Cloud Retrieval Research Articles

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.

LWR-Net: Robust and Lightweight Place Recognition Network for Noisy and Low-Density Point Clouds.

Point cloud-based retrieval for place recognition is essential in robotic applications like autonomous driving or simultaneous localization and mapping. However, this remains challenging in complex real-world scenes. Existing methods are sensitive to noisy, low-density point clouds and require extensive storage and computation, posing limitations for hardware-limited scenarios. To overcome these challenges, we propose LWR-Net, a lightweight place recognition network for efficient and robust point cloud retrieval in noisy, low-density conditions. Our approach incorporates a fast dilated sampling and grouping module with a residual MLP structure to learn geometric features from local neighborhoods. We also introduce a lightweight attentional weighting module to enhance global feature representation. By utilizing the Generalized Mean pooling structure, we aggregated the global descriptor for point cloud retrieval. We validated LWR-Net's efficiency and robustness on the Oxford robotcar dataset and three in-house datasets. The results demonstrate that our method efficiently and accurately retrieves matching scenes while being more robust to variations in point density and noise intensity. LWR-Net achieves state-of-the-art accuracy and robustness with a lightweight model size of 0.4M parameters. These efficiency, robustness, and lightweight advantages make our network highly suitable for robotic applications relying on point cloud-based place recognition.

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.

Rank-PointRetrieval: Reranking Point Cloud Retrieval via a Visually Consistent Registration Evaluation.

Point cloud-based place recognition is a fundamental part of the localization task, and it can be achieved through a retrieval process. Reranking is a critical step in improving the retrieval accuracy, yet little effort has been devoted to reranking in point cloud retrieval. In this paper, we investigate the versatility of rigid registration in reranking the point cloud retrieval results. Specifically, after obtaining the initial retrieval list based on the global point cloud feature distance, we perform registration between the query and point clouds in the retrieval list. We propose an efficient strategy based on visual consistency to evaluate each registration with a registration score in an unsupervised manner. The final reranked list is computed by considering both the original global feature distance and the registration score. In addition, we find that the registration score between two point clouds can also be used as a pseudo label to judge whether they represent the same place. Thus, we can create a self-supervised training dataset when there is no ground truth of positional information. Moreover, we develop a new probability-based loss to obtain more discriminative descriptors. The proposed reranking approach and the probability-based loss can be easily applied to current point cloud retrieval baselines to improve the retrieval accuracy. Experiments on various benchmark datasets show that both the reranking registration method and probability-based loss can significantly improve the current state-of-the-art baselines.

Collaborative development of the Lidar Processing Pipeline (LPP) for retrievals of atmospheric aerosols and clouds

Abstract. Atmospheric lidars can simultaneously measure clouds and aerosols with high temporal and spatial resolution and hence help understand cloud–aerosol interactions, which are the source of major uncertainties in future climate projections. However, atmospheric lidars are typically custom-built, with significant differences between them. In this sense, lidar networks play a crucial role as they coordinate the efforts of different groups, provide guidelines for quality-assured routine measurements and opportunities for side-by-side instrument comparisons, and enforce algorithm validation, all aiming to homogenize the physical retrievals from heterogeneous instruments in a network. Here we provide a high-level overview of the Lidar Processing Pipeline (LPP), an ongoing, collaborative, and open-source coordinated effort in Latin America. The LPP is a collection of tools with the ultimate goal of handling all the steps of a typical analysis of lidar measurements. The modular and configurable framework is generic enough to be applicable to any lidar instrument. The first publicly released version of the LPP produces data files at levels 0 (raw and metadata), 1 (averaging and layer mask), and 2 (aerosol optical properties). We assess the performance of the LPP through quantitative and qualitative analyses of simulated and measured elastic lidar signals. For noiseless synthetic 532 nm elastic signals with a constant lidar ratio (LR), the root mean square error (RMSE) in aerosol extinction within the boundary layer is about 0.1 %. In contrast, retrievals of aerosol backscatter from noisy elastic signals with a variable LR have an RMSE of 11 %, mostly due to assuming a constant LR in the inversion. The application of the LPP for measurements in São Paulo, further constrained by co-located AERONET data, retrieved a lidar ratio of 69.9 ± 5.2 sr at 532 nm, in agreement with reported values for urban aerosols. Over the Amazon, analysis of a 6 km thick multi-layer cirrus found a cloud optical depth of about 0.46, also in agreement with previous studies. From this exercise, we identify the need for new features and discuss a roadmap to guide future development, accommodating the needs of our community.

