Retrieval Of Cloud Properties Research Articles

Bayesian cloud-top phase determination for Meteosat Second Generation

Abstract. A comprehensive understanding of the cloud thermodynamic phase is crucial for assessing the cloud radiative effect and is a prerequisite for remote sensing retrievals of microphysical cloud properties. While previous algorithms mainly detected ice and liquid phases, there is now a growing awareness for the need to further distinguish between warm liquid, supercooled and mixed-phase clouds. To address this need, we introduce a novel method named ProPS (PRObabilistic cloud top Phase retrieval for SEVIRI), which enables cloud detection and the determination of cloud-top phase using SEVIRI (Spinning Enhanced Visible and Infrared Imager), the geostationary passive imager aboard Meteosat Second Generation. ProPS discriminates between clear sky, optically thin ice (TI) cloud, optically thick ice (IC) cloud, mixed-phase (MP) cloud, supercooled liquid (SC) cloud and warm liquid (LQ) cloud. Our method uses a Bayesian approach based on the cloud mask and cloud phase from the lidar–radar cloud product DARDAR (liDAR/raDAR). The validation of ProPS using 6 months of independent DARDAR data shows promising results: the daytime algorithm successfully detects 93 % of clouds and 86 % of clear-sky pixels. In addition, for phase determination, ProPS accurately classifies 91 % of IC, 78 % of TI, 52 % of MP, 58 % of SC and 86 % of LQ clouds, providing a significant improvement in accurate cloud-top phase discrimination compared to traditional retrieval methods.

Satellite-Based Estimation of the Role of Cloud-Radiative Interaction in Accelerating Tropical Cyclone Development

Abstract Recent modeling studies have suggested a potentially important role of cloud-radiative interactions in accelerating tropical cyclone (TC) development, but there has been only limited investigation of this in observations. Here, we investigate this by performing radiative transfer calculations based on cloud property retrievals from the CloudSat Tropical Cyclone (CSTC) dataset. We examine the radius–height structure of radiative heating anomalies, compute the resulting radiatively driven circulations, and use the moist static energy variance budget to compute radiative feedbacks. We find that inner-core midlevel ice water content and anomalous specific humidity increase with TC intensification rate, resulting in enhanced inner-core deep-layer longwave warming anomalies and shortwave cooling anomalies in rapidly intensifying TCs. This leads to a stronger radiatively driven deep in-up-and-out overturning circulation and inner-core radiative feedback in rapidly intensifying TCs. The longwave-driven circulation provides radially inward momentum fluxes and upward moisture fluxes, which benefit TC development, while the shortwave-driven circulation suppresses TC development. The longwave anomalies, which dominate the inner-core positive radiative feedback, are mainly generated from cloud-radiative interactions, with ice particles dominating the deep-layer circulation and liquid droplets and water vapor contributing to the shallow circulation. Moreover, the variability in ice water content, as opposed to the variability in liquid water content and the effective radii of ice particles and liquid droplets, dominates the uncertainty in TC-radiative interaction. These results provide observational evidence for the importance of cloud-radiative interactions in TC development and suggest that the amount and spatial structure of ice water content are critical for determining the strength of this interaction. Significance Statement The limited investigation of tropical cyclone (TC)-radiative interaction in observations impedes our understanding of TC development. This study aims to quantitatively show the spatial variation in radiation in TCs and their effect on TC development by using a set of satellite-based observations. We relate TC-radiative interaction to TC intensification and emphasize the inner-core features. Moreover, we quantitatively demonstrate the relative contribution from clouds, liquid droplets, ice particles, and water vapor to TC-radiative interaction as well as the source of the variation in radiative properties. These results provide an additional observational foundation for the importance of cloud-radiative interactions in TC development and support a quantitative validation for numerical modeling.

