Hydrometeor Types Research Articles

Contrasting lightning projection using the lightning potential index adapted in a convection-permitting regional climate model

Lightning climate change projections show large uncertainties caused by limited empirical knowledge and strong assumptions inherent to coarse-grid climate modeling. This study addresses the latter issue by implementing and applying the lightning potential index parameterization (LPI) into a fine-grid convection-permitting regional climate model (CPM). This setup takes advantage of the explicit representation of deep convection in CPMs and allows for process-oriented LPI inputs such as vertical velocity within convective cells and coexistence of microphysical hydrometeor types, which are known to contribute to charge separation mechanisms. The LPI output is compared to output from a simpler flash rate parameterization, namely the CAPE times PREC parameterization, applied in a non-CPM on a coarser grid. The LPI’s implementation into the regional climate model COSMO-CLM successfully reproduces the observed lightning climatology, including its latitudinal gradient, its daily and hourly probability distributions, and its diurnal and annual cycles. Besides, the simulated temperature dependence of lightning reflects the observed dependency. The LPI outperforms the CAPE times PREC parameterization in all applied diagnostics. Based on this satisfactory evaluation, we used the LPI to a climate change projection under the RCP8.5 scenario. For the domain under investigation centered over Germany, the LPI projects a decrease of 4.8% in flash rate by the end of the century, in opposition to a projected increase of 17.4% as projected using the CAPE times PREC parameterization. The future decrease of LPI occurs mostly during the summer afternoons and is related to (i) a change in convection occurrence and (ii) changes in the microphysical mixing. The two parameterizations differ because of different convection occurrences in the CPM and non-CPM and because of changes in the microphysical mixing, which is only represented in the LPI lightning parameterization.

Structure and Dynamics of Ensemble Correlations for Satellite All-Sky Observations in an FV3-Based Global-to-Regional Nested Convection-Permitting Ensemble Forecast of Hurricane Harvey

AbstractThere are ongoing efforts to establish an ensemble data assimilation and prediction system for tropical cyclones based on the FV3 (finite-volume cubed-sphere) dynamic core with the capability to assimilate satellite all-sky infrared and microwave observations. To complement the system developments and improve our understanding of the assimilation of all-sky infrared and microwave observations, this study assesses their potential impacts on the analysis of Hurricane Harvey (2017) through examinations of the structure and dynamics of the ensemble-based correlations as well as single observation data assimilation experiments, using an ensemble forecast generated by a global-to-regional nested FV3-based model. It is found that different infrared and microwave channels are sensitive to different types of hydrometeors within different layers of the atmosphere, and the correlations vanish beyond 200 km in the region covered by cloud or abundant hydrometeors. The spatial correlations between brightness temperatures and model states will adjust the structure and intensity of the hurricane in the model so that the simulated hurricane will better fit the “observed” brightness temperatures. In general, these results show how assimilating infrared and microwave together can improve the analyses of tropical cyclone intensity and structure, which may lead to improved intensity forecasts.

Assessment of a Microphysical Ensemble Used to Investigate the OWLeS IOP4 Lake-Effect Storm

AbstractMicrophysical processes within mixed-phase convective clouds can have cascading impacts on cloud properties and resultant precipitation. This paper investigates the role of microphysics in the lake-effect storm (LES) observed during intensive observing period 4 of the Ontario Winter Lake-effect Systems field campaign. A microphysical ensemble is composed of 24 simulations that differ in the microphysics scheme used (e.g., Weather Research and Forecasting Model microphysics options or a choice of two bulk adaptive habit models) along with changes in the representation of aerosol and potential ice nuclei concentrations, ice nucleation parameterizations, rain and ice fall speeds, spectral indices, ice habit assumptions, and the number of moments used for modeling ice-phase hydrometeors in each adaptive habit model. Each of these changes to microphysics resulted in varied precipitation types at the surface; 15 members forecast a mixture of snow, ice, and graupel, 7 members forecast only snow and ice, and the remaining 2 members forecast a combination of snow, ice, graupel, and rain. Observations from an optical disdrometer positioned to the south of the LES core indicate that 92% of the observed particles were snow and ice, 5% were graupel, and 3% were rain and drizzle. Analysis of observations spanning more than a point location, such as polarimetric radar observations and aircraft measurements of liquid water content, provides insight into cloud composition and processes leading to the differences at the surface. Ensemble spread is controlled by hydrometeor type differences spurred by processes or parameters (e.g., ice fall speed) that affect graupel mass.

