Ice Habit Research Articles

A statistical study of precipitation on the eastern Antarctic plateau (Dome-C) using remote sensing and in-situ instrumentation.

Studying precipitation at very high latitudes is a challenge, particularly during the polar winter. Direct monitoring of ice habit and size in high latitude precipitation is crucial for validating the algorithms used to derive precipitation from radar, and for improving the climatological modeling of polar areas. The high plateau lacks long-term direct observations of precipitation. In this work, carried out at Concordia Station (Dome-C (DC), -75°S, 123°E, 3233 m a.m.s.l), the use of a depolarization LIDAR, a flatbed scanner (ICECAMERA), a microwave profiler (HAMSTRAD) and meteorological instrumentation made possible the study, over the period 2014-2021, of shape, size, height and temperature of formation of precipitation. The precipitation sources were classified into four types: ice fogs, liquid fogs, mixed-phase clouds, and cirrus. Ten representative ice habits for Dome-C were chosen. The size distribution for every habit was calculated, allowing for the estimation of the corresponding radar reflectivity. The use of W-band radars, such as CLOUDSAT, with a sensitivity of -28dB, resulted in capturing all the crystals observed in Concordia. A positive trend was observed between grain size and height in ice habits that are typical of cloud precipitation. North West (NW) and North East (NE) winds at cloud height, blowing from coastal regions, caused the majority of precipitation from clouds. The study also examined the height trend of the ice habit composition of precipitation. The ice habit composition for each of the four types of precipitation source was analyzed, and the possibility of determining the source by simply observing the precipitation was explored. This work marks the first comprehensive investigation of precipitation on the eastern Antarctic plateau.

Explicit Habit‐Prediction in the Lagrangian Super‐Particle Ice Microphysics Model McSnow

AbstractThe Monte‐Carlo ice microphysics model McSnow is extended by an explicit habit prediction scheme, combined with the hydrodynamic theory of Böhm. Böhm's original cylindrical shape assumption for prolates is compared against recent lab results, showing that interpolation between cylinder and prolate yields the best agreement. For constant temperature and supersaturation, the predicted mass, size, and density of the ice crystals agree well with the laboratory results, and a comparison with real clouds using the polarizability ratio shows regimes capable of improvement. An updated form of the inherent growth function to describe the primary habit growth tendencies is proposed and combined with a habit‐dependent ventilation coefficient. The modifications contrast the results from general mass size relations and significantly impact the main ice microphysical processes. Depending on the thermodynamic regime, ice habits significantly alter depositional growth and affect aggregation and riming. The influence of primary ice behavior on precipitation formation needs to be considered in future development of microphysical parameterizations, but the consideration of secondary habits and the geometry of aggregates should be further improved in future work.

Arctic Cloud‐Base Ice Precipitation Properties Retrieved Using Bayesian Inference

AbstractCloud‐climate feedbacks are still the greatest source of uncertainty in current climate projections. Arctic clouds, which are predominantly stratiform and supercooled, often long‐lived, and nearly continuously precipitate ice particles, contribute roughly 10% of the uncertainty attributed to the global cloud feedback. This Arctic cloud uncertainty is driven by incomplete observational and theoretical knowledge required to estimate and explain the state and active processes occurring in those clouds. A focus on ice precipitation properties at Arctic cloud base rather than the surface deconfounds the product of cloud condensate sink processes from the influence of the atmospheric thermodynamic state below cloud base, rendering cloud‐base properties a more appealing target for inference and evaluation of model simulations. Here I describe an inverse model for the estimation of cloud base ice precipitation properties over Utqiagvik, North Slope of Alaska, using the synthesis of ground‐based radar and lidar measurements. By leveraging a Markov Chain Monte Carlo algorithm as the core of the inverse model, a wide range of particle size distributions are sampled, and different combinations of ice habit models are examined, both of which are typically fixed in other retrieval methods. Results show intriguing links between different cloud base thermodynamic and ice precipitation properties. Apparent ice number concentration enhancements at temperatures of −5 and −15°C suggest possible secondary ice production (SIP). The analysis alludes to an overestimation of SIP occurrence and intensity, especially in studies relying only on radar or lidar measurements. Finally, reflectivity‐dependent ice precipitation rate and ice water content parameterizations are presented.

