Variability Of Drop Size Distribution Research Articles

Forecasting Sauter mean droplet size and examining the range of droplet sizes in a Tenova liquid–liquid extraction column

A new type of Tenova pulsed extraction column was introduced in 2017. It is the newest generation of pulsed columns. Due to the internal equipment of this column and the lack of moving parts and the simplicity and speed of repairs and maintenance, it has been the focus of researchers in recent years. No correlations for predicting the mean drop size and drop size distribution of the Tenova column have been reported. The Sauter mean drop diameter and drop size distribution are investigated for a Tenova pulsed column with a diameter and an active height of 7.4 and 73 cm, respectively. Three standard chemical systems of isobutyl acetate-water, isobutanol-water, and toluene-water have been used. The effects of pulse intensity, dispersed and continuous phase flow rates have been taken into account. In each experiment, 200–300 drops have been analyzed in a total of 10,000 drops. The investigation covered a spectrum of physical properties, notably surface tension (within a range of 1.75–36 mN/m). Operating conditions including pulse intensity (in the range of 0.2–2 cm/s) and the flow rate of continuous and dispersed phases (in the range of 8–30 L/h) have been investigated. Methods based on artificial intelligence (AI) such as multilayer perceptron neural networks and gene expression programming were combined with a dimensional analysis approach to provide a new approach to estimating the mean drop diameter (d32). Experimental results have been compared with the equations found by other researchers in similar columns. The variation of drop size distribution has also been experimentally obtained.Methods based on artificial intelligence (AI) such as multilayer perceptron neural networks and gene expression programming were combined with a dimensional analysis approach to provide a new approach to estimating the mean drop diameter (d32). Experimental results have been compared with the equations found by other researchers in similar columns. The variation of drop size distribution has also been experimentally obtained.

How Accurately Can Warm Rain Realistically Be Retrieved with Satellite Sensors? Part I: DSD Uncertainties

Abstract A significant part of the uncertainty in satellite-based precipitation products stems from differing assumptions about drop size distributions (DSDs). Satellite radar-based retrieval algorithms rely on DSD assumptions that may be overly simplistic, whereas radiometers further struggle to distinguish cloud water from rain. We utilize the Ocean Rainfall and Ice-phase Precipitation Measurement Network (OceanRAIN), version 1.0, dataset to examine the impact of DSD variability on the ability of satellite measurements to accurately estimate rates of warm rainfall. We use the binned disdrometer counts and a simple model of the atmosphere to simulate observations for three satellite architectures. Two are similar to existing instrument combinations on the GPM Core Observatory and CloudSat, and the third is a theoretical triple-frequency radar–radiometer architecture. Using an optimal estimation framework, we find that the assumed DSD shape can have a large impact on retrieved rain rate. A three-parameter normalized gamma DSD model is sufficient for describing and retrieving the DSDs observed in the OceanRAIN dataset. Assuming simpler single-moment DSD models can lead to significant biases in retrieved rain rate, on the order of 100%. Differing DSD assumptions could thus plausibly explain a large portion of the disagreement in satellite-based precipitation estimates.

The extreme precipitation events of August 2018 and 2019 over southern Western Ghats, India: A microphysical analysis using in-situ measurements

The extreme rainfall events observed at the High-Altitude Cloud Physics Observatory (HACPO) in Rajamallay, Munnar (10° 9′19.94″N, 77° 1′6.65″E; 1820 m above MSL) over the southern Western Ghats (India) during the floods in 2018 and 2019 monsoon periods are investigated. The observations from Micro Rain Radar (MRR) and Ceilometer are used to analyze precipitation microphysics and the vertical distribution of clouds during the intense rainfall episodes on 14 - 16th August 2018 and on 8th August 2019. The drop size distribution (DSD) spectra during the 2018 event is characterized by a large number of small to medium-sized drops resulting in maximum reflectivity of 48dBZ with mass-weighted mean diameter (Dm) value of 1.2 mm. At the same time, the 2019 event is characterized by larger drops and resulted in high reflectivity of 53dBZ and Dm value shifted to 1.4 mm. The consistent increment of Dm and σm with slight variation in Nt during the intensive rain hours on 8th August 2019 shows a mixed-phase microphysical process that can invigorate the production of convective rainfall from deep cloud bands (217 K of cloud top temperature) with enhanced rain water content (22 gm−3). The parameters of scaled raindrop size distributions corresponding to higher rain rates (>50 mmhr−1) suggest that the microphysical process that control the variations in DSD is strongly number controlled during these extreme rainfall events. The DSDs are evolved from a consistent, widespread rainfall supported by anomalous moisture advection from the Arabian Sea in 2018 monsoon period. The moisture convergence occurred on the elevated terrains leads to an intense spell of rainfall in two consecutive hours and satisfies the occurrence of a mini-cloud burst (MCB) event on 8th August 2019 causing flash flood in the region.

