Background
An accurate forecast of the solar wind plasma and magnetic field properties is a crucial capability for space weather prediction. However, thus far, it has been limited to the largescale properties of the solar wind plasma or the arrival time of a coronal mass ejection from the Sun. As yet, there are no reliable forecasts for the northsouth interplanetary magnetic field (IMF) component, Bn (or, equivalently, Bz). On this web page, we use a statistics based pattern matching algorithm, along with four other models, to predict the magnetic and plasma state of the solar wind ∆t hours into the future (where ∆t can range from 12 hours to seven days ).
Data
Our data base dates back to the early 1970's and it is updated every hour using NOAA's Space Weather
Prediction Center
real time solar wind page
.The data includes the magnetic and plasma (speed, number density and proton temperature) properties of the solar wind.
Currently, our web page displays data and results only for the three components of the vector magnetic field and the speed.

Historic data from the early 1970's to the end of 2016 was obtained from NASA's OMNI_M dataset,
using the
COHOWeb data server

Real time data is obtained from NOAA's Space Weather Prediction Center
(SWPC)
This is high resolution 1minute data which we average to 1hour.

Past, 1minute high resolution data for 2017, is obtained from NOAA's DSCOVR Space Weather
Data Portal
This high resolution data is also averaged to 1hour.
Models
Five different models can be used to forecast the properties of the solar wind (magnetic field components and speed). Below is a brief description of the models,
for more details about the algorithms (in particular the pattern recognition) we refer the User to our
manuscript

Baseline Model
: This is our reference model. Since on average the zcomponent of the magnetic field is zero the Baseline model predicts
all components of the magnetic field to remain exactly zero. For other parameters, such as the speed, the Baseline model uses the average value
of the parameter during the previous ∆t hours as a baseline forecast for the next ∆t hours.

Persistence Model
: This model uses the current value of the parameter to predict it's value for the next ∆t hours.

Recurrence Model
: This model takes the data from 27 days before the current date and time and uses it as the prediction for the next ∆t hours.

Pattern Recognition
: This model uses a simple pattern recognition technique for identifying previous intervals in the entire solar wind dataset that are most like the interval recently observed and use the data that follows those intervals as a set of forecasts (realizations) for what is likely to occur in the next ∆t hours.

Turbulence Model
: This model is applied only to the three components of the magnetic field.
It assumes that the magnitude of each component is randomly distributed in a min/max range which we take to be 3nT/+3nT.
Coordinate System
The User can select to view the data and results using one of these three coordinate systems:

RTN
: A spacecraft centered system where
R
is a unit vector from the Sun to the spacecraft,
T
is the cross product of the solar rotational axis and
R
, and
N
completes the righthanded triad.

GSE
The Geocentric Solar Ecliptic system.
X
is the EarthSun line, and
Z
is aligned with the ecliptic north pole.

GSM
The Geocentric Solar Magentospheric system.
X
is the EarthSun line, and
Z
is the projection of dipole axis on the GSE YZ plane.
Technology
All the data and model analysis are done with R codes and displayed using R, shinyApp and plotly.