Retrieval Of Properties Research Articles

Airborne lidar intensity correction for mapping snow cover extent and effective grain size in mountainous terrain

ABSTRACT Differentially mapping snow depth in mountain watersheds from airborne laser altimetry is a valuable hydrologic technique that has seen an expanded use in recent years. Additionally, lidar systems also record the strength of the returned light pulse (i.e. intensity), which can be used to characterize snow surface properties. For near-infrared lidar systems, return intensity is relatively high over snow and inversely related to the effective grain size, a primary control on snow albedo. Raw intensity is also sensitive to laser range and incidence angle, however, and requires a correction for snow property retrieval that is especially pertinent in mountainous terrain. Here, we describe a workflow to correct the intensity using the plane trajectory, lidar scan angle, and lidar-derived topography. As a proof of concept for snow retrievals, we apply the workflow to an airborne 1064 nm lidar flight over a snow-covered mountain basin in the Colorado Rockies. Corrected intensity was empirically related to reflectance before delineating snow extent and retrieving grain size. Relative to the traditional snow classification derived from optical imagery, the lidar-derived snow extent covered 5.4% more area due to the fine resolution point cloud and absence of shadows common in optical imagery. The lidar-derived grain size retrievals had a MAE of 32 µm compared to those from field spectroscopy, which translated to a 1% error in snow albedo. We found high incidence angles yielded an overcorrection in intensity that introduced a high bias in the grain size distribution and, therefore, suggest using an incidence angle threshold (40°). Developing methods specifically for quantitative snow surface property retrievals from lidar intensity is timely and relevant as aerial lidar is increasingly being used to map snow depth for hydrologic and cryospheric studies.

Improved snow property retrievals by solving for topography in the inversion of at-sensor radiance measurements

Abstract. Accurately modelling optical snow properties like snow albedo and specific surface area (SSA) are essential for monitoring the cryosphere in a changing climate and are parameters that inform hydrologic and climate models. These snow surface properties can be modelled from spaceborne imaging spectroscopy measurements but rely on digital elevation models (DEMs) of relatively coarse spatial scales (e.g. Copernicus at 30 m), which degrade accuracy due to errors in derived products such as slope and aspect. In addition, snow deposition and redistribution can change the apparent topography, and thereby static DEMs may not be considered coincident with the imaging spectroscopy dataset. Testing in three different snow climates (tundra, maritime, alpine), we established a new method that simultaneously solves snow, atmospheric, and terrain parameters, enabling a solution that is more unified across sensors and introduces fewer sources of uncertainty. We leveraged imaging spectroscopy data from Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and PRecursore IperSpettrale della Missione Applicativa (PRISMA) (collected within 1 h) to validate this method and showed a 25 % increase in performance for the radiance-based method over the static method when estimating SSA. This concept can be implemented in missions such as Surface Biology and Geology (SBG), the Environmental Mapping and Analysis Program (EnMap), and the Copernicus Hyperspectral Imaging Mission for the Environment (CHIME).

Evaluation of Physical Microphysical Property Retrieval Algorithms During the 2020 IMPACTS Field Campaign

Abstract The NASA Investigation of Microphysics and Precipitation for Atlantic Coast Threatening Snowstorms (IMPACTS) field campaign provides high-quality, high-altitude aircraft lidar (532 nm), radar (W-band) and in-cloud microphysical aircraft data taken during wintertime storm events impacting the United States. This study evaluates two mass-dimensional relationships (Brown and Francis (1995, BF95); Heymsfield (2014, H14) and two lidar-radar microphysical retrieval algorithms (Cloudsat and CALIPSO Ice Cloud Property Product (2C-ICE); VarPy (a variational method derived from the satellite lidar-radar data community)) to estimate aircraft-retrieved volume extinction coefficient (σ), ice water content (IWC), and effective radius (re) during the 2020 IMPACTS deployment. BF95 and H14 have a close 1:1 correlation (R2 = 0.98) with in-situ observations of σ. However, only BF95 displays a linear, consistent, and almost temperature-independent low bias for IWC and re, which likely arises from the environmental conditions used to determine each. Unlike the field-campaign-derived BF95 and H14 relationships, VarPy and 2C-ICE directly ingest the aircraft-based lidar and radar data to simulate σ, IWC, and re. For all three microphysical parameters, VarPy and 2C-ICE retrieval errors became notably more pronounced around the dendritic growth zone (−15°C to −10°C) and near freezing (≥−5°C), which suggests that both algorithms experience difficulty addressing riming and aggregation processes and with larger particles (dendrites and plates) due in part to their simplified ice particle assumptions. However, the mean-melt diameter ice-particle assumption did yield more accurate IWC estimates, which led to slightly better overall results for VarPy.