Polarimetric Radar Quantitative Precipitation Estimation

Radar quantitative precipitation estimation (QPE) is one of the primary tasks of weather radars. The QPE quality was substantially improved after polarimetric upgrade of the radars. This study provides an overview of existing polarimetric methodologies for rain and snow estimation and their operational implementation. The variability of drop size distributions (DSDs) is a primary factor affecting the quality of rainfall estimation and its impact on the performance of various radar rainfall relations at S, C, and X microwave frequency bands is one of the focuses of this review. The radar rainfall estimation algorithms based on the use of specific attenuation A and specific differential phase KDP are the most efficient. Their brief description is presented and possible ways for their further optimization are discussed. Polarimetric techniques for the vertical profile of reflectivity (VPR) correction at longer distances from the radar are also summarized. Radar quantification of snow is particularly challenging and it is demonstrated that polarimetric methods for snow measurements show good promise. Finally, the article presents a summary of the latest operational radar QPE products available in the US by integration of the information from the WSR-88D radars via the Multi-Radar Multi-Sensor (MRMS) platform.

Drop Size Distribution Variability in Central Argentina during RELAMPAGO-CACTI

The Remote sensing of Electrification, Lightning, And Meso-scale/micro-scale Processes with Adaptive Ground Observations (RELAMPAGO) and the Cloud, Aerosol, and Complex Terrain Interactions Experiment Proposal (CACTI) field campaigns provided an unprecedented thirteen-disdrometer dataset in Central Argentina during the Intensive (IOP, 15 November to 15 December 2018) and Extended (EOP, 15 October 2018 to 30 April 2019) Observational Periods. The drop size distribution (DSD) parameters and their variability were analyzed across the region of interest, which was divided into three subregions characterized by the differing proximity to the Sierras de Córdoba (SDC), in order to assess the impact of complex terrain on the DSD parameters. A rigorous quality control of the data was first performed. The frequency distributions of DSD-derived parameters were analyzed, including the normalized intercept parameter (logNw), the mean volume diameter (D0), the mean mass diameter (Dm), the shape parameter (μ), the liquid water content (LWC), and the rain rate (R). The region closest to the SDC presented higher values of logNw, lower D0, and higher μ, while the opposite occurred in the farthest region, i.e., the concentration of small drops decreased while the concentration of bigger drops increased with the distance to the east of the SDC. Furthermore, the region closest to the SDC showed a bimodal distribution of D0: the lower values of D0 were associated with higher values of logNw and were found more frequently during the afternoon, while the higher D0 were associated with lower logNw and occurred more frequently during the night. The data were analyzed in comparison to the statistical analysis of Dolan et al. 2018 and sorted according to the classification proposed in the cited study. The logNw-D0 and LWC-D0 two-dimensional distributions allowed further discussion around the applicability of other mid-latitude and global precipitation classification schemes (startiform/convection) in the region of interest. Finally, three precipitation case studies were analyzed with supporting polarimetric radar data in order to relate the DSD characteristics to the precipitation type and the microphysical processes involved in each case.

