Polarization And Directionality Of The Earth's Reflectances Research Articles

Evaluation of the Snow Albedo Retrieved from the Snow Kernel Improved the Ross-Roujean BRDF Model

The original kernel-driven bidirectional reflectance distribution function (BRDF) models were developed based on soil-vegetation systems. To further improve the ability of the models to characterize the snow surface scattering properties, a snow kernel was derived from the asymptotic radiative transfer (ART) model and used in the kernel-driven BRDF model framework. However, there is a need to further evaluate the influence of using this snow kernel to improve the original kernel-driven models in snow albedo retrieval applications. The aim of this study is to perform such an evaluation using a variety of snow BRDF data. The RossThick-Roujean (RTR) model is used as a framework for taking in the new snow kernel (hereafter named the RTS model) since the Roujean geometric-optical (GO) kernel captures a neglectable hotspot effect and represents a more prominent dome-shaped BRDF, especially at a small solar zenith angle (SZA). We obtained the following results: (1) The RTR model has difficulties in reconstructing the snow BRDF shape, especially at large SZAs, which tends to underestimate the reflectance in the forward direction and overestimate reflectance in the backward direction for various data sources. In comparison, the RTS model performs very well in fitting snow BRDF data and shows high accuracy for all data. (2) The RTR model retrieved snow albedos at SZAs = 30°–70° are underestimated by 0.71% and 0.69% in the red and near-infrared (NIR) bands, respectively, compared with the simulation results of the bicontinuous photon tracking (bic-PT) model, which serve as “real” values. However, the albedo retrieved by the RTS model is significantly improved and generally agrees well with the simulation results of the bic-PT model, although the improved model still somewhat underestimates the albedo by 0.01% in the red band and overestimates the albedo by 0.05% in the NIR band, respectively, at SZAs = 30°–70°, which may be negligible. (3) The albedo derived by these two models shows a high correlation (R2 > 0.9) between the field-measured and Polarization and Directionality of the Earth's Reflectances (POLDER) data, especially for the black-sky albedo. However, the albedo derived using the RTR model is significantly underestimated compared with the RTS model. The RTR model underestimates the black-sky albedo (white-sky albedo) retrievals by 0.62% (1.51%) and 0.93% (2.08%) in the red and NIR bands, respectively, for the field-measured data. The shortwave black-sky and white-sky albedos derived using the RTR model for the POLDER data are underestimated by 1.43% and 1.54%, respectively, compared with the RTS model. These results indicate that the snow kernel in the kernel-driven BRDF model frame is more accurate in snow albedo retrievals and has the potential for application in the field of the regional and global energy budget.

Intercomparison in spatial distributions and temporal trends derived from multi-source satellite aerosol products

Abstract. Satellite-derived aerosol products provide long-term and large-scale observations for analysing aerosol distributions and variations, climate-scale aerosol simulations, and aerosol–climate interactions. Therefore, a better understanding of the consistencies and differences among multiple aerosol products is important. The objective of this study is to compare 11 global monthly aerosol optical depth (AOD) products, which are the European Space Agency Climate Change Initiative (ESA-CCI) Advanced Along-Track Scanning Radiometer (AATSR), Advanced Very High Resolution Radiometer (AVHRR), Multi-angle Imaging SpectroRadiometer (MISR), Moderate Resolution Imaging Spectroradiometer (MODIS), Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Visible Infrared Imaging Radiometer (VIIRS), and POLarization and Directionality of the Earth's Reflectance (POLDER) products. AErosol RObotic NEtwork (AERONET) Version 3 Level 2.0 monthly measurements at 308 sites around the world are selected for comparison. Our results illustrate that the spatial distributions and temporal variations of most aerosol products are highly consistent globally but exhibit certain differences on regional and site scales. In general, the AATSR Dual View (ADV) and SeaWiFS products show the lowest spatial coverage with numerous missing values, while the MODIS products can cover most areas (average of 87 %) of the world. The best performance is observed in September–October–November (SON) and the worst is in June–July–August (JJA). All the products perform unsatisfactorily over northern Africa and Middle East, southern and eastern Asia, and their coastal areas due to the influence from surface brightness and human activities. In general, the MODIS products show the best agreement with the AERONET-based AOD values on different spatial scales among all the products. Furthermore, all aerosol products can capture the correct aerosol trends at most cases, especially in areas where aerosols change significantly. The MODIS products perform best in capturing the global temporal variations in aerosols. These results provide a reference for users to select appropriate aerosol products for their particular studies.

