Formulation and validation of simulated data for the Atmospheric Infrared Sounder (AIRS)

Models for synthesizing radiance measurements by the Atmospheric Infrared Sounder (AIRS) are described. Synthetic radiances have been generated for developing and testing data processing algorithms. The radiances are calculated from geophysical states derived from weather forecasts and climatology using the AIRS rapid transmission algorithm. The data contain horizontal variability at the spatial resolution of AIRS from the surface and cloud fields. This is needed to test retrieval algorithms under partially cloudy conditions. The surface variability is added using vegetation and International Geosphere Biosphere Programme surface type maps, while cloud variability is added randomly. The radiances are spectrally averaged to create High Resolution Infrared Sounder (HIRS) data, and this is compared with actual HIRS2 data on the NOAA 14 satellite. The simulated data under-represent high-altitude equatorial cirrus clouds and have too much local variability. They agree in the mean to within 1-4 K, and global standard deviation agrees to better than 2 K. Simulated data have been a valuable tool for developing retrieval algorithms and studying error characteristics and will continue to be so after launch.     

Vertical Resolution Estimates in Version 5 of AIRS Operational Retrievals

In this paper, we present an overview of averaging-kernel computations from the Atmospheric Infrared Sounder (AIRS) Version 5 product retrieval software. Temperature and moisture retrievals form the focus of this paper; however, some results for all other retrieved gas amounts are presented. The theory and methodology required to utilize the averaging kernels for comparison of AIRS retrievals with correlative measurements are given. The averaging kernels are used to transform correlative measurements to AIRS effective resolution and are used to assess and derive the vertical resolution of Version 5 temperature and moisture retrievals in different atmospheric conditions. We find that depending on the scene, AIRS Version 5 tropospheric temperature (moisture) retrieval resolution, which is as determined by the full-width at half-maximum of the averaging kernels, ranges between 2.5 km (2.7 km) near the surface and 7.1 km (4.3 km) near the tropopause.      

Retrieval of atmospheric and surface parameters from AIRS/AMSU/HSB data in the presence of clouds

New state-of-the-art methodology is described to analyze the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit/Humidity Sounder for Brazil (AIRS/AMSU/HSB) data in the presence of multiple cloud formations. The methodology forms the basis for the AIRS Science Team algorithm, which will be used to analyze AIRS/AMSU/HSB data on the Earth Observing System Aqua platform. The cloud-clearing methodology requires no knowledge of the spectral properties of the clouds. The basic retrieval methodology is general and extracts the maximum information from the radiances, consistent with the channel noise covariance matrix. The retrieval methodology minimizes the dependence of the solution on the first-guess field and the first-guess error characteristics. Results are shown for AIRS Science Team simulation studies with multiple cloud formations. These simulation studies imply that clear column radiances can be reconstructed under partial cloud cover with an accuracy comparable to single spot channel noise in the temperature and water vapor sounding regions; temperature soundings can be produced under partial cloud cover with RMS errors on the order of, or better than, 1 K in 1-km-thick layers from the surface to 700 mb, 1-km layers from 700-300 mb, 3-km layers from 300-30 mb, and 5-km layers from 30-1 mb; and moisture profiles can be obtained with an accuracy better than 20% absolute errors in 1-km layers from the surface to nearly 200 mb.     

Coalignment and synchronization of the AIRS instrument suite

Previous multispectral sounders have consisted of infrared and microwave instruments operated asynchronously, with the data interpolated during ground processing to common fields of view (FOVs) for geophysical retrieval processing. To help achieve the high retrieval accuracy required for the Atmospheric Infrared Sounder (AIRS) system, the four instruments making up the AIRS suite are aligned and synchronized in such a way as to achieve common FOVs without interpolation. We describe the system, how the alignment is accomplished, and the plans to verify performance after launch and compensate for misalignments that might be revealed then.     