A Case Study on Microphysical Characteristics of Mesoscale Convective System Using Generalized DSD Parameters Retrieved from Dual-Polarimetric Radar Observations

The microphysical characteristics of a mesoscale convective system (MCS) during a summer monsoon of South Korea are investigated using the generalized drop size distributions (DSD) that are derived from S-band dual-polarization radar data. The characteristics parameters of generalized DSDs (generalized number concentration, N0′ and generalized mean diameter, Dm) are directly calculated from DSD’s two moments without any assumption on the DSD model. Relationships between ZDR and generalized DSD parameters normalized by ZH are derived in the form of the polynomial equation. Verification of the retrieved DSD parameters is conducted with the 2-D video disdrometer (2DVD) located about 23 km from the radar. The standard deviations (SD) of retrieved DSD parameters are about 0.26 for log N0′, and about 0.11 for Dm because of the variability of DSDs. The SD of the retrieved log N0′ from the dual-polarimetric measurement reaches to about 0.46 (almost double) for 11 rain events while the accuracy of retrieved Dm is quite higher (~0.19). This higher error in retrieved log N0′ is likely attributed to the larger discrepancy in radar-observed and DSD-calculated ZDR when ZH is low. This retrieval technique is applied to a mesoscale convective system (MCS) case to investigate the Lagrangian characteristics of the microphysical process. The MCS is classified into the leading edge and trailing stratiform region by using the storm classification algorithm. The leading edge dominated by strong updraft showed the broad DSD spectra with a steady temporal increase of Dm throughout the event, likely because of the dominant drop growth by the collision-coalescence process. On the other hand, the drop growth is less significant in the trailing stratiform region as shown by the nearly constant Dm for the entire period. The DSD variation is also controlled by the new generation of drops in the leading edge and less extent in the trailing stratiform during the early period when precipitation systems grow. When the system weakens, the characteristic number concentration decreases with time, indicating the new generation of drops becomes less significant in both regions.

Utilization of Specific Attenuation for Rainfall Estimation in Southern China

This study uses rain gauge observations to assess the performance of different radar estimators <i>R</i>(<i>Z</i>_H), <i>R</i>(<i>K</i>_DP) and <i>R</i>(<i>A</i>) in estimating precipitation based on the observations of an S-band polarimetric radar over southern China during a typical convective storm and an extremely severe typhoon, i. e., Typhoon Manghkut. These radar estimators were derived from observations of a local autonomous particle size and velocity (Parsivel) unit (APU) disdrometer. A key parameter, alpha (<i>α</i>), which is the ratio of specific attenuation <i>A</i> to specific differential phase <i>K</i>_DP with three fixed values (<i>α</i>=0.015 dB deg^-1, <i>α</i>=0.0185 dB deg^-1 and <i>α</i>=0.03 dB deg^-1) was examined to test the sensitivity of the <i>R</i>(<i>A</i>) rain retrievals. The results show that: (1) All radar estimators can capture the spatio-temporal patterns of two precipitation events, <i>R</i>(<i>A</i>) with <i>α</i>=0.0185 dB deg^-1 is well correlated with gauge measurement via higher Pearson's correlation coefficient (CC) of 0.87, lower relative bias (RB) of 16%, and lower root mean square error (RMSE) of 17.09 mm in the convective storm while it underestimates the typhoon event with RB of 35%; (2) <i>R</i>(<i>A</i>) with <i>α</i>=0.03 dB deg^-1 shows the best statistical scores with the highest CC (0.92), lowest RB (7%) and RMSE (25.74mm) corresponding to Typhoon Manghkut; (3) <i>R</i>(<i>A</i>) estimates are more efficient in mitigating the impact of partial beam blockage. The results indicate that <i>α</i> is remarkably influenced by the variation of drop size distribution. Thus, more work is needed to establish an automated and optimized <i>α</i> for the <i>R</i>(<i>A</i>) relation during different rainfall events over different regions.

