INVERSION OF MOISTURE PROFILES BY A NONLINEAR ITERATIVE PHYSICAL RETRIEVAL*

The Advanced TIROS-N Operational Vertical Sounder(ATOVS) measurements are used to generate the atmospheric parameters,such as temperature and moisture profiles,under both clear and cloudy situations.This paper describes briefly the nonlinear iterative physical retrieval method.By using this retrieval scheme,an experiment has been carried out to retrieve the moisture profiles from ATOVS measurements on the NOAA-16 satellite for July of 2002.ATOVS profile retrieval results are evaluated by root mean square(RMS) differences with respect to RAdiosonde OBservation(RAOB) profiles.The accuracy of the retrieval is about 15%-23% for the relative humidity profile in this study.     

The Capability of Atmospheric Profile Retrieval from Satellite High Resolution Infrared Sounder Radiances

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人工神经网络算法在红外高光谱资料反演大气温度廓线中的应用

基于红外高光谱大气探测器AIRS实况观测资料,尝试用人工神经网络算法来实现晴空时大气的温度垂直廓线反演,重点将该算法与目前已经发展比较成熟的特征向量统计反演算法进行比较。结果表明,两种算法在计算时间效率和反演精度上相当。鉴于人工神经网络算法的统计物理本质,误差反向传播BP神经网络可以求解非线性问题,在优化训练样本和继续调试网络种类和网络参数的基础上,有望能进一步提高反演精度。     

A Neural Network Based Single Footprint Temperature Retrieval for AtmosphericInfrared Sounder Measurements and Its Application to Study onStratospheric Gravity Wave

Satellite hyperspectral infrared sounder measurements have better horizontal resolution than other sounding techniques as it boasts the stratospheric gravity wave (GW) analysis. To accurately and efficiently derive the three- dimensional structure of the stratospheric GWs from the single-field-of-view (SFOV) Atmospheric InfraRed Sounder (AIRS) observations, this paper firstly focuses on the retrieval of the atmospheric temperature profiles in the altitude range of 20-60 km with an artificial neural network approach (ANN). The simulation experiments show that the retrieval bias is less than 0.5 K, and the root mean square error (RMSE) ranges from 1.8 to 4 K. Moreover, the retrieval results from 20 granules of the AIRS observations with the trained neural network (AIRS_SFOV) and the corresponding operational AIRS products (AIRS_L2) as well as the dual-regression results from the Cooperative Institute for Meteorological Satellite Studies (CIMSS) (AIRS_DR) are compared respectively with ECMWF T799 data. The comparison indicates that the standard deviation of the ANN retrieval errors is significantly less than that of the AIRS_DR. Furthermore, the analysis of the typical GW events induced by the mountain Andes and the typhoon "Soulik" using different data indicates that the AIRS_SFOV results capture more details of the stratospheric gravity waves in the perturbation amplitude and pattern than the operational AIRS products do.     

Atmospheric profile retrieval with AIRS data and validation at the ARM CART site

The physical retrieval algorithm of atmospheric temperature and moisture distribution from the Atmospheric InfraRed Sounder （AIRS） radiances is presented. The retrieval algorithm is applied to AIRS clear-sky radiance measurements. The algorithm employs a statistical retrieval followed by a subsequent nonlinear physical retrieval. The regression coefficients for the statistical retrieval are derived from a dataset of global radiosonde observations （RAOBs） comprising atmospheric temperature, moisture, and ozone profiles. Evaluation of the retrieved profiles is performed by a comparison with RAOBs from the Atmospheric Radiation Measurement （ARM） Program Cloud And Radiation Testbed （CART） in Oklahoma, U. S. A.. Comparisons show that the physically-based AIRS retrievals agree with the RAOBs from the ARM CART site with a Root Mean Square Error （RMSE） of 1K on average for temperature profiles above 850 hPa, and approximately 10% on average for relative humidity profiles. With its improved spectral resolution, AIRS depicts more detailed structure than the current Geostationary Operational Environmental Satellite （GOES） sounder when comparing AIRS sounding retrievals with the operational GOES sounding products.     

