Ice Cloud Microphysical Properties Research Articles

Retrieval of terahertz ice cloud properties from airborne measurements based on the irregularly shaped Voronoi ice scattering models

Abstract. Currently, terahertz remote sensing technology is one of the best ways to detect the microphysical properties of ice clouds. Influenced by the representativeness of the ice crystal scattering (ICS) model, the existing terahertz ice cloud remote sensing inversion algorithms still have significant uncertainties. In this study, based on the Voronoi ICS model, we developed a terahertz remote sensing inversion algorithm of the ice water path (IWP) and median mass diameter (Dme) of ice clouds. This study utilized the single-scattering properties (extinction efficiency, single-scattering albedo, and asymmetry factor) of the Voronoi, sphere, and hexagonal column ICS models in the terahertz region. Combined with 14 408 groups of particle size distributions obtained from aircraft-based measurements, we developed the Voronoi, sphere, and column ICS schemes based on the Voronoi, sphere, and column ICS models. The three schemes were applied to the radiative transfer model to carry out the sensitivity analysis of the top-of-cloud (TOC) terahertz brightness temperature differences between cloudy and clear skies (BTDs) on the IWP and Dme. The sensitivity results showed that the TOC BTDs between 640 and 874 GHz are functions of the IWP, and the TOC BTDs of 380, 640, and 874 GHz are functions of the Dme. The Voronoi ICS scheme possesses stronger sensitivity to the Dme than the sphere and column ICS schemes. Based on the sensitivity results, we built a multi-channel look-up table for BTDs. The IWP and Dme were searched from the look-up table using an optimal estimation algorithm. We used 2000 BTD test data randomly generated by the RSTAR model to assess the algorithm's accuracy. Test results showed that the correlation coefficients of the retrieved IWP and Dme reached 0.99 and 0.98, respectively. As an application, we used the inversion algorithm to retrieve the ice cloud IWP and Dme based on the Compact Scanning Submillimeter-wave Imaging Radiometer (CoSSIR) airborne terahertz radiation measurements. Validation against the retrievals of the Bayesian algorithm reveals that the Voronoi ICS model performs better than the sphere and hexagonal column ICS models, with enhancement of the mean absolute errors of 5.0 % and 12.8 % for IWP and Dme, respectively. In summary, the results of this study confirmed the practicality and effectiveness of the Voronoi ICS model in the terahertz remote sensing inversion of ice cloud microphysical properties.

Novel Parameterization of Ice Cloud Effective Diameter from Collocated CALIOP-IIR and CloudSat Retrievals

Abstract Satellite-based measurements of global ice cloud microphysical properties are sampled to develop a novel set of physical parameterizations, relating to cloud layer temperature and effective diameter De, that can be implemented for two separate applications: in numerical weather prediction models and lidar-based cloud radiative forcing studies. Ice cloud optical properties (i.e., spectral scattering and absorption) are estimated based on the effective size and habit mixture of the cloud particles. Historically, the ice cloud De has been parameterized from aircraft in situ measurements. However, aircraft-based parameterizations are opportunistic in that they only represent specific types of clouds (e.g., convective anvil, tropopause-topped cirrus) in the regions in which they were sampled and, in some cases, are limited in fully resolving the entire vertical cloud layer. Breaking away from the aircraft-based parameterization paradigm, this study is the first of its kind to attempt a parameterization of De as a function of temperature, ice water content (IWC), and lidar-derived extinction from satellite-based global oceanic measurements of ice clouds. Data from both active and passive remote sensing sensors from two of NASA’s A-Train satellites, CloudSat and CALIPSO, are collected to guide development of globally robust parameterizations of all ice cloud types and one exclusively for cirrus clouds. Significance Statement We derived unique parameterizations of ice crystal effective size from global satellite measurements in an effort to more robustly and consistently represent ice clouds in numerical models for weather forecasting and climate energy balance studies. Based on our results, effective ice crystal size is easily solved based on temperature and visible cloud translucence. By knowing the size of the ice crystals, we can then estimate cloud scattering and absorption. In comparison with aircraft-based parameterizations, the satellite data reveal that ice crystal effective sizes are much smaller, on global average, for ice clouds occurring in relatively warm layers (>230 K), indicating that many ice clouds are more reflective than previously believed.

