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The absorption Ångström exponent (AAE)-based methods are widely used to estimate brown carbon (BrC) absorption, and the estimated BrC absorption can be significantly different from 0 even for pure black carbon (BC). However, few studies have systematically quantified the effects of BC microphysical properties. Moreover, it is still unclear under which conditions the AAE-based method is applicable. In this work, we used BC models partially coated with non-absorbing materials to calculate the total absorption. Since the total absorption is entirely from BC, the estimated BrC absorption should be 0 if the retrieval methods are accurate. Thus, the estimated BrC absorption (ABSBrC) should be the absorption from BC that is incorrectly attributed to BrC. The results show that a BC AAE of 1 can generally provide reasonable estimates for freshly emitted BC, since at this time ABSBrC is generally in the range of -3 % to 4.5 %. However, when BC aerosols are aged, ABSBrC of about 35 % could be observed. The WDA method does not necessarily improve the estimates, sometimes a negative ABSBrC of -40 % can be found for partially coated BC. By combining simulations of a global chemical transport model, this work also quantified the effects of BC microphysical properties on BrC global optical absorption aerosol depth (AAOD) estimates. The AAE = 1 method could sometimes lead to a misassigned global mean AAOD of about -0.4 – 0.5 × 10-3 if BC aerosols have a complex morphology, leading to a global mean direct radiation factor (DRF) of about -0.068 ± 0.0172 to +0.085 ± 0.0215 W/m2 from BC, which is incorrectly assigned to BrC. The WDA method does not necessarily improve the estimates. In our cases, the WDA methods based on the spherical models can lead to a range of about -0.9 – 0.05 × 10-3 of misassigned AAOD, which could lead to a global mean DRF error range of -0.153 ± 0.0387 to +0.0085 ± 0.0022 W/m2. At the regional scale, the AAE = 1 method in East Asia sometimes leads to a distributed AAOD of over 3 × 10-3, resulting in a BC DRF of about +0.51 ± 0.129 W/m2, which is incorrectly attributed to BrC. Mie theory-based WDA methods would lead to an estimated AAOD error of more than 6 × 10-3 in some regions (e.g., East Asia), resulting in an estimated misattributed DRF of +1.0 ± 0.258 W/m2.

Permanent magnet synchronous motors (PMSM) show chaotic phenomena under certain working conditions. In this paper, both fractional order (FO) robust control via single-variable feedback and FO robust synchronization via linear feedback are achieved by the use of the FO-PMSM chaotic model with uncertainties. The proposed method decomposes the nonlinear fractional-order system (FOS) into a linear component and a nonlinear component. The linear component represents the nominal system, and the nonlinear component stands for the perturbation of the nominal system. The robustness of the system is investigated based on the study of the parameters or structural uncertainties of the nonlinear component. The FO Lyapunov matrix differential equation stability theory is proposed to obtain robust stability results of linear FOS with the order of 0

