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Impacts of small‐scale surface irregularities, or surface roughness, of atmospheric ice crystals on lidar backscattering properties are quantified. Geometric ice crystal models with various degrees of surface roughness and state‐of‐the‐science light‐scattering computational capabilities are utilized to simulate the single‐scattering properties across the entire practical size parameter range. The simulated bulk lidar and depolarization ratios of polydisperse ice crystals at wavelength 532 nm are strongly sensitive to the degree of surface roughness. Comparisons of these quantities between the theoretical simulations and counterparts inferred from spaceborne lidar observations for cold cirrus clouds suggest a typical surface‐roughness‐degree range of 0.03–0.15 in the cases of compact hexagonal ice crystals, which is most consistent with direct measurements of scanning electron microscopic images. To properly interpret lidar backscattering observations of ice clouds, it is necessary to account for the degree of surface roughness in light‐scattering computations involving ice crystals. Plain Language Summary: Lidar (Light Detection and Ranging) instruments on satellites use reflected, or backscattered, laser beams to investigate ice clouds in the atmosphere. However, it has long been a challenge to interpret lidar signals, called backscattering properties, to infer ice cloud characteristics accurately. This study uses theoretical simulations to investigate how small‐scale surface irregularities of ice crystals affect the lidar signals associated with ice clouds. These simulations demonstrate the significant impacts of small‐scale surface irregularities of ice crystals on backscattering. Based on comparisons between the theoretical simulations and satellite lidar observations, it is necessary to assume a moderate degree of small‐scale surface irregularities to explain lidar observations of typical ice clouds. Key Points: The sensitivity of the backscattering properties to the surface roughness of atmospheric ice crystals is theoretically investigatedThe depolarization ratio is substantially sensitive to the degree of surface roughness of ice crystalsCompact hexagonal ice models with degrees of surface roughness ranging 0.03–0.15 reasonably explain the Cloud‐Aerosol Lidar with Orthogonal Polarization backscattering signals [ABSTRACT FROM AUTHOR]

Abstract Impacts of small‐scale surface irregularities, or surface roughness, of atmospheric ice crystals on lidar backscattering properties are quantified. Geometric ice crystal models with various degrees of surface roughness and state‐of‐the‐science light‐scattering computational capabilities are utilized to simulate the single‐scattering properties across the entire practical size parameter range. The simulated bulk lidar and depolarization ratios of polydisperse ice crystals at wavelength 532 nm are strongly sensitive to the degree of surface roughness. Comparisons of these quantities between the theoretical simulations and counterparts inferred from spaceborne lidar observations for cold cirrus clouds suggest a typical surface‐roughness‐degree range of 0.03–0.15 in the cases of compact hexagonal ice crystals, which is most consistent with direct measurements of scanning electron microscopic images. To properly interpret lidar backscattering observations of ice clouds, it is necessary to account for the degree of surface roughness in light‐scattering computations involving ice crystals.

Ice cloud optical thickness (IOT) is an important parameter to characterize ice cloud properties and in the determination of cloud–radiation parameterization schemes, and the variation trend of ice clouds is more concerned with the study of weather and climate. In this paper, we analyzed the spatial and temporal distributions of IOT over the region between ±60° latitude. Cloud product data from March 2000 to February 2021 acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA's Aqua satellite were used in this study. Theil–Sen median trend analysis and EOF analysis methods were used to study the variation trend of IOT. The research results indicate that the monthly average IOT shows a "W" distribution from January to December, with a maximum reached in July (12.15) and a double bottom reached in March (10.7) and October (10.99), respectively. The average global IOT reaches the maximum in June–August, it tends to decrease with time, and its slope is −0.01 year−1. The statistical analysis results show that the area with an increase accounted for 49.4% of the total ice cloud coverage area; the area with a trend of significant increased and decreased is both 2.2%. The probability distribution of IOT reaches the maximum, around 3.25%, when the IOT is larger than 1.5 and less than or equal to 2. [ABSTRACT FROM AUTHOR]