GPM‐DPR Observations on TGFs Producing Storms

AbstractUnique spaceborne measurements of the three‐dimensional structure of convective clouds producing terrestrial gamma ray flashes (TGFs) were performed using both active and passive microwave sensors on board the Global Precipitation Measurement (GPM)‐Core Observatory satellite, finding coherent features for nine TGF‐producing storms. The delineation of cloud structure using the radar reflectivity factor shows convective cells with significant vertical development and thick layers with high ice content. Compared to other cumulonimbus clouds in the tropics, the TGFs counterparts have higher reflectivity values above 3 and 8 km altitude showing in all cases a cumulonimbus tower and the TGFs locations are very close, or coincident, to these high Z columns, where reflectivity exceeds 50 dBz. Using the GPM Microwave Imager radiometer, most thunderstorms show a very strong depression of polarization corrected temperature (PCT) at channel 89 GHz, indicating a strong scattering signal by ice in the upper cloud layers. At channel 166 GHZ, the difference between vertical and horizontal brightness temperature signal always returns positive values, from 0.2 up to 13.7 K indicating a complex structure with randomly/vertically oriented ice particles. The PCT was used to characterize the analyzed storms in terms of hydrometeor types, confirming in 7/9 cases a high likelihood of hail/graupel presence. To perform analysis on the TGFs parent flashes, radio atmospherics data from the Earth Networks Total Lightning Network lightning network were used. Waveform data indicate that all cases are intra‐cloud events and TGFs typically take place during the peak of flash rate production. Finally, the analysis of the most intense event is shown.

Radar reflectivity data assimilation method based on background‐dependent hydrometeor retrieval: Comparison with direct assimilation for real cases

AbstractAssimilating radar reflectivity into NWP models is one of the keys to improve the accuracy of convective‐scale numerical weather prediction (NWP). There are generally two major branches of research in radar reflectivity assimilation: directly assimilating radar reflectivity, and indirectly assimilating reflectivity, i.e. assimilating hydrometeors retrieved from radar reflectivity. In this study, the indirect assimilation method based on background‐dependent hydrometeor retrievals is compared with the direct assimilation method using frequent data assimilation cycles for five real data cases. The retrieved hydrometeors in the indirect assimilation method are first verified against the hydrometeor types obtained from a polarimetric hydrometeor classification method. It is illustrated that the background‐dependent hydrometeor retrieval method can obtain reasonable model‐equivalent hydrometeors from radar reflectivity. The analysis increments for hydrometeors with both radar data assimilation methods show similar patterns but their magnitudes are different. Both quantitative and qualitative evaluations of forecasted composite reflectivities and accumulated precipitation indicate that the indirect assimilation method predicts the location and intensity of the simulated convection more accurately than the direct method. Furthermore, the indirect assimilation method is more efficient, which is valuable in real‐time applications by helping deliver forecasts quickly and thus helping forecasters make more timely warning decisions.

