Feiyue Mao

Simple multiscale algorithm for layer detection with lidar

Lidar is a powerful active remote sensing device used in the detection of the optical properties of aerosols and clouds. However, there are difficulties in layer detection and classification. Many previous methods are too complex for large dataset analysis or limited to data with too high a signal-to-noise ratio (SNR). In this study, a mechanism of multiscale detection and overdetection rejection is proposed based on a trend index function that we define. Finally, we classify layers based on connected layers employing a quantity known as the threshold of the peak-to-base ratio. We find good consistency between retrieved results employing our method and visual analysis. The testing of synthetic signals shows that our algorithm performs well with SNRs higher than 4. The results demonstrate that our algorithm is simple, practical, and suited to large dataset applications.

Comparison of simultaneous signals obtained from a dual-field-of-view lidar and its application to noise reduction based on empirical mode decomposition

Although the empirical mode decomposition (EMD) method is an effective tool for noise reduction in lidar signals, evaluating the effectiveness of the denoising method is difficult. A dual-field-of-view lidar for observing atmospheric aerosols is described. The backscattering signals obtained from two channels have different signal-to-noise ratios (SNRs). The performance of noise reduction can be investigated by comparing the high SNR signal and the denoised low SNR signal without a simulation experiment. With this approach, the signal and noise are extracted to one intrinsic mode function (IMF) by the EMD-based denoising; thus, the threshold method is applied to the IMFs. Experimental results show that the improved threshold method can effectively perform noise reduction while preserving useful sudden-change information.

Validation of VIIRS AOD through a Comparison with a Sun Photometer and MODIS AODs over Wuhan

Visible Infrared Imaging Radiometer Suite (VIIRS) is a next-generation polar-orbiting operational environmental sensor with a capability for global aerosol observations. A comprehensive validation of VIIRS products is significant for improving product quality, assessing environment quality for human life, and studying regional climate change. In this study, three-year (from 1 January 2014 to 31 December 2016) records of VIIRS Intermediate Product (IP) data and Moderate Resolution Imaging Spectroradiometer (MODIS) retrievals on aerosol optical depth (AOD) at 550 nm were evaluated by comparing them to ground sun photometer measurements over Wuhan. Results indicated that VIIRS IP retrievals were underestimated by 5% for the city. A comparison of VIIRS IP retrievals and ground sun photometer measurements showed a lower R2 of 0.55 (0.79 for Terra-MODIS and 0.76 for Aqua-MODIS), with only 52% of retrievals falling within the expected error range established by MODIS over land (i.e., ±(0.05 + 0.15AOD)). Bias analyses with different Angstrom exponents (AE) demonstrated that land aerosol model selection of the VIIRS retrieval over Wuhan was appropriate. However, the larger standard deviations (i.e., uncertainty) of VIIRS AODs than MODIS AODs could be attributed to the less robust retrieval algorithm. Monthly variations displayed largely underestimated AODs of VIIRS in winter, which could be caused by a large positive bias in surface reflectance estimation due to the sparse vegetation and greater surface brightness of Wuhan in this season. The spatial distribution of VIIRS and MODIS AOD observations revealed that the VIIRS IP AODs over high-pollution areas (AOD > 0.8) with sparse vegetation were underestimated by more than 20% in Wuhan, and 40% in several regions. Analysis of several clear rural areas (AOD < 0.2) with native vegetation indicated an overestimation of about 20% in the northeastern region of the city. These findings showed that the VIIRS IP AOD at 550 nm can provide a solid dataset with a high resolution (750 m) for quantitative scientific investigations and environmental monitoring over Wuhan. However, the performance of dark target algorithms in VIIRS was associated with aerosol types and ground vegetation conditions.

Anti-noise algorithm of lidar data retrieval by combining the ensemble Kalman filter and the Fernald method

The lidar signal-to-noise ratio decreases rapidly with an increase in range, which severely affects the retrieval accuracy and the effective measure range of a lidar based on the Fernald method. To avoid this issue, an alternative approach is proposed to simultaneously retrieve lidar data accurately and obtain a de-noised signal as a by-product by combining the ensemble Kalman filter and the Fernald method. The dynamical model of the new algorithm is generated according to the lidar equation to forecast backscatter coefficients. In this paper, we use the ensemble sizes as 60 and the factor δ(1/2) as 1.2 after being weighed against the accuracy and the time cost based on the performance function we define. The retrieval and de-noising results of both simulated and real signals demonstrate that our method is practical and effective. An extensive application of our method can be useful for the long-term determining of the aerosol optical properties.

