Disdrometer Measurements Research Articles

Machine Learning Approach to Classify Rain Type Based on Thies Disdrometers and Cloud Observations

Rain microstructure parameters assessed by disdrometers are commonly used to classify rain into convective and stratiform. However, different types of disdrometer result in different values for these parameters. This in turn potentially deteriorates the quality of rain type classifications. Thies disdrometer measurements at two sites in Bavaria in southern Germany were combined with cloud observations to construct a set of clear convective and stratiform intervals. This reference dataset was used to study the performance of classification methods from the literature based on the rain microstructure. We also explored the possibility of improving the performance of these methods by tuning the decision boundary. We further identified highly discriminant rain microstructure parameters and used these parameters in five machine-learning classification models. Our results confirm the potential of achieving high classification performance by applying the concepts of machine learning compared to already available methods. Machine-learning classification methods provide a concrete and flexible procedure that is applicable regardless of the geographical location or the device. The suggested procedure for classifying rain types is recommended prior to studying rain microstructure variability or any attempts at improving radar estimations of rain intensity.

In situ terminal settling velocity measurements at Stromboli volcano: Input from physical characterization of ash

Ash particle terminal settling velocity is an important parameter to measure in order to constrain the internal dynamics and dispersion of volcanic ash plumes and clouds that emplace ash fall deposits from which source eruption conditions are often inferred. Whereas the total Particle Size Distribution (PSD) is the main parameter to constrain terminal velocities, many studies have empirically highlighted the need to consider shape descriptors such as the sphericity to refine ash settling velocity as a function of size. During radar remote sensing measurements of weak volcanic plumes erupted from Stromboli volcano in 2015, an optical disdrometer was used to measure the size and settling velocities of falling ash particles over time, while six ash fallout samples were collected at different distances from the vent. We focus on the implications of the physical parameters of ash for settling velocity measurements and modeling. Two-dimensional sizes and shapes are automatically characterized for a large number of ash particles using an optical morpho-grainsizer MORPHOLOGI G3. Manually sieved ash samples show sorted, relatively coarse PSDs spanning a few microns to 2000 μm with modal values between 180 and 355 μm. Although negligible in mass, a population of fine particles below 100 μm form a distinct PSD with a mode around 5–20 μm. All size distributions are offset compared to the indicated sieve limits. Accordingly, we use the diagonal of the upper mesh sizes as the upper sieve limit. Morphologically, particles show decreasing average form factors with increasing circle-equivalent diameter, the latter being equal to 0.92 times the average size between the length and intermediate axes of ash particles. Average particle densities measured by water pycnometry are 2755 ± 50 kg m−3 and increase slightly from 2645 to 2811 kg m−3 with decreasing particle size. The measured settling velocities under laboratory conditions with no wind, <3.6 m s−1, are in agreement with the field velocities expected for particles with sizes <460 μm. The Ganser (1993) empirical model for particle settling velocity is the most consistent with our disdrometer settling velocity results. Converting disdrometer detected size into circle equivalent diameter shows similar PSDs between disdrometer measurements and G3 analyses. This validates volcanological applications of the disdrometer to monitor volcanic ash sizes and settling velocities in real-time with ideal field conditions. We discuss ideal conditions and the measurement limitations. In addition to providing sedimentation rates in-situ, calculated reflectivities can be compared with radar reflectivity measurements inside ash plumes to infer first-order ash plume concentrations. Detailed PSDs and shape parameters may be used to further refine radar-derived mass loading retrievals of the ash plumes.

A Moment-Based Polarimetric Radar Forward Operator for Rain Microphysics

AbstractThere is growing interest in combining microphysical models and polarimetric radar observations to improve our understanding of storms and precipitation. Mapping model-predicted variables into the radar observational space necessitates a forward operator, which requires assumptions that introduce uncertainties into model–observation comparisons. These include uncertainties arising from the microphysics scheme a priori assumptions of a fixed drop size distribution (DSD) functional form, whereas natural DSDs display far greater variability. To address this concern, this study presents a moment-based polarimetric radar forward operator with no fundamental restrictions on the DSD form by linking radar observables to integrated DSD moments. The forward operator is built upon a dataset of &gt;200 million realistic DSDs from one-dimensional bin microphysical rain-shaft simulations, and surface disdrometer measurements from around the world. This allows for a robust statistical assessment of forward operator uncertainty and quantification of the relationship between polarimetric radar observables and DSD moments. Comparison of “truth” and forward-simulated vertical profiles of the polarimetric radar variables are shown for bin simulations using a variety of moment combinations. Higher-order moments (especially those optimized for use with the polarimetric radar variables: the sixth and ninth) perform better than the lower-order moments (zeroth and third) typically predicted by many bulk microphysics schemes.