Sensitivity of convectively driven tropical tropopause cirrus properties to ice habits in high-resolution simulations

Abstract. Cirrus clouds that form in the tropical tropopause layer (TTL) can play a key role in vertical transport through the upper troposphere and lower stratosphere, which can significantly impact the radiative energy budget and stratospheric chemistry. However, the lack of realistic representation of natural ice cloud habits in microphysical parameterizations can lead to uncertainties in cloud-related processes and cloud–climate feedbacks. The main goal of this study is to investigate the role of different cloud regimes and the associated ice habits in regulating the properties of the TTL. We compare aircraft measurements from the StratoClim field campaign to a set of numerical experiments at the scale of large-eddy simulations (LESs) for the same case study that employ different microphysics schemes. Aircraft measurements over the southern slopes of the Himalayas captured high ice water content (HIWC) up to 2400 ppmv and ice particle aggregates exceeding 700 µm in size with unusually long residence times. The observed ice particles were mainly of liquid origin, with a small amount formed in situ. The corresponding profile of ice water content (IWC) from the ERA5 reanalysis corroborates the presence of HIWC detrained from deep-convective plumes in the TTL but underestimates HIWC by an order of magnitude. In the TTL, only the scheme that predicts ice habits can reproduce the observed HIWC, ice number concentration, and bimodal ice particle size distribution. The lower range of particle sizes is mostly represented by planar and columnar habits, while the upper range is dominated by aggregates. Large aggregates with sizes between 600 and 800 µm have fall speeds of less than 20 cm s−1, which explains the long residence time of the aggregates in the TTL. Planar ice particles of liquid origin contribute substantially to HIWC. The columnar and aggregate habits are in the in situ range with lower IWC and number concentrations. For all habits, the ice number concentration increases with decreasing temperature. For the planar ice habit, relative humidity is inversely correlated with fall speed. This correlation is less evident for the other two ice habits. In the lower range of supersaturation with respect to ice, the columnar habit has the highest fall speed. The difference in ice number concentration across habits can be up to 4 orders of magnitude, with aggregates occurring in much smaller numbers. We demonstrate and quantify the linear relationship between the differential sedimentation of pristine ice crystals and the size of the aggregates that form when pristine crystals collide. The slope of this relationship depends on which pristine ice habit sediments faster. Each simulated ice habit is associated with distinct radiative and latent heating rates. This study suggests that a model configuration nested down to LES scales with a microphysical parameterization that predicts ice shape evolution is crucial to provide an accurate representation of the microphysical properties of TTL cirrus and thus the associated (de)hydration process.

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.

Diverse cloud radiative effects and global surface temperature simulations induced by different ice cloud optical property parameterizations

The representation of ice cloud optical properties in climate models has long been a difficult problem. Very different ice cloud optical property parameterization schemes developed based on very different assumptions of ice particle shape habits, particle size distributions, and surface roughness conditions, are used in various models. It is not clear as to how simulated climate variables are affected by the ice cloud optical property parameterizations. A total of five ice cloud optical property parameterization schemes, including three based on the ice habit mixtures suitable for general ice clouds, mid-latitude synoptic ice clouds, and tropical deep convective ice clouds, and the other two based on single ice habits (smooth hexagonal column and severely roughened column aggregate), are developed under a same framework and are implemented in the National Center for Atmospheric Research Community Atmospheric Model version 5. A series of atmosphere-only climate simulations are carried out for each of the five cases with different ice parameterizations. The differences in the simulated top of the atmosphere shortwave and longwave cloud radiative effects (CREs) are evaluated, and the global averaged net CRE differences among different cases range from − 1.93 to 1.03 Wm−2. The corresponding changes in simulated surface temperature are found to be most prominent on continental regions which amount to several degrees in Kelvin. Our results indicate the importance of choosing a reasonable ice cloud optical property parameterization in climate simulations.

