Simulation Of Evolution Research Articles

The Effect of Stellar Evolution on Dispersal of Protoplanetary Disks: Disk Fraction in Star-forming Regions

Abstract We study the effect of stellar evolution on the dispersal of protoplanetary disks by performing one-dimensional simulations of long-term disk evolution. Our simulations include viscous disk accretion, magnetohydrodynamic winds, and photoevaporation as important disk dispersal processes. We consider a wide range of stellar mass of 0.1–7 M ⊙ and incorporate the luminosity evolution of the central star. For solar-mass stars, stellar evolution delays the disk dispersal time as the far-ultraviolet (FUV) luminosity decreases toward the main sequence. In the case of intermediate-mass stars, the FUV luminosity increases significantly over a few million years, driving strong photoevaporation and enhancing disk mass loss during the later stages of disk evolution. This highlights the limitations of assuming a constant FUV luminosity throughout a simulation. Photoevaporation primarily impacts the outer regions of the disk and is the dominant disk dispersal process in the late evolutionary stage. Based on the results of a large set of simulations, we study the evolution of a population of star–disk systems and derive the disk fraction as a function of time. We demonstrate that the inclusion of stellar luminosity evolution can alter the disk fraction by several tens of percent, bringing the simulations into closer agreement with recent observations. We argue that it is important to include the stellar luminosity evolution in simulations of the long-term dispersal of protoplanetary disks.

The Observed and Simulated Evolution of a Microburst Using X‐Band Phased‐Array Radar Data Assimilation With EnKF

AbstractA microburst is a severe small‐scale meteorological event that develops rapidly, producing intense downdrafts that result in catastrophic divergent winds near the ground. The fine‐scale structure and evolution of real‐case microbursts are rarely analyzed due to the limitation of regular observational platforms and the computational capacity for numerical simulations. On 12 September 2020, a microburst was effectively captured by China's S‐band operational weather radar network and an X‐band polarimetric phased‐array radar (XPAR). XPAR observations can identify precursor signatures of the rapidly evolving microburst before the occurrence of surface wind disasters, demonstrating superior spatiotemporal resolution in monitoring compared to the S‐band radars. To disclose the evolution of small‐scale structure and the underlying physical processes, this study utilizes the Weather Research and Forecasting (WRF) model and the ensemble Kalman filter (EnKF) system to assimilate XPAR data. The simulation successfully reproduces the fine‐scale structure and evolution of the microburst with a misocyclone. The analysis indicates that the microburst's downdraft is primarily triggered by the middle‐level hydrometeor loading. The misocyclone generates a downward‐directed perturbation pressure gradient then accelerates the microburst's downdraft toward the surface. This study is the first observation and simulation of a real‐case microburst using the WRF‐EnKF system assimilating XPAR data. The investigation of the impact of misocyclone on the microburst enhances our understanding of microbursts' forcing mechanism.

Ensemble-based assisted history matching of DTS monitoring data in HT-ATES systems: application to a real-life case study

Underground heat storage is an important element in accelerating the energy transition. It can significantly contribute to CO2 emission reduction and cost savings since it is one of the cheapest forms of energy storage and it enables the seasonal storage of large energy surpluses from sustainable sources, e.g. wind, sun, geothermal. Numerical models are used for the prediction of thermal behavior important in establishing the high efficiency of the high temperature aquifer thermal energy storage (HT-ATES) systems. However, the lack of exact knowledge of the subsurface conditions introduces modeling uncertainty. It is therefore important to employ approaches that reduce subsurface uncertainty. History matching is a methodology where the numerical models are updated to match historical observations that will in turn not only increase understanding of the subsurface but also improve accuracy of the model predictability of future behavior. In this research, models of the first large-scale operational HT-ATES system in Middenmeer, the Netherlands, were used to evaluate the thermal evolution in the storage aquifer and the over- and underburden clay layers. The HT-ATES system, consisting of a hot and warm well, with a monitoring well inbetween, became operational in the summer of 2021. The extensive monitoring program implemented for the first few operational years provided an opportunity to study the performance of such a system from an environmental and operational point of view. A state-of-the-art assisted history matching approach was applied to the first storage cycle, using a coupling between history matching software and the thermal flow simulator. This approach was compared to a more traditional single-model manual history matching method. Rock properties of the aquifer and over- and underburden layers were updated in the randomly generated prior ensemble of models to fit the simulated temperature evolution measured down the monitoring well with the distributed temperature sensing (DTS) data. The observations gathered during the second year of operations were used to validate the accuracy of the prediction capabilities of the updated models. The obtained results indicate the value of history matching to improve understanding of the subsurface conditions for HT-ATES systems and obtain models with better predictability of the future behavior of heat in the storage reservoir and overburden formations. Such improved models are instrumental in providing engineers with a better quantitative grip on the environmentally responsible storage potential and heat deliverability of the target storage site, which is important to achieve cost-effective site-specific design (e.g. number of wells, well placement) and performing operational strategies (e.g. injection/production rates and temperatures) for new HT-ATES systems. Moreover, the benefits of the assisted history matching approach over manual method are highlighted and both approaches are validated where the assisted history matching method produced more accurate predictions than the manual approach.