Retrieval of snow and soil properties for forward radiative transfer modeling of airborne Ku-band SAR to estimate snow water equivalent: the Trail Valley Creek 2018/19 snow experiment

Abstract. Accurate snow information at high spatial and temporal resolution is needed to support climate services, water resource management, and environmental prediction services. However, snow remains the only element of the water cycle without a dedicated Earth observation mission. The snow scientific community has shown that Ku-band radar measurements provide quality snow information with its sensitivity to snow water equivalent and the wet/dry state of snow. With recent developments of tools like the snow micropenetrometer (SMP) to retrieve snow microstructure data in the field and radiative transfer models like the Snow Microwave Radiative Transfer (SMRT) model, it becomes possible to properly characterize the snow and how it translates into radar backscatter measurements. An experiment at Trail Valley Creek (TVC), Northwest Territories, Canada, was conducted during the winter of 2018/19 in order to characterize the impacts of varying snow geophysical properties on Ku-band radar backscatter at a 100 m scale. Airborne Ku-band data were acquired using the University of Massachusetts radar instrument. This study shows that it is possible to calibrate SMP data to retrieve statistical information on snow geophysical properties and properly characterize a representative snowpack at the experiment scale. The tundra snowpack measured during the campaign can be characterize by two layers corresponding to a rounded snow grain layer and a depth hoar layer. Using RADARSAT-2 and TerraSAR-X data, soil background roughness properties were retrieved (msssoil=0.010±0.002), and it was shown that a single value could be used for the entire domain. Microwave snow grain size polydispersity values of 0.74 and 1.11 for rounded and depth hoar snow grains, respectively, were retrieved. Using the geometrical optics surface backscatter model, the retrieved effective soil permittivity increased from C-band (εsoil=2.47) to X-band (εsoil=2.61) and to Ku-band (εsoil=2.77) for the TVC domain. Using the SMRT and the retrieved soil and snow parameterizations, an RMSE of 2.6 dB was obtained between the measured and simulated Ku-band backscatter values when using a global set of parameters for all measured sites. When using a distributed set of soil and snow parameters, the RMSE drops to 0.9 dB. This study thus shows that it is possible to link Ku-band radar backscatter measurements to snow conditions on the ground using a priori knowledge of the snow conditions to retrieve snow water equivalent (SWE) at the 100 m scale.

Using Ly α transits to constrain models of atmospheric escape

ABSTRACT Ly $\alpha$ transits provide an opportunity to test models of atmospheric escape directly. However, translating observations into constraints on the properties of the escaping atmosphere is challenging. The major reason for this is that the observable parts of the outflow often comes from material outside the planet’s Hill sphere, where the interaction between the planetary outflow and circumstellar environment is important. As a result, 3D models are required to match observations. Whilst 3D hydrodynamic simulations are able to match observational features qualitatively, they are too computationally expensive to perform a statistical retrieval of properties of the outflow. Here, we develop a model that determines the trajectory, ionization state, and 3D geometry of the outflow as a function of its properties and system parameters. We then couple this model to a ray tracing routine in order to produce synthetic transits. We demonstrate the validity of this approach, reproducing the trajectory of the outflows seen in 3D simulations. We illustrate the use of this model by performing a retrieval on the transit spectrum of GJ 436 b. The bound on planetary outflow velocity and mass-loss rates are consistent with a photoevaporative wind.

