Chih-Ting Hsu

Quantifying the Impact of Solar Irradiance Uncertainty on Thermosphere-Ionosphere Variability Using Ensemble Forecasts

whawkins — Wed, 10 Dec 2025 18:11:52 +0000

Quantifying the Impact of Solar Irradiance Uncertainty on Thermosphere-Ionosphere Variability Using Ensemble Forecasts whawkins Wed, 12/10/2025 - 11:11

Author:

whawkins

Dec 10, 2025

Histogram of the normalized model–data difference (Ai) for dayside Swarm neutral density. The grey histogram shows Ai computed from the WACCM-X ensemble using the new method (EXP 3). The green and red histograms represent Ai computed using the old method (EXP 1 and EXP 2). The blue histogram shows Ai computed with the new method, but with doubled spread (EXP 4). The top row presents the Ai distributions for Swarm-A, and the bottom row shows those for Swarm-C. Blue dashed lines indicate the ±3-standard-deviation thresholds.

Space Weather: This study investigates the sensitivity of the thermosphere and ionosphere to variations in solar spectral irradiance. Using data from the SDO and SORCE missions collected between 2010 and 2018, we quantified the variability and uncertainty of solar spectral irradiance across wavelengths from 0.1 to 190 nm and developed a data-driven method to generate perturbed versions of the irradiance. These perturbations were used to drive ensemble simulation experiments conducted during the 2021/2022 winter to assess sensitivity in the thermosphere and ionosphere. In addition to the experiment using statistically derived perturbations, three more ensemble experiments driven by different perturbation methods were also performed.
Results show that both neutral temperature and electron density are highly sensitive to uncertainty in solar spectral irradiance, especially above 200 km altitude. Electron density is particularly influenced by soft X-ray variability in the lower ionosphere. Comparisons with Swarm, ICON, and COSMIC-2 satellite observations confirm the performance of ensemble simulation experiments on capturing the realistic thermospheric and ionospheric variability. Among the experiments, the one driven by statistically derived perturbations produces ensemble spreads that best match the observed variability. This experiment shows good agreement with all three datasets, while the others tend to overestimate or underestimate the variability.
This work highlights the importance of accounting for uncertainty in external solar energy input in space weather models and demonstrates the value of data-informed ensemble simulations in improving the accuracy and reliability of thermosphere and ionosphere forecasts.

The Long-term Trend of Thermospheric Compositions from Whole Atmospheric Simulation and Satellite Observation

whawkins — Tue, 11 Nov 2025 20:32:51 +0000

The Long-term Trend of Thermospheric Compositions from Whole Atmospheric Simulation and Satellite Observation whawkins Tue, 11/11/2025 - 13:32

Author:

whawkins

Nov 11, 2025

JGR space physics: This study examines the long-term trend of column-integrated atomic oxygen to molecular nitrogen ratio, O/N2, in the upper atmosphere and investigates the cause of this long-term trend in O/N2. We first validate the feasibility of using a physics-based model for a long-term climate reanalysis by applying a model-data comparison between 2002 and 2018. O/N2 simulated by NSF NCAR's Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension (WACCM-X) and measured by Global Ultraviolet Imager (GUVI) aboard the Thermosphere Ionosphere Mesosphere Energetics and Dynamics (TIMED) mission is used to determine the long-term trend of O/N2 from 2002 to 2018 and validate the model result. The model and data show good agreement after removing the impact of solar irradiance and geomagnetic activity using a least-squares fitting method, revealing a decreasing trend of O/N2 of about -0.54% per decade relative to the O/N2 in 2018 in the model and about -0.45% per decade in data along the satellite orbit during the period between 2002 and 2018. A decreasing trend of global O/N2 of about -0.70% per decade is found in the model between 1960 and 2018. After that, four WACCM-X long-term simulations are performed from 1960 to 2018 to identify the cause of the decreasing trend of O/N2. The results show that this decreasing trend is mainly caused by the increase in greenhouse gas concentrations.

