Relevant bibliographies by topics / Disdrometer Observations

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Academic literature on the topic 'Disdrometer Observations'

Author: Grafiati
Published: 4 June 2021
Last updated: 4 February 2022

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Journal articles on the topic "Disdrometer Observations"

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Tokay, Ali, Paul G. Bashor, and Katherine R. Wolff. "Error Characteristics of Rainfall Measurements by Collocated Joss–Waldvogel Disdrometers." Journal of Atmospheric and Oceanic Technology 22, no. 5 (May 1, 2005): 513–27. http://dx.doi.org/10.1175/jtech1734.1.

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Abstract Error characteristics of rainfall measurements were studied using six collocated Joss–Waldvogel (JW) disdrometers that are located at NASA’s Wallops Flight Facility. The six disdrometer means of rain rate R, reflectivity Z, and differential reflectivity ZDR, for a given minute were considered as a reference. The maximum deviations of R, Z, and ZDR from the mean in a rain event were 0.6 mm h−1, 1.3 dB, and 0.05 dB, respectively. Rainfall statistics were then examined between disdrometer pairs. The root-mean-square (rms) difference of R, Z, and ZDR between paired disdrometers in a rain event were as high as 3.2 mm h−1, 3.7 dB, and 0.3 dB, respectively. The rms difference of R and ZDR were even higher when the disdrometer observations were stratified based on reflectivity intervals. The differences in disdrometer rainfall measurements have a potential impact when the disdrometers are considered as calibration tools for vertically pointing and scanning radars. The differences between the disdrometer measurements also result in differences in coefficients and exponents of the derived relations between radar parameters and rain rate. Among the four different relations between radar parameters and rain rate, the absolute difference in rain rate |ΔR| from two different JW disdrometers was highest in R(ZH, ZDR) and lowest in R(KDP, ZDR). The other two relations were R(Z) and R(KDP). The |ΔR| increases with increasing horizontally polarized reflectivity ZH, and differential specific phase KDP in both single- and dual-parameter rainfall estimators, while the |ΔR| increases with decreasing ZDR in dual-parameter rainfall estimators. Several sources of JW disdrometer malfunctions were also presented. The hardware problems were the leading cause for the malfunction of the JW disdrometers, as identified by the manufacturer. A single JW disdrometer could have inherent measurement errors that can only be identified in the presence of collocated (preferably two) rain-measuring instruments.
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Wood, N. B., T. S. L'Ecuyer, F. L. Bliven, and G. L. Stephens. "Characterization of disdrometer uncertainties and impacts on estimates of snowfall rate and radar reflectivity." Atmospheric Measurement Techniques Discussions 6, no. 4 (July 11, 2013): 6329–69. http://dx.doi.org/10.5194/amtd-6-6329-2013.

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Abstract. Estimates of snow microphysical properties obtained by analyzing collections of individual particles are often limited to short time scales and coarse time resolution. Retrievals using disdrometer observations coincident with bulk measurements such as radar reflectivity and snowfall amounts may overcome these limitations; however, retrieval techniques using such observations require uncertainty estimates not only for the bulk measurements themselves, but also for the simulated measurements modeled from the disdrometer observations. Disdrometer uncertainties arise due to sampling and analytic errors and to the discrete, potentially truncated form of the reported size distributions. Imaging disdrometers such as the Snowflake Video Imager and 2-D Video Disdrometer provide remarkably detailed representations of snow particles, but view limited projections of their three-dimensional shapes. Particle sizes determined by such instruments underestimate the true dimensions of the particles in a way that depends, in the mean, on particle shape, also contributing to uncertainties. An uncertainty model that accounts for these uncertainties is developed and used to establish their contributions to simulated radar reflectivity and snowfall rate. Viewing geometry effects are characterized by a parameter, φ, that relates disdrometer-observed particle size to the true maximum dimension of the particle. Values and uncertainties for φ are estimated using idealized ellipsoidal snow particles. The model is applied to observations from seven snow events from the Canadian CloudSat CALIPSO Validation Project (C3VP), a mid-latitude cold season cloud and precipitation field experiment. Typical total uncertainties are 4 dBZ for reflectivity and 40–60% for snowfall rate, are highly correlated, and are substantial compared to expected observational uncertainties. The dominant sources of errors are viewing geometry effects and the discrete, truncated form of the size distributions. While modeled Ze-S relationships are strongly affected by assumptions about snow particle mass properties, such relationships are only modestly sensitive to φ owing to partially compensating effects on both the reflectivity and snowfall rate.
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Chandrasekar, V., and Enrico G. Gori. "Multiple Disdrometer Observations of Rainfall." Journal of Applied Meteorology 30, no. 11 (November 1991): 1514–20. http://dx.doi.org/10.1175/1520-0450(1991)030<1514:mdoor>2.0.co;2.