Retrieval of High Spatial Resolution Aerosol Optical Depth from HJ-1 A/B CCD Data

A high-spatial resolution aerosol optical depth (AOD) dataset is critically important for regional meteorology and climate studies. Chinese Huanjing-1 (HJ-1) A/B charge-coupled diode (CCD) data are a suitable data source for retrieving AODs. However, AOD cannot be retrieved based on the dark target method due to the absence of a shortwave infrared band. In this study, an AOD estimation method based on the relationships between visible bands of HJ-1 A/B CCDs is proposed. The Polarization and Directionality of the Earth's Reflectances (POLDER) Bidirectional Reflectance Distribution Function (BRDF) dataset was used to construct a lookup table for interband regression coefficients that varied by solar/view angle and land cover type. Finally, high-spatial resolution AODs could be retrieved with the aerosol lookup table and constraints. The results showed that the AODs retrieved from the HJ-1 A/B CCD data had the same range of distribution and trends as a visual interpretation of the images and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products did. The validation results using four sites of the Aerosol Robotic Network (AERONET) in Beijing showed that the value of the correlation coefficient R was 0.866, the root mean square error (RMSE) was 0.167, the mean absolute error (MAE) was 0.131, and the expected error (EE) was 53.9%. If the measurements of an AERONET site were used as prior knowledge, AOD retrieval results could be much more accurately obtained by this method (R is 0.989, RMSE is 0.052, MAE is 0.042, and EE is 96.7%).

Algorithms for the classification and characterization of aerosols: utility verification of near-UV satellite observations

Aerosol types were characterized and classified using multispectral satellite data. The role of near-UV data in the detection of absorbing aerosols, such as biomass burning aerosols (BBA) or mineral dust particles (DUST), was examined on a global scale. An absorbing aerosol index (AAI) was proposed and defined as the ratio of the satellite-observed radiance (R) at a wavelength of 0.412 μm [R (0.412)] to that at 0.380 μm [R (0.380)] that can also detect nonabsorbing-type aerosols. Initially, the numerical AAI values were estimated for the BBAs and DUST from measurements collected by the Advanced Earth Observing Satellite-2/Global Imager (ADEOS-2/GLI). The Japanese short mission ADEOS-2 carried the GLI instrument with observation channels in the near-UV region. Not only the AAI index but also the short-wavelength infrared measurements were utilized to determine the dust detection index (DDI) defined as the ratio of R (2.210) to R (0.380) in order to discriminate BBAs from DUST. In addition, the AAI and DDI values were evaluated for the detection of clouds. The results allowed the classification criteria for DUST, BBA, other types of aerosols and clouds to be obtained. The Second-Generation Global Imager (SGLI) sensor is onboard the Japanese Global Change Observation Mission-Climate (GCOM-C) (SHIKISAI in Japanese) satellite launched on December 23, 2017. The SGLI has multiple channels (19) including near-UV and polarization sensors in the red and near-IR wavelengths. We also demonstrated the advantages of the SGLI for near-UV and polarization data for aerosol remote sensing. An understanding of aerosol types facilitated subsequent aerosol retrieval. Then, retrieval for classified aerosols was made based on the radiation simulations with multispectral radiance by GLI and polarization measurements by Polarization and Directionality of the Earth’s Reflectances (POLDER)-2, respectively, mounted on the ADEOS-2 satellite. The proposed algorithms are expected to be available not only for the analysis of the SGLI data but also for other future missions.

A Review of Ice Cloud Optical Property Models for Passive Satellite Remote Sensing

The current wealth of spaceborne passive and active measurements from ultraviolet to the infrared wavelengths provides an unprecedented opportunity to construct ice cloud bulk optical property models that lead to consistent ice cloud property retrievals across multiple sensors and platforms. To infer the microphysical and radiative properties of ice clouds from these satellite measurements, the general approach is to assume an ice cloud optical property model that implicitly assumes the habit (shape) and size distributions of the ice particles in these clouds. The assumption is that this ice optical property model will be adequate for global retrievals. In this review paper, we first summarize the key optical properties of individual particles and then the bulk radiative properties of their ensemble, followed by a review of the ice cloud models developed for application to satellite remote sensing. We illustrate that the random orientation condition assumed for ice particles is arguably justified for passive remote sensing applications based on radiometric measurements. The focus of the present discussion is on the ice models used by the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Clouds and Earth’s Radiant Energy System (CERES) science teams. In addition, we briefly review the ice cloud models adopted by the Polarization and Directionality of the Earth’s Reflectance (POLDER) and the Himawari-8 Advanced Himawari Imager (AHI) for ice cloud retrievals. We find that both the MODIS Collection 6 ice model and the CERES two-habit model result in spectrally consistent retrievals.

