Waves in-ice observations made during the SIPEX II voyage of the Aurora Australis, 2012
Antarctic sea-ice is highly influenced by the dynamic nature of the Southern Ocean. Ocean waves can propagate from tens to hundreds of kilometres into sea-ice, leaving behind a wake of broken ice sheets. As global climate change intensifies, storm intensity will increase in the Southern Ocean. Incre...
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Australian Antarctic Data Centre
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| Online Access: | https://researchdata.ands.org.au/waves-in-ice-australis-2012/701688 https://doi.org/10.4225/15/53266BEC9607F https://data.aad.gov.au/metadata/records/SIPEX_II_Waves http://nla.gov.au/nla.party-617536 |
| Summary: | Antarctic sea-ice is highly influenced by the dynamic nature of the Southern Ocean. Ocean waves can propagate from tens to hundreds of kilometres into sea-ice, leaving behind a wake of broken ice sheets. As global climate change intensifies, storm intensity will increase in the Southern Ocean. Increased storm intensity will bring stronger winds and bigger waves, which has the potential for waves to travel deeper into the ice pack and increase the likelihood that ice floes break apart. To enhance our understanding of this system, our aim during SIPEXII was to improve on the scarce Antarctic waves-in-ice dataset by collecting a set of wave observations in the MIZ. In order to achieve this, we designed and produced eight custom made wave sensors. The sensors were deployed in the Antarctic marginal ice zone along a transect line perpendicular to the ice edge and spread over approximately 200 km. Every three hours, the sensors simultaneously woke and recorded their location and a burst of wave acceleration data. Each sensor performed on-board data quality control and spectral analysis before returning the wave spectrum via satellite. The sensors were powered via lithium batteries and had enough battery power to last a minimum of 6 weeks. This project involved collaboration between the Australian Antarctic Division (AAD) and the NZ's National Institute for Water and Atmospheric Research (NIWA). The work was funded by a New Zealand Foundation of Research Science and Technology Postdoctoral award to A.L.K.; the Marsden Fund Council, administered by the Royal Society of New Zealand; NIWA, through core funding under the National Climate Centre Climate Systems programme; the Antarctic Climate and Ecosystems Cooperative Research Centre; and Australian Antarctic Science project 4073. Instruments were designed and built by Inprod PTY LTD. Below is a summary of the design and hardware: Accelerometer: Kistler ServoK-Beam accelerometer. Model 8330B3. IMU: Razor IMU (3 axis acceleration, 3 axis magnetometer and 3 axis gyro) ADC: TI ADS1247 Analog-to- Digital converter CPU (main): Arduino Mega 3.3V CPU (maths): BeagleBone from BeagleBoard.org who use Texas Instruments (TI) ARM processors GPS: Skytraq Venus634FLP Temperature readings: SHT15 from SparkFun Transmission: Iridium 9602 Battery: Lithium batteries (enough to survive a minimum of 6 weeks) Inner housing: Explorer 1908OE Outer housing: The case is fitted in a fork lift tyre ( .53 m diameter and .165 m height) with an inner tube to enable floating. Aerial housing: The aerial is housed in a plastic spherical container on top of a .5 m tube attached to the tyre. Feet: 3 screws stick out of the bottom to create friction with the ice. Onboard processing: Every 3 hours, the instruments wake and record wave accelerations for 35 mins. An initial low pass analogue filter is used. We over sample at 64 Hz and decimate down to 2 Hz. Downsampling from 64 Hz to 2 Hz is achieved through a multistage decimation of 8 followed by 4, to achieve a total decimation of 32. Prior to each downsampling stage, a second order lowpass Butterworth filter is applied to remove all components above the nyquist frequency. We first apply the Butterworth filter with a cut off of 1 Hz and sample at 8 Hz and secondly with a cut off of 0.5 Hz and sample at 2 Hz. A high-pass filter was then applied and the acceleration