Radar profiling algorithms are widely used for precipitation‐induced path‐attenuation correction. In this study, we investigate the impact of the four well‐known profiling algorithms on the efficacy ...of quantitative precipitation estimation retrievals. The first method, named the “simple PhiDP‐based method”, distributes precipitation‐induced path attenuation, or PhiDP (differential phase shift), of a rain segment to each radar gate according to radar reflectivity (Zh). The second one is a linear version of the ZPHI method derived from an approximate linear relationship between specific attenuation (Ah) and specific differential phase (KDP). The third one is a nonlinear version of the ZPHI that respects the nonlinearity of the Ah–KDP relationship. The fourth method is inspired from the self‐consistent method of Bringi. The two‐moment normalized raindrop size distribution (DSD) with two variables, scaled drop concentration Nw$$ {N}_{\mathrm{w}} $$ and mean drop diameter Dm$$ {D}_{\mathrm{m}} $$, is used to represent natural variability of DSD. Two configurations are considered: a constant Nw$$ {N}_{\mathrm{w}} $$ over a segment and a variable Nw$$ {N}_{\mathrm{w}} $$ over a segment. The Ah, KDP, and rain intensity R$$ R $$ are estimated by the four profiling algorithms. The sensitivity of each algorithm to radar measurements uncertainty is also investigated. The evaluation of the estimates shows the following. First, for the range‐constant Nw$$ {N}_{\mathrm{w}} $$ situation, the nonlinear ZPHI and the self‐consistent method yield the best estimates if the measurements errors are not considered. However, the nonlinear ZPHI and the self‐consistent method are significantly impacted by the Zh bias and the measurements noises respectively. Second, for the range‐variable Nw$$ {N}_{\mathrm{w}} $$ situation, the self‐consistent method does not have a good performance even when radar measurements are accurate. The performance of the nonlinear ZPHI method is similar to the simple PhiDP‐based method, whereas the latter is immune to the Zh calibration error. These results suggest that the benefit of introducing Nw$$ {N}_{\mathrm{w}} $$ in quantitative precipitation estimation algorithms can be significantly reduced if Nw$$ {N}_{\mathrm{w}} $$ is not constant in reality and/or if the radar measurements are biased and noisy.
Four radar profiling algorithms—Phi.(KDP) and Phi.(Ah): simple distribution of PhiDP of a rain segment to each pixel as function of its reflectivity; L_ZPHI: linear ZPHI; NLZPHI: nonlinear ZPHI; Self‐Cons: self‐consistent method proposed by Bringi—for quantitative rain estimate at S‐band are studies based on numerical experiments. Their rain estimate performances are illustrated by the boxplot of estimated/true rain ratios. In the first numerical experiment (a), the scaled raindrop concentration Nw$$ {N}_{\mathrm{w}} $$ is constant over rain profiles. Excellent performances of the nonlinear ZPHI and self‐consistent methods are illustrated if the radar measurements are perfect. However, when some Nw$$ {N}_{\mathrm{w}} $$ variation is introduced over the profiles in the second experiment (b), the performance of the nonlinear ZPHI and self‐consistent methods decrease significantly. Their outperformances are no longer seen compared with the simple PhiDP distribution method. This study shows the limits of these advanced radar profiling algorithms in real operational quantitative precipitation estimate application.
The raindrop size distribution (DSD) is essential information for understanding rain attenuation effects at millimeter wavelengths. The DSD was measured in Albuquerque, NM, USA, as a part of the ...W/V-band Terrestrial Link Experiment. An optical disdrometer from Thies Clima was used to measure both size and velocity of rain droplets. The measured DSD consistently showed a unique property of two log-linear distributions regionally separable by drop size under variable rain rates. The functional fit that best represents our measured data with rain rates under 40 mm/h is presented. Based on the DSD, rain specific attenuation is estimated at 72 and 84 GHz with Mie scattering theory. These estimated rain attenuations can be used and validated for rain attenuation analysis of the millimeter wave propagation experiments under similar climate conditions. This letter will guide millimeter wave communication system designers to estimate the rain attenuation based on their own DSD measurements.