Development of a snow kernel to better model the anisotropic reflectance of pure snow in a kernel-driven BRDF model framework

The linear kernel-driven RossThick-LiSparseReciprocal (RTLSR) bidirectional reflectance distribution function (BRDF) model was originally developed from the simplified scenarios of continuous and discrete vegetation canopies, and has been widely used to fit multiangle observations of vegetation-soil systems of the land surface in many fields. Although this model was not developed explicitly for snow surfaces, it can capture the geometric-optical effect caused by the shadowing of rugged or undulating snow surfaces. However, in this study, this model has been further developed to better characterize the scattering properties of snow surface, which can also exhibit strongly forward-scattering behavior. This study proposes a new snow kernel to characterize the reflectance anisotropy of pure snow based on the asymptotic radiative transfer (ART) model that assumes snow can be modeled as a semi-infinite, plane-parallel, weakly absorbing light scattering layer. This new snow kernel adopts a correction term with a free parameter α to correct the analytic form of the ART model that has been reported to underestimate observed snow reflectance in the forward-scattering direction in the principal plane (PP), particularly in cases of a large viewing zenith angle (>60°). This snow kernel has now been implemented in the kernel-driven RTLSR BRDF model framework in conjunction with two additional kernels (i.e., the volumetric scattering kernel and geometric-optical scattering kernel) and is validated using observed and simulated multiangle data from three data sources. Pure snow targets were selected from the extensive archive of the Polarization and Directionality of the Earth's Reflectance (POLDER) BRDF data. Antarctic snow field measurements, which were taken from the top of a 32-m-tall tower at Dome C Station and include 6336 spectral bidirectional reflectance factors (BRFs), were also utilized. Finally, a set of simulated BRFs, generated by a hybrid scattering snow model that combines the geometric optics with vector radiative transfer theory, were used to further assess the proposed method. We first retrieve the value of the free parameter α for a comprehensive analysis using single multiangle snow data with a sufficient BRDF sampling. Then, we determine the optimally fixed value of the α parameter as prior information for potential users. The new snow kernel method is shown to be quite accurate, presenting a high correlation coefficient (R2 = ~0.9) and a negligible bias between the modeled BRFs and the various snow BRDF validation data. The finding demonstrates that this snow kernel provides an improved potential compared to that of the original kernel-driven model framework for a pure snow surface in many applications, particularly those involving the global water cycle and radiation budget, where snow cover plays an important role.

Precipitable water vapor over oceans from the Maritime Aerosol Network: Evaluation of global models and satellite products under clear sky conditions

We present results from an evaluation of precipitable water vapor (W) over remote oceanic areas as derived from global reanalysis models and from satellites against observations from the Maritime Aerosol Network (MAN) for cloudless skies during the period of 2004–2017. They cover polar, mid latitude and tropical oceanic regions and represent a first effort to use MAN observations for such evaluation. The global reanalysis model products evaluated in this study are from the Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2), the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA I), and the Climate Forecast System Reanalysis (CFSR) model. The satellite products evaluated are from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Polarization and Directionality of the Earth's Reflectances (POLDER), the Global Ozone Monitoring Experiment (GOME-2), the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY), and the Atmospheric Infra-red Sounder (AIRS). Satellite retrievals of W are based on the attenuation of solar reflected light by water vapor absorption bands, except those from AIRS that rely on brightness temperature measurements. A very good agreement is observed between the model estimates and MAN, with mean differences of ~5% and standard deviations of ~15%. These results are within the uncertainties associated with the models and the measurements, indicating the skill of the reanalysis models to estimate W over oceans under clear sky conditions. Mean differences of W between the satellite and MAN products are ~11, 6.7, 12, −7, and 3% for MODIS, POLDER, GOME-2, SCIAMACHY and AIRS respectively, while their standard deviations are 31, 29, 28, 20 and 17%. These differences reveal the need to address inconsistencies among different satellite sensors and ground-based measurements to reduce the uncertainties associated with the retrievals.