Assessing Synergistic Radar and Radiometer Retrievals of Ice Cloud Microphysics for the Atmosphere Observing System (AOS) Architecture

After exploring numerous observing system designs, the NASA aerosols, clouds, convection, and precipitation (ACCP) study team arrived at the top candidate architecture referred to as the atmosphere observing system (AOS) that is composed of a suite of spaceborne instruments in two orbital inclinations to characterize the complexity of hydrometeors and aerosols in the Earth’s atmosphere. This study proposes hybrid Bayesian retrieval algorithms that combine the Monte Carlo integration (MCI) and cost function optimization approaches to quantitatively evaluate the AOS architecture for skill in constraining the ice cloud microphysical properties. The remote sensor candidates under evaluation include multiple-frequency radars with W-, Ka-, and Ku-band channels and a submillimeter-wave radiometer. Two optimization techniques, the optimal estimation method (OEM) and Markov chain Monte Carlo (MCMC), are developed to maximize the posterior distribution function to retrieve ice cloud microphysical quantities with uncertainty estimates. Observing system simulation experiments are conducted using simulated synergistic radar and radiometer observations to determine the pixel-level retrieval accuracies by comparing the retrieved parameters to the true values. Results demonstrate that the low-frequency Ka-/Ku-band radar observations are complementary to the W-band channel since they provide more constraints on the condensed cloud scenes that are composed of large particles. The brightness temperature measurements exhibit sensitivities to the ice cloud layers with large water content (WC), and the synergistic active and passive observations improve the ice water path retrieval accuracies significantly. The scores measuring how well the AOS architecture satisfies the desired retrieval uncertainties for different ice cloud geophysical variables are also derived.

Validation of Ice Cloud Microphysical Properties Retrieval Using a Markov Chain Monte Carlo Algorithm

AbstractThe Markov chain Monte Carlo (MCMC) algorithm has been used for retrieving ice cloud microphysical properties in this paper. The retrieval data include effective radius (re), ice water content (IWC), number density (N0), and distribution width parameter (w).First we examine the algorithm feasibility using simulated W‐band radar reflectivity. The retrievals locate in a solution space around the exact results. Two methods are applied to produce a unique solution with maximum likelihood function, that is, the mean value of all acceptable sets of parameters in the chain and the first convergence sample. The results of the second method have a better performance compared with that of the first method. The algorithm has better performance when one particle size distribution (PSD) parameter is known than that when three PSD parameters are unknown.Then we apply the algorithm for CloudSat Cloud Profiling Radar (CPR) observations. Four habits, including column, plate, bullet rosette and sphere, are considered to analyze the influence on the retrieval accuracy. The averaged deviation ofres andIWCs obtained from MCMC algorithm among different habit assumptions has a maximum of about 10% and 5%, respectively. It appears clearly that the MCMC algorithm retrieval for short hexagonal column (HEXS) are the closest to that of the official products (2B‐CWC‐RO), while thick hexagonal plate (HEXF) has the largest bias. Retrievedre,IWC,N0, andwfrom radar reflectivity are in reasonable agreement with 2B‐CWC‐RO product, which indicate that the MCMC algorithm can produce reliable cloud properties from radar observations.

