Shawn L. Young | Geology Reports

USGS2018

Operational nowcasting of electron flux levels in the outer zone of Earth's radiation belt

We describe a lightweight, accurate nowcasting model for electron flux levels measured by the Van Allen probes. Largely motivated by Rigler et al. ( 2004 , https://doi.org/10.1029/2003SW000036 ), we turn to a time‐varying linear filter of previous flux levels and K p . We train and test this model on data gathered from the 2.10 MeV channel of the Relativistic Electron‐Proton Telescope sensor onboard the Van Allen probes. Dynamic linear models are a specific case of state space models and can be made flexible enough to emulate the nonlinear behavior of particle fluxes within the radiation belts. Real‐time estimation of the parameters of the model is done using a Kalman filter, where the state of the model is exactly the parameters. Nowcast performance is assessed against several baseline interpolation schemes. Our model demonstrates significant improvements in performance over persistence nowcasting. In particular, during times of high geomagnetic activity, our model is able to attain performance substantially better than a persistence model. In addition, residual analysis is conducted in order to assess model fit and to suggest future improvements to the model.

Space Weather↗

USGS2015

Real-time validation of the Dst Predictor model

The Dst Predictor model, which has been running real-time in the Space Weather Analysis and Forecast System (SWAFS), provides 1-hour and 4-hour forecasts of the Dst index. This is useful for awareness of impending geomagnetic activity, as well as driving other real-time models that use Dst as an input. In this report, we examine the performance of this forecast model in detail. When validating indices it should be noted that performance is only with respect to a reference index as they are derived quantities assumed to reﬂect a state of the magnetosphere that cannot be directly measured. In this case U.S. Geological Survey (USGS) Deﬁnitive Dst is the reference index (Section 3). Whether or not the model better reﬂects the actual activity level is nearly impossible to discern and is outside the scope of this report. We evaluate the performance of the model by computing continuous predictant skill scores against USGS Deﬁnitive Dst values as “observations” (Section 4.2). The two sets of data are not well-correlated for both 1-hour and 4-hour forecasts. The Dst Predictor Prediction Efﬁciency for both the 1- and 4-hour forecasts suggests poor performance versus the climatological mean. However, the skill score against a nowcast persistence model is positive, suggesting value added by the Dst Predictor model. We further examine statistics for storm times (Section 4.3) with similar results: nowcast persistence performs worse than Dst Predictor. Dst Predictor is superior to the nowcast persistence model for the metric used in this study. We recommend continued use of the DstPredictor model for 1-and4-hour Dst predictions along with active study of other Dst forecast models that do not rely on nowcast inputs (Section 6). The lack of certiﬁed requirements makes further recommendations difﬁcult. A study of how the error in Dst translates to error in models and a better understanding of operational needs for magnetic storm warning are needed to determine such requirements. Nowcast persistence is often hard to beat for short term forecasts and speciﬁcation and Dst Predictor clearly performs well against that standard (with 1-hour and 4-hour skill-scores of 0.233 and 0.485 respectively), although poor in absolute terms (with1-hourand4-hour prediction efﬁciencies of-64.6and-43.1, respectively).

Air Force Research Laboratory Technical Report↗