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Wood, N. B., T. S. L'Ecuyer, F. L. Bliven, and G. L. Stephens. "Characterization of video disdrometer uncertainties and impacts on estimates of snowfall rate and radar reflectivity." Atmospheric Measurement Techniques 6, no. 12 (December 20, 2013): 3635–48. http://dx.doi.org/10.5194/amt-6-3635-2013.

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Abstract. Estimates of snow microphysical properties obtained by analyzing collections of individual particles are often limited to short timescales and coarse time resolution. Retrievals using disdrometer observations coincident with bulk measurements such as radar reflectivity and snowfall amounts may overcome these limitations; however, retrieval techniques using such observations require uncertainty estimates not only for the bulk measurements themselves, but also for the simulated measurements modeled from the disdrometer observations. Disdrometer uncertainties arise due to sampling and analytic errors and to the discrete, potentially truncated form of the reported size distributions. Imaging disdrometers such as the Snowflake Video Imager and 2-D Video Disdrometer provide remarkably detailed representations of snow particles, but view limited projections of their three-dimensional shapes. Particle sizes determined by such instruments underestimate the true dimensions of the particles in a way that depends, in the mean, on particle shape, also contributing to uncertainties. An uncertainty model that accounts for these uncertainties is developed and used to establish their contributions to simulated radar reflectivity and snowfall rate. Viewing geometry effects are characterized by a parameter, &amp;varphi;, that relates disdrometer-observed particle size to the true maximum dimension of the particle. Values and uncertainties for &amp;varphi; are estimated using idealized ellipsoidal snow particles. The model is applied to observations from seven snow events from the Canadian CloudSat/CALIPSO Validation Project (C3VP), a mid-latitude cold-season cloud and precipitation field experiment. Typical total uncertainties are 4 dB for reflectivity and 40–60% for snowfall rate, are highly correlated, and are substantial compared to expected uncertainties for radar and precipitation gauge observations. The dominant sources of errors are viewing geometry effects and the discrete, truncated form of the size distributions. While modeled Ze–S relationships are strongly affected by assumptions about snow particle mass properties, such relationships are only modestly sensitive to &amp;varphi; owing to partially compensating effects on both the reflectivity and snowfall rate.
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Tokay, Ali, and Paul G. Bashor. "An Experimental Study of Small-Scale Variability of Raindrop Size Distribution." Journal of Applied Meteorology and Climatology 49, no. 11 (November 1, 2010): 2348–65. http://dx.doi.org/10.1175/2010jamc2269.1.

