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The Weather Research and Forecasting (WRF) double-moment 6-class (WDM6) scheme was modified by incorporating predicted graupel density. Explicitly predicted graupel density, in turn, modifies graupel characteristics such as the fall velocity–diameter and mass–diameter relationships of graupel. The modified WDM6 has been evaluated based on a two-dimensional (2D) idealized squall line simulation and winter snowfall events that occurred during the International Collaborative Experiment for Pyeongchang Olympics and Paralympics (ICE-POP 2018) field campaign over the Korean Peninsula. From the 2D simulation, we confirmed that the modified WDM6 can simulate varying graupel densities, ranging from low values in an anvil cloud region to high values in the convective region at the mature stage of a squall line. Simulations with the modified WDM6 increased graupel amounts at the surface and decreased graupel aloft because of the faster sedimentation of graupel for two winter snowfall cases during the ICE-POP 2018 campaign, as simulated in the 2D idealized model. The altered graupel sedimentation in the modified WDM6 influenced the magnitude of the major microphysical processes of graupel and snow, subsequently reducing the surface snow amount and precipitation over the mountainous region. The reduced surface precipitation over the mountainous region mitigates the surface precipitation bias observed in the original WDM6, resulting in better statistical skill scores for the root mean square errors. Notably, the modified WDM6 reasonably captures the relationship between graupel density and its fall velocity, as retrieved from 2D video disdrometer measurements, thus emphasizing the necessity of including predicted graupel density to realistically represent the microphysical properties of graupel in models. [ABSTRACT FROM AUTHOR]

This study investigates the impact of initial conditions/boundary conditions (ICs/BCs) and horizontal resolutions on forecast for average weather conditions, focusing on low-level weather variables such as 2-m temperature (T2m), 2-m water vapor mixing ratio (Q2m), and 10-m wind speed (WS10). A Weather Research and Forecasting (WRF) Model is used for regional mesoscale model simulations and large-eddy simulations (LESs). The 6-h-interval forecast fields generated by the Global Forecast System of the National Centers for Environmental Prediction and the Korean Integrated Model of the Korea Meteorological Administration are utilized as ICs/BCs for the regional models. Numerical experiments are performed for 24 h starting at 0000 UTC on each day in April 2021 when the average monthly wind speed was strongest during 10 years (2011–20). A comparison of model simulations with observations obtained around the Yeongjong Island, where Incheon International Airport is situated, shows that the regional models capture the time series of T2m, Q2m, and WS10 more effectively than the global model forecasts. Moreover, the LES experiments with a 100-m horizontal grid spacing simulate higher Q2m and lower WS10 during the daytime compared to the 1-km WRF. This results in a deterioration of their time-series correlation with the observations. Meanwhile, the 100-m LES forecasts time series of T2m over ocean stations and Q2m over land stations, as well as probability density functions of low-level weather variables, more accurately than that of the 1-km WRF. Our study also emphasizes the need for caution when comparing high-resolution model results with observation values at specific stations due to the high spatial variability in low-level meteorological fields. [ABSTRACT FROM AUTHOR]

Accurately generating high-resolution surface grid datasets often involves merging multiple weather observation networks and addressing the challenge of network heterogeneity. This study aims to tackle the problem of accurately interpolating temperature data in regions with a complex topography. To achieve this, we introduce a deterministic interpolation method that incorporates elevation to enhance the accuracy of temperature datasets. This method is particularly valuable for areas with intricate terrains. Our robust methodology integrates a network harmonization method with radial basis function (RBF) interpolation for complex topographical regions. The method was tested on 10 min average temperature data from Jeju Island, South Korea, over 2 years that had a spatial resolution of 100 m. The results show a significant reduction of 5.5% in error rates, from an average of 0.73 °C to 0.69 °C, by incorporating all adjusted data. Integrating a parameterized nonlinear temperature profile further enhances accuracy, yielding an average reduction of 4.4% in error compared to the linear model. The spatial interpolation method, based on regression-based radial basis functions, demonstrates a 6.7% improvement over regression-based kriging for the same temperature profile. This research offers a valuable approach for precise temperature interpolation, especially in regions with a complex topography. [ABSTRACT FROM AUTHOR]

