Weizhen Hou

Retrieval of Aerosol Fine-Mode Fraction from Intensity and Polarization Measurements by PARASOL over East Asia

The fine-mode fraction (FMF) of aerosol optical depth (AOD) is a key optical parameter that represents the proportion of fine particles relative to total aerosols in the atmosphere. However, in comparison to ground-based measurements, the FMF is still difficult to retrieve from satellite observations, as attempted by a Moderate-resolution Imaging Spectroradiometer (MODIS) algorithm. In this paper, we introduce the retrieval of FMF based on Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar (PARASOL) data. This method takes advantage of the coincident multi-angle intensity and polarization measurements from a single satellite platform. In our method, we use intensity measurements to retrieve the total AOD and polarization measurements to retrieve the fine-mode AOD. The FMF is then calculated as the ratio of the retrieved fine-mode AOD to the total AOD. The important processes in our method include the estimation of the surface intensity and polarized reflectance by using two semi-empirical models, and the building of two sets of aerosol retrieval lookup tables for the intensity and polarized measurements via the 6SV radiative transfer code. We apply this method to East Asia, and comparisons of the retrieved FMFs for the Beijing, Xianghe and Seoul_SNU sites with those of the Aerosol Robotic Network (AERONET) ground-based observations produce correlation coefficients (R2) of 0.838, 0.818, and 0.877, respectively. However, the comparison results are relatively poor (R2 = 0.537) in low-AOD areas, such as the Osaka site, due to the low signal-to-noise ratio of the satellite observations.

Improving Remote Sensing of Aerosol Optical Depth over Land by Polarimetric Measurements at 1640 nm: Airborne Test in North China

An improved aerosol retrieval algorithm based on the Advanced Multi-angular Polarized Radiometer (AMPR) is presented to illustrate the utility of additional 1640-nm observations for measuring aerosol optical depth (AOD) over land using look-up table approaches. Spectral neutrality of the polarized surface reflectance over visible to short-wavelength infrared bands is verified, and the 1640-nm measurements corrected for atmospheric effects are used to estimate the polarized surface reflectance at shorter wavelengths. The AMPR measurements over the Beijing-Tianjin-Hebei region in north China reveal that the polarized surface reflectance of 670, 865 and 1640 nm are highly correlated with correlation slopes close to one (0.985 and 1.03) when the scattering angle is less than 145°. The 1640-nm measurements are then employed to estimate polarized surface reflectance at shorter wavelengths for each single viewing direction, which are then used to improve the retrieval of AOD over land. The comparison between AMPR retrievals and ground-based Sun-sky radiometer measurements during three experimental flights illustrates that this approach retrieves AOD at 865 nm with uncertainties ranging from 0.01 to 0.06, while AOD varies from 0.05 to 0.17.

High temporal resolution aerosol retrieval using Geostationary Ocean Color Imager: application and initial validation

Abstract The Geostationary Ocean Color Imager (GOCI) provides multispectral imagery of the East Asia region hourly from 9:00 to 16:00 local time ( GMT + 9 ) and collects multispectral imagery at eight spectral channels (412, 443, 490, 555, 660, 680, 745, and 865 nm) with a spatial resolution of 500 m. Thus, this technology brings significant advantages to high temporal resolution environmental monitoring. We present the retrieval of aerosol optical depth (AOD) in northern China based on GOCI data. Cross-calibration was performed against Moderate Resolution Imaging Spectrometer (MODIS) data in order to correct the land calibration bias of the GOCI sensor. AOD retrievals were then accomplished using a look-up table (LUT) strategy with assumptions of a quickly varying aerosol and a slowly varying surface with time. The AOD retrieval algorithm calculates AOD by minimizing the surface reflectance variations of a series of observations in a short period of time, such as several days. The monitoring of hourly AOD variations was implemented, and the retrieved AOD agreed well with AErosol RObotic NETwork (AERONET) ground-based measurements with a good R 2 of approximately 0.74 at validation sites at the cities of Beijing and Xianghe, although intercept bias may be high in specific cases. The comparisons with MODIS products also show a good agreement in AOD spatial distribution. This work suggests that GOCI imagery can provide high temporal resolution monitoring of atmospheric aerosols over land, which is of great interest in climate change studies and environmental monitoring.