Radar and ground-level measurements of precipitation collected by the École Polytechnique Fédérale de Lausanne during the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic winter games

Abstract. This article describes a 4-month dataset of precipitation and cloud measurements collected during the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic winter games (ICE-POP 2018). This paper aims to describe the data collected by the Environmental Remote Sensing Laboratory of the École Polytechnique Fédérale de Lausanne. The dataset includes observations from an X-band dual-polarisation Doppler radar, a W-band Doppler cloud profiler, a multi-angle snowflake camera and a two-dimensional video disdrometer (https://doi.org/10.1594/PANGAEA.918315, Gehring et al., 2020a). Classifications of hydrometeor types derived from dual-polarisation measurements and snowflake photographs are presented. The dataset covers the period from 15 November 2017 to 18 March 2018 and features nine precipitation events with a total accumulation of 195 mm of equivalent liquid precipitation. This represents 85 % of the climatological accumulation over this period. To illustrate the available data, measurements corresponding to the four precipitation events with the largest accumulation are presented. The synoptic situations of these events were contrasted and influenced the precipitation type and accumulation. The hydrometeor classifications reveal that aggregate snowflakes were dominant and that some events featured significant riming. The combination of dual-polarisation variables and high-resolution Doppler spectra with ground-level snowflake images makes this dataset particularly suited to study snowfall microphysics in a region where such measurements were not available before.

Hydrometeor classification of quasi-vertical profiles of polarimetric radar measurements using a top-down iterative hierarchical clustering method

Abstract. Correct, timely and meaningful interpretation of polarimetric weather radar observations requires an accurate understanding of hydrometeors and their associated microphysical processes along with well-developed techniques that automatize their recognition in both the spatial and temporal dimensions of the data. This study presents a novel technique for identifying different types of hydrometeors from quasi-vertical profiles (QVPs). In this new technique, the hydrometeor types are identified as clusters belonging to a hierarchical structure. The number of different hydrometeor types in the data is not predefined, and the method obtains the optimal number of clusters through a recursive process. The optimal clustering is then used to label the original data. Initial results using observations from the National Centre for Atmospheric Science (NCAS) X-band dual-polarization Doppler weather radar (NXPol) show that the technique provides stable and consistent results. Comparison with available airborne in situ measurements also indicates the value of this novel method for providing a physical delineation of radar observations. Although this demonstration uses NXPol data, the technique is generally applicable to similar multivariate data from other radar observations.

Numerical Experiments on Forecasting Glaze Phenomena

Methods and results of the numerical forecast of glaze phenomena in Central Russia for cold periods of 2003–2018 are presented. Effectiveness of glaze forecasting is compared by two methods: (i) analysis of hydrometeor types and air temperature near the Earth’s surface from the WRF-ARW model and (ii) using the thermobalance model with WRF-ARW model forecasts as initial data. Some advantages of glaze forecasts using the thermobalance model are demonstrated.

Evaluation of Simulated Winter Precipitation Using WRF-ARW during the ICE-POP 2018 Field Campaign

AbstractThis study evaluates the performance of several cloud microphysics parameterizations in simulating surface precipitation for two snowstorm cases during the International Collaborative Experiment held at the PyeongChang 2018 Olympics and Winter Paralympic Games (ICE-POP 2018) field campaign. We compared four different schemes in the Weather Research and Forecasting (WRF) Model, namely the double-moment 6-class (WDM6), the WRF single-moment 6-class (WSM6), and Thompson and Morrison parameterizations. Both WSM6 and WDM6 overestimated the precipitation amount for the shallow precipitation system because of the substantial amount of cloud ice, mostly generated by the deposition process. The simulated precipitation amount and distribution for the deep precipitation system showed no noticeable differences in the different cloud microphysics parameterizations. However, the simulated hydrometeor type at the surface using WSM6 and WDM6 showed good agreement with observations for all cases. The accuracy of the mean mass-weighted terminal velocity of cloud ice applied in WSM6 and WDM6 is ±20%. The number concentration of cloud ice and the ice microphysics processes are newly retrieved with 1.2 times increased . For the shallow snowstorm, the precipitation amount was reduced by approximately 8% because of the inefficient deposition and its effects on the subsequent ice microphysical processes, such as the accretion of cloud ice by snow and the conversion from cloud ice to snow.

