Ice Clouds Research Articles

Calculation of Ice Cloud Bulk Scattering Properties Based on Deep Reinforcement Learning

Calculation of Ice Cloud Bulk Scattering Properties Based on Deep Reinforcement Learning

Synergistic Retrievals of Ice Cloud Microphysics by Spaceborne Submillimeter and Infrared Observations

Synergistic Retrievals of Ice Cloud Microphysics by Spaceborne Submillimeter and Infrared Observations

A Robust Ice Cloud Optical Property Model for Lidar-Based Remote Sensing Applications

A Robust Ice Cloud Optical Property Model for Lidar-Based Remote Sensing Applications

Hyperspectral Remote Sensing Technology of Far-Infrared Radiation and Its Application in Ice Cloud Retrievals (Invited)

Hyperspectral Remote Sensing Technology of Far-Infrared Radiation and Its Application in Ice Cloud Retrievals (Invited)

A hybrid cloud detection and cloud phase classification algorithm using classic threshold-based tests and extra randomized tree model

The detection of clouds from the latest-generation satellites is crucial for the accuracy of downstream remote sensing products. Classic threshold-based algorithms or machine-learning-based algorithms have limitations in all-day cloud detection, usually sacrificing measurements from visible bands to develop all-day cloud detections. It is difficult to find a day-night consistent cloud detection that takes advantage of all band measurements even if there is an abrupt absence of visible bands on one sides of the terminator. This study explains a cloud detection algorithm for the Cloud, Atmospheric Radiation, and renewal Energy application (CARE) datasets that synergistically uses threshold-based tests and an extra randomized tree (ERT) model to detect all-day clouds from Himawari-8 full-disk measurements. First, we developed a visible-to-infrared band daytime cloud detection algorithm (D algorithm) to generate high-precision training datasets, based on which an infrared band all-day cloud detection algorithm (DN algorithm) is built with an ERT model. Second, we merge the D and DN algorithm into CARE algorithm by weighting the results of the two algorithms differently according to day/night and land/sea conditions. In developing D algorithm, we use two-layer curved-surface thresholds from a look-up-table (LUT), that can effectively improve the dusk-dawn cloud detection accuracy over high latitudes and reduce the misclassification of clouds over sunglint regions. Validations against CALIPSO measurements show that the hit rate (HR) of CARE results (daytime cloudy HR 76.95%, clear HR 87.97%, nighttime cloudy HR 73.19%, clear HR 81.77%) outperform MODIS (daytime cloudy HR 77.35%, clear HR 66.09%, nighttime cloudy HR 78.08%, clear HR 59.72%) especially in nighttime clear sky detections, daytime CARE results show lower and higher HR in water/ice cloud detections than JAXA and MODIS results, nighttime CARE (water cloud HR 47.82%, ice cloud HR 93.59%) results show higher HR in both water and ice clouds than MODIS (water cloud HR 43.39%, ice cloud HR 74.41%). The comparison between the CARE and JAXA products shows high consistency during the daytime, and the CARE products have better cloud detection in the sunglint center and in dusk-dawn scenarios.

High-altitude demonstration of LWIR polarimetry using uncooled microbolometers

An Infrared Channeled Spectro-Polarimeter (IRCSP) was successfully demonstrated in a near-space environment on a high-altitude balloon mission out of the NASA Columbia Scientific Balloon Facility in August 2021. The compact IRCSP is sensitive to linearly polarized thermal radiation between 7-12μm and targets polarized cloud top scattering. The IRCSP comprises a series of polarization modulation components followed by a diffraction grating and lens that images a polarization modulated spectrum at the focal plane. At altitudes greater than 30 km, the IRCSP reported brightness temperatures (Tb) between 250–285 K with an uncertainty of less than 1.5K. These IRCSP Tb values overlapped the collated GOES-16 cloud top temperatures within the pointing uncertainty of the instrument. IRCSP’s inaugural high-altitude deployment demonstrated the operation of uncooled microbolometers in the low-pressure environment and resulted in the first known observations of downward-viewing thermal polarized radiation from Earth’s atmosphere. Using the Lomb–Scargle periodogram with a maximum acceptable false alarm probability of 1%, the IRCSP detected statistically significant polarized signals with average degrees of linear polarization (DoLP) ranging from 1% to 20%. The top-of-atmosphere simulations that motivated IRCSP’s development predict that LWIR polarization is highest for ice clouds of moderate optical thickness. IRCSP observed cloud-top DoLP trends with brightness temperature that are consistent with these simulations. The highest and lowest Tb values occur for no clouds and optically thick clouds, respectively. The lowest DoLP measurements by IRCSP were at these extreme conditions. The DoLP peaked at intermediate Tb values. This preliminary work motivates IRCSP’s participation in future deployments that provide independent data products of cloud microphysical properties.

