Huazhe Shang

Development of a daytime cloud and haze detection algorithm for Himawari‐8 satellite measurements over central and eastern China

Cloud detection by passive satellite sensors is very challenging in hazy weather over China because the reflective characteristics of haze and clouds are very similar. Consequently, hazy areas tend to be mistaken as cloudy or clear areas by current cloud mask algorithms. The Advanced Himawari Imager (AHI) aboard Himawari-8 is a multispectral earth observation sensor with high temporal and spatial resolutions. A cloud and haze detection algorithm for AHI measurements is urgently needed for monitoring atmospheric pollution and its transport over China. This study presents the new Himawari-8 Cloud and Haze Mask (HCHM) algorithm that classifies image pixels from central and eastern China into one of three categories: clear, cloudy or hazy. Based on the observations that haze occurs near the ground and accumulates in low-elevation plains and basins while clouds form at high altitudes, the proposed HCHM algorithm incorporates altitude information to adjust the thresholds used in the selected threshold tests to separate haze and cloud pixels. We find that combining auxiliary digital elevation model (DEM) data with traditional indicators, such as the R0.86/R0.64, R0.86/R1.6 and BT11-BT3.9, improves the accuracy of cloud and haze discrimination. The HCHM algorithm is applied to Himawari-8 observations from Aug. 2015, Nov. 2015, Jan. 2016 and May 2016 and validated against the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) vertical feature mask (VFM) results. The validation shows that the average leakage rate (LR), false alarm rate (FAR) and haze missing rate (HMR) of the HCHM algorithm are 3.95%, 5.88% and 4.17%, respectively.

Synergetic Use of MODIS Cloud Parameters for Distinguishing High Aerosol Loadings From Clouds Over the North China Plain

The Moderate Resolution Imaging Spectroradiometer (MODIS) standard cloud product is prone to misidentifying areas that are heavily polluted with aerosols as cloudy regions over the North China Plain (NCP) and to retrieving aerosol characteristics as cloud parameters. Based on the differences in physical and optical properties between aerosols and clouds, we propose a new approach to distinguish aerosol-laden areas from cloudy regions using MODIS level 2 cloud properties (e.g., cloud fraction, cloud phase, and cloud top pressure products). The approach was applied to 22 haze-fog cases that occurred in the 2011 and 2012 winters over the NCP. The aerosol identification results were then compared with MODIS-flagged aerosol areas, which were inferred from the noncloud obstruction flag and the suspended dust flag in the MODIS cloud mask product. The results indicated that approximately 60% of the MODIS-flagged aerosol areas were correctly identified using our approach. Among the analyzed cases, two cases exhibited substantial differences; the aerosol areas detected using the newly proposed method were approximately 2.5 times larger than that of the MODIS-flagged area. Further comparisons with aerosol distributions along the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) orbit for the two cases demonstrated that approximately 60%-80% of the CALIOP observed aerosols were identified using our method, while less than 10% of the CALIOP observed aerosols were consistent with the MODIS flagging.

Diurnal cycle and seasonal variation of cloud cover over the Tibetan Plateau as determined from Himawari-8 new-generation geostationary satellite data

Analysis of cloud cover and its diurnal variation over the Tibetan Plateau (TP) is highly reliant on satellite data; however, the accuracy of cloud detection from both polar-orbiting and geostationary satellites over this area remains unclear. The new-generation geostationary Himawari-8 satellites provide high-resolution spatial and temporal information about clouds over the Tibetan Plateau. In this study, the cloud detection of MODIS and AHI is investigated and validated against CALIPSO measurements. For AHI and MODIS, the false alarm rate of AHI and MODIS in cloud identification over the TP was 7.51% and 1.94%, respectively, and the cloud hit rate was 73.55% and 80.15%, respectively. Using hourly cloud-cover data from the Himawari-8 satellites, we found that at the monthly scale, the diurnal cycle in cloud cover over the TP tends to increase throughout the day, with the minimum and maximum cloud fractions occurring at 10:00 a.m. and 18:00 p.m. local time. Due to the limited time resolution of polar-orbiting satellites, the underestimation of MODIS daytime average cloud cover is approximately 4.00% at the annual scale, with larger biases during the spring (5.40%) and winter (5.90%).