A unified synergistic retrieval of clouds, aerosols, and precipitation from EarthCARE: the ACM-CAP product

Abstract. ACM-CAP provides a synergistic best-estimate retrieval of all clouds, aerosols, and precipitation detected by the atmospheric lidar (ATLID), cloud-profiling radar (CPR), and multi-spectral imager (MSI) aboard EarthCARE (Earth Cloud Aerosol and Radiation Explorer). While synergistic retrievals are now mature in many contexts, ACM-CAP is unique in that it provides a unified retrieval of all hydrometeors and aerosols. The Cloud, Aerosol, and Precipitation from mulTiple Instruments using a VAriational TEchnique (CAPTIVATE) algorithm allows for a robust accounting of observational and retrieval errors and the contributions of passive and integrated measurements and for enforcing physical relationships between components (e.g. the conservation of precipitating mass flux through the melting layer). We apply ACM-CAP to EarthCARE scenes simulated from numerical weather model forecasts and evaluate the retrievals against “true” quantities from the numerical model. The retrievals are well-constrained by observations from active and passive instruments and overall closely resemble the bulk quantities (e.g. cloud water content, precipitation mass flux, and aerosol extinction) and microphysical properties (e.g. cloud effective radius and median volume diameter) from the model fields. The retrieval performs best where the active instruments have a strong and unambiguous signal, namely in ice clouds and snow, which is observed by both ATLID and CPR, and in light to moderate rain, where the CPR signal is strong. In precipitation, CPR's Doppler capability permits enhanced retrievals of snow particle density and raindrop size. In complex and layered scenes where ATLID is obscured, we have shown that making a simple assumption about the presence and vertical distribution of liquid cloud in rain and mixed-phase clouds allows improved assimilation of MSI solar radiances. In combination with a constraint on the CPR path-integrated attenuation from the ocean surface, this leads to improved retrievals of both liquid cloud and rain in midlatitude stratiform precipitation. In the heaviest precipitation, both active instruments are attenuated and dominated by multiple scattering; in these situations, ACM-CAP provides a seamless retrieval of cloud and precipitation but is subject to a high degree of uncertainty. ACM-CAP's aerosol retrieval is performed in hydrometeor-free parts of the atmosphere and constrained by ATLID and MSI solar radiances. While the aerosol optical depth is well-constrained in the test scenes, there is a high degree of noise in the profiles of extinction. The use of numerical models to simulate test scenes has helped to showcase the capabilities of the ACM-CAP clouds, aerosols, and precipitation product ahead of the launch of EarthCARE.

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.

Spectral Geometric Verification: Re-Ranking Point Cloud Retrieval for Metric Localization

In large-scale metric localization, an incorrect result during retrieval will lead to an incorrect pose estimate or loop closure. Re-ranking methods propose to take into account all the top retrieval candidates and re-order them to increase the likelihood of the top candidate being correct. However, state-of-the-art re-ranking methods are inefficient when re-ranking many potential candidates due to their need for resource intensive point cloud registration between the query and each candidate. In this work, we propose an efficient spectral method for geometric verification (named SpectralGV) that does not require registration. We demonstrate how the optimal inter-cluster score of the correspondence compatibility graph of two point clouds represents a robust fitness score measuring their spatial consistency. This score takes into account the subtle geometric differences between structurally similar point clouds and therefore can be used to identify the correct candidate among potential matches retrieved by global similarity search. SpectralGV is deterministic, robust to outlier correspondences, and can be computed in parallel for all potential candidates. We conduct extensive experiments on 5 large-scale datasets to demonstrate that SpectralGV outperforms other state-of-the-art re-ranking methods and show that it consistently improves the recall and pose estimation of 3 state-of-the-art metric localization architectures while having a negligible effect on their runtime. The open-source implementation and trained models are available at: https://github.com/csiro-robotics/SpectralGV.