Modeling Performance of SCALE‐AMPS: Simulations of Arctic Mixed‐Phase Clouds Observed During SHEBA

AbstractThe Advanced Microphysics Prediction System (AMPS), which adopts a two‐moment hybrid‐bin habit‐predicting scheme, was previously developed to study cloud microphysical processes that depend on ice habit; however, only one particular atmospheric model, the University of Wisconsin‐Nonhydrostatic Modeling System, has been used to test the AMPS. In this study, AMPS is implemented into the Scalable Computing for Advanced Library and Environment (SCALE) large‐eddy simulation model. The AMPS Eulerian advection scheme for non‐mass characteristic variables of ice particles, such as axis lengths, is refined to minimize numerical artifacts. The resulting SCALE‐AMPS model successfully reproduces features of mixed‐phase clouds observed during the Surface Heat Budget of the Arctic campaign, including liquid water path (LWP), ice particle size distributions, and ice habits, when ice particle number concentrations (Nice) are reproduced. Sensitivity studies show that increases in Nice result in reductions of LWP that are generally consistent with previous results. Interestingly, LWP reductions lead to changes in ice habits through increases in cloud temperature due to weaker cloud top radiative cooling. Furthermore, aspect ratios of precipitating particles also change following LWP reductions, because in Bigg's immersion freezing scheme, adopted in this study, the aspect ratios depend on the initial size of the ice particles and freezing rates depend on both temperature and droplet size. Because habits of ice particles affect their growth rates, fall speeds, and collision rates, the results obtained in this study reveal possible feedback processes of Arctic mixed‐phase clouds operating through ice habits.

Bulk hydrometeor optical properties for microwave and sub-millimetre radiative transfer in RTTOV-SCATT v13.0

Abstract. Satellite observations of radiation in the microwave and sub-millimetre spectral regions (broadly from 1 to 1000 GHz) can have strong sensitivity to cloud and precipitation particles in the atmosphere. These particles (known as hydrometeors) scatter, absorb, and emit radiation according to their mass, composition, shape, internal structure, and orientation. Hence, microwave and sub-millimetre observations have applications including weather forecasting, geophysical retrievals and model validation. To simulate these observations requires a scattering-capable radiative transfer model and an estimate of the bulk optical properties of the hydrometeors. This article describes the module used to integrate single-particle optical properties over a particle size distribution (PSD) to provide bulk optical properties for the Radiative Transfer for TOVS microwave and sub-millimetre scattering code, RTTOV-SCATT, a widely used fast model. Bulk optical properties can be derived from a range of particle models including Mie spheres (liquid and frozen) and non-spherical ice habits from the Liu and Atmospheric Radiative Transfer Simulator (ARTS) databases, which include pristine crystals, aggregates, and hail. The effects of different PSD and particle options on simulated brightness temperatures are explored, based on an analytical two-stream solution for a homogeneous cloud slab. The hydrometeor scattering “spectrum” below 1000 GHz is described, along with its sensitivities to particle composition (liquid or ice), size and shape. The optical behaviour of frozen particles changes in the frequencies above 200 GHz, moving towards an optically thick and emission-dominated regime more familiar from the infrared. This region is little explored but will soon be covered by the Ice Cloud Imager (ICI).

