Precipitation Type Research Articles

Multiple Characteristics of Precipitation Inferred from Wind Profiler Radar Doppler Spectra

A methodology to process radar wind profiler Doppler spectra is presented and implemented for an UHF Degreane PCL1300 system. First, double peak signal detection is conducted at each height level and, then, vertical continuity checks for each radar beam ensure physically consistent measurements. Second, horizontal and vertical wind, kinetic energy flux components, Doppler moments, and different precipitation-related variables are computed. The latter include a new precipitation type estimate, which considers rain, snow, and mixed types, and, finally, specific variables for liquid precipitation, including drop size distribution parameters, liquid water content and rainfall rate. The methodology is illustrated with a 48 h precipitation event, recorded during the Cerdanya-2017 field campaign, carried out in the Eastern Pyrenees. Verification is performed with a previously existing process for wind profiler data regarding wind components, plus precipitation estimates derived from Micro Rain Radar and disdrometer observations. The results indicated that the new methodology produced comparable estimates of wind components to the previous methodology (Bias < 0.1 m/s, RMSE ≈ 1.1 m/s), and was skilled in determining precipitation type when comparing the lowest estimate of disdrometer data for snow and rain, but did not correctly identify mixed precipitation cases. The proposed methodology, called UBWPP, is available at the GitHub repository.

Spatiotemporal Characteristics of US Floods: Current Status and Forecast Under a Future Warmer Climate

AbstractFloods in the US exhibit strong spatiotemporal variability, mainly controlled by precipitation types and catchment attributes. In a future warmer climate, it remains largely unclear how such features change, relative to the current climate. Global Climate Models at coarse resolution cannot resolve convective‐scale storms which is one of the major weather systems causing devastating floods in the US. Alternatively, coupled, high‐resolution and continental‐scale climate and flood simulations can advance our understanding. In this study, we couple a 4‐km convection‐permitting climate simulation model with a 1‐km hydrologic model to simulate the spatiotemporal variability of rainfall and flooding over the contiguous US. In particular, changes in rainfall and flood frequency, spatial scale, and seasonality are explored in major climate divisions. We found: (a) an overall increase in flood frequency (+101.7%) and spatial extent (+44.9%), mainly attributed to more extreme rainfall and variability in the future; (b) weakening rainfall and flood seasonality, resulting in more random and unpredictable events throughout the year; (c) earlier flooding season onsets in the West and snow‐dominated regions while delayed onsets in the East driven by drier antecedent soil moisture; (d) correlation between extreme rainfall and flood onsets is becoming stronger in the West yet weaker in the East in the future. Findings in this study can potentially serve as a basis for future flood exposure and risk assessments, as well as more scientific understanding of changing flood‐generating mechanisms across the CONUS.

Effect of aging temperature on microstructure evolution and strengthening behavior of L-PBF 18Ni(300) maraging steel

This work investigated the microstructure evolution with aging temperature of laser-powder bed fusion ( L -PBF) 18Ni(300) maraging steel at different scales. Important issues of cell structure changes, precipitation sequence, and austenite reversion were evaluated. The dominant strengthening factor in each state was elucidated based on yield strength modeling. The as built fully martensitic matrix consists of dislocation cell structures, and its yield strength comes from the martensite lath and solid solution strengthening with comparable contributions. Aged at 440 °C, the cell walls kept stable and massive ω particles prominently strengthened matrix by shearing mechanism. At 490 °C, the cell walls were partially destroyed and austenite reversion just began along the lath boundary. Densely distributed η-Ni 3 Ti particles produced the peak strength dominantly by the Orowan mechanism, but the shearing process may also exist meanwhile. At 540 °C, cell walls further degraded and reverted austenite formed along the lath boundaries and around some retained cell walls. Its strength decreased mildly mainly due to the wider inter-particle spacing, but was still maintained quite high by Laves-Fe 2 Mo and η-Ni 3 Ti particles via the Orowan process. The reverted austenite with limited fraction had a minor role in its strength decrease, but improved ductility significantly. • Cell structures change, precipitation sequence and autenite reversion were discussed. • Precipitate types were rigorious identified based on crystallographic information. • Dominant strengthening factors in as built and aged states were elucidated.