Application of M5 Model Tree in Passive Remote Sensing of Thin Ice Cloud Microphysical Properties in Terahertz Region

This article presents a new method for retrieving the Ice Water Path (IWP), the median volume equivalent sphere diameter (Dme) of thin ice clouds (IWP < 100 g/m2, Dme < 80 μm) in the Terahertz band. The upwelling brightness temperature depressions caused by the ice clouds at 325.15, 448.0, 664.0 and 874.0 GHz channels are simulated by the Atmospheric Radiative Transfer Simulator (ARTS). The simulated forward radiative transfer models are taken as historical data for the M5 model tree algorithm to construct a set of piecewise functions which represent the relation of simulated brightness temperature depressions and IWP. The inversion results are optimized by an empirical relation of the IWP and the Dme for thin ice clouds which is summarized by previous studies. We inverse IWP and Dme with the simulated brightness temperature and analyze the inversion performance of selected channels. The 874.4 ± 6.0 GHz channel provides the most accurate results, because of the strong brightness temperature response to the change of IWP in the forward radiative transfer model. In order to improve the thin ice clouds IWP and Dme retrieval accuracy at the middle-high frequency channels in Terahertz band, a dual-channel inversion method was proposed that combines the 448.0± 3.0 GHz and 664.0 ± 4.2 GHz channel. The error analysis shows that the results of the 874.4 ± 6.0 GHz channel and the dual-channel inversion are reliable, and the IWP inversion results meet the error requirement range proposed by previous studies.

Combining In Situ and Satellite Observations to Understand the Vertical Structure of Tropical Anvil Cloud Microphysical Properties During the TC4 Experiment.

Tropical anvil clouds have a profound impact on Earth's weather and climate. Their role in Earth's energy balance and hydrologic cycle is heavily modulated by the vertical structure of the microphysical properties for various hydrometeors in these clouds and their dependence on the ambient environmental conditions. Accurate representations of the variability and covariability of such vertical structures are key to both the satellite remote sensing of cloud and precipitation and numerical modeling of weather and climate, which remain a challenge. This study presents a new method to combine vertically resolved observations from CloudSat radar reflectivity and Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation cloud masks with probability distributions of cloud microphysical properties and the ambient atmospheric conditions from detailed in situ measurements on tropical anvils sampled during the National Aeronautics and Space Administration TC4 (Tropical Composition, Cloud and Climate Coupling) mission. We focus on the microphysical properties of the vertical distribution of ice water content, particle size distributions, and effective sizes for different hydrometeors, including ice particles and supercooled liquid droplets. Results from this method are compared with those from in situ data alone and various CloudSat/Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation cloud retrievals. The sampling limitation of the field experiment and algorithm limitations in the current retrievals is highlighted, especially for the liquid cloud particles, while a generally good agreement with ice cloud microphysical properties is seen from different methods. While the method presented in this study is applied to tropical anvil clouds observed during TC4, it can be readily employed to study a broad range of ice clouds sampled by various field campaigns.

Characteristics of Ice Cloud–Precipitation of Warm Season Mesoscale Convective Systems over the Great Plains

AbstractIn this study, the mesoscale convective systems (MCSs) are tracked using high-resolution radar and satellite observations over the U.S. Great Plains during April–August from 2010 to 2012. The spatiotemporal variability of MCS precipitation is then characterized using the Stage IV product. We found that the spatial variability and nocturnal peaks of MCS precipitation are primarily driven by the MCS occurrence rather than the precipitation intensity. The tracked MCSs are further classified into convective core (CC), stratiform rain (SR), and anvil clouds regions. The spatial variability and diurnal cycle of precipitation in the SR regions of MCSs are not as significant as those of MCS precipitation. In the SR regions, the high-resolution, long-term ice cloud microphysical properties [ice water content (IWC) and ice water paths (IWPs)] are provided. The IWCs generally decrease with height. Spatially, the IWC, IWP, and precipitation are all higher over the southern Great Plains than over the northern Great Plains. Seasonally, those ice and precipitation properties are all higher in summer than in spring. Comparing the peak timings of MCS precipitation and IWPs from the diurnal cycles and their composite evolutions, it is found that when using the peak timing of IWPSR as a reference, the heaviest precipitation in the MCS convective core occurs earlier, while the strongest SR precipitation occurs later. The shift of peak timings could be explained by the stratiform precipitation formation process. The IWP and precipitation relationships are different at MCS genesis, mature, and decay stages. The relationships and the transition processes from ice particles to precipitation also depend on the low-level humidity.