Validation of the Surface Air Temperature Products Retrieved From the Atmospheric Infrared Sounder Over China

The Atmospheric Infrared Sounder (AIRS) is the first of a new generation of high spectral resolution atmospheric sounders, which are expected to obtain atmospheric temperature and water vapor profiles with high accuracy. We are interested in investigating the validation of AIRS surface air temperature retrievals, particularly in the region of China. The surface air temperature observations obtained from 540 ground meteorological stations over China were collected, and quantitative comparisons were performed between the AIRS Version 4 retrievals and the ground observations. Then, the main causes of retrieval errors are discussed in detail. Results show that the rms errors of the AIRS surface air temperature retrievals are correlated with the terrain altitudes of the meteorological stations. With the altitude increasing, the rms errors have a trend of gradual increase. The rms errors are insensitive to the ground-observed cloud fraction. With the observed cloud fraction increasing, the small-scale oscillations of rms errors occur. In mountainous and desert regions, the rms errors are larger and can reach up to 11 K sometimes. Furthermore, the AIRS surface air temperature retrievals have better performance in January than in July. In central and eastern China, even the accuracy of accepted quality products in January approaches the goal of the AIRS Team.     

AIRS/AMSU/HSB on the Aqua mission: design, science objectives, data products, and processing systems

The Atmospheric Infrared Sounder (AIRS), the Advanced Microwave Sounding Unit (AMSU), and the Humidity Sounder for Brazil (HSB) form an integrated cross-track scanning temperature and humidity sounding system on the Aqua satellite of the Earth Observing System (EOS). AIRS is an infrared spectrometer/radiometer that covers the 3.7-15.4-/spl mu/m spectral range with 2378 spectral channels. AMSU is a 15-channel microwave radiometer operating between 23 and 89 GHz. HSB is a four-channel microwave radiometer that makes measurements between 150 and 190 GHz. In addition to supporting the National Aeronautics and Space Administration's interest in process study and climate research, AIRS is the first hyperspectral infrared radiometer designed to support the operational requirements for medium-range weather forecasting of the National Ocean and Atmospheric Administration's National Centers for Environmental Prediction (NCEP) and other numerical weather forecasting centers. AIRS, together with the AMSU and HSB microwave radiometers, will achieve global retrieval accuracy of better than 1 K in the lower troposphere under clear and partly cloudy conditions. This paper presents an overview of the science objectives, AIRS/AMSU/HSB data products, retrieval algorithms, and the ground-data processing concepts. The EOS Aqua was launched on May 4, 2002 from Vandenberg AFB, CA, into a 705-km-high, sun-synchronous orbit. Based on the excellent radiometric and spectral performance demonstrated by AIRS during prelaunch testing, which has by now been verified during on-orbit testing, we expect the assimilation of AIRS data into the numerical weather forecast to result in significant forecast range and reliability improvements.     

利用超光谱红外卫星数据反演大气廓线研究

建立新的物理反演法能同时反演大气温度廓线、水汽廓线、表层温度和地表发射率,该反演算法应用到我国黄海地区AIRS红外卫星资料中,可反演得到较高垂直分辨率的大气温度廓线和水汽廓+线,同时反演得到的表层温度和地表发射率、根据水汽廓线计算大气可降水量。     

Regression of Surface Spectral Emissivity From Hyperspectral Instruments

The operational Atmospheric Infrared Sounder (AIRS) emissivity retrieval uses a National Oceanic and Atmospheric Administration (NOAA) regression emissivity product as a first guess for its retrieval over land. The NOAA approach is based on clear radiances that are simulated from the European Centre for Medium-Range Weather Forecasting forecast and a surface emissivity training data set. The same approach has also been applied to simulated Infrared Atmospheric Sounding Interferometer (IASI) data. Resulted emissivity spectra and maps derived from AIRS and IASI will be presented and discussed.     