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Abstract An experimental study of small-scale variability of raindrop size distributions (DSDs) has been carried out at Wallops Island, Virginia. Three Joss–Waldvogel disdrometers were operated at a distance of 0.65, 1.05, and 1.70 km in a nearly straight line. The main purpose of the study was to examine the variability of DSDs and its integral parameters of liquid water content, rainfall, and reflectivity within a 2-km array: a typical size of Cartesian radar pixel. The composite DSD of rain events showed very good agreement among the disdrometers except where there were noticeable differences in midsize and large drops in a few events. For consideration of partial beam filling where the radar pixel was not completely covered by rain, a single disdrometer reported just over 10% more rainy minutes than the rainy minutes when all three disdrometers reported rainfall. Similarly two out of three disdrometers reported 5% more rainy minutes than when all three were reporting rainfall. These percentages were based on a 1-min average, and were less for longer averaging periods. Considering only the minutes when all three disdrometers were reporting rainfall, just over one quarter of the observations showed an increase in the difference in rainfall with distance. This finding was based on a 15-min average and was even less for shorter averaging periods. The probability and cumulative distributions of a gamma-fitted DSD and integral rain parameters between the three disdrometers had a very good agreement and no major variability. This was mainly due to the high percentage of light stratiform rain and to the number of storms that traveled along the track of the disdrometers. At a fixed time step, however, both DSDs and integral rain parameters showed substantial variability. The standard deviation (SD) of rain rate was near 3 mm h−1, while the SD of reflectivity exceeded 3 dBZ at the longest separation distance. These standard deviations were at 6-min average and were higher at shorter averaging periods. The correlations decreased with increasing separation distance. For rain rate, the correlations were higher than previous gauge-based studies. This was attributed to the differences in data processing and the difference in rainfall characteristics in different climate regions. It was also considered that the gauge sampling errors could be a factor. In this regard, gauge measurements were simulated employing existing disdrometer dataset. While a difference was noticed in cumulative distribution of rain occurrence between the simulated gauge and disdrometer observations, the correlations in simulated gauge measurements did not differ from the disdrometer measurements.
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Tokay, Ali, Leo Pio D’Adderio, David B. Wolff, and Walter A. Petersen. "Development and Evaluation of the Raindrop Size Distribution Parameters for the NASA Global Precipitation Measurement Mission Ground Validation Program." Journal of Atmospheric and Oceanic Technology 37, no. 1 (January 2020): 115–28. http://dx.doi.org/10.1175/jtech-d-18-0071.1.

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AbstractThe National Aeronautics and Space Administration Global Precipitation Measurement (GPM) mission ground validation program uses dual-polarization radar moments to estimate raindrop size distribution (DSD) parameters, the mass-weighted mean drop diameter Dmass, and normalized intercept parameter NW, to validate the GPM Core Observatory–derived DSD parameters. The disdrometer-based Dmass and NW are derived through empirical relationships between Dmass and differential reflectivity ZDR, and between NW, reflectivity ZH, and Dmass. This study employs large datasets collected from two-dimensional video disdrometers (2DVD) during six different field studies to derive the requisite empirical relationships. The uncertainty of the derived Dmass(ZDR) relationship is evaluated through comparisons of 2DVD-calculated and ZDR-estimated Dmass, where ZDR is calculated directly from 2DVD observations. Similarly, the uncertainty of the NW(ZH, Dmass) relationship is evaluated through 2DVD-calculated and Dmass and ZH-estimated NW, where Dmass and ZH are directly calculated from 2DVD observations. This study also presents the sensitivity of Dmass(ZDR) relationships to climate regime and to disdrometer type after developing three additional Dmass(ZDR) relationships from second-generation Particle Size Velocity (PARSIVEL2) disdrometer (P2) observations collected in the Pacific Northwest, in Iowa, and at Kwajalein Atoll in the tropical Pacific Ocean. The application of P2-derived Dmass(ZDR) relationship based on precipitation in the northwestern United States to P2 observations collected over the tropical ocean resulted in the highest error among comparisons of the three datasets.
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Xie, Xinxin, Raquel Evaristo, Clemens Simmer, Jan Handwerker, and Silke Trömel. "Precipitation and microphysical processes observed by three polarimetric X-band radars and ground-based instrumentation during HOPE." Atmospheric Chemistry and Physics 16, no. 11 (June 10, 2016): 7105–16. http://dx.doi.org/10.5194/acp-16-7105-2016.