Assessing the accuracy and efficiency of longwave radiative transfer models involving scattering effect with cloud optical property parameterizations

A radiative transfer model (RTM) that accurately and explicitly accounts for both absorbing and scattering effects requires a substantial amount of computational effort. In the longwave (LW) spectral regime, the atmosphere is optically opaque and absorbs a large portion of terrestrial thermal radiation. To alleviate the computational burden, clouds are assumed to be only absorptive in most general circulation models (GCMs) and their scattering effects are neglected. Using parameterizations of cloud bulk single-scattering properties derived from the latest cloud optical property models for satellite remote sensing, this study analyzes the numerical accuracy and efficiency of a variety of LW RTMs . The approaches considered in this study include the absorption approximation (AA), the absorption approximation with scattering parameterization (ASA), the two-stream approximation (2S), a hybrid two- and four-stream approximation (2/4S), and the discrete ordinate radiative transfer (DISORT) with multiple streams. These approximations are benchmarked against 128-stream DISORT calculation. After evaluating the full ranges of ice and water cloud optical and microphysical properties using these RTMs, we find that neglecting LW scattering effect causes simulation errors as large as ±15% by using the AA method. Among these RTMs, the 2/4S method provides an optimal balance between computational efficiency and accuracy, leading to the maximum cloud emissivity errors within ±5%, 25th to 75th percentile errors about ±1%, and almost zero net bias. Therefore, the 2/4S approximation is computationally accurate and yet affordable option for incorporating cloud longwave scattering effect into the radiation schemes used in weather and climate models.

Retrieval of ice cloud microphysical properties at the SACOL

Ice clouds are one of the most widely distributed cloud types in the world and play an important role in the radiation budget. The microphysical parameters of ice clouds, such as ice water content, effective radius and particle concentration, have a significant effect on the artificial influences on weather, the calculation of the radiation effect and climate feedback of ice clouds. Because of their unique physical and optical properties, ice clouds play an important and unique role in the radiation budget. Understanding and recognizing the microphysical properties of ice clouds are of great significance for studying the effects of ice clouds on climate change. Ka-band zenith radar (KAZR), which has been deployed at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) since July 2013, has continuously operated for many years. By using the observation data from KAZR and micropulse lidar (MPL), we compare the microphysical properties of ice clouds, including ice effective particle size (Dge) and ice water content (IWC), which were derived from three algorithms: a traditional algorithm (Microbase), the Z-IWC-T relationship algorithm (Hogan) and the KAZR-MPL retrieval algorithm (Combined). Then, we compute and examine the radiative fluxes using each set of cloud properties as inputs to a radiative transfer model and compare the top-of-atmosphere (TOA) radiative fluxes to the satellite measurements to assess the impact of the differences in the microphysical quantities of different ice clouds on radiation. Our analysis shows that ice clouds mainly appear from 2 to 14 km and peak by approximately 13.02% at 7 km. The ice water contents retrieved from the three different inversion methods show a unimodal distribution. The ice water content is mainly concentrated in the range of 0.01−10 mg/m3, and the corresponding maximum probability of ice water content is concentrated in the vicinity of 0.1 mg/m3. However, there are some differences between the degrees of data concentration and the peak probabilities of the different algorithms. The effective particle size distributions obtained by the three methods are quite different. The effective particle size of the ice crystal inversion by the Microbase algorithm is unimodal, and the data are mainly concentrated in the small particle radius region of 0−100 μm. The KAZR-MPL algorithm has a wider range of particle distributions, with two peaks at 40 and 96 μm. However, the distribution of the ice effective particle radius shows significant discrepancies, which sometimes translate into large differences in the cloud shortwave radiative effect. Using the results of three different microphysical properties, the longwave and shortwave radiation fluxes at the top of the atmosphere are calculated through the Fu-Liou radiation transfer model. The results show that the longwave radiation fluxes calculated by using different cloud microphysical characteristics are in good agreement with the satellite observation data. The average error is 5.04%, and the correlation coefficients are all greater than 0.6. Compared with the observed longwave radiation, the Hogan algorithm that considers the temperature effect has a better simulation effect, the correlation coefficient is larger, and the root mean square error is smaller. The shortwave radiation is smaller than the observed values in the case of clouds, and the maximum error reaches 24.61%. The shortwave radiation flux simulated by the traditional empirical algorithm is closest to the satellite observations, and its root mean square error is significantly reduced compared with that of the other methods.