The diagonal squall line that passed through the Korean Peninsula on the 18 May 2020 was examined using wind data retrieved from multiple Doppler radar synthesis focusing on its kinematic and dynamic aspects. The low-level jet, along with warm and moist air in the lower level, served as the primary source of moisture supply during the initiation and formation process. The presence of a cold pool accompanying the squall line played a role in retaining moisture at the surface. As the squall line approached the Korean Peninsula, the convective bands in the northern segment (NS) and southern segment (SS) of the squall line exhibited distinct evolutionary patterns. The vertical wind shear in the NS area was more pronounced compared to that in the SS. The ascending inflow associated with the tilted updraft in the NS reached an altitude of 7 km, whereas it was only up to 4 km in the SS. The difference was caused by the strong descending rear flow, which obstructed the ascending inflow and let to significant updraft in the SS. [ABSTRACT FROM AUTHOR]

The Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) scheme was modified by incorporating prognostic graupel density. Explicitly prognosed graupel density, in turn, modifies graupel characteristics such as the fall velocity–diameter and mass–diameter relationships of graupel. The modified WDM6 has been evaluated based on a two-dimensional (2D) idealized squall line simulation and winter snowfall events that occurred during the International Collaborative Experiment for Pyeongchang Olympics and Paralympics (ICE-POP 2018) field campaign over the Korean Peninsula. From the 2D simulation, we confirmed that the modified WDM6 can simulate varying graupel density, ranging from low values in an anvil clouds region to high values in the convective region at the mature stage of a squall line. Simulations with the modified WDM6 increase graupel amounts at the surface and decreased graupel aloft because of the faster sedimentation of graupel for two winter snowfall cases during the ICE-POP 2018 campaign, as simulated in the 2D idealized model. The altered graupel sedimentation in the modified WDM6 influenced the magnitude of the major microphysical processes of graupel and snow, subsequently reducing the surface snow amount and precipitation over the mountainous region. The reduced surface precipitation over the mountainous region mitigates the surface precipitation bias observed in the original WDM6, resulting in better statistical skill scores for the root mean square errors. Notably, the modified WDM6 reasonably captures the relationship between graupel density and its fall velocity, as retrieved from 2D video disdrometer measurements, thus emphasizing the necessity of including prognostic graupel density to realistically represent the microphysical properties of graupel in models. [ABSTRACT FROM AUTHOR]

Planetary boundary layer (PBL) flow over complex terrain during a cold surge event was investigated using 3-hourly radiosonde measurements in upwind, near ridge, and downwind locations of mountains in the northeastern part of Republic of Korea and using a high-resolution (333-m) numerical simulation. A cold surge occurred on 23 January 2018 and lasted for 4 days. We analyzed onset day of the cold surge when air temperature dropped rapidly. Analysis of the radiosonde data shows that the PBL was characterized by an adiabatic layer with strong capping inversion in early morning and evening as well as during daytime in the upwind and near-ridge sites. The PBL flow at the near-ridge site was strongest among three sites except at 0600 local standard time (LST) when the PBL flow in the lee was strongest. We performed high-resolution (333-m) numerical simulations using the Weather Research and Forecasting (WRF) model. The adiabatic PBL in the upwind site at 0600 LST was simulated, although its depth was underestimated. The model reproduced the strong low-level wind at 0600 LST and large wind shear during the daytime in the lee, but it did not capture the exact timing of the large wind shear. The model showed overall good performance in simulating the vertical profile of the virtual potential temperature and wind below 2 km above ground level at the three sites, with a high index of agreement (IOA) except for the wind at 1200 and 1500 LST in the lee. To examine the cause for the different behavior of PBL flow in the lee of mountains between 0600 LST and the daytime, we calculated the Froude number for PBL flow using radiosonde measurements based on reduced gravity shallow water (RGSW) theory. At 0600 LST, the upwind Froude number F0 was close to 1, while during the daytime, it was much lower than 1. The observed lee flow behavior was consistent with the flow regime change of a single layer over an obstacle with changing F0; the flow with a propagating lee jump changes into that with a stationary lee jump with decreasing F0. Numerical simulation shows that the steepening of streamlines of lee-wave field leads to a jump-like structure in the lee of mountains during the daytime. [ABSTRACT FROM AUTHOR]

