Crowd-Sensing Road Surface Quality Using Connected Vehicle Data

This work presents an approach for collecting road surface data using connected vehicles. Road surface readings from multiple production vehicles were collected and aggregated to estimate road roughness measured by the International Roughness Index (IRI). The analysis compared multiple instances of connected vehicle data with high speed pavement profile vehicle (Class 1 profiler) data. A separate analysis compared multiple instances of connected vehicle data to an advanced walking profiler. Results demonstrate the feasibility of harvesting road surface data from the existing connected vehicles to support continuous road surface monitoring applications. Benefits include more timely acquisition of pavement data, broader coverage of the road network, and potential for aiding existing survey fleet in targeting early signs of pavement degradation. Collected roughness measurements were found to be closely aligned with reference devices that were employed as part of this study. A regional experiment in the Detroit Metropolitan area that covered 64 mi of roadways found that the connected vehicle data was highly correlated with Class 1 profiler data where 83% of traveled miles had a 0.8 or higher correlation. Moreover, 85% of the measurements had small absolute errors less than 50 in./mi and half of the measurements had absolute errors less than 20 in./mi. A test track experiment at Virginia Tech Transportation Institute Smart Road facility compared the connected vehicle data to the advanced walking profiler and showed that the correlations for repeatability and reproducibility are 0.90 and 0.91, respectively, which are very close to the standard requirement for certified profilers.

Hydrometeor classification in convective clouds using cloud profiler data

<p>In this contribution, we investigate hydrometeors and their distribution in thunderclouds. We classify 5 kinds of hydrometeors using data of a Ka-band cloud profiler (35 GHz) situated at the weather station Milešovka in Central Europe. The classification of hydrometeors is based on calculated vertical air velocity, terminal velocity of a target, minimum and maximum terminal velocity of hydrometeor classes, and Linear Depolarization Ratio within three temperature intervals. We performed the classification for convective events that were observed at the station in 2018 and 2019 and were related to lightning in the vicinity of the station.</p><p>Results suggest that there is a link between lightning flashes observed close to the weather station (based on EUCLID data) and the presence of graupel, ice, snow, and hail. These are the hydrometeors (graupel and ice in particular) that are considered to play major role in thundercloud electrification by the collision of hydrometeors.</p>

Mass wasting on Alpha Ridge in the Arctic Ocean: new insights from multibeam bathymetry and sub-bottom profiler data

Marine geological and geophysical data from Alpha Ridge in the Arctic Ocean are sparse because of thick perennial sea-ice cover, which prevents access by most surface vessels. Rare seismic data in this area, acquired largely from drifting ice-camps, had shown the hemipelagic drape that covers most of the ridge is highly disrupted within a large (>90 000 km2) south central region. Here, evidence of pronounced seafloor erosion and debris flows infilling seafloor lows was previously interpreted to be the result of a possible bolide impact. In recent years, several icebreaker expeditions have successfully acquired multibeam bathymetry and sub-bottom profiler data in the western segment of this region. Analysis of these data reveals a complex seafloor morphology characterized by ridges and troughs, angular blocks and escarpments as well as seismic facies characterized by hyperbolic seafloor reflections, and convoluted to incoherent and transparent sub-bottom reflectivity. These features are interpreted as evidence of sediment mass movement with varying degrees of lateral transport deformation. At least two episodes of failure are interpreted based on the presence of both buried and surficial mass-transport features. As multiple events are interpreted, seismicity is the most plausible trigger mechanism rather than bolide impact.

Recent Sediment Analysis, Study Case: Sub Bottom Profiler Data Line 8 Geomarine Research Vessels

<p>North Aru Island’s offshore had a long exploration history since 1973 until present. The characteristics of seabed can be studied through the shapes, acoustic reflection pattern, type of substrate or sediment, or by living organisms at the seafloor. In Indonesia, the sub-bottom profiler data was previously only used to measure sea depth. This study was expected to provide overview and updated information about sea depth, seabed and sedimentary layers characteristics based on generated acoustical reflection values and to identify information about abiotic compounding seabed (grain size) used methods Folk 1974 and Spread. Resulted bathymetry data could explain the depth and topography of study areas, seabed characteristics, sea bed sediment classification that were expected to support the determination of shipping tracked lines, underwater pipelines construction, and to determine mineral compounds in the deep sea. The obtained data of field records were in digital *.odc format that is a standard format for BATHY-2010 software. In order to simplify data processing, there would be a series data conversion process into other formats. Data processing of sub-bottom profiling was conducted by Kogeo-imaging software. For more clear and better look than the playback data, the processing data was undergone some steps of treatments such as filtering, stacking and additional gain. Moreover, those data were interpreted at once time with digitizing to interpreted sediment layers. The sea depth of research location was around 52.59-97.03 below the sea surface. Sea bottom formed land (flat) was in the eastern part of the location. Steep basin or Aru Trough was in the western with type substrate of gravelly mud. In general, recorded seismic cut (section) was in the time domain which created vertical velocity distortion and lateral that would produce seismic records that would be different with its actual. Seismic only enabled to detect lithology border if there any acoustical impedance exchange which would be bigger than detectable limit of used seismic waves.</p>

Postdeployment Calibration of a Tropical UHF Profiling Radar via Surface- and Satellite-Based Methods

Wind profiling radars are usually not calibrated with respect to reflectivity because such calibrations are both unnecessary for good wind measurements and costly. However, reflectivity from calibrated profilers can reveal many atmospheric attributes beyond winds. Establishing ways to calibrate these radars even after they have been taken out of service would expand the utility of archived profiler data. We have calibrated one operating mode of a 915-MHz profiler deployed at Manus, Papua New Guinea (1992–2001), using two methods. The first method adjusts a radar parameter until the profiler’s estimate of rainfall during stratiform events closely matches surface observations. The second adjusts the parameter so that mean brightband heights observed by the profiler (July 1992–August 1994) match the mean brightband reflectivities over the profiler as observed by the TRMM Precipitation Radar (January 1998–July 2001). The results differ by about 5% and yield very similar precipitation errors during tested stratiform events. One or both of these methods could be used on many other wind profilers, whether they have been decommissioned or are currently operational. Data from such calibrated profilers will enable research employing the equivalent reflectivity factor observed by profilers to be compared with that from other radars, and will also enable turbulent studies with C n2.