Retrieval algorithms for the EOS Microwave limb sounder (MLS)

The retrieval algorithms for the Earth Observing System Microwave Limb Sounder (MLS) on the Aura spacecraft, launched on July 15, 2004, are described. These algorithms are used to produce estimates of geophysical parameters such as vertical profiles of atmospheric temperature and composition ("Level 2" data) from the calibrated MLS observations of microwave limb radiance ("Level 1" data). The MLS algorithms are based on the standard optimal estimation approach, a weighted nonlinear least squares optimization with a priori constraints. New aspects include adaptation to a two-dimensional system, and an approach to the issues of retrieval "phasing" and error propagation that differs from that taken for previous similar instruments. Important new aspects of the software that implements these algorithms are also described, along with the algorithm configuration for the "version 1.5" dataset. Some examples are shown from MLS in-orbit observations.     

AIRS near-real-time products and algorithms in support of operational numerical weather prediction

The assimilation of Atmospheric InfraRed Sounder, Advanced Microwave Sounding Unit-A, and Humidity Sounder for Brazil (AIRS/AMSU/HSB) data by Numerical Weather Prediction (NWP) centers is expected to result in improved forecasts. Specially tailored radiance and retrieval products derived from AIRS/AMSU/HSB data are being prepared for NWP centers. There are two types of products - thinned radiance data and full-resolution retrieval products of atmospheric and surface parameters. The radiances are thinned because of limitations in communication bandwidth and computational resources at NWP centers. There are two types of thinning: (1) spatial and spectral thinning and (2) data compression using principal component analysis (PCA). PCA is also used for quality control and for deriving the retrieval first guess used in the AIRS processing software. Results show that PCA is effective in estimating and filtering instrument noise. The PCA regression retrievals show layer mean temperature (1 km in troposphere, 3 km in stratosphere) accuracies of better than 1 K in most atmospheric regions from simulated AIRS data. Moisture errors are generally less than 15% in 2-km layers, and ozone errors are near 10% over approximately 5-km layers from simulation. The PCA and regression methodologies are described. The radiance products also include clear field-of-view (FOV) indicators. The residual cloud amount, based on simulated data, for FOVs estimated to be clear (free of clouds) is about 0.5% over ocean and 2.5% over land.     

Solving the inverse problem for soil moisture and temperatureprofiles by sequential assimilation of multifrequency remotely sensedobservations

An algorithm is developed to solve the inverse problem for the retrieval of the soil moisture and temperature profiles based on remotely sensed observations of multispectral irradiance. A model of coherent wave radiative transfer and a model of coupled heat and moisture diffusion in porous media are combined in order to estimate the liquid volumetric water content and temperature profiles in a soil column using low-frequency passive microwave and infrared emitted radiation observations and without the use of empirical relations. The central purpose of this mainly theoretical paper is to pose the inverse problem and present the physics-based algorithm as the solution. The algorithm is tested on a basic synthetic example in order to ascertain that the retrieval is feasible. Additional work in the future is necessary and planned in order to test the algorithm with field observations, extend it to include vegetation, and refine it for detail in the specification of heterogeneity in soil types and boundary conditions     

Prelaunch spectral calibration of the atmospheric infrared sounder (AIRS)

The Atmospheric Infrared Sounder (AIRS) is a high-resolution infrared sounder launched aboard the National Aeronautics and Space Administration's Aqua satellite on May 4, 2002. AIRS is a grating spectrometer with 2378 channels located between 15 and 3.8 /spl mu/m, with nominal resolving powers of /spl nu///spl Delta//spl nu/=1200. As the first of a new generation of upcoming infrared instruments with similar spectral coverage and resolution, there will be much interest in the performance of AIRS. The ability to retrieve good atmospheric profiles from AIRS observations will depend in part upon our knowledge of the spectral response of AIRS to the upwelling radiance. This paper discusses the spectral calibration of AIRS based upon an extensive set of laboratory test data generated by the instruments prime contractor, BAE. In particular, we describe the calibration of the AIRS spectral response functions, showing that our requirement for accuracies of "1% of a width" have been achieved.     