The study focuses on exploring the bright band (BB) characteristics, using Micro Rain Radar and ceilometer during the monsoon season of 2019 over three stations - coastal (20 m above mean sea level ...(MSL)), mid (400 m above MSL), and high (1820 m above MSL) altitude cloud physics observatories, setup by the National Centre for Earth Science Studies. The raindrop spectral characteristics are analyzed by classifying the BB events into three time periods, (i) prior to the occurrence of the bright band (pre-BB), (ii) during bright band (BB), and (iii) the subsequent hours after the dissipation of bright band (post-BB). Stronger BB events are noted in the high-altitude station with reflectivity of 33 dBZ at the melting layer while the occurrence of high duration (~2 h) events are noted at the mid-altitude station. Pre-BB hours are contributing the highest surface rainfall from all stages of BB evolution at the coastal and mid-altitude stations. The vertical profile of high rain rates indicates the presence of shallow and mixed-phase precipitating systems during pre-BB hours. At the coastal station, light to moderate rainfall with narrow raindrop spectra having dominance of mid-size drops are noticed. The efficiency of collision-coalescence processes is relatively more at the mid- and high-altitude stations resulting in small to mid-size drops and larger drops respectively. The raindrops generated from the melting layer fall into the shallow feeder clouds formed at the saturated surface in the high-altitude station that creates a seeder feeder mechanism of orographic rain enhancement. The distribution of mass-weighted mean diameter (Dm) varies from 0.5 to 1 mm with wider generalized intercept parameter (log10Nw) values (3–5 m−3 mm−1) at the mid- and high-altitude stations during pre-BB hours. In BB hours, the distribution of Dm values at the high-altitude station extends to 1.5 mm with high log10Nw values and the shape-slope (μ-Λ) relation also shows the extension of raindrop spectra to larger drops. The radar reflectivity-rain rate relations coherently explain the results concerning the evolution of drop spectra. The analysis provides a comprehensive understanding of the precipitation microphysical processes in the elevated terrain of the southwest coast of India, which acts as the gateway of the Indian summer monsoon.
•The evolution of bright band contributes 25% of the seasonal mean rainfall at mid- and high-altitude stations.•Maximum bright band events are observed at mid-altitude while stronger events occurred at high-altitude station.•Rain drop size spectra are initially evolved from shallow and mixed phase precipitating systems.•Regional orography greatly influence the bright band formation in the background of monsoon circulations.
•Raindrop size distribution for unit area and time is useful for erosion studies.•The ratio kinetic power Pn/intensity I is estimated by DSD and terminal velocity.•Relationship for estimating kinetic ...power is available for some theoretical DSD.•The relationship between Pn/I and rainfall intensity I is site specific.•The relationship between Pn/I and the median volume diameter is not site specific.
The knowledge of the rainfall drop size distribution (DSD) at the land surface is essential for understanding precipitation mechanisms affecting soil erosion processes. Rainfall erosivity is defined as the potential of rain to cause erosion and it can be evaluated by rainfall kinetic power, which is determined by DSD and raindrop terminal velocity.
This paper firstly deals with the raindrop terminal velocity estimate. Then the most widely used DSD are reviewed highlighting the difference between the raindrop size distribution per unit volume of air and that per unit area and time. The reliability of the available kinetic power-rainfall intensity relationships and their application in several part of the world is discussed, highlighting that the use of rainfall intensity is not sufficient to determine the rainfall kinetic power everywhere. Finally, the influence of seasonality on both raindrop size distribution and rainfall energy characteristics is investigated using DSD measurements carried out by an optical disdrometer placed at Palermo experimental area.
To improve typhoon forecasting in southern China, which is one of the most susceptible regions in China by typhoons, it is essential to investigate the microphysical characteristics of various types ...of typhoon precipitation. In this study, the dual-frequency precipitation radar (DPR) data from the Global Precipitation Measurement (GPM) satellite is adopted to reveal the raindrop size distribution (RSD) characteristics of 18 typhoons that made landfall in southern China during 2014 - 2022. The RSD characteristics of precipitation in the horizontal and vertical directions of summer typhoons (STs) and autumn typhoons (ATs) are examined. There is no obvious difference between the parameters corresponding to STs andATs without considering the precipitation types. The mass-weighted mean diameter (D
m
) both STs and ATs are primarily composed of small-sized (D
m
<1.4 mm) and medium-sized (1.4 mm ≤ D
m
<2.4 mm) precipitation particles, while STs contain a higher proportion of large-sized (D
m
≥2.4 mm) precipitation particles. Convective precipitation during STs contributes most to the large-sized precipitation particles. The STs and the ATs are both dominated by stratiform precipitation, the proportion of convective precipitation is higher in the STs than that in the ATs. While the strong precipitation centres of ATs are primarily made up of small-sized and medium-to-small-sized (1.4 mm ≤ D
m
<1.7 mm) precipitation particles, the strong precipitation centres of STs are primarily made up of medium-to-large-sized (1.7 mm ≤ D
m
<2.4 mm) precipitation particles at various vertical profiles. Compared to ATs, the storm height and freezing level height of STs are typically higher, and these variables may contribute to divert D
m
to relatively larger droplet diameters. An examination of the RSD characteristics of STs and ATs in southern China may assist in advancing our comprehension of the microphysical characteristics of typhoon precipitation.