Remote sensing of aerosols with small satellites in formation flight.

Determination of aerosol optical properties with orbital passive remote sensing is a difficult task, as observations often have limited information. Multi-angle instruments, such as the Multi-angle Imaging SpectroRadiometer (MISR) and the POlarization and Directionality of the Earth's Reflectances (POLDER), seek to address this by making information rich multi-angle observations, which can be used to better retrieve aerosol optical properties. The paradigm for such instruments is that each angle view is made from one platform, with, for example, a gimbaled sensor or multiple fixed view angle sensors. This restricts the observing geometry to a plane within the scene Bidirectional Reflectance Distribution Function (BRDF ) observed at the top of the atmosphere (TOA). New technological developments, however, support sensors on small satellites flying in formation, which could be a beneficial alternative. Such sensors may have only one viewing direction each, but the agility of small satellites allows one to control this direction and change it over time. When such agile satellites are flown in formation and their sensors pointed to the same location at approximately the same time, they could sample a distributed set of geometries within the scene BRDF . In other words, observations from multiple satellites can take a variety of view zenith and azimuth angles, and are not restricted to one azimuth plane as is the case with a single multi-angle instrument. It is not known, however, if this is as potentially capable as a multi-angle platform for the purposes of aerosol remote sensing. Using a systems engineering tool coupled with an information content analysis technique, we investigate the feasibility of such an approach for the remote sensing of aerosols. These tools test the mean results of all geometries encountered in an orbit. We find that small satellites in formation are equally capable as multi-angle platforms for aerosol remote sensing, as long as their calibration accuracies and measurement uncertainties are equivalent. As long as the viewing geometries are dispersed throughout the BRDF , it appears the quantity of view angles determines the information content of the observations, not the specific observation geometry. Given the smoothly varying nature of BRDF 's observed at the TOA, this is reasonable, and supports the viability of aerosol remote sensing with small satellites flying in formation. The incremental improvement in information content that we found with number of view angles also supports the concept of a resilient mission comprised of multiple satellites that are continuously replaced as they age or fail.

Improved retrieval of aerosol optical depth from POLDER/PARASOL polarization data based on a self-defined aerosol model

The characterization of surface reflection and the selection of an appropriate aerosol model are key problems in aerosol retrieval over land. Due to the amount of polarized light reflected by ground targets is small, the selection of an aerosol model is the main problem in aerosol retrieval based on polarization data. In this study, a self-defined aerosol model is proposed to retrieve aerosol optical depth (AOD) from 2008 to 2009 in the Yangtze River Delta from Polarization and Directionality of the Earth’s Reflectance (POLDER) and Polarization and Anisotropy of Reflectance for Atmospheric Sciences coupled with Observation from a Lidar (PARASOL) data. The model was established using the volume percentages of aerosol components retrieved by Aerosol Robotic Network (AERONET) ground-based measurements. The results show that the volume percentages of aerosol components in the study area are 0.31, 0.55, 0.01 and 0.13 for dust, water-soluble, oceanic and soot particles, respectively, which are quite different from those of the continental model defined in the standard radiation atmosphere (SRA) aerosol models. The accuracy of AOD retrieved by the self-defined aerosol model (r = 0.75, RMSE = 0.20) is higher than that by the continental model (r = 0.41, RMSE = 0.39). Therefore, selecting a more suitable aerosol model can significantly improve the retrieval precision of AOD.

A modified version of the kernel-driven model for correcting the diffuse light of ground multi-angular measurements