The Education and Research 3D Radiative Transfer Toolbox (EaR3T) – towards the mitigation of 3D bias in airborne and spaceborne passive imagery cloud retrievals

Abstract. We introduce the Education and Research 3D Radiative Transfer Toolbox (EaR3T, pronounced []) for quantifying and mitigating artifacts in atmospheric radiation science algorithms due to spatially inhomogeneous clouds and surfaces and show the benefits of automated, realistic radiance and irradiance generation along extended satellite orbits, flight tracks from entire aircraft field missions, and synthetic data generation from model data. EaR3T is a modularized Python package that provides high-level interfaces to automate the process of 3D radiative transfer (3D-RT) calculations. After introducing the package, we present initial findings from four applications, which are intended as blueprints to future in-depth scientific studies. The first two applications use EaR3T as a satellite radiance simulator for the NASA Orbiting Carbon Observatory 2 (OCO-2) and Moderate Resolution Imaging Spectroradiometer (MODIS) missions, which generate synthetic satellite observations with 3D-RT on the basis of cloud field properties from imagery-based retrievals and other input data. In the case of inhomogeneous cloud fields, we show that the synthetic radiances are often inconsistent with the original radiance measurements. This lack of radiance consistency points to biases in heritage imagery cloud retrievals due to sub-pixel resolution clouds and 3D-RT effects. They come to light because the simulator's 3D-RT engine replicates processes in nature that conventional 1D-RT retrievals do not capture. We argue that 3D radiance consistency (closure) can serve as a metric for assessing the performance of a cloud retrieval in presence of spatial cloud inhomogeneity even with limited independent validation data. The other two applications show how airborne measured irradiance data can be used to independently validate imagery-derived cloud products via radiative closure in irradiance. This is accomplished by simulating downwelling irradiance from geostationary cloud retrievals of Advanced Himawari Imager (AHI) along all the below-cloud aircraft flight tracks of the Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex, NASA 2019) and comparing the irradiances with the colocated airborne measurements. In contrast to case studies in the past, EaR3T facilitates the use of observations from entire field campaigns for the statistical validation of satellite-derived irradiance. From the CAMP2Ex mission, we find a low bias of 10 % in the satellite-derived cloud transmittance, which we are able to attribute to a combination of the coarse resolution of the geostationary imager and 3D-RT biases. Finally, we apply a recently developed context-aware Convolutional Neural Network (CNN) cloud retrieval framework to high-resolution airborne imagery from CAMP2Ex and show that the retrieved cloud optical thickness fields lead to better 3D radiance consistency than the heritage independent pixel algorithm, opening the door to future mitigation of 3D-RT cloud retrieval biases.

Ground-based remote sensing of aerosol properties using high-resolution infrared emission and lidar observations in the High Arctic