Spaceborne passive submillimeter instruments can provide higher sensitivity to a broader size range of ice hydrometeors than traditional millimeter and infrared observations. However, a plane‐parallel cloud assumption is commonly applied for simplicity in passive cloud retrieval algorithms, while observations from active instruments have already demonstrated the ice particle variabilities in the vertical dimension. This study is conducted to assess the impacts of ice cloud vertical inhomogeneity on passive submillimeter‐wave simulations. Specifically, a simplified two‐layer scheme for distributions of particle size and ice water content in ice clouds is utilized to explore the inhomogeneous effects on radiative‐transfer process via the brightness temperature differences at the top of the atmosphere. Then the effect is quantitively evaluated based on a generated synthetic ice cloud and compared under the assumption of five particle size distributions to simulate homogeneous and heterogeneous clouds. The simplified two‐layer model simulations show that before saturation, the upper layer generates a stronger brightness temperature depression due to scattering than does the lower layer, given the same ice water content (IWC). However, with increasing IWC or effective diameters, the saturation effect would lead to the opposite impact, especially for high‐frequency channels. The generated synthetic ice cloud simulations reveal that the average values and the root‐mean‐squared errors of the brightness temperature differences are between −6 and 8 K and within 10 K, respectively. The results in this study are of special significance for remote‐sensing ice hydrometeors from passive submillimeter observations for the upcoming Ice Cloud Imager (ICI), as well as for future assimilations of submillimeter radiances in all‐sky conditions. [ABSTRACT FROM AUTHOR]

Dust aerosols can participate in the heterogeneous nucleation process as effective ice nucleation particles, thus changing the physical properties of clouds. In this paper, we used an eye-safe 1550 nm micropulse polarization single photon lidar combined with meteorological stations, HYSPLIT backward trajectory analysis, ERA5 reanalysis data, CALIPSO, Himawari-8 and Terra-MODIS satellite data to compare the difference in cloud characteristics between dust and clean cirrus cases in Jinan from 26–29 March 2022. The study found that the aerosol affected the cloud effective radius, and the cloud top temperature impacted the properties of depolarization of dust ice clouds. According to the statistical results of the upper and lower quartiles, the depolarization ratio (DPR) range of dust cirrus on 26 March was 0.46–0.49, a similar range to the clean cirrus, while that of dust cirrus on 27 March was 0.54–0.59, which seemed much larger. Different height and temperature conditions lead to differences in the habits of ice crystals in clouds, thus changing the DPR. However, the range of the DPR between clean cirrus and dust cirrus showed no obvious difference, as the former was 0.43–0.53 and the latter was 0.46–0.59. Under the condition of higher aerosol loading, the lidar range-corrected signal (RCS) of cirrus clouds was stronger, and the cloud effective radius was 48 μm, larger than that of clean cirrus (32 μm). This may be the effect of dust on the microphysical properties of clouds. This study discusses the indirect effects of dust aerosols on cirrus clouds and the underlying mechanisms from the perspectives of microphysics and optics, which can provide more references for urban air pollution processes and aerosol-cloud interactions. [ABSTRACT FROM AUTHOR]

Ice cloud optical thickness (IOT) is an important parameter to characterize ice cloud properties and in the determination of cloud–radiation parameterization schemes, and the variation trend of ice clouds is more concerned with the study of weather and climate. In this paper, we analyzed the spatial and temporal distributions of IOT over the region between ±60° latitude. Cloud product data from March 2000 to February 2021 acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA’s Aqua satellite were used in this study. Theil–Sen median trend analysis and EOF analysis methods were used to study the variation trend of IOT. The research results indicate that the monthly average IOT shows a “W” distribution from January to December, with a maximum reached in July (12.15) and a double bottom reached in March (10.7) and October (10.99), respectively. The average global IOT reaches the maximum in June–August, it tends to decrease with time, and its slope is −0.01 year−1. The statistical analysis results show that the area with an increase accounted for 49.4% of the total ice cloud coverage area; the area with a trend of significant increased and decreased is both 2.2%. The probability distribution of IOT reaches the maximum, around 3.25%, when the IOT is larger than 1.5 and less than or equal to 2.

The ice cloud detection sensitivity of the millimeter cloud radar (MMCR) and the Ka-band Zenith radar (KAZR) is investigated using a collocated Raman lidar (RL) at the Atmospheric Radiation Measurement Program Southern Great Plains site. Only profiles that are transparent to the RL with ice clouds only are considered in this study. The MMCR underestimates the RL ice cloud optical depth (COD) by 20%. The MMCR detects no ice clouds in 37% of the profiles. These profiles where ice cloud goes undetected by the MMCR typically contain very optically thin clouds, with a mean RL ice COD of 0.03. Higher ice cloud detection sensitivity is found for the KAZR, which underestimates the RL ice COD by 15%. The decrease in the ice COD bias for the KAZR compared to the MMCR is largely due to a decrease in the ice COD bias for the situation where the transparent profiles with ice clouds are detected by both the RL and cloud radar. The climatic net ice cloud radiative effects (CREs) from the RL at the top of the atmosphere (TOA) and the surface are 3.2 W m−2 and −0.6 W m−2, respectively. The ice CREs at the TOA and surface are underestimated for the MMCR by 0.7 W m−2 and 0.16 W m−2 (21% and 29%) and underestimated for the KAZR by 0.6 W m−2 and 0.14 W m−2 (17% and 24%). The ice clouds undetected by the cloud radars led to underestimating the climatic net cloud heating rates below 150 hPa by about 0–0.04 K day−1. [ABSTRACT FROM AUTHOR]

