Abstract. Snowfall in the northeastern part of South Korea is the result of complex snowfall mechanisms due to a highly contrasting terrain combined with nearby warm waters and three synoptic pressure patterns. All these factors together create unique combinations, whose disentangling can provide new insights into the microphysics of snow on the planet. This study focuses on the impact of wind flow and topography on the microphysics drawing of 20 snowfall events during the ICE-POP 2018 (International Collaborative Experiment for PyeongChang 2018 Olympic and Paralympic winter games) field campaign in the Gangwon region. The vertical structure of precipitation and size distribution characteristics are investigated with collocated MRR (micro rain radar) and PARSIVEL (particle size velocity) disdrometers installed across the mountain range. The results indicate that wind shear and embedded turbulence were the cause of the riming process dominating the mountainous region. As the strength of these processes weakens from the mountainous region to the coastal region, riming became less significant and gave way to aggregation. This study specifically analyzes the microphysical characteristics under three major synoptic patterns: air–sea interaction, cold low, and warm low. Air–sea interaction pattern is characterized by more frequent snowfall and vertically deeper precipitation systems on the windward side, resulting in significant aggregation in the coastal region, with riming featuring as a primary growth mechanism in both mountainous and coastal regions. The cold-low pattern is characterized by a higher snowfall rate and vertically deep systems in the mountainous region, with the precipitation system becoming shallower in the coastal region and strong turbulence being found in the layer below 2 km in the mountainous upstream region (linked with dominant aggregation). The warm-low pattern features the deepest system: precipitation here is enhanced by the seeder–feeder mechanism with two different precipitation systems divided by the transition zone (easterly below and westerly above). Overall, it is found that strong shear and turbulence in the transition zone is a likely reason for the dominant riming process in the mountainous region, with aggregation being important in both mountainous and coastal regions.

This article proposes and presents a novel approach to the characterization of winter precipitation and modeling of radar observables through a synergistic use of advanced optical disdrometers for microphysical and geometrical measurements of ice and snow particles (in particular, a multi-angle snowflake camera-MASC), image processing methodology, advanced method-of-moments scattering computations, and state-of-the-art polarimetric radars. The article also describes the newly built and established MASCRAD (MASC + Radar) in-situ measurement site, under the umbrella of CSU-CHILL Radar, as well as the MASCRAD project and 2014/2015 winter campaign. We apply a visual hull method to reconstruct 3D shapes of ice particles based on high-resolution MASC images, and perform "particle-by-particle" scattering computations to obtain polarimetric radar observables. The article also presents and discusses selected illustrative observation data, results, and analyses for three cases with widely-differing meteorological settings that involve contrasting hydrometeor forms. Illustrative results of scattering calculations based on MASC images captured during these events, in comparison with radar data, as well as selected comparative studies of snow habits from MASC, 2D video-disdrometer, and CHILL radar data, are presented, along with the analysis of microphysical characteristics of particles. In the longer term, this work has potential to significantly improve the radar-based quantitative winter-precipitation estimation.

Differences in atmospheric environments can have a significant impact on microphysical processes of precipitation. Dominant warm (cold) rain processes in East Asia (southern Great Plains of USA) is implied by large (small or constant) gradient of reflectivity at low level in vertical reflectivity profiles. Long-term ground observations using two-dimensional video disdrometers were conducted in southern Korea (KOR) and Norman, Oklahoma, USA (OKL). Raindrop size distributions (RSD) and their moments in the two regions were analyzed in the framework of scaling normalized RSDs. Results show that the concentrations of small (big) raindrops were higher (smaller) in KOR than in OKL. KOR RSDs were also found to be characterized by relatively high characteristic number concentrations, N0′, and a small characteristic diameters, Dm′ when compared to OKL RSDs. The N0′ increases with increasing Dm′ in both KOR and OKL at the lower Z with the opposite trend at higher Z. In addition, OKL RSDs with Dm′ > 2.5 mm indicate the existence of an equilibrium between coalescence and break-up processes. Rainfall estimation relationships between the rain rate (R) and radar variables were retrieved from scattering simulations at S-, C-, and X-band wavelengths. KOR RSDs showed relatively small horizontal reflectivity and specific differential phase shift at the same R and same wavelength when compared to OKL RSDs. The regional dependency was significant due to the different microphysical process in KOR and OKL. The specific attenuation of KOR was however similar to that of OKL only at S-band, indicating an advantage of using specific attenuation in S-band in rainfall estimation.