Towards multi-views cloud retrieval accounting for the 3-D structure collected by directional polarization camera

The structure of a cloud is three-dimensional (3-D). Nevertheless, cloud research is predominantly developed in one-dimensional (1-D) hypothesis from passive optical satellite measurements. Cloud retrieval algorithms typically do not consider the parallax displacement of clouds in multi-view images. Inversion of cloud properties based on two-dimensional (2-D) coordinates cannot solve multi-view radiation changes which are caused by macro-structure of a cloud in 3-D space. In this study, we included the 3-D structure of clouds by repositioning cloud pixels within a 3-D coordinate system utilizing the directional polarization camera (DPC) data before conducting cloud property retrieval. It is followed by identification of location of cloud in each observational view’s image plane. Observational paths of clouds are rebuilt at each observational view. Furthermore, computer graphics methods are employed to establish spatial relationship among different cloud observational paths. The radiational and geometrical information are relocated in unified 3-D sub-pixel space. Finally, 3-D cloud radiation information could be applied to retrieve cloud properties. The case results demonstrate that our method can effectively reduce mixed pixels of clouds and clear sky by 5.79% while decrease indistinguishable pixels in cloud phase classification by 2.11%. It leads to an enhancement in the classification accuracy of water clouds and ice clouds by 3.30% and 4.03%, respectively. Therefore, it is concluded that considering the impact of cloud structure before retrieval would significantly improve accuracy.

A Lagrangian perspective on the lifecycle and cloud radiative effect of deep convective clouds over Africa

Abstract. The anvil clouds of tropical deep convection have large radiative effects in both the shortwave (SW) and longwave (LW) spectra with the average magnitudes of both over 100 W m−2. Despite this, due to the opposite sign of these fluxes, the net average of the anvil cloud radiative effect (CRE) over the tropics is observed to be neutral. Research into the response of the anvil CRE to climate change has primarily focused on the feedbacks of anvil cloud height and anvil cloud area, in particular regarding the LW feedback. However, tropical deep convection over land has a strong diurnal cycle which may couple with the shortwave component of the anvil cloud radiative effect. As this diurnal cycle is poorly represented in climate models it is vital to gain a better understanding of how its changes impact the anvil CRE. To study the connection between the deep convective cloud (DCC) lifecycle and CRE, we investigate the behaviour of both isolated and organised DCCs in a 4-month case study over sub-Saharan Africa (May–August 2016). Using a novel cloud tracking algorithm, we detect and track growing convective cores and their associated anvil clouds using geostationary satellite observations from the Meteosat Spinning Enhanced Visible and Infrared Imager (SEVIRI). Retrieved cloud properties and derived broadband radiative fluxes are provided by the Community Cloud retrieval for CLimate (CC4CL) algorithm. By collecting the cloud properties of the tracked DCCs, we produce a dataset of anvil cloud properties along their lifetimes. While the majority of DCCs tracked in this dataset are isolated, with only a single core, the overall coverage of anvil clouds is dominated by those of clustered, multi-core anvils due to their larger areas and lifetimes. We find that the anvil cloud CRE of our tracked DCCs has a bimodal distribution. The interaction between the lifecycles of DCCs and the diurnal cycle of insolation results in a wide range of the SW anvil CRE, while the LW component remains in a comparatively narrow range of values. The CRE of individual anvil clouds varies widely, with isolated DCCs tending to have large negative or positive CREs, while larger, organised systems tend to have a CRE closer to 0. Despite this, we find that the net anvil cloud CRE across all tracked DCCs is close to neutral (−0.94 ± 0.91 W m−2). Changes in the lifecycle of DCCs, such as shifts in the time of triggering, or the length of the dissipating phase, could have large impacts on the SW anvil CRE and lead to complex responses that are not considered by theories of LW anvil CRE feedbacks.

Efficient single-scattering lookup table for lidar and polarimeter phytoplankton studies.