Comparison of Raindrop Size Distribution Characteristics Across the Southeast Asia Region

Satellite communication requires reliable estimates of the channel characteristics, especially with the future use of higher frequencies. Regardless of the rain rate, the shape of rain drop size distribution (DSD) start to considerably effect the specific attenuation. In this study DSDs are studied using ground-based two-dimensional video disdrometer measurements taken from Johor, Malaysia as well as two similar datasets from Gan and Manus, two equatorial islands. Integral rain parameters are studied to explain DSD variations across the Southeast Asia region. Slightly higher raindrop concentrations and larger diameters were observed in Johor than in Gan or Manus, which is due to Johor being affected by not only oceanic rain- fall but land rainfall as well. The measured rainfall was classified into convective and stratiform precipitation types; the results showed that the Southeast Asia region is dominated by convective rain in terms of accumulated rainfall amount, but stratiform rain occurred more frequently. Further, seasonal variations observed in Johor were insignificant and the DSD variation was mostly due to changes in percentage occurrence of the precipitation types for each monsoon season.

Influence of Disdrometer Type on Weather Radar Algorithms from Measured DSD: Application to Italian Climatology

Relations for retrieving precipitation and attenuation information from radar measurements play a key role in radar meteorology. The uncertainty in such relations highly affects the precipitation and attenuation estimates. Weather radar algorithms are often derived by applying regression methods to precipitation measurements and radar observables simulated from datasets of drop size distributions (DSD) using microphysical and electromagnetic assumptions. DSD datasets can be derived from theoretical considerations or obtained from experimental measurements collected throughout the years by disdrometers. Although the relations obtained from experimental disdrometer datasets can be generally considered more representative of a specific climatology, the measuring errors, which depend on the specific type of disdrometer used, introduce an element of uncertainty to the final retrieval algorithms. Eventually, data quality checks and filtering procedures applied to disdrometer measurements play an important role. In this study, we pursue two main goals: (i) evaluate two different techniques for establishing weather radar algorithms from measured DSD, and (ii) investigate to what extent dual-polarization radar algorithms derived from experimental DSD datasets are influenced by the different error structures introduced by the various disdrometer types (namely 2D video disdrometer, first and second generation of OTT Parsivel disdrometer, and Thies Clima disdrometer) used to collect the data. Furthermore, weather radar algorithms optimized for Italian climatology are presented and discussed.

Vertically Resolved Precipitation Intensity Retrieved through a Synergy between the Ground-Based NASA MPLNET Lidar Network Measurements, Surface Disdrometer Datasets and an Analytical Model Solution

In this paper, we illustrate a new, simple and complementary ground-based methodology to retrieve the vertically resolved atmospheric precipitation intensity through a synergy between measurements from the National Aeronautics and Space Administration (NASA) Micropulse Lidar network (MPLNET), an analytical model solution and ground-based disdrometer measurements. The presented results are obtained at two mid-latitude MPLNET permanent observational sites, located respectively at NASA Goddard Space Flight Center, USA, and at the Universitat Politècnica de Catalunya, Barcelona, Spain. The methodology is suitable to be applied to existing and/or future lidar/ceilometer networks with the main objective of either providing near real-time (3 h latency) rainfall intensity measurements and/or to validate satellite missions, especially for critical light precipitation (&lt;3 mm h−1).

