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Garg, Piyush

Publications and source records attributed to Garg, Piyush.

Measuring Transverse Displacements Using Unmanned Aerial Systems Laser Doppler Vibrometer (UAS-LDV): Development and Field Validation

Measurement of bridge displacements is important for ensuring the safe operation of railway bridges. Traditionally, contact sensors such as Linear Variable Displacement Transducers (LVDT) and accelerometers have been used to measure the displacement of the railway bridges. However, these sensors need significant effort in installation and maintenance. Therefore, railroad management agencies are interested in new means to measure bridge displacements. This research focuses on mounting Laser Doppler Vibrometer (LDV) on an Unmanned Aerial System (UAS) to enable contact-free transverse dynamic displacement of railroad bridges. Researchers conducted three field tests by flying the Unmanned Aerial Systems Laser Doppler Vibrometer (UAS-LDV) 1.5 m away from the ground and measured the displacement of a moving target at various distances. The accuracy of the UAS-LDV measurements was compared to the Linear Variable Differential Transducer (LVDT) measurements. The results of the three field tests showed that the proposed system could measure non-contact, reference-free dynamic displacement with an average peak and root mean square (RMS) error for the three experiments of 10% and 8% compared to LVDT, respectively. Such errors are acceptable for field measurements in railroads, as the interest prior to bridge monitoring implementation of a new approach is to demonstrate similar success for different flights, as reported in the three results. This study also identified barriers for industrial adoption of this technology and proposed operational development practices for both technical and cost-effective implementation.

42 ENGINEERING↗

Diurnal Cycle of ASCAT-Identified Cold Pools and Associated Convective Systems in the Maritime Continent and South China Sea

Mesoscale convective cold pools are known to alter the turbulent air-sea fluxes in the regions of active convection such as maritime tropics. Satellite-based Ocean Vector Wind instruments have proven to be useful in observing these mesoscale outflows corresponding to convective cold pools. A new storm-centric, tensor-based wind-gradient identification algorithm identifies gradients associated with convective cold pools and other boundaries such as land-sea breeze fronts. We term these as Gradient Features or GFs; analyzed over tropical oceans (2007-2018) using horizontal winds from Advanced Scatterometer (ASCAT-A).

Nesbitt, Stephen W.↗

Comparing ASCAT and CYGNSS Winds near Tropical Convection

Gradient Features identified in ASCAT (Advanced Scatterometer) data correspond well to observed CYGNSS (Cyclone Global Navigation Satellite System) wind shifts: Comparing ASCAT and CYGNSS winds near tropical convection. Gradient wind magnitude in ASCAT observations has been recently shown to be a useful proxy for the presence of tropical convection cold pools. To help confirm this in the vicinity of precipitation we perform a comparison with the L-band CYGNSS wind dataset. Integrated Multi-satellite Retrievals for GPM (Global Precipitation Measurement)) IMERG.

Convection↗

Understanding Oceanic Heavy Precipitation Using Scatterometer, Satellite Precipitation, and Reanalysis Products

The primary aim of this study is to understand the heavy precipitation events over Oceanic regions using vector wind retrievals from space based scatterometers in combination with precipitation products from satellite and model reanalysis products. Heavy precipitation over oceans is a less understood phenomenon and this study tries to fill in the gaps which may lead us to a better understanding of heavy precipitation over oceans. Various phenomenon may lead to intense precipitation viz. MJO (Madden-Julian Oscillation), Extratropical cyclones, MCSs (Mesoscale Convective Systems), that occur inside or outside the tropics and if we can decipher the physical mechanisms behind occurrence of heavy precipitation, then it may lead us to a better understanding of such events which further may help us in building more robust weather and climate models. During a heavy precipitation event, scatterometer wind observations may lead us to understand the governing dynamics behind that event near the surface. We hypothesize that scatterometer winds can observe significant changes in the near-surface circulation and that there are global relationships among these quantities. To the degree to which this hypothesis fails, we will learn about the regional behavior of heavy precipitation-producing systems over the ocean. We use a "precipitation feature" (PF) approach to enable statistical analysis of a large database of raining features.

Winds↗