Disdrometer

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

Automated rain rate estimates using the Ka-band ARM zenith radar (KAZR)

Abstract: . The use of millimeter wavelength radars for probing precipitation has recently gained interest. However, estimation of precipitation variables is not straightforward due to strong signal attenuation, radar receiver saturation, antenna wet radome effects and natural microphysical variability. Here, an automated algorithm is developed for routinely retrieving rain rates from the profiling Ka-band (35-GHz) ARM (Atmospheric Radiation Measurement) zenith radars (KAZR). A 1-dimensional, simple, steady state microphysical model is used to estimate impacts of microphysical processes and attenuation on the profiles of radar observables at 35-GHz and thus provide criteria for identifying situations when attenuation or microphysical processes dominate KAZR observations. KAZR observations are also screened for signal saturation and wet radome effects. The algorithm is implemented in two steps: high rain rates are retrieved by using the amount of attenuation in rain layers, while low rain rates are retrieved from the reflectivity–rain rate (Ze–R) relation. Observations collected by the KAZR, rain gauge, disdrometer and scanning precipitating radars during the DYNAMO/AMIE field campaign at the Gan Island of the tropical Indian Ocean are used to validate the proposed approach. The differences in the rain accumulation from the proposed algorithm are quantified. The results indicate that the proposed algorithm has a potential for deriving continuous rain rate statistics in the tropics.

A Field Study of Pixel-Scale Variability of Raindrop Size Distribution in the Mid-Atlantic Region.

Abstract: The spatial variability of parameters of the raindrop size distribution and its derivatives is investigated through a field study where collocated Particle Size and Velocity (Parsivel2) and two-dimensional video disdrometers were operated at six sites at Wallops Flight Facility, Virginia, from December 2013 to March 2014. The three-parameter exponential function was employed to determine the spatial variability across the study domain where the maximum separation distance was 2.3 km. The nugget parameter of the exponential function was set to 0.99 and the correlation distance d0 and shape parameter s0 were retrieved by minimizing the root-mean-square error, after fitting it to the correlations of physical parameters. Fits were very good for almost all 15 physical parameters. The retrieved d0 and s0 were about 4.5 km and 1.1, respectively, for rain rate (RR) when all 12 disdrometers were reporting rainfall with a rain-rate threshold of 0.1 mm h−1 for 1-min averages. The d0 decreased noticeably when...

Sensitivity of Streamflow Simulations to Temporal Variability and Estimation of Z–R Relationships

Abstract: This study focuses on the sensitivity of streamflow simulations to temporal variations in radar reflectivity–rainfall (i.e., Z–R ) relationships. The physically based continuous-mode distributed hydrologic model—gridded surface subsurface hydrologic analysis—is used to predict runoff during three major rainfall-runoff periods observed in a 35 km2 experimental watershed in southern Louisiana. Z–R relationships are derived at a series of temporal scales ranging from a climatological scale, where interstorm Z–R variations are ignored, down to a subevent scale, where variations in rainfall type (convective versus stratiform) are taken into account. The analysis is first performed using Z and R data pairs derived directly from disdrometer drop size distribution measurements, and then repeated using WSR-88D radar reflectivity data. The degree of sensitivity in runoff simulations to temporal variations in Z–R relationships depends largely on the method used to derive the parameters of these relationships. Using ...

Evaluation of the Microphysical Assumptions within GPM-DPR Using Ground-Based Observations of Rain and Snow

Abstract: The Global Precipitation Measurement Dual-Frequency Precipitation Radar (GPM-DPR) provides an opportunity to investigate hydrometeor properties. Here, an evaluation of the microphysical framework used within the GPM-DPR retrieval was undertaken using ground-based disdrometer measurements in both rain and snow with an emphasis on the evaluation of snowfall retrieval. Disdrometer measurements of rain show support for the two separate prescribed relations within the GPM-DPR algorithm between the precipitation rate (R) and the mass weighted mean diameter ( D m ) with a mean absolute percent error ( M A P E ) on R of 29% and 47% and a mean bias percentage ( M B P ) of − 6% and − 20% for the stratiform and convective relation, respectively. Ground-based disdrometer measurements of snow show higher MAPE and MBP values in the retrieval of R, at 77% and − 52% , respectively, compared to the stratiform rain relation. An investigation using the disdrometer-measured fall velocity and mass in the calculation of R and D m illustrates that the variability found in hydrometeor mass causes a poor correlation between R and D m in snowfall. The results presented here suggest that R − D m retrieval is likely not optimal in snowfall, and other retrieval techniques for R should be explored.