double-integrated to provide displacement. Welch's method, using a 10% cosine window and de-trending on four segments with 50% overlap, was applied to estimate the power spectral density. Sample frequency: 2 Hz Sample duration - raw: 2048 sec Sample duration - fft: 1280 sec No. of discrete bins of fft: 512 No. of segments: 4 Below is a detailed description of each line of the raw output. Header info L1: Longitude (decimal degrees) L2: File name of attachment emailed via Iridium L3: Temperature inside the box (degrees Celsius) L4: Sensor identification number L5: Time wave record starts (24 hr format HHMMSS) L6: Date of wave record (yyyy-mm-dd) L7: Current voltage L8: Elevation (cm) L9: Latitude (decimal degrees) Wave spectrum L10-L64: The power spectral density for wave period bins (secs) centred on [24.38 19.69 18.96 18.28 17.65 17.06 16.51 16.00 15.51 15.05 14.62 14.22 13.83 13.47 3.12 12.80 12.48 12.19 11.90 11.63 11.37 11.13 10.89 10.66 10.44 10.24 10.03 9.84 9.66 9.48 9.30 9.14 8.98 8.82 8.67 8.53 8.39 8.25 8.12 8.00 7.64 7.31 7.01 6.73 6.48 6.24 6.02 5.81 5.50 5.22 4.97 4.74 4.53 4.33 4.16] Spectral moments L65-L70: m-2 - m4 Quality control L71: mean roll (degrees) L72: mean pitch (degrees) L73: mean yaw (degrees) L74: wave direction (degrees) L75: directional spread (degrees) L76: ratio term to evaluate quality of wave direction approximation (should be close to 1) L77: standard deviation of acceleration (m/s2) L78: standard deviation of gyro x axis (radians/s) L79: standard deviation of gyro y axis (radians/s) L80: standard deviation of gyro z axis (radians/s) L81: standard deviation of yaw (radians) L82: Accelerometer quality flag. 0 = good, 1 = accelerometer bad, 2 = accelerometer and imu bad L83: IMU quality flag. 0 = good, 1 = pitch/roll bad, 2 = yaw bad, 3 = both bad L84: mean acceleration removed (m/s2) L85: no. of flat spots in raw acceleration data L86: the maximum number of consecutive flat spots L87: no. of spikes (data point greater than 6 standard deviations of data set) L88: the maximum number of consecutive spikes L89: Quality flag indicating whether the total power in the time domain and frequency domain are equal. 0 = difference less than 0.01, 1 = difference greater than 0.01. Deployment method: The Helicopter Resources team, lead by Leigh Hornsby, and the Aurora crew, lead by Murray Doyle, were a crucial component to the success of the deployment. The first three sensors were deployed via helicopter. The sensor was lowered via a rope onto floe whilst the helicopter hovering about 2 m above floe. Due to weather constraints, the remaining five were deployed via crane. The ship pulled up beside a chosen floe and the sensors were lowered onto it via crane. Once deployed, the ship slowly moved forward until the floe was clear of the turbulence generated by the ship. Both the helicopter and crane deployment methods proved to be successful. See /Waves/Wave Observations/wiios_deployment.pdf for more details on the deployment procedure. Approximate floe dimensions in metres based on the images in /Waves/Ice Observations/Ice_floe/Sensor ID): Sensor ID,Freeboard,Width,Length 1,0.15,28,28 2,0.33,10,12.5 3,0.15,10,15 4,.1,12,12 5,0.15,10,16.5 6,1,10,16.5 7,0.5,11.5,24 8,1,28.5,9 Ice observations: A collection of images and movies of the ice conditions are provided in Waves/Ice Observations. The folders include: Aerial: This folder contains aerial images taken with a gopro hero 2 fixed to the underside of the helicopter. Note that the date stamp on the GoPro is incorrect. Use the following for calibration: 20121022 13:52:00 - GPS - Australian eastern standard (no daylight savings) 20110707 14:00:07 - GoPRO Ice floe: Images of floes the sensors were deployed on. Ship: Images of the ice conditions taken from the ship. /Waves/Wave Observations/raw/sensorID_yyyy-mm-dd_hhmmss.raw Maps and shapefiles.zip - contains an ArcGIS map and shapefiles containing track data. KML.zip - contains KML files (point data) showing point-in-time snapshots of the buoy positions. Raw_NIWA_data.zip - contains the raw data files. |
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