Observations of Anomalous Charge Structures in Supercell Thunderstorms in the Southeastern United States

AbstractAnomalous thunderstorm charge structures (ACSs), characterized by a dominant layer of positive charge in the lower mixed‐phase region, are uncommon and rarely reported outside of the Great Plains region of the United States. This study documents the kinematics, precipitation microphysics, and charge structures of two supercell thunderstorms exhibiting ACSs that were observed in the Southeastern United States. Ground‐based three‐dimensional total lightning observations, polarimetric Doppler radar observations, and environmental model analysis of these supercells presented the opportunity to evaluate conceptual models of ACS development and characteristics in an atypical parameter space. In both anomalous storms, prominent layers of positive charge were located in lower to middle mixed‐phase regions (−10°C to −30°C) of updrafts and were spatially associated with riming hydrometeor types. Simultaneously, negative charge regions were identified above the primary positive charge layers in the upper mixed‐phase and glaciated region of updrafts, collocated with graupel and small ice hydrometeors. While coarse charge structure observations were consistent with noninductive charging‐based models of anomalous storms, charge structure complexities were also observed that suggested variability in cloud microphysical conditions. Analysis of the environments in which these storms formed indicated that several parameters thought to increase mixed‐phase liquid water content in favor of anomalous charging were inconsistent with those documented in the Great Plains region. However, environmental humidity metrics were most comparable. Comparisons between these and other documented anomalous storms identified similarities in kinematic structure and microphysical conditions while motivating ongoing study of the environmental parameter space supportive of ACSs.

Hydrometeor Identification Using Multiple-Frequency Microwave Links: A Numerical Simulation

A method for identifying hydrometeor types (rain, graupel, and wet snow) based on a microwave link is proposed in this paper. The measured hydrometeor size distribution (HSD) data from the winters of 2014 to 2019 in Nanjing, China, were used to carry out simulation experiments to verify the performance of the model. Single-, dual-, and tri-frequency models (combinations of 15 GHz, 18 GHz, 25 GHz, 38 GHz, 50 GHz, 60 GHz, 70 GHz, and 80 GHz) were established with the extreme learning machine (ELM) algorithm. The results showed that the performance of the tri-frequency models was overall better than that of the dual-frequency models, for which the performance was better than that of the single-frequency models. The mean (maximum) test set accuracies of the single-frequency, dual-frequency, and tri-frequency models reached 75.8%, 80.7%, and 83.2% (83.0%, 84.4%, and 85.6%), respectively. For the dual-frequency and tri-frequency models, it was found that the accuracy increased with the overall frequency or the frequency difference. In addition, the influences of different noise levels on the model performance were also analyzed. Finally, the effects of position and length of link relative to precipitation cell were analyzed and are also discussed.

Radar reflectivity of thunderstorm clouds

The contribution of various types of hydrometeors to the total radar reflectivity was estimated based on numerical modeling of thunderstorm clouds. Isolines, time course and change of reflectivity with the height at two wavelengths due to the contribution of drops and hailstones are constructed. It is shown that when solving inverse problems of the scattering theory at a wavelength of 3.0 cm, it is necessary to take into account the contribution of raindrops to reflectivity, and at a wavelength of 10 cm, it can be ignored. The contribution of cloud droplets and crystals at both wavelengths can be ignored.