Probabilistic cellular automata simulation of microstructure evolution: the role of model parameters on precision and uncertainty

Abstract Probabilistic cellular automata (PCA) is a widely used and cost-efficient method for simulating microstructural evolution. In this method, probabilistic state change rules determine the evolution of cell states at each time step. However, its stochastic nature introduces inherent uncertainty, leading to non-repeatable results. Most microstructural simulation studies assess the accuracy of simulations by comparing predicted results with experimental observations, neglecting uncertainties in mathematical models and algorithms. In this study, the precision and stochastic behavior of microstructure evolution in the PCA simulations were investigated. The probabilities of transformations of cell states at each time step were formulated, and discrete probability distribution functions (dPDF) were introduced to analyze the frequency distribution of simulation outcomes. The performance and consistency of these dPDFs were assessed by comparing statistical analyzes of PCA simulation results with dPDF predictions, revealing that the variance of simulation results is less than that of the binomial distribution function. Additionally, the effects of modeling parameters, such as model size, cellular resolution, and probability distribution of state changes in two- and three-dimensional PCA modeling, on the precision and reliability of simulation results were studied. In PCA models, simulation uncertainty inversely relates to the square root of model size. Furthermore, in 2D simulations, uncertainty is inversely proportional to the square root of the cellular resolution, while in 3D simulations, it is inversely proportional to the cellular resolution itself. These findings provide a simple and computationally efficient method for evaluating PCA simulation uncertainty and determining optimal simulation parameters, including model size, cellular resolution, and dimensionality.

From Virtual Patients to AI-Powered Training: The Evolution of Medical Simulation

Simulation is a learning technique or tool that allows medical professionals to have dynamic training for diagnosing and treating clinical-surgical pathologies. It can also be employed on the patient as a distraction to reduce pain and anxiety using virtual reality. The objective of this research was to determine the usefulness of medical simulation and its current advances, for which a bibliographic search was carried out of 58 medical articles obtained from databases such as PubMed, ScienceDirect, Mendeley, Latindex, published in the last 5 years that included observational studies, randomized studies, systematic reviews and meta-analyses referring to the research topic. It is concluded that the advances of simulation in medicine and the vast majority of medical specialties recommend implementing this technique for teaching, diagnosis, and treatment. In addition, it can also be used through virtual reality, artificial intelligence, and mixed reality to reduce stress in patients, being an advance in development; however, it was found that there are areas where the help of expert evaluators is indispensable, in topics such as resuscitation and physical rehabilitation where simulation did not surpass conventional treatment. Keywords: Patient simulation; Training Simulation; Faculties of Medicine; Coroner; Medical Specialties.

Conditional cooperation with longer memory

Direct reciprocity is a wide-spread mechanism for the evolution of cooperation. In repeated interactions, players can condition their behavior on previous outcomes. A well-known approach is given by reactive strategies, which respond to the coplayer's previous move. Here, we extend reactive strategies to longer memories. A reactive-n strategy takes into account the sequence of the last n moves of the coplayer. A reactive-n counting strategy responds to how often the coplayer cooperated during the last n rounds. We derive an algorithm to identify the partner strategies within these strategy sets. Partner strategies are those that ensure mutual cooperation without exploitation. We give explicit conditions for all partner strategies among reactive-2, reactive-3 strategies, and reactive-n counting strategies. To further explore the role of memory, we perform evolutionary simulations. We vary several key parameters, such as the cost-to-benefit ratio of cooperation, the error rate, and the strength of selection. Within the strategy sets we consider, we find that longer memory tends to promote cooperation. This positive effect of memory is particularly pronounced when individuals take into account the precise sequence of moves.

