Micro Rain Radar Research Articles

Case studies of different types of precipitation at Ny-Ålesund, Arctic

Arctic precipitation plays a crucial role in shaping the surface mass balance of Arctic sea ice and has wide-ranging impacts on local climate, ecosystems, and global sea level dynamics. With the Arctic undergoing warming trends, historical data and climate models indicate a shift from primarily snowfall to a rise in liquid and mixed forms of precipitation. This study tried to explain the microphysical characteristics and atmospheric conditions associated with different forms of precipitation and their transitions. The phase changes were explained by the vertical precipitation profiles over Ny-Ålesund, Svalbard ( 55’ N, 56’ E), observed using a Micro Rain Radar (MRR) and vertical atmospheric profiles using the ground-based microwave radiometer (MWR). Atmospheric conditions were also analysed based on ERA5 reanalysis data at different pressure levels. For the cases studied here, it was found that the southerly warm-moist air mass plays a crucial role in the change of precipitation phase and intensity. Warm and moist winds at 2-3 km altitude facilitated high temperature and moisture that helped snow to melt in liquid, resulted in rainfall over the location. Additionally, hourly winds from ERA5 reanalysis indicated upward wind motion was responsible for the formation of graupel. The insight gained from this study will be useful to predict the further precipitation trend over the Arctic more accurately.

Rain detection for rain-contaminated ground-based microwave radiometer data using physics-informed machine learning method

Because the radiation signal is strongly influenced by emission and scattering from rain, microwave radiometer data suffer from rain contamination. The traditional method of using rain gauges to detect rain for microwave radiometers has limitations. For example, it can only detect rain that reaches the ground and is ineffective for raindrops suspended in the atmosphere that can still contaminate remote sensing data. This article presents a rain detection method for microwave radiometer measurements, based on Gradient Boosted Decision Trees (GBDT). First, the characteristic that the increase in microwave radiometer brightness temperature when raindrops are present in the atmosphere, along with the seasonal dependency of rainfall patterns, is combined with meteorological variables to form feature vectors. Then, the GBDT is employed to classify data into rain-free and rain-contaminated categories. Microwave radiometer (MWR) measurements and simultaneous Micro Rain Radar (MRR) target classification collected from the Swiss Plateau in 2008 are utilized to train the model, which is subsequently tested using two testing schemes: ten-fold cross-validation technique and time series test sets. Compared with the detection accuracy of the integrated liquid water (ILW) threshold method (73.6% and 68.3%) in both testing schemes, our GBDT-based method achieved superior accuracy, recording approximately 100% and 98.4%, respectively. The proposed method exhibits strong generalization capabilities, allowing it to directly detect rain contamination in time series data and effectively overcome the time dependence of rainfall occurrence. In addition, compared with the ILW threshold method, the GBDT-based method considers various rainfall patterns contained in various seasons. Features selected for this method enable its direct application to other tropospheric microwave radiometer systems.

Estimating the snow density using collocated Parsivel and Micro-Rain Radar measurements: a preliminary study from ICE-POP 2017/2018

Abstract. A new method is developed to derive the bulk density and bulk water fraction of a population of particles from collocated measurements from the Micro-Rain Radar (MRR) and Particle Size and Velocity disdrometer (Parsivel). A rigorous particle-scattering simulation, namely the T-matrix method, is applied to Parsivel's particle size distribution data 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 the minimum water fraction and maximum ice 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 ρbulk was examined independently by a comparison of the liquid-equivalent snowfall rate (SR) of collocated Pluvio devices. The bias values are adequately low (SR: −0.25–0.06 mm h−1). The retrieved bulk density also shows good consistency with collocated Precipitation Imaging Package (PIP) retrievals. The results indicate the capability of the proposed algorithm to derive reliable ρbulk, leveraging the compact and easily deployable designs of MRR and Parsivel. The derived bulk density of the two warm–low cases (28 February and 7 March 2018) shares a similar transition as the systems were decaying. The higher bulk density and bulk water fraction were found in the coastal sites (BKC and GWU have a median value of ρbulk and are 0.05 to 0.12 g cm−3), typically accompanied by higher liquid-water constituents (mean values of the top 5 % bulk water fraction are 0.07 to 0.45) than the inland sites (YPO and MHS have a median value of ρbulk and are 0.06 to 0.10, and mean values of the top 5 % bulk water fraction are 0.001 to 0.008) during such synoptic conditions.