Drop-Size Distribution Variations Associated with Different Storm Types in Southeast Texas

Drop-size distributions (DSDs) provide important microphysical information about rainfall and are used in rainfall estimates from radar. This study utilizes a four-year DSD dataset of 163 rain events obtained using a Joss–Waldvogel impact disdrometer located in southeast Texas. A seasonal comparison of the DSD data shows that small (~1 mm diameter) drops occur more frequently in winter and fall, whereas summer and spring months see an increase in the relative frequency of medium and large (~&gt;2 mm diameter) drops, with notable interannual variability in all seasons. Each rain event is classified by dynamic forcing and radar precipitation structure to more directly link environmental and storm organization properties to storm microphysics. Cold fronts and upper-level disturbances account for 80% of the rain events, whereas warm fronts, weakly forced situations, and tropical cyclones comprise the other 20%. Warm frontal storms and upper-level disturbances have smaller drops compared to the climatological DSD for southeast Texas, whereas the more dynamically vigorous cold fronts and weakly forced environments have larger drops. Tropical cyclones generally produce smaller drops than the climatology, but their DSD anomalies are sensitive to what part of the storm is sampled. Regardless of dynamic forcing, storms with precipitation structures that are mostly deep convective or stratiform rain formed from deep convection have larger drops, whereas stratiform rain formed from non-deep convection has smaller drops. Reflectivity-rain rate (Z-R) relationships that account for dynamic forcing and precipitation structures improve rainfall estimates compared to climatological Z-R relationships despite a wide spread in Z-R relationships by storm.

On the distinctiveness of observed oceanic raindrop distributions

Abstract. Representation of the drop size distribution (DSD) of rainfall is a key element of characterizing precipitation in models and observations, with a functional form necessary to calculate the precipitation flux and the drops' interaction with radiation. With newly available oceanic disdrometer measurements, this study investigates the validity of commonly used DSDs, potentially useful a priori constraints for retrievals, and the impacts of DSD variability on radiative transfer. These data are also compared with leading satellite-based estimates over ocean, with the disdrometers observing a larger number of small drops and significantly more variability in number concentrations. This indicates that previous appraisals of raindrop variability over ocean may have been underestimates. Forward model errors due to DSD variability are shown to be significant for both active and passive sensors. The modified gamma distribution is found to be generally adequate to describe rain DSDs but may cause systematic errors for high-latitude or stratocumulus rain retrievals. Depending on the application, an exponential or generalized gamma function may be preferable for representing oceanic DSDs. An unsupervised classification algorithm finds a variety of DSD shapes that differ from commonly used DSDs but does not find a singular set that best describes the global variability.

Seasonal Variations of Observed Raindrop Size Distribution in East China

Seasonal variations of rainfall microphysics in East China are investigated using data from the observations of a two-dimensional video disdrometer and a vertically pointing micro rain radar. The precipitation and rain drop size distribution (DSD) characteristics are revealed for different rain types and seasons. Summer rainfall is dominated by convective rain, while during the other seasons the contribution of stratiform rain to rainfall amount is equal to or even larger than that of convective rain. The mean mass-weighted diameter versus the generalized intercept parameter pairs of convective rain are plotted roughly around the “maritime” cluster, indicating a maritime nature of convective precipitation throughout the year in East China. The localized rainfall estimators, i.e., rainfall kinetic energy–rain rate, shape–slope, and radar reflectivity–rain rate relations are further derived. DSD variability is believed to be a major source of diversity of the aforementioned derived estimators. These newly derived relations would certainly improve the accuracy of rainfall kinetic energy estimation, DSD retrieval, and quantitative precipitation estimation in this specific region.

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.

Evaluation of the Polarimetric-Radar Quantitative Precipitation Estimates of an Extremely Heavy Rainfall Event and Nine Common Rainfall Events in Guangzhou

