Guided by radiative transfer modeling of the effects of dust (aerosol) on satellite thermal infrared radiance by many different imaging radiometers, in this article, we present the aerosol-effected satellite radiative signal changes in the top of atmosphere (TOA). The simulation of TOA radiance for Infrared Atmospheric Sounding Interferometer (IASI) is performed by using the RTTOV fast radiative transfer model. The model computation is carried out with setting representative geographical atmospheric models and typical default aerosol climatological models under clear sky condition. The radiative differences (in units of equivalent black body brightness temperature differences (BTDs)) between simulated radiances without consideration of the impact of aerosol (Aerosol-free) and with various aerosol models (Aerosol-modified) are calculated for the whole IASI spectrum between 3.62 and 15.5μm. The comparisons of BTDs are performed through 11 aerosol models in 5 classified atmospheric models. The results show that the Desert aerosol model has the most significant impact on IASI spectral simulated radiances than the other aerosol models (Continental, Urban, Maritime types and so on) in Mid-latitude Summer, contributing to the mineral aerosol components contained. The value of BTDs could reach up to 1K at peak points. The atmospheric window spectral region between 900 and 1100cm−1 (9.09–11.11μm) is concentrated after the investigation for the largest values of aerosol-affected radiance differences. BTDs in IASI spectral region between 645 and 1200cm−1 occupies the largest oscillation and the major part of the whole spectrum. The IASI highest window peak-points channels (such as 9.4 and 10.2μm) are obtained finally, which are the most sensitive ones to the simulated IASI radiance.

Abstract. The first results of the Fast Linear Inversion Trace gas System (FLITS) retrieval scheme are presented here for CO from IASI (Infrared Atmospheric Sounding Interferometer) measurements using RAQMS (Real time Air Quality Modelling System) as atmospheric background. FLITS is a simple linear inversion scheme with a stable performance that retrieves total column CO concentrations (molec cm−2) at single field-of-view (FOV) irrespective of cloud cover. A case study is presented here for a biomass burning plume over the Pacific on 29 March 2010. For each FOV a single tropospheric CO density, vertically integrated over 200–800 hPa, is retrieved with 12 channels in the spectral range 2050–2225 cm−1. Despite variations in cloud cover and temperature, the degrees of freedom for signal (DFS) of the solution ranges between 0.8 and 0.95. In addition, the retrieval error is at least half the background error of 10 %, with dominant contribution from uncertainty in the measurement and temperature. With its stability and processing speed, FLITS meet two of the key requirements for operational processing. We conclude that the linear combination of space-borne measurements with a chemical transport model in the FLITS retrieval scheme holds potential for real-time air quality monitoring and evaluation of pollutant transport at high spatial resolution.

The influence is investigated of the assumed ice particle microphysical and optical model on inferring ice cloud optical thickness (τ) from satellite measurements of the Earth's reflected shortwave radiance. Ice cloud τ are inferred, and subsequently compared, using products from MODIS (MODerate resolution Imaging Spectroradiometer) and POLDER (POLarization and Directionality of the Earth's Reflectances). POLDER τ values are found to be substantially smaller than those from collocated MODIS data. It is shown that this difference is caused primarily by the use of different ice particle bulk scattering models in the two retrievals, and more specifically, the scattering phase function. Furthermore, the influence of the ice particle model on the derivation of ice cloud radiative forcing (CRF) from satellite retrievals is studied. Three sets of shortwave CRF are calculated using different combinations of the retrieval and associated ice particle models. It is shown that the uncertainty associated with an ice particle model may lead to two types of errors in estimating CRF from satellite retrievals. One stems from the retrieval itself and the other is due to the optical properties, such as the asymmetry factor, used for CRF calculations. Although a comparison of the CRFs reveals that these two types of errors tend to cancel each other, significant differences are still found between the three CRFs, which indicates that the ice particle model affects not only optical thickness retrievals but also CRF calculations. In addition to CRF, the effect of the ice particle model on the derivation of seasonal variation of τ from satellite measurements is discussed. It is shown that optical thickness retrievals based on the same MODIS observations, but derived using different assumptions of the ice particle model, can be substantially different. These differences can be divided into two parts. The first-order difference is mainly caused by the differences in the asymmetry factor. The second-order difference is related to seasonal changes in the sampled scattering angles and therefore dependent on the sun-satellite viewing geometry. Because of this second-order difference, the use of different ice particle models may lead to a different understanding of the seasonal variation of τ.

Abstract. The influence is investigated of the assumed ice particle microphysical and optical model on inferring ice cloud optical thickness (τ) from satellite measurements of the Earth's reflected shortwave radiance. Ice cloud τ are inferred, and subsequently compared, using products from MODIS (MODerate resolution Imaging Spectroradiometer) and POLDER (POLarization and Directionality of the Earth's Reflectances). POLDER τ values are found to be substantially smaller than those from collocated MODIS data. It is shown that this difference is caused primarily by the use of different ice particle bulk scattering models in the two retrievals, and more specifically, the scattering phase function. Furthermore, the influence of the ice particle model on the derivation of ice cloud radiative forcing (CRF) from satellite retrievals is studied. Three sets of shortwave CRF are calculated using different combinations of the retrieval and associated ice particle models. It is shown that the uncertainty associated with an ice particle model may lead to two types of errors in estimating CRF from satellite retrievals. One stems from the retrieval itself and the other is due to the optical properties, such as the asymmetry factor, used for CRF calculations. Although a comparison of the CRFs reveals that these two types of errors tend to cancel each other, significant differences are still found between the three CRFs, which indicates that the ice particle model affects not only optical thickness retrievals but also CRF calculations. In addition to CRF, the effect of the ice particle model on the derivation of seasonal variation of τ from satellite measurements is discussed. It is shown that optical thickness retrievals based on the same MODIS observations, but derived using different assumptions of the ice particle model, can be substantially different. These differences can be divided into two parts. The first-order difference is mainly caused by the differences in the asymmetry factor. The second-order difference is related to seasonal changes in the sampled scattering angles and therefore dependent on the sun-satellite viewing geometry. Because of this second-order difference, the use of different ice particle models may lead to a different understanding of the seasonal variation of τ.