Evolution of DARDAR-CLOUD ice cloud retrievals: new parameters and impacts on the retrieved microphysical properties

Abstract. In this paper we present the latest refinements brought to the DARDAR-CLOUD product, which contains ice cloud microphysical properties retrieved from the cloud radar and lidar measurements from the A-Train mission. Based on a large dataset of in situ ice cloud measurements, the parameterizations used in the microphysical model of the algorithm – i.e. the normalized particle size distribution, the mass–size relationship, and the parameterization of the a priori value of the normalized number concentration as a function of temperature – were assessed and refined to better fit the measurements, keeping the same formalism as proposed in DARDAR basis papers. Additionally, in regions where lidar measurements are available, the lidar ratio retrieved for ice clouds is shown to be well constrained by the lidar–radar synergy. Using this information, the parameterization of the lidar ratio was also refined, and the new retrieval equals on average 35±10 sr in the temperature range between −60 and −20 ∘C. The impact of those changes on the retrieved ice cloud properties is presented in terms of ice water content (IWC) and effective radius. Overall, IWC values from the new DARDAR-CLOUD product are on average 16 % smaller than the previous version, leading to a 24 % reduction in the ice water path. In parallel, the retrieved effective radii increase by 5 % to 40 %, depending on temperature and the availability of the instruments, with an average difference of +15 %. Modifications of the microphysical model strongly affect the ice water content retrievals with differences that were found to range from −50 % to +40 %, depending on temperature and the availability of the instruments. The largest differences are found for the warmest temperatures (between −20 and 0 ∘C) in regions where the cloud microphysical processes are more complex and where the retrieval is almost exclusively based on radar-only measurements. The new lidar ratio values lead to a reduction of IWC at cold temperatures, the difference between the two versions increasing from around 0 % at −30 ∘C to 70 % below −80 ∘C, whereas effective radii are not impacted.

Understanding Ice Cloud‐Precipitation Properties of Three Modes of Mesoscale Convective Systems During PECAN

AbstractThis study analyzes the precipitation and ice cloud microphysical features of three common modes of linear mesoscale convective systems during the Plains Elevated Convection at Night (PECAN) campaign. Three cases, one for each linear mesoscale convective system archetype (trailing stratiform, leading stratiform, and parallel stratiform precipitation), are selected. We focus primarily on analyzing ice cloud microphysical properties and precipitation rates (PRs) over the classified convective core (CC) and stratiform rain (SR) regions, as well as the two stratiform regions that developed behind (SR1) and ahead (SR2) of the convective line relative to the storm motion. In the three selected cases, the ice water path (IWP) and PR have strong correlations in the CC, but not in the SR. In terms of the temporal evolution of the mean IWPs and PRs, both CC and SR IWPs, as well as CC PRs, reach peaks quickly but take a longer time to dissipate than the increase period. For all the three cases, both SR1 and SR2 IWPs are 20–70% of their corresponding CC values in both the leading stratiform and parallel stratiform cases and up to 95% for the trailing stratiform case, while all of their PRs are only 7–25% of their CC values. These values suggest not only that the SR PRs may depend on IWPs but also that the microphysical properties of ice particles such as habit and size distribution may play an important role. Utilizing cloud‐resolving simulations of these systems may provide better understanding of the physical meanings behind the results in the future.

