Uncertainty Estimates of Solar Wind Prediction using HMI Photospheric Vector and Spatial Standard Deviation Synoptic Maps
Bala Poduval, Gordon Petrie, Luca Bertello

TL;DR
This study estimates the uncertainties in solar wind speed predictions at 1 AU caused by errors in photospheric synoptic maps, using Monte Carlo simulations and comparing with observational data across different solar cycle phases.
Contribution
It introduces a method to quantify the impact of synoptic map uncertainties on solar wind predictions using Monte Carlo realizations and compares model predictions with observational data.
Findings
Uncertainty estimates vary across different Carrington rotations.
Predicted solar wind speeds show measurable root mean square errors.
Coronal hole predictions are compared with EUV synoptic maps.
Abstract
Current solar wind prediction is based on the Wang & Sheeley empirical relationship between the solar wind speed observed at 1 AU and the rate of magnetic flux tube expansion (FTE) between the photosphere and the inner corona, where FTE is computed by coronal models that take the photospheric flux density synoptic maps as their inner boundary conditions to extrapolate the photospheric magnetic fields to deduce the coronal and the heliospheric magnetic field configuration. Since these synoptic maps are among the most widely-used of all solar magnetic data products, the uncertainties in the model predictions that are caused by the uncertainties in the synoptic maps are worthy of study. However, such an estimate related to synoptic map construction was not available until Bertello et al. (Solar Physics, 289, 2014) obtained the spatial standard deviation synoptic maps; 98 Monte-Carlo…
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