Coupled atmosphere and ocean remote sensing retrievals of aerosol, cloud, and oceanic phytoplankton microphysical properties, such as those carried out by the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, involve single-scattering calculations that are time consuming. Lookup tables (LUTs) exist to speed up these calculations for aerosol and water droplets in the atmosphere. In our new Lorenz-Mie lookup table, we tabulate single scattering by an ensemble of coated isotropic spheres representing oceanic phytoplankton at wavelengths from 0.355 µm. The lookup table covers phytoplankton particles with radii in the range of 0.15-100 µm at an increase of up to 104 in computational speed compared to single-scattering calculations. The allowed complex refractive indices range from 1.05 to 1.24 for the shell's real part, from 10-7 to 0.3 for the shell's imaginary part, from 0 to 0.001 for the core's imaginary part, and equal to 1.02 for the core's real part. We show that we precisely compute inherent optical properties for the phytoplankton size distributions ranging up to 5 µm for the effective radius and up to 0.6 for the effective variance. We test wavelengths from 0.355 to 1.065 µm and find that all the inherent optical properties of interest agree with the single-scattering calculations to within 1% for 99.9% of cases. We also provide an example of using the lookup table to reproduce the phytoplankton optical datasets listed in the PANGAEA database for synthetic hyperspectral algorithm development. The table together with C++, Fortran, MATLAB, and Python codes to apply different complex refractive indices and phytoplankton size distributions is freely available online.

Assessment of the spectral misalignment effect (SMILE) on EarthCARE's Multi-Spectral Imager aerosol and cloud property retrievals

Abstract. The Multi-Spectral Imager (MSI) on board the Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) will provide horizontal information about aerosols and clouds. These measurements are needed to extend vertical cloud and aerosol property information, which is obtained from EarthCARE's active sensors, in order to obtain a full three-dimensional view of cloud and aerosol conditions. Mesoscale weather systems, in particular, will be characterized. The discovery of a non-compliance of the MSI visible–near-infrared–shortwave infrared (VNS) camera’s visible (VIS) and shortwave infrared (SWIR1) channels regarding a spectral central wavelength (CWVL) shift across-track of up to 14 nm (VIS) and 20 nm (SWIR1) led to the need for an analysis regarding its impact on MSI Level-2A aerosol and cloud products. A significant influence of the spectral misalignment effect (SMILE) on MSI retrievals is identified due to the spectral variation in gas absorption, surface reflectance, and aerosol and cloud properties within the spectral ranges of these MSI bands. For example, the VIS channel is positioned in close proximity to the red edge of green vegetation and is impacted by residual absorption of water vapor and ozone. Small central wavelength variations introduce uncertainties due to the rapid change in surface reflectance for conditions with low optical thickness. The present central wavelength shift in the VIS towards shorter wavelengths than at nadir introduces a relative error in transmission of up to 3.3 % due to the increasing influence of water vapor and ozone absorption. We found relative errors in the top-of-atmosphere (TOA) signal due to the SMILE of up to 30 % for low optical thickness over a land surface in that band. Since the magnitude of the impact strongly depends on the underlying surface and atmospheric conditions, we conclude that accounting for the SMILE in Level-2 retrievals or correcting the Level-1 signal will improve MSI aerosol and cloud product quality.

Influence of cloud retrieval errors due to three-dimensional radiative effects on calculations of broadband shortwave cloud radiative effect

Abstract. We investigate how cloud retrieval errors due to the three-dimensional (3D) radiative effects affect broadband shortwave (SW) cloud radiative effects (CREs) in shallow cumulus clouds. A framework based on the combination of large eddy simulations (LESs) and radiative transfer (RT) models was developed to simulate both one-dimensional (1D) and 3D radiance, as well as SW broadband fluxes. Results show that the broadband SW fluxes reflected at top of the domain, transmitted at the surface, and absorbed in the atmosphere, computed from the cloud retrievals using 1D RT (F1D∗), can provide reasonable broadband radiative energy estimates in comparison with those derived from the true cloud fields using 1D RT (F1D). The difference between these 1D-RT-simulated fluxes (F1D∗, F1D) and the benchmark 3D RT simulations computed from the true cloud field (F3D) depends primarily on the horizontal transport of photons in 3D RT, whose characteristics vary with the sun's geometry. When the solar zenith angle (SZA) is 5°, the domain-averaged F1D∗ values are in excellent agreement with the F3D, all within 7 % relative CRE bias. When the SZA is 60°, the CRE differences between calculations from F1D∗ and F3D are determined by how the cloud side-brightening and darkening effects offset each other in the radiance, retrieval, and broadband fluxes. This study suggests that although the cloud property retrievals based on the 1D RT theory may be biased due to the 3D radiative effects, they still provide CRE estimates that are comparable to or better than CREs calculated from the true cloud properties using 1D RT.

