Disdrometer

About: Disdrometer is a research topic. Over the lifetime, 930 publications have been published within this topic receiving 23092 citations.

Measurements of Size and Electrical Charges Carried by Precipitation Particles During RELAMPAGO Field Campaign

Abstract: The electrical charge carried by raindrops provides significant information about thunderstorm electrification mechanisms, since the charge acquired by hydrometeors is closely related to the microphysical processes that they undergo within clouds. Investigation of charges on raindrops was conducted during the Remote sensing of Electrification, Lightning, And Meso-scale/micro-scale Processes with Adaptive Ground Observations field campaign. A newly designed instrument was used to determine simultaneously the fall velocity and charge for precipitating particles. Hydrometeor size and charge were measured in Córdoba city, Argentina, during electrified storms. Temporal series of size-charge of single raindrops were recorded for two storms, which were also monitored with a Parsivel disdrometer and Lightning Mapping Array. The results show that the magnitude of the electric charges range between 1 and 50 pC and more than 90% of the charges are mainly carried by raindrops >1 mm, even though most of the raindrops are smaller than 1 mm. Furthermore, the measurement series show charged hydrometeors of both signs all the time. A correlation between the sizes and the charges carried by the raindrops was found in both storms.

Analyzing relationships between lightning and rain in order to improve estimation accuracy of rain

Abstract: The remote estimation of rainfall rate R is essential for the aviation industry, agriculture, and flood warning. Radar, the current means of R estimation, is not available in much of the world. In addition, this measurement involves a level of inaccuracy. Using lightning to detect rain is a relatively inexpensive alternative to radar systems and can be done from existing satellites. Previous research has revealed correlations between lightning and rain, suggesting either that it is possible to estimate R using lightning, or that it is possible to use it to correct for a portion of the radar inaccuracies. These correlations are not only between the amount of lightning and the amount of rain, but also between other parameters, including statistics describing raindrop size. Rain, lightning, and radar data were collected in Central Florida over a two month period in the summer of 2005. Rain data, including raindrop size statistics, were collected from a single point using a disdrometer. Lightning data were collected using the Los Alamos Sferic Array. Radar data were obtained from the WSR-88D radar network. Rain rate R and the raindrop size statistics were compared to lightning statistics to determine which rain/lightning parameter pairs were most correlated. The degree of correlation between rain and lightning parameters was evaluated using the correlation coefficient r. Different lightning types (Cloud-to-Ground, Intra-Cloud, Narrow-Bipolar-Event, Total) were considered, and various circular areas were used for lightning collection to optimize the strength of the correlations. Four models using lightning and/or radar for the estimation of R were developed and then compared for accuracy. The first model is based on the relationship between R and the radar reflectivity factor Z, as is the current practice. Two models using only lightning for the estimation of R were evaluated, and a final model used both radar and lightning data to estimate R. The performance of each model was evaluated using the RMS error. The correlations between rain and lightning parameters were generally weak (r < 0.5), although some pairs clearly produced stronger correlations than others. Results show that the strongest correlations are between lightning density (strokes/km/hr) and Λ, a parameter of the raindrop size distribution. This correlation was strongest for Intra-Cloud (IC) lightning measured on a 75 km

Dependence of Z-R Relations on the Rain Type Classification Scheme

Abstract: The TRMM Global Validation Program is giving us a unique opportunity to compare radar datasets from different sites since they are analyzed in a relatively uniform procedure. Monthly Ze-R relations for four different sites (i.e, Melbourne Florida, Houston Texas, Darwin Australia and Kwajalein Atoll) were derived. The relations were obtained using the Window Probability Matching Method (WPMM). This version of the PMM relies on matching unconditional probabilities of rain rates, R, and radar reflectivity, Ze, using rain gauge and radar data, respectively. This procedure was done separately for convective and stratiform rain type using the Steiner classification procedure. The radar and gauge data from all sites were quality controlled using the same algorithms, which include also an automatic procedure to filter unreliable rain gauge data upon comparison to radar data. An adjusted power law Z-R for each rain type was also derived by comparing the radar-gauge coincident pairs in order to adjust the total monthly rainfall to match the gauges. The obtained PMM based Ze-R relations are found to be curved lines in log-log space rather than any straight line power law. While the PMM based Ze-R curves were always distinctly different between the convective and stratiform rain, the power law based Z-R, in few cases, was found to be the same for both types. In general, a given reflectivity was matched to a much lower rain intensity in the convective rainfall as compared to that in stratiform rainfall. These findings are inherently contradictory to previous findings based on disdrometer data and suggest some precaution for using the latter Z-R relations on radar data when the partition of stratiform and convective rainfall amount is in concern. The inverse trends in the relations might be caused by effects such as partial beam fillings, the use of different classification schemes, as well as having a distinct difference in the Z-R relations between the initial convective and the trailing transition regions as suggested by recent findings.

A 3D drop-size distribution model based on the convolution of raindrops at terminal velocity

Abstract: Using a matrix of drop size distributions (DSDs), measured by a microscale array of disdrometers, a method of spatial and temporal DSD interpolation is presented. The goal of this interpolation technique is to estimate the DSD above the disdrometer array as a function of three spatial coordinates, time and drop diameter. This interpolation algorithm assumes simplified drop dynamics, based on cloud advection and terminal velocity of raindrops. Once a 3D DSD has been calculated, useful quantities such as radar reflectivity Z and rainfall rate R can be computed and compared with corresponding rain gauge and weather radar data.