Characteristics of the Evolution of Precipitation Particles during a Stratiform Precipitation Event in Liupan Mountains

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.

Experimental campaign for the characterization of precipitation in a complex terrain site using high resolution observations

Precipitation has an effect on wind power at several levels. It affects the wind current, blade status, wake development and power production. Power production is affected by the harmful effect of precipitation on the blades eroding its surface and altering their aerodynamic performance. In the past decades, wind has been characterized using different techniques, but less effort has been devoted to precipitation measurement. In this work, the results of an experimental campaign performed at a high altitude complex terrain site to characterize precipitation using high resolution observations are presented. The campaign, carried out at CENER’s experimental wind farm (Alaiz) during 2023 within the framework of the Horizon Europe AIRE project, lasted nine months and different precipitation types (rain, snow, graupel) were recorded using a Micro Rain Radar (MRR), a Parsivel disdrometer and a rain gauge co-located with an instrumented wind mast with anemometers and wind vanes at different heights. Two case studies are selected to illustrate the wide range of variability found in precipitation conditions, particularly during the cool season. Precipitation characterization is very challenging at high temporal resolution, making necessary measurement campaigns with different precipitation equipment to optimize their performance and optimise its calibration. The study of precipitation profiles with MRR will support the study of precipitation impingement on wind turbine blades responsible of blade erosion. Moreover, these measurements will contribute to create the link between in-field wind farm data, laboratory experiments in rain erosion test rig and blade damage models necessary to improve wind turbine and wind farm design and operation.

Seasonal Variation in Vertical Structure for Stratiform Rain at Mêdog Site in Southeastern Tibetan Plateau

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.

Micro Rain Radar and Radiometric Measurements to Unravel Contrasting Features of Rain Microstructure Below and Above the Boundary Layer

AbstractKa‐band Micro rain Doppler radar is an effective tool to investigate the profiles of precipitation microstructure in terms of the raindrop size distribution (DSD). The DSD parameters that vary appreciably with height are indicative of the associated atmospheric phenomena. Hence the present investigation endeavors to put light on the underlying physical processes responsible for the evolution of varied rain microstructure profiles using micro rain radar (MRR), and radiometric measurements complemented with re‐analysis outputs over an urban tropical location, Kolkata (22.57°N, 88.37°E), India. MRR unravels the prevalence of significant biases in the typical power law relationship (Dm = aRb) between rain rate (R) and mass‐weighted mean drop diameter (Dm) along the rain height, especially during intense convective rain events, above the atmospheric boundary layer (ABL). Consequently, an alternative empirical relation appropriate to account for the R‐Dm variability above the ABL is proposed. Further, radiometric measurements and re‐analysis outputs reveal that the presence of atmospheric instabilities coupled with wind shear impacts above the ABL contributes to the enhanced breakup of raindrops and the deviations in the usual R‐Dm relationship. Thus, the present study intends to highlight the applicability of ground‐based radar measurements over the tropics to devise quantitative precipitation algorithms for reliable rain estimates.

Comparisons of Rainfall Microphysical Characteristics between the Southeastern Tibetan Plateau and Low-Altitude Areas

Abstract The low air pressure and density over the Tibetan Plateau may have an impact on the microphysical features of rainfall. Using a two-dimensional video disdrometer (2DVD), a Micro Rain Radar (MRR), and a microwave radiometer (MWR), the features of the raindrop size distribution (DSD) on the southeastern Tibetan Plateau (SETP) are explored and compared with those in low-altitude regions. The falling speed of raindrops on the SETP is higher than that in low-altitude areas. Under different rainfall-rate categories, the number concentration and the maximum diameter of raindrops on the SETP are smaller than those in low-altitude regions. The convective rainfall on the SETP is more maritime-like because the South Asian summer monsoon brings water vapor from the ocean here. For stratiform and convective rainfall, the SETP has more small-sized raindrops than low-altitude locations. The mass-weighted mean diameters (Dm) on the SETP are the smallest among six sites. The generalized intercept parameter (Nw) of stratiform rainfall is balanced at a low rainfall rate, while that of convective rainfall is balanced at a high rainfall rate. Furthermore, for a given μ (the shape parameter of gamma distribution) value, the λ (the slope parameter of gamma distribution) value on the SETP is the highest of the six sites. Significance Statement For the occurrence and progression of rainfall, microphysical processes (for instance, collision, fragmentation, coalescence, and evaporation) that take place when rainfall particles descend are crucial. A key factor in the microphysical features of rainfall that varies with rainfall rates and types is the raindrop size distribution (DSD). The southeastern Tibetan Plateau (SETP)’s unique terrain ensures that there is enough moisture for rain to fall there, but little is known about the microphysical aspects of this type of precipitation. There has not been enough research done on how the high altitude affects the microphysical features of rainfall. The microphysical features of rainfall in this area must thus be studied.