When using the kernel-driven bidirectional reflectance distribution function (BRDF) model to process multi-angular measurements, the input multi-angular measurements must be corrected for atmospheric effects. However, in current databases, a significant number of ground-based multi-angular measurements contain either no corrections or only approximate corrections for atmospheric effects. Thus, the blended diffuse light in the total incident irradiance will result in considerable smoothing of the reflectance anisotropy retrieved by the kernel-driven model unless an atmospheric correction process is conducted. In this study, we propose a diffuse-light correction (DLC) form of the kernel-driven model that improves its ability to process multi-angular measurements blended with hemispherical diffuse light. The DLC form of the kernel-driven model can be used to retrieve the intrinsic reflectance anisotropy of the observed target from atmospheric-uncorrected multi-angular measurements. This study used multi-angular data simulated by the PROSAIL and Radiosity Applicable to Porous IndiviDual objects (RAPID) BRDF model, atmospheric-corrected Polarization and Directionality of the Earth's Reflectances (POLDER), Cloud Absorption Radiometer (CAR) multi-angular measurements and their simulated data based on the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) tools to validate the effectiveness of the DLC form of the kernel-driven model. The results indicated that the reflectance factors directly retrieved by the kernel-driven model are considerably smoothed by the blended diffuse light, especially in hotspot regions. Even under clear and cloudless sky conditions, the retrieved hotspot reflectance in the red band is still underestimated by an average of 9.25%, 7.72%, 11.0% and 13.8% for the PROSAIL, RAPID, POLDER and CAR data, respectively. In contrast, the hotspot reflectance retrieved by the DLC form of the kernel-driven model is very close to the intrinsic reflectance anisotropy of the targets; the average relative error of the DLC form of the kernel-driven model is only 1.99%, 1.50%, 4.57% and 3.42%, respectively. Although the reflectance reconstructed by the DLC form of the kernel-driven model in the hotspot region represents a considerable improvement compared with the reflectance retrieved by the original kernel-driven model, its improvement on the root mean square error (RMSE) and the bias of the entire datasets is not very apparent. Using the DLC form of the kernel-driven model can significantly improve the ability of the kernel-driven model to process multi-angular measurements blended with hemispherical diffuse irradiance.

A Global Multilayer Cloud Identification with POLDER/PARASOL

AbstractThe detection of multilayer cloud situations is important for satellite retrieval algorithms and for many climate-related applications. In this paper, the authors describe an algorithm based on the exploitation of the Polarization and Directionality of the Earth’s Reflectance (POLDER) observations to identify monolayered and multilayered cloudy situations along with a confidence index. The authors’ reference comes from the synergy of the active instruments of the A-Train satellite constellation. The algorithm is based upon a decision tree that uses a metric from information theory and a series of tests on POLDER level-2 products. The authors obtain a multilayer flag as the final result of a tree classification, which takes discrete values between 0 and 100. Values closest to 0 (100) indicate a higher confidence in the monolayer (multilayer) character. This indicator can be used as it is or with a threshold level that minimizes the risk of misclassification, as a binary index to distinguish between monolayer and multilayer clouds. For almost fully covered and optically thick enough cloud scenes, the risk of misclassification ranges from 29% to 34% over the period 2006–10, and the average confidences in the estimated monolayer and multilayer characters of the cloud scenes are 74.0% and 58.2%, respectively. With the binary distinction, POLDER provides a climatology of the mono–multilayer cloud character that exhibits some interesting features. Comparisons with the performance of the Moderate Resolution Imaging Spectroradiometer (MODIS) multilayer flag are given.

Impact of cloud horizontal inhomogeneity and directional sampling on the retrieval of cloud droplet size by the POLDER instrument

Abstract. The principles of cloud droplet size retrieval via Polarization and Directionality of the Earth's Reflectance (POLDER) requires that clouds be horizontally homogeneous. The retrieval is performed by combining all measurements from an area of 150 km × 150 km to compensate for POLDER's insufficient directional sampling. Using POLDER-like data simulated with the RT3 model, we investigate the impact of cloud horizontal inhomogeneity and directional sampling on the retrieval and analyze which spatial resolution is potentially accessible from the measurements. Case studies show that the sub-grid-scale variability in droplet effective radius (CDR) can significantly reduce valid retrievals and introduce small biases to the CDR (~ 1.5 μm) and effective variance (EV) estimates. Nevertheless, the sub-grid-scale variations in EV and cloud optical thickness (COT) only influence the EV retrievals and not the CDR estimate. In the directional sampling cases studied, the retrieval using limited observations is accurate and is largely free of random noise. Several improvements have been made to the original POLDER droplet size retrieval. For example, measurements in the primary rainbow region (137–145°) are used to ensure retrievals of large droplet (> 15 μm) and to reduce the uncertainties caused by cloud heterogeneity. We apply the improved method using the POLDER global L1B data from June 2008, and the new CDR results are compared with the operational CDRs. The comparison shows that the operational CDRs tend to be underestimated for large droplets because the cloudbow oscillations in the scattering angle region of 145–165° are weak for cloud fields with CDR > 15 μm. Finally, a sub-grid-scale retrieval case demonstrates that a higher resolution, e.g., 42 km × 42 km, can be used when inverting cloud droplet size distribution parameters from POLDER measurements.