Bayesian Classification of Nonmeteorological Targets in Polarimetric Doppler Radar Measurements

Abstract The latest established generation of weather radars provides polarimetric measurements of a wide variety of meteorological and nonmeteorological targets. While the classification of different precipitation types based on polarimetric data has been studied extensively, nonmeteorological targets have garnered relatively less attention beyond an effort to detect them for removal from meteorological products. In this paper we present a supervised learning classification system developed in the Finnish Meteorological Institute (FMI) that uses Bayesian inference with empirical probability density distributions to assign individual range gate samples into 7 meteorological and 12 nonmeteorological classes, belonging to five top-level categories of hydrometeors, terrain, zoogenic, anthropogenic, and immaterial. We demonstrate how the accuracy of the class probability estimates provided by a basic naive Bayes classifier can be further improved by introducing synthetic channels created through limited neighborhood filtering, by properly managing partial moment nonresponse, and by considering spatial correlation of class membership of adjacent range gates. The choice of Bayesian classification provides well-substantiated quality estimates for all meteorological products, a feature that is being increasingly requested by users of weather radar products. The availability of comprehensive, fine-grained classification of nonmeteorological targets also enables a large array of emerging applications, utilizing nonprecipitation echo types and demonstrating the need to move from a single, universal quality metric of radar observations to one that depends on the application, the measured target type, and the specificity of the customers’ requirements. Significance Statement In addition to meteorological echoes, weather radars observe a wide variety of nonmeteorological phenomena including birds, insects, and human-made objects like ships and aircraft. Conventionally, these data have been rejected as undesirable disturbance, but lately their value for applications like aeroecological monitoring of bird and insect migration has been understood. The utilization of these data, however, has been hampered by a lack of comprehensive classification of nonmeteorological echoes. In this paper we present a comprehensive, fine-grained, probabilistic classifier for all common types of nonmeteorological echoes which enables the implementation of a wide range of novel weather radar applications.

Implications From the Meteorological Data Effects on Water Level Fluctuations of the Lake Van (Eastern Anatolia/Türkiye)

Lake Van is located in the eastern part of Türkiye and forms the largest soda lake in the world. In this study, we present the relationship between instrumental data which belongs to Lake Van level changes and meteorological parameters by performing a multilevel analysis. The data set consists of monthly average levels of Lake Van and monthly meteorological parameters (temperature, precipitation and wind speed) between 1944-2019 years. Two different multilevel linear models; random intercept, random intercept and slope model were used. Unstructured (UN) covariance structure and Maximum Likelihood (ML) were used for repeated measurements and estimation, respectively. Log-likelihood (ll), Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC) and AIC Corrected (AICC) were used for the selection of the best model. The random intercept and slope model (Model II) is explained as the best model for the lake level changes in this study. Statistical results in this study indicated that the temperature and wind speed are the key parameters controlling the Lake Van water level fluctuations, whereas the precipitation effect is minimal due to the type of precipitation (snowfall). For this reason, temperature, wind speed and also the type of precipitation (snowfall or rain) must be considered for disaster modelling in settlements of Lake Van and similarly closed basin lakes.

Towards improved precipitation estimation with the GOES‐16 advanced baseline imager: Algorithm and evaluation

AbstractThe study introduces a new quantitative precipitation estimation (QPE) algorithm from Advanced Baseline Imager (ABI) observations from GOES‐16 across the contiguous USA (CONUS). It is developed and comprehensively evaluated using the Ground Validation Multi‐Radar/Multi‐Sensor (GV‐MRMS) system as a benchmark, and features Random Forest (RF) machine learning‐based QPE. The key innovations of the algorithm include a comprehensive set of satellite predictors derived from five infrared ABI channels, complemented by low‐level environmental conditions from the Rapid Refresh (RAP) numerical weather prediction (NWP) model, and outputs of probability of precipitation type for seamless integration of varying precipitation rates across types. A systematic analysis of the predictors is performed. The analysis reveals that satellite predictors contribute more to high‐intensity precipitation estimates, whereas environmental predictors primarily condition low‐intensity precipitation. Combining both categories of predictors improves scores (correlation coefficient, CC = 0.41) overall, with the greatest improvement in the warm stratiform precipitation type. Introducing precipitation type information through probabilities further improves correlation (0.46). An intercomparison of the RF model with the Self‐Calibrating Multivariate Precipitation Retrieval (SCaMPR) shows that RF has better detection and quantification scores than SCaMPR (Heidke Skill Score, HSS = 0.91 vs. 0.19; CC = 0.44 vs. 0.26). Both retrievals display similar performance patterns across different regions of the CONUS. For example, skill degrades in complex terrain. Precipitation processes in complex terrain and their variability may not be well captured, especially with the degraded ABI resolution in the western CONUS. It is recommended to complement the 11.2 μm legacy channel with at least the 6.2 μm channel for global precipitation retrievals using GEO sensors.