X-Net-Based Radar Data Assimilation Study over the Seoul Metropolitan Area

This study investigates the ability of the high-resolution Weather Research and Forecasting (WRF) model to simulate summer precipitation with assimilation of X-band radar network data (X-Net) over the Seoul metropolitan area. Numerical data assimilation (DA) experiments with X-Net (S- and X-band Doppler radar) radial velocity and reflectivity data for three events of convective systems along the Changma front are conducted. In addition to the conventional assimilation of radar data, which focuses on assimilating the radial velocity and reflectivity of precipitation echoes, this study assimilates null-echoes and analyzes the effect of null-echo data assimilation on short-term quantitative precipitation forecasting (QPF). A null-echo is defined as a region with non-precipitation echoes within the radar observation range. The model removes excessive humidity and four types of hydrometeors (wet and dry snow, graupel, and rain) based on the radar reflectivity by using a three-dimensional variational (3D-Var) data assimilation technique within the WRFDA system. Some procedures for preprocessing radar reflectivity data and using null-echoes in this assimilation are discussed. Numerical experiments with conventional radar DA over-predicted the precipitation. However, experiments with additional null-echo information removed excessive water vapor and hydrometeors and suppressed erroneous model precipitation. The results of statistical model verification showed improvements in the analysis and objective forecast scores, reducing the amount of over-predicted precipitation. An analysis of a contoured frequency by altitude diagram (CFAD) and time–height cross-sections showed that increased hydrometeors throughout the data assimilation period enhanced precipitation formation, and reflectivity under the melting layer was simulated similarly to the observations during the peak precipitation times. In addition, overestimated hydrometeors were reduced through null-echo data assimilation.

A Recognition Method of Hydrometeor in Tropical Cyclones by Using the GPM Dual‐Frequency Precipitation Radar

In this paper, we propose a fuzzy logic algorithm to recognize the types of hydrometeors by using Ka and Ku reflectivity factors, temperature thresholds and an asymmetric t-form membership function. The identifiable types of hydrometeors include snow, graupel, mixed-phase particles, large raindrops, and small raindrops. The reflectivity detection data of Ka and Ku with attenuation correction is from dual-frequency precipitation radar onboard the global precipitation measurement satellite. The temperature data from the European Centre for Medium-range Weather Forecasts are used to identify the hydrometeors, and the identified hydrometeors are compared with the vertical profiles of phase from official level-2 DPR products. The results show that the identified hydrometeors are reasonable and reveal the evolution process of tropical cyclones, which can be further applied to the study of precipitation microphysical process and artificial precipitation.

Characteristics of Warm Clouds and Precipitation in South China during the Pre-Flood Season Using Datasets from a Cloud Radar, a Ceilometer, and a Disdrometer

The millimeter-wave cloud radar, ceilometer, and disdrometer have been widely used to observe clouds and precipitation. However, there are some drawbacks when those three instruments are solely employed due to their own limitations, such as the fact that radars usually suffer from signal attenuation and ceilometers/disdrometers cannot provide measurements of the hydrometeors of aloft clouds and precipitation. Thus, in this paper, we developed an integrated technology by combining and utilizing the advantages of three instruments together to investigate the vertical structure and diurnal variation of warm clouds and precipitation, and the raindrop size distribution. Specifically, the technology consists of appropriate data processing, quality control, and retrieval methods. It was implemented to study the warm clouds and precipitation in South China during the pre-flood season of 2016. The results showed that the hydrometeors of warm clouds and precipitation were mainly distributed below 2.5 km and most of the rainfall events were very light with a rain rate less than 1 mm h−1, however, the stronger precipitation primarily contributed the accumulated rain amount. Furthermore, a rising trend of cloud base height from 1000 to 1900 BJT was found. The cloud top height and cloud thickness gradually increased from 1200 BJT to reach a maximum at 1600 BJT (Beijing Standard Time, UTC+8), and then decreased until 2000 BJT. Also, three periods of the apparent rainfall on the ground of the day, namely, 0400–0700 BJT, 1400–1800 BJT, and 2300–2400 BJT were observed. During three periods, the raindrops had wider size spectra, higher number concentrations, larger rain rates, and higher water contents than at other times. The hydrometeor type, size, and concentration were gradually changed in the vertical orientation. The raindrop size distributions of warm precipitation in the air and on the ground were different, which can be expressed by γ distributions N(D) = 1.49 × 104D−0.9484exp(−6.79D) in the air and N(D) = 1.875 × 103D0.862exp(−2.444D) on the ground, where D and N(D) denote the diameter and number concentration of the raindrops, respectively.