Opinion: Tropical cirrus – from micro-scale processes to climate-scale impacts

Abstract. Tropical cirrus clouds, i.e., any type of ice cloud with tops above 400 hPa, play a critical role in the climate system and are a major source of uncertainty in our understanding of global warming. Tropical cirrus clouds involve processes spanning a wide range of spatial and temporal scales, from ice microphysics on cloud scales to mesoscale convective organization and planetary wave dynamics. This complexity makes tropical cirrus clouds notoriously difficult to model and has left many important questions stubbornly unanswered. At the same time, their multi-scale nature makes them well-positioned to benefit from the rise of global, high-resolution simulations of Earth's atmosphere and a growing abundance of remotely sensed and in situ observations. Rapid progress on our understanding of tropical cirrus requires coordinated efforts to take advantage of these modern computational and observational abilities. In this opinion paper, we review recent progress in cirrus studies, highlight important unanswered questions, and discuss promising paths forward. Significant progress has been made in understanding the life cycle of convectively generated “anvil” cirrus and the response of their macrophysical properties to large-scale controls. On the other hand, much work remains to be done to fully understand how small-scale anvil processes and the climatological anvil radiative effect will respond to global warming. Thin, in situ formed cirrus clouds are now known to be closely tied to the thermal structure and humidity of the tropical tropopause layer, but microphysical uncertainties prevent a full understanding of this link, as well as the precise amount of water vapor entering the stratosphere. Model representation of ice-nucleating particles, water vapor supersaturation, and ice depositional growth continue to pose great challenges to cirrus modeling. We believe that major advances in the understanding of tropical cirrus can be made through a combination of cross-tool synthesis and cross-scale studies conducted by cross-disciplinary research teams.

Kilometer-scale global warming simulations and active sensors reveal changes in tropical deep convection

Changes in tropical deep convection with global warming are a leading source of uncertainty for future climate projections. A comparison of the responses of active sensor measurements of cloud ice to interannual variability and next-generation global storm-resolving model (also known as k-scale models) simulations to global warming shows similar changes for events with the highest column-integrated ice. The changes reveal that the ice loading decreases outside the most active convection but increases at a rate of several percent per Kelvin surface warming in the most active convection. Disentangling thermodynamic and vertical velocity changes shows that the ice signal is strongly modulated by structural changes of the vertical wind field towards an intensification of strong convective updrafts with warming, suggesting that changes in ice loading are strongly influenced by changes in convective velocities, as well as a path toward extracting information about convective velocities from observations.

Effect of Acidity on Ice Nucleation by Inorganic-Organic Mixed Droplets.

Aerosol acidity significantly influences heterogeneous chemical reactions and human health. Additionally, acidity may play a role in cloud formation by modifying the ice nucleation properties of inorganic and organic aerosols. In this work, we combined our well-established ice nucleation technique with Raman microspectroscopy to study ice nucleation in representative inorganic and organic aerosols across a range of pH conditions (pH -0.1 to 5.5). Homogeneous nucleation was observed in systems containing ammonium sulfate, sulfuric acid, and sucrose. In contrast, droplets containing ammonium sulfate mixed with diethyl sebacate, poly(ethylene glycol) 400, and 1,2,6-hexanetriol were found to undergo liquid-liquid phase separation, exhibiting core-shell morphologies with observed initiation of heterogeneous freezing in the cores. Our experimental findings demonstrate that an increased acidity reduces the ice nucleation ability of droplets. Changes in the ratio of bisulfate to sulfate coincided with shifts in ice nucleation temperatures, suggesting that the presence of bisulfate may decrease the ice nucleation efficiency. We also report on how the morphology and viscosity impact ice nucleation properties. This study aims to enhance our fundamental understanding of acidity's effect on ice nucleation ability, providing context for the role of acidity in atmospheric ice cloud formation.