Objective Characterization of Rain Microphysics: Validating a Scheme Suitable for Weather and Climate Models

Abstract Improving the atmospheric component of hydrological models is beneficial for applications such as water resources assessment and hydropower operations. Within this goal, precise characterization of rain microphysics is key for climate and weather modeling, and thus for hydrometeorological applications. Such characterization can be achieved by analyzing the evolution in time of the particle size distribution (PSD) of hydrometeors, which can be measured at ground using disdrometers for validation. The estimation, however, depends on the choice of the PSD form (the shape) and on the parameters to define the exact shape. In the case of modeling rain microphysics, two approaches compete: the use of the number concentration of drops decoupled from the shape of the distribution (the [NT, E(D), E(D2)] and the {NT, E(D), E[log(D)]} models), and the (N0, Λ, μ) model that embeds in N0 both the shape of the distribution and the number concentration of drops. Here we use a comprehensive dataset of disdrometer measurements to show that the NT-based approaches allow a more precise characterization of the drop size distribution (DSD) and also a physically based modeling of the microphysical processes of rain since NT is analytically independent of the shape of the DSD {parameterized by E(D), and E(D2) or E[log(D)]}. The implication is that numerical models would benefit from decoupling the number of drops from the shape of distribution in their modules of precipitation microphysics in order to improve outputs that eventually feed hydrological models.

Two months of disdrometer data in the Paris area

Abstract. The Hydrology, Meteorology, and Complexity laboratory of École des Ponts ParisTech (hmco.enpc.fr) has made a data set of optical disdrometer measurements available that come from a campaign involving three collocated devices from two different manufacturers, relying on different underlying technologies (one Campbell Scientific PWS100 and two OTT Parsivel2 instruments). The campaign took place in January–February 2016 in the Paris area (France). Disdrometers provide access to information on the size and velocity of drops falling through the sampling area of the devices of roughly a few tens of cm2. It enables the drop size distribution to be estimated and rainfall microphysics, kinetic energy, or radar quantities, for example, to be studied further. Raw data, i.e. basically a matrix containing a number of drops according to classes of size and velocity, along with more aggregated ones, such as the rain rate or drop size distribution with filtering, are available. Link to the data set: https://zenodo.org/record/1240168 (DOI: https://doi.org/10.5281/zenodo.1240168).

Rain attenuation studies from radiometric and rain DSD measurements at two tropical locations

Efficient use of satellite communication in tropical regions demands proper characterization of rain attenuation, particularly, in view of the available popular propagation models which are mostly based on temperate climatic data. Thus rain attenuations at frequencies 22.234, 23.834 and 31.4/30 GHz over two tropical locations Kolkata (22.57°N, 88.36°E, India) and Belem (1.45°S, 48.49° W, Brazil), have been estimated for the year 2010 and 2011, respectively. The estimation has been done utilizing ground-based disdrometer observations and radiometric measurements over Earth-space path. The results show that rain attenuation estimations from radiometric data are reliable only at low rain rates (<30 mm/h). However, the rain attenuation estimations from disdrometer measurements show good agreement with the ITU-R model, even at high rain rates (upto100 mm/h). Despite having significant variability in terms of drop size distribution (DSD), the attenuation values calculated from DSD data (disdrometer measurements) at Kolkata and Belem differ a little for the rain rates below 30 mm/h. However, the attenuation values, obtained from radiometric measurements at the two places, show significant deviations ranging from 0.54 dB to 3.2 dB up to a rain rate of 30 mm/h, on account of different rain heights, mean atmospheric temperatures and climatology of the two locations.

Polarimetric Radar Relations for Quantification of Snow Based on Disdrometer Data

AbstractAccurate 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 particles’ aspect ratios and densities, whereas the exponents are practically invariant. This novel approach is tested against the S(Z) relation using snow disdrometer measurements in three geographical regions (Oklahoma, Colorado, and Canada). Significant improvement in snow estimates relative to the traditional Z-based methods is demonstrated.

