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Disdrometer
About: Disdrometer is a research topic. Over the lifetime, 930 publications have been published within this topic receiving 23092 citations.
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TL;DR: In this article, a polynomial least-square fit of the data was proposed to improve the accuracy of the radar reflectivity measurements by a sorting and moving average (SMA) method and with a POSS (precipitation occurrence sensing system) disdrometer.
8 citations
TL;DR: A ground observation system for solid precipitation using two-dimensional video disdrometer (2DVD) and performance evaluation of two-class classification by Support Vector Machine (SVM) revealed that the average accuracy of classifying particles into snowflakes and graupels could reach around 95.4%, which had not been achieved by previous studies.
Abstract: We developed a ground observation system for solid precipitation using two-dimensional video disdrometer (2DVD). Among 16,010 particles observed by the system, around 10% of them were randomly sampled and manually classified into five classes which are snowflake, snowflake-like, intermediate, graupel-like, and graupel. At first, each particle was represented as a vector of 72 features containing fractal dimension and box-count to represent the complexity of particle shape. Feature analysis on the dataset clarified the importance of fractal dimension and box-count features for characterizing particles varying from snowflakes to graupels. On the other hand, performance evaluation of two-class classification by Support Vector Machine (SVM) was conducted. The experimental results revealed that, by selecting only 10 features out of 72, the average accuracy of classifying particles into snowflakes and graupels could reach around 95.4%, which had not been achieved by previous studies.
8 citations
TL;DR: In this article , the authors analyzed nearly 2 years of combined seismic and optical disdrometer measurements, where the latter enables the drop-based quantification of rainfall physical properties.
Abstract: • Rainfall is studied using colocated seismic and optical disdrometer measurements. • Seismic power is set by the impulse power of (particularly the largest) raindrops. • Rainfall intensity and kinetic energy show power-law relations with seismic power. • Seismic power vs. rainfall intensity may reveal changes in drop size distribution. • Seismic measurements may provide a spatially integrated measure for soil erosivity. Rainfall is a key driver of geomorphological processes ranging from impacting drops that lead to the small-scale dislodgement of soil particles to large-scale morphogenic floods and rainfall-induced hillslope processes. Although rainfall has been identified in seismic records, the associated power spectral density and its quantitative relation to the underlying physical processes have not yet been studied. Here, we analyze nearly 2 years of combined seismic and optical disdrometer measurements, where the latter enables the drop-based quantification of rainfall physical properties. Our measurements confirm the broadband observation of ground velocity power spectral density due to rainfall, allowing the seismic identification of rainfall at intensities as low as 1 mm/h. Seismic power, P , shows a power-law scaling with rainfall intensity, I , and kinetic energy, E : P ∝ I 2.1 and P ∝ E 1.6 . The observed scaling relations are consistent between the three monitored sites although there are absolute differences in seismic power of about 1 order of magnitude, which are likely due to variability in landcover and subsurface seismic properties. With a physical model, we demonstrate that the observed power-law relations are set by an underlying linear relation between seismic power and rainfall impulse power, and that the associated exponent values of I and E are due to the covariance of the raindrop size distribution with the total number of drops. The largest raindrop fractions, whose relative contribution increases with rainfall intensity, dominate the seismic signal where, in our case, 90% of the seismic power is attributed to drops larger than 3 mm. Using our model, we estimate the contributing area of rainfall to seismic observations to be within a radial distance of ∼5–25 m. The spatially integrated nature of the seismic measurements and their sensitivity to large raindrops, which control the disaggregation and the mobilization of soil particles, make seismic records well-suited for the investigation of soil erosion processes. More generally, our work provides a basis for the temporally-resolved seismic quantification of rainfall that drives the dynamics of various hydro-geomorphological processes.
8 citations
8 citations
TL;DR: In this article, a low-cost laser disdrometer (LLD) was developed and calibrated for raindrops, which is a type of high-speed line-image scanner, and measured hydrometeor's particle-size distribution and fall velocities.
Abstract: A low-cost laser disdrometer (LLD), which is a type of high-speed line-image scanner, has been developed and calibrated for raindrops. The disdrometer measures hydrometeor's particle-size distribution (PSD) and fall velocities, as well as record hydrometeor images. Hydrometeor imaging is advantageous for hydrometeor classification. All hydrometeor types, raindrops, graupels, snowflakes, and ice crystals, can mix with snowfall. Snowfall sensing requires a wide sensing light sheet to capture large-sized snowflakes. Accordingly, a new LLD equipped with a 35-mm sensing light sheet has been developed. This paper demonstrates and evaluates the new drisdrometer's snowfall-evaluation performance. The wide sensing light sheet captures images of large-sized (14 mm) snowflakes reasonably well, and the PSD and the fall velocities correlate with those measured with the commercially available Parsivel laser disdrometer. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
7 citations