Disdrometer

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

Vertical air motion estimates from the disdrometer flux conservation model and related experimental observations

Abstract: The use of meteorological radar reflectivity Z to estimate rainfall rate R is approached using a different perspective from the classical Z-R relation. Simultaneous rain measurements from different sensors are combined to construct a model that estimates the vertical air velocity by minimizing the error in reflectivity between the different sensors. This model is based on the fact that rain rate and reflectivity are both dependent on the integrals of rain drop size distribution (DSD) but only R depends on vertical air velocity. This study attempts to validate the vertical air velocity estimates and quantify their affects on the rainfall rate estimation. Disdrometer Flux Conservation Model (DFC) uses measurements from disdrometers and other sensors such as vertically pointing radar profilers and scanning radars. Disdrometers measure a drop size flux (Phi) (D), defined as the number of drops passing a horizontal surface per unit time, per unit area, per drop size. The flux is equal to the product of the drop size distribution near the ground NG(D) and drop velocity near the ground vG(D). The drop velocity is the difference between the droplet terminal velocity and the vertical component of the wind velocity, which varies with altitude. The estimates derived from the DFC model using two pair wise selected sensors are used to study the change of reflectivity and vertical air velocity with altitude. Sensitivity tests for the DFC model are also discussed and these outcomes are validated by comparison with independent profiler vertical velocity observations.© (2002) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Retrieval of lower-order moments of the drop size distribution using CSU-CHILL X-band polarimetric radar: a case study

Abstract: . The lower order moments of the drop size distribution (DSD) have generally been considered as difficult to retrieve accurately from polarimetric radar data because these are related to higher order moments. For example, the 4.5th moment is associated with specific differential phase, 6th moment with reflectivity and ratio of high order moments with differential reflectivity. Thus, conventionally, the emphasis has been to estimate rain rate (3.67th moment) or parameters of the exponential or gamma distribution. Many double-moment bulk microphysical schemes predict the total number concentration (the 0th moment or M0) and the mixing ratio (or equivalently, the 3rd moment M3). Thus, it is difficult to compare the model outputs directly with polarimetric radar observations or, given the model outputs, to forward model the radar observables. This article describes the use of double-moment normalization of DSDs and the resulting stable intrinsic shape that can be fitted to the generalized gamma (G-G) distribution. The two reference moments are M3 and M6 which are shown to be retrievable using the X-band radar reflectivity, differential reflectivity and specific attenuation (from the iterative ZPHI method). Along with the climatological shape parameters of the G-G fit to the scaled/normalized DSDs, the lower order moments are then retrieved more accurately than possible hitherto. The importance of measuring the complete DSD from 0.1 mm onwards is emphasized using, in our case, an optical array probe with 50 µm resolution collocated with a two-dimensional video disdrometer with 170 µm resolution. This avoids small drop truncation and hence the accurate calculation of lower order moments. A case study of a complex multi-cell storm which traversed an instrumented site near the CSU-CHILL radar is described for which the moments were retrieved and compared with directly computed moments from the complete spectrum measurements using the aforementioned two disdrometers. Our detailed validation analysis of the radar-retrieved moments showed relative bias of the moments M0 through M2 was 0.9. Both radar measurement and parameterization errors were estimated rigorously. We show that the temporal variation of the radar-retrieved characteristic diameter with M0 resulted in coherent time tracks that can potentially lead to studies of precipitation evolution that have not been possible so far.

Acoustic Radar Studies of Rain Microphysics

Abstract: Raindrop size distributions are obtained from the Doppler frequency spectrum of an acoustic radar. Number concentrations of 12 drop diameters with a minimum diameter 0.14 cm are obtained and averaged over 3–15 min at 20-m range gates from 20 to 220 m. The last three range gates are used to estimate rain intensity–dependent background noise, which is dynamically subtracted from the signals. Multifrequency sounding is also used. Intercomparisons with the vertical rain intensity profile from an X-band radar and with drop size distributions from an impact disdrometer show general agreement between instruments and demonstrate the usefulness of the acoustic profiler in giving vertical continuity below the range of electromagnetic radars. Temporal variations in raindrop size distributions are found to have an essentially flat spectrum for periodicities shorter than 12 min, although the step response to a sudden change in rainfall rate is a function of drop size. Principal component analysis applied to a...

A new algorithm for classification of tropical convective precipitating clouds over north- eastern region of india

Abstract: The North East (NE) region of India is prone to severe thunderstorm during pre monsoon period. These premonsoon precipitations are measured at Guwahati (26 o 17’ N, 91 o 77’ E) using a laser based particle size and velocity (PARSIVEL) disdrometer from 15 April to 31 May 2010 under a national field campaign named Severe Thunderstorm Observational and Regional Modeling (STORM). For the improved understanding of these premonsoon thunderstorm a new cloud classification hybrid algorithm is developed based on thermodynamics and microphysical characterstics of precipitation. This algorithm can classify the premonsoon precipitaitng clouds into thuderstrom (TS), non- thuderstrom (NTS) and futher into convective & stratiform cloud fractions based on thermodynamic indices and rain integral parameters. The observation results showed that raindrops of Small and mid (large) size are having same concentration in convective (stratiform) regions of both TS and NTS precipitations. There is a large spread in the mean diameter (Dm) and total concentration (NT) at higher rainrate of TS than NTS. The coefficient (A) of the radar reflectivity and rainrate relation (Z-R) is found to be smaller for TS than NTS. There is a significant difference in Raindrop concentration in stratiform, convective regions of TS and NTS precipitation.

Evaluation of modelled summertime convective storms using polarimetric radar observations

Abstract: Abstract. Ensemble simulations with the Terrestrial Systems Modelling Platform (TSMP) covering northwestern Germany are evaluated for three summertime convective storms using polarimetric X-band radar measurements. Using a forward operator, the simulated microphysical processes have been evaluated in radar observation space. Observed differential reflectivity (ZDR) columns, which are proxies for updrafts, and multi-variate fingerprints for size sorting and aggregation processes are captured by the model, but co-located specific differential phase (KDP) columns in observations are not reproduced in the simulations. Also, the simulated ZDR columns, generated by only small-sized supercooled drops, show smaller absolute ZDR values and a reduced width compared to their observational counterparts, which points to deficiencies in the cloud microphysics scheme as well as the forward operator, which does not have explicit information of water content of ice hydrometeors. Above the melting layer, the simulated polarimetric variables also show weak variability, which can be at least partly explained by the reduced particle diversity in the model and the inability of the T-matrix method to reproduce the polarimetric signatures of snow and graupel; i.e. current forward operators need to be further developed to fully exploit radar data for model evaluation and improvement. Below the melting level, the model captures the observed increase in reflectivity, ZDR and specific differential phase (KDP) towards the ground. The contoured frequency altitude diagrams (CFADs) of the synthetic and observed polarimetric variables were also used to evaluate the model microphysical processes statistically. In general, CFADs of the cross-correlation coefficient (ρhv) were poorly simulated. CFADs of ZDR and KDP were similar but the model exhibits a relatively narrow distribution above the melting layer for both, and a bimodal distribution for ZDR below the melting layer, indicating either differences in the mechanism of precipitation formation or errors in forward operator which uses a functional form of drop size distribution. In general, the model was found to underestimate the convective area fraction, high reflectivities, and the width/magnitude of ZDR columns, all leading to an underestimation of the frequency distribution for high precipitation values.