Winter Precipitation Liquid–Ice Phase Transitions Revealed with Polarimetric Radar and 2DVD Observations in Central Oklahoma

AbstractObservations and analysis of an ice–liquid phase precipitation event, collected with an S-band polarimetric KOUN radar and a two-dimensional video disdrometer (2DVD) in central Oklahoma on 20 January 2007, are presented. Using the disdrometer measurements, precipitation is classified either as ice pellets or rain/freezing rain. The disdrometer observations showed fast-falling and slow-falling particles of similar size. The vast majority (&gt;99%) were fast falling with observed velocities close to those of raindrops with similar sizes. In contrast to the smaller particles (&lt;1 mm in diameter), bigger ice pellets (&gt;1.5 mm) were relatively easy to distinguish because their shapes differ from the raindrops. The ice pellets were challenging to detect by looking at conventional polarimetric radar data because of the localized and patchy nature of the ice phase and their occurrence close to the ground. Previously published findings referred to cases in which ice pellet areas were centered on the radar location and showed a ringlike structure of enhanced differential reflectivity ZDR and reduced copolar correlation coefficient ρhv and horizontal reflectivity ZH in PPI images. In this study, a new, unconventional way of looking at polarimetric radar data is introduced: slanted vertical profiles (SVPs) at low (0°–1°) radar elevations. From the analysis of the localized and patchy structures using SVPs, the polarimetric refreezing signature, reflected in local enhancement in ZDR and reduction in ZH and ρhv, became much more evident. Model simulations of sequential drop freezing using Marshall–Palmer DSDs along with the observations suggest that preferential freezing of small drops may be responsible for the refreezing polarimetric signature, as suggested in previous studies.

Scattering Calculations at C-Band for Asymmetric Raindrops Reconstructed from 2D Video Disdrometer Measurements

AbstractThe distribution of raindrop shapes is well known to be important in deriving retrieval algorithms for drop size distribution parameters (such as the mass-weighted mean diameter) and rain rate, as well as for attenuation correction using the differential propagation phase constraint. While past work has shown that in the vast majority of rain events the most “probable” shapes conform to those arising primarily from the axisymmetric (2,0) oscillation mode, a more recent event analysis has shown that drop collisions can give rise to mixed-mode oscillations and that for high collision rate scenarios, a significant percentage of drops can become “asymmetric” at any given instant.As a follow-up to such studies, this study involved performing scattering calculations for 3D-reconstructed shapes of asymmetric drops using the shape measurements from a 2D video disdrometer (2DVD) during the above-mentioned rain event. A recently developed technique is applied to facilitate the 3D reconstruction from the 2DVD camera data for these asymmetric drops. The reconstruction requires a specific technique to correct for the drop image distortions due to horizontal velocities. Scattering calculations for the reconstructed asymmetric drops have been performed using a higher-order method of moments solution to the electric and magnetic field surface integral equations. Results show that the C-band scattering amplitudes of asymmetric drops are markedly different from those of oblate spheroids. The intention for future studies is to automate the entire procedure so that more realistic simulations can be performed using the 2DVD-based data, particularly for cases where collision-induced drop oscillations give rise to considerable numbers of asymmetric drops.

An Improved Dual-Polarization Radar Rainfall Algorithm (DROPS2.0): Application in NASA IFloodS Field Campaign

Abstract Compared to traditional single-polarization radar, dual-polarization radar has a number of advantages for quantitative precipitation estimation because more information about the drop size distribution and hydrometeor type can be gleaned. In this paper, an improved dual-polarization rainfall methodology is proposed, which is driven by a region-based hydrometeor classification mechanism. The objective of this study is to incorporate the spatial coherence and self-aggregation of dual-polarization observables in hydrometeor classification and to produce robust rainfall estimates for operational applications. The S-band dual-polarization data collected from the NASA Polarimetric (NPOL) radar during the GPM Iowa Flood Studies (IFloodS) ground validation field campaign are used to demonstrate and evaluate the proposed rainfall algorithm. Results show that the improved rainfall method provides better performance than a few single- and dual-polarization algorithms in previous studies. This paper also investigates the impact of radar beam broadening on various rainfall algorithms. It is found that the radar-based rainfall products are less correlated with ground disdrometer measurements as the distance from the radar increases.