The Disdrometer Verification Network (DiVeN): a UK network of laser precipitation instruments

Abstract. Starting in February 2017, a network of 14 Thies laser precipitation monitors (LPMs) were installed at various locations around the United Kingdom to create the Disdrometer Verification Network (DiVeN). The instruments were installed for verification of radar hydrometeor classification algorithms but are valuable for much wider use in the scientific and operational meteorological community. Every Thies LPM is able to designate each observed hydrometeor into one of 20 diameter bins from ≥0.125 to >8 mm and one of 22 speed bins from >0.0 to >20.0 m s−1. Using empirically derived relationships, the instrument classifies precipitation into one of 11 possible hydrometeor classes in the form of a present weather code, with an associated indicator of uncertainty. To provide immediate feedback to data users, the observations are plotted in near-real time (NRT) and made publicly available on a website within 7 min. Here we describe the Disdrometer Verification Network and present specific cases from the first year of observations. Cases shown here suggest that the Thies LPM performs well at identifying transitions between rain and snow, but struggles with detection of graupel and pristine ice crystals (which occur infrequently in the United Kingdom) inherently, due to internal processing. The present weather code quality index is shown to have some skill without the supplementary sensors recommended by the manufacturer. Overall the Thies LPM is a useful tool for detecting hydrometeor type at the surface and DiVeN provides a novel dataset not previously observed for the United Kingdom.

An Assessment of the Role of Ice Hydrometeor-Types in WRF Bulk Microphysical Schemes in Simulating Two Heavy Rainfall Events over Southern Nigeria

The role of ice hydrometeor-types in bulk schemes available in the Weather Research and Forecasting (WRF) model has been assessed in this study to simulate two heavy rainfall events reported over the southern part of Nigeria. This has been done with a view to provide necessary information on the convective cloud hydrometeor types and compositions in the area to improve heavy rainfall forecasts with the selection of appropriate bulk microphysical schemes. Results from the statistical validation of the simulated rainfall by different schemes showed that for the first event, the WSM5 scheme with less dense snow ice particles performed relatively better than other schemes like WSM6, Morrison with graupel (MORR_G), and Morrison with hail (MORR_H), while the WDM6 scheme performed least effectively when compared to TRMM data. Conversely, the second event showed that a WDM6 scheme with graupel as dense ice particle performed better than other schemes. Further analysis using a spatial distribution plot of simulated rainfall over the area of study shows that for both cases, almost all the schemes fail to capture the intensity and location of the heavy rainfall shown by TRMM data. In addition, the surface accumulated rainfall area average of all schemes for the first (second) event shows an underestimation (overestimation). Vertical profile plots of mass mixing ratios of different ice hydrometeor compositions showed that the WSM5 scheme contains a greater mass of snow than other type ice particles for both cases, while the hydrometeor path calculation of total mass content showed the WSM5 scheme having more snow mass content than other schemes during the period of analysis in both cases. A pressure-time plot of the differences between simulated air temperature and water vapor of the WSM5 scheme showed that WSM5 simulated the higher air temperature that was needed and water vapor at the mid and upper troposphere more than other schemes. In conclusion, results from this study has shown that less dense ice particle (e.g., snow) and high dense ice particle (e.g., graupel and hail) type-bulk schemes can both be suitable for simulating heavy rainfall events that are produced by convective system(s) that are common in the area.

Development and validation of a supervised machine learning radar Doppler spectra peak-finding algorithm

