Search Results (20)

Winter mixed-phase precipitation (P) impacts transportation, electric power grids, and homes. Forecasting winter precipitation such as freezing precipitation (ZP), freezing rain (ZR), freezing drizzle (ZL), ice pellets (IPs), and the snow (S) and rain (R) boundary remains challenging due to the complex cloud microphysical and dynamical processes involved, which are difficult to predict with the current numerical weather prediction (NWP) models. Understanding these processes based on observations is crucial for improving NWP models. To aid this effort, Environment and Climate Change Canada deployed specialized instruments such as the Vaisala FD71P and OTT PARSIVEL disdrometers, which measure P type (PT), particle size distributions, and fall velocity (V). The liquid water content (LWC) and mean mass-weighted diameter (${\mathrm{D}}_{\mathrm{m}}$) were derived based on the PARSIVEL data during ZP events. Additionally, a Micro Rain Radar (MRR) and an OTT Pluvio2 P gauge were used as part of the Winter Precipitation Type Research Multi-Scale Experiment (WINTRE-MIX) field campaign at Sorel, Quebec. The dataset included manual measurements of the snow water equivalent (SWE), PT, and radiosonde profiles. The analysis revealed that the FD71P and PARSIVEL instruments generally agreed in detecting P and snow events. However, FD71P tended to overestimate ZR and underestimate IPs, while PARSIVEL showed superior detection of R, ZR, and S. Conversely, the FD71P performed better in identifying ZL. These discrepancies may stem from uncertainties in the velocity–diameter (V-D) relationship used to diagnose ZR and IPs. Observations from the MRR, radiosondes, and surface data linked ZR and IP events to melting layers (MLs). IP events were associated with colder surface temperatures (Ts) compared to ZP events. Most ZR and ZL occurrences were characterized by light P with low LWC and specific intensity and ${\mathrm{D}}_{\mathrm{m}}$ thresholds. Additionally, snow events were more common at warmer T compared to liquid P under low surface relative humidity conditions. The Pluvio2 gauge significantly underestimated snowfall compared to the optical probes and manual measurements. However, snowfall estimates derived from PARSIVEL data, adjusted for snow density to account for riming effects, closely matched measurements from the FD71P and manual observations. Full article

This research presents a novel fuzzy-logic-based algorithm aimed at detecting and removing interference lines from Micro Rain Radar (MRR-2) data. Interference lines, which are non-meteorological echoes with unknown origins, can severely obscure meteorological signals. Leveraging an understanding of interference line characteristics, such as temporal continuity, we identified and utilized eight key variables to distinguish interference lines from meteorological signals. These variables include radar moments, Doppler spectrum peaks, and the spatial/temporal continuity of Doppler velocity. The algorithm was developed and validated using data from MRR installations at three sites (Seoul, Suwon, and Incheon) in South Korea, from June to September 2021–2023. While there is a slight tendency to eliminate some weak precipitation, results indicate that the algorithm effectively removes interference lines while preserving the majority of genuine precipitation signals, even in complex scenarios where both interference and precipitation signals are present. The developed software, written in Python 3 and available as open-source, outputs in NetCDF4 format, with customizable parameters for user flexibility. This tool offers a significant contribution to the field, facilitating the accurate interpretation of MRR-2 data contaminated by interference. Full article

This study utilizes comprehensive observational data from a stratiform mixed-cloud precipitation event in Liupan Mountains, combined with ground-based millimeter-wave cloud radar (CR), micro rain radar (MRR), and microwave radiometer (MR) data, to study the evolution characteristics and conversion efficiency of precipitation particles in the ice–water mixed layer, melting layer, and below these layers during the formation and dissipation of precipitation. The results show the following: (1) When precipitation particles occupy more than 20% of cloud layers detected by cloud radar, the ice–water mixed cloud layer descends and evolves into a precipitating cloud. (2) During surface precipitation periods, the proportion of raindrops forming precipitation was equivalent to that of small-scale precipitation particles in the cloud layers. The proportion of precipitation particles in the cloud layers with temperatures below 0 °C averaged 25%. Ice-phase particles within the bright band (BB) melted, coalesced, and grew into larger precipitation particles, increasing their proportion to 55%. (3) After surface precipitation ended, the water content and precipitation rate of the cloud layer were 60% and 52% of those during the precipitation process, respectively. The proportion of small-scale precipitation particles in the cloud layers was approximately half of that during the precipitation period. A large number of evaporated small-scale precipitation particles floated in the air layer below the clouds, occupying less than 6.0% of the cloud layers. Full article