The development and application of operational polarimetric radar (PR) in China is still in its infancy. In this study, an operational PR quantitative precipitation estimation (QPE) algorithm is suggested based on data for PR hydrometeor classification and local drop size distribution (DSD). Even though this algorithm performs well for conventional rainfall events, in which hourly rainfall accumulations are less than 50 mm, the capability of a PR to estimate extremely heavy rainfall remains unclear. The proposed algorithm is used for nine different types of rainfall events that occurred in Guangzhou, China, in 2016 and for an extremely heavy rainfall event that occurred in Guangzhou on 6 May 2017. It performs well for all data of these nine rainfall events and for light-to-moderate rain (hourly accumulation &lt;50 mm) in this extremely heavy rainfall event. However, it severely underestimated heavy rain (&gt;50 mm) and the extremely heavy rain at stations where total rainfall exceeded 300 mm within 5 h in this extremely heavy rainfall event. To analyze the reasons for underestimation, a rain microphysics retrieval algorithm is presented to retrieve Dm and Nw from the PR measurements. The DSD characteristics and the factors affecting QPE are analyzed based on Dm and Nw. The results indicate that compared with statistical DSD data in Yangjiang (estimators are derived from these data), the average raindrop diameter during this rainfall event occurred on 6 May 2017 was much smaller and the number concentration was higher. The algorithm underestimated the precipitation with small and midsize particles, but overestimated the precipitation with midsize and large particles. Underestimations occurred when Dm and Nw are both very large, and the severe underestimations for heavy rain are mainly due to these particles. It is verified that some of these particles are associated with melting hail. Owing to the big differences in DSD characteristics, R(KDP, ZDR) underestimates most heavy rain. Therefore, R(AH), which is least sensitive to DSD variations, replaces R(KDP, ZDR) to estimate precipitation. This improved algorithm performs well even for extremely heavy rain. These results are important for evaluating S-band Doppler radar polarization updates in China.

Rain characterization based on maritime and continental origin at a tropical location

Quantitative precipitation estimations (QPE) from remote sensing observations requires understanding of the rain characteristics. Rain drop size distribution (DSD) is the fundamental parameter for characterizing rain and depends strongly on the climate, origin and precipitation type. Past studies are mostly concentrated on the DSD variations on locations and rain type, but only a few studies focused on the rain DSD difference between maritime and continental circulation. The aim of the present study is to analyse the different precipitation properties associated with maritime and continental rain at a coastal Indian location. The rain rate probability, DSD parameters, rain rate(R)-radar reflectivity (Z) relation and rain attenuation under different rain conditions are presented here based on three years of DSD measurements at Trivandrum (08°52′, 76°93′). Higher value of mean rain rate is observed for maritime rain than continental rain. The Z-R relation is also found to be significantly different for these two types of rain. Abundance of smaller drops is indicated in maritime rain whereas large raindrops has dominance in continental rain. The rain attenuation associated with maritime rain also found to be notably higher than the continental rain. The coefficients for converting the rain rate to attenuation values for different frequencies are estimated for the present location. The results indicate the scope of improvement in QPE by adjusting the Z-R relation from the characteristics difference in rain microstructure/attenuation for continental and maritime rain.

A Novel Radar-Based Visibility Estimator

Remote visibility (Vis) estimation by radar is of interest to aviation, road traffic, and other fields. Millimeter-wave radars are suitable candidates because of such advantages as high spatial resolution and sensitivity to small droplets in reflection and attenuation. To investigate remote Vis estimation and to develop physics-based models and algorithms, a 35 GHz cloud radar at the Cabauw Experimental Site for Atmospheric Research (CESAR) in the western part of the Netherlands has acquired data during fog periods in “fog mode.” The advantage in using millimeter-waves for remote sensing of fog is that they interact strong enough with fog for sensing it, but not too strong so that they can penetrate the fog allowing to sense fog top. Simultaneously, fog drop size distribution (DSD) and Vis are continuously automatically measured by the in situ optical sensors at CESAR. Radar reflectivity (Z) and Vis can be linked theoretically since they are related to the sixth and the second moments of an assumed Gamma-shaped DSD. However, in reality the fog DSD is not always Gamma-shaped, leading to errors in the Vis estimation. A further development of the Vis-Z model includes the attenuation factor (La), which is proportional to the liquid water content at a given radar wavelength. This improves the estimated accuracy of Vis, in the theoretical moments-based model. Finally, we were able to arrive at a higher accuracy by introducing an empirical exponential model, estimating Vis from Z and La. A test based on DSD data sets for various fog types in the literature showed robust performance of the Vis-Z-La model for large variations in DSD. The Vis-Z-La model is also validated with the actual fog data sets that were collected by the in situ and remote sensing instruments synergy at CESAR.

