ESS 261 Lecture April 28, 2008 Marissa Vogt

Overview  “Probabilistic forecasting of geomagnetic indices using solar wind air mass analysis” by McPherron and Siscoe (2004, Space Weather)  “The Solar Wind and Geomagnetic Activity as a Function of Time Relative to Corotating Interaction Regions” by McPherron and Weygand (2006, Recurrent Magnetic Storms: Corotating Solar Wind Streams)˜˜  “Probabilistic forecasting of geomagnetic indices using solar wind air mass analysis” by McPherron and Siscoe (2004, Space Weather)  “The Solar Wind and Geomagnetic Activity as a Function of Time Relative to Corotating Interaction Regions” by McPherron and Weygand (2006, Recurrent Magnetic Storms: Corotating Solar Wind Streams)˜˜

McPherron and Siscoe - Overview  The authors compare space weather forecasting to air mass climatology (predicting weather based on atmospheric weather fronts)  IMF Bz (and also vBz) is hard to predict because it is predominantly the product of in- transit turbulence. However, it is the main controller of geomagnetic disturbance.  They apply the air mass concept to fast/slow streams and corotating compression ridges (CCRs). They conclude that the position relative to the stream interface can be used to predict the probability of ap values.  The authors compare space weather forecasting to air mass climatology (predicting weather based on atmospheric weather fronts)  IMF Bz (and also vBz) is hard to predict because it is predominantly the product of in- transit turbulence. However, it is the main controller of geomagnetic disturbance.  They apply the air mass concept to fast/slow streams and corotating compression ridges (CCRs). They conclude that the position relative to the stream interface can be used to predict the probability of ap values.

 Problem: outside of ICMEs, which maintain their N-S magnetic structure, there are typically > 700 N-S alternations of the IMF at any one time.  As a result, forecasters must make probabilistic rather than deterministic predictions.  Goal: use archival data to derive statistics of Vsw, Bz, and -VBz tied to solar wind conditions that are midrange (~3 days) predictable.  Problem: outside of ICMEs, which maintain their N-S magnetic structure, there are typically > 700 N-S alternations of the IMF at any one time.  As a result, forecasters must make probabilistic rather than deterministic predictions.  Goal: use archival data to derive statistics of Vsw, Bz, and -VBz tied to solar wind conditions that are midrange (~3 days) predictable.

Air Mass Concept  Are there bounded volume of solar wind with uniform statistical geoeffective properties that would correspond to a solar wind analog of an air mass?  If so, is there more than one such type of volume, each having distinct statistical propserties such that the concept of identifiable air mass types would make sense?  If so, can the presence of these air mass types at 1 AU be predicted from solar measurements?  Are there bounded volume of solar wind with uniform statistical geoeffective properties that would correspond to a solar wind analog of an air mass?  If so, is there more than one such type of volume, each having distinct statistical propserties such that the concept of identifiable air mass types would make sense?  If so, can the presence of these air mass types at 1 AU be predicted from solar measurements?

 The study used data from 1995, when there were long, well-developed fast and slow streams and relatively few ICMEs.  To differentiate between fast and slow streams they located the stream interface within the CCR (corotating compression region, between fast and slow streams). The interface in the CCR is better- defined than the interface in the CRS.  The Wang-Sheely-Arge model can be used to predict the arrival time of the stream interface using solar data.  Stream interfaces were defined by the bipolar east-west deflection of the solar wind flow arising when the fast stream pushes against the preceding slow stream.  The study used data from 1995, when there were long, well-developed fast and slow streams and relatively few ICMEs.  To differentiate between fast and slow streams they located the stream interface within the CCR (corotating compression region, between fast and slow streams). The interface in the CCR is better- defined than the interface in the CRS.  The Wang-Sheely-Arge model can be used to predict the arrival time of the stream interface using solar data.  Stream interfaces were defined by the bipolar east-west deflection of the solar wind flow arising when the fast stream pushes against the preceding slow stream.

 26 deflections were identified in 1995 data.  All bipolar deflections in 1995 were well-defined.  The sizes of the streams as gauged by the change in flow speed varied.  26 deflections were identified in 1995 data.  All bipolar deflections in 1995 were well-defined.  The sizes of the streams as gauged by the change in flow speed varied.