Mêdog is located at the entrance of the water vapor channel of the Yarlung Tsangpo Great Canyon on the southeastern Tibetan Plateau (TP). In this study, the seasonal variation in the microphysical vertical structure of stratiform precipitation at the Mêdog site in 2022 was investigated using micro rain radar (MRR) observations, as there is a lack of similar studies in this region. The average melting layer height is the lowest in February, after which it gradually increases, reaches its peak in August, and then gradually decreases. For lower rain categories, the vertical distribution of small drops remains uniform in winter below the melting layer. The medium-sized drops show slight increases, leading to negative gradients in the microphysical profiles. Slight or evident decreases in concentrations of small drops are observed with decreasing height in the premonsoon, monsoon, and postmonsoon seasons, likely due to significant evaporation. The radar reflectivity, rain rate, and liquid water content profiles decrease with decreasing height according to the decrease in concentrations of small drops. With increasing rain rate, the drop size distribution (DSD) displays significant variations in winter, and the fall velocity decreases rapidly with decreasing height. In the premonsoon, monsoon, and postmonsoon seasons, the concentrations of large drops significantly decrease below the melting layer because of the breakup mechanism, leading to the decreases in the fall velocity profiles with decreasing height during these seasons. Raindrops with sizes ranging from 0.3–0.5 mm are predominant in terms of the total drop number concentration in all seasons. Precipitation in winter and postmonsoon seasons is mainly characterized by small raindrops, while that in premonsoon and monsoon seasons mainly comprises medium-sized raindrops. Understanding the seasonal variation in the vertical structure of precipitation in Mêdog will improve the radar quantitative estimation and the use of microphysical parameterization schemes in numerical weather forecast models over the TP. Full article

The rescaling of rainfall requires measurements of rainfall rates over many dimensions. This paper develops one approach using 10 m vertical spatial observations of the Doppler spectra of falling rain every 10 s over intervals varying from 15 up to 41 min in two different locations and in two different years using two different micro-rain radars (MRR). The transformation of the temporal domain into spatial observations uses the Taylor “frozen” turbulence hypothesis to estimate an average advection speed over an entire observation interval. Thus, when no other advection estimates are possible, this paper offers a new approach for estimating the appropriate frozen turbulence advection speed by minimizing power spectral differences between the ensemble of purely spatial radial power spectra observed at all times in the vertical and those using the ensemble of temporal spectra at all heights to yield statistically reliable scaling relations. Thus, it is likely that MRR and other vertically pointing Doppler radars may often help to obviate the need for expensive and immobile large networks of instruments in order to determine such scaling relations but not the need of those radars for surveillance. Full article

A field campaign in Liupan Mountains was carried out by the Weather Modification Center of the China Meteorological Administration to study the impact of terrain on precipitation in Northwest China. The vertical structures and microphysical characteristics of a mixed cloud and precipitation process, which means stratiform clouds with embedded convection, over three topographic positions of the Liupan Mountains, namely, the Longde (LD, located on the windward slope), Liupan (LP, located on the mountain top), and Dawan sites (DW, located on the leeward slope), are compared using measurements from ground-based cloud radar (CR), micro rain radar (MRR), and disdrometer (OTT). The 17 h process is classified into cumulus mixed (1149 min), shallow (528 min), and stratiform (570 min) cloud and precipitation stages. Among them, the vertical structures over the three sites are relatively similar in the third stage, while the differences, mainly in cloud-top heights (CTHs) and rain rates (Rs), are significant in the second stage due to the strong instability. Overall, the characteristics of higher concentrations and smaller diameters of raindrops are found in this study, especially at the LP site. Topographic forcing makes the microphysical and dynamic processes of mountaintop clouds and precipitation more intense. The updrafts are the strongest at the LP, caused by orographic uplifting, and the DW is dominated by the downdrafts due to the topography impact on the dynamic structure. Meanwhile, particle falling velocities (V_ts) and downdrafts rapidly increase within 0.6 km near the ground over the LP, forming positive feedback, and the collision–coalescence process is dominant. Full article

