scholarly journals Measurements of Rainfall Velocity and Raindrop Size Distribution Using Coherent Doppler Lidar

2016 ◽  
Vol 33 (9) ◽  
pp. 1949-1966 ◽  
Author(s):  
Makoto Aoki ◽  
Hironori Iwai ◽  
Katsuhiro Nakagawa ◽  
Shoken Ishii ◽  
Kohei Mizutani
Keyword(s):  
Size Distribution ◽  
Wind Velocity ◽  
Vertical Wind ◽  
Doppler Spectrum ◽  
Doppler Lidar ◽  
Stratiform Rain ◽  
Raindrop Size Distribution ◽  
Raindrop Size ◽  
Rain Events ◽  
Vertical Wind Velocity

AbstractRainfall velocity, raindrop size distribution (DSD), and vertical wind velocity were simultaneously observed with 2.05- and 1.54-μm coherent Doppler lidars during convective and stratiform rain events. A retrieval method is based on identifying two separate spectra from the convolution of the aerosol and precipitation Doppler lidar spectra. The vertical wind velocity was retrieved from the aerosol spectrum peak and then the terminal rainfall velocity corrected by the vertical air motion from the precipitation spectrum peak was obtained. The DSD was derived from the precipitation spectrum using the relationship between the raindrop size and the terminal rainfall velocity. A comparison of the 1-min-averaged rainfall velocity from Doppler lidar measurements at a minimum range and that from a collocated ground-based optical disdrometer revealed high correlation coefficients of over 0.89 for both convective and stratiform rain events. The 1-min-averaged DSDs retrieved from the Doppler lidar spectrum using parametric and nonparametric methods are also in good agreement with those measured with the optical disdrometer with a correlation coefficient of over 0.80 for all rain events. To retrieve the DSD, the parametric method assumes a mathematical function for the DSD and the nonparametric method computes the direct deconvolution of the measured Doppler lidar spectrum without assuming a DSD function. It is confirmed that the Doppler lidar can retrieve the rainfall velocity and DSD during relatively heavy rain, whereas the ratio of valid data significantly decreases in light rain events because it is extremely difficult to separate the overlapping rain and aerosol peaks in the Doppler spectrum.

scholarly journals 4-D-VAR assimilation of disdrometer data and radar spectral reflectivities for raindrop size distribution and vertical wind retrievals

2016 ◽  
Vol 9 (7) ◽  
pp. 3145-3163 ◽  
Author(s):  
François Mercier ◽  
Aymeric Chazottes ◽  
Laurent Barthès ◽  
Cécile Mallet
Keyword(s):  
Data Assimilation ◽  
Size Distribution ◽  
Vertical Wind ◽  
Data Sets ◽  
Raindrop Size Distribution ◽  
Raindrop Size ◽  
Microphysical Processes ◽  
Rain Events ◽  
Wind Profiles ◽  
Vertical Winds

Abstract. This paper presents a novel framework for retrieving the vertical raindrop size distribution (DSD) and vertical wind profiles during light rain events. This is also intended as a tool to better characterize rainfall microphysical processes. It consists in coupling K band Doppler spectra and ground disdrometer measurements (raindrop fluxes) in a 2-D numerical model propagating the DSD from the clouds to the ground level. The coupling is done via a 4-D-VAR data assimilation algorithm. As a first step, in this paper, the dynamical model and the geometry of the problem are quite simple. They do not allow the complexity implied by all rain microphysical processes to be encompassed (evaporation, coalescence breakup and horizontal air motion are not taken into account). In the end, the model is limited to the fall of droplets under gravity, modulated by the effects of vertical winds. The framework is thus illustrated with light, stratiform rain events. We firstly use simulated data sets (data assimilation twin experiment) to show that the algorithm is able to retrieve the DSD profiles and vertical winds. It also demonstrates the ability of the algorithm to deal with the atmospheric turbulence (broadening of the Doppler spectra) and the instrumental noise. The method is then applied to a real case study which was conducted in the southwest of France during the autumn 2013. The data set collected during a long, quiet event (6 h duration, rain rate between 2 and 7 mm h−1) comes from an optical disdrometer and a 24 GHz vertically pointing Doppler radar. We show that the algorithm is able to reproduce the observations and retrieve realistic DSD and vertical wind profiles, when compared to what could be expected for such a rain event. A goal for this study is to apply it to extended data sets for a validation with independent data, which could not be done with our limited 2013 data. Other data sets would also help to parameterize more processes needed in the model (evaporation, coalescence/breakup) to apply the algorithm to convective rain and to evaluate the adequacy of the model's parameterization.