International audience; Polarimetry is one of the most promising types of remote sensing for improved characterization of atmospheric aerosol. Indeed, aerosol particles constitute a highly variable atmospheric component characterized by a large number of parameters describing particle sizes, morphologies (including shape and internal structure), absorption and scattering properties, amounts, horizontal and vertical distribution, etc. Reliable monitoring of all these parameters is very challenging, and therefore the aerosol effects on climate and environment are considered to be among the most uncertain factors in climate and environmental research. In this regard, observations that provide both the angular distribution of the scattered atmospheric radiation as well as its polarization state at multiple wavelengths covering the UV-SWIR spectral range carry substantial implicit information on the atmospheric composition. Therefore, high expectations in improving aerosol characterization are associated with detailed passive photopolarimetric observations. 475 The critical need to use space-borne polarimetry for global accurate monitoring of detailed aerosol properties was first articulated in the late 1980s and early 1990s. By now, several orbital instruments have already provided polarization observations from space, and a number of advanced missions are scheduled for launch in the coming years by international and national space agencies. The first and most extensive record of polarimetric imagery was provided by POLDER-I, POLDER-II, and POLDER/PARASOL multi-angle multi-spectral polarization sensors. Polarimetric observations with the POLDER-like design intended for collecting extensive multi-angular multi-spectral measurements will be provided by several instruments, such as the MAI/TG-2, CAPI/TanSat, and DPC/GF-5 sensors recently launched by the Chinese Space Agency. Instruments such as the 3MI/MetOp-SG, MAIA, SpexOne and HARP2 on PACE, POSP, SMAC, PCF, DPC-Lidar, ScanPol and MSIP/Aerosol-UA, MAP/Copernicus CO2 Monitoring, etc. are planned to be launched by different space agencies in the coming decade. The concepts of these future instruments, their technical designs, and the accompanying algorithm development have been tested intensively and analyzed using diverse airborne prototypes. Certain polarimetric capabilities have also been implemented in such satellite sensors as GOME-2/MetOp and SGLI/GCOM-C. A number of aerosol retrieval products have been developed based on the available measurements and successfully used for different scientific applications. However, the completeness and accuracy of aerosol data operationally derived from polarimetry do not yet appear to have reached the accuracy levels implied by theoretical sensitivity studies that analyzed the potential information content of satellite po-larimetry. As a result, the dataset provided by MODIS is still most frequently used by the scientific community , yet this sensor has neither polarimetric nor multi-angular capabilities. Admittedly polarimetric multi-angular observations are highly complex and have extra sensitivities to aerosol particle morphology, vertical variability of aerosol properties, polarization of surface reflectance, etc. As such, they necessitate state-of-the-art forward modeling based on first-principles physics which remains rare, and conventional retrieval approaches based on look-up tables turn out to be unsuitable to fully exploit the information implicit in the measurements. Several new-generation retrieval approaches have recently been proposed to address these challenges. These methods use improved forward modeling of atmospheric (polarized) radiances and implement a search in the continuous space of solutions using rigorous statistically optimized inversions. Such techniques provide more accurate retrievals of the main aerosol parameters such as aerosol optical thickness and yield additional parameters such as aerosol absorption. However, the operational implementation of advanced retrieval approaches generally requires a significant extra effort, and the forward-modeling part of such retrievals still needs to be substantially improved. Ground-based passive polarimetric measurements have also been evolving over the past decade. Although polarimetry helps improve aerosol characterization, especially of the fine aerosol mode, the operators of major observational networks such as AERONET remain reluctant to include polarimetric measurements as part of routine retrievals owing to their high complexity and notable increase in effort required to acquire and interpret polarization data. In addition to remote-sensing observations, polarimetric characteristics of aerosol scattering have been measured in situ as well as in the laboratory using polar nephelometers. Such measurements constitute direct observations of single scattering with no contributions from multiple scattering effects and therefore provide unique data for the validation of aerosol optical models and retrieval concepts. This article overviews the above-mentioned polarimetric observations, their history and expected developments , and the state of resulting aerosol products. It also discusses the main achievements and challenges in the exploitation of polarimetry for the improved characterization of atmospheric aerosols.

International audience

The Directional Polarimetric Camera (DPC) is the first Chinese multi-angle polarized earth observation satellite sensor, which has been launched onboard the GaoFen-5 Satellite in Chinese High-resolution Earth Observation Program. GaoFen-5 runs in a sun-synchronous orbit with the 2-days revisiting period. DPC employed a charge coupled device detection unit, and can realize spatial resolution of 3.3 km under a swath width of 1850 km. Moreover, DPC has 3 polarized channels together with 5 non-polarized bands, and is able to obtain at least 9 viewing angles by continuously capturing series images over the same target on orbit. Based on the Directional Polarization Camera (DPC) onboard GF-5 satellite, the first global high-resolution (3.3 km) map of fine-mode aerosol optical depth (AODf) over land has been obtained together by Aerospace Information Institute, Chinese Academy of Sciences, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences (the manufacturer of DPC sensor) and other institutes. This AODf remote sensing observation dataset has the highest spatial resolution in the world. It can reflect the spatial information of major air pollutants (PM2.5, etc.) and provide critical basic products for "decryption" of global haze distribution., Comment: 15 pages, 5 figures

Data from the directional polarimetric camera (DPC) instrument onboard Chinese Gaofen-5 satellite dedicated to aerosol monitoring have been available recently. By measuring the spectral, angular and polarization properties of the radiance at the top of atmosphere (TOA), a DPC provides the aerosol optical depths (AODs) as well as partial microphysical aerosol properties. In order to evaluate the capability and the retrieval uncertainty of DPC sensor systematically, the information content and a posteriori error analysis are applied to the synthetic data of DPC multi-angle observation in this paper, which inherits from the optimal estimate theoretical framework. The forward simulation is conducted by the unified linearized vector radiative transfer model (UNL-VRTM), and the Jacobians of four Stokes elements with respect to aerosol and surface model parameters can be obtained simultaneously. Firstly, the error influences of surface parameter on the TOA measurements are simulated. The results indicate that a 10% relative error of parameter k1 in the improved BRDF model results in about 4.65% error of the TOA reflectance, while the error of TOA polarized reflectance caused by the same error of parameter C in BPDF model is negligibly small. Secondly, the multi-angle dependence of total information content in DPC measurements is investigated. It is shown that the information content increases significantly with the number of viewing angles, especially for the measurements of the first 9 angles. The DPC multi-angle observation can provide extra 5 degrees of freedom for signal (DFS) for the retrieval of aerosol and surface parameters, in which the retrieval of aerosol parameters is more sensitive to observation geometries than the retrieval of surface parameters in most cases. In addition, the total aerosol DFS increases with the range extension of scattering angle under the same number of viewing angles. After that, the DFS of each retrieved aerosol and surface parameter are given. For the aerosols, the volume concentration, real-part refractive index and effective radius show a high DFS (greater than 0.8). For the surfaces, the mean DFS of each parameter is greater than 0.5, which indicates the well capability of DPC in the surface retrieval. Finally, the a posteriori error of each aerosol, surface parameter and corresponding vary with the number of viewing angles, and the observation error and aerosol model error are discussed. The a posteriori error decrease significantly with the number of viewing angles, and the influence of the aerosol model error on the a posteriori error is not remarkable. In general, the observation error is the main influence factor on the uncertainty of the inversion results.

