Abstract:
A forecast model of the northern highlatitude ionospheric plasma motion as observed by the SuperDARN radars. The model comprises a set of regression coefficients. The user needs to specify the dayofyear and the monthly mean of the solar radio flux at 10.7 cm/2800 MHz, often called the f10.7 index. They also need to provide the value of the interplanetary magnetic field (IMF) component By and the SunEarth component of the solar wind velocity Vx, both in geocentric solar magnetospheric (GSM) coordinates. The regression coefficients are provided as two files, one can be used to model the northsouth (NS) component of the plasma motion and the other to model the eastwest (EW) component of the motion.
Funding was provided by NERC standard grant numbers: NE/V002732/1, NE/N01099X/1, NE/V00283X/1, NE/V002686/1 and NE/T000937/1.
Keywords:
Ionospheric electric field, Solar wind effects on ionosphere, SuperDARN, Upper atmosphere dynamics, onospheric plasma convection
Lam, M., Shore, R., Chisham, G., & Freeman, M. (2023). Forecast regression model of the northern Super Dual Auroral Radar Network (SuperDARN) highlatitude ionospheric plasma motion built from data interval 19972008 inclusive (Version 1.0) [Data set]. NERC EDS UK Polar Data Centre. https://doi.org/10.5285/22272b8e1aa3483b9867224fe02db4e8
Access Constraints:  None 

Use Constraints:  Data released under Open Government Licence V3.0: http://www.nationalarchives.gov.uk/doc/opengovernmentlicence/version/3/. 
Creation Date:  20230113 

Dataset Progress:  Complete 
Dataset Language:  English 
ISO Topic Categories: 

Parameters: 

Personnel:  
Name  UK PDC 
Role(s)  Metadata Author 
Organisation  British Antarctic Survey 
Name  Mai Mai Lam 
Role(s)  Investigator, Technical Contact 
Organisation  British Antarctic Survey 
Name  Robert M Shore 
Role(s)  Investigator 
Organisation  British Antarctic Survey 
Name  Gareth Chisham 
Role(s)  Investigator 
Organisation  British Antarctic Survey 
Name  Mervyn Freeman 
Role(s)  Investigator 
Organisation  British Antarctic Survey 
Parent Dataset:  N/A 
Quality:  The SuperDARN radar observations were processed to remove ground scatter, and to eliminate measurements with too low power (lower than 3dB), or which had a poorquality flag (identified in RSTv4.0). When binning the data, range gates below 11 and above 150 (where those values correspond to a multiple of the 45 km range distance from the radar array location) were not used, since these gave inaccurate locational estimates. The Shore et al. (2022) eigenanalyses of the radar data were performed on a spatial grid with 559 locations, so we keep the same grid for consistency. We recombined all 10 spatial and temporal modes for the 12year interval. The resulting velocities have NaN (not a number) values for colatitudes greater than 30 degrees, and for the single point exactly at the pole. For this reason, our forecast model regression coefficients also have NaN values for colatitudes greater than 30 degrees and the pole. 


Lineage:  Our data set was derived from a reanalysis of SuperDARN plasma velocity data: Shore, R., Freeman, M., Chisham, G., Lam, M. M., & Breen, P. (2022). Dominant spatial and temporal patterns of horizontal ionospheric plasma velocity variation covering the northern polar region, from 1997.0 to 2009.0  VERSION 2.0 (Version 2.0) [Data set]. NERC EDS UK Polar Data Centre. https://doi.org/10.5285/2b9f0e9f34ec44679e02abc771070cd9. The Shore et al. (2022) reanalysis data covers the period 1997.0 to 2009.0 at 5min resolution. The scientific motivation and details of the methodology of its production are described in Shore et al., 2021, https://doi.org/10.1029/2021JA029272. We performed a regression analysis on the Shore et al. (2022) reanalysis data, which resulted in the forecast model regression coefficients published here. The regression analysis occurred via two steps described in Section 3 of Lam et al. (see References below), submitted to the journal Space Weather in January 2023: Step A: see Section 3.2 of Lam et al. (2023). Regression of each component of the SuperDARN plasma velocity with respect to 5min averages of the epsilon solar wind coupling function (Koskinen and Tanskanen, 2012 https://doi.org/10.1029/2002JA009283), IMF By, and a constant. This produced 3 regression coefficients (the slope in epsilon, the slope in By and the constant) for every location and month (3 step A coefficients x 559 locations x 144 months). Step B: see Section 3.3 of Lam et al. (2023). Regression of each set of regression coefficients (each of size 559 x 144) produced in Step A with respect to 4 variables: sin x, cos x, the monthly mean value of f10.7, and a constant, where x = 2pi (tj  79)/365.25 and tj is the dayofyear in the middle of the 12 months of the year (j = 1 to 12). This produced 12 regression coefficients for each velocity component, so 24 in total (3 step A coefficients x 4 step B coefficients x 2 velocity components). Use of regression coefficients. The plasma velocity can be forecast (or hindcast) by reading in these 24 regression coefficients from two ACSII files and using them in Equations 6, and subsequently Equations 5 of Lam et al. (2023). An example of how this is done is given by the IDL programme 'read_BAS_convection.pro'. Please see Data structure and data format below for details. 
Temporal Coverage:  

