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A convective-scale static background-error covariance (BEC) matrix is further developed to include the capability of direct reflectivity assimilation and evaluated within the GSI-based three-dimensional variational (3DVar) and hybrid ensemble–variational (EnVar) methods. Specific developments are summarized as follows: 1) Control variables (CVs) are extended to include reflectivity, vertical velocity, and all hydrometeor types. Various horizontal momentum and moisture CV options are included. 2) Cross correlations between all CVs are established. 3) A storm intensity-dependent binning method is adopted to separately calculate static error matrices for clear-air and storms with varying intensities. The resultant static BEC matrices are simultaneously applied at proper locations guided by the observed reflectivity. 4) The EnVar is extended to adaptively incorporate static BECs based on the quality of ensemble covariances. Evaluation and examination of the new static BECs are first performed on the 8 May 2003 Oklahoma City supercell. Detailed diagnostics and 3DVar examinations suggest zonal/meridional winds and pseudo–relative humidity are selected as horizontal momentum and moisture CVs for direct reflectivity assimilation, respectively; inclusion of cross correlations favors spin up and maintains the analyzed storms; application of binning improves characteristics and persistence of the simulated storm. Relative to an experiment using the full ensemble BECs (Exp-PureEnVar), incorporating static BECs in hybrid EnVar reduces spinup time and better analyzes reflectivity distributions while the background ensemble is deficient in sampling errors. Compared to both pure 3DVar and Exp-PureEnVar, hybrid EnVar better predicts reflectivity distributions and better maintains a strong mesocyclone. Further examination through the 20 May 2013 Oklahoma supercells confirms these results and additionally demonstrates the effectiveness of adaptive hybridization.

Polarimetric radars provide variables like the specific differential phase (KDP) to detect fingerprints of dendritic growth in the dendritic growth layer (DGL) and secondary ice production, both critical for precipitation formation. A key challenge in interpreting radar observations is the lack of in situ validation of particle properties within the radar measurement volume. While high KDP in snow is usually associated with high particle number concentrations, only few studies attributed KDP to certain hydrometeor types and sizes. To address this, we combined surface in situ observations from the Video In Situ Snowfall Sensor (VISSS) with remote sensing data from a polarimetric W-band radar and an X-band radar, along with modeling approaches. Data were collected during the CORSIPP project, part of the ARM SAIL campaign (winter 2022/2023, Colorado Rocky Mountains). We found that at W-band, KDP > 2 ° km−1 can result from a broad range of particle number concentrations, between 1 and 100 L−1. Blowing snow and increased ice collisional fragmentation in a turbulent layer enhanced observed KDP values. T-matrix simulations indicated that high KDP values were primarily produced by particles smaller than 0.8 mm in the DGL and 1.5 mm near the surface. Discrete dipole approximation simulations based on VISSS data suggested that dendritic aggregates larger than 2.5 mm contributed 10 %–20 % to the measured W-band KDP near the surface. These findings highlight the complexity of interpreting W-band KDP in snowfall and emphasize the need for combined in situ observations and radar forward simulations to better understand snowfall microphysical processes.

Cloud microphysical processes occur at the smallest end of scales among cloud-related processes and thus must be parameterized not only in large-scale global circulation models (GCMs) but also in various higher-resolution limited-area models such as cloud-resolving models (CRMs) and large-eddy simulation (LES) models. Instead of giving a comprehensive review of existing microphysical parameterizations that have been developed over the years, this study concentrates purposely on several topics that we believe are understudied but hold great potential for further advancing bulk microphysics parameterizations: multi-moment bulk microphysics parameterizations and the role of the spectral shape of hydrometeor size distributions; discrete vs “continuous” representation of hydrometeor types; turbulence-microphysics interactions including turbulent entrainment-mixing processes and stochastic condensation; theoretical foundations for the mathematical expressions used to describe hydrometeor size distributions and hydrometeor morphology; and approaches for developing bulk microphysics parameterizations. Also presented are the spectral bin scheme and particle-based scheme (especially, super-droplet method) for representing explicit microphysics. Their advantages and disadvantages are elucidated for constructing cloud models with detailed microphysics that are essential to developing processes understanding and bulk microphysics parameterizations. Particle-resolved direct numerical simulation (DNS) models are described as an emerging technique to investigate turbulence-microphysics interactions at the most fundamental level by tracking individual particles and resolving the smallest turbulent eddies in turbulent clouds. Outstanding challenges and future research directions are explored as well.