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Abstract. This study presents a first analysis of precipitation and related microphysical processes observed by three polarimetric X-band Doppler radars (BoXPol, JuXPol and KiXPol) in conjunction with a ground-based network of disdrometers, rain gauges and vertically pointing micro rain radars (MRRs) during the High Definition Clouds and Precipitation for advancing Climate Prediction (HD(CP)2) Observational Prototype Experiment (HOPE) during April and May 2013 in Germany. While JuXPol and KiXPol were continuously observing the central HOPE area near Forschungszentrum Jülich at a close distance, BoXPol observed the area from a distance of about 48.5 km. MRRs were deployed in the central HOPE area and one MRR close to BoXPol in Bonn, Germany. Seven disdrometers and three rain gauges providing point precipitation observations were deployed at five locations within a 5 km × 5 km region, while three other disdrometers were collocated with the MRR in Bonn. The daily rainfall accumulation at each rain gauge/disdrometer location estimated from the three X-band polarimetric radar observations showed very good agreement. Accompanying microphysical processes during the evolution of precipitation systems were well captured by the polarimetric X-band radars and corroborated by independent observations from the other ground-based instruments.
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Jameson, A. R., M. L. Larsen, and A. B. Kostinski. "Disdrometer Network Observations of Finescale Spatial–Temporal Clustering in Rain." Journal of the Atmospheric Sciences 72, no. 4 (March 31, 2015): 1648–66. http://dx.doi.org/10.1175/jas-d-14-0136.1.

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Abstract The spatial clustering of drops is a defining characteristic of rain on all scales from centimeters to kilometers. It is the physical basis for much of the observed variability in rain. The authors report here on the temporal–spatial 1-min counts using a network of 21 optical disdrometers over a small area near Charleston, South Carolina. These observations reveal significant differences between spatial and temporal structures (i.e., clustering) for different sizes of drops, which suggest that temporal observations of clustering cannot be used to infer spatial clustering simply using by an advection velocity as has been done in past studies. It is also shown that both spatial and temporal clustering play a role in rain variability depending upon the drop size. The more convective rain is dominated by spatial clustering while the opposite holds for the more stratiform rain. Like previous time series measurements by a single disdrometer but in contradiction with widely accepted drop size distribution power-law relations, it is also shown that there is a linear relation between 1-min averages of the rainfall rate R over the network and the average total number of drops Nt. However, the network (area) R–Nt relation differs from those derived strictly from time series observations by individual disdrometers. These differences imply that the temporal and spatial size distributions and their variabilities are not equivalent.
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Moisseev, Dmitri N., and V. Chandrasekar. "Examination of the μ–Λ Relation Suggested for Drop Size Distribution Parameters." Journal of Atmospheric and Oceanic Technology 24, no. 5 (May 1, 2007): 847–55. http://dx.doi.org/10.1175/jtech2010.1.

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Abstract Raindrop size distributions are often assumed to follow a three-parameter gamma distribution. Since rain intensity retrieval from radar observations is an underdetermined problem, there is great interest in finding physical correlations between the parameters of the gamma distribution. One of the more common approaches is to measure naturally occurring drop size distributions (DSDs) using a disdrometer and to find DSD parameters by fitting a gamma distribution to these observations. Often the method of moments is used to retrieve the parameters of a gamma distribution from disdrometer observations. In this work the effect of the method of moments and data filtering on the relation between the parameters of the DSD is investigated, namely, the shape μ and the slope Λ parameters. For this study the disdrometer observations were simulated. In these simulations the gamma distribution parameters Nw, D0, and μ were randomly selected from a wide range of values that are found in rainfall. Then, using simulated disdrometer measurements, DSD parameters were estimated using the method of moments. It is shown that the statistical errors associated with data filtering of disdrometer measurements might produce a spurious relation between μ and Λ parameters. It is also shown that three independent disdrometer measurements can be used to verify the existence of such a relation.
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Ghada, Wael, Nicole Estrella, and Annette Menzel. "Machine Learning Approach to Classify Rain Type Based on Thies Disdrometers and Cloud Observations." Atmosphere 10, no. 5 (May 7, 2019): 251. http://dx.doi.org/10.3390/atmos10050251.