基于加密探空资料的上海不同云天下AIRS/Aqua相对湿度廓线反演精度研究

目前云对卫星相对湿度廓线反演精度的影响研究大多是针对云量,对其他云属性的影响研究尚少,云高也是影响卫星相对湿度廓线反演精度的重要因素。利用上海宝山站L波段(1型)加密探空资料,分析了上海地区7—9月不同质量控制标识、云量和云顶高度条件下大气红外探测器AIRS/Aqua (Atmospheric Infrared Sounder)相对湿度廓线的反演精度,以期为今后开展AIRS等卫星资料的同化研究提供科学依据。结果表明：（1）AIRS相对湿度廓线反演误差随着云量的增加而逐渐增大,并且随着气压值的升高,少云与多云时的均方根误差(Root Mean Squared Error, RMSE)之差有逐渐增大的趋势;（2）云顶高度越高,AIRS相对湿度廓线反演精度越差,云顶以上湿度廓线反演精度更高,而云顶以下高度的反演误差较大;（3）高云且多云时,AIRS相对湿度廓线的反演精度最差,850 hPa处,AIRS相对湿度反演数据与探空资料绝对误差的下限达到了-63.51%;(4)虽然质量控制标识为0时,AIRS湿度廓线在对流层范围内的反演精度仍达不到无线电探空的水平,但是相对于质量控制标识1时,反演精度...     

A Study on Retrieving Atmospheric Profiles from EOS/AIRS Observations

1. IntroductionThe development of global climate and weathermodels requires accurate monitoring of atmospherictemperature and moisture profiles, as well as the con-tents of trace gases and aerosols. It is quite difficultto monitor continuously these parameters on a globalscale.Until recently. AIRS (Atmospheric InfraredSounder) offers a new opportunity to improve globalmonitoring of temperature, moisture, and ozone distri-butions and changes therein. The high spectral resolu-tion (v/Δv ? 12…     

利用AIRS卫星资料反演大气廓线Ⅰ.特征向量统计反演法

引进美国威斯康星大学的IMAPP（International MODIS／AIRS Preprocessing Package）软件包，介绍了利用高光谱分辨率大气红外探测器AIRS（Atmospheric Infrared Sounder）观测辐射值，用特征向量统计法反演大气温度、湿度等垂直廓线的算法，采用亮度温度分类和扫描角分类回归后，减小了反演误差。并将其应用到中国地区，通过与无线电探空值及欧洲中期天气预报中心ECMWF（European Center of Medium-range Weather Forecasts）客观分析场的比较，结果表明：该方法所获得的温度、水汽反演结果与探空观测及ECMWF大气廓线分布一致，且AIRS因其高光谱分辨率（即高垂直空间分辨率）显示了精细的大气结构。     

RETRIEVING ATMOSPHERIC SOUNDING PROFILES AROUND TYPHOON YUNNA USING INFRARED HYPERSPECTRAL MEASUREMENTS AIRS

In this study,we derived atmospheric profiles of temperature,moisture,and ozone,along with surface emissivity,skin temperature,and surface pressure,from infrared-sounder radiances under clear sky (cloudless) condition.Clouds were detected objectively using the Atmospheric Infrared Sounder under a relatively low spatial resolution and cloud-mask information from the Moderate Resolution Imaging Spectroradiometer under a high horizontal resolution;this detection was conducted using space matching.Newton’s nonlinear physical iterative solution technique is applied to the radiative transfer equation (RTE) to retrieve temperature profiles,relative humidity profiles,and surface variables simultaneously.This technique is carried out by using the results of an eigenvector regression retrieval as the background profile and using corresponding iterative forms for the weighting functions of temperature and water-vapor mixing ratio.The iterative forms are obtained by applying the variational principle to the RTE.We also compared the retrievals obtained with different types of observations.The results show that the retrieved atmospheric sounding profile has great superiority over other observations by accuracy and resolution.Retrieved profiles can be used to improve the initial conditions of numerical models and used in areas where conventional observations are sparse,such as plateaus,deserts,and seas.     

Simultaneous Non-linear Retrieval of Atmospheric Temperature and Absorbing Constituent Profiles from Satellite Infrared Sounder Radiances

Based on Zeng’s theory (1974), a successive linearized form of radiative transfer equation (RTE) is derived for simultaneous retrieval of atmospheric temperature and absorbing constituent profiles from satellite infrared observa-tions. It contains the temperature component weighting function and absorbing constituent (H2O, O3, CH4 etc.) com-ponent weighting functions. All these weighting functions reach maximum at their own “optimum information levels”, and make the remote sensing equations well-conditional. Then the atmospheric profiles are derived by Newton’s non-linear iteration method. Experiments of retrieval from both TIROS-N operational High Resolution Infrared Sounder (HIRS) and the simulated Atmospheric infRared Sounder (AIRS) show an significant improvement.     