Traditional radar-based rainfall estimation is typically done by known functional relationships between the rainfall intensity (R) and radar measurables, such as R–Zh, R–(Zh, ZDR), etc. One of the biggest advantages of machine learning algorithms is the applicability to a non-linear relationship between a dependent variable and independent variables without any predefined relationships. We explored the potential use of two supervised machine learning methods (regression tree and random forest) in rainfall estimation using dual-polarization radar variables. The regression tree does not require normalization and scaling of data; however, this method is quite unstable since each split depends on the parent split. Since the random forest is an ensemble method of regression trees, it has less variability in prediction compared with regression trees, but consumes more computer resources. We considered several different configurations for machine learning algorithms with different sets of dependent and independent variables. The random forest model was appropriately tuned. In the test of variable importance, the specific differential phase (differential reflectivity) was the most important variable to predict the rainfall rate (residual that is the difference between the true rainfall rate and the one estimated from the R–Z relationship). The models were evaluated by 10-fold cross-validation. The best model was the random forest model using a residual with the non-classified training set. The results indicated that the machine learning algorithms outperformed the traditional R–Z relationship. Then, we applied the best machine learning model to an S-band dual-polarization radar (Mt. Myeonbong) and validated the result with ground rain gauges. The results of the application to radar data showed that the estimates of the residuals had spatial variability. The stratiform and weak rain areas had positive residuals while convective areas had negative residuals, indicating that the spatial error structure driven by the R–Z relationship was well captured by the model. The rainfall rates of all pixels over the study area were adjusted with the estimated residuals. The rainfall rates adjusted by residual showed excellent agreement with the rain gauge, especially at high rainfall rates.

Abstract. Snowfall in north-eastern part of South Korea is the result of complex snowfall mechanisms due to a highly-contrasting terrain combined with nearby warm waters and three synoptic pressure patterns. All these factors together create unique combinations, whose disentangling can provide new insights into the microphysics of snow in the planet. This study focuses on the impact of wind flow and topography on the microphysics drawing of twenty snowfall events during the ICE-POP 2018 (International Collaborative Experiment for Pyeongchang 2018 Olympic and Paralympic winter games) field campaign in the Gangwon region. The vertical structure of precipitation and size distribution characteristics are investigated with collocated MRR (Micro Rain Radar) and PARSIVEL (PARticle SIze VELocity) disdrometers installed across the mountain range. The results indicate that wind shear and embedded turbulence were the cause of the riming process dominating the mountainous region. As the strength of these processes weaken from the mountainous region to the coastal region, riming became less significant and gave way to aggregation. This study specifically analyzes the microphysical characteristics under three major synoptic patterns: air-sea interaction, cold low, and warm low. Air–sea interaction pattern is characterized by more frequent snowfall and vertically deeper precipitation systems in the windward side, resulting in significant aggregation in the coastal region, with riming featuring as a primary growth mechanism in both mountainous and coastal regions. The cold low pattern is characterized by a higher snowfall rate and vertically deep systems in mountainous region, with the precipitation system becoming shallower in the coastal region and strong turbulence being found in the layer below 2 km in the mountainous upstream region (linked with dominant aggregation). The warm low pattern features the deepest system: precipitation here is enhanced by the seeder–feeder mechanism with two different precipitation systems divided by the transition zone (easterly below and westerly above). Overall, it is found that strong shear and turbulence in the transition zone is a likely reason for the dominant riming process in mountainous region, with aggregation being important in both mountainous and coastal regions.