Strong aerosol cooling alone does not explain cold-biased mid-century temperatures in CMIP6 models

Abstract. The current generation of global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) exhibits a surprisingly cold-biased ensemble-mean mid-20th century global-mean surface temperature anomaly, compared to the previous generation Phase 5 (CMIP5) and to the observed mid-century (1940–1970) temperature anomaly. Most CMIP6 models, 31 of 36 models in contrast to 17 of 27 CMIP5 models, are colder than the uncertainty range of the observed anomaly, indicating that the CMIP6 suppressed warming is not caused by a few cold models. However, no clear cause that sufficiently explains the tendency towards suppressed mid-20th century warming emerges. Whereas models that best match observations exclusively exhibit weaker aerosol forcing than that exhibited by colder models, there is not a clear relationship between mid-century temperatures and aerosol forcing. Likewise, no systematic differences emerge among other model aerosol representations, such as inclusion of aerosol–cloud interactions for ice clouds in the model or the type of aerosol model input data set used, nor variations in greenhouse gas forcing or climate sensitivity, that could explain the suppressed warming. This indicates the presence of another cause, or more likely a set of causes, of the suppressed warming in many CMIP6 models. Thus, the prospect of a strong constraint on present-day aerosol forcing based on the mid-century warming is weakened, even if it is encouraging that those models that do match the observed warming best all have relatively weak aerosol forcing.

Comparison of Cloud/Rain Band Structures of Typhoon Muifa (2022) Revealed in FY‐3E MWHS‐2 Observations With All‐Sky Simulations

AbstractAll‐sky simulations of brightness temperature (TB) at Microwave Humidity Sounder‐2 (MWHS‐2) channel 15 from Fengyun‐3E (FY‐3E) are generated based on the European Center for Medium‐Range Weather Forecasts (ECMWF) reanalysis version 5 (ERA5) and the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) analysis for four typhoon cases. For Typhoon Muifa (2022), the MWHS‐2 TB observations have a strongest symmetric component within about 210‐km radial distances. Although much weaker, the symmetric component in the GFS all‐sky simulations compares more favorably with that of observations than the ERA5 all‐sky simulations. Due to ice scattering effects at 183.31 ± 7 GHz, spatial distribution patterns of TB observations and all‐sky simulations are quite similar to those of ice water path. The more cloud ice, the lower the TB. Deep convective systems within Muifa are captured by TB observations but not by simulations. Using an objective azimuthal‐spectral‐analysis based center‐fixing algorithm, the center positions of Typhoon Muifa can be well determined from both MWHS‐2 observations and GFS all‐sky simulations, but not from the ERA5 all‐sky TB simulations. Compared with the best track, the average center position errors for Muifa are 16.2, 24.9 and 74.4 km based on FY‐3E MWHS‐2 TB observations, the GFS all‐sky TB simulations, and the ERA5 all‐sky simulations, respectively. The conclusions of TC cloud/rain band structures and center positions of all‐sky simulations using the NCEP‐GFS analysis being better matching with those of FY‐3E MWHS‐2 observations than the ERA5 all‐sky simulations for Typhoon Muifa also hold true for three other typhoons.

