The Use of Superspheroids as Surrogates for Modeling Electromagnetic Wave Scattering by Ice Crystals

Electromagnetic wave scattering by ice particles is commonly modeled by defining representative habits, including droxtals, columns, plates, and aggregates, although actual particles in the atmosphere can be even much more complex. In this study, we examined a superspheroidal approximation method for modeling electromagnetic wave scattering by ice crystals. Superspheroid can be associated with a shape index (SI) defined by the particle volume and average projected area. Corresponding to realistic ice crystals, suitable superspheroid models with the same SI (that means, identical volume and average projected area) and aspect ratio can be identified as surrogates for optical property calculations. We systematically compared the optical properties of ice crystals and superspheroids at 33 microwave bands in the range of 3–640 GHz and at three representative visible or infrared wavelengths (0.66, 2.13, and 11 μm). It was found that the single-scattering properties of compact ice crystal habits and their superspheroidal model particles were quite close. For an aggregate with sparse distribution of elements, a superspheroid model produces relatively large errors because the aspect ratio may not be sufficient to describe a particle shape. However, the optical similarity of a superspheroid and an aggregate is still encouraging.

Aircraft Observations of Characteristics and Growth of Ice Particles of Two Different Snowfall Clouds in Shanxi Province, China

The ice crystal habits, distributions and growth processes in two snowfall cloud cases on 29 November 2009 and 3 March 2012 in northern China were compared and analyzed with aircraft data. The results showed that ice crystal habits were affected by the height of ice clouds. Ice crystals in clouds with cloud top temperatures of −12.6 °C were predominantly needle, plate, dendrite and irregular. When the cloud top temperature was lower than −19.5 °C, plates, dendrites and irregular ice crystals were observed in addition to needles, capped-column crystals were observed in the lower and middle layers of clouds, and column crystals were observed in the upper layer of clouds. The liquid water content of the two snowfall processes was lower than 0.1 g·m–3. Ice particles grew mainly via deposition, riming and aggregation processes. On 29 November, the liquid water content of the stratospheric mixed snowfall cloud was distributed in the lower part of the cloud. The maximum values of particle concentration and ice water content detected by a cloud imaging probe were 187 L–1and 1.05 g·m–3, which were at −8.7 °C, and the ice water content was higher. On 3 March, the liquid water content of snowfall in stratiform clouds was located in the middle layer, and the maximum ice water was low, which was only 0.052 g m–3. The ice water value on 29 November was higher, which was mainly due to the convective zone embedded in the cumulus mixed cloud containing a large number of riming and aggregated snow crystals. Using an exponential function to fit the crystal spectrum of the two snowfall processes, N0 and λ were 109–1011 m–4 and 108–1010 m–4 and 103–104 m–1 and 104 m–1, respectively. Compared with 3 March, N0 on 29 November was larger and the variation range of λ was one more order of magnitude. N0 and λ conformed to a power function distribution. By analyzing the scatter plot of the correlation coefficient and slope, it was found that the exponential function can accurately express the crystal spectrum of snow clouds.

A Classification of Ice Crystal Habits Using Combined Lidar and Scanning Polarimeter Observations during the SEAC4RS Campaign

Using collocated NASA Cloud Physics Lidar (CPL) and Research Scanning Polarimeter (RSP) data from the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) campaign, a new observational-based method was developed which uses a K-means clustering technique to classify ice crystal habit types into seven categories: column, plates, rosettes, spheroids, and three different type of irregulars. Intercompared with the collocated SPEC, Inc., Cloud Particle Imager (CPI) data, the frequency of the detected ice crystal habits from the proposed method presented in the study agrees within 5% with the CPI-reported values for columns, irregulars, rosettes, and spheroids, with more disagreement for plates. This study suggests that a detailed ice crystal habit retrieval could be applied to combined space-based lidar and polarimeter observations such as CALIPSO and POLDER in addition to future missions such as the Aerosols, Clouds, Convection, and Precipitation (A-CCP).

Ice Particle Mass–Dimensional Relationship Retrieval and Uncertainty Evaluation Using the Optimal Estimation Methodology Applied to the MACPEX Data

A Bayesian optimal estimation methodology is applied to retrieve the time-varying ice particle mass–dimensional (M–D) relationships (i.e., M = amDbm) and the associated uncertainties using the in situ data that were collected by the NASA WB-57 during the Midlatitude Airborne Cirrus Properties Experiment (MACPEX) in March and April 2011. The authors utilize the coincident measurements of bulk ice water content and projected cross-sectional area to constrain M–D relationships and estimate the uncertainties. It is demonstrated that the additional information provided by the particle area with respect to size could contribute considerable improvements to the algorithm performance. Extreme variability of M–D properties is found among cases as well as within individual cases, indicating the nondiscrete nature of ice crystal habits within cloud volumes and further suggesting the risk of assuming a constant M–D relationship in different conditions. Relative uncertainties of am are approximately from 50% to 80%, and relative uncertainties of bm range from 6% to 9.5%, which would cause approximately 2.5-dB uncertainty in forward-modeled radar reflectivity or a factor-of-2 uncertainty in ice water content.