Occurrence, spatial heterogeneity, and risk assessment of perfluoroalkyl acids (PFAAs) in the major rivers of the Tibetan Plateau

The Tibetan Plateau (TP) is home to the headwaters of major rivers in Asia, yet their water quality security on a large spatial scale is scarcely studied, especially in regard to emerging organic pollutants. In this study, a systematic field campaign was carried out along Yarlung Tsangpo River, Nu River, Lancang River and Jinsha River, and 13 perfluoroalkyl acids (PFAAs) were analyzed. The total concentrations of PFAAs in the river waters of the TP were in the range of 0.58–7.46 ng/L, containing a high proportion of perfluorobutanoic acid (PFBA) and perfluorobutane sulfonate (PFBS) with average values of 56.7 %. Elevated PFAA loadings were found for the midstream of Yarlung Tsangpo River in central Tibet. Geodetector results indicated that precipitation, solar radiation and vegetation type were the top three influential factors contributing to the observed spatial heterogeneity. When interactions with human activities were taken into account, the explanatory power was significantly enhanced and rose above 0.70, highlighting the increased risks for TP rivers from the combined effects of natural environments and anthropogenic activities. Risk assessments suggest a low risk is posed to the alpine aquatic ecosystems and human health. The discharge fluxes of PFAAs via riverine export were estimated at 94–425 kg/year, which is one to two orders of magnitude lower than their mass loadings in major rivers worldwide. Our study underlined the need for further attention to the increased risk of water resource quality on the central TP in the context of long-range transport, increased cryosphere melting and local emission.

Growth mechanism of αp and interface relationships between αp and αs during cooling of a near α titanium alloy

This paper focused on the interface relationships between primary α (αp) and the precipitation secondary α (αs) phase at different cooling rates from 965 °C. The rapid cooling rate favors the precipitation of the secondary αs. And the low cooling rate is conducive to the epitaxial growth of the primary αp. One key finding is that once the grain boundary (αGB) nucleated on the αp could grow forward without maintaining the Burgers orientation relationship (BOR) with adjacent β grains. What’s more, HADDF and Fourier-filtered HRTEM images elucidated that two adjacent αp particles share a common plane but not the same atomic arrangement direction in crystal spatial. Three precipitation types of the αs were discussed through EBSD and 3D spatial crystalline orientation. Specifically, the αs shares common {1 1 2¯ 0} plane with the αp grains respectively in Type-I and Type-II, but displaying different c axis angle (60° in Type-I and 80° in Type-II). For the Type-III, the secondary αs shares common {1 0 1¯ 0} plane with the primary αp, and the angle of c axis between them is 60°. Moreover, the primary αp do not maintain the BOR with the pre-existing β, except for Type-I.

Impact on Variation in Genome Content of Wild Cymbopogon flexuosus (Nees ex Steud) Collected from Different Ecological Locations of Karnataka, India

The present work explores the variation in genome content of wild Cymbopogon flexuosus from different locations in Karnataka. Collected plant samples showed genotypic and phenotypic variations. The samples of wild Cymbopogon flexuosus (E1 and E2) were collected from Bannerghatta hills, Bangalore and Charmadi Ghat, Dakshina Kannada district, respectively. The locations for plant collection were selected based on ecological differences such as altitude, temperature, precipitation and soil type. The soil analysis revealed variation in macronutrient and micronutrient content and composition. The studies on E1 and E2 showed significant differences concerning morphological characteristics such as plant height, leaf length, inflorescence colour and essential oil content. Flow cytometry studies showed differences in genome size between E1 (2718 Mbp) and E2 (2211Mbp), even though their ploidy status was similar. Due to environmental variations in these ecotypes, the plants could be further explored for crop improvement as they continue to evolve in the wild and develop special traits like pest resistance and drought tolerance.

