospheric species

Bernath¹,

Meier²

2000

J. Geophys. Res.

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Abstract. The similarity transform method provides a new, highly robust, and stable parametric representation of geophysical functions for use in retrieving such functions from remote sensing observations. The present discussion focuses on the approximation of altitude profiles of upper atmospheric species concentration and on the development of parametric forward models for use with discrete inverse theory (DIT). Of equal importance, the similarity transform approach provides a framework for extracting generic profile shape information, in the form of a nondimensional shape function, from observations or detailed numerical simulations. In this way the method facilitates analysis of general characteristics of species concentration variations with altitude and with other geophysical parameters. For DIT retrievals of concentration profiles from observations a similarity transformation-based forward model embeds the generic ("basis") shape information directly into a parametric representation of each species profile. The presentation covers the extraction of nondimensional shape functions from discrete data or simulations, the basic forward model representation, and generalizations of the basic approach. We include simple examples of similarity transform fitting calculations in which the species concentration profiles to be approximated are generated by the Mass Spectrometer Incoherent Scatter Empirical 1990 (MSISE-90) atmospheric model, as are the basis profiles that define the shape information.

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Section: Approachmentioning

confidence: 99%

Section: This Approach Contrasts With Discrete Inverse Theory (Dit)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Similarity transformations for fitting of geophysical properties: Application to altitude profiles of upper atmospheric species

Bernath¹,

Meier²

2000

J. Geophys. Res.

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“…A subset of these coincident sets will be selected for a regression analysis, and the remaining coincident data will provide a basis for testing of the new empirical mapping from SSULI 83.4 nm to hmF2. A discrete inverse theory algorithm and code written by Dr. J. Michael Picone [2,3] (formerly at NRL, now at George Mason Univ.) will then be used to fit the coincident SSULI limb intensity profiles.…”

Section: Approachmentioning

confidence: 99%

Operational SSULI Algorithm for Dayside Ionosphere HmF2

Stephan¹

2009

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Award Number: N0001408AF00002 http://www.nrl.navy.mil LONG-TERM GOALSThe ultimate goal of this research is the development of an operational algorithm for retrieving daytime ionospheric hmF2 from the measurement of the O II 83.4 nm emission feature. This algorithm will provide data for ingestion into ionospheric models. The reward of a successful algorithm is very high, as no capability presently exists to measure the global variation of hmF2 within the F-region of the ionosphere during the day. Further, this work will provide a new assessment of the state of the ionosphere during significant space weather events, which is critical to the use of GPS and other operational systems that rely on the propagation of radio waves within the near-Earth space environment. In addition, this research could lead to a method for global monitoring of the ionosphere and its response to solar, geomagnetic and lower atmospheric waves and tides. OBJECTIVESThe technological objective of this research is to develop a new algorithm that can be used to obtain routine, global ionospheric measurements for incorporation into operational codes and assimilative ionospheric models, including the Global Assimilation of Ionospheric Measurements (GAIM) model. GAIM, used at AFWA as the state-of-the-art ionospheric predictive model for Navy and DoD needs, currently is limited by the absence of data that can be ingested to constrain the daytime hmF2 parameter. The hmF2 specifies the height of the peak of the ionosphere and is used as both a metric for the ionosphere and a parameter to specify the ionospheric density profile. The software to be developed will be directly applicable to dayside data from the Special Sensor Ultraviolet Limb Imager (SSULI) sensors on the DMSP F18-20 satellites. The proposed additional capability of the SSULI Ground Data Analysis Software (GDAS) to generate operational hmF2 values from 83.4 nm measurements would provide data that could be used as hmF2 parameter constraints with GAIM at AFWA. The scientific objective of this effort is to develop an understanding of the ionospheric response to variability in solar and geomagnetic forcing. In addition, this effort will lead to pioneering research in the fundamental physics behind the dayside 83.4 nm emission and the conditions under which it presents a viable measure of this ionospheric variability.

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Ionospheric‐thermospheric UV tomography: 2. Comparison with incoherent scatter radar measurements

et al. 2017

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The Special Sensor Ultraviolet Limb Imager (SSULI) instruments are ultraviolet limb scanning sensors that fly on the Defense Meteorological Satellite Program F16‐F19 satellites. The SSULIs cover the 80–170 nm wavelength range which contains emissions at 91 and 136 nm, which are produced by radiative recombination of the ionosphere. We invert the 91.1 nm emission tomographically using a newly developed algorithm that includes optical depth effects due to pure absorption and resonant scattering. We present the details of our approach including how the optimal altitude and along‐track sampling were determined and the newly developed approach we are using for regularizing the SSULI tomographic inversions. Finally, we conclude with validations of the SSULI inversions against Advanced Research Project Agency Long‐range Tracking and Identification Radar (ALTAIR) incoherent scatter radar measurements and demonstrate excellent agreement between the measurements. As part of this study, we include the effects of pure absorption by O2, N2, and O in the inversions and find that best agreement between the ALTAIR and SSULI measurements is obtained when only O2 and O are included, but the agreement degrades when N2 absorption is included. This suggests that the absorption cross section of N2 needs to be reinvestigated near 91.1 nm wavelengths.

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Discrete inverse theory for 834‐Å ionospheric remote sensing

Cited by 20 publications

References 18 publications

Similarity transformations for fitting of geophysical properties: Application to altitude profiles of upper atmospheric species

Similarity transformations for fitting of geophysical properties: Application to altitude profiles of upper atmospheric species

Operational SSULI Algorithm for Dayside Ionosphere HmF2

Ionospheric‐thermospheric UV tomography: 2. Comparison with incoherent scatter radar measurements

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