Challenges in Measuring Aerosol Cloud-Mediated Radiative Forcing

Satellites are required for the global measurement of aerosol cloud-mediated radiative forcing, but satellite retrievals of aerosols and cloud properties still have challenges to overcome.

Lessons Learned from the Updated GEWEX Cloud Assessment Database

AbstractSince the first Global Energy and Water Exchanges cloud assessment a decade ago, existing cloud property retrievals have been revised and new retrievals have been developed. The new global long-term cloud datasets show, in general, similar results to those of the previous assessment. A notable exception is the reduced cloud amount provided by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Science Team, resulting from an improved aerosol–cloud distinction. Height, opacity and thermodynamic phase determine the radiative effect of clouds. Their distributions as well as relative occurrences of cloud types distinguished by height and optical depth are discussed. The similar results of the two assessments indicate that further improvement, in particular on vertical cloud layering, can only be achieved by combining complementary information. We suggest such combination methods to estimate the amount of all clouds within the atmospheric column, including those hidden by clouds aloft. The results compare well with those from CloudSat-CALIPSO radar–lidar geometrical profiles as well as with results from the International Satellite Cloud Climatology Project (ISCCP) corrected by the cloud vertical layer model, which is used for the computation of the ISCCP-derived radiative fluxes. Furthermore, we highlight studies on cloud monitoring using the information from the histograms of the database and give guidelines for: (1) the use of satellite-retrieved cloud properties in climate studies and climate model evaluation and (2) improved retrieval strategies.

Physics-Driven Machine Learning Algorithm Facilitates Multilayer Cloud Property Retrievals From Geostationary Passive Imager Measurements

Physics-Driven Machine Learning Algorithm Facilitates Multilayer Cloud Property Retrievals From Geostationary Passive Imager Measurements

Integration of a physics-based direct normal irradiance (DNI) model to enhance the National Solar Radiation Database (NSRDB)

The National Solar Radiation Database (NSRDB) is an extensively used dataset that furnishes satellite-retrieved solar resource data across the United States and an expanding list of other countries. Although the NSRDB uses a physical model to compute global horizontal irradiance (GHI), it currently employs an empirical approach based on surface observations to estimate cloudy-sky direct normal irradiance (DNI). Recently, a new physics-based approach, known as the Fast All-sky Radiation Model for Solar applications with DNI (FARMS-DNI), was developed to improve DNI forecasting. FARMS-DNI integrates direct and scattered solar radiances within the circumsolar region, resulting in improved day-ahead forecasting of DNI by incorporating it into the Weather Research and Forecasting model with Solar extensions (WRF-Solar). This study incorporates FARMS-DNI into the NSRDB algorithm to produce high-spatiotemporal-resolution DNI data from satellite data. The accuracy of the NSRDB based on FARMS-DNI is analyzed using surface observations from 19 sites situated within the National Oceanic and Atmospheric Administration (NOAA) Surface Radiation Budget (SURFRAD) and Solar Radiation (SOLRAD) networks, the University of Oregon (UO) network, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) network, and at the National Renewable Energy Laboratory (NREL). The results demonstrate that FARMS-DNI reduces the significant overestimation of DNI in the conventional NSRDB at all surface sites, particularly in cloud overcast conditions classified using both satellite retrievals and surface observations. Consequently, this new model can effectively improve the overall accuracy of the NSRDB. The results also suggest that further improvement of DNI estimates at individual time steps, however, requires advanced satellite techniques and precise identification of clouds and retrieval of cloud properties.

