A 27 day persistence model of near‐Earth solar wind conditions: A long lead‐time forecast and a benchmark for dynamical models

Abstract

[1] Geomagnetic activity has long been known to exhibit approximately 27 day periodicity, resulting from solar wind structures repeating each solar rotation. Thus a very simple near-Earth solar wind forecast is 27 day persistence, wherein the near-Earth solar wind conditions today are assumed to be identical to those 27 days previously. Effective use of such a persistence model as a forecast tool, however, requires the performance and uncertainty to be fully characterized. The first half of this study determines which solar wind parameters can be reliably forecast by persistence and how the forecast skill varies with the solar cycle. The second half of the study shows how persistence can provide a useful benchmark for more sophisticated forecast schemes, namely physics-based numerical models. Point-by-point assessment methods, such as correlation and mean-square error, find persistence skill comparable to numerical models during solar minimum, despite the 27 day lead time of persistence forecasts, versus 2-5 days for numerical schemes. At solar maximum, however, the dynamic nature of the corona means 27 day persistence is no longer a good approximation and skill scores suggest persistence is out-performed by numerical models for almost all solar wind parameters. But point-by-point assessment techniques are not always a reliable indicator of usefulness as a forecast tool. An event-based assessment method, which focusses key solar wind structures, finds persistence to be the most valuable forecast throughout the solar cycle. This reiterates the fact that the means of assessing the "best" forecast model must be specifically tailored to its intended use.

Probabilistic Solar Wind and Geomagnetic Forecasting Using an Analogue Ensemble or “Similar Day” Approach

“…The high autocorrelation in V R means that this is an extremely good forecast in terms of RMSE, at least over short lead times (e.g. averaged over forecast lead times from 1 to 24 hours, it outperforms all current numerical solar-wind models (Owens et al, 2008), as well as 27-day persistence (Owens et al, 2013)), although that does not necessarily make it a useful forecast for operational decision making. For this six-month test period, persistence results in an RMSE of 63.2 km s −1 , lower than any of the AnEn forecasts considered in Figure 2a.…”

“…There is a small drop in the RMSE of the AnEn median and persistence forecasts of V R and N P at lead times of approximately 27 days (approximately 650 hours), which is due to the recurrence of solar-wind structures with solar rotation (e.g. Chree and Stagg, 1928;Owens et al, 2013). For B N and B T forecasts, the AnEn median beats persistence and climatology for all lead times, although the AnEn median essentially regresses towards climatology for lead times of around 100 hours for B T and 10 hours for B N .…”

Coronal hole evolution from multi-viewpoint data as input for a STEREO solar wind speed persistence model

“…We present three different persistence models that are evaluated against actual ACE measurements: ACEþ27, based on ACE data forward shifted in time by 27.2753 days (cf. Owens et al, 2013); STEREO persistence, based on STEREO data dynamically forward shifted, according to the angle between the STEREO-B (STEREO-A) spacecraft and Earth; the new concept model STEREOþCH, based on the STEREO persistence model taking into account the evolution of CH.…”

“…Based on this, typically single viewpoint in situ measurements are used to perform persistence models for solar wind speed forecasting, giving very reasonable results (see e.g. Owens et al, 2013). A similar performance as a persistence model built on ACE (Advanced Composition Explorer) measurements, has the empirical solar wind forecasting (ESWF 1 ) model which is built on the linear CH area-solar wind speed relation using an empirical formula that relates the CH area observed remotely in the EUV wavelength range with the speed at 1 AU (see Vr snak et al, 2007;Rotter et al, 2012Rotter et al, , 2015.…”

Characterizing interplanetary shocks for development and optimization of an automated solar wind shock detection algorithm

“…Recently, the technique of quantifying forecast performance using a variety of skill score metrics based on those used by the terrestrial meteorological community has been applied to the evaluation of several space weather forecast models [Mozer and Briggs, 2003;Smith et al, 2009;Crown, 2012;Owens et al, 2013]. This shift represents a necessary and positive move for the space physics community toward adopting a standard set of forecast skill measures to allow for easy comparison between various algorithms and prediction methods.…”