Start Date  19970101 
End Date  20081231 
Spatial Coverage:  
Latitude  
Southernmost  60.9 
Northernmost  90 
Longitude  
Westernmost  180 
Easternmost  180 
Altitude  
Min Altitude  250 km 
Max Altitude  400 km 
Depth  
Min Depth  N/A 
Max Depth  N/A 
Location:  
Location  Ionosphere 
Detailed Location  Fregion 
Data Collection:  The authors gratefully acknowledge the use of SuperDARN data. The original plasma velocity observations were gathered using the northern hemisphere radars of the SuperDARN global array. SuperDARN is a collection of radars funded by the national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom, and the United States. The fitted Doppler velocities were processed from the original autocorrelation functions using version 4.5 of the radar software toolkit (RSTv4.5) and within that toolkit, fitting routine 'FitACF v2.5'. The fitted Doppler velocities were analysed using multiple datainterpolating empirical orthogonal function eigenanalyses for the period 1997.0 to 2009.0 at 5min resolution, extending between the north magnetic pole and 30 degrees colatitude, as described in Shore et al., 2021 DOI: 10.1029/2021JA029272. 

Distribution:  

Distribution Media  Online Internet (HTTP) 
Distribution Size  268 kB 
Distribution Format  ASCII 
Fees  N/A 
Data Storage:  We present two ASCIIformatted datasets. The volumes of each of the files are as follows: PlasmaVelocityForecastCoefficientsNS.asc 130 kB PlasmaVelocityForecastCoefficientsEW.asc 130 kB Each file contains the following: A header of 4 lines of metadata followed by 16 sets of values (arrays), each with a single line of text description to introduce it. Each numerical value is in exponential format E12.4. The first 4 arrays in each ASCIIformatted file contain the location information for the twodimensional spatial bins used in this analysis as specified by Shore et al. (2021, 2022). The coordinates are in the QuasiDipolar (QD) reference frame and at these latitudes the values coincide quite strongly with the equivalent AltitudeAdjusted Corrected Geomagnetic (AACGM) coordinates. There are 559 location bins in the northern polar region, which is the area of focus for this analysis. They are ordered approximately by latitude, then longitude, but this does not always apply near the 0/360degree longitude boundary. The 'centroids' are the locations of the centre of each bin. Array #1: The colatitude of the bin centroids in degrees latitude (QD). 559 values. Array #2: The longitude of the bin centroids in degrees longitude (QD). 559 values. Array #3: The colatitude of the limits of the bins in degrees latitude (QD). 2 x 559 values. Array #4: The longitude of the limits of the bins in degrees longitude (QD). 2 x 559 values. The next 12 arrays in each ASCIIformatted file give the values of the 12 regression coefficients relating to Equation 6 and Figures 4 and 5 of Lam et al., (submitted Jan 2023). The arrays are all of size 559. They are plotted in the 12 subfigures of Figure 4(al) (NS component) and the 12 subfigures of Figure 5(al) (EW component) of Lam et al. (2023). The NS regression coefficients can be used in Equations 6ac and then Equations 5ab of Lam et al. (2023) to produce the NS (and similarly for the EW velocity component). This is done in the IDL programme 'read_BAS_convection.pro'. Array #5: Coefficient values (metres/sec/watt) for the sin(x) term in the epsilon slope (see Eqn 6a, Fig 4b). There is an equivalent version of Eqn 6 for the EW component (and see Fig 5b). Array #6: Coefficient values (m/s/W) for cos(x) term in the epsilon slope. Eqn 6a, Fig 4/5e. Array #7: Coefficient values (m/s/W) for f10.7 term in the epsilon slope. Eqn 6a, Fig 4/5h. Array #8: Coefficient values (m/s/W) for intercept term in the epsilon slope. Eqn 6a. Fig 4/5k. Array #9: Coefficient values (metres/sec/nanotesla) for sin(x) term in IMF By slope. Eqn 6b, Fig 4/5c. Array #10: Coefficient values (m/s/nT) for cos(x) term in IMF By slope. Eqn 6b. Fig 4/5f. Array #11: Coefficient values (m/s/nT) for f10.7 term in IMF By slope. Eqn 6b, Fig 4/5i. Array #12: Coefficient values (m/s/nT) for intercept term in IMF By slope. Eqn 6b, Fig 4/5l. Array #13: Coefficient values (metres/sec) for sin(x) term in constant. Eqn 6c. Fig 4/5a. Array #14: Coefficient values (m/s) for cos(x) term in the constant. Eqn 6c. Fig 4/5d. Array #15: Coefficient values (m/s) for f10.7 term in the constant. Eqn 6c. Fig 4/5g. Array #16: Coefficient values (m/s) for intercept term in the constant. Eqn 6c. Fig 4/5j. 