Since the ability of anthropogenic aerosols to act as ice nucleation particles has been recognized, the effect of anthropogenic aerosols on ice clouds has attracted increasing attentions. In recent years, some progress has been made in investigating the effects of anthropogenic aerosols on ice clouds. In this paper, we briefly review the study on the impact of anthropogenic aerosols on ice nuclei, properties and radiative forcing of ice clouds. Anthropogenic aerosols can form ice nuclei through homogeneous nucleation and heterogeneous nucleation. Convective strength can modulate the response of ice clouds to anthropogenic aerosols by affecting the nucleation activities. There have been large uncertainties in calculating the radiative forcing of anthropogenic aerosols on ice clouds in climate models. Further studies on the impact of anthropogenic aerosols on ice clouds are imperative to provide better parameterization schemes and reduce the uncertainties of aerosol indirect effects. [ABSTRACT FROM AUTHOR]

Investigations of the effects of clouds on Earth’s radiation budget demand accurate representations of cloud top parameters, which can be efficiently obtained by large-scale satellite remote sensing approaches. However, the insufficient utilization of multiband information is one of the major sources of uncertainty in cloud top products derived from geostationary satellites. In this study, we developed a new algorithm to estimate Cloud, Atmospheric Radiation and renewal Energy application (CARE) version 1.0 cloud top properties [cloud top height (CTH), cloud top pressure (CTP), and cloud top temperature (CTT)]. The algorithm is constructed from ten thermal spectral measurements in Himawari-8 observations by using the random forest (RF) method to comprehensively consider the contribution of each band to the cloud top parameters. We chose the highly accurate Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) products in 2018 as the true values. The sensitivity analysis demonstrated that the products can be fully reproduced by using multiple Himawari-8 channels with the addition of the digital elevation model (DEM) data. The validation results of the 2019 CALIOP data confirm that the new algorithm shows an effective performance, with correlation coefficients ($R$) of 0.89, 0.89, and 0.90 for CTH, CTP, and CTT, respectively. Moreover, a significant improvement in the ice cloud estimation is achieved, in which the CTT $R$ value increased from 0.46 to 0.70, as well as an improvement in the sea area, where the CTT $R$ value increased from 0.71 to 0.84 compared with the Himawari-8 products of the Japan Aerospace Exploration Agency (JAXA) P-tree system. The further analyses performed here capture the diurnal cycle of cloud top parameters well in different temporal scales over the Asia–Pacific region. [ABSTRACT FROM AUTHOR]

This study investigated the temperature and fraction of lower‐tropospheric ice cloud over Antarctica and the Southern Ocean (SO) using Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation satellite data. Over the SO, the maximum low‐level ice‐cloud fraction below 2 km is observed at cold temperatures (<−25°C); however, local maxima of low‐level ice‐cloud fraction are observed at temperatures >−7.5°C (>−17.5°C) during summer (winter). High fractions of low‐level ice cloud observed at higher temperatures over near‐coastal Antarctic sea ice areas in summer, coincident with the highest chlorophyll‐a concentrations, and over coastal Antarctic ice‐covered areas in winter, suggest that marine aerosols act as ice‐nucleating particles for ice‐cloud formation during summer and winter. Plain Language Summary: Clouds over Antarctica and the Southern Ocean (SO) play an important role in Earth's climate. Although climate models are useful for investigating Southern Hemisphere clouds, overestimation of the modeled ice‐cloud fraction over the SO causes large uncertainties regarding atmospheric and oceanic circulations. Therefore, improved understanding of cloud characteristics over Antarctica and the SO would enhance projections of the climate system. We investigated ice‐cloud fractions over Antarctica and the SO using satellite data. Although ice cloud generally occurs under very cold conditions (e.g., <−38°C), low‐level ice cloud is observed over the SO under higher temperature conditions because various aerosols influence ice‐cloud formation. During summer, high fractions of low‐level ice cloud at higher temperatures (>−7.5°C) are observed over coastal Antarctic sea ice areas, coincident with the highest chlorophyll‐a concentrations, an indicator of phytoplankton abundance in the upper layer of the ocean. During winter, the highest fractions of low‐level ice cloud at higher temperatures (>−17.5°C) are observed near coastal Antarctic ice‐covered areas, coincident with considerable heat exchange from the ocean to the air. These findings suggest that oceanic aerosols contribute to low‐level ice‐cloud formation over the SO at higher temperatures during summer and winter. Key Points: The highest ice‐cloud fractions were found in different temperature environments between Antarctica and the Southern Ocean (SO)Local maxima of low‐level ice‐cloud fraction over the SO were detected at temperatures between −7.5°C and 0°C during summerHigh fractions of low‐level ice cloud in summer at temperatures between −7.5°C and 0°C coincided with high chlorophyll‐a concentrations [ABSTRACT FROM AUTHOR]