Rain type classification into convective and stratiform is an essential step required to improve quantitative precipitation estimations by remote sensing instruments. Previous studies with Micro Rain Radar (MRR) measurements and subjective rules have been performed to classify rain events. However, automating this process by using machine learning (ML) models provides the advantages of fast and reliable classification with the possibility to classify rain minute by minute. A total of 20,979 min of rain data measured by an MRR at Das in northeast Spain were used to build seven types of ML models for stratiform and convective rain type classification. The proposed classification models use a set of 22 parameters that summarize the reflectivity, the Doppler velocity, and the spectral width (SW) above and below the so-called separation level (SL). This level is defined as the level with the highest increase in Doppler velocity and corresponds with the bright band in stratiform rain. A pre-classification of the rain type for each minute based on the rain microstructure provided by the collocated disdrometer was performed. Our results indicate that complex ML models, particularly tree-based ensembles such as xgboost and random forest which capture the interactions of different features, perform better than simpler models. Applying methods from the field of interpretable ML, we identified reflectivity at the lowest layer and the average spectral width in the layers below SL as the most important features. High reflectivity and low SW values indicate a higher probability of convective rain. Full article

(1) Raindrop size distribution (DSD) is a vital microphysical characteristic of clouds and precipitations. The vertical evolution of DSD also provides a reference for the microphysical mechanisms and dynamic processes involved in clouds and precipitations. (2) Here we analyzed the characteristics and vertical evolution of DSDs, which were obtained from Micro Rain Radar (MRR) data of two typical stratiform rain cases. (3) First, we compared MRR-observed reflectivity (Z) and DSD at 400 m with data from a distrometer on the ground. This ensured the reliability of the MRR data of the two cases. Then it was found that the DSD was wider just below the 0 °C level than at lower levels. The larger DSDs width formed a bulge shape in the vertical direction, and large particles in the ‘bulge’ then constantly collided as they were falling down. The DSD was broadened and the echo of the warm layer was strengthened. We referred to this as the bulge phenomenon (BP), which appeared occasionally, and broader DSD propagated from high to low intermittently during the stratiform rain. Next, by combining the detailed cloud structures detected by cloud radar with BP, we found that a BP was always accompanied by higher developing cloud tops, stronger Z and larger falling velocity. It was inferred that ice particles formed near cloud top intermittently and fell through the underlying cloud, causing the gustiness and instability of particle aggregation, which was reflected by the BP below the 0 °C layer. BP triggered quick collision and falling down along the warm layer, enhancing the Z and falling velocity transiently. Thus, BP was considered as one of the mechanisms of rain variation in stratocumulus and stratiform rain in North China. Finally, we defined the cycle time of a BP (BPT), which was composed of broadening stage (BS) and stable stage (SS). We found that changes of DSD parameters for both MRR and distrometer responded to each BP occurring, showing the same intermittency. From each BP occurring time to the corresponding BS ending time, D_m basically grew from small to large. After this, D_m decreased immediately or maintained for a while and then decreased. N_w had the opposite trend to D_m. Also, it was found that larger R accelerated the fluency of BP occurring (BPT). Full article