Optimal Use of High Resolution Infrared Sounder Channels in Atmospheric Prof

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Impact of 4DVAR Assimilation of AIRS Total Column Ozone Observations on the Simulation of Hurricane Earl

The Atmospheric Infrared Sounder(AIRS) provides twice-daily global observations of brightness temperature, which can be used to retrieve the total column ozone with high spatial and temporal resolution.In order to apply the AIRS ozone data to numerical prediction of tropical cyclones, a four-dimensional variational(4DVAR) assimilation scheme on selected model levels is adopted and implemented in the mesoscale non-hydrostatic model MM5. Based on the correlation between total column ozone and potential vorticity(PV), the observation operator of each level is established and five levels with highest correlation coefficients are selected for the 4DVAR assimilation of the AIRS total column ozone observations. The results from the numerical experiments using the proposed assimilation scheme for Hurricane Earl show that the ozone data assimilation affects the PV distributions with more mesoscale information at high levels first and then influences those at middle and low levels through the so-called asymmetric penetration of PV anomalies.With the AIRS ozone data being assimilated, the warm core of Hurricane Earl is intensified, resulting in the improvement of other fields near the hurricane center. The track prediction is improved mainly due to adjustment of the steering flows in the assimilation experiment.     

人工神经网络法反演晴空大气湿度廓线的研究

高光谱分辨率大气红外探测器AIRS(Atmospheric Infrared Sounder)作为第一个超高光谱大气红外探测仪,开辟了卫星大气探测的新时代.以无线电探空值与SARTA(Stand-Alone Radiative Transfer Algorithm辐射传输模式)v1.05版的前向模式模拟出的AIRS辐射...     

Use of total precipitable water classification of a priori error and quality control in atmospheric temperature and water vapor sounding retrieval

This study investigates the use of dynamic a priori error information according to atmospheric moistness and the use of quality controls in temperature and water vapor profile retrievals from hyperspectral infrared (IR) sounders. Temperature and water vapor profiles are retrieved from Atmospheric InfraRed Sounder (AIRS) radiance measurements by applying a physical iterative method using regression retrieval as the first guess. Based on the dependency of first-guess errors on the degree of atmospheric moistness, the a priori first-guess errors classified by total precipitable water (TPW) are applied in the AIRS physical retrieval procedure. Compared to the retrieval results from a fixed a priori error, boundary layer moisture retrievals appear to be improved via TPW classification of a priori first-guess errors. Six quality control (QC) tests, which check non-converged or bad retrievals, large residuals, high terrain and desert areas, and large temperature and moisture deviations from the first guess regression retrieval, are also applied in the AIRS physical retrievals. Significantly large errors are found for the retrievals rejected by these six QCs, and the retrieval errors are substantially reduced via QC over land, which suggest the usefulness and high impact of the QCs, especially over land. In conclusion, the use of dynamic a priori error information according to atmospheric moistness, and the use of appropriate QCs dealing with the geographical information and the deviation from the first-guess as well as the conventional inverse performance are suggested to improve temperature and moisture retrievals and their applications.     

Estimating the retrievability of temperature profiles from satellite infrared measurements

A method is developed to assess retrievability, namely the retrieval potential for atmospheric temperature profiles, from satellite infrared measurements in clear-sky conditions. This technique is based upon generalized linear inverse theory and empirical orthogonal function analysis. Utilizing the NCEP global temperature reanalysis data in January and July from 1999 to 2003, the retrievabilities obtained with the Atmospheric Infrared Sounder （AIRS） and the High Resolution Infrared Radiation Sounder/3 （HIRS/3） sounding channel data are derived respectively for each standard pressure level on a global scale. As an incidental result of this study, the optimum truncation number in the method of generalized linear inverse is deduced too. The results show that the retrievabilities of temperature obtained with the two datasets are similar in spatial distribution and seasonal change characteristics. As for the vertical distribution, the retrievabilities are low in the upper and lower atmosphere, and high between 400 hPa and 850 hPa. For the geographical distribution, the retrievabilities are low in the low-latitude oceanic regions and in some regions in Antarctica, and relatively high in mid-high latitudes and continental regions. Compared with the HIRS/3 data, the retrievability obtained with the AIRS data can be improved by an amount between 0.15 and 0.40.     

METOP星载干涉式超高光谱分辨率红外大气探测仪(IASI)及其产品

为了深入了解国际卫星红外大气探测仪器的新特点,以欧洲METOP卫星装载的超高光谱红外大气探测仪(IASI:Infrared Atmospheric Sounding Interferometer)为例,介绍卫星红外探测仪研发背景、仪器特征和数据产品处理,并与第1个高光谱大气红外探测仪(AIRS:Atmosphenic Inftared Sounder)作了比较分析.采用干涉分光技术的IASI为地球大气遥感提供了丰富的研究资料,可用于反演大气、海洋、云和大气成分,对我国风云卫星的高光谱红外大气垂直探测仪器的研发具有重要参考价值.     