Retrieval of Aerosol Optical Depth Using the Empirical Orthogonal Functions (EOFs) Based on PARASOL Multi-Angle Intensity Data

Aerosol optical depth (AOD) is a widely used aerosol optical parameter in atmospheric physics. To obtain this parameter precisely, many institutions plan to launch satellites with multi-angle measurement sensors, but one important step in aerosol retrieval, the estimation of surface reflectance, is still a pressing issue. This paper presents an AOD retrieval method based on the multi-angle intensity data from the Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar (PARASOL) platform using empirical orthogonal functions (EOFs), which can be universally applied to multi-angle observations. The function of EOFs in this study is to estimate surface intensity contributions, associated with aerosol lookup tables (LUTs), so that the retrieval of AOD can be implemented. A comparison of the retrieved AODs for the Beijing, Xianghe, Taihu, and Hongkong_PolyU sites with those from the Aerosol Robotic Network (AERONET) ground-based observations produced high correlation coefficients (r) of 0.892, 0.915, 0.831, and 0.897, respectively, while the corresponding root mean square errors (RMSEs) are 0.095, 0.093, 0.099, and 0.076, respectively.

Study the polarization and depolarization properties of atmospheric aerosol multiple scattering based on the successive order of scattering

With the polynomial fitting of source function in each order of scattering calculation and the effective process of aerosol forward scattering peak, a polarized radiative transfer (RT) model based on the improved successive order of scattering (SOS) method has been developed to solve the vector radiative transfer equation. By our RT model, not only the total Stokes parameters [I, Q, U] measured by the satellite (aircraft) and ground-based sensors with linear polarization could be approximately simulated, but also the results of parameters for each scattering order event could conveniently calculated, which are very helpful to study the polarization properties for the atmospheric aerosol multiple scattering. In this study, the synchronous measured aerosol results including aerosol optical depth, complex refractive index and particle size distribution from AERONET under different air conditions, are considered as the input parameters for the successive scattering simulations. With our polarized RT model and the Mie code combined, the Stokes parameters as well as the degree of polarization for each scattering order are simulated and presented; meanwhile, the polarization (depolarization) properties of multiply scattering are preliminary analyzed and compared with different air quality (clear and pollution). Those results could provide a significant support for the further research of polarized aerosol remote sensing and inversion. Polarization properties of aerosol, successive order of scattering, vector radiative transfer equation, polynomial fitting of source function , multiply scattering

Improving Remote Sensing of Aerosol Microphysical Properties by Near‐Infrared Polarimetric Measurements Over Vegetated Land: Information Content Analysis

While polarimetric measurements contain valuable information regarding aerosol microphysical properties, polarization data in the near-infrared (NIR) bands have not been widely utilized. This paper evaluates whether the aerosol property information contents from single-viewing satellite polarimetric measurements at 1,610 and 2,250 nm can be used to improve the retrieval of aerosol parameters over vegetated land, in combination with shorter-wavelength bands (490, 670, and 870 nm). The a priori information and errors for the analysis are derived by assuming that the surface reflectance at visible wavelengths can be derived from the top of atmosphere at 2,250 nm. The information content in the synthetic data set is investigated for 10 aerosol parameters characterizing the columnar aerosol volumes (V f 0 and V0), particle size distributions (r f eff, v f eff, r c eff, and v c eff), and refractive indices (m f r , m f i , m c r , and m c i ) for the fineand coarse-mode aerosol models, respectively, and one parameter C characterizing the surface polarization. The results indicate that the degrees of freedom for signal can be increased by at least 2 with the addition of NIR measurements and that one to three additional parameters could be further retrieved with significantly decreased uncertainties. In addition, the 1,610 nm band is necessary for the simultaneous retrieval of V f 0, m f r , and r f eff for the fine mode dominated aerosols, while the 1,610 and 2,250 nm bands are both indispensable for retrieving V f 0, V c 0, m c r , r f eff , and r c eff in tandem for the coarse mode dominated aerosols. The analysis also reveals that C could be further retrieved by including scalar radiance and that measurement errors have significantly larger influences on the retrieval uncertainties than model errors.