An Inverse Problem Approach for the Retrieval of Ice Particle Mass from In Situ Measurements

AbstractMass–dimensional relationships have been published for decades to characterize the microphysical properties of ice cloud particles. Classical retrieval methods employ a simplifying assumption that restricts the form of the mass–dimensional relationship to a power law, an assumption that was proved inaccurate in recent studies. In this paper, a nonstandard approach that leverages optimal use of in situ measurements to remove the power-law constraint is presented. A model formulated as a linear system of equations relating ice particle mass to particle size distribution (PSD) and ice water content (IWC) is established, and the mass retrieval process consists of solving the inverse problem with numerical optimization algorithms. First, the method is applied to a synthetic crystal dataset in order to validate the selected algorithms and to tune the regularization strategy. Subsequently, the method is applied to in situ measurements collected during the High Altitude Ice Crystal–High Ice Water Content field campaigns. Preliminary results confirm the method is efficient at retrieving size-dependent masses from real data despite a significant amount of noise: the IWC values calculated from the retrieved masses are in good agreement with reference IWC measurements (errors on the order of 10%–15%). The possibility to retrieve ice particle size–dependent masses combined with the flexibility left for sorting datasets as a function of parameters such as cloud temperature, cloud type, or convective index makes this approach well suited for studying ice cloud microphysical properties.

Potential of Higher-Order Moments and Slopes of the Radar Doppler Spectrum for Retrieving Microphysical and Kinematic Properties of Arctic Ice Clouds

AbstractRetrievals of ice-cloud properties from cloud-radar observations are challenging because the retrieval methods are typically underdetermined. Here, the authors investigate whether additional information can be obtained from higher-order moments and the slopes of the radar Doppler spectrum such as skewness and kurtosis as well as the slopes of the Doppler peak. To estimate quantitatively the additional information content, a generalized Bayesian retrieval framework that is based on optimal estimation is developed. Real and synthetic cloud-radar observations of the Indirect and Semi-Direct Aerosol Campaign (ISDAC) dataset obtained around Barrow, Alaska, are used in this study. The state vector consists of the microphysical (particle-size distribution, mass–size relation, and cross section–area relation) and kinematic (vertical wind and turbulence) quantities required to forward model the moments and slopes of the radar Doppler spectrum. It is found that, for a single radar frequency, more information can be retrieved when including higher-order moments and slopes than when using only reflectivity and mean Doppler velocity but two radar frequencies. When using all moments and slopes with two or even three frequencies, the uncertainties of all state variables, including the mass–size relation, can be considerably reduced with respect to the prior knowledge.

Dependence of the Ice Water Content and Snowfall Rate on Temperature, Globally: Comparison of in Situ Observations, Satellite Active Remote Sensing Retrievals, and Global Climate Model Simulations

AbstractCloud ice microphysical properties measured or estimated from in situ aircraft observations are compared with global climate models and satellite active remote sensor retrievals. Two large datasets, with direct measurements of the ice water content (IWC) and encompassing data from polar to tropical regions, are combined to yield a large database of in situ measurements. The intention of this study is to identify strengths and weaknesses of the various methods used to derive ice cloud microphysical properties. The in situ data are measured with total water hygrometers, condensed water probes, and particle spectrometers. Data from polar, midlatitude, and tropical locations are included. The satellite data are retrieved from CloudSat/CALIPSO [the CloudSat Ice Cloud Property Product (2C-ICE) and 2C-SNOW-PROFILE] and Global Precipitation Measurement (GPM) Level2A. Although the 2C-ICE retrieval is for IWC, a method to use the IWC to get snowfall rates S is developed. The GPM retrievals are for snowfall rate only. Model results are derived using the Community Atmosphere Model (CAM5) and the Met Office Unified Model [Global Atmosphere 7 (GA7)]. The retrievals and model results are related to the in situ observations using temperature and are partitioned by geographical region. Specific variables compared between the in situ observations, models, and retrievals are the IWC and S. Satellite-retrieved IWCs are reasonably close in value to the in situ observations, whereas the models’ values are relatively low by comparison. Differences between the in situ IWCs and those from the other methods are compounded when S is considered, leading to model snowfall rates that are considerably lower than those derived from the in situ data. Anomalous trends with temperature are noted in some instances.