Improving the representation of aggregation in a two-moment microphysical scheme with statistics of multi-frequency Doppler radar observations

Abstract. Aggregation is a key microphysical process for the formation of precipitable ice particles. Its theoretical description involves many parameters and dependencies among different variables that are either insufficiently understood or difficult to accurately represent in bulk microphysics schemes. Previous studies have demonstrated the valuable information content of multi-frequency Doppler radar observations to characterize aggregation with respect to environmental parameters such as temperature. Comparisons with model simulations can reveal discrepancies, but the main challenge is to identify the most critical parameters in the aggregation parameterization, which can then be improved by using the observations as constraints. In this study, we systematically investigate the sensitivity of physical variables, such as number and mass density, as well as the forward-simulated multi-frequency and Doppler radar observables, to different parameters in a two-moment microphysics scheme. Our approach includes modifying key aggregation parameters such as the sticking efficiency or the shape of the size distribution. We also revise and test the impact of changing functional relationships (e.g., the terminal velocity–size relation) and underlying assumptions (e.g., the definition of the aggregation kernel). We test the sensitivity of the various components first in a single-column “snowshaft” model, which allows fast and efficient identification of the parameter combination optimally matching the observations. We find that particle properties, definition of the aggregation kernel, and size distribution width prove to be most important, while the sticking efficiency and the cloud ice habit have less influence. The setting which optimally matches the observations is then implemented in a 3D model using the identical scheme setup. Rerunning the 3D model with the new scheme setup for a multi-week period revealed that the large overestimation of aggregate size and terminal velocity in the model could be substantially reduced. The method presented is expected to be applicable to constrain other ice microphysical processes or to evaluate and improve other schemes.

Vertical Structure of a Snowfall Event Based on Observations From the Aircraft and Mountain Station in Beijing

An aircraft platform, ground-based disdrometer, cloud radar, radiometer, and automatic station were combined to study a snowfall case (16:30–21:00 observed by ground cloud radar) on the Yangqing Mountains in Beijing. Comparing the variation of ice habit and number concentration at aircraft altitude (2.9–3.2 km) and ground, we discussed the ice growth mechanisms in the Beijing Mountains. Results indicated that the snowfall was steady but not strong with reflectivity less than 20dBZ, and cloud top altitude less than 4.5 km. The number concentrations for both liquid and ice crystals at aircraft altitude and ground were very similar, both dominated by small particles at diameters of 0.1–1.2 mm, and the proportion of mean number concentrations at small diameters both in the aircraft and on the ground was large, peaking at 44 L−1 mm−1 and 8826 L−1 mm−1 respectively, and decreased rapidly as the diameter increased. There was no mixed phase in clouds with little liquid water. Particles were relatively regular, and were transparent with dendritic and disk-hexagonal shapes. The ice crystals and snowflakes were mainly grown by the deposition and aggregation, rarely by the riming process, and no secondary ice formation was observed.

Crossover from lamellar to intersected ice morphologies within a single ice crystal during unidirectional freezing of an aqueous solution

Ice growth from liquid phase has been extensively investigated in various conditions, especially for ice freely grown in undercooled water and aqueous solutions. Although unidirectional ice growth plays a significant role in various domains, the detailed pattern formation of unidirectionally grown ice in an aqueous solution remains elusive. For the first time, we in situ proved a crossover from lamellar to intersected ice morphologies of a single ice crystal via unidirectional freezing of an aqueous solution. It is revealed that the tilted lamellar morphology occurs within a single ice crystal and consists of two groups of tilted lamellar ice with different tilting directions with respect to basal plane of ice. Further observations also confirm a competition relation between the two groups of ice lamellae which are either “divergent” or “convergent”. This intersected morphology which originates from peculiar growth habit of ice is crystallographically different from the competitive growth of bi-crystal composed of two differently orientated grains which usually appears in directional solidification. These results are suggested to provide a more complete physical picture of unidirectionally grown ice from aqueous solution and are believed to promote our understanding of various pattern of ice in many relevant domains where pattern formation of ice crystal is vital.