In this research, a summer orographic precipitation process that occurred over the Tian Shan Mountains on 27 July 2019, was investigated, focusing on the impact of vertical wind shear on clouds. Multiple remote sensors were deployed to measure the ambient conditions and the fine structures of clouds and precipitation, including a radiometer, a vertically pointing micro-rain radar (MRR), and a cloud radar on a truck. In addition, a convection-permitting simulation was conducted to investigate the role of vertical wind shear. The results show that (1) according to the MRR measurements, the precipitation was mainly due to a warm rain process and was mostly light to moderate, with no strong convection occurring; (2) the cloud structures observed by the cloud radar were very different above and below the shear level, and the cloud evolution was strongly controlled by the vertical wind shear, and (3) radar observations and model simulations indicated that vertical wind shear had an inhibiting impact on the vertical development of clouds and was responsible for the formation of multi-layer clouds. The analysis highlights the advantages of the use of millimeter radars to measure the fine structures of orographic clouds; thus, they can be powerful tools with which to improve our understanding of the interactions occurring between vertical wind shear and clouds over complex terrain. Full article

Studies of snow virga precipitation are rare. In this study, we investigated data from a vertically pointing Doppler Micro Rain Radar (MRR) in Bern, Switzerland, from 2008 to 2013 for snow virga precipitation events. The MRR data were reprocessed using the radar data processing algorithm of Garcia-Benardi et al., which allows the reliable determination of the snow virga precipitation rate. We focus on a long-lasting snow virga event from 17 March 2013, supported by atmospheric reanalysis data and atmospheric back trajectories. The snow virga was associated with a wind shear carrying moist air and snow precipitation in the upper air layers and dry air in the lower air layers. The lowest altitudes reached by the precipitation varied between 300 m and 1500 m above the ground over the course of the event. The duration of the snow virga was 22 h. In disagreement with the MRR observations, ERA5 reanalysis indicated drizzle at the ground over a time segment of 4 h during the snow virga event. Full article

The detection and characterisation of the radar Bright Band (BB) are essential for many applications of weather radar quantitative precipitation estimates, such as heavy rainfall surveillance, hydrological modelling or numerical weather prediction data assimilation. This study presents a new technique to detect the radar BB levels (top, peak and bottom) for Doppler radar spectral moments from the vertically pointing radars applied here to a K-band radar, the MRR-Pro (Micro Rain Radar). The methodology includes signal and noise detection and dealiasing schemes to provide realistic vertical Doppler velocities of precipitating hydrometeors, subsequent calculation of Doppler moments and associated parameters and BB detection and characterisation. Retrieved BB properties are compared with the melting level provided by the MRR-Pro manufacturer software and also with the 0 °C levels for both dry-bulb temperature (freezing level) and wet-bulb temperature from co-located radio soundings in 39 days. In addition, a co-located Parsivel disdrometer is used to analyse the equivalent reflectivity of the lowest radar height bins confirming consistent results of the new signal and noise detection scheme. The processing methodology is coded in a Python program called RaProM-Pro which is freely available in the GitHub repository. Full article

Evaluation of the cloud seeding effect is a challenge due to lack of directly physical observational evidence. In this study, an approach for directly observing the cloud seeding effect is proposed using a 1548 nm coherent Doppler wind lidar (CDWL). Normalized skewness was employed to identify the components of the reflectivity spectrum. The spectrum detection capability of a CDWL was verified by a 24.23-GHz Micro Rain Radar (MRR) in Hefei, China (117°15′ E, 31°50′ N), and different types of lidar spectra were detected and separated, including aerosol, turbulence, cloud droplet, and precipitation. Spectrum analysis was applied as a field experiment performed in Inner Mongolia, China (112°39′ E, 42°21′ N ) to support the cloud seeding operation for the 70th anniversary of China’s national day. The CDWL can monitor the cloud motion and provide windshear and turbulence information ensuring operation safety. The cloud-precipitation process is detected by the CDWL, microwave radiometer (MWR) and Advanced Geosynchronous Radiation Imager (AGRI) in FY4A satellites. In particular, the spectrum width and skewness of seeded cloud show a two-layer structure, which reflects cloud component changes, and it is possibly related to cloud seeding effects. Multi-component spectra are separated into four clusters, which are well distinguished by spectrum width and vertical velocity. In general, our findings provide new evidence that the reflectivity spectrum of CDWL has potential for assessing cloud seeding effects. Full article

