SuperDARN operations Pasha Ponomarenko. Outline Operational principles Antenna design and coverage Pulse sequence and ACF Data processing and presentation.

SuperDARN operations

Pasha Ponomarenko

Outline

• Operational principles

• Antenna design and coverage

• Pulse sequence and ACF

• Data processing and presentation

Just to remind: The Main Objective• Mapping 2D ionospheric plasma circulation at

high latitudes

Operation principles

• Resolving 2D ionospheric plasma velocities in range-azimuth (horizontal) domain– Azimuth: multi-beam pattern– Range: pulsed mode (time delay)– Line-of-sight Velocity: Doppler shift– Velocity vector:

• receiving echoes from the same areas by different radars (i.e. from different directions)

• fitting to a velocity model

phase shift

0

Phased array: Azimuthal scan

• SuperDARN:– main array: 16 log-periodic

antennae

– interferometer: 4 log-periodic antennae

– resolution: ~3.5 deg

– coverage (16 beams): ~54 deg

Field of view (FoV)

• Each radar consecutively scans through 16 directions (“beams”) every 1or 2 minutes.

• Along each beam the returned echoes are sampled at 45-km steps forming 70-75 “range gates” (max. range~3500 km)

• Total FoV consists of ~1200 range-beam cells

Pulse sequence & complex ACF

• For each range gate a complex autocorrelation function (ACF) is calculated

• Radar emits a sequence of 7 or 8 pulses

FITACF

Experimental ACFSuperDARN software: FITACF

ptab[mppul] short mppul Pulse table.ltab[2][mplgs] short 2,mplgs Lag table.pwr0[nrng] float nrng Lag zero power.slist[0-nrng] short 0-nrng List of stored ranges.nlag[0-nrng] short 0-nrng Number of points in the fit.qflg[0-nrng] char 0-nrng Quality of fit flag for ACF.gflg[0-nrng] char 0-nrng Ground scatter flag for ACF.p_l[0-nrng] float 0-nrng Power from lambda fit of ACF.p_l_e[0-nrng] float 0-nrng Power error from lambda fit of ACF.p_s[0-nrng] float 0-nrng Power from sigma fit of ACF..p_s_e[0-nrng] float 0-nrng Powr error from sigma fit of ACF.v[0-nrng] float 0-nrng Velocity from ACF.v_e[0-nrng] float 0-nrng Velocity error from ACF.w_l[0-nrng] float 0-nrng Spectral width from lambda fit of ACF.w_l_e[0-nrng] float 0-nrng Spectral width error from lambda fit of ACF.w_s[0-nrng] float 0-nrng Spectral width from sigma fit of ACF.w_s_e[0-nrng] float 0-nrng Spectral width error from sigma fit of ACF.sd_l[0-nrng] float 0-nrng Standard deviation of sigma fit.sd_s[0-nrng] float 0-nrng Standard deviation of lambda fit.sd_phi[0-nrng] float 0-nrng Standard deviation of phase fit of ACF.x_qflg[0-nrng] char 0-nrng Quality of fit flag for XCF.x_gflg[0-nrng] char 0-nrng Ground scatter flag for XCF.x_p_l[0-nrng] float 0-nrng Power from lambda fit of XCF.x_p_l_e[0-nrng] float 0-nrng Power error from lambda fit of XCF.x_p_s[0-nrng] float 0-nrng Power from sigma fit of XCF.x_p_s_e[0-nrng] float 0-nrng Power error from sigma fit of XCF.x_v[0-nrng] float 0-nrng Velocity from XCF.x_v_e[0-nrng] float 0-nrng Velocity error from XCF.x_w_l[0-nrng] float 0-nrng Spectral width from lambda fit of XCF.x_w_l_e[0-nrng] float 0-nrng Spectral width error from lambda fit of XCF.x_w_s[0-nrng] float 0-nrng Spectral width from sigma fit of XCF.x_w_s_e[0-nrng] float 0-nrng Spectral width error from sigma fit of XCF.phi0[0-nrng] float 0-nrng Phase determination at lag zero of the ACF.phi0_e[0-nrng] float 0-nrng Phase determination error at lag zero of the ACF.elv[0-nrng] float 0-nrng Angle of arrival estimate.elv_low[0-nrng] float 0-nrng Lowest estimate of angle of arrival.elv_high[0-nrng] float 0-nrng Highest estimat of angle of arrival.x_sd_l[0-nrng] float 0-nrng Standard deviation of lambda fit of XCF.x_sd_s[0-nrng] float 0-nrng Standard deviation of sigma fit of XCF.x_sd_phi[0-nrng] float 0-nrng Standard deviation of phase fit of XCF.

Main estimated parameters

• Signal-to-noise ratio (“power”) [dB] – maximum ACF power

• Spectral width [m/s] – ACF power decay time

• Line-of-sight velocity (“velocity”) [m/s] – ACF phase slope

ACF power

ACF phase

Velocity vector measurements

• Overlapping FoVs provide line-of-sight measuremets from two different directions at the same location

• They are combined into a 2D velocity vector

Plasma circulation (“convection”) maps

• Velocity vectors from different radars are combined into a plasma circulation map

• Electric field distribution is calculated from velocities based on ExB assumption

Plotting data: range-time maps

Plotting data: fan plots

Attention: Software availability!

• SuperDARN “starter’s kit” that contains ready-to-use IDL routines which have been tested on Chapman:– reading fitted data from the standard radar output files into

IDL variables

– range-time plotting

– plotting “fan” diagrams (maps, coordinate systems etc)

• PDF files describing SuperDARN data formats/variables/parameters