Abstract. Arctic amplification, the phenomenon that the Arctic is warming faster than the global mean, is still not fully understood. The Transregional Collaborative Research Centre “TRR 172: ArctiC Amplification: Climate Relevant Atmospheric and SurfaCe Processes, and Feedback Mechanisms (AC)3” program, funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), contributes towards this research topic. For the purpose of measuring aerosol components, a Fourier transform infrared spectrometer (FTIR), for measuring downwelling emission (in operation since 2019), and a Raman lidar are operated at the joint Alfred Wegener Institute for Polar and Marine Research and Paul Emile Victor Institute (AWIPEV) research base in Ny-Ålesund, Spitsbergen (79∘ N, 12∘ E). To carry out aerosol retrieval using measurements from the FTS, the LBLDIS retrieval algorithm, based on a combination of the Line-by-Line Radiative Transfer Model (LBLRTM) and the DIScrete Ordinate Radiative Transfer (DISORT) algorithm, is modified for different aerosol types (dust, sea salt, black carbon, and sulfate), aerosol optical depth (AOD), and effective radius (Reff). Using lidar measurement, an aerosol and cloud classification method is developed to provide basic information about the distribution of aerosols or clouds in the atmosphere and is used as an indicator to perform aerosol or cloud retrievals with the FTS. Therefore, a two-instrument joint-observation scheme is designed and subsequently used on the data measured from 2019 to the present. In order to introduce this measurement technique in detail, an aerosol-only case study is presented using data from 10 June 2020. In the aerosol-only case, the retrieval results show that sulfate is the dominant aerosol throughout the day (τ900cm-1 = 0.007 ± 0.0027), followed by dust (τ900cm-1 = 0.0039 ± 0.0029) and black carbon (τ900cm-1 = 0.0017 ± 0.0007). Sea salt (τ900cm-1 = 0.0012 ± 0.0002), which has the weakest emission ability in the infrared wave band, shows the lowest AOD value. Such proportions of sulfate, dust, and BC also show good agreement with Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) reanalysis data. Additionally, comparison with a Sun photometer (AErosol RObotic NETwork – AERONET) shows the daily variation in the AOD retrieved from FTS to be similar to that retrieved by Sun photometer. Using this method, long-term observations (from April to August 2020) are retrieved and presented. We find that sulfate is often present in the Arctic; it is higher in spring and lower in summer. Similarly, BC is also frequently observed in the Arctic, with less obvious seasonal variation than sulfate. A BC outburst event is observed each spring and summer. In spring, sulfate and BC are dominant, whereas sea salt and dust are relatively low. In addition, a sea salt enhancement event is observed in summertime, which might be due to the melting of sea ice and emissions from nearby open water. From the retrieved results over a long time period, no clear correlations are found; thus, the aforementioned species can be retrieved independently of one another.

Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 1: Model description and Jacobian calculation

Abstract. Our global understanding of clouds and aerosols relies on the remote sensing of their optical, microphysical, and macrophysical properties using, in part, scattered solar radiation. These retrievals assume that clouds and aerosols form plane-parallel, homogeneous layers and utilize 1D radiative transfer (RT) models, limiting the detail that can be retrieved about the 3D variability in cloud and aerosol fields and inducing biases in the retrieved properties for highly heterogeneous structures such as cumulus clouds and smoke plumes. To overcome these limitations, we introduce and validate an algorithm for retrieving the 3D optical or microphysical properties of atmospheric particles using multi-angle, multi-pixel radiances and a 3D RT model. The retrieval software, which we have made publicly available, is called Atmospheric Tomography with 3D Radiative Transfer (AT3D). It uses an iterative, local optimization technique to solve a generalized least squares problem and thereby find a best-fitting atmospheric state. The iterative retrieval uses a fast, approximate Jacobian calculation, which we have extended from Levis et al. (2020) to accommodate open and periodic horizontal boundary conditions (BCs) and an improved treatment of non-black surfaces. We validated the accuracy of the approximate Jacobian calculation for derivatives with respect to both the 3D volume extinction coefficient and the parameters controlling the open horizontal boundary conditions across media with a range of optical depths and single-scattering properties and find that it is highly accurate for a majority of cloud and aerosol fields over oceanic surfaces. Relative root mean square errors in the approximate Jacobian for a 3D volume extinction coefficient in media with cloud-like single-scattering properties increase from 2 % to 12 % as the maximum optical depths (MODs) of the medium increase from 0.2 to 100.0 over surfaces with Lambertian albedos <0.2. Over surfaces with albedos of 0.7, these errors increase to 20 %. Errors in the approximate Jacobian for the optimization of open horizontal boundary conditions exceed 50 %, unless the plane-parallel media providing the boundary conditions are optically very thin (∼0.1). We use the theory of linear inverse RT to provide insight into the physical processes that control the cloud tomography problem and identify its limitations, supported by numerical experiments. We show that the Jacobian matrix becomes increasing ill-posed as the optical size of the medium increases and the forward-scattering peak of the phase function decreases. This suggests that tomographic retrievals of clouds will become increasingly difficult as clouds become optically thicker. Retrievals of asymptotically thick clouds will likely require other sources of information to be successful. In Loveridge et al. (2023a; hereafter Part 2), we examine how the accuracy of the retrieved 3D volume extinction coefficient varies as the optical size of the target medium increases using synthetic data. We do this to explore how the increasing error in the approximate Jacobian and the increasingly ill-posed nature of the inversion in the optically thick limit affect the retrieval. We also assess the accuracy of retrieved optical depths and compare them to retrievals using 1D radiative transfer.