Ground-Level NO2 Concentrations over China Inferred from the Satellite OMI and CMAQ Model Simulations

In the past decades, continuous efforts have been made at a national level to reduce Nitrogen Dioxide (NO2) emissions in the atmosphere over China. However, public concern and related research mostly deal with tropospheric NO2 columns rather than ground-level NO2 concentrations, but actually ground-level NO2 concentrations are more closely related to anthropogenic emissions, and directly affect human health. This paper presents one method to derive the ground-level NO2 concentrations using the total column of NO2 observed from the Ozone Monitoring Instrument (OMI) and the simulations from the Community Multi-scale Air Quality (CMAQ) model in China. One year’s worth of data from 2014 was processed and the results compared with ground-based NO2 measurements from a network of China’s National Environmental Monitoring Centre (CNEMC). The standard deviation between ground-level NO2 concentrations over China, the CMAQ simulated measurements and in-situ measurements by CNEMC for January was 21.79 μg/m3, which was improved to a standard deviation of 18.90 μg/m3 between our method and CNEMC data. Correlation coefficients between the CMAQ simulation and in-situ measurements were 0.75 for January and July, and they were improved to 0.80 and 0.78, respectively. Our results revealed that the method presented in this paper can be used to better measure ground-level NO2 concentrations over China.

Assessment and Improvement of MISR Angstrom Exponent and Single-Scattering Albedo Products Using AERONET Data in China

Mapping the components, size, and absorbing/scattering properties of particle pollution is of great interest in the environmental and public health fields. Although the Multi-angle Imaging SpectroRadiometer (MISR) can detect a greater number of aerosol microphysical properties than most other spaceborne sensors, the Angstrom exponent (AE) and single-scattering albedo (SSA) products are not widely utilized or as robust as the aerosol optical depth (AOD) product. This study focused on validating MISR AE and SSA data using AErosol RObotic NETwork (AERONET) data for China from 2004 to 2014. The national mean value of the MISR data (1.08) was 0.095 lower than that of the AERONET data. However, the MISR SSA average (0.99) was significantly higher than that of AERONET (0.89). In this study, we developed a method to improve the AE and SSA by narrowing the selection of MISR mixtures via the introduction of the following group thresholds obtained from an 11-year AERONET dataset: minimum and maximum values (for the method of MISR_Imp_All) and the top 10% and bottom 10% of the averaged values (for MISR_Imp_10%). Overall, our improved AE values were closer to the AERONET AE values, and additional samples (MISR_Imp_All: 28.04% and 64.72%, MISR_Imp_10%: 34.11% and 73.13%) had absolute differences of less than 0.1 and 0.3 (defined by the expected error tests, e.g., EE_0.1) compared with the original MISR product (18.46% and 50.23%). For the SSA product, our method also improved the mean, EE_0.05, and EE_0.1 from 0.99, 16.13%, and 56.45% (MISR original product) to 0.96, 40.32%, and 70.97% (MISR_Imp_All), and 0.94, 54.84%, and 90.32% (MISR_Imp_10%), respectively.

The effect of cloud optical thickness, ground surface albedo and above-cloud absorbing dust layer on the cloudbow structure

The cloudbow structure is directly related to the retrieval of cloud droplet size distribution (droplet effective radius and effective variance). This study investigated the effect of the cloud optical thickness, ground surface albedo and the above-cloud absorbing dust layer on the cloudbow structure based on the modeled airborne directional polarimetric camera (DPC) measurements, which are simulated in 670 nm using Mie scattering theory and the vector radiative transfer mode. It is found that the polarized reflectance increase as the increase of the cloud optical thickness (COT) and saturate when COT=10. The absorbing dust layers signal would cover the signal from the cloud layer as the aerosol optical thickness increased to 1. Additionally, the surface albedo has negligible effect on the cloudbow structure.

A new cloud mask algorithm used in aerosol retrieval over land for Suo-NPP VIIRS

Launching in October 2011, the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument has been stable operating for more than 5 years on board the Suomi National Polar-orbiting Partnership (S-NPP) spacecraft. Some researchers indicated that its aerosol optical thickness (AOT) product had larger biases than MODIS over land. Although the VIIRS-AOT algorithm is based on MODIS Dark-Target algorithm, some differs exist, including cloud mask. In considering the algorithm independence and identification complexity, we develop a new quick cloud mask algorithm for aerosol retrieval. Based on the spatial variability test inherent from MODIS, we add a expand test to remove the pixels mixed with cloud or effected by neighboring pixels. The results illustrate that this new test can screen out the confident cloudy pixels that VIIRS algorithm regard as clear sky. Throughout hundreds test in different weather conditions, the algorithm perform well.