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Understanding drop size distribution (DSD) variability has important implications for remote sensing and numerical modeling applications. Twelve disdrometer datasets across three latitude bands are analyzed in this study, spanning a broad range of precipitation regimes: light rain, orographic, deep convective, organized midlatitude, and tropical oceanic. Principal component analysis (PCA) is used to reveal comprehensive modes of global DSD spatial and temporal variability. Although the locations contain different distributions of individual DSD parameters, all locations are found to have the same modes of variability. Based on PCA, six groups of points with unique DSD characteristics emerge. The physical processes that underpin these groups are revealed through supporting radar observations. Group 1 (group 2) is characterized by high (low) liquid water content (LWC), broad (narrow) distribution widths, and large (small) median drop diameters D 0 . Radar analysis identifies group 1 (group 2) as convective (stratiform) rainfall. Group 3 is characterized by weak, shallow radar echoes and large concentrations of small drops, indicative of warm rain showers. Group 4 identifies heavy stratiform precipitation. The low latitudes exhibit distinct bimodal distributions of the normalized intercept parameter N w , LWC, and D 0 and are found to have a clustering of points (group 5) with high rain rates, large N w , and moderate D 0 , a signature of robust warm rain processes. A distinct group associated with ice-based convection (group 6) emerges in the midlatitudes. Although all locations exhibit the same covariance of parameters associated with these groups, it is likely that the physical processes responsible for shaping the DSDs vary as a function of location.

In this study, significant rainfall microphysical variability is revealed for the extremely heavy rainfall event over Henan Province in July 2021 (the “21·7” Henan EHR event) using a dense network of disdrometers and two polarimetric radars. The broad distributions of specific drop size distribution (DSD) parameters are identified in heavy rainfall from the disdrometer observations, indicating obvious microphysical variability on the surface. A K-means clustering algorithm is adopted to objectively classify the disdrometer datasets into separate groups, and distinct DSD characteristics are found among these heavy rainfall groups. Combined with the supporting microphysical structures obtained through radar observations, comprehensive microphysical features of the DSD groups are derived. An extreme rainfall group is dominantly formed in the deep convection over the plain regions, where the high number of concentrations and large mean sizes of surface raindrops are underpinned by both active ice-phase processes and efficient warm-rain collision-coalescence processes in the vertical direction. Convection located near orographic regions is characterized by restricted ice-phase processes and high coalescence efficiency of liquid hydrometeors, causing the dominant DSD group to comprise negligible large raindrops. Multiple DSD groups can coexist within certain precipitation episodes at the disdrometer stations, indicating the potential microphysical variability during the passage of convective system on the plain regions.

Effects of eccentricity and horizontal electric field (EH) on the binary‐collision outcomes of water drops are examined using numerically calculated collision characteristics from previous studies and results of simulation experiment conducted by the authors. For a fixed collision kinetic energy (CKE), filament breakups can occur at all values of eccentricity but events of coalescence decrease, and that of sheet breakup increase with increasing eccentricity in absence of EH. However, as EH increases to ∼300 kVm−1 it opposes the variability of the coalescence and sheet breakup events with eccentricity. When EH exceeds ∼300 kVm−1 the collision outcomes might be determined only by the CKE and EH. The calculated value of coalescence efficiency and total number of fragments after a binary collision decreases with an increase in EH. It is argued that an electric field can significantly modify drop size distribution in thunderclouds and needs to be considered for development of precipitation. Plain Language Summary Growth of water drops in clouds is mostly governed by the drop size distribution in them. When two drops collide with each other, they can either coalesce to form a single larger drop, or disintegrate into many smaller drops, or bounce back. These different outcomes after their collisions are mostly determined by whether the collisions are centric where the eccentricity of the collision is zero or grazing where the eccentricity is one or somewhere in between the two extremes. The present study shows that if the collisions occur in presence of a horizontal electric field, it opposes the effect of eccentricity on the outcomes of the collisions. In this study, simultaneous effects of eccentricity and horizontal electric field are examined from numerically calculated collision characteristics from previous studies and utilizing the results of a simulation experiment recently conducted by the authors. Simultaneous effects of eccentricity and electric field on coalescence efficiency and total and spectral size distribution of fragments generated after the collision have also been evaluated. The results suggest that the electric field can significantly modify drop size distribution in thunderclouds and need to be considered for the development of precipitation. Key Points Horizontal electric field opposes the variability of coalescence/sheet breakup of water drops with eccentricity in binary collisions Number of fragments close to small (large) parent drop size decreases (increases) after collisions in the higher horizontal electric field Binary collisions in horizontal electric fields can substantially modify drop size distribution in thunderclouds

Remote sensing instruments are heavily used to provide observations for both the operational and research communities. These sensors do not provide direct observations of the desired atmospheric variables, but instead, retrieval algorithms are necessary to convert the indirect observations into the variable of interest. It is critical to be aware of the underlying assumptions made by many retrieval algorithms, including that the retrieval problem is often ill posed and that there are various sources of uncertainty that need to be treated properly. In short, the retrieval challenge is to invert a set of noisy observations to obtain estimates of atmospheric quantities. The problem is often complicated by imperfect forward models, by imperfect prior knowledge, and by the existence of nonunique solutions. Optimal estimation (OE) is a widely used physical retrieval method that combines measurements, prior information, and the corresponding uncertainties based on Bayes’s theorem to find an optimal solution for the atmospheric state. Furthermore, OE also allows the relative contributions of the different sources of error to the uncertainty in the final retrieved atmospheric state to be understood. Here, we provide a novel Python library to illustrate the use of OE for inverse problems in the atmospheric sciences. We introduce two example problems: how to retrieve drop size distribution parameters from radar observations and how to retrieve the temperature profile from ground-based microwave sensors. Using these examples, we discuss common pitfalls, how the various error sources impact the retrieval, and how the quality of the retrieval results can be quantified.