Abstract. In many types of clouds, multiple hydrometeor populations can be present at the same time and height. Studying the evolution of these different hydrometeors in a time–height perspective can give valuable information on cloud particle composition and microphysical growth processes. However, as a prerequisite, the number of different hydrometeor types in a certain cloud volume needs to be quantified. This can be accomplished using cloud radar Doppler velocity spectra from profiling cloud radars if the different hydrometeor types have sufficiently different terminal fall velocities to produce individual Doppler spectrum peaks. Here we present a newly developed supervised machine learning radar Doppler spectra peak-finding algorithm (named PEAKO). In this approach, three adjustable parameters (spectrum smoothing span, prominence threshold, and minimum peak width at half-height) are varied to obtain the set of parameters which yields the best agreement of user-classified and machine-marked peaks. The algorithm was developed for Ka-band ARM zenith-pointing radar (KAZR) observations obtained in thick snowfall systems during the Atmospheric Radiation Measurement Program (ARM) mobile facility AMF2 deployment at Hyytiälä, Finland, during the Biogenic Aerosols – Effects on Clouds and Climate (BAECC) field campaign. The performance of PEAKO is evaluated by comparing its results to existing Doppler peak-finding algorithms. The new algorithm consistently identifies Doppler spectra peaks and outperforms other algorithms by reducing noise and increasing temporal and height consistency in detected features. In the future, the PEAKO algorithm will be adapted to other cloud radars and other types of clouds consisting of multiple hydrometeors in the same cloud volume.

Sensitivity of Simulated GMI Brightness Temperatures to Variations in Particle Size Distributions in a Severe Hailstorm

AbstractGlobal Precipitation Measurement (GPM) Microwave Imager (GMI) brightness temperatures (BTs) were simulated over a case of severe convection in Texas using ground-based S-band radar and the Atmospheric Radiative Transfer Simulator. The median particle diameter Do of a normalized gamma distribution was varied for different hydrometeor types under the constraint of fixed radar reflectivity to better understand how simulated GMI BTs respond to changing particle size distribution parameters. In addition, simulations were conducted to assess how low BTs may be expected to reach from realistic (although extreme) particle sizes or concentrations. Results indicate that increasing Do for cloud ice, graupel, and/or hail leads to warmer BTs (i.e., weaker scattering signature) at various frequencies. Channels at 166.0 and 183.31 ± 7 GHz are most sensitive to changing Do of cloud ice, channels at ≥89.0 GHz are most sensitive to changing Do of graupel, and at 18.7 and 36.5 GHz they show the greatest sensitivity to hail Do. Simulations contrasting BTs above high concentrations of small (0.5-cm diameter) and low concentrations of large (20-cm diameter) hailstones distributed evenly across a satellite pixel showed much greater scattering using the higher concentration of smaller hailstones with BTs as low as ~110, ~33, ~22, ~46, ~100, and ~106 K at 10.65, 18.7, 36.5, 89.0, 166.0, and 183.31 ± 7 GHz, respectively. These results suggest that number concentration is more important for scattering than particle size given a constant S-band radar reflectivity.

An Investigation of Hydrometeor Latent Cooling upon Convective Cold Pool Formation, Sustainment, and Properties

Abstract Downdrafts extending from convective clouds can produce cold pools that propagate outward, sometimes initiating new convection along their leading edges. Models operating at scales requiring convective parameterizations usually lack representation of this detail, and thus fail to predict this convective regeneration and longer episodes of convective activity. Developing such parameterizations requires an improved understanding of the physical drivers of cold pools, and detailed studies of the roles of all the contributing microphysical processes have been lacking. This study utilizes a set of 12 simulations conducted within a single convective environment, but with variability in the microphysical fields produced by varying parameters influencing warm-rain or ice processes. Time-integrated microphysical budgets quantify the contribution of each hydrometeor type to the total latent cooling occurring in the downdrafts that form and sustain the cold pool. The timing of the onset of the cold pool is earlier in cases with a stronger warm rain process, but both graupel and rain were equally as likely to be the dominant hydrometeor in the downdraft first forming the cold pool. Graupel sublimation is the dominant term in sustaining the cold pool in all simulations, but the evaporation of rain has the strongest correlation to the cold pool expansion rate, depth, and intensity. Reconciling the current results with past studies elucidates the importance of considering: graupel sublimation, the latent cooling only in downdrafts contributing to the cold pool, and latent cooling in those downdrafts at altitudes that may be significantly higher than the melting level.