Linear segmentation algorithm for detecting layer boundary with lidar

The automatic detection of aerosol- and cloud-layer boundary (base and top) is important in atmospheric lidar data processing, because the boundary information is not only useful for environment and climate studies, but can also be used as input for further data processing. Previous methods have demonstrated limitations in defining the base and top, window-size setting, and have neglected the in-layer attenuation. To overcome these limitations, we present a new layer detection scheme for up-looking lidars based on linear segmentation with a reasonable threshold setting, boundary selecting, and false positive removing strategies. Preliminary results from both real and simulated data show that this algorithm cannot only detect the layer-base as accurate as the simple multi-scale method, but can also detect the layer-top more accurately than that of the simple multi-scale method. Our algorithm can be directly applied to uncalibrated data without requiring any additional measurements or window size selections.

Measurements for profiles of aerosol extinction coeffcient, backscatter coeffcient, and lidar ratio over Wuhan in China with Raman/Mie lidar

The profiles of aerosol extinction, backscatter coeffcient, and lidar ratio in the lower troposphere over Wuhan are measured by a multi-channel Raman/Mie lidar. Using the lidar ratio retrieved by Raman scattering principle, the profiles of aerosol extinction and backscatter coefficients are also retrieved by Mie scattering signals, without a prior assumption about their relation in the traditional pure Mie signals data analyses. The observations by both Raman and Mie are in good agreement with each other. The high coherence shows that the system is reliable, and the Mie and Raman channels are in good adjustment and have the same field of view.

Macrophysical and optical properties of clouds over East Asia measured by CALIPSO

The macrophysical and optical properties of clouds over East Asia (18°N–54°N, 73°E–145°E) from 1 March 2007 to 28 February 2015 are investigated using Cloud-Aerosol Lidar with Orthogonal Polarization data. Data analysis determines the macrophysical properties, such as cloud fraction, cloud vertical structure, cloud top height (CTH), cloud base height, and cloud geometrical depth (CGD), as well as the optical properties of clouds. Statistical analysis shows that the annual cloud fractions of single-layer (SL), multilayer (ML), and total clouds over East Asia are 41.4 ± 0.7%, 25.1 ± 0.9%, and 66.5 ± 1.6%, respectively, with a slight interannual variation. The maximum annual cloud fraction that appeared over the Sichuan Basin is mainly attributed to unique occlusive topographic features. Moreover, the annual vertical distribution of cloud occurrence frequency over East Asia presents a multipeak structure. Furthermore, at a height below 2 km, cloud frequency distribution exhibits a large peak over the south, north, northeast, eastern sea, and East Asia, a small peak over the northwest, and the smallest peak over Tibet, which is mainly ascribed to terrain topographies. For the average uppermost CTH and cloud fraction, the same seasonal characteristic is demonstrated; that is, CTH and cloud fraction are highest in summer and lowest in winter, except in the northwest. This seasonal characteristic mainly results from the East Asian summer monsoon circulation. Overall, the annual cloud optical depths (CODs) of SL, ML, and total cloud over East Asia are 0.98 ± 0.02, 0.83 ± 0.09, and 1.81 ± 0.12, respectively. Moreover, the COD of each layer is mainly below 0.5 (52.3%), and the second peak of probability (10.4%) exists from 2.5 to 3.0. The two crests of probability are caused by clouds of different types. Overall, the annual cloud layer over East Asia mainly consists of cirrus (44.4%), which indicates that cirrus clouds play a leading role. Most geometrically thick clouds (CGD > 2 km) are cirrus and deep convective clouds. In general, annual CGD decreases with the increase in the number of ML cloud system layers, and CGD increases with the increase in altitude, whereas the COD of each layer exhibits a reverse trend.