Dynamical and Chemical Features of a Cutoff Low over Northeast China in July 2007: Results from Satellite Measurements and Reanalysis

The European Centre for Medium-Range Weather Forecasts Re-Analysis Interim (ERA-Interim) meteorology and measurements from the Microwave Limb Sounder, High Resolution Dynamics Limb Sounder, and Ozone Monitoring Instrument onboard the Earth Observing System Aura satellite were applied to analyze the dynamical and chemical features of a cutoff low (COL) event over northeast China in early July 2007. The results showed the polar stratospheric origin of an upper-level warm-core cyclone at 100--300 hPa, associated with a funnel-shaped tropopause intruding into the mid-troposphere just above the COL center. The impacts of the stratospheric intrusion on both column ozone and ozone profiles were investigated using satellite measurements. When the intensity of the COL peaked on 10 July 2007, the total column ozone (TCO) increase reached a maximum (40--70 DU). This could be dynamically attributed to both the descent of the tropopause (～75%) and the downward transport of stratospheric ozone across the tropopause (～25%). Analysis of the tropospheric ozone profiles provided evidence for irreversible transport/mixing of ozone-rich stratospheric air across the tropopause near the upper-level front region ahead of the COL center. This ozone intrusion underwent downstream transport by the upper tropospheric winds, leading to further increase in TCO by 12--16 DU over broad regions extending from east China toward the northern Japan Sea via South Korea. Meteorological analysis also showed the precedence of the stratospheric intrusion ahead of the development of cyclones in the middle and lower troposphere.     

Ensemble retrieval of atmospheric temperature profiles from AIRS

ABSTRACT Satellite-based observations provide great opportunities for improving weather forecasting. Physical retrieval of atmo spheric profiles from satellite observations is sensitive to the uncertainty of the first guess and other factors. In order to improve the accuracy of the physical retrieval, an ensemble methodology was developed with an emphasis on perturbing the first guess. In the methodology, a normal probability density function （PDF） is used to select the optimal profile from the ensemble retrievals. The ensemble retrieval algorithm contains four steps： （1） regression retrieval for original first guess; （2） perturbation of the original first guess to generate new first guesses （ensemble first guesses）; （3） using the ensemble first guesses and nonlinear iterative physical retrieval to generate ensemble physical results; and （4） the final optimal profile is selected from the ensemble physical results by using PDE Temperature eigenvectors （EVs） were used to generate the pertur- bation and generate the ensemble first guess. Compared with the regular temperature profile retrievals from the Atmospheric InfraRed Sounder （AIRS）, the ensemble retrievals RMSE of temperature profiles selected by the PDF was reduced between 150 and 320 hPa and below 400 hPa, with a maximum improvement of 0.3 K at 400 hPa. The bias was also reduced in many layers, with a maximum improvement of 0.69 K at 460 hPa. The combined optimal （CombOpt） profile and a mean optimal （MeanOpt） profile of all ensemble physical results were improved below 150 hPa. The MeanOpt profile was better than the CombOpt profile, and was regarded as the final optimal （FinOpt） profile. This study lays the foundation for improving temperature retrievals from hyper-spectral infrared radiance measurements.     

Nonlinear Retrieval of Atmospheric Ozone Profile from Solar Backscatter Ultraviolet Measurements:Theory and Simulation

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DRP-4DVar方法同化AIRS反演资料在一次江淮流域暴雨中的应用

利用经济省时的降维投影四维变分同化方法(DRP-4DVar),在2009年7月22～23日江淮流域的一次大暴雨过程中同化晴空条件下高光谱大气红外探测仪(AIRS)反演温度、湿度廓线,改进此次强降水过程的模拟。试验结果分析显示,同化AIRS反演的温度及湿度场后,基于四维变分同化系统的模式约束,能够改进湿度场、高度场、高低层散度场。从累积降水量偏差图及同化试验增量图可以看到,正降水量偏差对应于正湿度增量、负位势高度增量及低层负散度高层正散度增量,负降水量偏差则与之相反。同化试验较参照试验可更好地模拟出暴雨的天气形势、对暴雨的落区及强度有更好的反映。此外,从单次同化与连续同化的试验对比结果看出,连续同化试验结果较单次同化结果有进一步的改进,说明不断加入新的观测资料可以更好地模拟强降水过程。