The Prestorm Environment and Prediction for Local‐ and Nonlocal‐Scale Precipitation: Insights Gained From High‐Resolution Radiosonde Measurements Across China

AbstractDifferentiating local‐scale precipitation (LP) and nonlocal‐scale precipitation (NLP) and understanding their corresponding prestorm environment are of importance for accurate severe weather analysis and forecasting. However, the difference in the transient prestorm environments of LP and NLP is largely undetermined, thereby fundamentally limiting the predictability of convective storms. The present study focuses on the precursor signals of LP and their differences from those of NLP based on explicit observational analyses using high‐resolution radiosonde measurements from the China radiosonde network with a vertical resolution of 5‐m, combined with 1‐min rain gauge data from the China rain gauge network during 2013–2020. LP and NLP can be recognized by most proximity sounding parameters. For example, the upcoming LP is characterized by larger most‐unstable convective available potential energy (MUCAPE), K index, and total precipitable water (TPW), and a lower lifting condensation level (LCL), compared to the NLP scenario. By taking proximity sounding parameters as inputs and precipitation types as learning targets, a nu‐support vector machine algorithm can effectively predict LP or NLP events, achieving an overall precision of 97%. However, the precision significantly drops by approximately 20% after removing the variable of low‐level wind shear, indicating the crucial role of wind observation when predicting the LP event. Furthermore, more intensive LPs are characterized by larger MUCAPE, K index, TPW, and moist static energy, and lower LCL. Among others, MUCAPE is the most important feature, according to the gradient boosting machine algorithm.

The Effect of Acidic Ternary Deep Eutectic Solvent Treatment on Native Lignin

Ternary deep eutectic solvents (DESs) are gaining increased attention to serve as a cheap green alternative medium for the processing of lignocellulosic biomass. For example, mixtures of choline chloride (ChCl), ethylene glycol (EG), and oxalic acid (OA) were recently explored for the fractionation of lignocellulosic biomass into its main components. Interestingly, during this processing, the recovered lignin was structurally modified by incorporation of EG, which altered its solubility properties and led to the need for different lignin recovery strategies. This offers an excellent starting point for a deeper investigation of the effect of acidic DES systems on the structure of lignin. In particular, native-like residual enzyme lignins (RELs) that are hard to completely dissolve in organic solvents are specifically suitable for this task. Here, a ternary DES is used consisting of ChCl/EG with OA or trifluoromethanesulfonic acid (HOTf) as a third component. The results showed that both solvent systems led to high EG incorporation into REL. The HOTf system showed a lesser extent of lignin depolymerization at similar modification levels as it already induced modification at lower temperature (25–30 °C). Low recovery yields from typical acidic precipitation were observed for treatment with both acidic DES systems. Analysis of THF and DCM extracts showed that the products in the water phase included small EG modified lignin fragments and aromatic monomers released from lignin aryl ether linkage cleavage. This analysis details the types of other products that can be expected and where these will end up during fractionation. These results show that the treatment of lignin with acidic DES in the presence of alcohols leads to low- and high-molecular-weight products that are not effectively recovered by typical precipitation procedures.

