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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 statistical inversion technique based upon Bayesian methodology was used to decompose heterogeneous rain into five-seven statistically homogeneous components, each characterized by its own steady drop size distribution.
Abstract: Most variables in meteorology are statistically heterogeneous. The statistics of data from several different locations, then, can be thought of as an amalgamation of information contained in several contributing probability density functions (PDFs) having different sets of parameters, different parametric forms, and different mean values. The frequency distribution of such data, then, will often be multimodal. Usually, however, in order to achieve better sampling, measurements of these variables over an entire set of data gathered at widely disparate locations are processed as though the data were statistically homogeneous, that is, as though they were fully characterized by just one PDF and one single set of parameters having one mean value. Is there, instead, a better way of treating the data in a manner that is consistent with this statistical heterogeneity? This question is addressed here using a statistical inversion technique developed by Tarantola based upon Bayesian methodology. Two examples of disdrometer measurements in real rain, one 16 h and the other 3 min long, reveal the presence of multiple mean values of the counts at all the different drop sizes. In both cases the heterogeneous rain can be decomposed into five–seven statistically homogeneous components, each characterized by its own steady drop size distribution. Concepts such as stratiform versus convective rain can be given more precise meaning in terms of the contributions each component makes to the rain. Furthermore, this discovery permits the explicit inclusion of statistical heterogeneity into some analytic theories.

17 citations

Journal ArticleDOI
TL;DR: Fall velocity-diameter relationships for four different snowflake types (dendrite, plate, needle, and graupel) were investigated in northeastern South Korea, and a new algorithm for classifying hydrometeors is proposed for distrometric measurements based on the new relationships as discussed by the authors.
Abstract: Fall velocity-diameter relationships for four different snowflake types (dendrite, plate, needle, and graupel) were investigated in northeastern South Korea, and a new algorithm for classifying hydrometeors is proposed for distrometric measurements based on the new relationships. Falling ice crystals (approximately 40 000 particles) were measured with a two-dimensional video disdrometer (2DVD) during a winter experiment from 15 January to 9 April 2010. The fall velocity-diameter relationships were derived for the four types of snowflakes based on manual classification by experts using snow photos and 2DVD measurements: the coefficients (exponents) for different snowflake types were 0.82 (0.24) for dendrite, 0.74 (0.35) for plate, 1.03 (0.71) for needle, and 1.30 (0.94) for graupel, respectively. These new relationships established in the present study (PS) were compared with those from two previous studies. Hydrometeor types were classified with the derived fall velocity-diameter relationships, and the classification algorithm was evaluated using 3× 3 contingency tables for one rain-snow transition event and three snowfall events. The algorithm showed good performance for the transition event: the critical success indices (CSIs) were 0.89, 0.61 and 0.71 for snow, wet-snow and rain, respectively. For snow events, the algorithm performance for dendrite and plate (CSIs = 1.0 and 1.0, respectively) was better than for needle and graupel (CSIs = 0.67 and 0.50, respectively).

17 citations

Journal ArticleDOI
TL;DR: Polarimetric radar and 2D video disdrometer observations provide new insights into the precipitation microphysical processes and characteristics in the inner rainband of tropical cyclone (TC) Kajiki (2019) in the South China Sea for the first time.
Abstract: Polarimetric radar and 2D video disdrometer observations provide new insights into the precipitation microphysical processes and characteristics in the inner rainband of tropical cyclone (TC) Kajiki (2019) in the South China Sea for the first time. The precipitation of Kajiki is dominated by high concentrations and small (< 3 mm) raindrops, which contribute more than 98% to the total precipitation. The average mass-weighted mean diameter and logarithmic normalized intercept are 1.49 mm and 4.47, respectively, indicating a larger mean diameter and a lower concentration compared to the TCs making landfall in eastern China. The ice processes of the inner rainband are dramatically different among different stages. The riming process is dominant during the mature stage, while during the decay stage the aggregation process is dominant. The vertical profiles of the polarimetric radar variables together with ice and liquid water contents in the convective region indicate that the formation of precipitation is dominated by warm-rain processes. Large raindrops collect cloud droplets and other raindrops, causing reflectivity, differential reflectivity, and specific differential phase to increase with decreasing height. That is, accretion and coalescence play a critical role in the formation of heavy rainfall. The melting of different particles generated by the ice process has a great influence on the initial raindrop size distribution (DSD) to further affect the warm-rain processes. The DSD above heavy rain with the effect of graupel has a wider spectral width than the region without the effect of graupel.

17 citations

Journal ArticleDOI
TL;DR: In this article, the authors examined the performance of two collocated Joss-Waldvogel disdrometers (JWD) at Wallops Island, VA and two correlated profilers deployed at Ji-Parana, Brazil during Tropical Rainfall Measuring Mission (TRMM) Large-Scale Biosphere-Atmosphere Experiment.
Abstract: [1] Serial reflectivity measurements from paired instruments are examined during two field campaigns in order to examine the precision of the measurements. The instruments studied are two collocated Joss-Waldvogel disdrometers (JWD) at Wallops Island, VA and two collocated profilers deployed at Ji-Parana, Brazil during Tropical Rainfall Measuring Mission (TRMM) Large-Scale Biosphere-Atmosphere Experiment. Differencing the measured reflectivity from the instrument pairs eliminated most of the temporal and large-scale precipitation variability, reducing the error fluctuations to those of the instrument precision plus fluctuations due to precipitation variability over the small differences in sample volume and distances between the instruments. For both pairs of calibrated instruments we found that the observed time-series of one-minute dBZ differences were not autocorrelated and exhibited a Gaussian-like distribution. Consequently, the difference time-series could be meaningfully characterized by their standard statistics, including the rms difference or standard deviation, and the standard error about the mean. While the disdrometer pair exhibited an rms difference of 2.1 dBZ, a standard error about the mean of less than 0.1 dBZ for the 12-hour rain event was achieved. The profiler pair exhibited an rms difference of 0.4 dBZ, with a standard error of only 0.05 dBZ for the 90-minute stratiform rain event. Since it is currently difficult to routinely calibrate radars in an absolute sense to better than 1–3 dBZ, the precisions of a few tenths of a dBZ obtained here suggest the potential for substantially improving these calibrations, and open the door to examination of subtle sampling and stability effects.

16 citations

Journal ArticleDOI
TL;DR: In this paper, a decision tree algorithm was proposed to classify snowfall according to microphysical properties of single hydrometeors (e.g. shape and fall velocity) measured by means of a 2D video disdrometers.

16 citations


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