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Rain microstructure parameters assessed by disdrometers are commonly used to classify rain into convective and stratiform. However, different types of disdrometer result in different values for these parameters. This in turn potentially deteriorates the quality of rain type classifications. Thies disdrometer measurements at two sites in Bavaria in southern Germany were combined with cloud observations to construct a set of clear convective and stratiform intervals. This reference dataset was used to study the performance of classification methods from the literature based on the rain microstructure. We also explored the possibility of improving the performance of these methods by tuning the decision boundary. We further identified highly discriminant rain microstructure parameters and used these parameters in five machine-learning classification models. Our results confirm the potential of achieving high classification performance by applying the concepts of machine learning compared to already available methods. Machine-learning classification methods provide a concrete and flexible procedure that is applicable regardless of the geographical location or the device. The suggested procedure for classifying rain types is recommended prior to studying rain microstructure variability or any attempts at improving radar estimations of rain intensity.
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Dissertations / Theses on the topic "Disdrometer Observations"

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Bracci, Alessandro. "Analysis of precipitation from ground observations over the Antarctic coast." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16875/.

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The primary mass input of the Antarctic ice sheet is represented by snow precipitation. Despite of their crucial role, the estimates of precipitation over Antarctica are sparse and characterized by large uncertainties. Recently, the Italian Mario Zucchelli station (MZS) at Terra Nova Bay was equipped with instruments for monitoring precipitation. This thesis is part of the APP-PNRA project (Antarctic precipitation properties from ground-based instruments), whose object is to set up an observatory to characterize precipitation at MZS. The present study was focused on the evaluation of the response of solid hydrometeors to the electromagnetic radiation and on the microphysical characterization of precipitation. The former was investigated using a pre-computed discrete dipole approximation (DDA) database for complex-shape snowflakes and a T-Matrix code for soft-spheroids. The backscattering cross sections, calculated at the K-band by the two methods, were compared. In case of aggregate particles the methods show a poor agreement, comparable values were found when pristine crystals were considered. The latter was examined through in-situ observations by a Parsivel disdrometer and Micro Rain Radar. By exploiting the Parsivel data collected during the summer seasons 2016-17 and 2017-18, the particle size distributions (PSD) of hydrometeors were derived, showing a high number of particles with very small diameter. Numerical simulations, driven by DDA and T-Matrix, were also performed by using the PSDs, to obtain the simulated radar reflectivity. The comparative analysis of simulated and actual reflectivity allowed inferring microphysical characterization of precipitation. Based on this methodology, 16 out of 22 snow days were categorized: 6 as having aggregate-like features and 10 as pristine crystal-like. These results will be of practical interest, giving an important contribution toward a more accurate quantification of snow accumulation in Antarctica.
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(10285328), Connor Paul Belak. "Verification of simulated DSDs and sensitivity to CCN concentration in EnKF analysis and ensemble forecasts of the 30 April 2017 tornadic QLCS during VORTEX-SE." Thesis, 2021.

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Storms in the SE-US often evolve in different environments than those in the central Plains. Many poorly understood aspects of these differing environments may impact the tornadic potential of SE-US storms. Among these differences are potential variations in the CCN concentration owing to differences in land cover, combustion, industrial and urban activity, and proximity to maritime environments. The relative influence of warm and cold rain processes is sensitive to CCN concentration, with higher CCN concentrations producing smaller cloud droplets and more efficient cold rain processes. Cold rain processes result in DSDs with relatively larger drops from melting ice compared to warm rain processes. Differences in DSDs impact cold pool and downdraft size and strength, that influence tornado potential. This study investigates the impact of CCN concentration on DSDs in the SE-US by comparing DSDs from ARPS-EnKF model analyses and forecasts to observed DSDs from portable disdrometer-equipped probes collected by a collaboration between Purdue University, the University of Oklahoma (OU), the National Severe Storms Laboratory (NSSL), and the University of Massachusetts in a tornadic QLCS on 30 April 2017 during VORTEX-SE.

The ARPS-EnKF configuration, which consists of 40 ensemble members, is used with the NSSL triple-moment microphysics scheme. Surface and radar observations are both assimilated. Data assimilation experiments with CCN concentrations ranging from 100 cm-3 (maritime) to 2,000 cm-3 (continental) are conducted to characterize the variability of DSDs and the model output DSDs are verified against the disdrometer observations. The sensitivity of the DSD variability to CCN concentrations is evaluated. Results indicate continental CCN concentrations (close to CCN 1,000 cm3) produce DSDs that align closest to the observed DSDs. Other thermodynamic variables also accord better to observations in intermediate CCN concentration environments.