Geodetector based identification of influencing factors on spatial distribution patterns of heavy metals in soil: A case in the upper reaches of the Yangtze River, China

With the acceleration of urbanization and industrialization, soil heavy metal pollution has become a serious problem threatening ecological security. Identification of influencing factors on the variations and distribution of heavy metals in soils becomes a critical problem to be solved. Here, the geographical detector (geodetector) method was applied to study the influencing factors on the spatial distribution pattern of heavy metals (As, Cd, Cr and Hg) in soils in the upper reaches of the Yangtze River, China. Based on the geodetector model, eight influencing factors were selected, including 5 natural factors of pH, precipitation, soil type, rock type and landform, 2 anthropogenic factors of land use and road density, and 1 comprehensive factor of deposit density). The influence of each factor on the spatial distribution of 4 heavy metals shows that 1) for As, the dominant influencing factors were pH and deposit density, and the secondary influencing factors were precipitation and rock type, 2) for Cd, precipitation had the greatest explanatory power, while pH, land uses, road density and deposit density also had significant effects, 3) for Cr, the main influencing factors were rock type and soil type, and the secondary factors were pH and deposit density, 4) for Hg, the deposit density and rock type dominated its distribution. Compared with single factors, the interaction between factors significantly improved the explanatory power of heavy metals. Taking Cd as an example, the single factor contributions of road density and precipitation are 12.4% and 13.2%, respectively, while the explanatory power of their interaction was as high as 49.8%. This finding indicated that the geodetector can be a helpful method for us to understand the factors affecting the distribution patterns of heavy metals in soil, and it can further service for land management and pollution remediation.

Decreasing Solid Aerosols from Small Heat Sources Using the Optimized Electrostatic Precipitator

Air quality and air pollution are important issues, and thus it is necessary to look at possibilities for how to decrease emissions in the atmosphere affordably and effectively. This article focuses on solid aerosols, specifically particulate matter, and the design of a low-cost solution for their decrease in the atmosphere. The mass concentration of particulate matter with the proposed optimized electrostatic precipitator was measured and compared with measurement without its implementation. Based on the results, it can be concluded that the designed ESP type could catch approximately 71% of solid particles. However, the real ability to capture particulate matter could be expected to be approximately 50–60% due to possible clogging, irregular cleaning and maintenance under normal conditions. Further, the article deals with the application of the village model with this type of electrostatic precipitator in households. Based on this model, the production of particulate matter would be reduced from 12.24 t/year to 6.12 t/year considering 50% separation efficiency and to 5.25 t/year considering 60% separation efficiency by the application to all heat sources for burning solid fuels for all emission classes. However, the reduction in particulate matter would be lower in real conditions.

Winter Orographic Precipitation and ENSO in Sapporo, Japan

The effect of global climate change on the distribution of snow water is a great concern. Thus, it is important to clarify the characteristics of winter precipitation variability, including mountain precipitation, together with climate indices. In this study, regional snowfall characteristics were investigated with the daily gridded precipitation over Sapporo City (located on the Japan Sea side of Hokkaido in northern Japan), which was quantified by the APHRODITE method and by adding local precipitation observation data. We found places of showing large interannual variability that is different from that of daily precipitation variability. Applying an EOF analysis to the daily grid precipitation, we defined four local precipitation types. The occurrence of each precipitation type and associated atmospheric circulation was analyzed, and the results revealed that (except for the Super El Niño winter of 1997/1998) more snow fell in the southwestern mountains and inland areas during El Niño winters, and more snow fell in the northeastern plains and along the sea during La Niña winters. Continued development and evaluation of the precise data that incorporate local precipitation network is needed.

Impact of Optimally Growing Initial Errors on the Mesoscale Predictability of Heavy Precipitation Events along the Mei-Yu Front in China

Abstract Based on the conditional nonlinear optimal perturbation (CNOP) approach, the impact of the optimally growing initial errors on the mesoscale predictability of typical mei-yu front heavy precipitation events over eastern China was explored. First, based on a nonlinear optimization system built using the high-resolution Weather Research and Forecasting Model and particle swarm optimization algorithm, the CNOPs for three heavy precipitation cases were obtained. The CNOPs as the optimally growing initial errors caused the largest forecast errors and made the 24-h accumulated precipitation stronger than any other kind of initial errors. Moreover, the CNOPs showed faster growth and saturation than the random errors in space, highlighting the importance of the initial error with specific spatial structure in the meso- and convective-scale processes. Despite different CNOPs having particular spatial structures, the large amplitudes of the CNOPs at lower levels were mainly located in the rainband along the mei-yu front. Although the spectral energies of the CNOPs increased with increasing scales, the forecast error growth for the CNOPs generally followed the well-known three-stage conceptual model. Moreover, the large-scale and large-amplitude initial errors in the CNOPs were the most influential in terms of the forecast quality. This suggests that reducing large-scale initial errors can potentially improve the forecast accuracy. However, the mesoscale predictability of the mei-yu front heavy precipitation events is inherently limited, for which the moist convection was found to be the main reason.