Evaluation of the impacts of ice cloud vertical inhomogeneity on spaceborne passive submillimeter‐wave simulations

AbstractSpaceborne 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.

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.

Multi-Channel Spectral Band Adjustment Factors for Thermal Infrared Measurements of Geostationary Passive Imagers

The newest and upcoming geostationary passive imagers have thermal infrared channels comparable to those of more established instruments, but their spectral response functions still differ significantly. Therefore, retrievals developed for a certain type of radiometer cannot simply be applied to another imager. Here, a set of spectral band adjustment factors is determined for MSG/SEVIRI, Himawari-8/AHI, and MTG1/FCI from a training dataset based on MetOp/IASI hyperspectral observations. These correction functions allow to turn the observation of one sensor into an analogue observation of another sensor. This way, the same satellite retrieval—that has been usually developed for a specific instrument with a specific spectral response function—can be applied to produce long time series that go beyond one single satellite/satellite series or to cover the entire geostationary ring in a consistent way. It is shown that the mean uncorrected brightness temperature differences between corresponding channels of two imagers can be >1 K, in particular for the channels centered around 13.4 μm in the carbon dioxide absorption band and even when comparing different imager realizations of the same series, such as the four SEVIRI sensors aboard MSG1 to MSG4. The spectral band adjustment factors can remove the bias and even reduce the standard deviation in the brightness temperature difference by more than 80%, with the effect being dependent on the spectral channel and the complexity of the correction function. Further tests include the application of the spectral band adjustment factors in combination with (a) a volcanic ash cloud retrieval to Himawari-8/AHI observations of the Raikoke eruption 2019 and a comparison to an ICON-ART model simulation, and (b) an ice cloud retrieval to simulated MTG1/FCI test data with the outcome compared to the retrieval results using real MSG3/SEVIRI measurements for the same scene.

Comparison of Cloud Properties between SGLI Aboard GCOM-C Satellite and MODIS Aboard Terra Satellite

This study presents a comprehensive comparison of Level 2.0 cloud properties between a Second-generation Global Imager (SGLI) aboard the GCOM-C satellite and a Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite, to better understand the qualities of cloud properties obtained from SGLI/GCOM-C launched on 23 December 2017. The cloud pixels identified as water phase by both satellite sensors are highly consistent to each other by more than 90%, although the consistency is only ~60% for ice phase cloud pixels. A comparison of cloud properties—cloud optical thickness (COT) and cloud particle effective radius (CER)—between these two satellite sensors reveals that water and ice cloud properties can have different degrees of agreement depending on underlying surface. The relative difference (RD) values of 22% (18%) and 37% (24%) for water cloud COT (CER) comparison over ocean and land surfaces and respective values of 35% (42%) and 35% (62%) for comparisons of ice cloud properties, and also other comparison metrics, suggest better agreements for water cloud properties than for ice cloud properties, and for ocean surface than for land surface. Though cloud properties differences between MODIS and SGLI can arise from inherent features of cloud retrieval algorithms, such as differences in ancillary data, surface reflectance, cloud droplet size distribution function, model for ice particle habit, etc., this study further identifies the important roles of cloud thickness and Sun and satellite positions for differences in cloud properties between SGLI and MODIS: the differences in cloud properties are found to increase for thinner clouds, higher solar zenith angle, and higher differences in viewing zenith and azimuth angles between these satellite sensors, and such differences are more distinct for water cloud properties than for ice cloud properties.

