Clouds are crucial regulators of both weather and climate. Properties such as the amount,type,height,distribution and movement of them have an impact on the earth's radiation budget and the hydrological cycle,thus...Clouds are crucial regulators of both weather and climate. Properties such as the amount,type,height,distribution and movement of them have an impact on the earth's radiation budget and the hydrological cycle,thus cloud observation is very important. The disadvantages of zenith pointing measuring instruments and whole sky visible imagers limit the application of them.A summary of the actuality and application of ground-based whole sky infrared cloud measuring instruments and analyses of the techniques of radiometric calibrations,removal of atmospheric emission and calculation of cloud cover,amount,type are conducted to promote the automatically observation of the whole sky. Fully considering whole sky infrared cloud sounding theories,techniques and applications,there are still a lot of studies on improving the properties of instruments,enhancing the techniques of cloud base height measurements and establishing instrumental cloud classification criterion before actual operations.展开更多
Passive Fourier transform infrared （FTIR） remote sensing measurement of chemical gas cloud is a vital technology. It takes an important part in many fields for the detection of released gases. The principle of conce...Passive Fourier transform infrared （FTIR） remote sensing measurement of chemical gas cloud is a vital technology. It takes an important part in many fields for the detection of released gases. The principle of concentration measurement is based on the Beer-Lambert law. Unlike the active measurement, for the passive remote sensing, in most cases, the difference between the temperature of the gas cloud and the brightness temperature of the background is usually a few kelvins. The gas cloud emission is almost equal to the background emission, thereby the emission of the gas cloud cannot be ignored. The concentration retrieval algorithm is quite different from the active measurement. In this paper, the concentration retrieval algorithm for the passive FTIR remote measurement of gas cloud is presented in detail, which involves radiative transfer model, radiometric calibration, absorption coefficient calculation, et al. The background spectrum has a broad feature, which is a slowly varying function of frequency. In this paper, the background spectrum is fitted with a polynomial by using the Levenberg-Marquardt method which is a kind of nonlinear least squares fitting algorithm. No background spectra are required. Thus, this method allows mobile, real-time and fast measurements of gas clouds.展开更多
It has been several years since the Greenhouse Gases Observing Satellite （GOSAT） began to observe the distribution of CO2 and CH4 over the globe from space. Results from Thermal and Near-infrared Sensor for Carbon O...It has been several years since the Greenhouse Gases Observing Satellite （GOSAT） began to observe the distribution of CO2 and CH4 over the globe from space. Results from Thermal and Near-infrared Sensor for Carbon Observation-Cloud and Aerosol Imager （TANSO-CAI） cloud screening are necessary for the retrieval of CO2 and CH4 gas concentrations for GOSAT TANSO-Fourier Transform Spectrometer （FTS） observations. In this study, TANSO-CAI cloud flag data were compared with ground-based cloud data collected by an all-sky imager （ASI） over Beijing from June 2009 to May 2012 to examine the data quality. The results showed that the CAI has an obvious cloudy tendency bias over Beijing, especially in winter. The main reason might be that heavy aerosols in the sky are incorrectly determined as cloudy pixels by the CAI algorithm. Results also showed that the CAI algorithm sometimes neglects some high thin cirrus cloud over this area.展开更多
全天相机云图是监测云量的重要手段,提出了一种新的云量测量量化指标——云分布密度(Cloud Distribution Density of ASI Images,ASICDD),并基于该指标建立全天相机云图自动分类系统。首先对云图进行去噪,利用最大类间方差法(Otsu)分割...全天相机云图是监测云量的重要手段,提出了一种新的云量测量量化指标——云分布密度(Cloud Distribution Density of ASI Images,ASICDD),并基于该指标建立全天相机云图自动分类系统。首先对云图进行去噪,利用最大类间方差法(Otsu)分割云区域;然后对去除背景的云区域图像使用云分布密度计算云量;最后使用4种传统的分类器(支持向量机、K最近邻、决策树和随机森林)根据计算数值进行自动分类并评估各分类器的性能。结果表明,云分布密度可作为评判全天相机云图云量的数值指标;基于云分布密度建立的云图自动分类系统实现了较高的识别准确率,其中随机森林法的分类效果最好,各类云图的识别准确率达到95%以上。展开更多
基金supported by National Natural Science Foundation of China ( Grant No. 41575024 and Grant No. 41205125)
文摘Clouds are crucial regulators of both weather and climate. Properties such as the amount,type,height,distribution and movement of them have an impact on the earth's radiation budget and the hydrological cycle,thus cloud observation is very important. The disadvantages of zenith pointing measuring instruments and whole sky visible imagers limit the application of them.A summary of the actuality and application of ground-based whole sky infrared cloud measuring instruments and analyses of the techniques of radiometric calibrations,removal of atmospheric emission and calculation of cloud cover,amount,type are conducted to promote the automatically observation of the whole sky. Fully considering whole sky infrared cloud sounding theories,techniques and applications,there are still a lot of studies on improving the properties of instruments,enhancing the techniques of cloud base height measurements and establishing instrumental cloud classification criterion before actual operations.
