Two collections of substorms are created: 28,464 substorms identified with jumps in the SuperMAG AL index in the years 1979–2015 and 16,025 substorms identified with electron injections into geosynchronous orbit in the years 1989–2007. Substorm occurrence rates and substorm recurrence‐time distributions are examined as functions of the phase of the solar cycle, the season of the year, the Russell‐McPherron favorability, the type of solar wind plasma at Earth, the geomagnetic‐activity level, and as functions of various solar and solar wind properties. Three populations of substorm occurrences are seen: (1) quasiperiodically occurring substorms with recurrence times (waiting times) of 2–4 h, (2) randomly occurring substorms with recurrence times of about 6–15 h, and (3) long intervals wherein no substorms occur. A working model is suggested wherein (1) the period of periodic substorms is set by the magnetosphere with variations in the actual recurrence times caused by the need for a solar wind driving interval to occur, (2) the mesoscale structure of the solar wind magnetic field triggers the occurrence of the random substorms, and (3) the large‐scale structure of the solar wind plasma is responsible for the long intervals wherein no substorms occur. Statistically, the recurrence period of periodically occurring substorms is slightly shorter when the ram pressure of the solar wind is high, when the magnetic field strength of the solar wind is strong, when the Mach number of the solar wind is low, and when the polar‐cap potential saturation parameter is high.

We present a four‐category classification algorithm for the solar wind, based on Gaussian Process. The four categories are the ones previously adopted in Xu and Borovsky (2015): ejecta, coronal hole origin plasma, streamer belt origin plasma, and sector reversal origin plasma. The algorithm is trained and tested on a labeled portion of the OMNI data set. It uses seven inputs: the solar wind speed Vsw, the temperature standard deviation σT, the sunspot number R, the F10.7 index, the Alfven speed vA, the proton specific entropy Sp, and the proton temperature Tp compared to a velocity‐dependent expected temperature. The output of the Gaussian Process classifier is a four‐element vector containing the probabilities that an event (one reading from the hourly averaged OMNI database) belongs to each category. The probabilistic nature of the prediction allows for a more informative and flexible interpretation of the results, for instance, being able to classify events as “undecided.” The new method has a median accuracy larger than 90% for all categories, even using a small set of data for training. The Receiver Operating Characteristic curve and the reliability diagram also demonstrate the excellent quality of this new method. Finally, we use the algorithm to classify a large portion of the OMNI data set, and we present for the first time transition probabilities between different solar wind categories. Such probabilities represent the “climatological” statistics that determine the solar wind baseline.

In situ measurements of the solar wind have been available for almost 60 years, and in that time plasma physics simulation capabilities have commenced and ground‐based solar observations have expanded into space‐based solar observations. These observations and simulations have yielded an increasingly improved knowledge of fundamental physics and have delivered a remarkable understanding of the solar wind and its complexity. Yet there are longstanding major unsolved questions. Synthesizing inputs from the solar wind research community, nine outstanding questions of solar wind physics are developed and discussed in this commentary. These involve questions about the formation of the solar wind, about the inherent properties of the solar wind (and what the properties say about its formation), and about the evolution of the solar wind. The questions focus on (1) origin locations on the Sun, (2) plasma release, (3) acceleration, (4) heavy‐ion abundances and charge states, (5) magnetic structure, (6) Alfven waves, (7) turbulence, (8) distribution‐function evolution, and (9) energetic‐particle transport. On these nine questions we offer suggestions for future progress, forward looking on what is likely to be accomplished in near future with data from Parker Solar Probe, from Solar Orbiter, from the Daniel K. Inouye Solar Telescope (DKIST), and from Polarimeter to Unify the Corona and Heliosphere (PUNCH). Calls are made for improved measurements, for higher‐resolution simulations, and for advances in plasma physics theory.

Time‐integral correlations are examined between the geosynchronous relativistic electron flux index Fe1.2 and 31 variables of the solar wind and magnetosphere. An “evolutionary algorithm” is used to maximize correlations. Time integrations (into the past) of the variables are found to be superior to time‐lagged variables for maximizing correlations with the radiation belt. Physical arguments are given as to why. Dominant correlations are found for the substorm‐injected electron flux at geosynchronous orbit and for the pressure of the ion plasma sheet. Different sets of variables are constructed and correlated with Fe1.2: some sets maximize the correlations, and some sets are based on purely solar wind variables. Examining known physical mechanisms that act on the radiation belt, sets of correlations are constructed (1) using magnetospheric variables that control those physical mechanisms and (2) using the solar wind variables that control those magnetospheric variables. Fe1.2‐increasing intervals are correlated separately from Fe1.2‐decreasing intervals, and the introduction of autoregression into the time‐integral correlations is explored. A great impediment to discerning physical cause and effect from the correlations is the fact that all solar wind variables are intercorrelated and carry much of the same information about the time sequence of the solar wind that drives the time sequence of the magnetosphere.