Disdrometer

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

Error Structure of Multiparameter Radar and Surface Measurements of Rainfall Part I : Differential Reflectivity

Abstract: Fluctuations in the radar measurements of ZDR are due to both signal power fluctuations and the cross-correlation between the horizontal and vertical polarized signals. In Part I of this study, these signals are simulated for an S-band radar for backscatter from rain media, which is characterized by a gamma model of the raindrop size distribution (RSD). The parameters N0, D0, m of the gamma RSD are then varied over the entire range found in natural rainfall. Thus, the radar simulations contain the effects of both statistical fluctuations and physical variations. We also simulate sampling of raindrops by disdrometer. The sampling errors are related to the Poisson statistics of the total number of drops in the fixed sample volume and to the statistics that govern the gamma distribution of drops as a function of size. We simulate disdrometer RSD samples over the entire range of N0, D0, m values found in rainfall, so that the effects of statistical fluctuations and physical variations are introduced....

Winter Precipitation Microphysics Characterized by Polarimetric Radar and Video Disdrometer Observations in Central Oklahoma

Abstract: The study of precipitation in different phases is important to understanding the physical processes that occur in storms, as well as to improving their representation in numerical weather prediction models. A 2D video disdrometer was deployed about 30 km from a polarimetric weather radar in Norman, Oklahoma, (KOUN) to observe winter precipitation events during the 2006/07 winter season. These events contained periods of rain, snow, and mixed-phase precipitation. Five-minute particle size distributions were generated from the disdrometer data and fitted to a gamma distribution; polarimetric radar variables were also calculated for comparison with KOUN data. It is found that snow density adjustment improves the comparison substantially, indicating the importance of accounting for the density variability in representing model microphysics.

Impact of cloud microphysical processes on hurricane intensity, part 2: Sensitivity experiments

Abstract: Seven different microphysical sensitivity experiments were designed with an objective to evaluate their respective impacts in modulating hurricane intensity forecasts using mesoscale model MM5. Microphysical processes such as melting of graupel, snow and cloud ice hydrometeors, suppression of evaporation of falling rain, the intercept parameter and fall speed of snow and graupel hydrometeors are modified in the existing NASA Goddard Space Flight Center (GSFC) microphysical parameterization scheme. We studied the impacts of cloud microphysical processes by means of track, intensity, precipitation, propagation speed, kinematic and thermodynamic vertical structural characteristics of hurricane inner core. These results suggest that the set of experiments where (a) melting of snow, graupel and cloud ice were suppressed (b) melting of snow and graupel were suppressed and (c) where the evaporation of rain water was suppressed all produced most intense storms. The major findings of this study are the interconversion processes such as melting and evaporation among hydrometeors and associated feedback mechanism are significantly modulate the intensity of the hurricane. In particular an experiment where the melting of graupel, snow and cloud ice hydrometeors was eliminated from the model parameterization scheme produced the most explosively intensified storm. In the experiment where rain water evaporation was eliminated from the model, it produced a stronger storm as compared to the control run but it was not as strong as the storms produced from absence of melting processes. The impact on intensity due to variations made in intercept parameters of the hydrometeors (i.e., snow and graupel) were not that evident compared to other experiments. The weakest storm was noted in the experiment where the fall speeds of the snow hydrometeors were increased two fold. This study has isolated some of the factors that contributed to a stronger hurricane and concludes with a motivation that the findings from this study will help in further improvement in the design of sophisticated explicit microphysical parameterization for the mesoscale non-hydrostatic model for realistic hurricane intensity forecasts.

Polarimetric Radar Relations for Quantification of Snow Based on Disdrometer Data

Abstract: Accurate measurements of snow amounts by radar are very difficult to achieve. The inherent uncertainty in radar snow estimates that are based on the radar reflectivity factor Z is caused by the variability of snow particle size distributions and snow particle density as well as the large diversity among snow growth habits. In this study, a novel method for snow quantification that is based on the joint use of radar reflectivity Z and specific differential phase KDP is introduced. An extensive dataset of 2D-video-disdrometer measurements of snow in central Oklahoma is used to derive polarimetric relations for liquid-equivalent snowfall rate S and ice water content IWC in the forms of bivariate power-law relations S = and along with similar relations for the intercept N0s and slope Λs of the exponential snow size distribution. The physical basis of these relations is explained. Their multipliers are sensitive to variations in the width of the canting angle distribution and to a lesser extent the par...