Quantifying the Impact of Precipitation-Type Algorithm Selection on the Representation of Freezing Rain in an Ensemble of Regional Climate Model Simulations

Abstract Given their potentially severe impacts, understanding how freezing rain events may change as the climate changes is of great importance to stakeholders including electrical utility companies and local governments. Identification of freezing rain in climate models requires the use of precipitation-type algorithms, and differences between algorithms may lead to differences in the types of precipitation identified for a given thermodynamic profile. We explore the uncertainty associated with algorithm selection by applying four algorithms (Cantin and Bachand, Baldwin, Ramer, and Bourgouin) offline to an ensemble of simulations of the fifth-generation Canadian Regional Climate Model (CRCM5) at 0.22° grid spacing. First, we examine results for the CRCM5 driven by ERA-Interim reanalysis to analyze how well the algorithms reproduce the recent climatology of freezing rain and how results vary depending on algorithm parameters and the characteristics of available model output. We find that while the Ramer and Baldwin algorithms tend to be better correlated with observations than Cantin and Bachand or Bourgouin, their results are highly sensitive to algorithm parameters and to the number of pressure levels used. We also apply the algorithms to four CRCM5 simulations driven by different global climate models (GCMs) and find that the uncertainty associated with algorithm selection is generally similar to or greater than that associated with choice of driving GCM for the recent past climate. Our results provide guidance for future studies on freezing rain in climate simulations and demonstrate the importance of accounting for uncertainty between algorithms when identifying precipitation type from climate model output. Significance Statement Freezing rain events and ice storms can have major consequences, including power outages and dangerous road conditions. It is therefore important to understand how climate change might affect the frequency and severity of these events. One source of uncertainty in climate studies of these events is related to the choice of algorithm used to detect freezing rain in model output. We compare the frequency of freezing rain identified using four different algorithms and find sometimes large differences depending on the algorithm chosen over some regions. Our findings highlight the importance of taking this source of uncertainty into account and will provide researchers with guidance as to which algorithms are best suited for climate studies of freezing rain.

Raindrop size distribution characteristics in summer of a nival glacial zone in eastern Tianshan, Central Asia

Precipitation is a key component of the hydrological cycle, which is critical to understanding its formation and evolution. In this study, based on the observation data of the PWS100 located at the meteorological observation site at the terminal of Urumqi Glacier No. 1, eastern Tianshan Mountains, the statistical characteristics of the summer raindrop size distribution (DSD) were analyzed, and the DSD characteristics of five different rainfall rates(R) and two rainfall types (convective and stratiform) were investigated for the daytime and nighttime. The average raindrop spectral width was the largest in class III (1 < R < 5 mm h−1). The result showed that the raindrop concentration increased with the rainfall rate. The maximum raindrop concentration was at class IV (5 < R < 10 mm h−1), when the raindrop diameter was higher than 1.74 mm. The small and medium size raindrops played a dominant role in precipitation composition in the head watershed of the Urumqi River, contributing 98% of the total raindrop. The convective precipitation at the headwaters was divided into continental clusters. The stratiform/convective Dm-log10Nw was characterized by a large mass-weighted mean diameter Dm = 1.523/2.608, and a generalized intercept log10Nw = 2.841/3.469. N(D) of convective precipitation was significantly different between the daytime and nighttime, while that of stratiform precipitation was almost the same. The constraint relationship between R-Dm and R-log10Nw of these two precipitation types was deduced, the exponent of the R-log10Nw relationship of the two precipitation types was negative, and the Dm value of stratiform precipitation tended to be stable at a higher rainfall rate (1–2 mm). Finally, we deduced the power-law relationship between radar reflectivity (Z) and rain rate (R) [Z = A*Rb] for stratiform and convective precipitation at the headwaters. Z = 698.8R2.0 was for stratiform, and Z = 47.1R2.0 was for convective. These results, for the first time, offer insights into the microphysical nature of precipitation in the head watershed of the Urumqi River during the summer and provide essential information that could be useful for precipitation retrievals based on weather radar observations.