Parallel development of object recognition in newborn chicks and deep neural networks.

How do newborns learn to see? We propose that visual systems are space-time fitters, meaning visual development can be understood as a blind fitting process (akin to evolution) in which visual systems gradually adapt to the spatiotemporal data distributions in the newborn's environment. To test whether space-time fitting is a viable theory for learning how to see, we performed parallel controlled-rearing experiments on newborn chicks and deep neural networks (DNNs), including CNNs and transformers. First, we raised newborn chicks in impoverished environments containing a single object, then simulated those environments in a video game engine. Second, we recorded first-person images from agents moving through the virtual animal chambers and used those images to train DNNs. Third, we compared the viewpoint-invariant object recognition performance of the chicks and DNNs. When DNNs received the same visual diet (training data) as chicks, the models developed common object recognition skills as chicks. DNNs that used time as a teaching signal-space-time fitters-also showed common patterns of successes and failures across the test viewpoints as chicks. Thus, DNNs can learn object recognition in the same impoverished environments as newborn animals. We argue that space-time fitters can serve as formal scientific models of newborn visual systems, providing image-computable models for studying how newborns learn to see from raw visual experiences.

Simulation and multi-dimensional damage evolution analysis of detailed micro-model representing ancient brick masonry compressive behavior

To further investigate the crack propagation and damage evolution mechanisms of ancient masonry under uniaxial compression, this study employs numerical simulation techniques alongside laboratory experiments to characterize and predict both the apparent and internal cracking behaviors of ancient masonry, utilizing a detailed micro-model constructed from the constitutive equations governing brick blocks and mortar. The findings indicate that this comprehensive micro-model effectively replicates the crack development process. The microscopic damage evolution is delineated by distinct stages: initiation, propagation, penetration, and ultimate failure. Furthermore, the mesoscopic fracture degree serves as a quantitative measure for assessing the mesoscopic evolution of ancient masonry during damage events. An internal damage assessment model has been developed to intricately describe the crack propagation process from within to outside. Additionally, recognizing the inherent randomness and complexity associated with crack development, we introduce a fractal dimension index which significantly enhances tracking accuracy throughout all phases of crack progression. Concurrently, in conjunction with a progressive damage model, multi-faceted predictions and evaluations regarding crack development are conducted to provide an effective framework for understanding the damage processes affecting ancient masonry.

Phenotype selection due to mutational robustness.

The mutation-selection mechanism of Darwinian evolution gives rise not only to adaptation to environmental conditions but also to the enhancement of robustness against mutations. When two or more phenotypes have the same fitness value, the robustness distribution for different phenotypes can vary. Thus, we expect that some phenotypes are favored in evolution and that some are hardly selected because of a selection bias for mutational robustness. In this study, we investigated this selection bias for phenotypes in a model of gene regulatory networks (GRNs) using numerical simulations. The model had one input gene accepting a signal from the outside and one output gene producing a target protein, and the fitness was high if the output for the full signal was much higher than that for no signal. The model exhibited three types of responses to changes in the input signal: monostable, toggle switch, and one-way switch. We regarded these three response types as three distinguishable phenotypes. We constructed a randomly generated set of GRNs using the multicanonical Monte Carlo method originally developed in statistical physics and compared it to the outcomes of evolutionary simulations. One-way switches were strongly suppressed during evolution because of their lack of mutational robustness. By examining one-way switch GRNs in detail, we found that mutationally robust GRNs obtained by evolutionary simulations and non-robust GRNs obtained by McMC have different network structures. While robust GRNs have a common core motif, non-robust GRNs lack this motif. The bistability of non-robust GRNs is considered to be realized cooperatively by many genes, and these cooperative genotypes have been suppressed by evolution.

Research on the Collaborative Mechanism of Value Creation in Public–Private Partnership Projects Under Dynamic Trust

ABSTRACTPublic–private partnership (PPP) is now focused on project value creation, with trust playing a crucial role. This study models trust evolution among stakeholders using evolutionary game theory and simulations, revealing that higher initial and sustained trust boosts project value. However, overreliance on trust can lead to deregulation and neglect of contracts, harming outcomes. Spillover effects and speculative behavior also threaten value creation. The study identifies an optimal incentive range for reputation gains, highlighting the need for balanced trust mechanisms, effective incentives, and penalties to ensure collaboration and project success.