 Next, the goal is to quantify the statistics of the geoeffective solar wind element (-vBz).  Positive Ey corresponds to geomagnetically disturbed intervals.  Next, the goal is to quantify the statistics of the geoeffective solar wind element (-vBz).  Positive Ey corresponds to geomagnetically disturbed intervals.

 Plots similar to this and the previous one show similar tendencies except fast stream curves are not as well-separated from the CCR curves.

McPherron and Weygand - Overview  This work is similar to McPherron and Siscoe in that they examine cumulative distribution functions (CDFs) of parameters (density, speed, IMF, etc.) relative to a stream interface.  In this study they examine data from 1995 and 2004, both in the declining phase of the solar cycle.  They conclude that for space weather forecasting it is necessary to develop separate climatologies for even and odd solar cycles.  This work is similar to McPherron and Siscoe in that they examine cumulative distribution functions (CDFs) of parameters (density, speed, IMF, etc.) relative to a stream interface.  In this study they examine data from 1995 and 2004, both in the declining phase of the solar cycle.  They conclude that for space weather forecasting it is necessary to develop separate climatologies for even and odd solar cycles.

 They examine data from 1995 (declining phase of solar cycle 22) and 2004 (almost declining phase of cycle 23).  They find that all solar wind variables exhibit highly systematic behavior relative to the interface time, but there is a quantitative difference between the two solar cycles.  They contribute the differences to three factors:  The Russell-McPherron effect  The Rosenberg-Coleman effect (also important at equinoxes, says that the dominant polarity of the IMF is the same as the corresponding pole on the Sun)  The Hale cycle (22-year cycle in geomagnetic activity)  They examine data from 1995 (declining phase of solar cycle 22) and 2004 (almost declining phase of cycle 23).  They find that all solar wind variables exhibit highly systematic behavior relative to the interface time, but there is a quantitative difference between the two solar cycles.  They contribute the differences to three factors:  The Russell-McPherron effect  The Rosenberg-Coleman effect (also important at equinoxes, says that the dominant polarity of the IMF is the same as the corresponding pole on the Sun)  The Hale cycle (22-year cycle in geomagnetic activity)

 Stream interfaces were defined by:  The solar wind velocity changed rapidly from below to above 500 km/s  Velocity decreased slowly after it peak over a number of days  A density peak followed by a peak in |B| was associated with the velocity increase  The azimuthal flow changed from positive to negative (the zero crossing was selected as the time of the stream interface)  26 interfaces were found for 1995; 42 for 2004  Stream interfaces were defined by:  The solar wind velocity changed rapidly from below to above 500 km/s  Velocity decreased slowly after it peak over a number of days  A density peak followed by a peak in |B| was associated with the velocity increase  The azimuthal flow changed from positive to negative (the zero crossing was selected as the time of the stream interface)  26 interfaces were found for 1995; 42 for 2004

 Qualitatively the behavior in 1995 is the same as in 2004, but quantitatively the peak values are 25-50% lower in 2004 than in 1995.

 Ey has a negative bias in the median value in 1995 but not in This is due to the Russell-McPherron effect.  Bz fluctuations were weaker in 2004 than in  Ey has a negative bias in the median value in 1995 but not in This is due to the Russell-McPherron effect.  Bz fluctuations were weaker in 2004 than in 1995.

 A serious obstacle to the success of the prediction schemes is the change in probability distribution functions between cycles.  One possible explanation is that 2004 (~1-2 years before minimum) was in a different phase in the solar cycle than 1995 (~6 months before minimum). However, data from 1994 (a year similar to 2004 in solar cycle phase) show similar results to the 1995 data.  Therefore, it is likely that the results are explained by true differences in the Sun and solar wind between cycles and not by slight differences in phase.  A serious obstacle to the success of the prediction schemes is the change in probability distribution functions between cycles.  One possible explanation is that 2004 (~1-2 years before minimum) was in a different phase in the solar cycle than 1995 (~6 months before minimum). However, data from 1994 (a year similar to 2004 in solar cycle phase) show similar results to the 1995 data.  Therefore, it is likely that the results are explained by true differences in the Sun and solar wind between cycles and not by slight differences in phase.

The End