MEnvData-SWE: Planetary Space Weather Data

• MEnvData-SWE is a comprehensive, multiparameter data ecosystem that normalizes and integrates solar wind, meteoroid, and environmental datasets to support space weather research.
• It employs advanced techniques like double superposed epoch analysis to generate canonical event profiles and validated simulation inputs for varied planetary environments.
• The platform enables ensemble forecasting, probabilistic modeling, and rigorous benchmarking for planetary magnetospheric and environmental hazard analyses.

MEnvData-SWE is a rigorously structured, multiparameter data product and modeling ecosystem encapsulating the salient properties of space and environmental drivers that influence planetary magnetospheres and surface environments. It integrates and curates normalized, empirically derived, and simulated solar-wind, magnetospheric, meteoroid, and environmental data streams, primarily to support research, simulation, and forecasting in planetary space physics and environmental hazard analysis.

1. Scope and Conceptual Framework

MEnvData-SWE (Meteoroid Environment Data – Space Weather Environment) serves as both a repository and methodological framework for the systematic collection, normalization, and deployment of environmental driver data relevant to planetary and spacecraft environments. It draws upon multi-decadal empirical analyses, parametric MHD simulations, standardized event alignment/averaging methodologies, and advanced probabilistic/ensemble solar-wind modeling. The data and derived quantities cover phenomena ranging from hydrodynamic solar wind parameters, CME-driven disturbances, magnetospheric boundary conditions, to the flux and directionality of meteoroid backgrounds.

The system leverages normalization strategies (e.g., double superposed epoch analysis), multiphase sequence parsing, and detailed domain-specific parameterization to produce canonical event profiles, lookup tables, and scaling laws—enabling robust driver specification for downstream models of planetary space weather, atmospheric erosion, or surface impact processes (Yermolaev et al., 2015, Varela et al., 2016, Moorhead et al., 2024).

2. Normalized Solar-Wind Driver Library

A central pillar of MEnvData-SWE is the normalized library of solar-wind driver time series derived via the double superposed epoch analysis (DSEA) method (Yermolaev et al., 2015). DSEA aligns and rescales empirical event intervals to a common phase axis $\tau$ for inter-comparability across heterogeneous, variable-duration phenomena such as CIRs, ICMEs (Ejecta, magnetic clouds), Sheaths, and interplanetary shocks.

Key method features:

• Event selection from OMNI 1-hour data for 1976–2000, tagged using strict thresholds on speed $V$, density $N$, temperature $T$, $\beta$, and IMF.
• Uniform rescaling of each event’s timeline onto $\tau \in [0,1]$, with subinterval stitching for multiphase structures.
• Averaging over hundreds of events per class to obtain high-SNR canonical profiles for 20 plasma and field parameters (e.g., $V$, $N$, $T$, $B$, $B_z$, dynamic pressure $P_d$, $\beta$, $Kp$, $AE$, $Dst$, $T/T_\text{exp}$).

Canonical sequences encoded:

• CIR only; IS/CIR; Ejecta only; Sheath/Ejecta; IS/Sheath/Ejecta
• MC (magnetic cloud) only; Sheath/MC; IS/Sheath/MC

Significance:

This provides, for each event class, the “average” normalized waveform of driver parameters (e.g., rising ramp in $V$ and peaking $P_d$ in CIRs/sheaths, falling $V$ and persistent low $\beta$ in ICMEs), with variance estimates. These profiles are used to initialize magnetospheric simulations, storm forecasting models, or for comparative analysis of compression vs. ejecta-driven effects in space weather, Dst, Kp, or AE index responses (Yermolaev et al., 2015).

3. Hydrodynamic and Magnetospheric Boundary Parameterization

MEnvData-SWE encodes detailed parameter grids, scaling laws, and simulation references for planetary solar wind–magnetosphere interaction, with particular emphasis on Mercury and weak-IMF scenarios (Varela et al., 2016).

Parameter Ranges (Mercury Sunward SW):

• Number density $n = [12, 60, 180]$ cm$^{-3}$
• Bulk speed $v = [200, 250, 500]$ km/s
• Temperature $$T = [2 \times 10^4, 5.8 \times 10^4, 1.8 \times 10^5]$$ K
• IMF: $B_\text{sw} \approx 7.3$ nT northward

Governing equations:

• Full 3D ideal MHD conservation laws in spherical coordinates.
• Multipolar internal field expansion for planetary dipole-plus-higher moments.

Empirical scalings:

• Magnetopause standoff $$R_{MP}/R_M = \left( \frac{B_\text{dip}^2}{\mu_0\rho v^2} \right)^{1/6}$$
• Simulated $R_{MP,sim} \approx 1.08 \times R_{MP,theory}$ (northward IMF enhancement)

Mass and energy deposition lookup tables:

• Mass deposition rates and hemispheric partitioning tabulated against $n$, $v$, and $T$.
• Bow shock and sheath expansion/compression metrics given as $\Delta R$ as a function of sonic Mach $M_s$ and $T$.

Usage:

These parameterizations provide strict driver specifications for simulation boundaries in coupled MHD/plasma models, facilitating systematic exploration and benchmarking across hydrodynamic and IMF regimes (Varela et al., 2016).