Models assume that mixed‐phase clouds consist of uniformly mixed ice crystals and liquid cloud droplets when observations have shown that they consist of clusters, or “pockets,” of ice crystals and liquid cloud droplets. We characterize the spatial distribution of cloud phase over the Arctic and the Southern Ocean using active satellite observations and determine the relative importance of collocated meteorological parameters and aerosols from reanalysis to predict how uniformly mixed mixed‐phase clouds are for the first time. We performed a multi‐linear regression fit to the data set to predict the spatial distribution of the ice and liquid pockets. Contrary to what models suggest, mixed‐phase clouds are rarely perfectly homogeneous. Our results suggest that high temperatures are associated with homogeneously mixed ice and liquid pockets. We also find that a high mixing ratio of black carbon is associated with heterogeneously mixed ice and liquid pockets. Plain Language Summary The representation of clouds in numerical models remains one of the largest uncertainties in predicting our future climate. Clouds can consist solely of liquid droplets, ice crystals, or the coexistence of both hydrometeor types. The latter cloud type is referred to as mixed phase. Climate models assume that liquid droplets and ice crystals are uniformly mixed in space in mixed‐phase clouds, but observations show that mixed‐phase clouds are organized in separate pockets of clustered liquid droplets and ice crystals. This difference in representation has a large impact on the lifetime of clouds and on their role in climate change. Using satellite observations over the Arctic and the Southern Ocean, we quantify the spatial distribution of ice and liquid in clouds. We used a statistical method to determine the relationship between meteorology and aerosols and the spatial distribution of ice and liquid. Our results suggest that high temperatures are associated with homogeneously mixed mixed‐phase clouds and high concentrations of soot are associated with heterogeneously mixed mixed‐phase clouds. Furthermore, pockets of liquid within ice clouds are larger than pockets of ice within liquid clouds. These results will improve the representation of mixed‐phase clouds in large‐scale models. Key Points Space‐based observations and reanalysis are considered to determine the factors that control how mixed mixed‐phase clouds are Liquid dominated clouds contain small and isolated ice pockets whereas ice dominated clouds contain large and isolated liquid pockets Temperature and black carbon play an important role in controlling the cloud phase spatial distribution and increasing phase heterogeneity

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 × 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 × 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 × 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.

Polarimetric radar variables are simulated from members of the 2013 Center for Analysis and Prediction of Storms (CAPS) Storm-Scale Ensemble Forecasts (SSEF) with varying microphysics (MP) schemes and compared with observations. The polarimetric variables provide information on hydrometeor types and particle size distributions (PSDs), neither of which can be obtained through reflectivity (Z) alone. The polarimetric radar simulator pays close attention to how each MP scheme [including single- (SM) and double-moment (DM) schemes] treats hydrometeor types and PSDs. The recent dual-polarization upgrade to the entire WSR-88D network provides nationwide polarimetric observations, allowing for direct evaluation of the simulated polarimetric variables. Simulations for a mesoscale convective system (MCS) and supercell cases are examined. Five different MP schemes—Thompson, DM Milbrandt and Yau (MY), DM Morrison, WRF DM 6-category (WDM6), and WRF SM 6-category (WSM6)—are used in the ensemble forecasts. Forecasts using the partially DM Thompson and fully DM MY and Morrison schemes better replicate the MCS structure and stratiform precipitation coverage, as well as supercell structure compared to WDM6 and WSM6. Forecasts using the MY and Morrison schemes better replicate observed polarimetric signatures associated with size sorting than those using the Thompson, WDM6, and WSM6 schemes, in which such signatures are either absent or occur at abnormal locations. Several biases are suggested in these schemes, including too much wet graupel in MY, Morrison, and WDM6; a small raindrop bias in WDM6 and WSM6; and the underforecast of liquid water content in regions of pure rain for all schemes.

In this study, we investigate how the regional climate model HIRHAM5 reproduces the spatial and temporal distribution of Arctic snowfall when compared to CloudSat satellite observations during the examined period of 2007–2010. For this purpose, both approaches, i.e., the assessments of the surface snowfall rate (observation-to-model) and the radar reflectivity factor profiles (model-to-observation), are carried out considering spatial and temporal sampling differences. The HIRHAM5 model, which is constrained in its synoptic representation by nudging to ERA-Interim, represents the snowfall in the Arctic region well in comparison to CloudSat products. The spatial distribution of the snowfall patterns is similar in both identifying the southeastern coast of Greenland and the North Atlantic corridor as regions gaining more than twice as much snowfall as the Arctic average, defined here for latitudes between 66 and 81∘ N. Excellent agreement (difference less than 1 %) in the Arctic-averaged annual snowfall rate between HIRHAM5 and CloudSat is found, whereas ERA-Interim reanalysis shows an underestimation of 45 % and significant deficits in the representation of the snowfall rate distribution. From the spatial analysis, it can be seen that the largest differences in the mean annual snowfall rates are an overestimation near the coastlines of Greenland and other regions with large orographic variations as well as an underestimation in the northern North Atlantic Ocean. To a large extent, the differences can be explained by clutter contamination, blind zone or higher resolution of CloudSat measurements, but clearly HIRHAM5 overestimates the orographic-driven precipitation. The underestimation of HIRHAM5 within the North Atlantic corridor south of Svalbard is likely connected to a poor description of the marine cold air outbreaks which could be identified by separating snowfall into different circulation weather type regimes. By simulating the radar reflectivity factor profiles from HIRHAM5 utilizing the Passive and Active Microwave TRAnsfer (PAMTRA) forward-modeling operator, the contribution of individual hydrometeor types can be assessed. Looking at a latitude band at 72–73∘ N, snow can be identified as the hydrometeor type dominating radar reflectivity factor values across all seasons. The largest differences between the observed and simulated reflectivity factor values are related to the contribution of cloud ice particles, which is underestimated in the model, most likely due to the small sizes of the particles. The model-to-observation approach offers a promising diagnostic when improving cloud schemes, as illustrated by comparison of different schemes available for HIRHAM5.