Single field-of-view sounder atmospheric product retrieval algorithm: establishing radiometric consistency for hyper-spectral sounder retrievals

Abstract. The single field-of-view (SFOV) sounder atmospheric product (SiFSAP) retrieval algorithm has been developed to address the need to retrieve high-spatial-resolution atmospheric data products from hyper-spectral sounders and ensure the radiometric consistency between the retrieved properties and measured spectral radiances. It is based on an integrated optimal-estimation inversion scheme that processes data from the satellite-based synergistic microwave (MW) and infrared (IR) spectral measurements from advanced sounders. The retrieval system utilizes the principal component radiative transfer model (PCRTM), which performs radiative transfer calculations monochromatically and includes accurate cloud-scattering simulations. SiFSAP includes temperature, water vapor, surface skin temperature and emissivity, cloud height and microphysical properties, and concentrations of essential trace gases for each SFOV at a native instrument spatial resolution. Error estimations are provided based on a rigorous analysis for uncertainty propagation from the top-of-atmosphere (TOA) spectral radiances to the retrieved geophysical properties. As a comparison, the spatial resolution for the traditional hyper-spectral sounder retrieval products is much coarser than the native resolution of the instruments due to the common use of the “cloud-clearing” technique to compensate for the lack of cloud-scattering simulation in the forward model. The degraded spatial resolution in traditional cloud-clearing sounder retrieval products limits their applications for capturing meteorological or climate signals at finer spatial scales. Moreover, a rigorous uncertainty propagation estimation needed for long-term climate trend studies cannot be given due to the lack of direct radiative transfer relationships between the observed TOA radiances and the retrieved geophysical properties. With the advantages of the higher spatial resolution; the simultaneous retrieval of atmospheric, cloud, and surface properties using all available spectral information; and the establishment of “radiance closure” in the sounder spectral measurements, the SiFSAP provides additional information needed for various weather and climate studies and applications using sounding observations. This paper gives an overview of the SiFSAP retrieval algorithm and assessment of SiFSAP atmospheric temperature, water vapor, clouds, and surface products derived from the Cross-track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) data.

On the Consistency of Ice Cloud Optical Models for Spaceborne Remote Sensing Applications and Broadband Radiative Transfer Simulations

AbstractAqua satellite Moderate Resolution Imaging Spectroradiometer (MODIS) 1‐km observations are collocated with Clouds and the Earth's Radiant Energy System (CERES) fields of view taken during July 2008 afternoon satellite passes over the equatorial western Pacific Ocean. Radiation simulations are compared with collocated CERES observations to better understand the sensitivity of computed fluxes to two ice cloud broadband radiation parameterization schemes and inferred ice cloud characteristics. In particular, the radiation computational schemes and ice cloud property retrievals are based on two respective ice particle models, the MODIS Collection 6 (MC6) aggregate model and a more microphysically consistent two‐habit model (THM). The simulation results show that both MC6 and THM overestimate the shortwave (SW) and longwave (LW) cloud radiative effects at the top of the atmosphere, as compared to the CERES observations; the difference between the MC6 and THM‐based ice cloud retrievals is too small to compensate for the differences between the two model‐based radiation schemes. Therefore, the present finding suggests that broadband radiative simulations are more sensitive to the radiation parameterization scheme than to the input cloud properties retrieved using the corresponding ice cloud particle optical property model.

Rapid saturation of cloud water adjustments to shipping emissions

Abstract. Human aerosol emissions change cloud properties by providing additional cloud condensation nuclei. This increases cloud droplet numbers, which in turn affects other cloud properties like liquid-water content and ultimately cloud albedo. These adjustments are poorly constrained, making aerosol effects the most uncertain part of anthropogenic climate forcing. Here we show that cloud droplet number and water content react differently to changing emission amounts in shipping exhausts. We use information about ship positions and modeled emission amounts together with reanalysis winds and satellite retrievals of cloud properties. The analysis reveals that cloud droplet numbers respond linearly to emission amount over a large range (1–10 kg h−1) before the response saturates. Liquid water increases in raining clouds, and the anomalies are constant over the emission ranges observed. There is evidence that this independence of emissions is due to compensating effects under drier and more humid conditions, consistent with suppression of rain by enhanced aerosol. This has implications for our understanding of cloud processes and may improve the way clouds are represented in climate models, in particular by changing parameterizations of liquid-water responses to aerosol.