PATMOS-x Version 6.0: 40 Years of Merged AVHRR and HIRS Global Cloud Data

Abstract A new version of the PATMOS-x multidecadal cloud record, version 6.0, has been produced and is available from the NOAA National Centers for Environmental Information. A description of the processes and methods used for generating the dataset are presented, with a focus on the differences between version 6.0 and the previous version of PATMOS-x, version 5.3. The new version appears both to be more stable, with less intersatellite variability, and to have more consistent polar cloud detection, phase distribution, and cloud-top height distribution when compared against the MODIS EOS record. Improvements in consistency and performance are attributed to the addition of multidimensional variables for cloud detection, constraining cloud retrievals to radiometric bands available throughout the record, and the addition of data from the HIRS instrument. Significance Statement The PATMOS-x project produces multidecadal cloudiness records from polar-orbiting satellites. Version 6.0 combines imager and sounder data from 15 satellites and shows significant improvements in accuracy and stability.

Evaluation of the spectral misalignment on the Earth Clouds, Aerosols and Radiation Explorer/multi-spectral imager cloud product

Abstract. A cloud identification and profiling algorithm is being developed for the multi-spectral imager (MSI), which is one of the four instruments that the Earth Clouds, Aerosols, and Radiation Explorer (EarthCARE) spacecraft will feature. During recent work, we noticed that the MSI response function could shift substantially among some wavelengths (0.67 and 1.65 µm bands) owing to the spectral misalignment (SMILE), in which a shift in the center wavelength appears as a distortion in the spectral image. We evaluated how SMILE affects the cloud retrieval product qualitatively and quantitatively. We chose four detector pixels from bands 1 and 3 with the nadir pixel as the reference to elucidate how the SMILE error affects the cloud optical thickness (τ) and effective cloud droplet radius (re) by simulating the MSI forward radiation with Comprehensive Analysis Program for Cloud Optical Measurement (CAPCOM). We also evaluated the error in simulated scenes from a global cloud system-resolving model and a satellite simulator to measure the effect on actual observation scenes. For typical shallow warm clouds (τ = 8, re = 8 µm), the SMILE error on the cloud retrieval was not significant in most cases (up to 6 % error). For typical deep convective clouds (τ = 8, re = 40 µm), the SMILE error on the cloud retrieval was even less significant in most cases (up to 4 % error). Moreover, our results from two oceanic scenes using the synthetic MSI data agreed well with the forward radiation simulation, indicating that the SMILE error was generally within 10 %. Generally, this negligible impact of the SMILE is true for water surfaces, but it still needs to be investigated further for land surfaces in future works.

VIIRS Edition 1 Cloud Properties for CERES, Part 1: Algorithm Adjustments and Results

Cloud properties are essential for the Clouds and the Earth’s Radiant Energy System (CERES) Project, enabling accurate interpretation of measured broadband radiances, providing a means to understand global cloud-radiation interactions, and constituting an important climate record. Producing consistent cloud retrievals across multiple platforms is critical for generating a multidecadal cloud and radiation record. Techniques used by CERES for retrievals from measurements by the MODerate-Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua platforms are adapted for the application to radiances from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership to continue the CERES record beyond the MODIS era. The algorithm adjustments account for spectral and channel differences, use revised reflectance models, and set new thresholds for detecting thin cirrus clouds at night. Cloud amounts from VIIRS are less than their MODIS counterparts by 0.016 during the day and 0.026 at night, but trend consistently over the 2012–2020 period. The VIIRS mean liquid water cloud fraction differs by ~0.01 from the MODIS amount. The average cloud heights from VIIRS differ from the MODIS heights by less than 0.2 km, except the VIIRS daytime ice cloud heights, which are 0.4 km higher. The mean VIIRS nonpolar optical depths are 17% (1%) larger (smaller) than those from MODIS for liquid (ice) clouds. The VIIRS cloud hydrometeor sizes are generally smaller than their MODIS counterparts. Discrepancies between the MODIS and VIIRS properties stem from spectral and spatial resolution differences, new tests at night, calibration inconsistencies, and new reflectance models. Many of those differences will be addressed in future editions.