Improvements on the ice cloud modeling capabilities of the Community Radiative Transfer Model

AbstractNoticeable improvements on the ice cloud modeling capabilities of the Community Radiative Transfer Model (CRTM) are reported, which are based on the most recent advances in understanding ice cloud microphysical (particularly, ice particle habit/shape characteristics) and optical properties. The new CRTM ice cloud model is derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) collection 6 ice cloud habit model, which represents ice particles as severely roughened hexagonal ice column aggregates with a gamma size distribution. The single‐scattering properties of the new ice particle model are derived from a state‐of‐the‐art ice optical property library and are constructed as look‐up tables for rapid CRTM computations. Various sensitivity studies concerning instrument‐specific applications and simulations are performed to validate CRTM against satellite observations. In particular, radiances in a spectral region covering the infrared wavelengths are simulated. Comparisons of brightness temperatures between CRTM simulations and observations (from MODIS, the Atmospheric Infrared Sounder, and the Advanced Microwave Sounding Unit) show that the new ice cloud optical property look‐up table substantially enhances the performance of the CRTM under ice cloud conditions.

Retrievals of ice cloud microphysical properties of deep convective systems using radar measurements

AbstractThis study presents newly developed algorithms for retrieving ice cloud microphysical properties (ice water content (IWC) and median mass diameter (Dm)) for the stratiform rain and thick anvil regions of deep convective systems (DCSs) using Next Generation Radar (NEXRAD) reflectivity and empirical relationships from aircraft in situ measurements. A typical DCS case (20 May 2011) during the Midlatitude Continental Convective Clouds Experiment (MC3E) is selected as an example to demonstrate the 4‐D retrievals. The vertical distributions of retrieved IWC are compared with previous studies and cloud‐resolving model simulations. The statistics from six selected cases during MC3E show that the aircraft in situ derived IWC and Dm are 0.47 ± 0.29 g m−3 and 2.02 ± 1.3 mm, while the mean values of retrievals have a positive bias of 0.19 g m−3 (40%) and negative bias of 0.41 mm (20%), respectively. To evaluate the new retrieval algorithms, IWC and Dm are retrieved for other DCSs observed during the Bow Echo and Mesoscale Convective Vortex Experiment (BAMEX) using NEXRAD reflectivity and compared with aircraft in situ measurements. During BAMEX, a total of 63, 1 min collocated aircraft and radar samples are available for comparisons, and the averages of radar retrieved and aircraft in situ measured IWC values are 1.52 g m−3 and 1.25 g m−3 with a correlation of 0.55, and their averaged Dm values are 2.08 and 1.77 mm. In general, the new retrieval algorithms are suitable for continental DCSs during BAMEX, especially within stratiform rain and thick anvil regions.

Developing and bounding ice particle mass- and area-dimension expressions for use in atmospheric models and remote sensing

Abstract. Ice particle mass- and projected area-dimension (m-D and A-D) power laws are commonly used in the treatment of ice cloud microphysical and optical properties and the remote sensing of ice cloud properties. Although there has long been evidence that a single m-D or A-D power law is often not valid over all ice particle sizes, few studies have addressed this fact. This study develops self-consistent m-D and A-D expressions that are not power laws but can easily be reduced to power laws for the ice particle size (maximum dimension or D) range of interest, and they are valid over a much larger D range than power laws. This was done by combining ground measurements of individual ice particle m and D formed at temperature T < −20 °C during a cloud seeding field campaign with 2-D stereo (2D-S) and cloud particle imager (CPI) probe measurements of D and A, and estimates of m, in synoptic and anvil ice clouds at similar temperatures. The resulting m-D and A-D expressions are functions of temperature and cloud type (synoptic vs. anvil), and are in good agreement with m-D power laws developed from recent field studies considering the same temperature range (−60 °C < T < −20 °C).