O/N2 measured by TIMED/GUVI and simulated by WACCM-X Case4 and their linear trends. (a) Black and grey lines are the O/N2 data derived from TIMED/GUVI and simulated by WACCM-X, respectively. (b) Dark and light green lines are the fitting curves of the black and grey lines in (a) using the least-squares fitting method with an equation that includes the annual, semi-annual, F10.7 index, and Ap index variations. (c) The blue line is the residual term of TIMED/GUVI O/N2 by subtracting the dark green line from the black line, while the cyan line is the residual term of WACCM-X O/N2 by subtracting the light green line from the grey line. (d) Red and magenta lines are the fitting results of blue and cyan lines using the least-squares fitting method with a linear equation, respectively.

A Community Ionosphere-Thermosphere Observing System Simulation Experiment (OSSE) Tool

whawkins — Tue, 09 Apr 2024 15:17:06 +0000

A Community Ionosphere-Thermosphere Observing System Simulation Experiment (OSSE) Tool whawkins Tue, 04/09/2024 - 09:17

Geospace Dynamics Constellation Example

Author:

whawkins

Apr 9, 2024

The longitude-latitude distribution of neutral temperature from the pressure level 19 that corresponds to about 350 km altitude at UT 01 on 17 March 2013 from OSSE5. (a) True distribution from the nature run overlaid with GDC observation locations indicated by white dots. (b) Mean distribution from the control ensemble simulation with no DA. (c) Differences between the experiment result and truth before DA. (d) Differences between the experiment result and truth after DA.

Earth and Space Science: Observing System Simulation Experiments (OSSEs) provide an effective way to evaluate the impact of assimilating data from a specific observing system on hindcasting, nowcasting, and forecasting of environmental systems. The NSF NCAR's Data Assimilation Research Testbed/Thermosphere-Ionosphere-Electrodynamics General Circulation Model (DART/TIEGCM) tool, to be hosted at the NASA Community Coordinated Modeling Center (CCMC), serves as a valuable and accessible community resource for quantitatively evaluating the impact of observations from both current and future ionosphere-thermosphere (IT) observing systems.
Chih-Ting Hsu, et al. in this study demonstrate the utility of DART/TIEGCM as an IT OSSE tool, using synthetic observations simulated using a currently planned NASA Geospace Dynamics Constellation (GDC) observing system design. Five sets of OSSEs are carried out to compare the effects of assimilating various combinations of prospective GDC observations (e.g., neutral temperature, neutral wind, neutral composition, atomic oxygen ion density, and ion and electron temperature) during a major geomagnetic storm period of the St Patrick's Day Storm on March 17, 2013. These OSSEs indicate the benefits of coupled IT data assimilation approaches implemented in DART/TIEGCM to maximize the impact of multi-parameter IT observations, such as those expected from the GDC mission. Although more work is required to draw any definitive conclusion on the GDC data impact, the study provides an illustrative example of how the DART/TIEGCM community tool can be used to evaluate observational impacts of planned or existing missions for geospace research and applications.

Impact of Thermospheric Wind Data Assimilation on Ionospheric Electrodynamics using a Coupled Whole Atmosphere Data Assimilation System

kolinski — Thu, 03 Mar 2022 21:39:21 +0000

Impact of Thermospheric Wind Data Assimilation on Ionospheric Electrodynamics using a Coupled Whole Atmosphere Data Assimilation System kolinski Thu, 03/03/2022 - 14:39

Publication: Journal of Geophysical Research, Space Physics; Authors: Chih-Ting Hsu, Nick Pedatella, Jeff Anderson

Author:

kolinski

Mar 3, 2022

Three Observing System Simulation Experiments (OSSEs) are carried out in this study. Thermospheric wind and temperature states are updated using synthetic data sampled from a Nature Run (NR) in this experiment. The updated thermospheric states will further change the plasma drift and electron density in the ionosphere. This figure shows the longitude-latitude maps of NmF2 from the NR (first column) and the difference of NmF2 between OSSEs and NR (second to fourth columns) at six different times. From top to bottom rows are maps at 1200 UT on 05, 10, 15, 20, 25, and 30 December 2009. From the second to fourth columns are the difference of NmF2 between OSSE-MIGHTI-ELF, OSSE-MIGHTI-GC, and OSSE-GC and NR.