Geo-Intelligent Retrieval Framework Based on Machine Learning in the Cloud Environment: A Case Study of Soil Moisture Retrieval

Soil moisture is one of the important parameters in Earth system models. In recent years, the retrieval based on machine learning and data fusion of multi-source satellite observation data has become one of the effective methods to obtain soil moisture information at a large scale. However, most retrieval studies need to download remote sensing original data first, then preprocess, train the retrieval models, and finally generate products in the offline environment. In order to meet the requirements of long temporal series of large-scale area retrieval, and with the widespread use of machine learning in retrieval studies, the amount of remote sensing data and necessary computing resources are gradually increasing. Moreover, studies usually use a single machine learning retrieval model for the entire study area, which lacks the consideration of geographical differences and spatial heterogeneity of soil moisture. Therefore, we established a geo-intelligent soil moisture retrieval framework completely based on the cloud environment. In this study, a variety of machine learning algorithms were used to fuse multi-source observation data mainly including MODIS data and other auxiliary data, and the Continental United States (CONUS) was taken as the experimental area to generate soil moisture data with a resolution of 500m. In addition, this study combines geographical correlation with machine learning models to cope with the spatial heterogeneity of surface soil moisture. Overall, on the basis of site-based validation, the retrieval model trained under the framework performed well, with estimation accuracy of 0.716 and 0.0383 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ·m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> in terms of coefficient of determination (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) and unbiased root mean square error (ubRMSE). The establishment of the cloud retrieval framework provides convenience for the whole retrieval process and also provides a new idea for other retrieval studies of geoscience parameters.

Cloud parameter retrieval for Environmental trace gases Monitoring Instrument (EMI) using the O2-O2 absorption band

This study proposes an improved cloud parameter retrieval algorithm for the existing cloud parameter algorithm of the Environmental trace gases Monitoring Instrument (EMI) using the collision complex of the oxygen (O2-O2) absorption feature. The main improvements are the evaluation of the impact of temperature on the O2-O2 slant column densities (SCDs), and the well-corrected EMI measured reflectance. The cloud parameters will help improve the accuracy of EMI trace gas retrievals. The O2-O2 SCDs were fitted based on EMI-observed radiances and solar irradiance, and the impact of temperature on the O2-O2 SCD was evaluated. A simulated reflectance look-up table (LUT), as well as the O2-O2 SCD LUT, were established through the SCIATRAN radiative transfer model. Reflectance and O2-O2 SCD LUTs are functions of effective cloud fraction (ECF) and cloud pressure (CP), which can be inverted to ECF and CP LUTs. On this basis, the EMI ECF and CP were obtained using the EMI observation geometric information, EMI measured reflectance, and O2-O2 SCD to interpolate the ECF and CP LUTs. To evaluate the results, we performed an inter-comparison between the EMI cloud parameters, TROPOspheric Monitoring Instrument (TROPOMI) cloud products, Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) CP, and ground-based lidar cloud altitude. Of them, the one-day global ECF and CP of EMI and TROPOMI exhibited good agreement; the correlation coefficients were 0.925 and 0.866, respectively. Additionally, EMI was closer to the CALIOP results when the cloud pressure was >600 hPa. The comparison with the ground-based lidar cloud altitude also yielded good agreement during the continuous observation period from September 21 to November 10, 2019. The results prove that the improved EMI cloud retrieval algorithm could provide effective cloud products for EMI trace gas inversion.