Radar‐Derived Snowfall Microphysical Properties at Davis, Antarctica

AbstractAntarctic precipitation remains poorly characterized and understood, especially within the boundary layer. This is due in part to a still‐limited amount of surface‐based remote sensing observations. A suite of cloud and precipitation remote‐sensing instruments including a W‐band cloud radar and a K‐band Micro Rain Radar (MRR) were used to characterize snowfall over Davis (69°S, 78°E). Surface snowfall events occurred when boundary layer wind speeds were weaker, temperatures were warmer, and relative humidity over ice higher than when virga were present. The presence of virga is associated with Föhn winds due to the location of Davis in the lee of an ice ridgeline. Dual wavelength ratio values from the summer indicate particle aggregation at temperatures of −14° to −10°C, consistent with observations made elsewhere, including in the Arctic. Riming frequency increases for temperatures above −10°C and reaches 6.5% at −3°C. No temperature dependence of rime mass fraction was found. Sublimation of snowfall mass aloft was 50% between the snow peak at 1.2 km and 205 m altitude, which occurs within CloudSat’s “blind zone.” Given the common prevailing wind direction and numerous ice ridgelines along much of the East Antarctic coastline, these Davis results can be used as a basis to further understand snowfall across the Antarctic region.

Detection of Bright Band Heights from Vertical Profile of Radar Reflectivity in Akure, South Western Nigeria

Bright band is an important phenomenon that occurs in radar observations of precipitation. It is a layer of intense reflectivity caused by the melting of the snowflakes in the atmosphere. Accurate detection of bright band and its associated properties are essential for the estimation of precipitation rates and characterization of precipitation types as required in Agriculture, Aviation, Navigation, Telecommunication sectors etc. Bright Band Height (BBH) is a vital parameter in the determination of rain height, rain-induced radio signal attenuation and mitigation techniques. This paper presents an empirical study on the measurement of BBH and Zero Degree Isotherm Height (ZDIH) from Micro Rain Radar (MRR) data obtained in Akure, Nigeria. Vertical profile of radar obtained during stratiform rainy events were examined to detect bright bands and its features. Analysis of the result showed that the ZDIH varies between 4.48 and 4.60 km while the BBH also vary from 4.16 to 4.48 km during intense rainy events in September and October 2010. The derived propagation data would be needed for the optimization and improvement of quality of service (QoS) offered by earth-space links during rainy events in Akure and its environs.

South-West and North-East Monsoon Precipitations Bright Band Characteristics over Kadapa, Semi-arid Regions of India

bstract: Long-term (3-yrs) observation of precipitation during Southwest (SW) and Northeast (NE) With and without Bright Band/melting layer using a Micro Rain Radar (MRR) is investigated. Profiles of DSD in Bright Band (BB) and Non-Bright Band (NBB) cases demonstrate that the BB rain events are associated with higher number concentration of larger drops and NBB events are associated with higher concentration of smaller drops.

A Radar-Based Quantitative Precipitation Estimation Algorithm to Overcome the Impact of Vertical Gradients of Warm-Rain Precipitation: The Flood in Western Germany on 14 July 2021

Abstract The demand of accurate, near-real-time radar-based quantitative precipitation estimation (QPE), which is key to feed hydrological models and enable reliable flash flood predictions, was highlighted again by the disastrous floods following after an intense stratiform precipitation field passing western Germany on 14 July 2021. Three state-of-the-art rainfall algorithms based on reflectivity Z, specific differential phase KDP, and specific attenuation A were applied to observations of four polarimetric C-band radars operated by the German Meteorological Service [DWD (Deutscher Wetterdienst)]. Due to the large vertical gradients of precipitation below the melting layer suggesting warm-rain processes, all QPE products significantly underestimate surface precipitation. We propose two mitigation approaches: (i) vertical profile (VP) corrections for Z and KDP and (ii) gap filling using observations of a local X-band radar, JuXPol. We also derive rainfall retrievals from vertically pointing Micro Rain Radar (MRR) profiles, which indirectly take precipitation gradients in the lower few hundreds of meters into account. When evaluated with DWD rain gauge measurements, those retrievals result in pronounced improvements, especially for the A-based retrieval partly due to its lower sensitivity to the variability of raindrop size distributions. The VP correction further improves QPE by reducing the normalized root-mean-square error by 23% and the normalized mean bias by 20%. With the use of gap-filling JuXPol data, the A-based retrieval gives the lowest errors followed by the Z-based retrievals in combination with VP corrections. The presented algorithms demonstrate the increased value of radar-based QPE application for warm-rain events and related potential flash flooding warnings.