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

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.

Improving Polarimetric C-Band Radar Rainfall Estimation with Two-Dimensional Video Disdrometer Observations in Eastern China

Abstract In this study, the capability of using a C-band polarimetric Doppler radar and a two-dimensional video disdrometer (2DVD) to estimate monsoon-influenced summer rainfall during the Observation, Prediction and Analysis of Severe Convection of China (OPACC) field campaign in 2014 and 2015 in eastern China is investigated. Three different rainfall R estimators, for reflectivity at horizontal polarization [R(Zh)], for reflectivity at horizontal polarization and differential reflectivity factor [R(Zh, Zdr)], and for specific differential phase [R(KDP)], are derived from 2-yr 2DVD observations of summer precipitation systems. The radar-estimated rainfall is compared to gauge observations from eight rainfall episodes. Results show that the two polarimetric estimators, R(Zh, Zdr) and R(KDP), perform better than the traditional Zh–R relation [i.e., R(Zh)]. The KDP-based estimator [i.e., R(KDP)] produces the best rainfall accumulations. The radar rainfall estimators perform differently across the three organized convective systems (mei-yu rainband, typhoon rainband, and squall line). Estimator R(Zh) overestimates rainfall in the mei-yu rainband and squall line, and R(Zh, Zdr) mitigates the overestimation in the mei-yu rainband but has a large bias in the squall line. QPE from R(KDP) is the most accurate among the three estimators, but it possesses a relatively large bias for the squall line compared to the mei-yu case. The high variability of drop size distribution (DSD) related to the precipitation microphysics in different types of rain is largely responsible for the case-dependent QPE performance using any single radar rainfall estimator. The squall line has a distinct ice-phase process with a large mean size of raindrops, while the mei-yu rainband and typhoon rainband are composed of smaller raindrops. Based on the statistical QPE error in the ZH–ZDR space, a new composite rainfall estimator is constructed by combining R(Zh), R(Zh, Zdr), and R(KDP) and is proven to outperform any single rainfall estimator.

Investigating raindrop size distributions in the (L‐)skewness–(L‐)kurtosis plane

Skewness–kurtosis (β3−β4) and L‐skewness–L‐kurtosis (τ3−τ4) planes are proposed here as diagnostic tools to guide the identification of drop size distributions (DSDs) of rainfall at the ground. Firstly, we have determined β3−β4 and τ3−τ4 domains of 13 distribution families, namely normal, exponential, gamma, truncated gamma, log‐normal, truncated log‐normal, Weibull, hyperbolic, generalized hyperbolic, log‐logistic, skewed Laplace, Johnson SB and Johnson SU. These include those most used to represent DSDs and, in general, particle size distributions (PSDs). Secondly, we have considered 1 min and 2 min disdrometric data, collected at six sites in the United States, and reported the empirical couples (β3,β4) and (τ3,τ4) in the moment diagrams. The location uncertainty of the empirical couples in the diagrams, mostly due to the occurrence of sampling errors, has been thoroughly investigated. The variability of the empirical DSD couples (β3,β4) and (τ3,τ4) is well described by truncated gamma, truncated log‐normal and Johnson SB over the other considered distributions. However, a Monte Carlo analysis has shown that the Johnson SB is the most adequate distribution in describing the drop size variability, being characterized by the lowest level of uncertainty.