Passive microwave remote sensing of snow constrained byhydrological simulations

This paper describes a snow parameter retrieval algorithm from passive microwave remote sensing measurements. The three components of the retrieval algorithm include a dense media radiative transfer (DMRT) model, which is based on the quasicrystalline approximation (QCA) with the sticky particle assumption, a physically-based snow hydrology model (SHM) that incorporates meteorological and topographical data, and a neural network (NN) for computational efficient inversions. The DMRT model relates physical snow parameters to brightness temperatures. The SHM simulates the mass and heat balance and provides initial guesses for the neural network. The NN is used to speed up the inversion of parameters. The retrieval algorithm can provide speedy parameter retrievals for desired temporal and spatial resolutions, Four channels of brightness temperature measurements: 19V, 19H, 37V, and 37H are used. The algorithm was applied to stations in the northern hemisphere. Two sets of results are shown. For these cases, the authors use ground-truth precipitation data, and estimates of snow water equivalent (SWE) from SHM give good results. For the second set, a weather forecast model is used to provide precipitation inputs for SHM. Additional constraints in grain size and density are used. They show that inversion results compare favorably with ground truth observations     

Soil moisture retrieval using the C-band polarimetric scanning radiometer during the Southern Great Plains 1999 Experiment

The Advanced Microwave Scanning Radiometer (AMSR) holds promise for retrieving soil moisture in regions with low levels of vegetation. Algorithms for this purpose have been proposed, but none have been rigorously evaluated due to a lack of datasets. Accordingly, the Southern Great Plains 1999 Experiment (SGP99) was designed to provide C-band datasets for AMSR algorithm development and validation. Ground observations of soil moisture and related variables were collected in conjunction with aircraft measurements using a C-band radiometer similar to the AMSR sensor (6.92 GHz), the Polarimetric Scanning Radiometer with its C-band scanhead (PSR/C). The study region has been the focus of several previous remote sensing field experiments and contains vegetation conditions compatible with the expected capabilities of C-band for soil moisture retrieval. Flights were conducted under a wide range of soil moisture conditions, thus providing a robust dataset for validation. A significant issue found in data processing was the removal of anthropogenic radio-frequency interference. Several approaches to estimating the parameters of a single-channel soil moisture retrieval algorithm were used. PSR/C soil moisture images show spatial and temporal patterns consistent with meteorological and soil conditions, and the dynamic range of the PSR/C observations indicates that the AMSR instrument can provide useful soil moisture information.     

AIRS/AMSU/HSB validation

The Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit/Humidity Sounder for Brazil (AIRS/AMSU/HSB) instrument suite onboard Aqua observes infrared and microwave radiances twice daily over most of the planet. AIRS offers unprecedented radiometric accuracy and signal to noise throughout the thermal infrared. Observations from the combined suite of AIRS, AMSU, and HSB are processed into retrievals of atmospheric parameters such as temperature, water vapor, and trace gases under all but the cloudiest conditions. A more limited retrieval set based on the microwave radiances is obtained under heavy cloud cover. Before measurements and retrievals from AIRS/AMSU/HSB instruments can be fully utilized they must be compared with the best possible in situ and other ancillary "truth" observations. Validation is the process of estimating the measurement and retrieval uncertainties through comparison with a set of correlative data of known uncertainties. The ultimate goal of the validation effort is retrieved product uncertainties constrained to those of radiosondes: tropospheric rms uncertainties of 1.0 degC over a 1-km layer for temperature, and 10% over 2-km layers for water vapor. This paper describes the data sources and approaches to be used for validation of the AIRS/AMSU/HSB instrument suite, including validation of the forward models necessary for calculating observed radiances, validation of the observed radiances themselves, and validation of products retrieved from the observed radiances. Constraint of the AIRS product uncertainties to within the claimed specification of 1 K/1 km over well-instrumented regions is feasible within 12 months of launch, but global validation of all AIRS/AMSU/HSB products may require considerably more time due to the novelty and complexity of this dataset and the sparsity of some types of correlative observations.     

