Data Assimilation Across Disciplines

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.

Analysis Of Short-term Solar Activity Variability and Estimating Timings of Next Enhanced Bursts

whawkins — Tue, 11 Nov 2025 21:45:29 +0000

Analysis Of Short-term Solar Activity Variability and Estimating Timings of Next Enhanced Bursts whawkins Tue, 11/11/2025 - 14:45

Author:

whawkins

Nov 11, 2025

Zoomed-in burst-envelope forecasts from January 2024 to April 2026. Top: The Northern Hemisphere SSN with inferred RF (Random Forest) bursts properties. Middle: The Southern hemisphere counterpart. Bottom: Total (Northern Hemisphere + Southern Hemisphere) SSN, highlighting the same burst timings and amplitudes.

The Astrophysical Journal: A novel machine-learning-based hybrid forecasting strategy for predicting next enhanced burst of solar activity is presented. This hybrid forecast-system combines numerical, statistical, and machine-learning techniques to detect the occurrence of the next bursts of solar activity. These enhanced bursts are called “space weather seasons” that occur on intermediate timescales (6–18 months). Monthly smoothed sunspot number (SSN) data from 1878 to 2025 are analyzed using Gaussian fitting techniques to identify burst events and their properties such as amplitude and duration. The SSN data are divided into training, test, and forecast, which shows hindcast and forecast. Each hemisphere is modeled via a Seasonal Autoregressive Integrated Moving Average (SARIMA) approach, refined with an asymmetric Gaussian override to capture rapid burst rise and gradual decay, and burst amplitudes and duration are predicted using a Random Forest (RF) regression model. This hybrid approach successfully hindcasts burst timing in between November 2024 and May 2025, with a peak SSN of ∼70 around March 2025 for the Northern Hemisphere. The next burst in the Northern Hemisphere is forecast to be in December 2025 with a slightly lower SSN of 60. By contrast, the Southern Hemisphere shows relatively complicated behavior, where the bursts show multiple amplitudes starting approximately in October 2024 and ending in October 2025. The main burst shows an amplitude of 130 SSN. The next burst in the Southern Hemisphere is forecast to occur approximately in December 2025. Combining SSN properties in both hemispheres, we find that the total SSN is mainly influenced by a stronger cycle in the Southern Hemisphere.

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.

Data Assimilation Across Disciplines

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

Recent News

Influence of the Stratospheric Quasi-Biennial Oscillation on the Seasonal Variation in the Mesosphere and Lower Thermosphere Based on a Long-Term Reanalysis JAWARA

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

Polarization fringes in optical systems: a compendium

Contribution of Gravity Waves to the Lower Thermospheric Winter-to-summer Meridional Circulation in High-resolution WACCM-X

Spectropolarimetric Inversion in Four Dimensions with Deep Learning (SPIn4D): II. A Physics-Informed Machine Learning Method for 3D Solar Photosphere Reconstruction

NASA selects NSF NCAR Heliophysics Mission for Continued Development

New AI Based Methods for 3D Reconstruction of the Solar Photosphere

MHD simulations of CME with associated prominence eruption

Local Time Variability of Gravity Wave Activity Revealed by SABER Temperature Observations

Efficiency of Electromagnetic Energy Transfer from Solar Wind to Ionosphere through Magnetospheric Ultra-Low Frequency Waves

Analysis Of Short-term Solar Activity Variability and Estimating Timings of Next Enhanced Bursts

Recent News

Influence of the Stratospheric Quasi-Biennial Oscillation on the Seasonal Variation in the Mesosphere and Lower Thermosphere Based on a Long-Term Reanalysis JAWARA

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

Polarization fringes in optical systems: a compendium

Contribution of Gravity Waves to the Lower Thermospheric Winter-to-summer Meridional Circulation in High-resolution WACCM-X

Spectropolarimetric Inversion in Four Dimensions with Deep Learning (SPIn4D): II. A Physics-Informed Machine Learning Method for 3D Solar Photosphere Reconstruction

NASA selects NSF NCAR Heliophysics Mission for Continued Development

New AI Based Methods for 3D Reconstruction of the Solar Photosphere

MHD simulations of CME with associated prominence eruption

Local Time Variability of Gravity Wave Activity Revealed by SABER Temperature Observations

Efficiency of Electromagnetic Energy Transfer from Solar Wind to Ionosphere through Magnetospheric Ultra-Low Frequency Waves

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

Recent News

Influence of the Stratospheric Quasi-Biennial Oscillation on the Seasonal Variation in the Mesosphere and Lower Thermosphere Based on a Long-Term Reanalysis JAWARA

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

Polarization fringes in optical systems: a compendium

Contribution of Gravity Waves to the Lower Thermospheric Winter-to-summer Meridional Circulation in High-resolution WACCM-X

Spectropolarimetric Inversion in Four Dimensions with Deep Learning (SPIn4D): II. A Physics-Informed Machine Learning Method for 3D Solar Photosphere Reconstruction

NASA selects NSF NCAR Heliophysics Mission for Continued Development

New AI Based Methods for 3D Reconstruction of the Solar Photosphere

MHD simulations of CME with associated prominence eruption

Local Time Variability of Gravity Wave Activity Revealed by SABER Temperature Observations

Efficiency of Electromagnetic Energy Transfer from Solar Wind to Ionosphere through Magnetospheric Ultra-Low Frequency Waves