A Practical Approach for Determining Multi-Dimensional Spatial Rainfall Scaling Relations Using High-Resolution Time–Height Doppler Data from a Single Mobile Vertical Pointing Radar

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.

Radar Reflectivity of Micro Rain Radar (MRR2) At 16.44180N, 80.620E of India

To improve accurate standards of Radar Reflectivity Z, Rainfall Rates RR, and even to monitor small size precipitation particles Z-R relation is derived in this work with the help of Micro Rain Radar. Formerly, taking rain/precipitation data from ground-based rain gauges (Cylindrical, Optical, Weighing, Tipping Bucket Rain Gauges, Disdrometers, etc.,) currently, in this work using METEK MRR (Micro Rain Radar) of Frequency Modulated Continuous Wave System which reads the vertical structure of precipitation particles and hence named as vertical profile radar which operates at 24.2 GHz and height up to 6000m/6kms with an increment step size of 200m respectively to monitor the frozen hydrometeors. It is installed at (16.440 N, 80.620 E) K L University, 29 meters above the sea level (ASL) in CARE LAB. The METEK is manufacturer of micro rain radar, to know the Radar Reflectivity of precipitation, it is observing the amount of power received to the radar receiver after hitting the precipitation particles with respect to the transmitted power of the radar. This proposed work considered distinctive rain conditions at different heights ASL ranging from 200m 1200m 2200m, and derived Z-R relations respectively. This work has been provided significant natural disasters such as Geophysical, Hydrological, Climatological and Meteorological pre-intimation easily using METEK MRR.

A Comparative Study on the Vertical Structures and Microphysical Properties of a Mixed Precipitation Process over Different Topographic Positions of the Liupan Mountains in Northwest China

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 (Vts) and downdrafts rapidly increase within 0.6 km near the ground over the LP, forming positive feedback, and the collision–coalescence process is dominant.

Consistency test of precipitating ice cloud retrieval properties obtained from the observations of different instruments operating at Dome C (Antarctica)

Abstract. Selected case studies of precipitating ice clouds at Dome C (Antarctic Plateau) were used to test a new approach for the estimation of ice cloud reflectivity at 24 GHz (12.37 mm wavelength) using ground-based far infrared spectral measurements from the REFIR-PAD Fourier transform spectroradiometer and backscattering/depolarization lidar profiles. The resulting reflectivity was evaluated with the direct reflectivity measurements provided by a co-located micro rain radar (MRR) operating at 24 GHz, that was able to detect falling crystals with large particle size, typically above 600 µm. To obtain the 24 GHz reflectivity, we used the particle effective diameter and the cloud optical depth retrieved from the far infrared spectral radiances provided by REFIR-PAD and the tropospheric co-located backscattering lidar to calculate the modal radius and the intercept of the particle size distribution. These parameters spanned in the wide ranges between 570–2400 µm and 10−2–104 cm−5, respectively. The retrieved effective sizes and optical depths mostly varied in the ranges 70–250 µm and 0.1–5, respectively. From these parameters, the theoretical reflectivity at 24 GHz was obtained by integrating the size distribution over different cross sections for various habit crystals provided by Eriksson et al. (2018) databases. From the comparison with the radar reflectivity measurements, we found that the hexagonal column-like habits, the columnar crystal aggregates, and the 5/6 branches bullet rosettes showed the best agreement with the MRR observations. The dispersion coefficient of the crystal particle size distribution was assumed in the range 0–2 according to the temperature dependence found in previous studies. The retrieved values of the intercept and slope were found in good agreement with these studies. The presence of the inferred habits was confirmed by the crystal images taken by the ICE-CAMERA, operating in proximity of REFIR-PAD and the MRR. In particular, the occurrence of hexagonal column-like ice crystals was confirmed by the presence of 22∘ solar halos, detected by the HALO-CAMERA. The average crystal lengths obtained from the retrieved size distribution were also compared to those estimated from the ICE-CAMERA images. The agreement between the two results confirmed that the retrieved parameters of the particle size distributions correctly reproduced the observations.

