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Carl A. Friehe
and
Kurt F. Schmitt

Abstract

The parameterizations of the sensible heat and moisture fluxes by the bulk aerodynamic formulas are determined from a compilation of existing data, together with some new results. The data set comprised 152 determinations of the sensible heat flux and 30 of the moisture flux from experiments in which the fluxes were measured directly over water with suitable turbulence instrumentation. Least-square-error fits were performed on the data. The moisture flux (and therefore the latent heat flux) is adequately described by the bulk formula with a coefficient of 1.32 × 10−3. The parameterization of the sensible heat flux is complicated, for the data show 1) a small positive heat flux for zero temperature difference between the air and sea surface, 2) the coefficient for stable conditions is smaller than for unstable conditions, and 3) the coefficient appears to increase at high wind speeds, as shown by the data of Smith and Banke (1975). Separate bulk formulas are presented for the sensible heat flux for the different conditions of the temperature field.

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Kurt F. Schmitt
,
Carl A. Friehe
, and
Carl H. Gibson

Abstract

Measurements of turbulent wind velocity, humidity and temperature spectra for stable and unstable stratification in the atmospheric surface layer obtained during an experiment over the North Pacific Ocean are presented. The velocity field appears to be in a state of local isotropy as measured by the ratio of vertical to streamwise velocity spectra S u(n>/ S u(n> at the measurement height of 29 m above the sea surface. Using Monin-Obukhov scaling, spectral shapes for humidity are similar to those for overland temperature. Evidence is presented which suggests that previous departures of marine temperature measurements from Monin-Obukhobzv similarity may be due to humidity sensitivity of salt-spray-contaminated temperature probes. Overland humidity data from the AFCRL-UCSD 1973 Minnesota Experiment (Champagne et al., 1977) were analyzed and also found to exhibit Monin-Obukhov similarity.

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Andrew J. Heymsfield
,
Carl Schmitt
, and
Aaron Bansemer

Abstract

The primary goal of this study is to derive ice particle terminal velocities from micron to centimeter sizes and for atmospheric pressures covering the range 200–1000 hPa from data spanning a wide range of locations, temperatures, and altitudes and to parameterize the results for use in cloud through cloud models. The study uses data from 10 field programs spanning the temperature range −86° to 0°C and encompassing a total of about 800 000 km of cloud horizontal pathlengths and includes measurements of ice particle size distributions (PSDs) and direct measurements of the ice water content (IWC). The necessary ice particle variables are derived using variables that are interconnected rather than varying independently from observations reported in the literature. A secondary goal of the study is to quantify the properties of ice cloud particle ensembles over a wide range of temperatures to further the understanding of how ice particle ensembles and ice clouds develop.

Functional forms for the PSDs and mass– and area–dimensional relationships are developed from the observations and summarized in a table. The PSDs are found to be nearly exponential at temperatures from about −40° to −10°C although deviations from exponentiality are noted outside of this range. It is demonstrated that previous pressure-dependent corrections to ice fall speeds lead to overestimated terminal velocities for particles smaller than 1 mm, particularly so for sizes below 100 μm, with consequent effects on modeled lifetimes of cold ice clouds.

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Andrew J. Heymsfield
,
Carl Schmitt
,
Aaron Bansemer
, and
Cynthia H. Twohy

Abstract

The mass–dimensional relationship put forth by Brown and Francis has been widely used for developing parameterizations for representing ice cloud microphysical properties. This relationship forms the cornerstone for past and forthcoming retrievals of ice cloud properties from ground-based and spaceborne active and passive sensors but has yet to be rigorously evaluated. This study uses data from six field campaigns to evaluate this mass–dimensional relationship in a variety of ice cloud types and temperatures and to account for the deviations observed with temperature and size, based on properties of the ice particle ensembles. Although the Brown and Francis relationship provides a good match to the observations in a mean sense, it fails to capture dependences on temperature and particle size that are a result of the complex microphysical processes operative within most ice cloud layers. Mass–dimensional relationships that provide a better fit to the observations are developed.