Magneto‐Optical Control of Ordering Kinetics and Vacancy Behavior in Fe–Al Thin Films Quenched by Laser

One of the issues arising in materials science is the behavior of nonequilibrium point defects in the atomic lattice, which defines the rates of chemical reactions and relaxation processes as well as affects the physical properties of solids. It is previously theoretically predicted that melting and rapid solidification of metals and alloys provide a vacancy concentration in the quenched material, which can be comparable to that quantity at the point of melting. Here, the vacancy behavior is studied experimentally in thin films of the near equiatomic Fe–Al alloy subjected to nanosecond laser annealing with intensities up to film ablation. The effects of laser irradiation are studied by monitoring magneto‐optically the ordering kinetics in the alloy at the very ablation edge, within a narrow (micron‐scale) ring‐shaped region around the ablation zone. Quantitatively, the vacancy supersaturation in the quenched alloy has been estimated by fitting a simulated temporal evolution of the long‐range chemical order to the obtained experimental data. Laser quenching (LQ) of alloys and single‐element materials will be a tool for obtaining novel phase states within a small volume of the crystal.

High-fidelity modelling of unburnt coal flow in an industry-scale blast furnace using a hybrid CFD-DEM method

Solid fuels, such as coal or biochar, can be injected into a blast furnace for low-carbon ironmaking. However, unburnt coal or biochar powders may accumulate in the coke bed, potentially reducing bed permeability and compromising furnace stability. Current CFD-DEM methods struggle to simulate systems with significant size differences between coke particles and coal or biochar powders, where the diameter ratio dck/dcl is 100–200 times. In this work, a novel multi-resolution hybrid CFD-DEM model is developed to simulate gas-unburnt powders-coke particles flow dynamics within and around the raceway with high fidelity. The model’s accuracy is validated by comparing the simulated evolution of the raceway cavity shape with experimental results. Subsequently, the hybrid model is used to simulate unburnt powder flow through the raceway and the adjacent coke bed (dck/dcl = 100), comparing its performance with the conventional smoothed CFD-DEM model. The effects of gas inlet velocity and powder wettability are also analysed. Results show that the hybrid CFD-DEM model effectively simulates detailed pore fluid flow in the coke bed, which the smoothed model fails to capture, demonstrating the hybrid model’s superiority. Increasing gas inlet velocity enlarges the raceway cavity, intensifies high-speed pore flows, and accelerates powder transport into the coke bed. Additionally, higher cohesion energy density (kCED) reduces powder penetration, aligns the peak holdup position and penetration angle, and decreases permeability at key probe positions. This work provides an effective and efficient numerical tool to help understand and optimise the injection operation in blast furnaces.

Predicting arbitrary state properties from single Hamiltonian quench dynamics

Analog quantum simulation is an essential routine for quantum computing and plays a crucial role in studying quantum many-body physics. Typically, the quantum evolution of an analog simulator is largely determined by its physical characteristics, lacking the precise control or versatility of quantum gates. This limitation poses challenges in extracting physical properties on analog quantum simulators, an essential step of quantum simulations. To address this issue, we introduce the protocol, which uses a single quench Hamiltonian for estimating arbitrary state properties, eliminating the need for ancillary systems and random unitaries. Additionally, we derive the sample complexity of this protocol and show that it performs comparably to the classical shadow protocol. The Hamiltonian shadow protocol does not require sophisticated control and can be applied to a wide range of analog quantum simulators. We demonstrate its utility through numerical demonstrations with Rydberg atom arrays under realistic parameter settings. The new protocol significantly broadens the application of randomized measurements for analog quantum simulators without precise control and ancillary systems. Published by the American Physical Society 2024

Applying a computer model to evaluate the evolution of resistance by western corn rootworm to multiple Bt traits in transgenic maize.