Evaluating Mesoscale Convective Systems Over the US in Conventional and Multiscale Modeling Framework Configurations of E3SMv1

AbstractOrganized mesoscale convective systems (MCSs) contribute a significant amount of precipitation in the Central and Eastern US during spring and summer, which impacts the availability of freshwater and flooding events. However, current global Earth system models cannot capture MCSs well and misrepresent the statistics of precipitation in the region. In this study, we investigate the representation of MCSs in three configurations of the Energy Exascale Earth System Model (E3SMv1) by tracking individual storms based on outgoing longwave radiation using a new application of TempestExtremes. Our results indicate that conventional parameterizations of convection, implemented in both low (LR; ∼150 km) and high (HR; ∼25 km) resolution configurations, fail to capture almost all MCS‐like events, in‐part because they underestimate high‐level cloud ice associated with deep convection. On the other hand, the multiscale modeling framework (MMF; cloud‐resolving models embedded in each grid‐column of ∼150 km resolution E3SMv1) configuration represents MCSs and their annual cycle better. Nevertheless, relative to observations, the E3SMv1‐MMF spatial distribution of MCSs and associated precipitation is shifted eastward, and the diurnal timing is lagged. A comparison between the large‐scale environment in E3SMv1‐MMF and ERA5 reanalysis suggests that the biases during the summer in E3SMv1‐MMF are associated with biases in low‐level humidity and meridional moisture transport within the low‐level jet. The fact that conventional parameterizations of convection, even with high‐resolution, cannot capture MCSs over the US suggests that methods with explicit representation of kilometer‐scale convective organization, such as the MMF, may be necessary for improving the simulation of these convective systems.

JWST/NIRSpec Observations of the Coldest Known Brown Dwarf* *Based on observations made with the NASA/ESA/CSA James Webb Space Telescope.

We present 1–5 μm spectroscopy of the coldest known brown dwarf, WISE J085510.83−071442.5 (WISE 0855), performed with the Near-Infrared Spectrograph (NIRSpec) on board the James Webb Space Telescope (JWST). NIRSpec has dramatically improved the measurement of the spectral energy distribution (SED) of WISE 0855 in terms of wavelength coverage, signal-to-noise ratios, and spectral resolution. We have performed preliminary modeling of the NIRSpec data using the ATMO 2020 models of cloudless atmospheres, arriving at a best-fitting model that has T eff = 285 K. That temperature is ∼20 K higher than the value derived by combining our luminosity estimate with evolutionary models (i.e., the radius in the model fit to the SED is somewhat smaller than expected from evolutionary models). Through comparisons to the model spectra, we detect absorption in the fundamental band of CO, which is consistent with an earlier detection in a ground-based spectrum and indicates the presence of vertical mixing. Although PH3 is expected in Y dwarfs that experience vertical mixing, it is not detected in WISE 0855. Previous ground-based M-band spectroscopy of WISE 0855 has been cited for evidence of H2O ice clouds, but we find that the NIRSpec data in that wavelength range are matched well by our cloudless model. Thus, clear evidence of H2O ice clouds in WISE 0855 has not been identified yet, but it may still be present in the NIRSpec data. The physical properties of WISE 0855, including the presence of H2O clouds, can be better constrained by more detailed fitting with both cloudless and cloudy models and the incorporation of unpublished 5–28 μm data from the Mid-infrared Instrument on JWST.

Heterogeneous Processes in the Atmosphere of Mars and Impact on H2O2 and O3 Abundances

AbstractCurrent models underestimate the highest observed ozone (O3) column densities on Mars. These estimates could be improved by including the uptake of odd hydrogen species (HOx) on water ice clouds, but the reported uptake coefficient of HO2 is likely overestimated for atmospheric conditions. This leaves a fundamental problem in Mars' atmospheric chemistry unsolved. Here, using the GEM‐Mars general circulation model, we explore a range of processes involving multiple phases (gas, adsorbed and solid) that may contribute to an alternative solution. First, we focus on hydrogen peroxide (H2O2) and discuss its physical states on Mars and its chemical impact. We also conjecture its photolytic destruction in ices with model simulations and Compact Reconnaissance Imaging Spectrometer for Mars observations. Then, we include in the model all relevant (for Mars) heterogeneous reactions, both on dust and water ice, recommended by the International Union of Pure and Applied Chemistry for terrestrial atmospheric studies. We find that only the uptake of HO2 and H2O2 on dust are efficient on Mars. Finally, we find that attenuation of sunlight by water ice clouds in the calculation of photolysis rates leads to increased O3 and H2O2 abundances below the ice clouds. The combination of the proposed processes leads to O3 increases without the need for strong uptake of HO2 on ice, but it remains difficult to find a good agreement with O3 and H2O2 observations on the global scale. We provide specific recommendations for future work in observations, laboratory experiments and modeling to advance our understanding of fundamental chemistry on Mars.