4. Meteoroid Environment Data Products

MEnvData-SWE includes canonical meteoroid environment data for the inner Solar System, directly referencing the NASA MEM 3/3.1 framework and the Moorhead et al. sample library (Moorhead et al., 2024, Moorhead et al., 9 Dec 2025).

Core outputs:

• Look-up tables and histograms for meteoroid flux ($\Phi$), speed ($v$), impact directionality (azimuth/elevation maps), and bulk density distributions, generated per trajectory (e.g., MESSENGER at Mercury, LADEE at Moon), stratified by two density populations.
• MEM 3.1 computes spatial number density $\rho(r,\beta)$, directional flux $F(r,\beta,\varphi,\theta)$ at arbitrary ecliptic latitude $\beta$ (unlike MEM 3.0, which was planar), covering $r = 0.2$–$4.6$ au, crucial for high-inclination and out-of-ecliptic orbits (Moorhead et al., 9 Dec 2025).

Recommended file artifacts:

• Cube flux tables (cube_avg.txt), speed histograms, density histograms, SVG heatmaps, and full trajectory/environment reproduction files with associated run options.
• Sampling guidance to ensure $<1\%$ uncertainty via CLT-based subsampling and chi-squared uniformity tests on orbit anomalies.

Validation and limits:

• Near-ecliptic comparisons yield $<3\%$ flux variance between MEM 3.1 and 3.0.
• For missions at high $\beta$, MEM 3.1 predicts strong latitude-dependence, with $\sim20\%$ of ecliptic flux at $\beta=60^\circ$ (Moorhead et al., 9 Dec 2025).

5. Ensemble and Probabilistic Solar Wind Modeling

MEnvData-SWE supports ensemble MHD solar wind forecasts, emphasizing the ADAPT–WSA–MS-FLUKSS chain, which quantifies uncertainty propagation from photospheric magnetogram boundary perturbations to solar wind environments in the inner heliosphere (Hegde et al., 28 May 2025).

Boundary ensemble:

• ADAPT generates $N=12$ stochastic realizations of the photospheric field ingesting SDO/HMI data, encoding ensemble-averaged uncertainties especially at poorly observed longitudes/poles.
• WSA is driven per realization, outputting $\{B_r,V_r\}$ maps at $21.5\,R_\odot$; these drive MS-FLUKSS for global 3D wind predictions.

Validation metrics:

• Comparison to PSP, Solar Orbiter, STEREO-A, and OMNI in-situ data for $V_r$, $N$, $B_r$, $T$ across $r=0.1$–$1$ au, using RMSE, Pearson $r$, sector-boundary ($SB$) crossing time errors.
• Typical ensemble mean RMSE at Earth: $V_r$ $\sim100$ km/s; $SB$ timing $\sim1.2$ days.

Forecast product:

• Probabilistic confidence intervals and median forecasts for all key plasma variables at arbitrary IHS points.
• Recommended operational practice: update boundary ensemble every 12 h; combine with CME ensemble models for full event geoeffectiveness prediction (Hegde et al., 28 May 2025).

6. Data Structures, Access, and Integration

MEnvData-SWE enforces strict artifact and file-naming conventions across drivers, environments, and trajectory-linked reference data, ensuring reproducibility and direct integration into simulation or analytics pipelines.

Directory structure:

• Each event/trajectory class is stored under a standardized path with time/space-indexed files for high-frequency access (trajectory.txt, options, results per run).
• Canonical parameter grids, analytical fits, and auto-generated lookup tables are provided in directly ingestible formats (e.g., for R_MP, bow-shock position, mass precipitation).
• SVG plots, tables, histograms, and statistical summaries enable immediate visualization and automated QA.

Integration:

• All key driver datasets (solar wind, meteoroids, magnetospheric boundaries) are harmonized for coupling with physics-based, empirical, and data-assimilative modeling environments.
• MEnvData-SWE intermediates coupled simulations (magnetosphere, exosphere, surface/radiation models), operational nowcasting tools, and meta-analyses for planetary comparative space physics.

7. Applications and Implications

MEnvData-SWE underpins a diverse breadth of planetary and heliophysical investigations:

• Magnetospheric response modeling (e.g., Mercury, Earth, Mars) and benchmarking of CME/stream ingress and boundary dynamics.
• Comparison and validation of geoeffectiveness across event classes and sequences, as normalized parameter profiles for Dst, Kp, AE, and $\beta$ are mapped to predicted storm intensities.
• Engineering of planetary missions for environmental hazard robustness using canonical meteoroid/solar-wind driver tables and probabilistic risk models.
• Data-driven calibration and assimilation in global MHD, empirical, and hybrid simulation frameworks supporting operational forecasting and space environment characterization (Ritter et al., 2015, Moorhead et al., 2024, Hegde et al., 28 May 2025).

In summary, MEnvData-SWE constitutes a comprehensive, rigorously parameterized, and empirically validated data ecosystem for planetary and space environment modeling, integrating multi-modal driver streams and ensemble-propagated uncertainties to advance both fundamental research and operational applications (Yermolaev et al., 2015, Varela et al., 2016, Moorhead et al., 2024, Moorhead et al., 9 Dec 2025, Hegde et al., 28 May 2025).