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.

The impact of the collisional warm-rain microphysical processes on the polarimetric radar variables is quantified using a coupled microphysics–electromagnetic scattering model. A one-dimensional bin-microphysical rain shaft model that resolves explicitly the evolution of the drop size distribution (DSD) under the influence of collisional coalescence and breakup, drop settling, and aerodynamic breakup is coupled with electromagnetic scattering calculations that simulate vertical profiles of the polarimetric radar variables: reflectivity factor at horizontal polarization ZH, differential reflectivity ZDR, and specific differential phase KDP. The polarimetric radar fingerprint of each individual microphysical process is quantified as a function of the shape of the initial DSD and for different values of nominal rainfall rate. Results indicate that individual microphysical processes (collisional processes, evaporation) display a distinctive signature and evolve within specific areas of ZH–ZDR and ZDR–KDP space. Furthermore, a comparison of the resulting simulated vertical profiles of the polarimetric variables with radar and disdrometer observations suggests that bin-microphysical parameterizations of drop breakup most frequently used are overly aggressive for the largest rainfall rates, resulting in very “tropical” DSDs heavily skewed toward smaller drops.

Two-dimensional video disdrometer (2DVD) data were analyzed from two equatorial Indian (Gan) and west Pacific Ocean (Manus) islands where precipitation is primarily organized by the intertropical convergence zone and the Madden–Julian oscillation (MJO). The 18 (3.5) months of 2DVD data from Manus (Gan) Island show that 1) the two sites have similar drop size distribution (DSD) spectra of liquid water content, median diameter, rain rate R, radar reflectivity z, normalized gamma number concentration Nw, and other integral rain parameters; 2) there is a robust Nw-based separation between convective (C) and stratiform (S) DSDs at both sites that produces consistent separation in other parameter spaces. The 2DVD data indicate an equatorial, maritime average C/S rainfall accumulation fraction (frequency) of 81/19 (41/59) at these locations. It is hypothesized that convective fraction and frequency estimates are slightly higher than previous radar-based studies, because the ubiquitous weak, shallow convection (<10 mm h−1) characteristic of the tropical warm pool is properly resolved by this high-resolution DSD dataset and identification method. This type of convection accounted for about 30% of all rain events and 15% of total rain volume. These rain statistics were reproduced when newly derived C/S R(z) equations were applied to 2DVD-simulated reflectivity. However, the benefits of using separate C/S R(z) equations are only realizable when C/S partitioning properly classifies each rain type. A single R(z) relationship fit to all 2DVD data yielded accurate total rainfall amounts but overestimated (underestimated) the stratiform (convective) rain fraction by ±10% and overestimated (underestimated) stratiform (convective) rain accumulation by +50% (−15%).

Opportunistic sensing of rainfall and water vapor using commercial microwave links operated within cellular networks was conceived more than a decade ago. It has since been further investigated in numerous studies, predominantly concentrating on the frequency region of 15–40 GHz. This article provides the first evaluation of rainfall and water vapor sensing with microwave links operating at E-band frequencies (specifically 71–76 and 81–86 GHz). These microwave links are increasingly being updated (and are frequently replacing) older communication infrastructure. Attenuation–rainfall relations are investigated theoretically on drop size distribution data. Furthermore, quantitative rainfall estimates from six microwave links, operated within cellular backhaul, are compared with observed rainfall intensities. Finally, the capability to detect water vapor is demonstrated on the longest microwave link measuring 4.86 km in path length. The results show that E-band microwave links are markedly more sensitive to rainfall than devices operating in the 15–40 GHz range and can observe even light rainfalls, a feat practically impossible to achieve previously. The E-band links are, however, substantially more affected by errors related to variable drop size distribution. Water vapor retrieval might be possible from long E-band microwave links; nevertheless, the efficient separation of gaseous attenuation from other signal losses will be challenging in practice.

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.

Accurate precipitation forecasting hinges on the representation of microphysical processes within numerical models. A key approach to understanding these processes is through the analysis of hydrometeor drop size distribution (DSD). The characteristics of DSD bulk parameters – Mass Weighted Mean Diameter (Dm) and the Normalized Intercept Parameter (Nw), are estimated from the double moment cloud microphysical scheme (CASIM: Cloud-Aerosol Interacting Microphysics) employed in the operational convection permitted model of National Centre for Medium-Range Weather Forecasting (NCUM-R). The observations from the Joss-Valdvogel Disdrometer (JWD) and the Global Precipitation Mission – Dual Frequency Precipitation Radar (GPM-DPR) are analyzed for providing essential validation. An algorithm for separating the monsoon precipitation into convective and stratiform types in NCUM-R and a new parameter estimation module to obtain DSD parameters from the CASIM are established in the study. The model exhibits agreement with the characteristics of the DSD of raindrops with Dm ranging from 0.5 to 2.5 mm marking the majority of the monsoon precipitation events. However, the underestimation when it comes to the larger drops (with Dm > 3.25 mm and Rainrate ≥ 8 mm h−1) demands a reassessment in microphysical parameterizations. The advanced autoconversion parameterization scheme applied in CASIM favored the growth of large drops compared to the existing scheme. The enhanced growth of larger drops is reflected in the increased accuracy in the prediction of extreme precipitation associated with a convective event. The current study underscores the importance of refining microphysical parameterizations to improve the accuracy of precipitation forecasts offering a pathway for enhanced model performance in future operational forecasting systems.