基金Project supported by the National Natural Science Foundation of China (Grant No 083H311501)the National High Technology Research and Development Program of China (Grant No 073H3f1514)
文摘Passive Fourier transform infrared （FTIR） remote sensing measurement of chemical gas cloud is a vital technology. It takes an important part in many fields for the detection of released gases. The principle of concentration measurement is based on the Beer-Lambert law. Unlike the active measurement, for the passive remote sensing, in most cases, the difference between the temperature of the gas cloud and the brightness temperature of the background is usually a few kelvins. The gas cloud emission is almost equal to the background emission, thereby the emission of the gas cloud cannot be ignored. The concentration retrieval algorithm is quite different from the active measurement. In this paper, the concentration retrieval algorithm for the passive FTIR remote measurement of gas cloud is presented in detail, which involves radiative transfer model, radiometric calibration, absorption coefficient calculation, et al. The background spectrum has a broad feature, which is a slowly varying function of frequency. In this paper, the background spectrum is fitted with a polynomial by using the Levenberg-Marquardt method which is a kind of nonlinear least squares fitting algorithm. No background spectra are required. Thus, this method allows mobile, real-time and fast measurements of gas clouds.
基金support from the Strategic Pilot Science and Technology project of the Chinese Academy of Sciences(Grant No.XDA05040200)the National Natural Science Foundation of China(Grant No.41275040)
文摘It has been several years since the Greenhouse Gases Observing Satellite （GOSAT） began to observe the distribution of CO2 and CH4 over the globe from space. Results from Thermal and Near-infrared Sensor for Carbon Observation-Cloud and Aerosol Imager （TANSO-CAI） cloud screening are necessary for the retrieval of CO2 and CH4 gas concentrations for GOSAT TANSO-Fourier Transform Spectrometer （FTS） observations. In this study, TANSO-CAI cloud flag data were compared with ground-based cloud data collected by an all-sky imager （ASI） over Beijing from June 2009 to May 2012 to examine the data quality. The results showed that the CAI has an obvious cloudy tendency bias over Beijing, especially in winter. The main reason might be that heavy aerosols in the sky are incorrectly determined as cloudy pixels by the CAI algorithm. Results also showed that the CAI algorithm sometimes neglects some high thin cirrus cloud over this area.
文摘全天相机云图是监测云量的重要手段,提出了一种新的云量测量量化指标——云分布密度(Cloud Distribution Density of ASI Images,ASICDD),并基于该指标建立全天相机云图自动分类系统。首先对云图进行去噪,利用最大类间方差法(Otsu)分割云区域;然后对去除背景的云区域图像使用云分布密度计算云量;最后使用4种传统的分类器(支持向量机、K最近邻、决策树和随机森林)根据计算数值进行自动分类并评估各分类器的性能。结果表明,云分布密度可作为评判全天相机云图云量的数值指标;基于云分布密度建立的云图自动分类系统实现了较高的识别准确率,其中随机森林法的分类效果最好,各类云图的识别准确率达到95%以上。