Satellite images often feature sun glints caused by the specular reflection of sunlight from water surfaces or from horizontally oriented ice crystals occurring in clouds. Such glints can prevent accurate retrievals of atmospheric and surface properties using existing algorithms, but the glints can also be used to infer more about the glint-causing objects—for example about the microphysical properties and radiative effects of ice clouds. This paper introduces the recently released operational glint product of the Earth Polychromatic Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) spacecraft. Most importantly, the paper describes the algorithm used for generating the key component of the new product: a glint mask indicating the presence of sun glint caused by the specular reflection of sunlight from ice clouds and smooth water surfaces. After describing the glint detection algorithm and glint product, the paper shows some examples of the detected glints and discusses some basic statistics of the glint population in a yearlong dataset of EPIC images. These statistics provide insights into the performance of glint detection and point toward possibilities for using the glint product to gain scientific insights about ice clouds and water surfaces.

Dust aerosols can participate in the heterogeneous nucleation process as effective ice nucleation particles, thus changing the physical properties of clouds. In this paper, we used an eye-safe 1550 nm micropulse polarization single photon lidar combined with meteorological stations, HYSPLIT backward trajectory analysis, ERA5 reanalysis data, CALIPSO, Himawari-8 and Terra-MODIS satellite data to compare the difference in cloud characteristics between dust and clean cirrus cases in Jinan from 26–29 March 2022. The study found that the aerosol affected the cloud effective radius, and the cloud top temperature impacted the properties of depolarization of dust ice clouds. According to the statistical results of the upper and lower quartiles, the depolarization ratio (DPR) range of dust cirrus on 26 March was 0.46–0.49, a similar range to the clean cirrus, while that of dust cirrus on 27 March was 0.54–0.59, which seemed much larger. Different height and temperature conditions lead to differences in the habits of ice crystals in clouds, thus changing the DPR. However, the range of the DPR between clean cirrus and dust cirrus showed no obvious difference, as the former was 0.43–0.53 and the latter was 0.46–0.59. Under the condition of higher aerosol loading, the lidar range-corrected signal (RCS) of cirrus clouds was stronger, and the cloud effective radius was 48 μm, larger than that of clean cirrus (32 μm). This may be the effect of dust on the microphysical properties of clouds. This study discusses the indirect effects of dust aerosols on cirrus clouds and the underlying mechanisms from the perspectives of microphysics and optics, which can provide more references for urban air pollution processes and aerosol-cloud interactions.

Abstract The 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 of res and IWCs 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. Retrieved re, IWC, N0, and w from 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.

The 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 of res and IWCs 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. Retrieved re, IWC, N0, and w from 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. Plain Language Summary: Ice clouds are globally widespread and their microphysical properties play an important role in the radiative balance of Earth, which are still not well understood and poorly represented. Thus, the algorithm that drive the microphysical properties of ice clouds precisely from radar observations is necessary. Here, we address this issue using Markov chain Monte Carlo algorithm for deriving ice cloud microphysical properties. The feasibility of Markov chain Monte Carlo (MCMC) algorithm has been tested using simulated W‐band radar reflectivity and CloudSat Cloud Profiling Radar observations. The retrievals are in reasonable agreement with CloudSat 2B‐CWC‐RO product. The study will be very useful for analyzing the role that ice clouds play in climate. Key Points: The Markov chain Monte Carlo algorithm has been validated with simulated W‐band radar reflectivity and CloudSat Cloud Profiling Radar (CPR) observationsRetrieved ice cloud microphysical properties are in reasonable agreement with CloudSat 2B‐CWC‐RO productThe crystal habit assumption has significant influence on the accuracy of retrievals [ABSTRACT FROM AUTHOR]