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Sergey Y. Matrosov
,
Carl G. Schmitt
,
Maximilian Maahn
, and
Gijs de Boer

Abstract

A remote sensing approach to retrieve the degree of nonsphericity of ice hydrometeors using scanning polarimetric Ka-band radar measurements from a U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Program cloud radar operated in an alternate transmission–simultaneous reception mode is introduced. Nonsphericity is characterized by aspect ratios representing the ratios of particle minor-to-major dimensions. The approach is based on the use of a circular depolarization ratio (CDR) proxy reconstructed from differential reflectivity Z DR and copolar correlation coefficient ρ linear polarization measurements. Essentially combining information contained in Z DR and ρ , CDR-based retrievals of aspect ratios are fairly insensitive to hydrometeor orientation if measurements are performed at elevation angles of around 40°–50°. The suggested approach is applied to data collected using the third ARM Mobile Facility (AMF3), deployed to Oliktok Point, Alaska. Aspect ratio retrievals were also performed using Z DR measurements that are more strongly (compared to CDR) influenced by hydrometeor orientation. The results of radar-based retrievals are compared with in situ measurements from the tethered balloon system (TBS)-based video ice particle sampler and the ground-based multiangle snowflake camera. The observed ice hydrometeors were predominantly irregular-shaped ice crystals and aggregates, with aspect ratios varying between approximately 0.3 and 0.8. The retrievals assume that particle bulk density influencing (besides the particle shape) observed polarimetric variables can be deduced from the estimates of particle characteristic size. Uncertainties of CDR-based aspect ratio retrievals are estimated at about 0.1–0.15. Given these uncertainties, radar-based retrievals generally agreed with in situ measurements. The advantages of using the CDR proxy compared to the linear depolarization ratio are discussed.

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Zhiyuan Jiang
,
Johannes Verlinde
,
Eugene E. Clothiaux
,
Kultegin Aydin
, and
Carl Schmitt

Abstract

Testing the often-made assumption that ice particle aggregates (snowflakes) are well represented by oblate spheroids, ellipsoid fits are applied to aggregate images. An algorithm to retrieve both the ellipsoidal parameters and the orientations of the fitted ellipsoids is applied to Multi-Angle Snowflake Camera measurements of ice particle aggregates observed in Alaska. The resulting ellipsoids have shapes closer to prolate spheroids than the oft-assumed oblate spheroids. A robust linear relationship exists between the two characteristic aspect ratios of the ellipsoids. The most probable orientation of the maximum dimension of the retrieved ellipsoids is not in the horizontal plane, and the rotational angles of the maximum dimensions in the horizontal plane are not uniform, but instead display some correlation with the wind direction at the times of the measurements. The retrieval results can be used to improve the representation of aggregates in microphysics and/or electromagnetic radiation scattering models applicable to radar and satellite measurements.

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Andrew J. Heymsfield
,
Aaron Bansemer
,
Alexander Theis
, and
Carl Schmitt

Abstract

This study quantifies how far snow can fall into the melting layer (ML) before all snow has melted by examining a combination of in situ observations from aircraft measurements in Lagrangian spiral descents from above through the ML and descents and ascents into the ML, as well as an extensive database of NOAA surface observer reports during the past 50 years. The airborne data contain information on the particle phase (solid, mixed, or liquid), population size distributions and shapes, along with temperature, relative humidity, and vertical velocity. A wide range of temperatures and ambient relative humidities are used for both the airborne and ground-based data. It is shown that an ice-bulb temperature of 0°C, together with the air temperature and pressure (altitude), are good first-order predictors of the highest temperature snowflakes can survive in the melting layer before completely melting. Particle size is also important, as is whether the particles are graupel or hail. If the relative humidity is too low, the particles will sublimate completely as they fall into the melting layer. Snow as warm as +7°C is observed from aircraft measurements and surface observations. Snow pellets survive to even warmer temperatures. Relationships are developed to represent the primary findings.

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Andrew J. Heymsfield
,
Aaron Bansemer
,
Carl Schmitt
,
Cynthia Twohy
, and
Michael R. Poellot

Abstract

In this study, aircraft data are used to derive effective ice particle densities. This density is defined as the ice particle mass divided by the volume of an equivalent diameter sphere. Measured ice particle size distributions and total ice water contents are used to derive effective ice densities for ice particle populations ( ρ e ) as a function of particle size [ρ e (D)]. The density values are critical for modeling and remote sensing applications.