Improvements and complications involved with adding an 85-GHzchannel to cloud liquid water radiometers

The improvement in cloud-liquid estimates by a microwave radiometer with the addition of measurements at 85 GHz is quantified. Atmospheric emission is simulated from radiosonde data at frequencies commonly used by ground-based water-vapor radiometers (22.235 and 31.65 GHz) and also at 85.5 GHz. Retrieval algorithms are developed from opacities based on full Mie extinction by cloud droplets and under an assumption that ice effects are not significant for downwelling emission. The algorithms use either three frequencies or only the lower two. The inclusion of 85-GHz information significantly improves liquid-water path estimates at all levels of integrated liquid water. The Rayleigh approximation is shown to be valid for most cloudy conditions. Uncertainty in the calculated opacities due to varying cloud droplet-size distributions and liquid-water content profiles is quantified. The accuracy of a retrieval algorithm trained by Rayleigh approximation opacities and including the additional uncertainty is shown to provide estimates with error levels similar to those from the algorithm trained with full Mie opacities     

A method for retrieving high-resolution surface soil moisture from hydros L-band radiometer and Radar observations

NASA's Earth System Science Pathfinder Hydrospheric States (Hydros) mission will provide the first global scale space-borne observations of Earth's soil moisture using both L-band microwave radiometer and radar technologies. In preparation for the Hydros mission, an observation system simulation experiment (OSSE) has been conducted. As a part of this OSSE, the potential for retrieving useful surface soil moisture at spatial resolutions of 9 and 3 km was explored. The approach involved optimally merging relatively accurate 36-km radiometer brightness temperature and relatively noisy 3-km radar backscatter cross section observations using a Bayesian method. Based on the Hydros OSSE data sets with low and high noises added to the simulated observations or model parameters, the Bayesian method performed better than direct inversion of either the brightness temperature or radar backscatter observations alone. The root-mean-square errors of 9-km soil moisture retrievals from the Bayesian merging method were reduced by 0.5 %vol/vol and 1.4 %vol/vol from the errors of direct radar inversions for the entire OSSE domain of all 34 consecutive days for the low and high noise data sets, respectively. Improvement in soil moisture estimates using the Bayesian merging method over the direct inversions of radar or radiometer data were even more significant for soil moisture retrieval at 3-km resolution. However, to address the representativeness of these results at the global and multiyear scales, further performance comparison studies are needed, particularly with actual field data.     

大气温度和湿度廓线的CT遥感方法仿真研究

本文讨论了ＣＴ（计算机层析扫描）技术在廓线反演中的应用，７个适合本研究特点的红外光谱通道被确定，对温度和湿度廓线截面作了数值仿真，并与常规的正对地观测反演作了比较，结果表明ＣＴ方法可以提高温度反演精度达２０％以上，而湿度的反演则表现出对先验信息的较强依赖性．     

Parameter sensitivity of soil moisture retrievals from airborne C- and X-band radiometer measurements in SMEX02

Among passive microwave frequencies, sensors operating at C- and X-band frequencies have been used with some success to estimate near-surface soil moisture from aircraft and satellite platforms. The objective of this paper is to quantify the sensitivities of soil moisture retrieved via a single-channel single-polarization algorithm to the observed brightness temperature and to retrieval algorithm parameters of surface roughness, vegetation B parameter, and single-scattering albedo. Examination of the regions within the parameter space that produce accurate soil moisture retrievals reveals that reasonably accurate retrievals can be made over a range of conditions using a fixed set of input parameters. Retrievals with horizontally polarized brightness temperature observations are more consistent than with vertically polarized observations. At horizontal polarization, sensitivity to the input parameters is much greater for wet soils than for dry soils, whereas for vertical polarization the moisture dependence is much weaker. At vertical polarization, sensitivities to variations in all parameters are much lower. To ensure that retrieval accuracy specifications are consistently met, high soil moisture conditions should be used in defining parameter accuracy requirements. Given the spatial and temporal variability of vegetation and soil conditions, it seems unlikely that, for regions with substantial rapidly growing vegetation, the accuracy requirements for model parameters in a single-frequency, single-polarization retrieval algorithm can be met with current satellite products. For such conditions, any soil moisture retrieval algorithm using parameterizations similar to those of this study may require multiple frequencies, polarizations, or look angles to produce stable, reliable soil moisture estimates.