The ice cloud detection sensitivity of the millimeter cloud radar (MMCR) and the Ka-band Zenith radar (KAZR) is investigated using a collocated Raman lidar (RL) at the Atmospheric Radiation Measurement Program Southern Great Plains site. Only profiles that are transparent to the RL with ice clouds only are considered in this study. The MMCR underestimates the RL ice cloud optical depth (COD) by 20%. The MMCR detects no ice clouds in 37% of the profiles. These profiles where ice cloud goes undetected by the MMCR typically contain very optically thin clouds, with a mean RL ice COD of 0.03. Higher ice cloud detection sensitivity is found for the KAZR, which underestimates the RL ice COD by 15%. The decrease in the ice COD bias for the KAZR compared to the MMCR is largely due to a decrease in the ice COD bias for the situation where the transparent profiles with ice clouds are detected by both the RL and cloud radar. The climatic net ice cloud radiative effects (CREs) from the RL at the top of the atmosphere (TOA) and the surface are 3.2 W m−2 and −0.6 W m−2, respectively. The ice CREs at the TOA and surface are underestimated for the MMCR by 0.7 W m−2 and 0.16 W m−2 (21% and 29%) and underestimated for the KAZR by 0.6 W m−2 and 0.14 W m−2 (17% and 24%). The ice clouds undetected by the cloud radars led to underestimating the climatic net cloud heating rates below 150 hPa by about 0–0.04 K day−1.

Two upcoming missions are scheduled to provide novel spaceborne observations of upwelling far‐infrared spectra. In this study, the accuracy of ice cloud property retrievals using spaceborne middle‐to‐far‐infrared (MIR‐FIR) measurements is examined toward a better understanding of retrieval biases and uncertainties. Theoretical sensitivity studies demonstrate that the MIR‐FIR spectra are sensitive to ice cloud properties, thereby providing a robust means for retrieving cloud properties under nighttime conditions. However, the temperature dependence of the ice refractive index and relevant ice particle shape models need to be incorporated into the retrieval procedure to avoid systematic biases in inferring cloud optical thickness and effective particle radius. Furthermore, prior information of subpixel cloud fractions is essential to mitigation of substantial systematic retrieval biases due to inconsistent subpixel cloud fractions. Plain Language Summary: Two upcoming satellite missions will provide the first measurements of spectrally resolved radiation emitted by the Earth across the so‐called "far‐infrared" (15–100 μm). We examined the uncertainty of ice cloud property estimations based on simulated middle‐to‐far‐IR spectra observed at the top of the atmosphere. The present results suggest that the aforementioned upcoming satellite missions will offer an opportunity to improve ice cloud property estimations particularly for optically thick clouds. In addition, we demonstrate a pressing need for a better understanding of ice crystal shapes and ice cloud temperature in order to fully exploit the capabilities of the upcoming spaceborne observations. Key Points: Spaceborne middle‐ and far‐infrared measurements improve cloud property retrievals in optically thick ice clouds under nighttime conditionsTemperature and particle habit dependence of ice optical properties at far‐infrared wavelengths have substantial impact on the retrievalsPrior information of subpixel cloud fractions is vital for retrieval accuracy [ABSTRACT FROM AUTHOR]

Simulations of the radiometric and polarimetric properties of ice clouds based on the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 ice cloud model (aggregates in conjunction with the gamma distribution) are performed at wavelengths in the Infrared (IR) and Submillimeter/Millimeter (sub‐mm/mm) regimes. The scattering/absorption/polarization properties from the precalculated database are incorporated into a radiative transfer model, the Atmospheric Radiative Transfer Model (ARTS). A number of sub‐mm/mm bands have been considered from radiometric and polarimetric studies for potential applications to retrieving ice cloud properties at shorter sub‐mm/mm wavelengths (higher frequencies). The IR atmospheric window has also been thoroughly investigated for its feasibility to observe ice clouds. However, in the literature there is a lack of studies utilizing these high frequency bands or polarimetric IR observations to infer ice cloud properties. This study focuses on performing simulations of ice clouds between 680 and 183 GHz (sub‐mm/mm regime) and between 8.6 and 12 μm wavelengths (IR‐window regime) toward determining potential applications to simultaneous retrievals of ice water path (IWP) and effective diameter (Deff) with a primary focus on optically thin cirrus clouds. IWP and Deff isoline plots (lookup tables) are created to conduct sensitivity analyses and to perform preliminary retrievals. Results reveal that the utilization of both sub‐mm/mm and IR wavelengths combined with linearly polarized and unpolarized measurements leads to the most accurate IWP and Deff retrievals with errors lower than 10%. Key Points: IR wavelengths mainly sensitive to optically thin ice clouds while sub‐mm/mm wavelengths mainly sensitive to optically thick cloudsSub‐mm/mm wavelengths corresponding to higher frequencies (>600 GHz) could be used to retrieve properties from optically thin cloudsLinearly polarized sub‐mm/mm and IR wavelengths have the potential to retrieve ice water path and effective diameter [ABSTRACT FROM AUTHOR]

