On Lidar Application for Remote Sensing of the Atmosphere

This paper introduces some advanced subjects on lidar remote sensing of the atmosphere, emphasizing recent studies and developments in lidar application for measuring ozone, cloud, aerosol, atmospheric temperature, moisture, pressure and wind.

Study on Microwave Remote Sensing of Atmosphere,Cloud and Rain

In this paper, recent research of microwave remote sensing of atmosphere, cloud and rain in China is presented. It includes the following aspects:(1) Progress in the development of multifrequency radiometer and its characteristics and parameters;(2) Application of microwave remote sensing in prediction of atmospheric boundary layer. The atmospheric temperature profiles are derived with 5 mm (54.5 GHz) radiometer angle-scanning observations. Due to the fact that microwave radiometer could monitor the atmospheric temperature profile continuously and make the initialization of numerical model any time, it is helpful for improving the accuracy in prediction of the evolution of atmospheric boundary layer;(3) Theory and application of microwave radiometers in monitoring atmospheric temperature, humidity and water content in cloud. The field experiment of International Satellite Cloud Climatology Project (ISCCP) at Shionomisaki and Amami Oshima of Japan for studies of cloud and weather has been described;(4) Satellite remote sensing of atmosphere and colud. The TIROS-N TOYS satellite data are used to obtain atmospheric temperature profile. The results are compared with those of radiosonde, with rms deviation smaller than that of the current operational TOVS processing;(5) Microwave remote sensing and communication. The atmospheric attenuations are derived with microwave remote sensing methods such as solar radiation method etc., in order to obtain the local value instantaneously. The characteristics of Beijing's rainfall have been analysed and the probability of microwave attenuation of rain is predicted;(6) For improvement of the accuracy of rainfall measurement, a radiometer-radar system (λ= 3.2 cm) has been developed The variation of rainfall distribution and area-rainfall may be obtained by its measurements, which mav be helpful for hydrological prediction.The prospect of microwave remote sensing in meteorology is also discussed.

Cloud Top Pressure Retrieval Using Polarized and Oxygen A-band Measurements from GF5 and PARASOL Satellites

Cloud top pressure(CTP)is one of the critical cloud properties that significantly affects the radiative effect of clouds.Multi-angle polarized sensors can employ polarized bands(490 nm)or O_(2)A-bands(763 and 765 nm)to retrieve the CTP.However,the CTP retrieved by the two methods shows inconsistent results in certain cases,and large uncertainties in low and thin cloud retrievals,which may lead to challenges in subsequent applications.This study proposes a synergistic algorithm that considers both O_(2)A-bands and polarized bands using a random forest(RF)model.LiDAR CTP data are used as the true values and the polarized and non-polarized measurements are concatenated to train the RF model to determine CTP.Additionally,through analysis,we proposed that the polarized signal becomes saturated as the cloud optical thickness(COT)increases,necessitating a particular treatment for cases where COT<10 to improve the algorithm's stability.The synergistic method was then applied to the directional polarized camera(DPC)and Polarized and Directionality of the Earth’s Reflectance(POLDER)measurements for evaluation,and the resulting retrieval accuracy of the POLDER-based measurements(RMSEPOLDER=205.176 hPa,RMSEDPC=171.141 hPa,R^(2)POLDER=0.636,R^(2)DPC=0.663,respectively)were higher than that of the MODIS and POLDER Rayleigh pressure measurements.The synergistic algorithm also showed good performance with the application of DPC data.This algorithm is expected to provide data support for atmosphere-related fields as an atmospheric remote sensing algorithm within the Cloud Application for Remote Sensing,Atmospheric Radiation,and Updating Energy(CARE)platform.

Performance evaluation of operational atmospheric correction algorithms over the East China Seas

To acquire high-quality operational data products for Chinese in-orbit and scheduled ocean color sensors, the performances of two operational atmospheric correction(AC) algorithms(ESA MEGS 7.4.1 and NASA Sea DAS 6.1) were evaluated over the East China Seas(ECS) using MERIS data. The spectral remote sensing reflectance R_(rs)(λ), aerosol optical thickness(AOT), and ?ngstr?m exponent(α) retrieved using the two algorithms were validated using in situ measurements obtained between May 2002 and October 2009. Match-ups of R_(rs), AOT, and α between the in situ and MERIS data were obtained through strict exclusion criteria. Statistical analysis of R_(rs)(λ) showed a mean percentage difference(MPD) of 9%–13% in the 490–560 nm spectral range, and significant overestimation was observed at 413 nm(MPD>72%). The AOTs were overestimated(MPD>32%), and although the ESA algorithm outperformed the NASA algorithm in the blue-green bands, the situation was reversed in the red-near-infrared bands. The value of α was obviously underestimated by the ESA algorithm(MPD=41%) but not by the NASA algorithm(MPD=35%). To clarify why the NASA algorithm performed better in the retrieval of α, scatter plots of the α single scattering albedo(SSA) density were prepared. These α-SSA density scatter plots showed that the applicability of the aerosol models used by the NASA algorithm over the ECS is better than that used by the ESA algorithm, although neither aerosol model is suitable for the ECS region. The results of this study provide a reference to both data users and data agencies regarding the use of operational data products and the investigation into the improvement of current AC schemes over the ECS.

Application of improved discrepancy principle in inversion of atmosphere infrared remote sensing

With the use of techniques in nonlinear problems, the IDP (improved discrepancy principle) method has been proposed and applied to the optimal smooth factor (parameterγ) in the inversion process of atmosphere profiles from satellite observation. This method has also been used to inverse atmospheric parameters from the observation of new generation geostationary operational environmental satellite (GOES-8). Results show that this method is more accurate than that in use.