Classification of Several Optically Complex Waters in China Using in Situ Remote Sensing Reflectance

Determining the dominant optically active substances in water bodies via classification can improve the accuracy of bio-optical and water quality parameters estimated by remote sensing. This study provides four robust centroid sets from in situ remote sensing reflectance (Rrs (λ)) data presenting typical optical types obtained by plugging different similarity measures into fuzzy c-means (FCM) clustering. Four typical types of waters were studied: (1) highly mixed eutrophic waters, with the proportion of absorption of colored dissolved organic matter (CDOM), phytoplankton, and non-living particulate matter at approximately 20%, 20%, and 60% respectively; (2) CDOM-dominated relatively clear waters, with approximately 45% by proportion of CDOM absorption; (3) nonliving solids-dominated waters, with approximately 88% by proportion of absorption of nonliving particulate matter; and (4) cyanobacteria-composed scum. We also simulated spectra from seven ocean color satellite sensors to assess their classification ability. POLarization and Directionality of the Earth's Reflectances (POLDER), Sentinel-2A, and MEdium Resolution Imaging Spectrometer (MERIS) were found to perform better than the rest. Further, a classification tree for MERIS, in which the characteristics of Rrs (709)/Rrs (681), Rrs (560)/Rrs (709), Rrs (560)/Rrs (620), and Rrs (709)/Rrs (761) are integrated, is also proposed in this paper. The overall accuracy and Kappa coefficient of the proposed classification tree are 76.2% and 0.632, respectively.

Polarized properties of typical surface types over China based on the multi-angular polarized remote sensing measurements

The surface polarized reflectance is able to fully reflect the physical characteristics of surface, such as vegetation classification, plant biomass estimation, leaf angle distribution and surface water content. The bidirectional polarization distribution function is a useful tool for quantitatively describing the surface polarized reflectance. The multi-angle polarized remote sensing reveals a significant advantage in capturing the radiation and polarization information. The polarization and directionality of the earth reflectance (POLDER) instrument is the only sensor which has provided a long-term trend of polarized measurements. Its combination of multi-spectral, polarization and multi-angle observations has considerable capability for retrieving ocean, land, cloud and aerosol properties. Until now, data obtained from POLDER has been widely used to study various surface bidirectional polarized reflectance models, especially Nadal model. However, parameters of Nadal model reveal a low accuracy in China region. In this study, the parameters of Nadal model suitable for China region are obtained and analyzed based on the POLDER-2 polarized reflectance data. Based on the modified parameters of Nadal model, polarized reflectance under different surface types is further analyzed. Our results show that the polarized reflectance retrieved from modified parameters of Nadal model reveals better correlation with the POLDER-2 products than the polarized reflectance from Nadal official parameters under different surface types. The polarization properties of three typical surfaces (forest, grassland and desert) are further investigated and reveal that 1) different surface polarized reflectances decrease with the increase of the scattering angle, and the polarized reflectance of the same object decreases as the normalized difference vegetation index increases; 2) significant discrepancies exist between the polarized reflectances of different surfaces, the polarized reflectance of forest is the lowest in the three surface types, then that of grass is the second lowest, and desert reveal the largest value (about twice that of forest), 3) the discrepancies of polarized reflectance between different surfaces have an increasing trend as satellite view zenith angle increases. This study will provide a priori knowledge for the detection of surface polarization properties and aerosol parameters based on multi-angle polarization remote sensing data, and also establish a good foundation for the quantitative applications of GF-5 satellite multi-angle polarization imager to be launched soon in China.

A study of aerosol pollution episode due to agriculture biomass burning in the east-central China using satellite data

Aerosol distributions in East Asia are complicated owing to both natural factors and human activities. The concentration of air pollutants has become severe, and in recent years, influence of yellow sand and biomass burning aerosols generated by agricultural biomass burning (ABB) has been noted upon the severe haze episodes. This work intends to make the retrieval algorithm more efficient and to make the aerosol model more tractable as far as heavy air pollution caused by ABB is concerned. Aerosol retrieval in the hazy atmosphere is achieved based on radiation simulation method of successive order of scattering (MSOS) for the moderate resolution imaging spectroradiometer (MODIS) on AQUA data. The retrieved aerosol properties agree well with the ground measurements and numerical model simulations. Multi-angle observations with the polarization and directionality of the Earth's reflectances (POLDER) on the polarization & anisotropy of reflectances for atmospheric sciences coupled with observations from a lidar (PARASOL) are available for aerosol retrieval from a mixture of cloud and haze, and many potential applications of MSOS involving polarization information are expected to be available for cloud as well as aerosol episode. This study should draw our attention to the retrieval of aerosol/cloud from a variety of perspectives.