The upward plasma drift and equatorial ionization anomaly (EIA) in the Earth's ionosphere are strongly influenced by the zonal electric field, which is generated by the wind dynamo. Specification and forecasting of thermospheric winds thus plays an important role in ionospheric weather prediction. In this study, we assess the impact of assimilating thermospheric wind observations from the Michelson Interferometer for Global High-resolution Thermospheric Imaging (MIGHTI) instrument on NASA's Ionospheric CONnection (ICON) explorer satellite on the ionospheric electrodynamics. Empirical Localization Functions (ELFs) of ICON/MIGHTI zonal and meridional winds are also computed and applied to our data assimilation experiments, enabling improved assimilation of the ICON/MIGHTI wind observations. A set of Observing System Simulation Experiments (OSSEs) are performed using the National Center for Atmospheric Research (NCAR) Whole Atmosphere Community Climate Model with thermosphere-ionosphere eXtension (WACCMX) with data assimilation provided by the Data Assimilation Research Testbed (DART) ensemble adjustment Kalman filter. The results show that assimilating the ICON/MIGHTI wind observations with the ELF improves the zonal and meridional wind root mean square error (RMSE) by 16-18% and 7-10%, respectively. The improved wind specification further improves the low-latitude ExB vertical drift RMSE by about 18%. The response of electron densities is slower, and the overall impact is smaller. The improvement of the ionosphere F-region maximum electron density (NmF2) between -45 to 45 degree is about 8% after 18 days of data assimilation.

Data-Driven Ensemble Modeling of Equatorial Ionospheric Electrodynamics

kolinski — Tue, 16 Nov 2021 21:09:20 +0000

Data-Driven Ensemble Modeling of Equatorial Ionospheric Electrodynamics kolinski Tue, 11/16/2021 - 14:09

Publication: JGR: Space Physics; First HAO Author: Chih-Ting Hsu; Authors as listed in article: Chih-Ting Hsu, Tomoko Matsuo, Astrid Maute, Russel Stoneback, and Chuan-Ping Lien

Author:

kolinski

Nov 16, 2021

A Case Study During a Minor Storm Period Under Solar Minimum Conditions: The dayside equatorial ionospheric electrodynamics exhibits strong variability driven simultaneously by highly changeable external forcings that originate from the Sun, magnetosphere, and lower atmosphere. In this paper, to investigate this variability, a comprehensive data-driven ensemble modeling is carried out by using a coupled model of the thermosphere and ionosphere, with the focus on the $\mathbf{E \times B}$ drift variability, during a solar minimum and minor storm period.

Root-Mean-Square Difference of the plasma drift between C/NOFS data and FORMOSAT-3/COSMIC DART/TIE-GCM ensemble data assimilation computed separately for the four geomagnetic longitudinal sectors. The ensemble data assimilation result is shown by triangle marks, and the control experiment (ensemble simulation without assimilating any data) result is shown by asterisk marks. The TIE-GCM ensemble used in this in these is driven by MERRA-TIMEGCM and AMGeO. Generally, by updating neutral winds using FORMOSAT-3/COSMIC DART/TIE-GCM ensemble data assimilation and driven the model ensemble using MERRA and AMGeO, the specification of plasma drift can be improved.

The variability of $\mathbf{E \times B}$ drift in response to the changes and uncertainty of primary forcings (e.g., solar EUV, high-latitude plasma convection and auroral particle precipitation, and lower atmospheric wave forcing) is investigated by ensemble forcing sensitivity experiments that incorporate data-driven stochastic perturbations of these forcings into the model. Secondly, the impact of assimilating FORMsa SATellite-3/Constellation Observing System for Meteorology, Ionosphere and Climate (FORMOSAT-3/COSMIC) electron density profiles (EDPs) on the reduction of uncertainty of the modeled $\mathbf{E \times B}$ drift variability. The Communication and Navigation Outage Forecasting System (C/NOFS) ion drift velocity observations are used for validation, and the validation results support importance of the use of data-driven forcing perturbation methods in ensemble modeling and data assimilation experiments. The specific main findings are as follows. The solar EUV dominates the global-scale day-to-day variability, while the lower atmosphere wave forcing is critical to determining the regional day-to-day variability. Uncertainty of the modeled $\mathbf{E \times B}$ drift variability is affected considerably by the magnetospheric forcing specification methods. The ensemble data assimilation of FORMOSAT-3/COSMIC EDPs helps reduce the uncertainty and improve an agreement of the modeled $\mathbf{E \times B}$ drifts with C/NOFS observations.