Ice multiplication from ice–ice collisions in the high Arctic: sensitivity to ice habit, rimed fraction, ice type and uncertainties in the numerical description of the process

Abstract. Atmospheric models often fail to correctly reproduce the microphysical structure of Arctic mixed-phase clouds and underpredict ice water content even when the simulations are constrained by observed levels of ice nucleating particles. In this study we investigate whether ice multiplication from breakup upon ice–ice collisions, a process missing in most models, can account for the observed cloud ice in a stratocumulus cloud observed during the Arctic Summer Cloud Ocean Study (ASCOS) campaign. Our results indicate that the efficiency of this process in these conditions is weak; increases in fragment generation are compensated for by subsequent enhancement of precipitation and subcloud sublimation. Activation of collisional breakup improves the representation of cloud ice content, but cloud liquid remains overestimated. In most sensitivity simulations, variations in ice habit and prescribed rimed fraction have little effect on the results. A few simulations result in explosive multiplication and cloud dissipation; however, in most setups, the overall multiplication effects become substantially weaker if the precipitation sink is enhanced through cloud-ice-to-snow autoconversion. The largest uncertainty stems from the correction factor for ice enhancement due to sublimation included in the breakup parameterization; excluding this correction results in rapid glaciation, especially in simulations with plates. Our results indicate that the lack of a detailed treatment of ice habit and rimed fraction in most bulk microphysics schemes is not detrimental for the description of the collisional breakup process in the examined conditions as long as cloud-ice-to-snow autoconversion is considered.

Version 4 CALIPSO Imaging Infrared Radiometer ice and liquid water cloud microphysical properties – Part II: Results over oceans

Abstract. Following the release of the version 4 Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data products from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission, a new version 4 (V4) of the CALIPSO Imaging Infrared Radiometer (IIR) Level 2 data products has been developed. The IIR Level 2 data products include cloud effective emissivities and cloud microphysical properties such as effective diameter (De) and water path estimates for ice and liquid clouds. This paper (Part II) shows retrievals over ocean and describes the improvements made with respect to version 3 (V3) as a result of the significant changes implemented in the V4 algorithms, which are presented in a companion paper (Part I). The analysis of the three-channel IIR observations (08.65, 10.6, and 12.05 µm) is informed by the scene classification provided in the V4 CALIOP 5 km cloud layer and aerosol layer products. Thanks to the reduction of inter-channel effective emissivity biases in semi-transparent (ST) clouds when the oceanic background radiance is derived from model computations, the number of unbiased emissivity retrievals is increased by a factor of 3 in V4. In V3, these biases caused inconsistencies between the effective diameters retrieved from the 12/10 (βeff12/10 = τa,12/τa,10) and 12/08 (βeff12/08 = τa,12/τa,08) pairs of channels at emissivities smaller than 0.5. In V4, microphysical retrievals in ST ice clouds are possible in more than 80 % of the pixels down to effective emissivities of 0.05 (or visible optical depth ∼0.1). For the month of January 2008, which was chosen to illustrate the results, median ice De and ice water path (IWP) are, respectively, 38 µm and 3 g m−2 in ST clouds, with random uncertainty estimates of 50 %. The relationship between the V4 IIR 12/10 and 12/08 microphysical indices is in better agreement with the “severely roughened single column” ice habit model than with the “severely roughened eight-element aggregate” model for 80 % of the pixels in the coldest clouds (<210 K) and 60 % in the warmest clouds (>230 K). Retrievals in opaque ice clouds are improved in V4, especially at night and for 12/10 pair of channels, due to corrections of the V3 radiative temperature estimates derived from CALIOP geometric altitudes. Median ice De and IWP are 58 µm and 97 g m−2 at night in opaque clouds, with again random uncertainty estimates of 50 %. Comparisons of ice retrievals with Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua in the tropics show a better agreement of IIR De with MODIS visible–3.7 µm than with MODIS visible–2.1 µm in the coldest ST clouds and the opposite for opaque clouds. In prevailingly supercooled liquid water clouds with centroid altitudes above 4 km, retrieved median De and liquid water path are 13 µm and 3.4 g m−2 in ST clouds, with estimated random uncertainties of 45 % and 35 %, respectively. In opaque liquid clouds, these values are 18 µm and 31 g m−2 at night, with estimated uncertainties of 50 %. IIR De in opaque liquid clouds is smaller than MODIS visible–2.1 µm and visible–3.7 µm by 8 and 3 µm, respectively.