The classification of atmospheric hydrometeors and aerosols from the EarthCARE radar and lidar: the A-TC, C-TC and AC-TC products

Abstract. The EarthCARE mission aims to probe the Earth's atmosphere by measuring cloud and aerosol profiles using its active instruments, the Cloud Profiling Radar (CPR) and ATmospheric LIDar (ATLID). The correct identification of hydrometeors and aerosols from atmospheric profiles is an important step in retrieving the properties of clouds, aerosols and precipitation. Ambiguities in the nature of atmospheric targets can be removed using the synergy of collocated radar and lidar measurements, which is based on the complementary spectral response of radar and lidar relative to atmospheric targets present in the profiles. The instruments are sensitive to different parts of the particle size distribution and provide independent but overlapping information in optical and microwave wavelengths. ATLID is sensitive to aerosols and small cloud particles, and CPR is sensitive to large ice particles, snowflakes and raindrops. It is therefore possible to better classify atmospheric targets when collocated radar and lidar measurements exist compared to using a single instrument. The cloud phase, precipitation and aerosol type within the column sampled by the two instruments can then be identified. ATLID-CPR target classification (AC-TC) is the product created for this purpose by combining the ATLID target classification (A-TC) and CPR target classification (C-TC). AC-TC is crucial for the subsequent synergistic retrieval of cloud, aerosol and precipitation properties. AC-TC builds upon previous target classifications using CloudSat and CALIPSO synergy while providing richer target classification using the enhanced capabilities of EarthCARE's instruments, specifically CPR's Doppler velocity measurements to distinguish snow and rimed snow from ice clouds and ATLID's lidar ratio measurements to objectively discriminate between different aerosol species and optically thin ice clouds. In this paper, we first describe how the single-instrument A-TC and C-TC products are derived from ATLID and CPR measurements. Then the AC-TC product, which combines the A-TC and C-TC classifications using a synergistic decision matrix, is presented. Simulated EarthCARE observations based on combined cloud-resolving and aerosol model data are used to test the processors generating the target classifications. Finally, the target classifications are evaluated by quantifying the fractions of ice and snow, liquid clouds, rain, and aerosols in the atmosphere that can be successfully identified by each instrument and their synergy. We show that radar–lidar synergy helps better detect ice and snow, with ATLID detecting radiatively important optically thin cirrus and cloud tops, while CPR penetrates most deep and highly concentrated ice clouds. The detection of rain and drizzle is entirely due to C-TC, while that of liquid clouds and aerosols is due to A-TC. The evaluation also shows that simple assumptions can be made to compensate for when the instruments are obscured by extinction (ATLID) or surface clutter and multiple scattering (CPR); this allows for the recovery of the majority of liquid cloud not detected by the active instruments.

Retrieval of cloud microphysical properties from Himawari-8/AHI infrared channels and its application in surface shortwave downward radiation estimation in the sun glint region

Satellite remote sensing of cloud property retrieval and shortwave downward radiation (SWDR) estimation is essential for global radiation budget and climate change studies. Sun glint areas remain a challenge for the existing cloud and SWDR algorithms based on the visible channel since surface specular reflection has a significant impact on satellite retrieval. In this study, a set of algorithms for cloud detection and cloud microphysical parameter estimation were developed using infrared multichannel data from the new generation geostationary satellite Himawari-8 based on the random forest method. The results indicated that the cloud retrieval algorithm exhibited better performance in the sun glint areas where the official Himawari-8 products (cloud detection and cloud optical thickness) were overestimated. We developed a new SWDR estimation algorithm combining the radiative transfer model and machine learning techniques by considering the cloud properties from the cloud retrieval algorithm. The results indicated that the SWDR and cloud radiative forcing derived by the new algorithm were more consistent with those of the well-known radiation products Cloud and the Earth's Radiant Energy System than those estimated using the official-based cloud product, with decreases in the root mean square error of approximately 22% and 41%, respectively. The new algorithms effectively addressed sun glint contamination by providing more data coverage and exhibiting stable performance on a spatiotemporal scale.