With the highlight of environmental problems over the Tibetan Plateau (TP), aerosol pollution and the influence of this pollution on cloud properties are becoming a new area of research. Based on the aerosol index and cloud property parameters derived from satellite observations, in this study, the inconsistent effects of aerosols on ice cloud properties between daytime and nighttime over the TP are investigated. The results indicate that ice clouds are mainly distributed over the TP margin area, especially over the north slope, during both daytime and nighttime. The occurrence frequency of ice cloud is higher during the daytime than during the nighttime over the margin areas of the TP. Similarly, aerosols are mainly concentrated over the northern margin of the TP. A potential relationship may exist between the aerosol index and ice cloud properties. When the aerosol index increases from 0.05 to 0.17, the ice cloud droplet radius (ICDR) during the daytime decreases from 32.1 to 27.9 μm, while the ICDR during the nighttime remains almost constant (approximately 25 μm); furthermore, the ice water path (IWP) during the daytime decreases slightly due to the saturation effect, while the nocturnal IWP increases significantly. The changes in ice cloud optical depth (ICOD) during daytime and nighttime show significant and completely opposite trends. The removal of the influence of meteorological factors showed that aerosols have a more dominant influence than meteorological conditions on ice cloud properties (except for the nocturnal ICDR and IWP during the daytime). Key Points: The inconsistent effects of aerosols on ice cloud properties between daytime and nighttime over the Tibetan Plateau are investigatedA potential relationship may exist between the aerosol index and ice cloud propertiesAerosols show dominant influence on daytime ICDR and nocturnal IWP and ICOD [ABSTRACT FROM AUTHOR]

Spaceborne passive instruments are widely used to infer long‐term ice cloud properties due to their large temporal and spatial coverage. Although observations from active instruments demonstrate ice particle variability in the vertical dimension, a pragmatic assumption made in passive cloud retrieval algorithms is that the observed scene consists of a plane‐parallel cloud. In this study, a theoretical exploration on how ice cloud vertical heterogeneity (ICVH) influences passive retrievals (i.e., cloud optical thickness, cloud effective radius, and ice water path, IWP) is implemented at the pixel scale. Specifically, with an established ice cloud profile database inferred from 1‐year Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation/CloudSat, we quantitatively estimate ICVH‐induced biases on monthly averaged cloud macrophysical and radiative properties. Results show an average underestimation (−35%) of Moderate Resolution Imaging Spectroradiometer (MODIS) monthly IWP due to the ICVH for global ice clouds over ocean. The ICVH impacts are enhanced at large IWPs (e.g., > 500 g/m2) and solar zenith angles, resulting in a profound underestimation of MODIS IWP (up to −50%) in deep convective regions and middle to high‐latitude regions in the winter hemisphere. The global‐averaged ice cloudy‐sky reflected solar radiation and outgoing longwave radiation derived from MODIS retrievals are slightly overestimated, suggesting that the ICVH has little impact on cloud radiative properties. Relatively large reflected solar radiation (0.3 W/m2) and outgoing longwave radiation (0.1 W/m2) flux differences occur at high and low IWPs, respectively. The largest total flux difference (~2 W/m2), mainly contributed by shortwave part, is associated with deep convection where the typical IWP is greater than 2,000 g/m2. Key Points: Derived IWP based on the plane‐parallel approximation can be underestimated by more than 50% due to ice cloud vertical heterogeneityICVH has little impact on flux calculations as long as the same assumptions are made in both the calculations and the cloud retrievalsIWP differences between the MODIS‐like and active sensor‐based retrievals can be partly explained by ice cloud vertical heterogeneity [ABSTRACT FROM AUTHOR]