Intercomparison of clumping index estimates from POLDER, MODIS, and MISR satellite data over reference sites

Clumping index is the measure of foliage grouping relative to a random distribution of leaves in space. It is a key structural parameter of plant canopies that influences canopy radiation regimes and controls canopy photosynthesis and other land–atmosphere interactions. The Normalized Difference between Hotspot and Darkspot (NDHD) index has been previously used to retrieve global clumping index maps from POLarization and Directionality of the Earth’s Reflectances (POLDER) data at ∼6km resolution and the Bidirectional Reflectance Distribution Function (BRDF) product from Moderate Resolution Imaging Spectroradiometer (MODIS) at 500m resolution. Most recently the algorithm was also applied with Multi-angle Imaging SpectroRadiometer (MISR) data at 275m resolution over selected areas. In this study for the first time we characterized and compared the three products over a set of sites representing diverse biomes and different canopy structures. The products were also directly validated with both in-situ vertical profiles and available seasonal trajectories of clumping index over several sites. We demonstrated that the vertical distribution of foliage and especially the effect of understory need to be taken into account while validating foliage clumping products from remote sensing products with values measured in the field. Satellite measurements responded to the structural effects near the top of canopies, while ground measurements may be biased by the lower vegetation layers. Additionally, caution should be taken regarding the misclassification in land cover maps as their errors can propagate into the foliage clumping maps. Our results indicate that MODIS data and MISR data, with 275m in particular, can provide good quality clumping index estimates at spatial scales pertinent for modeling local carbon and energy fluxes.

Variation of ice crystal size, shape, and asymmetry parameter in tops of tropical deep convective clouds

The variation of ice crystal properties in the tops of deep convective clouds off the north coast of Australia is analyzed. Cloud optical thickness, ice effective radius, aspect ratio of ice crystal components, crystal distortion parameter and asymmetry parameter are simultaneously retrieved from combined measurements of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Polarization and Directionality of the Earth's Reflectances (POLDER) satellite instruments. The data are divided into periods with alternating weak and strong convection. Mostly plate-like particle components with aspect ratios closer to unity and lower asymmetry parameters characterize strongly convective periods, while weakly convective periods generally show lower aspect ratios, relatively more column-like shapes and somewhat greater asymmetry parameters. Results for strongly convective periods show that, with increasing cloud top temperature, the distortion parameter generally decreases, while the asymmetry parameter and effective radius increase. For one of the strongly convective periods, the rate at which effective radii increase with cloud top temperature is more than double that of the other periods, while the temperature dependence of the other microphysical quantities for this period is substantially weaker. Atmospheric state analysis indicates that these differences are concurrent with differences in middle-to-upper tropospheric zonal wind shear. The observed variation of microphysical properties may have significant effects on the shortwave radiative fluxes and cloud absorption associated with deep convection. Additionally, MODIS collection 5 effective radii are estimated to be biased small with an artificially narrow range. Collection 6 products are expected to have less severe biases that depend on cloud top temperature and atmospheric conditions.

Effect of mineral dust aerosol aspect ratio on polarized reflectance

The effects of dust particle aspect ratios on single- and multiple-scattering processes are studied using the spheroidal model in order to obtain a better understanding of the radiance and polarization signals at the top of the atmosphere (TOA) under various dust-aerosol-loading conditions. Specifically, the impact of the particle aspect ratio on the polarization state of the TOA radiation field is demonstrated by comparing the normalized polarized radiances observed by the POLDER (POLarization and Directionality of the Earth׳s Reflectances) instrument on board the PARASOL (Polarisation et Anisotropie des Reflectances au sommet de l׳Atmosphère, couples avec un Satellite d׳Observation emportant un Lidar) satellite with the corresponding theoretical counterparts. Furthermore, presented are the aspect ratio values inferred from multi-angular polarized radiance measurements of Saharan and Asian dust by the POLDER/PARASOL.