Monitoring the Absolute Calibration of a Polarimetric Weather Radar

AbstractThe absolute calibration of a dual-polarization radar of the German Weather Service is continuously monitored using the operational birdbath scan and collocated disdrometer measurements at the Hohenpeissenberg observatory. The goal is to measure the radar reflectivity constant Z better than ±1 dB. The assumption is that a disdrometer measurement close to the surface can be related to the radar measurement at the first far-field range bin. This is verified using a Micro Rain Radar (MRR). The MRR data fill the gap between the measurement near the surface and the far-field range bin at 650 m. Using data from the first half of the warm season in 2014, a bias in radar calibration of 1.8 dB is found. Data from only stratiform precipitation events are considered. After adjusting the radar calibration and using an independent data sample, very good agreement is found between the radar, the MRR, and the disdrometer with a bias in smaller than 1 dB. The bias in is not captured with the classic one-point calibration, which is performed twice a day using a built-in test signal generator. This is attributed to the fact that the characterization of the transmit and receive path is not accurate enough. Solar interferences during the operational scanning are used to characterize the receiver. There, the bias found is small, about 0.2 dB, so that bias based on the comparison of the radar with external sensors is attributed to the transmit path. The representativeness of the disdrometer measurements are assessed using two additional disdrometers located within 200-m distance.

Characteristic Raindrop Size Retrievals from Measurements of Differences in Vertical Doppler Velocities at Ka- and W-Band Radar Frequencies

AbstractA Ka-band (~35 GHz) and W-band (~94 GHz) radar approach to retrieve profiles of characteristic raindrop sizes, such as mean mass-weighted drop diameters Dm, from measurements of the difference in the mean vertical Doppler velocities (DDV) is analyzed. This retrieval approach is insensitive to radar calibration errors, vertical air motions, and attenuation effects. The Dm–DDV relations are derived using long-term measurements of drop size distributions (DSDs) from different observational sites and do not assume a functional DSD shape. Unambiguous retrievals using this approach are shown to be available in the Dm range of approximately 0.5–2 mm, with average uncertainties of around 21%. Potential retrieval ambiguities occurring when larger drop populations exist can be avoided by using a Ka-band vertical Doppler velocity threshold. The performance of the retrievals is illustrated using a long predominantly stratiform rain event observed at the Atmospheric Radiation Measurement (ARM) Southern Great Plains site. An intercomparison of DDV-based estimates of characteristic raindrop sizes with independent estimates available from ground-based disdrometer measurements reveal good agreement, with a correlation coefficient of 0.88, and mean differences between radar and disdrometer-based Dm of approximately 14% for the entire range of unambiguous retrievals. The Ka–W-band DDV method to retrieve mean mass-weighted drop sizes is applicable to measurements from new dual-wavelength ARM cloud radars that are being deployed at a variety of observational facilities. An illustration for the retrievals at the Oliktok Point ARM facility is also given.

Assessment of DSDs of GPM‐DPR with ground‐based disdrometer at seasonal scale over Gadanki, India

AbstractCharacteristics of raindrop size distribution (DSD) obtained by Global Precipitation Measurement (GPM) mission dual‐frequency precipitation radar (DPR) are assessed over Gadanki region during southwest (SW) and northeast (NE) monsoon seasons utilizing 2 years (2014–2015) of DSD measurements by an impact‐type disdrometer. The mass weighted mean diameter (Dm in mm) and normalized DSD scaling parameter for concentration (Nw in mm−1 m−3) show pronounced seasonal differences at low to medium rain rates in the disdrometer data, in accordance with the previous studies, but not in the GPM‐DPR data. Similar features are observed every year in disdrometer measurements and over different spatial domains in GPM‐DPR measurements, indicating that sampling mismatch errors are insignificant. The reasons for the absence of seasonal differences in DSDs derived from GPM‐DPR are examined by simulating the reflectivities at Ku‐ and Ka‐bands, utilizing the disdrometer measurements and T‐matrix scattering indices. Results suggest that the Dm and Nw retrieved from single‐frequency and dual‐frequency algorithms utilizing the disdrometer data also show seasonal differences in accordance with the observations with under and overestimation of Dm and Nw, respectively. When compared with the disdrometer, the Dm values retrieved from the GPM‐DPR (official products) are severely underestimated at high rain rates (R &gt; 8 mm h−1) during the SW monsoon season. On the other hand, during low rain rates (R &lt; 8 mm h−1), a slight underestimation (overestimation) of Dm is seen during the SW (NE) monsoon. The mean Nw values retrieved from GPM‐DPR agree poorly with disdrometer data during both the monsoon seasons.