Ice clouds play a critical role in the balance of the earth–atmosphere radiation system, but there are some limitations in the existing remote sensing methods for ice clouds. Terahertz wave is expected to be the best waveband for retrieving ice clouds, with terahertz wavelengths in the order of the size of typical ice cloud particles. An inversion method for the remote sensing of ice clouds at terahertz wavelengths based on genetic algorithm is proposed in this paper. First, suitable channel sets in the terahertz band, which are mainly a combination of absorption lines and window regions, are determined. Then, to improve the efficiency of the generation of the retrieval database, based on the brightness temperature simulated by the atmospheric radiative transfer simulator (ARTS) for different cloud parameters, a fast forward operator is constructed using three-dimensional interpolation to simulate the brightness temperature difference between clear sky and a cloudy scene. Finally, an inversion model to retrieve the ice cloud base height, the effective particle diameter and the ice water path is established based on the genetic algorithm, and an analysis of the inversion errors is performed. The results show that the forward operator, constructed by the nearest interpolation, can accurately calculate the brightness temperature difference at a high speed. The proposed inversion method at terahertz wavelengths based on the genetic algorithm can achieve the expected scientific requirement. The absolute error of the cloud height is around 0.2 km, and the absolute error of the low ice water path (below 20 g/m2) is small, while the relative error of the high ice water path is generally maintained at around 10%, and the absolute error of the effective particle diameter is mostly around 4 μm.

Dust–cloud–surface radiation interactions are a complex nonlinear relation referring to the influences of both atmospheric dust and dust-on-snow on surface albedo. A ‘Tiramisu’ snow event occurred on 1 December 2018, in Urumqi, China, providing an excellent testbed for exploring the comprehensive effect induced by atmospheric dust and those deposited atop fresh snowpack on surface radiation. A detailed analysis indicates that the decrease of snow albedo by 0.17–0.26 (22%–34%) is contributed by the effects both the dust–cloud interactions and dust-on-snow at synoptic scale in this case. In particular, dust well mixed with ice clouds at altitudes of 2.5–5.5 km disrupted the ‘seeder–feeder’ structure of clouds and heterogeneous ice nucleation. Dust-induced changes in the low layer of ice clouds (3.3–5.5 km) under a low temperature of –20 °C resulted in a 31.8% increase in the ice particle radius and 84.6% increase in the ice water path, which acted to indirectly buffer the incident solar radiation reaching the surface. Dust particles deposited on the snow surface further caused snow darkening since the snow albedo was found to decrease by 11.8%–23.3%. These findings underscore the importance of considering the comprehensive effect of dust–cloud–radiation interactions in the future.

The Ice Cloud Imager (ICI) will be launched on the next generation of EUMETSAT polar-orbiting weather satellites and make passive observations between 183 and 664 GHz which are sensitive to scattering from cloud ice. These observations have the potential to improve weather forecasts through direct assimilation using "all-sky" methods which have been successfully applied to microwave observations up to 200 GHz in current operational systems. This requires sufficiently accurate representations of cloud ice in both numerical weather prediction (NWP) and radiative transfer models. In this study, atmospheric fields from a high-resolution NWP model are used to drive radiative transfer simulations using the Atmospheric Radiative Transfer Simulator (ARTS) and a recently released database of cloud ice optical properties. The simulations are evaluated using measurements between 89 and 874 GHz from five case studies of ice and mixed-phase clouds observed by the Facility for Airborne Atmospheric Measurements (FAAM) BAe-146 research aircraft. The simulations are strongly sensitive to the assumed cloud ice optical properties, but by choosing an appropriate ice crystal model it is possible to simulate realistic brightness temperatures over the full range of sub-millimetre frequencies. This suggests that sub-millimetre observations have the potential to be assimilated into NWP models using the all-sky method.