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Chen, Ruei-siang, and 陳睿祥. "Observation of TRAMI typhoon precipitations made with Chung-Li VHF radar andground-based disdrometer." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/61393944895996706899.

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碩士
國立中央大學
太空科學研究所
102
The research is to record the rainfall phenomenon of TRAMI-typhoon.We can obtain precipitation terminal velocity, echo power, rain fall rate, DSD and the spectral width etc by using the Chung-Li 52MHz VHF radar and ground 2DVD- Disdrometer. The data is the events from August 20th 15:01~August 22th,2013.We can distinguish the cold precipitation events and the warm precipitation events with the air vertical velocity and bright band,and make scatter diagram by using the relation between precipitation terminal velocity and echo power in air to obtain the N_0 from exponential and gamma of DSD of precipitation particle and the N_0 from ground 2DVD- Disdrometer. The slope is closer to 1 and the correlation coefficient is very well,when only takes not only the cold precipitation events but the DSD is exponential. We take the time from August 20th 19:02:04 to 20:03:10 to make the scatter diagram of air echo power with the spectral width,and air echo power with the air vertical velocity,because we found the presence of the strong updraft, average Doppler velocity maximum nearly 15m/s on August 20 at about 19:42 near 8-10 km.Then we found the correlation coefficient ratio of the range 7.05km~12.3km is better than the range 1.65km~6.9km. We can use the filter criteria to distinguish the character between ice and supercooled above melting layer(5.4km) by terminal velocity greater than 2.5m/s.The average echo power between ice and supercooled water is 8-11dB,and supercooled water is greater than ice about 0.15Hz in average spectral width.We have lightning echo signals in this observing data.
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Book chapters on the topic "Disdrometer Observations"

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Willems, Patrick, and Thomas Einfalt. "Sensors for rain measurements." In Metrology in Urban Drainage and Stormwater Management: Plug and Pray, 11–33. IWA Publishing, 2021. http://dx.doi.org/10.2166/9781789060119_0011.

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Abstract Rain measurements based on rain gauges, disdrometers, weather radars and microwave links provide essential input data for urban drainage and stormwater modelling, management, and planning. Their quality strongly depends on the sensor type and calibration, but also on the data post-processing that includes quality control and data adjustment after comparison with reference observations. This chapter provides an overview of traditional techniques and recent developments, and practical advice on the selection of the type of instrument, the installation and calibration aspects to be considered, and the measurement data processing and adjustment needs.
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Conference papers on the topic "Disdrometer Observations"

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Ibrahim, I. A., V. Chandrasekar, V. N. Bringi, P. C. Kennedy, M. Schoenhuber, H. E. Urban, and W. L. Randen. "Simultaneous multiparameter radar and 2D-video disdrometer observations of snow." In IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174). IEEE, 1998. http://dx.doi.org/10.1109/igarss.1998.702931.

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Cao, Q., G. Zhang, T. Schuur, A. Ryzhkov, E. Brandes, and K. Ikeda. "Characterization of Rain Microphysics based on Disdrometer and Polarimetric Radar Observations." In 2006 IEEE International Symposium on Geoscience and Remote Sensing. IEEE, 2006. http://dx.doi.org/10.1109/igarss.2006.139.

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Ahammad, Parvez, Christopher R. Williams, Takis Kasparis, John Lane, Francis Merceret, and Linwood Jones. "Vertical air motion estimates from the disdrometer flux conservation model and related experimental observations." In AeroSense 2002, edited by Ivan Kadar. SPIE, 2002. http://dx.doi.org/10.1117/12.477624.

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Thurai, Merhala, Sophie Steger, Franz Teschl, Michael Schonhuber, and David B. Wolff. "Rain Drop Shapes and Scattering Calculations: A Case Study using 2D Video Disdrometer Measurements and Polarimetric Radar Observations at S-band During Hurricane Dorian Rain-Bands." In 2021 15th European Conference on Antennas and Propagation (EuCAP). IEEE, 2021. http://dx.doi.org/10.23919/eucap51087.2021.9411181.

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