Retrieval of Aerosol and Surface Properties at High Spatial Resolution: Hybrid Approach and Demonstration Using Sentinel‐5p/TROPOMI and PRISMA

AbstractSatellite remote sensing of aerosol is largely conducted at moderate or coarse spatial resolution around 1–10 km. Nevertheless, at urban areas with high human activity, aerosol can originate from complex emission sources and may also vary strongly in space. Therefore, aerosol characterization at fine spatial resolution is essential for air quality study and assessment of anthropogenic pollution as well as climate effects. However, space‐borne instruments with high spatial resolution are usually limited in swath width or spectral coverage which result in lowering information content required for aerosol and surface retrieval. Based on the Generalized Retrieval of Atmosphere and Surface Properties (GRASP) algorithm, we propose a hybrid approach by combining fine and coarse spatial resolution measurements to retrieve aerosol and surface properties simulataneously at fine spatial resolution. The instruments with coarse spatial resolution and high revisting time can provide advanced aerosol characterization. At the same time, the instruments with fine spatial resolution are sensitive to spatial variability of aerosol nearby sources. In this study, the GRASP/Hybrid approach is demonstrated and tested based on the European Space Agency Sentinel‐5p/TROPOMI together with the Italian Space Agency PRISMA satellite data. Specifically, the detailed aerosol microphysical properties from Sentinel‐5p/TROPOMI 10 km retrievals are used as a priori information for PRISMA to derive aerosol loading and surface properties at 100 meter (m) spatial resolution. The PRISMA 100 m aerosol and surface retrieval based on the developed GRASP/Hybrid approach are evaluated using available ground‐based and satellite measurements, including AERONET, VIIRS/DB aerosol and PRISMA Level 2 surface reflectance products.

Ground-Based Remote Sensing of Aerosol, Clouds, Dynamics, and Precipitation in Antarctica: First Results from the 1-Year COALA Campaign at Neumayer Station III in 2023

Abstract Novel observations of aerosol and clouds by means of ground-based remote sensing have been performed in Antarctica over the Ekström Ice Shelf on the coast of Dronning Maud Land at Neumayer Station III (70.67°S, 8.27°W) from January to December 2023. The deployment of the OCEANET-Atmosphere remote sensing observatory in the framework of the Continuous Observations of Aerosol-Cloud Interaction (COALA) campaign has brought Aerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS) aerosol and cloud profiling capabilities next to meteorological and air chemistry in situ observations at the Antarctic station. We present an overview of the site, the instrumental setup, and data analysis strategy and introduce 3 scientific highlights from austral fall and winter, namely, 1) observations of a persistent mixed-phase cloud embedded in a plume of marine aerosol. Remote sensing–based retrievals of cloud-relevant aerosol properties and cloud microphysical parameters confirm that the free-tropospheric mixed-phase cloud layer formed in an aerosol-limited environment. 2) Two extraordinary warm air intrusions: one with intense snowfall produced the equivalent of 10% of the yearly snow accumulation and a second one with record-breaking maximum temperatures and heavy icing due to supercooled drizzle. 3) Omnipresent aerosol layers in the stratosphere. Our profiling capabilities could show that 50% of the 500-nm aerosol optical depth of 0.06 was caused by stratospheric aerosol, while the troposphere was usually pristine. As demonstrated by these highlights, the 1-yr COALA observations will serve as a reference dataset for the vertical structure of aerosol and clouds above the region, enabling future observational and modeling studies to advance understanding of atmospheric processes in Antarctica.