Development of a Method for Classifying Convective and Stratiform Rains from Micro Rain Radar (MRR) Observation Data Using Artificial Neural Network

This study examined the performance of Artificial Neural Network (ANN)-backpropagation to classify rain types from observations of Micro Rain Radar (MRR) in Serpong (6.359oSL; 106.673oEL). The inputs of ANN are radar reflectivity, Doppler velocity, and Liquid Water Content (LWC). Rain events on January 5, 2017; at 16.28 – 21.21 local time were used as training data. The ANN results were validated with rain classified by the Bright Band (BB) and Countour Frequency by Altitude Diagram (CFAD) methods. The most appropriate ANN-backpropagation architecture is the 3-6-1 architecture (input layer-hidden layer-output layer), with an activation-transfer function being competitive and a learning rate of 0.9. The Mean Square Error (MSE) of the training step was 0.0098735, and the average percentage of accuracy for the test step was 94%. A rain event with a single type of rain can be classified accurately by ANN and gives the same results as the CFAD method. Thus, the ANN can be a solution to the shortcomings of the BB method, which sometimes classification results of a single type of rain events is interspersed with another type, which is physically impossible.

Stratiform and Convective Rain Classification Using Machine Learning Models and Micro Rain Radar

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.

Ice Aggregation in Low‐Level Mixed‐Phase Clouds at a High Arctic Site: Enhanced by Dendritic Growth and Absent Close to the Melting Level

AbstractLow‐level mixed‐phase clouds (MPCs) occur extensively in the Arctic, and are known to play a key role for the energy budget. While their characteristic structure is nowadays well understood, the significance of different precipitation‐formation processes, such as aggregation and riming, is still unclear. Using a 3‐year data set of vertically pointing W‐band cloud radar and K‐band Micro Rain Radar (MRR) observations from Ny‐Ålesund, Svalbard, we statistically assess the relevance of aggregation in Arctic low‐level MPCs. Combining radar observations with thermodynamic profiling, we find that larger snowflakes (mass median diameter larger than 1 mm) are predominantly produced in low‐level MPCs whose mixed‐phase layer is at temperatures between −15 and −10°C. This coincides with the temperature regime known for favoring aggregation due to growth and subsequent mechanical entanglement of dendritic crystals. Doppler velocity information confirms that these signatures are likely due to enhanced ice particle growth by aggregation. Signatures indicative of enhanced aggregation are however not distributed uniformly across the cloud deck, and only observed in limited regions, suggesting a link with dynamical effects. Low Doppler velocity values further indicate that significant riming of large particles is unlikely at temperatures colder than −5°C. Surprisingly, we find no evidence of enhanced aggregation at temperatures warmer than −5°C, as is typically observed in deeper cloud systems. Possible reasons are discussed, likely connected to the ice habits that form at temperatures warmer than −10°C, increased riming, and lack of particle populations characterized by broader size distributions precipitating from higher altitudes.

Cloud Vertical Structure of Stratiform Clouds with Embedded Convections Occurring in the Mei-Yu Front

Cloud Vertical Structure (CVS) plays a crucial role in determining atmospheric circulation and the hydrological cycle. We analyzed the CVS in Stratiform Clouds with Embedded Convection (SCEC) occurring in the mei-yu front over central-eastern China based on the conjunction of the S-band Doppler weather radar, the C-band Frequency Modulation Continuous Wave (C-FMCW) rad ar, and the Microrain Radar (MRR). Our results showed that both the melting layers and the rain rate were unevenly distributed in the three SCEC cases, and there was a thicker melting layer and a larger rain rate in the embedded convection. In the stratiform regions, the vertical velocity of particles in the upper region of the melting layer was generally in the range of 0–4 m·s−1, and increased rapidly to 4–12 m·s−1 near the bottom of the melting layer. In the case of June 28, due to the vigorous development of embedded convection, the cloud particles in the upper layer showed upward movement, and the growth rate of the particles in this region was faster than that in the surrounding stratiform regions. The vertical distributions of Drop Spectrum Distributions (DSDs) showed that the average concentration of drops larger than 3 mm increased as they fell from 3 km to 1 km, and the collision–coalescence process of drops in the embedded convection was stronger.