Study of global cloud droplet number concentration with A-Train satellites

Abstract. Cloud droplet number concentration (CDNC) is an important microphysical property of liquid clouds that impacts radiative forcing, precipitation and is pivotal for understanding cloud–aerosol interactions. Current studies of this parameter at global scales with satellite observations are still challenging, especially because retrieval algorithms developed for passive sensors (i.e., MODerate Resolution Imaging Spectroradiometer (MODIS)/Aqua) have to rely on the assumption of cloud adiabatic growth. The active sensor component of the A-Train constellation (i.e., Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP)/CALIPSO) allows retrievals of CDNC from depolarization measurements at 532 nm. For such a case, the retrieval does not rely on the adiabatic assumption but instead must use a priori information on effective radius (re), which can be obtained from other passive sensors. In this paper, re values obtained from MODIS/Aqua and Polarization and Directionality of the Earth Reflectance (POLDER)/PARASOL (two passive sensors, components of the A-Train) are used to constrain CDNC retrievals from CALIOP. Intercomparison of CDNC products retrieved from MODIS and CALIOP sensors is performed, and the impacts of cloud entrainment, drizzling, horizontal heterogeneity and effective radius are discussed. By analyzing the strengths and weaknesses of different retrieval techniques, this study aims to better understand global CDNC distribution and eventually determine cloud structure and atmospheric conditions in which they develop. The improved understanding of CDNC can contribute to future studies of global cloud–aerosol–precipitation interaction and parameterization of clouds in global climate models (GCMs).

Direct radiative effect of the Russian wildfires and its impact on air temperature and atmospheric dynamics during August 2010

Abstract. In this study, we investigate the shortwave aerosol direct radiative forcing (ADRF) and its feedback on air temperature and atmospheric dynamics during a major fire event that occurred in Russia during August 2010. The methodology is based on an offline coupling between the CHIMERE chemistry-transport and the Weather Research and Forecasting (WRF) models. First, simulations for the period 5–12 August 2010 have been evaluated by using AERONET (AErosol RObotic NETwork) and satellite measurements of the POLarization and Directionality of the Earth's Reflectance (POLDER) and the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) sensors. During this period, elevated POLDER aerosol optical thickness (AOT) is found over a large part of eastern Europe, with values above 2 (at 550 nm) in the aerosol plume. According to CALIOP observations, particles remain confined to the first five kilometres of the atmospheric layer. Comparisons with satellite measurements show the ability of CHIMERE to reproduce the regional and vertical distribution of aerosols during their transport from the source region. Over Moscow, AERONET measurements indicate an important increase of AOT (340 nm) from 0.7 on 5 August to 2–4 between 6 and 10 August when the aerosol plume was advected over the city. Particles are mainly observed in the fine size mode (radius in the range 0.2–0.4 μm) and are characterized by elevated single-scattering albedo (SSA) (0.95–0.96 between 440 and 1020 nm). Comparisons of simulations with AERONET measurements show that aerosol physical–optical properties (size distribution, AOT, SSA) have been well simulated over Moscow in terms of intensity and/or spectral dependence. Secondly, modelled aerosol optical properties have been used as input in the radiative transfer code of WRF to evaluate their direct radiative impact. Simulations indicate a significant reduction of solar radiation at the ground (up to 80–150 W m−2 in diurnal averages over a large part of eastern Europe due to the presence of the aerosol plume. This ADRF causes an important reduction of the near-surface air temperature between 0.2 and 2.6° on a regional scale. Moscow has been affected by the aerosol plume, especially between 6 and 10 August. During this period, aerosol causes a significant reduction of surface shortwave radiation (up to 70–84 W m−2 in diurnal averages) with a moderate part (20–30%) due to solar absorption within the aerosol layer. The resulting feedbacks lead to a cooling of the air up to 1.6° at the surface and 0.1° at an altitude of 1500–2000 m (in diurnal averages), that contribute to stabilize the atmospheric boundary layer (ABL). Indeed, a reduction of the ABL height of 13 to 65% has been simulated during daytime in presence of aerosols. This decrease is the result of a lower air entrainment as the vertical wind speed in the ABL is shown to be reduced by 5 to 80% (at midday) when the feedback of the ADRF is taken into account. However, the ADRF is shown to have a lower impact on the horizontal wind speed, suggesting that the dilution of particles would be mainly affected by the weakening of the ABL development and associated vertical entrainment. Indeed, CHIMERE simulations driven by the WRF meteorological fields including this ADRF feedback result in a large increase in the modelled near-surface PM10 concentrations (up to 99%). This is due to their lower vertical dilution in the ABL, which tend to reduce model biases with the ground PM10 values observed over Moscow during this specific period.