Close-range radar rainfall estimation and error analysis

Abstract. Quantitative precipitation estimation (QPE) using ground-based weather radar is affected by many sources of error. The most important of these are (1) radar calibration, (2) ground clutter, (3) wet-radome attenuation, (4) rain-induced attenuation, (5) vertical variability in rain drop size distribution (DSD), (6) non-uniform beam filling and (7) variations in DSD. This study presents an attempt to separate and quantify these sources of error in flat terrain very close to the radar (1–2 km), where (4), (5) and (6) only play a minor role. Other important errors exist, like beam blockage, WLAN interferences and hail contamination and are briefly mentioned, but not considered in the analysis. A 3-day rainfall event (25–27 August 2010) that produced more than 50 mm of precipitation in De Bilt, the Netherlands, is analyzed using radar, rain gauge and disdrometer data. Without any correction, it is found that the radar severely underestimates the total rain amount (by more than 50 %). The calibration of the radar receiver is operationally monitored by analyzing the received power from the sun. This turns out to cause a 1 dB underestimation. The operational clutter filter applied by KNMI is found to incorrectly identify precipitation as clutter, especially at near-zero Doppler velocities. An alternative simple clutter removal scheme using a clear sky clutter map improves the rainfall estimation slightly. To investigate the effect of wet-radome attenuation, stable returns from buildings close to the radar are analyzed. It is shown that this may have caused an underestimation of up to 4 dB. Finally, a disdrometer is used to derive event and intra-event specific Z–R relations due to variations in the observed DSDs. Such variations may result in errors when applying the operational Marshall–Palmer Z–R relation. Correcting for all of these effects has a large positive impact on the radar-derived precipitation estimates and yields a good match between radar QPE and gauge measurements, with a difference of 5–8 %. This shows the potential of radar as a tool for rainfall estimation, especially at close ranges, but also underlines the importance of applying radar correction methods as individual errors can have a large detrimental impact on the QPE performance of the radar.

Small-Scale Variability of the Raindrop Size Distribution and Its Effect on Areal Rainfall Retrieval

Abstract The drop size distribution (DSD) describes the microstructure of liquid precipitation. The high variability of the DSD reflects the variety of microphysical processes controlling raindrop properties and affects the retrieval of rainfall. An analysis of the effects of DSD subgrid variability on areal estimation of precipitation is presented. Data used were recorded with a network of disdrometers in Ardèche, France. DSD variability was studied over two typical scales: 5 km × 5 km, similar to the ground footprint size of the Global Precipitation Measurement (GPM) spaceborne weather radar, and 2.8 km × 2.8 km, an operational pixel size of the Consortium for Small-Scale Modeling (COSMO) numerical weather model. Stochastic simulation was used to generate high-resolution grids of DSD estimates over the regions of interest, constrained by experimental DSDs measured by disdrometers. From these grids, areal DSD estimates were derived. The error introduced by assuming a point measurement to be representative of the areal DSD was quantitatively characterized and was shown to increase with the size of the considered area and with drop size and to decrease with the integration time. The controlled framework allowed for the accuracy of retrieval algorithms to be investigated. Rainfall variables derived by idealized simulations of GPM- and COSMO-style algorithms were compared to subgrid distributions of the same variables. While rain rate and radar reflectivity were well represented, the estimated drop concentration and mass-weighted mean drop diameter were often less representative of subgrid values.

On the Mechanisms of Rain Formation in an Idealized Supercell Storm

Abstract Deep convective storms produce raindrops through three mechanisms: condensation and coalescence growth of cloud liquid droplets (i.e., warm processes), melting of ice hydrometeors, and shedding from wet hailstones. To investigate the relative importance of these mechanisms and their contributions to exotic drop size distributions (DSDs) observed near the surface in supercell storms, an idealized simulation of a supercell is performed using a modified version of the Morrison two-moment microphysics scheme. The modified scheme includes separate categories for warm, shed, and melted rain. Rain originating from melting ice dominates the rain mass at low levels, especially along the right forward-flank precipitation shield, whereas shed-rain drops dominate a region within the left forward flank. Warm rain is only dominant in the upshear portion of the rear flank of the storm at low levels, though it dominates the total rain mass within the main updraft aloft. The warm-rain mass at low levels is associated with strong low-level downdrafts, consistent with previously published hypotheses based on polarimetric radar observations. Raindrops produced via warm processes are smaller on average than those produced by shedding and melting; drops in the latter class tend to be the largest. Overall, the simulations fail to reproduce the diverse nature of observed supercell DSDs, although the modified microphysics scheme does increase the variability of surface DSDs compared to the Control run. This implies that more sophisticated treatment of rain microphysics is needed to capture the natural variability of supercell DSDs, including the ability to evolve the DSD spectral shape through sedimentation and collisional processes.