Drizzle microphysical property and vertical air motions retrieval using Doppler lidar and radar measurements

The microphysical changes in cloud formation and development are closely related to the vertical air motions. It is difficult to simultaneously detect microphysical parameters of drizzle and vertical air motions. This study proposes a method for the drizzle microphysical property and vertical air motions retrieval using Doppler lidar and radar measurements. The wavelength of lidar is 1.55 µm, and it undergoes Mie scattering or geometric scattering in drizzle. The wavelength of radar is 8.6 mm, and it undergoes Rayleigh scattering or Mie scattering in drizzle. The difference in scattering mechanisms of two wavelengths enables them to retrieve the microphysical parameters of vertical air motions and raindrops. This wavelength-dependent backscattering cross section causes differently shaped reflectivity-weighted Doppler velocity spectra leading to wavelength-dependent mean Doppler velocity, and spectra width. In this algorithm, the echo power intensity, mean Doppler velocity and spectra width of lidar and radar are used for the retrieval of microphysical parameters and vertical air motions. The feasibility of the proposed method is simulated and analyzed, which is suitable for stratiform clouds rainfall with low turbulence. Finally, an observation case is provided and analyzed.

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.

A new spectrally-invariant approach to the remote sensing of inhomogeneous clouds

Current operational satellite retrievals of cloud optical and microphysical properties go back to the Nakajima-King technique developed at the end of the 1980 s. This technique is based on library calculations for plane-parallel homogeneous clouds. It often works well for overcast skies but leads to substantial errors for inhomogeneous and broken cloud fields where the plane-parallel-geometry assumption is no longer valid. The basic concept of a new technique was first introduced in nuclear physics to quantify the critical conditions of reactors. Based on the eigenvalues of the radiative transfer equation, their approach provides a powerful means to parameterize the structure of 3D media. This parameterization was later successfully applied to relate surface reflectance spectra to 3D canopy structure and is known as the spectrally-invariant approximation. The proposed approach adapts this technique to the remote sensing of cloud properties such as droplet single scattering albedo and the average number of scatterings (which are the fundamental parameters in radiative transfer theory), with an emphasis on quantifying the associated errors and uncertainties. This retrieval is free from the plane-parallel homogeneous cloud assumption.

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.

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.

The Effect of Physically Based Ice Radiative Processes on Greenland Ice Sheet Albedo and Surface Mass Balance in E3SM

AbstractA significant portion of surface melt on the Greenland Ice Sheet (GrIS) is due to dark ice regions in the ablation zone, where solar absorption is influenced by the physical properties of the ice, light absorbing constituents (LACs), and the overlying crustal surface or melt ponds. Earth system models (ESMs) typically prescribe the albedo of ice surfaces as a constant value in the visible and near‐infrared spectral regions. This work advances ESM ice radiative transfer modeling by (a) incorporating a physically based radiative transfer model (SNow, ICe and Aerosol Radiation model Adding‐Doubling Version 4; SNICAR‐ADv4) into the Energy Exascale Earth System Model (E3SM), (b) determining spatially and temporally varying bare ice physical properties over the GrIS ablation zone from satellite observations to inform SNICAR‐ADv4, and (c) assessing the impacts on simulated GrIS albedo and surface mass balance associated with modeling of more realistic bare ice albedo. GrIS‐wide bare ice albedo in E3SMv2 is overestimated by ∼4% in the visible and ∼7% in the near‐infrared wavelengths compared to the Moderate Resolution Imaging Spectroradiometer. Our bare ice physical property retrieval method found that LACs, ice crustal surfaces, and melt ponds reduce visible albedo by 30% in the bare ice region of the GrIS ablation zone. The realistic bare ice albedo reduces surface mass balance by ∼145 Gt, or 0.4 mm of sea‐level equivalent between 2000 and 2021 compared to the default E3SM. This work highlights the importance of simulating bare ice albedo accurately and realistically to improve our ability to quantify changes in the GrIS surface mass and radiative energy budgets.