Differences in Ice Cloud Microphysical Properties between Western and Eastern Tropical Pacific Regions Derived from CloudSat and CALIPSO Measurements

Differences in Ice Cloud Microphysical Properties between Western and Eastern Tropical Pacific Regions Derived from CloudSat and CALIPSO Measurements

The Characteristics of Ice Cloud Properties Derived from CloudSat and CALIPSO Measurements

Abstract The characteristics of ice clouds with a wide range of optical depths are studied based on satellite retrievals and radiative transfer modeling. Results show that the global-mean ice cloud optical depth, ice water path, and effective radius are approximately 2, 109 g m−2, and 48 , respectively. Ice cloud occurrence frequency varies depending not only on regions and seasons, but also on the types of ice clouds as defined by optical depth values. Ice clouds with different values show differently preferential locations on the planet; optically thinner ones ( < 3) are most frequently observed in the tropics around 15 km and in midlatitudes below 5 km, while thicker ones ( > 3) occur frequently in tropical convective areas and along midlatitude storm tracks. It is also found that ice water content and effective radius show different temperature dependence among the tropics, midlatitudes, and high latitudes. Based on analyzed ice cloud frequencies and microphysical properties, cloud radiative forcing is evaluated using a radiative transfer model. The results show that globally radiative forcing due to ice clouds introduces a net warming of the earth–atmosphere system. Those with < 4.0 all have a positive (warming) net forcing with the largest contribution by ice clouds with ~ 1.2. Regionally, ice clouds in high latitudes show a warming effect throughout the year, while they cause cooling during warm seasons but warming during cold seasons in midlatitudes. Ice cloud properties revealed in this study enhance the understanding of ice cloud climatology and can be used for validating climate models.

Investigation of ice cloud microphysical properties of DCSs using aircraft in situ measurements during MC3E over the ARM SGP site

AbstractSix deep convective systems (DCSs) with a total of 5589 five‐second samples and a range of temperatures from −41°C to 0°C during the Midlatitude Continental Convective Clouds Experiment (MC3E) were selected to investigate the ice cloud microphysical properties of DCSs over the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site. The ice cloud measurements of the DCS cases were made by the University of North Dakota Citation II research aircraft, and the ice cloud properties were derived through the following processes. First, the instances of supercooled liquid water in the ice‐dominated cloud layers of DCSs have been eliminated using multisensor detection, including the Rosemount Icing Detector, King and Cloud Droplet Probes, as well as 2DC and Cloud Imaging Probe images. Then the Nevzorov‐measured ice water contents (IWCs) at maximum diameter Dmax < 4000 µm are used as the best estimation to determine a new mass‐dimensional relationship. Finally, the newly derived mass‐dimensional relationship (a = 0.00365, b = 2.1) has been applied to a full spectrum of particle size distributions (PSDs, 120–30,000 µm) constructed from both 2DC and High‐Volume Precipitation Spectrometer measurements to calculate the best‐estimated IWCs of DCSs during MC3E. The averages of the total number concentrations (Nt), median mass diameter (Dm), maximum diameter (Dmax), and IWC from six selected cases are 0.035 cm−3, 1666 µm, 8841 µm, and 0.45 g m−3, respectively. The gamma‐type‐size distributions are then generated matching the observed PSDs (120–30,000 µm), and the fitted gamma parameters are compared with the observed PSDs through multimoment assessments including first moment (Dm), third moment (IWC), and sixth moment (equivalent radar reflectivity, Ze). For application of observed PSDs to the remote sensing community, a series of empirical relationships between fitted parameters and Ze values has been derived, and the bullet rosette ice crystal backscattering relationship has been suggested for ground‐based remote sensing.