Modeling the Sunspot Number Distribution with a Fokker-Planck Equation

TL;DR

This paper introduces a Fokker-Planck based statistical framework for modeling and forecasting sunspot number variability, capturing both short-term randomness and long-term solar cycle trends.

Contribution

It presents a novel, flexible Fokker-Planck approach that models sunspot numbers with a specified driver function, accommodating physical processes and empirical features.

Findings

Model aligns well with observed sunspot data

Framework is effective during both solar maximum and minimum

Analytic approximation enables efficient parameter estimation

Abstract

Sunspot numbers exhibit large short-timescale (daily-monthly) variation in addition to longer timescale variation due to solar cycles. A formal statistical framework is presented for estimating and forecasting randomness in sunspot numbers on top of deterministic (including chaotic) models for solar cycles. The Fokker-Planck approach formulated assumes a specified long-term or secular variation in sunspot number over an underlying solar cycle via a driver function. The model then describes the observed randomness in sunspot number on top of this driver function. We consider a simple harmonic choice for the driver function, but the approach is general and can easily be extended to include other drivers which account for underlying physical processes and/or empirical features of the sunspot numbers. The framework is consistent during both solar maximum and minimum, and requires no parameter restrictions to ensure non-negative sunspot numbers. Model parameters are estimated using statistically optimal techniques. The model agrees both qualitatively and quantitatively with monthly sunspot data even with the simplistic representation of the periodic solar cycle. This framework should be particularly useful for solar cycle forecasters and is complementary to existing modeling techniques. An analytic approximation for the Fokker-Planck equation is presented, which is analogous to the Euler approximation, which which allows for efficient maximum likelihood estimation of large data sets and/or when using difficult to evaluate driver functions.