README file for SCaRP Hover and truck lidar data. 15-Jul-2021 The data contained in this repository were obtained during the Storm CoAstal Response Project (SCaRP) field experiment of Winter 2019-2020. The research was supported by the U.S. Army Corps of Engineers (W912HZ192) and California Department of Parks and Recreation (C19E0026). Thanks to Brian Woodward and the Scripps Institution of Oceanography Coastal Processes Group (CPG) field crew including Lucian Parry, Kent Smith, Greg Boyd and Shane Finnerty. Michele Okihiro, Bonnie Ludka, and Hiro Matsumoto assisted with experiment logistics. Cassandra Henderson, Athina Lange, Mika Siegelman, and Mele Johnson assisted with field data collection. The LiDAR data are from: 14 Dec 2019 and 24 Feb 2020. There are 4 types of MATLAB data files. 1) Gridded LiDAR data (example: 20200224_4.mat) 2) Processed runup line at 5cm, 10cm (Drone) 3) Processed runup line at 5cm, 10cm (Truck) 4) Pressure sensors (P3,P4,P5) Instruments used: LiDAR: Truck-mounted terrestrial LiDAR (1550 nm Riegl VMZ-2000) Drone-mounted 905 nm Riegl miniVUX-1UAV LiDAR pressure: Paroscientific model 245A-102 combined with custom electronics Please email if you have any questions. jfiedler@ucsd.edu ***************************************************** EXAMPLE DATA and STRUCTURE ******** 20200224_4.mat: ******** Collected 24 Feb 2020, 4th Hover. Data are gridded to 10Hz and 0.1m. Name Size Class Description Hz_lidar 1x1 double sampling rate TXdrone 601x10189 double interpolated drone lidar Z NAVD88 data TXdrone2 601x10189 double gridded drone lidar Z NAVD88 data TXtruck 601x10189 double interpolated truck lidar Z NAVD88 data TXtruck2 601x10189 double gridded truck lidar Z NAVD88 data Tgrid 601x10189 datetime gridded time (timezone: GMT) Xgrid 601x10189 double gridded cross-shore values (m) tvecHover 1x10189 datetime time (timezone: GMT, matlab datetime format) x 601x1 double Cross-shore (m) t 1x10189 double time (timezone: GMT, matlab datenum format) ********** Drone_ or Truck_ Hover_04_L1_runupstats_10cm.mat: ********** 4 matlab structures: Bulk, Info, Spec, Tseries Bulk: Bulk runup statistics, processed in spectral domain following Stockdon et al. 2006 swashparams: [0.4533 0.7541 1.5808] (units = meters, numbers slightly different for truck/drone) swashparamsLO: [0.3989 0.6822 1.5808] (lower bound estimate) swashparamsUP: [0.5251 0.8432 1.5808] (upper bound estimate) swashParamsNames: {'Sig' 'Sinc' 'eta'} beta: slope of foreshore from +/-2 std(eta) foreshore: m ZNAVD88, mean of moving minimum foreshore under variance threshold foreshoreX: cross-shore x(m) Info: Hz: 10 Hz, gridded frequency sampling of the LiDAR data threshold: 0.0500 m datahour: 24-Feb-2020 18:33:17 (GMT) duration: 16.9800 minutes processedFilename: '/Volumes/FiedlerBot8000/scarp/mat/timestacks/20200224_4.mat' Spec: runup line spectrum, processing details in Fiedler et al. 2020 (nothing extraordinary here) f: [1×1501 double] frequency (Hz) S: [1501×1 double] vertical runup energy spectrum (m^2/Hz) Slo: [1501×1 double] lower bounds of spectrum Sup: [1501×1 double] upper bounds of spectrum dof: 13.3333 degrees of freedom Tseries: timeseries of runup line T: [1×10189 double], time (MATLAB datnum format) Zrunup: [1×10189 double], vertical runup location (m, ZNAVD88) Xrunup: [1×10189 double], horizontal runup location (x (m)) idxrunup: [1×10189 double], indices of runup on gridded LiDAR data ******** pressure ******** Organized by pressure sensor number (P3,4,5). Buried pressure sensors (battery-powered Paroscientific model 245A-102 combined with custom electronics), sampling at 2 Hz on near hourly intervals (7167 samples at the start of each hour). For the variable etaCorrected, surface-correction follows linear theory for water depth and sand burial (Raubenheimer et al, 1998). To avoid over-amplification of high-frequency signals, a hard cutoff of 0.33 Hz (3-sec waves) is used. No high-frequency tail corrections are applied. For the variable etaCorrectedB18, pressure was corrected to the seabed following linear theory for burial in completely saturated sand (Yamamoto 1978, Raubenheimer et al. 1998) and surface corrected using a nonlinear weakly dispersive method for the water column (Bonneton et al. 2018). Data were first low-pass filtered with a frequency cutoff 0.33 Hz (3-sec waves) to ensure smooth time derivatives in the surface correction. A hard cutoff of 0.5 Hz (2-sec waves) was used in the burial correction to avoid over-amplification of high-frequency signals. Significant wave heights below are calculated using the surface elevation time series (etaCorrectedB18), or Hs = 4*std(etaCorrectedB18). Name Size Description Hs 1x2976 significant wave height (m) UTMx 1x1 UTM coordinate, eastings UTMy 1x1 UTM coordinate, northings alongshore 1x1 y coordinate in local coordinate system crossshore 1x1 x coordinate in local coordinate system eta 7167x2976 elevation time series (corrected only for barometric pressure) etaCorrected 7167x2976 linearly corrected hourly sea surface elevation time series (m) etaCorrectedB18 7167x2976 nonlinearly corrected hourly sea surface elevation time series (m) h 1x2976 water depth (hourly mean) latitude 1x1 latitude local_coordinate_alongshore_origin 1x1 back-beach y location of local coordinate system local_coordinate_crosshore_origin 1x1 back-beach x location of local coordinate system local_coordinate_theta 1x1 angle of local coordinate system longitude 1x1 longitude name 1x10 sensor name offset 1x1 offset applied (m) t 1x2976 time, in MATLAB datetime t_datenum 1x2976 time, in MATLAB datenum z 1x1 measured vertical location