Spectrum Synergy for Investigating Cloud Microphysics

Abstract Observations from spaceborne microwave (MW) and infrared (IR) passive sensors are the backbone of current satellite meteorology, essential for data assimilation into modern numerical weather prediction and for climate benchmarking. While MW and IR observations from space offer complementary features with respect to cloud properties, their synergy for cloud investigation is currently underexplored, despite the presence of both MW and IR sensors on operational meteorological satellites such as the EUMETSAT Polar System (EPS) MetOp series. As such, several key cloud microphysical properties are not part of the operational products available from EPS MetOp sensors. In addition, the EPS Second Generation (EPS-SG) series, scheduled for launch starting from 2024 onward, will carry sensors such as the Microwave Sounder (MWS) and IASI Next Generation (IASI-NG), enhancing spatial and spectral resolutions and thus capacity to retrieve cloud properties. This article presents the Combined MWS and IASI-NG Soundings for Cloud Properties (ComboCloud) project, funded by EUMETSAT with the overall objective to specify, prototype, and validate algorithms for the retrieval of cloud microphysical properties (e.g., water content and drop effective radius) from the synergy of passive MW and IR observations. The article presents the synergy rationale, the algorithm design, and the results obtained exploiting simulated observations from EPS and EPS-SG sensors, quantifying the benefits to be expected from the MW–IR synergy and the new generation sensors.

Time-Dependent Systematic Biases in Inferring Ice Cloud Properties from Geostationary Satellite Observations

Geostationary satellite-based remote sensing is a powerful tool to observe and understand the spatiotemporal variation of cloud optical-microphysical properties and their climatologies. Solar reflectances measured from the Advanced Baseline Imager (ABI) instruments aboard Geostationary Operational Environmental Satellites 16 and 17 correspond to different spatial pixel resolutions, from 0.5 km in a visible band, up to 2 km in infrared bands. For multi-band retrievals of cloud properties, reflectances with finer spatial resolution need to be resampled (averaged or sub-sampled) to match the coarsest resolution. Averaging all small pixels within a larger pixel footprint is more accurate but computationally demanding when the data volume is large. Thus, NOAA operational cloud products incorporate sub-sampling (selecting one high-resolution pixel) to resample the reflectance data, which could cause potential retrieval biases. In this study, we examine various error sources of retrieval biases of cloud optical thickness (COT) and cloud effective radius (CER) caused by sub-sampling, including the solar zenith angle, viewing zenith angle, pixel resolutions, and cloud types. CER retrievals from ice clouds based on sub-sampling have larger biases and uncertainties than COT retrievals. The relative error compared to pixel averaging is positive for clouds that have small COT or CER, and negative for clouds that have large COT or CER. The relative error of COT decreases as the pixel resolution becomes coarser. The COT retrieval biases are attributed mainly to cirrus and cirrostratus clouds, while the largest biases of CER retrievals are associated with cirrus clouds.

A Case Study of a West Sumatra Squall Line Using Satellite Observations

Abstract A West Sumatra squall line occurred on 10 January 2016, with a clear offshore propagation of convection. Satellite-derived products from Himawari-8 Advanced Himawari Imager and the Geostationary Cloud Algorithm Testbed Geocat are used to investigate the westward propagation of cloudiness from Sumatra to the Indian Ocean with a lifetime of 1.5 days. A convective mask based on deep convective cell detection and a cell-tracking algorithm are used to estimate the propagation speed of the cloud system. Two distinct mesoscale convective responses are identified: 1) a rapid development in South Sumatra is influenced by the convective environment over the Indian Ocean. The propagation speed is estimated to be ∼5 m s−1 within the first 200 km from the coast. This speed is consistent with density currents. In contrast, 2) the coupling to the inertia–gravity wave is only evident for the northwest of Sumatra with speeds of ∼12 m s−1. The analysis of brightness temperature from the 10.4-μm spectral band and cloud-top temperature showed that the lifetime of the squall line is approximately 30 h with a propagating distance of ∼1000 km. Retrieved cloud properties and tracking of the offshore propagation indicated that the cloud structure consisted of multiple types of cells, propagating as envelopes of convection, and revealed the influence of large-scale variability of the Indian Ocean. Filtered OLR anomalies, satellite-derived rainfall, moisture flux convergence, and background winds flow around Sumatra are used to explore the effects of Kelvin wave activity that likely influenced the lifetime of the squall line.

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.