Clouds play a significant role in regulating the Arctic climate and water cycle due to their impacts on radiative balance through various complex feedback processes. However, there are still large discrepancies in satellite and numerical model-derived cloud datasets over the Arctic region due to a lack of observations. Here, we report observations of cloud base height (CBH) characteristics measured using a Vaisala CL51 ceilometer at Ny-Ålesund, Svalbard. The study highlights the monthly and seasonal CBH characteristics at the location. It is found that almost 40% of the lowest CBHs fall within a height range of 0.5–1 km. The second and third cloud bases that could be detected by the ceilometer are mostly concentrated below 3 km during summer but possess more vertical spread during the winter season. Thin and low-level clouds appear to be dominant during the summer. Low-level clouds are found to be dominant and observed in 76% of cases. The mid and high-level clouds occur in ~16% and ~7% of cases, respectively. Further, micro rain radar (MRR2) observed enhanced precipitation and snowfall events during the winter and spring which are found to be associated with the lowest CBHs within 2 km from the ground. The frontal process associated with synoptic-scale meteorological conditions explains the variabilities in CBH and precipitation at the observation site when compared for two contrasting winter precipitation events. The findings of the study could be useful for model evaluation of cloud precipitation relationships and satellite data validation in the Arctic environment. Full article

Lack of rainfall information at high temporal resolution in areas with a complex topography as the Tropical Andes is one of the main obstacles to study its rainfall dynamics. Furthermore, rainfall types (e.g., stratiform, convective) are usually defined by using thresholds of some rainfall characteristics such as intensity and velocity. However, these thresholds highly depend on the local climate and the study area. In consequence, these thresholds are a constraining factor for the rainfall class definitions because they cannot be generalized. Thus, this study aims to analyze rainfall-event types by using a data-driven clustering approach based on the k-means algorithm that allows accounting for the similarities of rainfall characteristics of each rainfall type. It was carried out using three years of data retrieved from a vertically pointing Micro Rain Radar (MRR) and a laser disdrometer. The results show two main rainfall types (convective and stratiform) in the area which highly differ in their rainfall features. In addition, a mixed type was found as a subgroup of the stratiform type. The stratiform type was found more frequently throughout the year. Furthermore, rainfall events of short duration (less than 70 min) were prevalent in the study area. This study will contribute to analyze the rainfall formation processes and the vertical profile. Full article

This paper describes a methodology for processing spectral raw data from Micro Rain Radar (MRR), a K-band vertically pointing Doppler radar designed to observe precipitation profiles. The objective is to provide a set of radar integral parameters and derived variables, including a precipitation type classification. The methodology first includes an improved noise level determination, peak signal detection and Doppler dealiasing, allowing us to consider the upward movements of precipitation particles. A second step computes for each of the height bin radar moments, such as equivalent reflectivity (Z_e), average Doppler vertical speed (W), spectral width (σ), the skewness and kurtosis. A third step performs a precipitation type classification for each bin height, considering snow, drizzle, rain, hail, and mixed (rain and snow or graupel). For liquid precipitation types, additional variables are computed, such as liquid water content (LWC), rain rate (RR), or gamma distribution parameters, such as the liquid water content normalized intercept (N_w) or the mean mass-weighted raindrop diameter (D_m) to classify stratiform or convective rainfall regimes. The methodology is applied to data recorded at the Eastern Pyrenees mountains (NE Spain), first with a detailed case study where results are compared with different instruments and, finally, with a 32-day analysis where the hydrometeor classification is compared with co-located Parsivel disdrometer precipitation-type present weather observations. The hydrometeor classification is evaluated with contingency table scores, including Probability of Detection (POD), False Alarm Rate (FAR), and Odds Ratio Skill Score (ORSS). The results indicate a very good capacity of Method3 to distinguish rainfall and snow (PODs equal or greater than 0.97), satisfactory results for mixed and drizzle (PODs of 0.79 and 0.69) and acceptable for a reduced number of hail cases (0.55), with relatively low rate of false alarms and good skill compared to random chance in all cases (FAR < 0.30, ORSS > 0.70). The methodology is available as a Python language program called RaProM at the public github repository. Full article