Data Assimilation for Ionospheric Space-Weather Forecasting in the Presence of Model Bias

Durazo, Juan; Kostelich, Eric J.; Mahalov, Alex

doi:10.3389/fams.2021.679477

Cited by 6 publications

(4 citation statements)

References 56 publications

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“…There are several major limitations to our studies, such as the assumption of a perfect model (no model error). These issues can perhaps be accounted for by continually improving the model development or by using a data assimilation approach, such as the Kalman filter [ 79 , 80 ]. We also do not employ patient-specific data for our simulation study.…”

Section: Discussionmentioning

confidence: 99%

Practical Understanding of Cancer Model Identifiability in Clinical Applications

Phan

Bennett

Patten

2023

Life

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Mathematical models are a core component in the foundation of cancer theory and have been developed as clinical tools in precision medicine. Modeling studies for clinical applications often assume an individual’s characteristics can be represented as parameters in a model and are used to explain, predict, and optimize treatment outcomes. However, this approach relies on the identifiability of the underlying mathematical models. In this study, we build on the framework of an observing-system simulation experiment to study the identifiability of several models of cancer growth, focusing on the prognostic parameters of each model. Our results demonstrate that the frequency of data collection, the types of data, such as cancer proxy, and the accuracy of measurements all play crucial roles in determining the identifiability of the model. We also found that highly accurate data can allow for reasonably accurate estimates of some parameters, which may be the key to achieving model identifiability in practice. As more complex models required more data for identification, our results support the idea of using models with a clear mechanism that tracks disease progression in clinical settings. For such a model, the subset of model parameters associated with disease progression naturally minimizes the required data for model identifiability.

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Section: Discussionmentioning

confidence: 99%

Practical Understanding of Cancer Model Identifiability in Clinical Applications

Phan

Bennett

Patten

2023

Life

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“…Scherliess et al (2006) have studied the model error of the GAIM-FP model. Durazo et al (2021) and Koshin et al (2019) study the model systematic bias of the TIEGCM algorithm and of a four-dimensional local ensemble transform Kalman filter (4D-LETKF) respectively. Calibration of the background climate models was analyzed by Mehta and Linares (2018).…”

Section: Introductionmentioning

confidence: 99%

“…Durazo et al. (2021) and Koshin et al. (2019) study the model systematic bias of the TIEGCM algorithm and of a four‐dimensional local ensemble transform Kalman filter (4D‐LETKF) respectively.…”

Section: Introductionmentioning

confidence: 99%

Quantification of Representation Error in the Neutral Winds and Ion Drifts Using Data Assimilation

Hu,

López Rubio,

Chartier

et al. 2024

Space Weather

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In this work we quantify the representation error of the algorithm Estimating Model Parameters from Ionospheric Reverse Engineering (EMPIRE), which estimates global neutral winds and ion drifts given time‐varying plasma densities. SAMI3 (Sami3 is A Model of the Ionosphere) serves as the background climate model and pseudo‐measurements for the EMPIRE observation system. This configuration allows the data assimilation inputs to be self‐consistent between each other and with the validation data. The estimated neutral winds and ion drifts are compared to the Horizontal Wind Model (HWM14) and SAMI3 “truth.” For both the quiet period on 25 August 2018 and subs7equent storm on 26 August, the EMPIRE estimation of ion drifts is better at low‐to‐mid geomagnetic latitudes with mean error up to 20 m/s. For the high latitudes (poleward of ±60° magnetic), the mean errors exceed 50 m/s with variances up to 200 m/s, and the relative errors are higher than the “truth.” At latitudes of ±87°, the large errors are attributed to a boundary effect. However, the neutral wind mean errors peak at 20 m/s at mid‐latitudes (40°–60° magnetic), with larger uncertainties, then converge to 0 approaching higher latitudes. By conducting this study, we define a method for obtaining the representation error covariance for future use of EMPIRE with SAMI3 as background.

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“…Empirical models, such as International Reference Ionosphere (IRI-2016) [1] and NeQuick [2], have a basic description of global 3D ionospheric N e profiles and Total Electron Content (TEC). Near-real-time data analysis such as global ionosphere maps (GIMs) from the International GNSS Service (IGS) have been frequently used to validate the vertical TEC (vTEC) from empirical models [3][4][5] and other forecast models [6,7]. However, lack of detailed vertical information on N e profiles leaves the modeled plasma structural variabilities poorly constrained within the ionosphere.…”

Section: Introductionmentioning

confidence: 99%

Optimal Estimation Inversion of Ionospheric Electron Density from GNSS-POD Limb Measurements: Part I-Algorithm and Morphology

Swarnalingam

Salinas

et al. 2023

Remote Sensing

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GNSS-LEO radio links from Precise Orbital Determination (POD) and Radio Occultation (RO) antennas have been used increasingly in characterizing the global 3D distribution and variability of ionospheric electron density (Ne). In this study, we developed an optimal estimation (OE) method to retrieve Ne profiles from the slant total electron content (hTEC) measurements acquired by the GNSS-POD links at negative elevation angles (ε < 0°). Although both OE and onion-peeling (OP) methods use the Abel weighting function in the Ne inversion, they are significantly different in terms of performance in the lower ionosphere. The new OE results can overcome the large Ne oscillations, sometimes negative values, seen in the OP retrievals in the E-region ionosphere. In the companion paper in this Special Issue, the HmF2 and NmF2 from the OE retrieval are validated against ground-based ionosondes and radar observations, showing generally good agreements in NmF2 from all sites. Nighttime hmF2 measurements tend to agree better than the daytime when the ionosonde heights tend to be slightly lower. The OE algorithm has been applied to all GNSS-POD data acquired from the COSMIC-1 (2006–2019), COSMIC-2 (2019–present), and Spire (2019–present) constellations, showing a consistent ionospheric Ne morphology. The unprecedented spatiotemporal sampling of the ionosphere from these constellations now allows a detailed analysis of the frequency–wavenumber spectra for the Ne variability at different heights. In the lower ionosphere (~150 km), we found significant spectral power in DE1, DW6, DW4, SW5, and SE4 wave components, in addition to well-known DW1, SW2, and DE3 waves. In the upper ionosphere (~450 km), additional wave components are still present, including DE4, DW4, DW6, SE4, and SW4. The co-existence of eastward- and westward-propagating wave4 components implies the presence of a stationary wave4 (SPW4), as suggested by other earlier studies. Further improvements to the OE method are proposed, including a tomographic inversion technique that leverages the asymmetric sampling about the tangent point associated with GNSS-LEO links.

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Data Assimilation for Ionospheric Space-Weather Forecasting in the Presence of Model Bias

Cited by 6 publications

References 56 publications

Practical Understanding of Cancer Model Identifiability in Clinical Applications

Practical Understanding of Cancer Model Identifiability in Clinical Applications

Quantification of Representation Error in the Neutral Winds and Ion Drifts Using Data Assimilation

Optimal Estimation Inversion of Ionospheric Electron Density from GNSS-POD Limb Measurements: Part I-Algorithm and Morphology

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