Western corn rootworm, Diabrotica virgifera virgifera (LeConte) (Coleoptera: Chrysomelidae), is a major pest of maize in the United States. Transgenic maize producing insecticidal toxins from the bacterium Bacillus thuringiensis (Bt) have been used to manage this pest since 2003. Refuges of non-Bt maize have been used to delay resistance to Bt maize by western corn rootworm, and are planted in conjunction with maize producing single or multiple (i.e., pyramids) Bt toxins. Two Bt toxins, Cry3Bb1 and Gpp34/Tpp35Ab1, were used individually before being combined as a pyramid, at which point resistance had already evolved to Cry3Bb1. Pyramids targeting western corn rootworm therefore contained at least one toxin to which resistance had evolved. Western corn rootworm has now evolved resistance to all four commercially available Bt toxins used for rootworm management. We used laboratory and field-generated data to parameterize a deterministic model to simulate the effectiveness of refuges and Bt pyramids to delay resistance to Bt maize in western corn rootworm. Resistance to the pyramid of Cry3Bb1 with Gpp34/Tpp35Ab1 evolved more rapidly when resistance to Cry3Bb1 was already present. This effect arose when model conditions affecting initial resistance allele frequency, inheritance of resistance, and fitness costs were varied. Generally, resistance evolved faster when initial resistance allele frequencies were higher, inheritance of resistance was nonrecessive, and fitness costs were absent, which is consistent with previous models that simulated resistance evolution. We conclude that new transgenic pyramids should pair novel, independently acting toxins with abundant refuges to minimize the risk of rapid resistance evolution.

Investigation of morphodynamic response to the storm-induced currents and waves in the Bay of Bengal

Storm surges, driven by Tropical Cyclones (TCs), pose a threat to the coastal areas of the Bay of Bengal (BoB), causing sediment transport and beach erosion. This study introduces a coupled hydrodynamic (TELEMAC-2D) -wave (TOMAWAC) -morphodynamic (GAIA) model to investigate morphodynamic changes during storm surges. The cyclonic wind is crucial for precisely predicting storm surges, wind waves, and morphodynamic bed evolution. Thus, this study introduced a modified parametric wind model to enhance wind field representation near and far from the cyclone center. The simulated storm surges and wind waves were validated against in-situ observations for TC Hudhud (2014) and TC Varadah (2016), and the simulated morphodynamic bed evolution was validated with field-measured data collected for TC Nivar (2020). Further, this study evaluates the performance of different bed load transport formulations for predicting morphodynamic bed evolution during TC Nivar (2020) under storm-induced currents and waves. Results indicate that the Engelund-Hansen model is most effective for currents alone, while the Bijker model excels for combined currents and waves along the open coast of BoB. The results also indicate that the incorporation of the effect of waves along with currents has enhanced the predictive capability of the coupled model framework.

Zero-dimensional simulations of DC ns-pulsed plasma jet in N2 at near atmospheric pressure: validation of the vibrational kinetics

Abstract A Direct Current (DC) nanosecond (ns)-pulsed plasma jet fed with N2 at near atmospheric pressure (20 000 Pa) is studied using a transient zero-dimensional (0-D) model coupled with a two-term Boltzmann equation solver. Good agreement is observed between the simulated and measured plasma properties: including the electron density and the ratios of the N2(v = 1, 2, 3, 4) densities to the gas density. A variety of theoretical approaches are considered to determine the Vibrational-Vibrational (V-V) and Vibrational-Translational (V-T) rate coefficients. For the V-V kinetics, the simple form of an Harmonic Oscillator (sfHO), the Schwartz–Slawsky–Herzfeld (SSH) and the Forced Harmonic Oscillator (FHO) approaches are used. The SSH approach used in this study is an improved version based on the pure SSH approach. For the V-T kinetics, the sfHO, a fit function of the Semi-Classical (ffSC) calculations and a fit function of the Quasi-Classical Trajectory (ffQCT) calculations are used. The influence of these different approaches on the calculated temporal evolution profiles of the vibrational distribution functions (VDFs) within one pulse modulation cycle is revealed. It is observed that the use of these different approaches does not strongly affect the densities of the low vibrational levels (v < 5), whereas larger influences on the higher level densities are found. In addition, it is found that the simulated evolution of the VDFs are sensitive to the probabilities of the neutral wall reaction N2(v) + wall → N2(v − 1) in the range of 1 × 10−2 to 1 × 10−4. A further analysis of the wall quenching probabilities of N2(0 < v < 58) is of importance for a more accurate prediction of the VDFs.

Evolvability in Artificial Development of Large, Complex Structures and the Principle of Terminal Addition.