The ground-based polarization remote sensing adds the polarization dimension information to traditional intensity detection, which provides a new method to detect atmospheric aerosols properties. In this paper, the polarization measurements achieved by a new multi-wavelength sun photometer, CE318-DP, are used for the ground-based remote sensing of atmospheric aerosols. In addition, a polarized vector radiative transfer model is introduced to simulate the DOLP (Degree Of Linear Polarization) under different sky conditions. At last, the correlative analysis between mass density of PM2.5 and multi-wavelength and multi-angular DOLP is carried out. The result shows that DOLP has a high correlation with mass density of PM2.5, R2>0.85. As a consequence, this work provides a new method to estimate the mass density of PM2.5 by using the comprehensive network of ground-based sun photometer.

The Chinese environmental satellites HJ1 A and B can play a significant role in the aerosol retrieval due to their high spatial and temporal resolution. The current Aerosol Optical Depth (AOD) retrieval methods from HJ1-CCD are almost based on the LUT (Look-Up Table), by selecting the best fitting result to determine the AOD. However, aerosol model selection has an important impact on the retrieval results when creating the lookup table; inappropriate choice of aerosol model will significantly affect the accuracy and applicability of the method. This paper determined the local aerosol physical properties (such as complex refractive index, and size distribution) based on the observational data, thus we defined the aerosol type and retrieved the AOD of the local aerosol. Furthermore we compared the results retrieved from the measurement aerosol model with those retrieved from the inherent aerosol model in the radiative transfer model and then evaluate its effect on the aerosol type.

Support vector machine (SVM), as a novel and powerful machine learning tool, can be used for the prediction of PM10 and PM2.5 (particulate matter less or equal than 10 and 2.5 micrometer) in the atmosphere. This paper describes the development of a successive over relaxation support vector regress (SOR-SVR) model for the PM10 and PM2.5 prediction, based on the daily average aerosol optical depth (AOD) and meteorological parameters (atmospheric pressure, relative humidity, air temperature, wind speed), which were all measured in Beijing during the year of 2010–2012. The Gaussian kernel function, as well as the k-fold crosses validation and grid search method, are used in SVR model to obtain the optimal parameters to get a better generalization capability. The result shows that predicted values by the SOR-SVR model agree well with the actual data and have a good generalization ability to predict PM10 and PM2.5. In addition, AOD plays an important role in predicting particulate matter with SVR model, which should be included in the prediction model. If only considering the meteorological parameters and eliminating AOD from the SVR model, the prediction results of predict particulate matter will be not satisfying.

Due to lack of the middle infrared wavelength, it is difficult to employ the classic dark target method to retrieve aerosol optical depth (AOD) from Chinese Environmental satellite HJ1-CCD data. Therefore, focusing on extracting weak information from mixed satellite signals of atmosphere and land surface, we developed the Multi-wavelength, Multi-sensor, Multi-day (3M) approach in order to utilize maximally the observation information, and with the consideration of a prior knowledge, e.g. the surface property changes quickly with location but slowly with time. We present the AOD retrieval algorithm based on HJ1-CCD blue and green bands, based on the look-up table approach constructed using 6S radiative transfer model to provide the simultaneous determination of AOD and the ground reflectance. The aerosol and particle size information obtained from ground-based sun-sky radiometer are then used to estimate PM2.5 on the surface level, while air humidity and height of planetary boundary layer from reanalysis data are employed to improve the correlation between AOD and PM2.5. Validation of the retrieval results with different spatial resolutions (300, 500 and 1000 m), are also performed by comparison with ground-based measurements at three sites of North China regions.