The method uses particle size distributions (PSDs) measured by several particle spectrometers to compute the total particle volume per unit volume of air, assuming that the particles are spheres. Simultaneous direct measurements of ice water content from a counterflow virtual impactor (CVI) yield values for the number of grams of ice per unit volume of air, enabling the overall effective ice density for a population to be calculated. The measured PSD together with the CVI measurements are used to derive mass–dimension relationships.

The methods are applied to measurements acquired in two field programs. More than 1200 population densities were derived from the Atmospheric Radiation Measurement (ARM) program and more than 5500 for the Cirrus Regional Study of Tropical Anvils and Cirrus Layers (CRYSTAL) Florida Area Cirrus Experiment (FACE) in southern Florida during July 2002. The population densities are represented in terms of two properties of particle size distributions: the spectral slope and the median mass diameter. The datasets have been divided into populations associated with predominantly synoptically generated ice cloud regions, convectively generated ice cloud regions, regions with moderately to heavily rimed and graupel particles, and those within the melting layer. Average particle density relationships are derived for each regime.

Values of ρ e are generally higher in synoptically than convectively generated cloud layers, and rimed particles are denser than unrimed ones. Values of ρ e also decrease systematically downward within the ice clouds except in the melting layer, where they increase downward.

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Carl G. Schmitt
,
Martin Schnaiter
,
Andrew J. Heymsfield
,
Ping Yang
,
Edwin Hirst
, and
Aaron Bansemer

Abstract

A reliable understanding of the microphysical properties of ice particles in atmospheric clouds is critical for assessing cloud radiative forcing effects in climate studies. Ice particle microphysical properties such as size, shape, and surface roughness all have substantial effects on the single-scattering characteristics of the particles. A recently developed ice particle probe, the Small Ice Detector-3 (SID-3), measures the two-dimensional near-forward light-scattering patterns of sampled ice particles. These scattering patterns provide a wealth of information for understanding the microphysical and radiative characteristics of ice particles. The SID-3 was operated successfully on 12 aircraft flights during the NASA Midlatitude Airborne Cirrus Properties Experiment (MACPEX) field campaign in April 2011. In this study, SID-3 measurements are used to investigate the frequency of occurrence of a number of ice particle properties observed during MACPEX. Individual scattering patterns (7.5°–23°) are used to infer properties of the observed particles as well as to calculate partial scattering functions (PSFs) for ensembles of particles in the measured size range (~5–100 μm). PSFs are compared to ray-tracing-based phase functions to infer additional properties of the particles. Two quantitative values—halo ratio and steepness ratio—are used to characterize PSFs. The MACPEX dataset suggests that most atmospheric ice particles have rough surfaces or are complex in nature. PSFs calculated for particles that were characterized as having smooth surfaces also appeared to more closely resemble rough crystal PSFs. PSFs measured with SID-3 compare well with those calculated for droxtals with rough surfaces.

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Vanessa M. Przybylo
,
Kara J. Sulia
,
Carl G. Schmitt
, and
Zachary J. Lebo

Abstract

A vast amount of ice crystal imagery exists from a variety of field campaign initiatives that can be utilized for cloud microphysical research. Here, nine convolutional neural networks are used to classify particles into nine regimes on over 10 million images from the Cloud Particle Imager probe, including liquid and frozen states and particles with evidence of riming. A transfer learning approach proves that the Visual Geometry Group (VGG-16) network best classifies imagery with respect to multiple performance metrics. Classification accuracies on a validation dataset reach 97% and surpass traditional automated classification. Furthermore, after initial model training and preprocessing, 10 000 images can be classified in approximately 35 s using 20 central processing unit cores and two graphics processing units, which reaches real-time classification capabilities. Statistical analysis of the classified images indicates that a large portion (57%) of the dataset is unusable, meaning the images are too blurry or represent indistinguishable small fragments. In addition, 19% of the dataset is classified as liquid drops. After removal of fragments, blurry images, and cloud drops, 38% of the remaining ice particles are largely intersecting the image border (≥10% cutoff) and therefore are considered unusable because of the inability to properly classify and dimensionalize. After this filtering, an unprecedented database of 1 560 364 images across all campaigns is available for parameter extraction and bulk statistics on specific particle types in a wide variety of storm systems, which can act to improve the current state of microphysical parameterizations.

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