Investigation of liquid cloud microphysical properties of deep convective systems: 1. Parameterization raindrop size distribution and its application for stratiform rain estimation

AbstractTo investigate liquid‐phase (T &gt; 3°C) cloud and precipitation microphysical properties within Deep Convective Systems (DCSs), eight DCS cases sampled by the University of North Dakota Citation II research aircraft during Midlatitude Continental Convective Clouds Experiment were selected. A full spectrum of raindrop size distribution (DSD) was constructed from 120 µm to 4000 µm through a combination of two‐dimensional cloud probe (120 to 900 µm) and High Volume Precipitation Spectrometer (900 to 4000 µm) data sets. A total of 1126 five second DSDs have been used to fit to Gamma and Exponential functions within the stratiform rain (SR) regions of DCSs. The Gamma shape μΓ and slope λΓ parameters are then compared with those derived from surface disdrometer measurements. The similar μΓ‐λΓ relationships but different μΓ and λΓ value ranges from two independent platforms at different elevations may represent the real nature of DSD shape information in clouds and at the surface. To apply the exponentially fitted DSD parameters to precipitation estimation using Next Generation Weather Radar (NEXRAD) radar reflectivity factor Ze, the terms N0E and λE have been parameterized as a function of Ze using an empirical N0E‐λE relationship. The averaged SR rain rate retrieved from this study is almost identical to the surface measurements, while the NEXRAD Q2 precipitation is twice as large. The comparisons indicate that the new DSD parameterization scheme is robust, while the Q2 SR precipitation estimation based on Marshall‐Palmer Z‐R relationship, where a constant DSD intercept parameter (N0E) was assumed, needs to be improved for heavy precipitation cases.

On the seasonal variability of raindrop size distribution and associated variations in reflectivity – Rainrate relations at Tirupati, a tropical station

Three years of continuous OTT Parsivel disdrometer measurements made at Tirupati (13.6°N, 79.4°E), a tropical station near the foothills of Nallamala mountains, have been used to examine the climatological seasonal differences in bulk rainfall parameters, gamma parameters, raindrop size distributions (DSDs) and reflectivity – rainfall (Z–R) relationships. These relations are derived for both stratiform and convective rain during southwest and northeast monsoon (SWM and NEM) seasons, the two primary rainfall seasons for this region. The probability distribution functions for bulk rainfall and gamma parameters during the SWM and NEM suggest the dominance of evaporation and drop sorting during the SWM. The seasonal variations are also clearly apparent in DSD with fewer big drops and more small drops during the NEM than in SWM. These differences are seen more prominently at smaller R. As a result, the retrieved Z–R relations are found to be distinctly different during the monsoon seasons. The seasonal variations in Z–R relations are not only observed for the total data but also for the rain type-segregated data. The prefactor of the Z–R relation is found to be larger for SWM and also for stratiform rain, consistent with earlier reports from southeast India, indicating that these features are robust and representative of southeast India. The observed differences in Z–R relations are discussed in the light of microphysical differences between the seasons and rain types.

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

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.

Tropical Convective Cloud Characterization Using Ground-Based Microwave Radiometric Observations

Characterization of the microphysical and thermodynamical properties of convective events over the tropical coastal station Thiruvananthapuram (TVM) has been carried out based on multiyear microwave radiometer profiler observations. The analyses have been extended to develop a methodology to identify convective events, which is based on the radiometric brightness temperature $(T_b)$ difference threshold, at 30 and 22.23 GHz channels, and the results are compared with reflectivity and rainfall intensity deduced from concurrent and collocated disdrometer measurements. Eighty-four of such convections were identified using the aforementioned methodology over the station during 2010–2013, i.e., both for pre- and post-Indian summer monsoon months, and further evaluated by computing their stability indexes. The occurrence of convective systems peaks in the afternoon and early-morning hours with genesis, respectively, over the land and the sea.