The planetary boundary layer height (PBLH) is a very important parameter in the atmosphere, because it determines the range where the most effective dispersion processes take place, and serves as a constraint on the vertical transport of heat, moisture, and pollutants. As the only space-borne lidar, Cloud-Aerosol Lidar with Orthogonal Polarization onboard Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) measures the vertical distribution of aerosol signals and thus offers the potential to retrieve large-scale PBLH climatology. In this study, we explore different techniques for retrieving PBLH from CALIPSO measurements and validate the results against those obtained from ground-based micropulse lidar (MPL) and radiosonde (RS) data over Hong Kong, where long-term MPL and RS measurements are available. Two methods, namely maximum standard deviation (MSD) and wavelet covariance transform (WCT), are used to retrieve PBLH from CALIPSO. Results show that the RS- and MPL-derived PBLHs share similar interannual variation and seasonality and can complement each other. Both MSD and WCT perform reasonably well compared with MPL/RS products, especially under sufficient aerosol loading. Uncertainties increase when aerosol loading is low and the CALIPSO signal consequently becomes noisier. Overall, CALIPSO captures the general PBLH seasonal variability over Hong Kong, despite a high bias in spring and a low bias in summer. The spring high bias is likely associated with elevated aerosol layers due to transport, while the summer low bias can be attributed to higher noise level associated with weaker aerosol signal.

Due to the limited spatial coverage of surface PM2.5 monitoring sites, satellite AOD (aerosol optical depth) products have been widely used to estimate surface PM2.5 in different parts of the world. A major difficulty as well as source of uncertainty in converting AOD to PM2.5 is the determination of aerosol vertical distribution, usually represented by the boundary layer height (BLH). In this study, we evaluate the performance of different approaches of estimating aerosol vertical distributions in the AOD-PM2.5 conversion process, using long-term and multi-source data acquired at a super station, Yuen Long, Hong Kong. The monthly climatology of aerosol vertical distribution and BLH products derived from lidar, radiosonde, and MERRA reanalysis data are respectively applied for converting AOD to surface aerosol extinction coefficients. Seasonal empirical hygroscopic growth functions are constructed to convert aerosol extinction to dry PM2.5 mass concentration. Results indicate that different vertical distribution estimation approaches can have highly varying effect on the converted PM2.5 concentration. Using lidar-derived BLHs shows the best agreement, with a correlation coefficient of 0.73 and a relative bias of 30.6% between retrievals and observations. Since continuous lidar measurements are not available for most regions, the climatology pattern of aerosol structure and radiosonde-derived BLHs are found to be suitable alternatives with a correlation coefficient of ∼0.6, and considerably outperform the results using BLHs derived from reanalysis data. Elevated aerosol layers appear to be the major source of uncertainty and result in an overestimate of satellite results, especially during the spring and summer seasons.

The total column-averaged volume mixing ratio of atmospheric carbon dioxide (\( {\text{X}}_{{{\text{CO}}_{ 2} }} \)) has been retrieved with high spectral resolution solar absorption data obtained from ground-based Fourier transform spectrometer (FTS) measurements at Xichong, a coastal site in the district of Shenzhen in southern China. Based on differential optical absorption spectroscopy (DOAS) theory, the \( {\text{X}}_{{{\text{CO}}_{ 2} }} \) was retrieved by finding the best match of observed high spectral resolution solar absorption data and monochromatic radiation transfer model calculations. The averaged \( {\text{X}}_{{{\text{CO}}_{ 2} }} \) in the whole observation period was about 394.9 ppm. The uncertainty of the retrieval was estimated to be 2.0 ppm (0.51 %) by comparing retrievals at two bands. The preliminary results show that \( {\text{X}}_{{{\text{CO}}_{ 2} }} \) retrieved by this method can be used to validate satellite remote sensing of \( {\text{X}}_{{{\text{CO}}_{ 2} }} .\)

The PM10 mass concentration from 428 ground sites in Eastern China in 2011 were used to investigate the temporal representative of satellites carrying MODIS for air quality monitoring.The daily,monthly,seasonal and yearly averaged ground measurements of PM10 mass concentration at the time when the satellite data is available(SATPM) were compared with the corresponding 24 hours averaged ground measurements(ALLPM).The data with the Aqua-MODIS time are more close to ALLPM than those with Terra-MODIS time,and most relative errors fall into the range of ±20%,indicating a high reliability of temporal representative on Aqua time.Data from both satellites were incorporated together through a linear fitting to get the validated daily and yearly SATPM.The results show a better correlation and own lower root mean square errors(RMSE) with ALLPM.Because PM2.5 is more correlated with optical observations,the results are also of significant implications for the reliability of PM2.5 retrieval from satellite.