Retrieval of aerosol optical thickness over land from airborne polarized measurements in Tianjin and Tangshan

New developed sensor was called Atmosphere Multi-angle Polarization Radiometer (AMPR). It provides airborne multi-spectral, multi-angular and polarized measurements. Based on the measurements, a method to retrieve aerosol optical thickness (AOT) was developed. To reduce the ambiguity in retrieval algorithm, the key characteristics of aerosol model over East Asia are constrained. Initial surface reflectance was estimated from measurements at 1640 nm. With iteration the surface polarized reflectance tends to the real value together with AOT. Retrieved cases were selected from measurements in Tianjin. Validation between AOTs from AMPR and CE318 is encouraging. The AOTs along the track shows reasonable temporal and spatial variation.

Comparison between dust and haze aerosol properties of the 2015 Spring in Beijing using ground-based sun photometer and lidar

Because of the special geographical location and meteorology conditions, Beijing is a dust-prone city for a long history especially in the spring season. But these years, the most common air pollution in Beijing is haze which is mainly composed of fine particles. The dust is transported from north (Inner Mongolia province and Mongolia country), and the haze is transported from south (Hebei, Shandong and other provinces). Generally, the severities of dust and haze are opposite for the different weather causes. On March 28, 2015, the spring coming earlier for the relatively high temperature, a severe dust weather process happened suddenly in the long-term hazy days. In this dust process, the PM10 concentration was more than 1000μg/m3; the visibility was no more than 3km; and the aerosol optical depth was more than 2, which reached a severe pollution level. We used ground-based remote sensing instruments to observing the heavy dust episode. The data of two conditions were analyzed optical and microphysical parameters contrastively including the Aerosol Optical Depth, Single Scattering Albedo, Size distribution, Complex refractive index, Fine-mode Fraction. The vertical distribution characteristics were also analyzed by the lidar measurements. The results show that big differences between the dust and haze aerosol properties. But we found that fine mode particle pollution was assignable in the dust pollution weather in 2015 spring in Beijing. Our preliminary inference is that this dust episode was not only caused by transportation, but also contributed by the local raise dust.

A comparison of two stream approximation for the discrete ordinate method and the SOS method

The two-stream discrete ordinates method and the two-stream successive orders of scattering method are compared, and the key features of two methods are discussed. Based on the convergence characteristics of successive scattering, we use a semi-empirical model to improve the computing efficiency in the SOS method. Using the delta-M method, we investigate the effect of the two two-stream method for non-absorbing and absorbing case respectively. The 32-stream DISORT is used as the benchmark for assessments of the relative accuracy of the two methods investigated. With the comparisons for the accuracy of flux, the results of the two two-stream methods are almost the same in general and the absorbing media lead to larger errors of flux compared with that of the non-absorbing case.

A parallel method of atmospheric correction for multispectral high spatial resolution remote sensing images

The remote sensing image is usually polluted by atmosphere components especially like aerosol particles. For the quantitative remote sensing applications, the radiative transfer model based atmospheric correction is used to get the reflectance with decoupling the atmosphere and surface by consuming a long computational time. The parallel computing is a solution method for the temporal acceleration. The parallel strategy which uses multi-CPU to work simultaneously is designed to do atmospheric correction for a multispectral remote sensing image. The parallel framework’s flow and the main parallel body of atmospheric correction are described. Then, the multispectral remote sensing image of the Chinese Gaofen-2 satellite is used to test the acceleration efficiency. When the CPU number is increasing from 1 to 8, the computational speed is also increasing. The biggest acceleration rate is 6.5. Under the 8 CPU working mode, the whole image atmospheric correction costs 4 minutes.