Three-Dimensional Structure Analysis and Droplet Spectrum Characteristics of Southwest Vortex Precipitation System Based on GPM-DPR

This study is the first in the region to use Global Precipitation Mission Dual-Frequency Precipitation Radar (GPM-DPR) and Fengyun-2G (FY-2G) observations to qualitatively and quantitatively study the Southwest Vortex evolution characteristics during the flood season from 2019 to 2021. Furthermore, vertical characteristics of the two main precipitation types in the Southwest Vortex, stratiform and convective, were statistically analyzed at different life stages, including horizontal and vertical distribution of precipitation particles, droplet spectrum characteristics, and vertically layered precipitation contribution. The results showed that: (1) The typical convective precipitation (CP) in the developing and mature stages has strong reflectivity distribution centers in the upper and lower layers, showing characteristics related to terrain. Additionally, the high-level hydrometeor particles are mainly solid precipitation particles, and particles in the lower layers collide and coalesce in the violent vertical motion of the airflow. (2) For the three stages of CP, the reflectivity below melting layer (ML) first showed a rapid weakening trend toward the surface and then remained unchanged, significantly changing its vertical structure. The main rainfall type of the Southwest Vortex system was stratiform precipitation (SP) in the three stages. (3) In the two types of cloud precipitation, the developing stage is generally composed of large and sparse precipitation particles, the mature stage of large and dense precipitation particles, and the dissipating stage of small and sparse precipitation particles. The findings of this study reveal the three-dimensional refined structure and vertical variation characteristics of different life stages of the Southwest Vortex precipitation cloud system and provide important tools and references for improving the accuracy of numerical models and the forecast level of short-term heavy precipitation under complex terrain.

Numerical Simulation of a Typical Convective Precipitation and Its Cloud Microphysical Process in the Yushu Area, Based on the WRF Model

Cloud microphysical processes significantly impact the time variation and intensity of precipitation. However, due to the high altitude of the Tibetan Plateau (TP) and the lack of observational data, the understanding of cloud microphysical processes on the TP is relatively insufficient, affecting the accuracy of precipitation simulations around the TP. To further reveal the characteristics of convective precipitation and cloud microphysical structure over the TP, the mesoscale numerical model, WRF, and various observational data were used to simulate and evaluate typical convective precipitation in the Yushu area, which was recorded from 11 to 12 August 2020. The results showed that the combination of the Lin scheme in the WRF model could effectively reproduce this case’s characteristics and evolution process. In the simulation process, the particles of each phase were distributed at different altitudes, and their mass and density over time reflected the characteristics of surface precipitation changes. Among the particles mentioned above, rainwater contributed the most to the initiation and growth of graupel particles. Further research established that the initiation of graupel was mainly affected by the freezing effect of rainwater and cloud ice, while the growth of graupel was influenced primarily by the collision of graupel particles and rainwater. On the whole, from the evolution characteristics of microphysical processes over time, it was found that the ice phase process plays an essential role in this typical convective precipitation.

Validation of Precipitation Type Forecasts Based on ECMWF’s Ensemble Model for Hungary

Forecasts of precipitation type are of high priority, as they have a large influence on human safety, the economy and the environment. In recent decades, methods of statistical post-processing of numerical weather prediction (NWP) outputs were only applied beside the experience of the operational forecasters. In the last few years, numerical models developed significantly; thus, precipitation type has become a variable directly calculated in some models. In the European Centre for Medium-Range Weather Forecasts (ECMWF) integrated forecast system (IFS) model, a new method has been used since 2015 to predict the type of precipitation. In this study, we examine the forecast of the ECMWF IFS ensemble model concerning precipitation type through ensemble verification and a case study on a freezing-rain situation for the territory of Hungary. We put emphasis on the investigation of the usability of ensemble forecasts. We introduce the developed forms of visualization supporting the interpretation of ensemble precipitation-type forecasts.