# A Novel Algorithm of Haze Identification Based on FY3D/MERSI-II Remote Sensing Data
## 1 Introduction  
According to the explicit provisions of the World Meteorological Organization, haze is defined as a day when the average visibility is less than 10 km, the average daily relative humidity is less than 80%, and other atmospheric haze days that can lead to low visibility, such as precipitation, are excluded (WMO, 2005). Haze particles are mainly composed of extremely small particles suspended in the air, such as dust, sulfate and nitrate, which are drier than fog and wetter than dust [1]. Due to the influence of the energy consumption structure, the air pollution in China has evolved into regional and complex air pollution. In recent decades, continuous high-intensity atmospheric haze pollution has swept across central and eastern China, which has aroused the attention of domestic scholars from all walks of life [2; 3; 4]. Using the discrete ground stations that are distributed in a small area, continuous observations over a short period of time are feasible to analyze the reasons for the formation of special pollution processes and the changing trend of the pollutants. For example, the AOD increases persistently during the daytime, and the diurnal variation varies from about 15% in summer to about 45% in winter based on thirty-three months of aerosol data in Beijing [5]. In addition, a severe haze occurred on 20-27 September 2011 in the megacity of Beijing [6] and several serious haze events in Beijing between January 1 and 1 February 2013 have been observed [7]. Further, the North China Plain was analyzed by [PERSON] et al. (2014) [8] and biomass burning impacted the haze pollution in the Yangtze River Delta, China [9]. Previous studies have focused on monitoring the process of haze events and the variation characteristics of the haze formation mechanism; the distribution and variations of the haze process have seldom been analyzed over continuous spatio-temporal scales. Subsequently, there have been some studies that have discussed the spatio-temporal characteristics of haze by using the products retrieved from satellites. For example, the AOD datasets from the Collection 6 enhanced Deep Blue algorithm of MODIS aboard the Terra and Aqua satellites, the UV Aerosol Index from Ozone Monitoring Instrument (OMI) on the Aura satellite, the vertical detection of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and other data were jointly used to analyze the variation characteristics and occurrence frequency of regional haze when the AOD > 1.0, which basically stabilized at 70-90 days, which was one year after 2009 [10; 11; 12]. Although the AOD data retrieved by satellites is used to analyze the characteristics of haze variation, some cloud-contaminated hazy areas have no valid AOD retrievals, which means not all of the aerosol retrieval algorithms are suitable over the heavily polluted regions [13]. Cloud detection algorithms based on satellite remote sensing data are generally designed to generate a cloud mask as an the input for the downstream cloud-relevant, environmental and continental elements retrieval [14]. Many studies revealed that the operational cloud detection products developed by MODIS, VIIRS and AHI lack the ability to classify haze aerosols, differentiate the thick aerosol layer from the cloudy pixels and they include certain uncertainties in desert and non-desert transition areas [7; 15; 16]. In the MOD/MYD4 aerosol retrieval algorithms, fast cloud detection has been designed to combine the standard derivation of the reflectance at 0.47 \(\upmu\)m and 1.38 \(\upmu\)m to distinguish the cloud edge from the reflectance identification [17]. Considering that the texture characteristic of haze is smoother than that of clouds, the variance information of a matrix composed of 3 \(\times\) 3 pixels at 0.412 \(\upmu\)m and 0.47 \(\upmu\)m is added to the VIIRS cloud identification to avoid identifying a haze as a cloud [18].