Aerosol and cloud data processing and optical property retrieval algorithms for the spaceborne ACDL/DQ-1

Abstract. The new-generation atmospheric environment monitoring satellite DQ-1, launched successfully in April 2022, carries the Aerosol and Carbon Detection Lidar (ACDL), which is capable of globally profiling aerosol and cloud optical properties with high accuracy. The ACDL/DQ-1 is a high-spectral-resolution lidar (HSRL) that separates molecular backscatter signals using an iodine filter and has 532 nm polarization detection and dual-wavelength detection at 532 and 1064 nm, which can be utilized to derive aerosol optical properties. The methods have been specifically developed for data processing and optical property retrieval according to the specific characteristics of the ACDL system and are introduced in detail in this paper. Considering the different signal characteristics and different background noise behaviors of each channel during daytime and nighttime, the procedures of data pre-processing, denoising process and quality control are applied to the original measurement signals. The aerosol and cloud optical property products of the ACDL/DQ-1, including the total depolarization ratio, backscatter coefficient, extinction coefficient, lidar ratio and color ratio, can be calculated by the retrieval algorithms presented in this paper. Two measurement cases with use of the ACDL/DQ-1 on 27 June 2022 and the global averaged aerosol optical depth (AOD) from 1 June to 4 August 2022 are provided and analyzed, demonstrating the measurement capability of the ACDL/DQ-1.

Algorithm evaluation for polarimetric remote sensing of atmospheric aerosols

Abstract. From a passive satellite remote sensing point of view, the richest set of information on aerosol properties can be obtained from instruments that measure both intensity and polarization of backscattered sunlight at multiple wavelengths and multiple viewing angles for one ground pixel. However, it is challenging to exploit this information at a global scale because complex algorithms are needed with many fit parameters (aerosol and land/ocean reflection), based on online radiative transfer models. So far, two such algorithms have demonstrated this capability at a global scale: the Generalized Retrieval of Atmosphere and Surface Properties (GRASP) algorithm and the Remote sensing of Trace gas and Aerosol Products (RemoTAP) algorithm. In this paper, we present a detailed comparison of the most recent versions of RemoTAP and GRASP. We evaluate both algorithms for synthetic observations, for real PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar) observations against AERONET (Aerosol Robotic Network) for common pixels, and for global PARASOL retrievals for the year 2008. For the aerosol optical depth (AOD) over land, both algorithms show a root mean square error (RMSE) of 0.10 (at 550 nm). For single scattering albedo (SSA), both algorithms show a good performance in terms of RMSE (0.04), but RemoTAP has a smaller bias (0.002) compared to GRASP (0.021). For the Ångström exponent (AE), GRASP has a smaller RMSE (0.367) than RemoTAP (0.387), mainly caused by a small overestimate of AE at low values (large particles). Over ocean both algorithms perform very well. For AOD, RemoTAP has an RMSE of 0.057 and GRASP an even smaller RMSE of 0.047. For AE, the RMSEs of RemoTAP and GRASP are 0.285 and 0.224, respectively. Based on the AERONET comparison, we conclude that both algorithms show very similar overall performance, where both algorithms have stronger and weaker points. For the global data products, we find a root mean square difference (RMSD) between RemoTAP and GRASP AOD of 0.12 and 0.038 over land and ocean, respectively. The largest differences occur over the biomass burning region in equatorial Africa. The global mean values are virtually unbiased with respect to each other. For AE the RMSD between RemoTAP and GRASP is 0.33 over land and 0.23 over ocean. For SSA, we find much better agreement over land (bias = −0.01, RMSD = 0.043 for retrievals with AOD > 0.2) than over ocean (bias = 0.053, RMSD = 0.074). As expected, the differences increase towards low AOD, over both land and ocean. We also compared the GRASP and RemoTAP AOD and AE products against MODIS. For AOD over land, the agreement of either GRASP or RemoTAP with MODIS is worse than the agreement between the two PARASOL algorithms themselves. Over ocean, the agreement is very similar among the three products for AOD. For AE, the agreement between GRASP and RemoTAP is much better than the agreement of both products with MODIS. The agreement of the latest product versions with each other and with AERONET improved significantly compared to the previous version of the global products of GRASP and RemoTAP. The results demonstrate that the dedicated effort in algorithm development for multi-angle polarimetric (MAP) aerosol retrievals still leads to substantial improvement of the resulting aerosol products, and this is still an ongoing process.

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.