Version 4 CALIPSO Imaging Infrared Radiometer ice and liquid water cloud microphysical properties – Part I: The retrieval algorithms

Abstract. Following the release of the version 4 Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data products from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission, a new version (version 4; V4) of the CALIPSO Imaging Infrared Radiometer (IIR) Level 2 data products has been developed. The IIR Level 2 data products include cloud effective emissivities and cloud microphysical properties such as effective diameter and ice or liquid water path estimates. Dedicated retrievals for water clouds were added in V4, taking advantage of the high sensitivity of the IIR retrieval technique to small particle sizes. This paper (Part I) describes the improvements in the V4 algorithms compared to those used in the version 3 (V3) release, while results will be presented in a companion (Part II) paper. The IIR Level 2 algorithm has been modified in the V4 data release to improve the accuracy of the retrievals in clouds of very small (close to 0) and very large (close to 1) effective emissivities. To reduce biases at very small emissivities that were made evident in V3, the radiative transfer model used to compute clear-sky brightness temperatures over oceans has been updated and tuned for the simulations using Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) data to match IIR observations in clear-sky conditions. Furthermore, the clear-sky mask has been refined compared to V3 by taking advantage of additional information now available in the V4 CALIOP 5 km layer products used as an input to the IIR algorithm. After sea surface emissivity adjustments, observed and computed brightness temperatures differ by less than ±0.2 K at night for the three IIR channels centered at 08.65, 10.6, and 12.05 µm, and inter-channel biases are reduced from several tens of Kelvin in V3 to less than 0.1 K in V4. We have also improved retrievals in ice clouds having large emissivity by refining the determination of the radiative temperature needed for emissivity computation. The initial V3 estimate, namely the cloud centroid temperature derived from CALIOP, is corrected using a parameterized function of temperature difference between cloud base and top altitudes, cloud absorption optical depth, and CALIOP multiple scattering correction factor. As shown in Part II, this improvement reduces the low biases at large optical depths that were seen in V3 and increases the number of retrievals. As in V3, the IIR microphysical retrievals use the concept of microphysical indices applied to the pairs of IIR channels at 12.05 and 10.6 µm and at 12.05 and 08.65 µm. The V4 algorithm uses ice look-up tables (LUTs) built using two ice habit models from the recent “TAMUice2016” database, namely the single-hexagonal-column model and the eight-element column aggregate model, from which bulk properties are synthesized using a gamma size distribution. Four sets of effective diameters derived from a second approach are also reported in V4. Here, the LUTs are analytical functions relating microphysical index applied to IIR channels 12.05 and 10.6 µm and effective diameter as derived from in situ measurements at tropical and midlatitudes during the Tropical Composition, Cloud, and Climate Coupling (TC4) and Small Particles in Cirrus Science and Operations Plan (SPARTICUS) field experiments.

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.

The Effects of Ice Habit on Simulated Orographic Snowfall

AbstractMany factors are at play in determining the amount and distribution of mountain snowfall that is predicted by weather models; among them is the influence of assumed ice habit on snowfall distribution. Ice habit is necessarily greatly simplified in microphysics schemes and uncertainty remains in how best to model ice processes. In this study we simulate a Sierra Nevada snowfall event driven by an extratropical cyclone in February 2014. We have simulated the storm with four fixed habit types as well as with an ice habit scheme that is variable in time and space. In contrast to some previous studies, we found substantially smaller sensitivity of total accumulated precipitation amount and negligible changes in spatial distribution to the ice habit specification. The reason for smaller sensitivity seems to be linked to strong aggregation of ice crystals in the model. Nonetheless, while changes in total accumulated precipitation were small, changes in accumulated ice hydrometeors were larger. The variable-habit simulation produced up to 37% more ice precipitation than any of the fixed-habit simulations with an average increase of 14%. The variable-habit simulation led to a maximization of ice growth in the atmosphere and, subsequently, ice accumulation at the surface. This result points to the potential importance of accounting for the time and space variation of ice crystal properties in simulations of orographic precipitation.