To date, there remains a noticeable gap in reliable techniques for retrieving the shape and orientation of ice particles from observational data. This paper introduces a method using ground-based scanning polarimetric Doppler cloud radar to retrieve the shape and orientation of multiple hydrometeor types within deep mixed-phase clouds. Building on the strong performance of an existing method, which is effective in retrieving the shape and orientation of pristine ice particles in stratiform clouds, we extended this technique by analyzing the entire Doppler spectrum. The previously developed main-peak approach focuses only on the part of the Doppler spectrum with the highest signal-to-noise ratio to retrieve the shape and orientation of the dominant hydrometeor types within stratiform clouds. With the extended technique, referred to as the spectrally resolved approach, the section of the Doppler spectrum containing valid data points exceeding the noise level is analyzed by dividing it into five equally spaced parts. This allows us to retrieve up to five distinct velocity-segregated hydrometeor types. The technique utilizes range–height indicator (RHI) scans (ranging from 30 to 90° elevation) of the Doppler spectra of differential reflectivity (ZDR) and correlation coefficient (RHV) from a polarimetric Ka-band cloud radar. The potential of the improved approach is presented by means of two case studies. The first case demonstrates the effectiveness of the spectrally resolved approach, and in the second case secondary ice production is investigated. These findings contribute to a profound understanding of hydrometeor characteristics, shedding light on dynamic cloud processes, especially in the context of precipitation and ice particle formation.

Polarimetric radar-based hydrometeor classification is the procedure of identifying different types of hydrometeors by exploiting polarimetric radar observations. The main drawback of the existing supervised classification methods, mostly based on fuzzy logic, is a significant dependency on a presumed electromagnetic behaviour of different hydrometeor types. Namely, the results of the classification largely rely upon the quality of scattering simulations. When it comes to the unsupervised approach, it lacks the constraints related to the hydrometeor microphysics. The idea of the proposed method is to compensate for these drawbacks by combining the two approaches in a way that microphysical hypotheses can, to a degree, adjust the content of the classes obtained statistically from the observations. This is done by means of an iterative approach, performed offline, which, in a statistical framework, examines clustered representative polarimetric observations by comparing them to the presumed polarimetric properties of each hydrometeor class. Aside from comparing, a routine alters the content of clusters by encouraging further statistical clustering in case of non-identification. By merging all identified clusters, the multi-dimensional polarimetric signatures of various hydrometeor types are obtained for each of the studied representative datasets, i.e. for each radar system of interest. These are depicted by sets of centroids which are then employed in operational labelling of different hydrometeors. The method has been applied on three C-band datasets, each acquired by different operational radar from the MeteoSwiss Rad4Alp network, as well as on two X-band datasets acquired by two research mobile radars. The results are discussed through a comparative analysis which includes a corresponding supervised and unsupervised approach, emphasising the operational potential of the proposed method.

Large hail events are typically infrequent, with significant time gaps between occurrences at specific locations. However, when these events do happen, they can cause rapid and substantial economic losses within a matter of minutes. Therefore, it is crucial to have the ability to accurately observe and understand hail phenomena to improve the mitigation of this impact. While in situ observations are accurate, they are limited in number for an individual storm. Weather radars, on the other hand, provide a larger observation footprint, but current radar-derived hail size estimates exhibit low accuracy due to horizontal advection of hailstones as they fall, the variability of hail size distributions (HSDs), complex scattering and attenuation, and mixed hydrometeor types. In this paper, we propose a new radar-derived hail product developed using a large dataset of hail damage insurance claims and radar observations. We use these datasets coupled with environmental information to calculate a hail damage estimate (HDE) using a deep neural network approach aiming to quantify hail impact, with a critical success index of 0.88 and a coefficient of determination against observed damage of 0.79. Furthermore, we compared HDE to a popular hail size product (MESH), allowing us to identify meteorological conditions that are associated with biases on MESH. Environments with relatively low specific humidity, high CAPE and CIN, low wind speeds aloft, and southerly winds at the ground are associated with a negative MESH bias, potentially due to differences in HSD, hail hardness, or mixed hydrometeors. In contrast, environments with low CAPE, high CIN, and relatively high specific humidity aloft are associated with a positive MESH bias.