Disdrometer

The drop size distribution (DSD) has awesome variety in various sorts of precipitation condition. The DSD can likewise decide diverse snapshot of rainfall. Image processing tool can help to analyse rain in the space. An enhanced mean filter is executed for de-noising of exceptionally tainted and edge protection of a picture. Picture division regularly used to recognize the frontal area from background. Biclustering calculations in view of vertical and horizontal arrays mean value an incentive to choose the limit at the nearby mean. Elective calculation is tried with same information; separation estimation is utilized to converge to pixels. It specifically approaches the feasibility of assessing the decency of each match and naturally gathering the nearer combine ought to be nearest in the feeling of mean separation. All means are rehashed until accomplishing two clusters. Results obtained from automatic thesholding of picture are demonstrating legitimacy of the method. Morphological operators are most helpful for depiction of the state of the objects.

s u m m a r y The aim of this study is to evaluate the potential use of a low-cost single polarization X-band weather radar, verified by a disdrometer and a dense rain gauge network, installed as a supporting tool for hydrological applications and for monitoring the urban area of Palermo (Italy). Moreover, this study focuses on studying the temporal variability of the Z–R relation for Mediterranean areas. The radar device is provided with an automatic operational ground-clutter filter developed by the producer. Attention has been paid to the development of blending procedures between radar measurements and other auxiliary instruments and to their suitability for both meteorological and hydrological applications. A general scheme enveloping these procedures and achieving the combination of data retrieved from the weather radar, the optical disdrometer, and the rain gauge network distributed within the monitored area has been designed. The first step of the procedure consists in the calibration of the radar equation by comparing the match between the radar raw data and the disdrometer reflectivity. The second step is the calibration of the Z–R relationship based on the retrieval of parameters that optimize the transformation of dis-drometer reflectivity into rainfall intensity, starting from the disdrometer rainfall intensity measurements. The Z–R calibration has been applied to the disdrometer measurements retrieved during a 1 year observation period, after a preliminary segmentation into separated rainfall events. This analysis allows for the characterization of the variability of the Z–R relationship from event to event, deriving some considerations about its predictability as well. Results obtained from this analysis provide a geographical specific record, for the Mediterranean area, for the study of the spatial variability of the Z–R relationship. Finally, the set of operational procedures also includes a correction procedure of radar estimates based on rain gauge data. Each application has been evaluated with reference to its suitability in supporting proper monitoring usage, with emphasis on the readiness of observations, and hydrological modelling, where the robust-ness of the quantitative estimates is focused.

Laser disdrometers measure the particle size distribution (PSD) of hydrometeors through a small cross-sectional (tens of square centimeters) surface. Such a limited area induces a sampling effect in the estimates of the PSD, which translates to error in the reflectivity–rain-rate (Z–R) relationship used for ground radar estimates of rainfall, estimates of kinetic energy of precipitation, and any other hydrometeorological application relying on particle size information. Here, the results of a dedicated experiment to estimate the extent of the effect of limited area sampling of rainfall are presented. Using 14 Parsivel, version 1 (Parsivel-1), disdrometers placed within 6 m 2 , it was found that the combined area of at least seven disdrometers is required for the estimates to start converging to a stable value. The results can be used to quantify the degree of over-/underestimation of precipitation parameters for a single instrument due to the limited collecting area effect. It has been found that a single disdrometer may underestimate instantaneous rain rate by 70%.

There have been debates and differences of opinion over the validity of using drop size distribution (DSD) models to characterize precipitation microphysics and to retrieve DSD parameters from multiparameter radar measurements. In this paper, simulated and observed rain DSDs are used to evaluate moment estimators. Seven estimators for gamma DSD parameters are evaluated in terms of the biases and fractional errors of five integral parameters: radar reflectivity (ZH), differential reflectivity (ZDR), rainfall rate (R), mean volume diameter (Dm), and total number concentration (NT). It is shown that middle-moment estimators such as M234 (using the second-third-fourth moments) produce smaller errors than lower- and higher-moment estimators if the DSD follows the gamma distribution. However, if there are model errors, the performance of M234 degrades. Even though the DSD parameters can be biased in moment estimators, integral parameters are usually not. Maximum likelihood (ML) and L-mome...

Abstract An introduction to the use of real time data acquisition and analysis in meteorological applications is given, with examples of areas in which such techniques are becoming important. A detailed description is given of a readout system being constructed for a disdrometer (raindrop measuring gauge) using a VLSI neural network chip. Tests on simulated data give promising results down to droplet diameters of 0.05 mm, about a factor of 10 lower than what has been possible with previous methods

An introduction to the use of real time data acquisition and analysis in meteorological applications is given, with examples of areas in which such techniques are becoming important. A detailed description is given of a readout system being constructed for a disdrometer (raindrop measuring gauge) using a VLSI neural network chip. Tests on simulated data give promising results down to droplet diameters of 0.05 mm, about a factor of 10 lower than what has been possible with previous methods