A Parameterization of the Cloud Scattering Polarization Signal Derived From GPM Observations for Microwave Fast Radative Transfer Models

Microwave cloud polarized observations have shown the potential to improve precipitation retrievals since they are linked to the orientation and shape of ice habits. Stratiform clouds show larger brightness temperature (TB) polarization differences (PDs), defined as the vertically polarized TB (TBV) minus the horizontally polarized TB (TBH), with ~10 K PD values at 89 GHz due to the presence of horizontally aligned snowflakes, while convective regions show smaller PD signals, as graupel and/or hail in the updraft tend to become randomly oriented. The launch of the global precipitation measurement (GPM) microwave imager (GMI) has extended the availability of microwave polarized observations to higher frequencies (166 GHz) in the tropics and midlatitudes, previously only available up to 89 GHz. This study analyzes one year of GMI observations to explore further the previously reported stable relationship between the PD and the observed TBs at 89 and 166 GHz, respectively. The latitudinal and seasonal variability is analyzed to propose a cloud scattering polarization parameterization of the PD-TB relationship, capable of reconstructing the PD signal from simulated TBs. Given that operational radiative transfer (RT) models do not currently simulate the cloud polarized signals, this is an alternative and simple solution to exploit the large number of cloud polarized observations available. The atmospheric radiative transfer simulator (ARTS) is coupled with the weather research and forecasting (WRF) model, in order to apply the proposed parameterization to the RT simulated TBs and hence infer the corresponding PD values, which show to reproduce the observed GMI PDs well.

Simulated reflectance above snow constrained by airborne measurements of solar radiation: implications for the snow grain morphology in the Arctic

Abstract. Accurate knowledge of the reflectance from snow/ice-covered surfaces is of fundamental importance for the retrieval of snow parameters and atmospheric constituents from space-based and airborne observations. In this paper, we simulate the reflectance in a snow–atmosphere system, using the phenomenological radiative transfer model SCIATRAN, and compare the results with that of airborne measurements. To minimize the differences between measurements and simulation, we determine and employ the key atmospheric and surface parameters, such as snow grain morphologies (or habits). First, we report on a sensitivity study. This addresses the requirement for adequate a priori knowledge about snow models and ancillary information about the atmosphere. For this aim, we use the well-validated phenomenological radiative transfer model, SCIATRAN. Second, we present and apply a two-stage snow grain morphology (i.e., size and shape of ice crystals in the snow) retrieval algorithm. We then describe the use of this new retrieval for estimating the most representative snow model, using different types of snow morphologies, for the airborne observation conditions performed by NASA's Cloud Absorption Radiometer (CAR). Third, we present a comprehensive comparison of the simulated reflectance (using retrieved snow grain size and shape and independent atmospheric data) with that from airborne CAR measurements in the visible (0.670 µm) and near infrared (NIR; 0.870 and 1.6 µm) wavelength range. The results of this comparison are used to assess the quality and accuracy of the radiative transfer model in the simulation of the reflectance in a coupled snow–atmosphere system. Assuming that the snow layer consists of ice crystals with aggregates of eight column ice habit and having an effective radius of ∼99 µm, we find that, for a surface covered by old snow, the Pearson correlation coefficient, R, between measurements and simulations is 0.98 (R2∼0.96). For freshly fallen snow, assuming that the snow layer consists of the aggregate of five plates ice habit with an effective radius of ∼83 µm and having surface inhomogeneity, the correlation is ∼0.97 (R2∼0.94) in the infrared and 0.88 (R2∼0.77) in the visible wavelengths. The largest differences between simulated and measured values are observed in the glint area (i.e., in the angular regions of specular and near-specular reflection), with relative azimuth angles <±40∘ in the forward-scattering direction. The absolute difference between the modeled results and measurements in off-glint regions, with a viewing zenith angle of less than 50∘, is generally small ∼±0.025 and does not exceed ±0.05. These results will help to improve the calculation of snow surface reflectance and relevant assumptions in the snow–atmosphere system algorithms (e.g., aerosol optical thickness retrieval algorithms in the polar regions).