Retrieval of ice cloud properties from passive remote satellite instruments using multispectral observations of the upwelling radiance at the top of atmosphere is challenging. This requires an accurate separation of the surface spectral reflectance below the cloud and that coming from the ice cloud particle. In this paper, a weighting-function based method is described, which retrieves ice cloud optical thickness (COT) and ice cloud crystal effective radius (CER). The retrieval algorithm belongs to the newly developed eXtensible Bremen Aerosol Retrieval (XBAER) algorithm family, and is called XBAER-ice. The XBAER-ice retrieval uses a surface reflectance parameterization minimizing the impact of surface scattering on the retrieval of optically thin ice cloud properties. A fractal model is used to describe the ice cloud optical properties. The comparison of COT and CER between XBAER-ice and latest Moderate Resolution Imaging Spectroradiometer (MODIS) cloud products shows good agreement. We note that ice cloud property retrievals are particularly sensitive to assumptions about the ice particle shape. The comparison between satellite derived COTs using both the MODIS C6 product and XBAER-ice algorithm agree well with Atmospheric Radiation Measurement (ARM) in-situ measurements (with correlation coefficient above 0.9) whereas CERs show poorer agreement (~0.6). The global spatial distribution patterns and values of COT from ice clouds retrieved by using the MODIS C6 and XBAER-ice algorithms show a high degree of consistency. The XBAER-ice derived ice cloud CER values are systematically smaller than the MODIS C6 product, but both products have similar spatial distribution patterns. The XBAER-ice offers a potential alternative algorithm for the synergistic operational retrieval of ice cloud properties from the observations of Ocean and Land Colour Instrument (OLCI) and Sea and Land Surface Temperature Radiometer (SLSTR) onboard Sentinel-3. [ABSTRACT FROM AUTHOR]

Abstract: Despite the vertically inhomogeneous (VIH) structure of ice clouds, current passive remote sensing methods assume plane‐parallel homogeneous (PPH) layers, which can lead to retrieval errors. An adequate VIH cloud model is required to improve retrieval performance. In this study, CloudSat and CALIPSO satellite measurements in a 1‐year period were analyzed to model cloud vertical inhomogeneity, and its impacts on cloud retrieval were assessed using thermal infrared (TIR) measurements. The satellite measurements revealed that the peak ice water content (IWC) located around the cloud vertical midpoint moved toward the cloud base as the ice water path (IWP) increased in clouds with small IWP values; thicker clouds exhibited a gradual shift in IWC peak location toward the cloud top as IWP increased. The vertical profiles of both the cloud‐particle effective radius (CER) and a proxy of cloud‐particle number concentration showed close associations with the vertical IWC profile. An empirical model linking cloud geometrical thickness to columnar optical properties (IWP and column‐mean CER) as a function of cloud‐top temperature was also proposed. Compared with a model assuming PPH clouds, the VIH cloud model improved retrieval performance by reducing the retrieval error of the TIR‐based passive remote sensing algorithm. Further, by increasing the retrieved values of cloud‐top height noticeably for high‐level clouds and column‐mean CER considerably, with minimal effects on cloud optical thickness retrieval, the VIH cloud model yielded results that were in better agreement with radar/lidar ice cloud products than those obtained under the assumption of PPH cloud layers. [ABSTRACT FROM AUTHOR]

We investigated the difference in the fraction of ice (water) cloud bins between eastern and western parts of Eurasia using Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data in January 2007. The fraction of ice cloud bins between −25 and 0 °C was clearly larger in eastern Eurasia than in western Eurasia. A significantly increased (about 20–30%) fraction of ice‐containing clouds is observed in eastern Eurasia in the cloud top temperatures range from −30 to −15 °C. The difference in ice cloud bin fractions between eastern and western Eurasia was remarkable between −20 and −5 °C, being about 20% greater in eastern Eurasia. The fraction of ice cloud bins in the lower troposphere (below 3 km) was larger in eastern Eurasia than in western Eurasia. These results indicate that the ice formation process was more promoted in the lower troposphere in eastern Eurasia than in western Eurasia. This is the first time such results have been obtained from cloud internal structure observations using the CALIPSO active sensor. [ABSTRACT FROM AUTHOR]

Abstract: The diurnal variation of tropical ice clouds has been well observed and examined in terms of the occurring frequency and total mass but rarely from the viewpoint of ice microphysical parameters. It accounts for a large portion of uncertainties in evaluating ice clouds' role on global radiation and hydrological budgets. Owing to the advantage of precession orbit design and paired polarized observations at a high‐frequency microwave band that is particularly sensitive to ice particle microphysical properties, 3 years of polarimetric difference (PD) measurements using the 166 GHz channel of Global Precipitation Measurement Microwave Imager (GPM‐GMI) are compiled to reveal a strong diurnal cycle over tropical land (30°S–30°N) with peak amplitude varying up to 38%. Since the PD signal is dominantly determined by ice crystal size, shape, and orientation, the diurnal cycle observed by GMI can be used to infer changes in ice crystal properties. Moreover, PD change is found to lead the diurnal changes of ice cloud occurring frequency and total ice mass by about 2 h, which strongly implies that understanding ice microphysics is critical to predict, infer, and model ice cloud evolution and precipitation processes. [ABSTRACT FROM AUTHOR]