The MPL observation data in Beijing is used to develop two new algorithms which will not need to refer to the low SNR signal in high altitude.In the first algorithm,the boundary in mixing layer is chosen in the Fernald’s theory.Then the lidar constant can be retrieved by combining AOD.Compared with the data from the automatic meteorological station locates in the same place,this algorithm seems viable for lidar’s extinction coefficient retrieval.In the second algorithm,since the range corrected lidar data near the surface is linear relate to the surface extinction coefficient,the visibility data in the surface could be used to calculate the lidar constant.The result of the lidar constant and the average extinction-to-backscatter ratio during the experiment period are similar to the first algorithm.In these two algorithms,the lidar constant is retrieved without using the high altitude signal.In the end,the lidar observation of a classic aerosol case when a Siberian High passes through Beijing is analyzed.By comparing the lidar observation with the Nanjiao Observation Station data,the lidar retrieval and the new algorithm are considered to be reliable.

Lidar has unique advantages in temporal and spatial resolution to measure the atmospheric mixing layer height (MLH), which is important for analyzing atmospheric phenomena. However, long‐term MLH information over several years, which has important significance in air quality and climate studies, is seldom obtained from lidar data due to the scarcity of long‐running lidar observations. In this paper, we retrieve and analyze daytime MLH from a data set of a lidar that operated continuously over 6.5 years at Yuen Long, Hong Kong. A new algorithm has been developed for consistently retrieving MLH from this large data set, handling all possible weather conditions and aerosol layer structures. We analyze the diurnal, seasonal and inter‐annual variation of MLH over Hong Kong and find a unique phenomenon that the afternoon MLH is higher in autumn than in summer, which is verified by radiosonde results and explained by thermal stability and humidity effect. Moreover, we find a slightly decreasing trend of the daily maximum of MLH, which implies a continually compressed air volume into which pollutants and their precursors are emitted, which is one of the possible factors leading to deteriorated air quality over Hong Kong region.

The aerosol optical depth(AOD) products from Moderate Resolution Imaging Spectroradiometer(MODIS),aerosol extinction coefficient profiles from Light Detection and Ranging(LIDAR),surface relative humidity data and particulate matter(PM) mass concentration data over Yuan Long,Hong Kong in 2008 were used in the remote sensing of surface suspended particulate matter mass distribution.LIDAR data were used to get the relationship among surface aerosol extinction coefficient,LIDAR AOD and aerosol scale height,which was further applied in the retrieval of the distribution of surface aerosol extinction coefficients with satellite AOD.After considering relative humidity effect,the correlation between satellite estimated aerosol extinction coefficients and the corresponding surface PM mass was investigated.Finally,the surface PM mass distribution was obtained by synergy usage of satellite and LIDAR measurements.The results show that the correlation coefficients between the estimated aerosol extinction coefficients and the surface PM mass are 0.57–0.86 for PM2.5 and 0.59–0.78 for PM10,respectively.The Root Mean Square Errors(RMSEs) between estimated PM and surface measured PM mass are 11.64–25.34 μg/m3 for PM2.5 and 24.64–91.64 μg/m3 for PM10.Satellite remote sensing provides a promising way in atmospheric suspended particulate matter monitoring.The 1-km resolution AOD product is more suitable for describing the pollution in the urban areas with complicated topography.

How to use the measurement from the visible channel of Chinese Fengyun-2C geostationary satellite to retrieve aerosol optical depth(AOD) is discussed.By calculating mean surface reflectance at the same local time of each day in one month,the randomicity of the estimated surface reflectance can be reduced.The influence of different values of AOD assumed in the cleanest days on the quality of final AOD product is analyzed.In addition,the data in May 2008 was used to test the proposed algorithm and the results were compared with the AOD product from six AERONET sites in East Asian and MODIS AOD product respectively.Last,the error sources were analyzed in the retrieval of AOD from FY2C satellite,and the corresponding possible schemes to decrease the error influence and improve the quality of FY2C AOD product were investigated.The comparison indicates that in East Asian the AOD product can display the pattern of aerosol distribution,but overestimates the values of AOD in southwest of China and low latitude areas,and underestimates the values of AOD in east of China.

本文深入探讨了如何利用中国的风云2C静止卫星可见光资料反演气溶胶光学厚度（AOD）的数值方法，重点讨论了通过计算一个月中每日同一时刻平均地表反射率来降低地表反射率估计随机性的方法，以及其中对清洁的天AOD值的不同假设对结果的影响。并将2008年5月由风云2C可见光资料反演得到的AOD产品分别与东亚7个AERONET站点的AOD产品和MODIS的AOD产品进行了比较，分析了本文所述风云2C卫星的AOD产品算法的误差来源和降低误差影响以及改善产品质量的方案。对比的结果表明，在东亚地区利用风云2C可见光资料反演的AOD产品可以展示气溶胶的分布样式，但是目前的算法高估了中国西南和低纬度一些地区的AOD值而低估了中国东部地区的AOD值。