Investigation of Titan’s South Polar HCN Cloud during Southern Fall Using Microphysical Modeling

Ice clouds in Titan’s polar stratosphere are implicated in radiative heating and cooling and in transporting volatile organic compounds from where they form in the upper atmosphere to the surface of the moon. In early southern fall, Cassini detected a large, unexpected cloud at an altitude of 300 km over Titan’s south pole. The cloud, which was found to contain HCN ice, was inconsistent with the most recent measurements of temperature in the same location and suggested that the atmosphere had to be 100 K cooler than expected. However, changes to Cassini’s orbit shortly after the cloud’s appearance precluded further observations, and, consequently, the atmospheric conditions and the details of the formation and evolution of the cloud remain unknown. We address this gap in the observational record by using microphysical cloud modeling to estimate the parameter space consistent with published measurements. Based on the nearest available temperature profile retrievals and other observations, we hypothesize that the cloud forms around 300 km and then descends until it reaches the cold lower stratosphere by late southern fall. The observations can be simulated using a cloud microphysical model by introducing a descending cold layer with temperatures near 100 K. In simulations of this scenario, the precipitation from this cloud rapidly removes over 70% of the HCN vapor from the stratosphere. This result suggests that vapor descending into the polar stratosphere during early fall is mostly removed from the stratosphere before the onset of winter and does not circulate to lower latitudes.

Interactions between Gravity Waves and Cirrus Clouds: Asymptotic Modeling of Wave-Induced Ice Nucleation

Abstract We present an asymptotic approach for the systematic investigation of the effect of gravity waves (GWs) on ice clouds formed through homogeneous nucleation. In particular, we consider high- and midfrequency GWs in the tropopause region driving the formation of ice clouds, modeled with a double-moment bulk ice microphysics scheme. The asymptotic approach allows for identifying reduced equations for self-consistent description of the ice dynamics forced by GWs including the effects of diffusional growth and nucleation of ice crystals. Further, corresponding analytical solutions for a monochromatic GW are derived under a single-parcel approximation. The results provide a simple expression for the nucleated number of ice crystals in a nucleation event. It is demonstrated that the asymptotic solutions capture the dynamics of the full ice model and accurately predict the nucleated ice crystal number. The present approach is extended to allow for superposition of GWs, as well as for variable ice crystal mean mass in the deposition. Implications of the results for an improved representation of GW variability in cirrus parameterizations are discussed.

Ice Cloud Formation Related to Oceanic Supply of Ice‐Nucleating Particles: A Case Study in the Southern Ocean Near an Atmospheric River in Late Summer

AbstractThis study investigated ice cloud formation associated with marine bioaerosols over the Southern Ocean (SO) using a combination of cloud particle sensor (CPS) sonde observations, satellite products, reanalysis data, and backward trajectory analysis. The CPS sonde detected ice clouds at temperatures higher than −10°C in the mid‐troposphere near an atmospheric river at high‐latitudes over the SO. Backward trajectory analyses indicated that a mid‐latitude air mass with a high concentration of atmospheric dimethylsulfide (DMS) in the atmospheric boundary layer (<1 km) arrived at the ice cloud formation layer over the high‐latitudes. The DMS in the boundary layer began to increase under high wave conditions, coincident with the highest chlorophyll‐a concentrations in the ocean. These results suggest that bioaerosols emitted from the ocean over the mid‐latitudes acted as ice‐nucleating particles for ice cloud formation over high‐latitudes.