Deriving Hourly PM2.5 Concentrations from Himawari-8 AODs over Beijing–Tianjin–Hebei in China

Monitoring fine particulate matter with diameters of less than 2.5 μm (PM2.5) is a critical endeavor in the Beijing–Tianjin–Hebei (BTH) region, which is one of the most polluted areas in China. Polar orbit satellites are limited by observation frequency, which is insufficient for understanding PM2.5 evolution. As a geostationary satellite, Himawari-8 can obtain hourly optical depths (AODs) and overcome the estimated PM2.5 concentrations with low time resolution. In this study, the evaluation of Himawari-8 AODs by comparing with Aerosol Robotic Network (AERONET) measurements showed Himawari-8 retrievals (Level 3) with a mild underestimate of about −0.06 and approximately 57% of AODs falling within the expected error established by the Moderate-resolution Imaging Spectroradiometer (MODIS) (±(0.05 + 0.15AOD)). Furthermore, the improved linear mixed-effect model was proposed to derive the surface hourly PM2.5 from Himawari-8 AODs from July 2015 to March 2017. The estimated hourly PM2.5 concentrations agreed well with the surface PM2.5 measurements with high R2 (0.86) and low RMSE (24.5 μg/m3). The average estimated PM2.5 in the BTH region during the study time range was about 55 μg/m3. The estimated hourly PM2.5 concentrations ranged extensively from 35.2 ± 26.9 μg/m3 (1600 local time) to 65.5 ± 54.6 μg/m3 (1100 local time) at different hours.

Measurement and Study of Lidar Ratio by Using a Raman Lidar in Central China

We comprehensively evaluated particle lidar ratios (i.e., particle extinction to backscatter ratio) at 532 nm over Wuhan in Central China by using a Raman lidar from July 2013 to May 2015. We utilized the Raman lidar data to obtain homogeneous aerosol lidar ratios near the surface through the Raman method during no-rain nights. The lidar ratios were approximately 57 ± 7 sr, 50 ± 5 sr, and 22 ± 4 sr under the three cases with obviously different pollution levels. The haze layer below 1.8 km has a large particle extinction coefficient (from 5.4e-4 m−1 to 1.6e-4 m−1) and particle backscatter coefficient (between 1.1e-05 m−1sr−1 and 1.7e-06 m−1sr−1) in the heavily polluted case. Furthermore, the particle lidar ratios varied according to season, especially between winter (57 ± 13 sr) and summer (33 ± 10 sr). The seasonal variation in lidar ratios at Wuhan suggests that the East Asian monsoon significantly affects the primary aerosol types and aerosol optical properties in this region. The relationships between particle lidar ratios and wind indicate that large lidar ratio values correspond well with weak winds and strong northerly winds, whereas significantly low lidar ratio values are associated with prevailing southwesterly and southerly wind.

Investigating the impact of haze on MODIS cloud detection

The cloud detection algorithm for passive sensors is usually based on a fuzzy logic system with thresholds determined from previous observations. In recent years, haze and high aerosol concentrations with high aerosol optical depth (AOD) occur frequently in China and may critically impact the accuracy of the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud detection. Thus, we comprehensively explore this impact by comparing the results from MODIS/Aqua (passive sensor), Cloud-Aerosol Lidar with Orthogonal Polarization/CALIPSO (lidar sensor), and Cloud Profiling Radar/CloudSat (microwave sensor) of the A-Train suite of instruments using an averaged AOD as an index for an aerosol concentration value. Case studies concerning the comparison of the three sensors indicate that MODIS cloud detection is reduced during haze events. In addition, statistical studies show that an increase in AOD creates an increase in the percentage of uncertain flags and a decrease in hit rate, a consistency index between consecutive sets of cloud retrievals. On average, AOD values lower than 0.1 give hit rate values up to 80.0% and uncertainty values lower than 16.8%, while AOD values greater than 1.0 reduce the hit rate below to 66.6% and increase the percentage of uncertain flags up to 46.6%. Therefore, we can conclude that the ability of MODIS cloud detection is weakened by large concentrations of aerosols. This suggests that use of the MODIS cloud mask, and derived higher-level products, in situations with haze requires caution. Further improvement of this retrieval algorithm is desired as haze studies based on MODIS products are of great interest in a number of related fields.