The impact of ICON/MIGHTI zonal and meridional winds on upper atmosphere weather specification in a whole atmosphere data assimilation system

kolinski — Mon, 15 Nov 2021 21:19:40 +0000

The impact of ICON/MIGHTI zonal and meridional winds on upper atmosphere weather specification in a whole atmosphere data assimilation system kolinski Mon, 11/15/2021 - 14:19

Publication: JGR: Space Physics; HAO Author: Chih-Ting Hsu; Authors as listed in article: Chih-Ting Hsu, Nicholas Pedatella

Author:

kolinski

Nov 15, 2021

The thermospheric data assimilation is limited due to the lack of continuous observation of the neutral state. Recently, the thermospheric wind data from the Michelson Interferometer for Global High-resolution Thermospheric Imaging (MIGHTI) on NASA's Ionospheric CONnection (ICON) became available. ICON/MIGHTI provides near-continuous observations of the mid- and low-latitude thermospheric meridional and zonal winds. This study assesses the impact of assimilating ICON/MIGHTI winds in the National Center for Atmospheric Research (NCAR) Whole Atmosphere Community Climate Model with thermosphere-ionosphere eXtension (WACCM-X) + Data Assimilation Research Testbed (DART) on the specification and short-term forecasting of the thermosphere. Observing system simulation experiments of WACCM-X+DART with and without assimilating synthetic ICON/MIGHTI meridional and zonal wind profiles are performed.

The difference between zonal wind field in the NR and the prior ensemble mean of OSSEs at 0000 UT of December 31. The first column shows the difference between zonal wind in the NR and the prior ensemble mean of OSSE1 at 150 km, 200 km, and 250 km altitude. The second column shows the difference between zonal wind in the NR and the prior ensemble mean of OSSE2. Positive/negative value means zonal wind from the NR is larger/smaller than that from the ensemble mean. The eastward wind is shown on the red scale, and the westward wind is shown on the blue scale. Black dots represent the observation location of assimilated ICON/MIGHTI wind data at about 150 km, 200km, and 250 km altitude used in the previous update from 16:30 UT to 19:30 UT of 30 December 2009. Red bold lines indicate the regions where the absolute difference between winds from OSSE1 and NR is larger than the absolute difference between winds from OSSE2 and NR by 8 m/s. Blue bold lines indicate the regions where the absolute difference between winds from OSSE1 and NR is smaller than the absolute difference between winds from OSSE2 and NR by 8 m/s.

The result shows that this new dataset can correct the wind specification throughout the mid-and low-latitude thermosphere, especially around the 90 to 160 km altitude region. A notable impact is also shown in the region above 300 km altitude, which is above the altitude of ICON/MIGHTI wind observations. The impact of ICON/MIGHTI data on the zonal wind field is larger than on the meridional wind field. The errors of meridional and zonal wind fields in the mid- and low-latitude region are reduced by 6\% and 12\%, respectively, with the help of ICON/MIGHTI wind data.

Chih-Ting Hsu

Quantifying the Impact of Solar Irradiance Uncertainty on Thermosphere-Ionosphere Variability Using Ensemble Forecasts

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The Long-term Trend of Thermospheric Compositions from Whole Atmospheric Simulation and Satellite Observation

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A Community Ionosphere-Thermosphere Observing System Simulation Experiment (OSSE) Tool

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Impact of Thermospheric Wind Data Assimilation on Ionospheric Electrodynamics using a Coupled Whole Atmosphere Data Assimilation System

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Data-Driven Ensemble Modeling of Equatorial Ionospheric Electrodynamics

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The impact of ICON/MIGHTI zonal and meridional winds on upper atmosphere weather specification in a whole atmosphere data assimilation system

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