Ephemeral Ice Clouds in the Upper Mesosphere of Venus

AbstractThe conditions in Venus' upper mesosphere at around 120 km have some similarities to the upper mesosphere of Earth and Mars where ice clouds form. Here we show, using published satellite products and numerical modeling, that the upper mesosphere of Venus can be sufficiently cold that both H2O and CO2 may condense to form particles. We show that amorphous solid water particles (ASW) are likely to nucleate both heterogeneously on meteoric smoke and also homogeneously, resulting in clouds of nano‐scaled particles at around 120 km that will occur globally. The temperatures may then become sufficiently low, below ∼90 K, that CO2 particles can nucleate on ASW particles. Given the uncertainty associated with retrievals of temperature in the upper mesosphere, it is unclear how frequently this occurs, but it could be >30% of the time poleward of 60°. Since the main component of Venus' tenuous atmosphere is CO2, any CO2 crystals that form will grow and sediment on a timescale of 10–20 min. Mie calculations show that these Venusian mesospheric clouds (VMCs) should be observable by contemporary satellite instruments, although their short lifetime means that the probability of detection is small. We suggest that VMCs are important for the redistribution of meteoric smoke and may serve as a cold‐trap, removing some water vapor from the very upper mesosphere of Venus through the growth and sedimentation of cloud particles, and possibly reducing the loss of water to space.

A Cloud Water Path-Based Model for Cloudy-Sky Downward Longwave Radiation Estimation from FY-4A Data

Clouds are a critical factor in regulating the climate system, and estimating cloudy-sky Surface Downward Longwave Radiation (SDLR) from satellite data is significant for global climate change research. The models based on cloud water path (CWP) are less affected by cloud parameter uncertainties and have superior accuracy in SDLR satellite estimation when compared to those empirical and parameterized models relying mainly on cloud fraction or cloud-base temperature. However, existing CWP-based models tend to overestimate the low SDLR values and underestimate the larger SDLR. This study found that this phenomenon was caused by the fact that the models do not account for the varying relationships between cloud radiative effects and key parameters under different Liquid Water Path (LWP) and Precipitable Water Vapor (PWV) ranges. Based upon this observation, this study utilized Fengyun-4A (FY-4A) cloud parameters and ERA5 data as data sources to develop a new CWP-based model where the model coefficients depend on the cloud phase and cloud water path range. The accuracy of the new model’s estimated SDLR is 20.8 W/m2 for cloudy pixels, with accuracies of 19.4 W/m2 and 23.5 W/m2 for overcast and partly cloudy conditions, respectively. In contrast, the accuracy of the old CWP-based model was 22.4, 21.2, and 24.8 W/m2, respectively. The underestimation and overestimation present in the old CWP-based model are effectively corrected by the new model. The new model exhibited higher accuracy under various station locations, cloud cover scenarios, and cloud phase conditions compared to the old one. Comparatively, the new model showcased its most remarkable improvements in situations involving overcast conditions, water clouds with low PWV and low LWP values, ice clouds with large PWV, and conditions with PWV ≥ 5 cm. Over a temporal scale, the new model effectively captured the seasonal variations in SDLR.

Multiscale hypothesis testing theory and methods for aerosol and cloud layer detection of lidar

Lidar is a unique instrument for profiling aerosol and cloud layers. Detecting the boundaries of these layers is crucial because a missing layer will not be retrieved later. Many layer detection methods, which can be divided into slope-type and threshold-type methods, have been proposed and applied to a one-dimensional (1D) profile or a two-dimensional (2D) scene using numerous empirical settings. However, many eye-visible missing layers remain, indicating the need for a more effective detector. Herein, we demonstrated that layer detection is essentially a hypothesis testing issue. Theoretically, after providing a possible distribution of the background air signal, the task of layer detection is to test whether a given signal conforms to the provided hypothesis of a background air distribution under a particular significance. Consequently, we propose multiscale hypothesis testing methods (MHTs) for cloud and aerosol layer detection, which can work on 1D profiles and 2D scenes and are named 1D-MHT and 2D-MHT, respectively. The 2D-MHT detected 1.67 times layer area as the CALIOP Version 4.2 Level 2 product totally. Particularly, the results show that the 2D-MHT at 5 km resolution can detect 16.3% and 0.5% more layer area than the CALIOP official product at 5–80 km resolutions at daytime and nighttime, respectively. Evaluation using the extinction retrievals from aircraft lidar and the depolarization ratio of ice clouds shows the reliability of the additional detected layers by 2D-MHT. Thus, the hypothesis testing methods have high potential to effectively improve the accuracy and resolution for retrieving lidar products in the future.