A new method is developed to derive the hydrometer's bulk density and bulk water fraction from collocated measurements from Micro-Rain Radar (MRR) and Particle Size and Velocity disdrometer (Parsivel). Rigorous particle scattering simulation, namely the T-matrix method, is applied to particle size distribution data of Parsivel to calculate the reflectivity (ZHH). The possible combinations of the particle's ice, air, and water are derived to compare them with the MRR-measured ZHH. The combination of minimum water fraction subsequently determines the bulk density (ρbulk). The proposed method is applied to the data collected from the International Collaborative Experiments for Pyeongchang 2018 Olympic and Paralympic Winter Games (ICE-POP 2018) Projects and its pre-campaign. The estimated was examined by self-evaluation of reflectivity weighted fall velocity (Vz) of MRR and independent comparison of the liquid-equivalent snowfall rate (SR) of collocated Pluvio. The bias values are adequately low (Vz: -0.27~0.14 m s-1, SR: 0.52~0.74 mm hr-1). The correlation coefficient of calculated SR from estimated ρbulk and observed SR from Pluvio can be up to 0.74. The results indicate the capability to derive reliable ρbulk through the proposed method, leveraging the compact and easily deployable designs of MRR and Parsivel. The derived bulk density of the two warm-low cases (28 February and 07 March 2018) shares a similar transition as the systems were decaying. The particles with higher bulk density and bulk water fraction were found in the coastal sites (BKC and GWU: mean ρbulk values are 0.12 to 0.25 g cm-3), typically accompanied by higher liquid-water constituents (mean values of the top 5 % bulk water fraction are 0.33 to 0.50) than the inland sites (YPO and MHS: mean ρbulk values are 0.09 to 0.08 and mean values of the top 5 % bulk water fraction are 0.001 to 0.029) during such synoptic conditions. [ABSTRACT FROM AUTHOR]

Retrieving raindrop size distribution (DSD) is essential to understanding precipitation processes. Conventional approaches based on polarimetric radar (e.g., polynomial regression) struggle to accurately capture the inherent nonlinearity between DSD parameters and radar measurables. In contrast, machine learning (ML) algorithms offer a promising solution as it effectively models the complex non‐linear relationship. We have developed an ML algorithm to retrieve DSD parameters using polarimetric radar variables in a framework of double‐moment normalization. The potentially stable and invariant double‐moment normalized DSD enables the applicability of the algorithm in any climatic regime or any precipitation system. To improve the robustness of the model to measurement noises, we employed training samples with random noise. All ML algorithms outperformed the conventional method, with the random forest being the best model. This study highlights the effectiveness of the developed algorithm as a tool for understanding the DSD characteristics from polarimetric radar measurements. Plain Language Summary: Raindrop size distribution (DSD) is an essential component of precipitation characteristics. DSD is often represented by a particular mathematical functional form which is composed of a few parameters. Retrieving the parameters from radar measurements can assist in identifying the microphysics of precipitation systems at high spatiotemporal resolution. This study developed a machine learning algorithm to retrieve parameters of double‐moment normalized DSD, which offers advantages over the other DSD forms thanks to two characteristic parameters and potentially universal normalized DSD shape. This study also demonstrates the practical application of the trained model by retrieving DSD information from real radar measurements. Key Points: We present a machine learning algorithm for the retrieval of raindrop size distribution parameters based on polarimetric radar measurementThe retrieval is based on the double‐moment normalization for potential universal applicationsThe suggested model was applied to the X‐band polarimetric radar data and outperformed the conventional polynomial regression model [ABSTRACT FROM AUTHOR]

A new version of the Weather Research and Forecasting (WRF) double-moment 6-class (WDM6) microphysics scheme was developed based on the existing WDM6 scheme by predicting snow and graupel number concentrations. The new WDM6 scheme was tested for summer rainfall and winter snowfall cases to evaluate the effects of prognostic number concentration of snow and graupel on the simulated precipitation. The number concentration of snow decreases at the upper layers and the one of graupel also decreases at all layers in the new WDM6 scheme compared to the diagnosed ones in the original WDM6 scheme. Rain number concentration is remarkably reduced in the new WDM6 scheme due to the newly added and modified sink processes. Therefore, the new scheme produces a larger size of raindrops with a reduced number concentration than the original scheme, which hinders raindrop evaporation and produces more surface rain. Even though the enhanced surface rainfall in the new scheme deteriorates the bias score, the new scheme improves the statistical skill of the equitable threat score and probability of detection in most cases. These scores all improved for warm-type summer cases in the new scheme. The new scheme also shows more comparable features to the observation for the probability density functions of simulated liquid equivalent precipitation rates by alleviating the overprediction problem of precipitation frequencies belonging to heavy precipitation categories. Therefore, the new scheme improves the precipitation forecast for warm-type summer cases, which occur most frequently during the summer season over the Korean Peninsula. [ABSTRACT FROM AUTHOR]