Clarification of the Water Saturation Represented on Ice Crystal Growth Diagrams

It has been known for a long time that the shape of ice crystals depends on both the air temperature and the relative humidity of the environment. The relationships among these factors have been summarized in classification diagrams and are intensively referred to in the cloud physics literature. To put in perspective the atmospheric conditions in which the different ice crystal habits grow with respect to the level of saturation in the atmosphere, the vapor density excess and supersaturation with respect to ice at liquid water saturation have been included on those diagrams as a function of air temperature. Over the years, the definition of the water saturation included in those types of diagrams has been misdefined. The goal of this study is to show that an error has been introduced in the definition of the excess of water vapor with respect to ice.

Ice Crystal Habits and Growth Processes in Stratiform Clouds with Embedded Convection Examined through Aircraft Observation in Northern China

Ice crystal habits and growth processes in two cases of stratiform clouds with embedded convection are investigated using data observed simultaneously from three aircraft on 18 April 2009 and 1 May 2009 as part of the Beijing Cloud Experiment (BCE). The results show that the majority of ice crystal habits found in the two cases at temperatures between 0° and −16°C included platelike, needle column, capped column, dendrite, and irregular. A mixture of several ice crystal habits was identified in all of the clouds studied. However, the ice crystals recorded in the embedded convection regions contained more dendrites and possessed heavier riming degrees, and the ice crystals identified in the stratiform clouds contained more hexagonal plate crystals. Both riming and aggregation processes played central roles in the broadening of particle size distributions (PSDs), and these processes were more active in embedded convection regions than in stratiform regions. However, riming was more prevalent in the 18 April case than aggregation, though aggregates were evident. In contrast, the 1 May case had a more dominant aggregation processes, but also riming. With the decrease in height, PSDs broadened in both embedded convection regions and stratiform regions, but the broadening rates between 4.8 km (T ≈ −11.6°C) and 4.2 km (T ≈ −8°C) were larger than those between 4.2 km (T ≈ −8°C) and 3.6 km (T ≈ −5°C). In addition, the broadening rates of PSDs in the embedded convection regions were larger than those in the stratiform clouds, as the aggregation and riming processes of ice particles in embedded convection regions were active. High supercooled water content is critical to enhancing riming and aggregation processes in embedded convection regions.

Observed Microphysical Evolution for Two East Coast Winter Storms and the Associated Snow Bands

This paper presents the observed microphysical evolution of two coastal extratropical cyclones (19–20 December 2009 and 12 January 2011) and the associated passage of heavy snowbands in the cyclone comma head. The observations were made approximately 93 km east of New York City at Stony Brook, New York. Surface microphysical measurements of snow habit and degree of riming were taken every 15–30 min using a stereo microscope and camera, and snow depth and snow density were also recorded. A vertically pointing Ku-band radar observed the vertical evolution of reflectivity and Doppler vertical velocities. There were rapid variations in the snow habits and densities related to the changes in vertical motion and depth of saturation. At any one time, a mixture of different ice habits was observed. Certain ice habits were dominant at the surface when the maximum vertical motion aloft occurred at their favored temperature for depositional growth. Convective seeder cells above 4 km MSL resulted in relatively cold (less than −15°C) ice crystal habits (side planes, bullets, and dendrites). Needlelike crystals were prevalent during the preband period when the maximum vertical motion was in the layer from −5° to −10°C. Moderately rimed dendritic crystals were observed at snowband maturity associated with the strongest frontogenetical ascent on the warm (east) side of the bands. Riming rapidly decreased and more platelike crystals became more numerous as the strongest ascent moved east of Stony Brook. Snow-to-liquid density ratios ranged from 8:1 to 13:1 in both events, except during the period of graupel, when the ratio was as low as 4:1.

Ice Crystal Linear Growth Rates from −20° to −70°C: Confirmation from Wave Cloud Studies

As a result of recent comprehensive laboratory and field studies, many details have been clarified concerning atmospheric ice crystal habits below −20°C as a function of temperature, ice supersaturation, air pressure, and growth history. A predominance of complex shapes has been revealed that is not reflected in most models, with symmetric shapes often incorrectly emphasized. From the laboratory study, linear (maximum dimension), projected area, and volume growth rates of complex and simple habits have been measured under simulated atmospheric conditions for temperatures from −20° to −70°C. Presently, only a few in situ cases of measured ice crystal growth rates are available for comparison with laboratory results. Observations from the Interaction of Aerosol and Cold Clouds (INTACC) field study of a well-characterized wave cloud at −24°C are compared with the laboratory results using a simple method of habit averaging to derive a range of expected growth rates. Laboratory results are also compared with recently reported wave cloud results from the Ice in Clouds Experiment–Layer Clouds (ICE-L) study between −20° and −32°C, in addition to a much colder wave cloud at −65°C. Considerable agreement is observed in these cases, confirming the reliability of the laboratory measurements. This is the first of two companion papers that compare laboratory growth rates and characteristics with in situ measurements, confirming that the laboratory results effectively provide a predictive capability for cloud particle and particle ensemble growth.