Vertical Inhomogeneity Effect of Frozen Hydrometeor Habits in All‐Sky Passive Microwave Simulations

AbstractIn this study, we attempted to address the frozen hydrometeor habit vertical inhomogeneity (FHHVI) effects on microwave radiative transfer simulations. For simplification, we focused on a “two habits and two layers” FHHVI scheme in which two different ice habits can be assumed to be above and below a fixed habit boundary line of 325 hPa as suggested by studies of tropical cyclone vertical structure. The aforementioned FHHVI scheme was implemented into a radiative transfer for the Television Infrared Observation Satellite (TIROS) operational vertical sounder (RTTOV), which allows for close examination of the FHHVI effect on the radiative transfer process. Four scenarios (thin plate, dendrite, thin plate over dendrite, and dendrite over thin plate) were considered in the simulations at frequencies ranging from 10 to 183 GHz. We found that FHHVI has an obvious impact on the brightness temperature (BT) simulations. For real case studies, we considered the 2018 tropical cyclone Jebi in the Western Pacific basin. The hydrometeor profiles forecasted by the Global/Regional Assimilation and Prediction System (GRAPES) regional numerical weather prediction (NWP) model were used, and the simulated results were compared with observations from the instruments aboard on the Fengyun‐3D satellite, which recorded 61 overpasses during the life span of typhoon Jebi. We found that the overall performance of the BT simulations over multichannels could be improved by considering FHHVI effects. Specifically, the total penalty values that quantify the spectral consistency were 63.05 (dendrite), 43.88 (thin plate), 46.23 (dendrite over thin plate), and 39.03 (thin plate over dendrite), respectively.

Relationships between Immersion Freezing and Crystal Habit for Arctic Mixed-Phase Clouds—A Numerical Study

AbstractThe number concentration of ice particles in Arctic mixed-phase clouds is a major controlling factor of cloud lifetime. The relationships between ice nucleation mode and ice crystal habit development are not yet constrained by observations. This study uses a habit-predicting microphysical scheme within a 3D large-eddy simulation model to evaluate the relationship between immersion freezing and ice habit in a simulated Arctic mixed-phase cloud case. Three immersion freezing parameterizations are considered: a volume-dependent freezing scheme (VF), a parameterization limited to activated droplets (C-AC), and a parameterization limited to coarse aerosol particles (C-CM). Both C-AC and C-CM are based on classical nucleation theory. The freezing rate with VF is found to be greater in downdraft regions than in updraft regions due to the downdraft having a higher number concentration of large droplets. The C-AC cases show active freezing of small droplets near cloud top, whereas in the C-CM cases, mainly the 8–32-μm-sized droplets freeze in updraft regions near the cloud base. Because the initial crystal size is assumed to affect the axis ratio of hexagonal plates, the VF cases produce crystals with larger axis ratios, resulting in smaller mode radii than the C-AC cases. In all cases, irregular polycrystals dominate near cloud top and a band-like structure develops within the cloud, which qualitatively agrees with previous observations. In the VF and C-CM cases, unactivated large droplets arising from coarse-mode aerosol particles contributed significantly to the freezing rate, producing an important influence on crystal habit.