Raindrop size distributions collected by the DROPLET MEASUREMENT TECHNOLogies Precipitation imaging probe from 17 flights through 6 hurricanes during National Oceanic and Atmospheric Administration’s ...hurricane field program in 2020 are used to study reflectivity (Z) and rainfall rate (RR) (R) relationship (i.e., Z‐R relationship). The results show that the Z‐R distribution is highly scattered and the scatter increases with RR and reflectivity up to 48 dBZ or 25 mm hr−1, after which it decreases rapidly. The range of the estimated RR from a power‐law Z‐R relationship can be as large as 50 mm hr−1 at reflectivity of 40 dBZ. The result from random forest regression model demonstrates that including the information of mass‐weighted‐diameter (Dm) along with radar reflectivity improves the estimated RR significantly.
Plain Language Summary
One of the main applications of radar measurements is to estimate rainfall rate (RR) based on its power‐law relationships with radar reflectivity. Different relationships have been obtained for different weather scenarios. Hurricanes, in comparison with other severe weather systems, have a much longer life cycle and travel across much greater zonal and meridional extent. Therefore, in order to accurately estimate the RR for a specific time at a specific location in a hurricane, the power‐law relationship with constant coefficients needs to be improved. The uncertainties of the estimated RR using power‐law relationship are related to the raindrop sizes. The usage of a machine learning model (random forest regression) demonstrates that the uncertainties in the estimated rainfall are reduced significantly when both radar reflectivity and the drop sizes are taken into account.
Key Points
The scatter in reflectivity (Z) ‐rainfall rate (R) distribution increases up to 48 dBZ or 25 mm hr−1, after which it decreases rapidly
There are significant uncertainties associated with using a power law Z‐R relationship to estimate rainfall rate from radar reflectivity
The result from a machine learning model shows that rainfall estimation is improved if drop size information and reflectivity are both used
We made an effort to inspect the raindrop size distribution (RSD) characteristics of Indian Ocean and Pacific Ocean tropical cyclones (TCs) using ground-based disdrometer measurements from ...observational sites in India and Taiwan. Five TCs (2010–2013) from the Indian Ocean and six TCs (2014–2016) from the Pacific Ocean were measured using particle size and velocity disdrometers installed in south India and south Taiwan, respectively. Significant differences between the RSDs of Indian Ocean and Pacific Ocean TCs are noticed. For example, a higher number of small drops is observed in Indian Ocean TCs, whereas Pacific Ocean TCs have more mid-size and large drops. RSDs of Pacific Ocean TCs have higher mass-weighted mean diameter and lower normalized intercept parameter than Indian Ocean TCs. RSD values quantified based on rainfall rate and precipitation types also showed similar characteristics between Indian Ocean and Pacific Ocean TCs. The radar reflectivity and rainfall rate (Z-R) relations and shape and slope (μ-Λ) relations of both oceanic (Indian and Pacific) TCs are found to be distinctly different. Possible causes for the dissimilarities in RSD features between Indian Ocean and Pacific Ocean TCs are due to relative differences in water vapor availability and convective activity between TCs in these two oceanic basins.
The knowledge of raindrop size distribution (DSD) is crucial for understanding the microphysical processes involved with the precipitation. Different empirical relationships established with DSD ...parameters, like radar reflectivity– rainfall rate (Z–R) relationships and shape–slope (μ–Ʌ) relationships, can progress the rainfall estimation algorithms and cloud modeling simulations. In the present study, long-term (2018–2021) measurements of a Laser Precipitation Monitor (LPM) disdrometer installed at the National Institute of Technology, Rourkela, India is used to investigate the DSD characteristics of pre-monsoon (March–May) rainfall. Along with the disdrometer data, auxiliary parameters like convective available potential energy (CAPE), total column water vapor (TCWV), vertical profiles of temperature and relative humidity from reanalysis data sets of ECMWF (European Centre for Medium-Range Weather Forecasts) fifth-generation reanalysis (ERA5) are also used in this study. Based on standardized rainfall anomaly, the pre-monsoon precipitation days are classified into strong, moderate, and weak rainy days, and they contributed to 58.69%, 32.7%, and 8.61% of total rainfall, respectively. The average DSD indicated noteworthy variations among strong, moderate, and weak rainy days with maximum (minimum) concentration of raindrops in strong (weak) rainy days. The mean value of rain rate (R), normalized intercept parameter (Nw), and mass-weighted mean diameter (Dm) is maximum during days of strong rainfall. Strong rainy days showed high-value CAPE, TCWV and vertical profile of relative humidity. The majority of R is contributed by moderate-sized raindrops with a significant difference in the Z–R and μ–Λ relationships among three types of rainy days.
•The strong and weak rainy days has same occurrence time of precipitation.•The strong rainy days contribution in the total precipitation is ∼58.69%.•Empirical relations of microphysical parameters differ for strong, moderate and weak rainy days.•The moderate-sized drops have highest contribution to rain rate.
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