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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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Journal ArticleDOI
Kang Pu, Xichuan Liu, Hongbing He, Yu Sun, Shuai Hu, Yi Wu 
24 Mar 2020-Water
TL;DR: Wang et al. as mentioned in this paper used the empirical V-D (velocity-diameter) relationships and observed surface temperature for matching precipitation types, and the precipitation data were divided into rain, graupel, wet snow and dry snow.
Abstract: To improve solid precipitation monitoring in the hydrology and meteorology field, 1-min precipitation data observed by the PARticle SIze VELocity (PARSIVEL) disdrometer in Nanjing, eastern China, from February 2014 to February 2019 for all days with solid precipitation, were used to study the microphysical characteristics of winter precipitation. In this study, the empirical V-D (velocity–diameter) relationships and observed surface temperature are used for matching precipitation types, and the precipitation data are divided into rain, graupel, wet snow and dry snow. The results show that dry snow and wet snow have maximum Dm (mass-weighted mean diameter) and minimum log10Nw (normalized intercept parameter), while rain shows the opposite. Additionally, the μ-Λ (shape parameter–slope parameter) curve of dry snow and wet snow is very close, and the μ value of dry snow and wet snow is higher than that of graupel and higher than that of rain for the same Λ value. Furthermore, the Ze-S (equivalent reflectivity factor–precipitation intensity) relationships among different types of precipitation are significantly different. If only the Ze-S relationship of rain is used for quantitative precipitation estimation (QPE), then, for small precipitation intensity, solid precipitation will be overestimated, while, for large precipitation intensity, it will be underestimated.

6 citations

01 Jan 2010
TL;DR: In this article, the authors describe an acoustic instrument that determines rain parameters from the sound of raindrops falling into a tank of water, which is a direct relationship between the kinetic energy of a raindrop and the acoustic energy that it creates upon impact.
Abstract: Microwave engineers and geomorphologists require rainfall data with a much greater temporal resolution, and a better representation of the numbers of large raindrops than is available from current commercial instruments. This paper describes an acoustic instrument that determines rain parameters from the sound of raindrops falling into a tank of water. There is a direct relationship between the kinetic energy of a raindrop and the acoustic energy that it creates upon impact. Rain kinetic energy flux density (KE) is estimated from measurements of the sound field in the tank and these have been compared to measurements from a co-sited commercial disdrometer. Six months data has been collected in the Eastern UK. Comparisons of rain KE estimated by the two instruments are presented and links between KR and rainfall intensity (RI) are discussed. The sampling errors of the two instruments are analysed to show that the acoustic instrument can produce rain KE measurements with a onesecond integration time with sampling uncertainty of the same size as commercial instruments using a oneminute integration time.

6 citations

Journal ArticleDOI
TL;DR: The results indicate the preliminary feasibility of using cellphone signals to detect rain by establishing a 2-GHz ML and a detection method to classify dry/rainy periods by using statistical parameters from attenuation measurements.
Abstract: Microwave links (MLs), ranging from 10 to 30 GHz, have been widely applied for estimating rainfall; however, links below 10 GHz have rarely been applied, although they are more widespread via applications such as cellphone signals. This letter analyzes the feasibility of using cellular device like transmission signal to detect rain by establishing a 2-GHz ML and presents a detection method to classify dry/rainy periods by using statistical parameters from attenuation measurements. The detection model is trained using the C4.5 algorithm based on the combination of the average, standard deviation, minimum, and maximum of the attenuation measurements over the course of 1 min. The method is then applied for seven rain events, using a disdrometer to validate the results. The true positive rates of dry periods are all greater than 70%, and those of rainy periods are greater than 60%, indicating that the method performs well and could detect most of dry and rainy periods correctly. The results indicate the preliminary feasibility of using cellphone signals to detect rain.

6 citations

01 Jan 2004
TL;DR: In this paper, the size distribution during the growing phase of a rain event is found biased towards larger drops compared to that during the later phase of the event for identical rain rates.
Abstract: Measurements of rain drop size distributions (DSD) at Kolkata (22°34' N, 88°29' E), India, have been carried out using a Josstype disdrometer since June 2004. The size distribution during the growing phase of a rain event is found biased towards larger drops compared to that during the later phase of the event for identical rain rates. The three-parameter distributions, lognormal and gamma, are fitted to the disdrometer data to model DSD for a number of rain events. The same data are also used to find integral rainfall parameters (IRP), such as liquid water content, radar reflectivity factor and specific attenuation which are compared to that obtained with the modelled DSD and the Marshall-Palmer distribution.

6 citations

Journal ArticleDOI
TL;DR: In this article , the authors describe the dual-frequency precipitation radar (DPR) retrieval algorithm that uses an adjustable relationship between rain rate and the mass-weighted diameter (Dm) or an R-Dm relationship in solving for R and Dm simultaneously.
Abstract: The primary goal of the dual-frequency precipitation radar (DPR) aboard the Global Precipitation Measurement (GPM) Core Observatory satellite is to infer precipitation rate and raindrop/particle size distributions (DSD/PSD). The focus of this paper is threefold: (1) to describe the DPR retrieval algorithm that uses an adjustable relationship between rain rate (R) and the mass-weighted diameter (Dm) or an R-Dm relationship in solving for R and Dm simultaneously; (2) to evaluate the DPR algorithm based on the physical simulations that employ measured DSD/PSD to understand the mechanism and error characteristics of the retrieval method; (3) to review ground validation studies for DPR products as well as to analyze the strengths and weaknesses of ground radar and rain gauge/disdrometer validations. Overall, the DPR Version 6 algorithm provides reasonably accurate estimates of R and Dm in rain. Non-uniformity in the rain profile, however, tends to degrade the accuracy of the R and Dm estimates to some extent as the range-independent assumption of the adjustable parameter (ε) of the R-Dm relation is not able to fully account for natural variation of DSD in the vertical profile. The DPR snow rate is underestimated as compared with the independent dual-frequency ratio (DFR) technique. This is possibly the result of the constraint associated with the path integral attenuation (PIA)/differential PIA (δPIA) used in the DPR algorithm to find the best ε and range-independent ε assumption. A range-variable ε model, proposed in the DPR Version 7 algorithm, is expected to improve rain and snow retrieval.

6 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202378
2022114
202151
202059
201972
201840