Epigenetic tracking (ET) is a model of development that is capable of generating diverse, arbitrary, complex three-dimensional cellular structures starting from a single cell. The generated structures have a level of complexity (in terms of the number of cells) comparable to multicellular biological organisms. In this article, we investigate the evolvability of the development of a complex structure inspired by the "French flag" problem: an "Italian Anubis" (a three-dimensional, doglike figure patterned in three colors). Genes during development are triggered in ET at specific developmental stages, and the fitness of individuals during simulated evolution is calculated after a certain stage. When this evaluation stage was allowed to evolve, genes that were triggered at later stages of development tended to be incorporated into the genome later during evolutionary runs. This suggests the emergence of the property of terminal addition in this system. When the principle of terminal addition was explicitly incorporated into ET, and was the sole mechanism for introducing morphological innovation, evolvability improved markedly, leading to the development of structures much more closely approximating the target at a much lower computational cost.

Determination of the Gurson-Tvergaard-Needleman damage model parameters for simulating small punch tests of heat-resistant alloys

The small punch (SP) test is utilized to assess the mechanical characteristics and damage progression of heat-resistant alloys. The inverse finite element analysis method incorporating SP tests is a parameter identification method based on adjusting the accuracy of the simulated load-displacement curves. In this paper, the elastoplastic parameters of the Hollomon model and the damage parameters of the Gurson-Tvergaard-Needleman (GTN) model are determined based on the undamaged and damaged stages of the load-displacement curves, respectively. The whole stress-strain curves of the tested materials are then built using the results of finite element simulations of the tensile specimens of ZG15Cr2Mo1, P91, 316H, and Hastelloy X at room and elevated temperatures. Comparison with uniaxial tensile tests indicates that the simulated stress-strain curves closely resemble the experimental data from the tensile testing. In addition, the simulated damage evolution characteristics of the SP specimens are consistent with the mechanical model based on the actual deformation behavior. It is possible to comprehend the damage evolution process by analyzing the SP specimens’ stress and strain change characteristics.

Development of a chemical mechanism for ammonia/hydrogen blends for engines

ABSTRACT Ammonia/hydrogen blend fuels have been extensively used as alternative fuel for engines. The combustion and emissions characteristics of ammonia/hydrogen engines have been investigated utilising a CFD model. Developing a robust and accurate chemical reaction mechanism for ammonia/hydrogen blend fuels is of utmost importance. In this paper, a chemical mechanism has been developed and validated for the combustion characteristics of ammonia/hydrogen, which includes 31 species and 223 reactions. Extensive validation has been conducted using experimental data as the basis. The obtained data indicate that the simulated values for the ignition delay times of the shock tube and the concentration of the main components in the stirred jet reactor align well with the measured values. The simulated laminar flame speed under turbulent flow conditions shows a remarkable agreement with the corresponding experimental data. The study compared simulated flame evolution values with experimental data from a constant volume combustion bomb. The results indicate that the proposed mechanism is able to produce favourable flame evolution and combustion characteristics. The presented detailed mechanism demonstrates credible overall performance in terms of combustion behaviours, indicating its suitability for effectively modelling ammonia/hydrogen combustion in real engines.

Simulated particle evolution within a winter storm: contributions of riming to radar moments and precipitation fallout

Abstract. Remote sensing radars from airborne and spaceborne platforms provide critical observations of clouds to estimate precipitation rates across the globe. The ability of these radars to detect changes in precipitation properties is advanced by Doppler measurements of particle fall speed. Within mixed-phase clouds, precipitation mass and its fall characteristics are especially sensitive to the effects of riming. In this study, we quantified these effects and investigated the distinction of riming from aggregation in Doppler radar vertical profiles using quasi-idealized particle-based model simulations. Observational constraints of a control simulation were determined from airborne in situ and remote sensing measurements collected during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) for a wintry–mixed precipitation event over the northeastern United States on 4 February 2022. From the upper boundary of a one-dimensional column, particle evolution was simulated through vapor deposition, aggregation, and riming processes, producing realistic Doppler radar profiles. Despite a modest observed amount of supercooled liquid water (0.05 g m−3), riming accounted for 55 % of the ice-phase precipitation mass, cumulatively increasing reflectivity by 44 % and Doppler velocity by 68 %. Independent evaluation of process-based sensitivities showed that, while radar reflectivity is comparably sensitive to either riming- or aggregation-based particle morphology, the Doppler velocity profile is uniquely sensitive to particle density changes during riming. Thus, Doppler velocity profiles advance the diagnosis of riming as a dominant microphysical process in stratiform clouds from single-wavelength radars, which has implications for quantitative constraints of particle properties in remote sensing applications.