scholarly journals A Synergy Approach to Estimate Properties of Raindrop Size Distributions Using a Doppler Lidar and Cloud Radar

2010 ◽  
Vol 27 (6) ◽  
pp. 1095-1100 ◽  
Author(s):  
Katja Träumner ◽  
Jan Handwerker ◽  
Andreas Wieser ◽  
Jens Grenzhäuser
Keyword(s):  
Size Distribution ◽  
Analytical Description ◽  
Doppler Lidar ◽  
Fall Velocity ◽  
Rayleigh Approximation ◽  
Size Dependent ◽  
Cloud Radar ◽  
Raindrop Size Distribution ◽  
Remote Sensing Systems ◽  
Raindrop Size

Abstract Remote sensing systems like radars and lidars are frequently used in atmospheric measurement campaigns. Because of their different wavelengths, they operate in different scattering regimes. Combined use may result in new measurement options. Here, an approach to estimate raindrop size distribution using vertical velocities measured by a lidar–radar combination is introduced and tested using a 2-μm Doppler lidar and a 35.5-GHz cloud radar. The lidar spectra are evaluated to deduce air motion from the aerosol peak and the fall velocity of the raindrops from the rain peak. The latter is weighted by the area (D2) of the scatters. The fall velocity derived from radar measurements is weighted by D6 (Rayleigh approximation). Assuming a size-dependent fall velocity and an analytical description of the drop size distribution, its parameters are calculated from these data. Comparison of the raindrop size distribution from the lidar–radar combination with in situ measurements on the ground yields satisfying results.

scholarly journals Invariance of the Double-Moment Normalized Raindrop Size Distribution through 3D Spatial Displacement in Stratiform Rain

2017 ◽  
Vol 56 (6) ◽  
pp. 1663-1680 ◽  
Author(s):  
Timothy H. Raupach ◽  
Alexis Berne
Keyword(s):  
Size Distribution ◽  
Vertical Displacement ◽  
Statistical Moments ◽  
Stratiform Rain ◽  
Vertical Range ◽  
Raindrop Size Distribution ◽  
Double Moment ◽  
Raindrop Size ◽  
Polarimetric Weather Radar ◽  
Using Data

AbstractDouble-moment normalization of the drop size distribution (DSD) summarizes the DSD in a compact way, using two of its statistical moments and a “generic” double-moment normalized DSD function. Results are presented of an investigation into the invariance of the double-moment normalized DSD through horizontal and vertical displacement in space, using data from disdrometers, vertically pointing K-band Micro Rain Radars, and an X-band polarimetric weather radar. The invariance of the double-moment normalized DSD is tested over a vertical range of up to 1.8 km and a horizontal range of up to approximately 100 km. The results suggest that for practical use, with well-chosen input moments, the double-moment normalized DSD can be assumed invariant in space in stratiform rain. The choice of moments used to characterize the DSD affects the amount of DSD variability captured by the normalization. It is shown that in stratiform rain, it is possible to capture more than 85% of the variability in DSD moments zero to seven using the technique. Most DSD variability in stratiform rain can thus be explained through the variability of two of its statistical moments. The results suggest similar behavior exists in transition and convective rain, but the limited data samples available do not allow for robust conclusions for these rain types. The results have implications for practical uses of double-moment DSD normalization, including the study of DSD variability and microphysics, DSD-retrieval algorithms, and DSD models used in rainfall retrieval.

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