We present a general methodology and first results of total column trace gas retrievals from hyperspectral infrared observations. More specifically, we made use of a physical scheme to retrieve methane (CH 4 ) profiles from atmospheric infrared sounder (AIRS) measurements. The method firstly required a forward calculation that maps the a priori state vector into measurement space, and for this we used the fast forward model, SARTA. Secondly, the calculation was inverted to derive at a posterior state of the atmosphere. We used gas profiles from the atmospheric chemistry experiment (ACE) together with ECMWF model fields for the assimilation of the state vector. ACE profiles were also used in the validation procedure. The retrievals were improved by up to 5% at the surface. Following the same scheme, we retrieved total column concentration of CH 4 for an entire AIRS granule, thereby demonstrating its potential for use in operational air quality monitoring schemes at regional scales.

Low density parity check (LDPC) codes have the ability to transmit data at rates approaching channel capacity with very low error probability. Our study of LDPC codes on JPEG2000-compressed ultraspectral sounder data shows that for an LDPC encoder designed with certain input parameters, the LDPC decoder is able to correct all single errors and two-bit burst errors. Furthermore, our analysis on a massive variety of 3- and 4-bit burst error scenarios indicates that the locations of LDPC-uncorrected errors for 3- and 4-bit burst errors appear to be fixed, regardless of the content of the input data. We propose a look-up table based error correction method to correct errors surviving from the LDPC decoder for 3- and 4-bit burst errors

Numerical simulations have been carried out to understand the effects of overlapping cirrus clouds and mineral dust on the high‐spectral‐resolution infrared spectrum in the 600–2400 cm−1 region. A combination of the discrete ordinates radiative transfer and line‐by‐line models that account for the multiple scattering and monochromatic molecular absorption in the atmosphere is utilized to simulate the down‐looking infrared spectrum at the top of the atmosphere with a resolution of 0.2 cm−1. It is demonstrated that the spectral slope of the infrared radiance in the 800–1000 cm−1 region can be used to discriminate coexisting cirrus‐dust scenes from those associated only with cirrus clouds or dust alone. In a case for a cirrus cloud overlapping with a dust layer, the spectral features in the 1100–1200 cm−1 and 1400–1850 cm−1 regions can be potentially useful for retrieving the optical thicknesses of the dust layer and cirrus cloud, respectively.

Reversible variable-length codes (RVLCs) allow instantaneous decoding in both directions, which affords better detection of bit errors due to synchronization losses over a noisy channel. Earlier, we developed 3D wavelet reversible variable-length coding (3DWT-RVLC) for lossless compression of ultraspectral sounder data, which has significantly better error resilience than JPEG2000 Part 2 at only a small reduction in compression gain [1]. To explore the feasibility of 3DWT-RVLC for real-time satellite onboard processing, we implement a memory-limited DSP version of 3DWT-RVLC. Experimental results for the 10 AIRS ultraspectral test granules show that the DSP-based 3DWT-RVLC yields an average compression ratio of 2.36, comparable to 2.51 of the original 3DWT-RVLC from our previous work.

A revolutionary satellite weather forecasting instrument, called the "GIFTS" which stands for the "Geostationary Imaging Fourier Transform Spectrometer", was recently completed and successfully tested in a space chamber at the Utah State University's Space Dynamics Laboratory. The GIFTS was originally proposed by the NASA Langley Research Center, the University of Wisconsin, and the Utah State University and selected for flight demonstration as NASA's New Millennium Program (NMP) Earth Observing-3 (EO-3) mission, which was unfortunately cancelled in 2004. GIFTS is like a digital 3-d movie camera that, when mounted on a geostationary satellite, would provide from space a revolutionary four-dimensional view of the Earth's atmosphere. GIFTS will measure the distribution, change, and movement of atmospheric moisture, temperature, and certain pollutant gases, such as carbon monoxide and ozone. The observation of the convergence of invisible water vapor, and the change of atmospheric temperature, provides meteorologists with the observations needed to predict where, and when, severe thunderstorms, and possibly tornados, would occur, before they are visible on radar or in satellite cloud imagery. The ability of GIFTS to observe the motion of moisture and clouds at different altitudes enables atmospheric winds to be observed over vast, and otherwise data sparse, oceanic regions of the globe. These wind observations would provide the means to greatly improve the forecast of where tropical storms and hurricanes will move and where and when they will come ashore (i.e., their landfall position and time). GIFTS, if flown into geostationary orbit, would provide about 80,000 vertical profiles per minute, each one like a low vertical resolution (1-2km) weather balloon sounding, but with a spacing of 4 km. GIFTS is a revolutionary atmospheric sensing tool. A glimpse of the science measurement capabilities of GIFTS is provided through airborne measurements with the NPOESS Airborne Sounding Testbed - Interferometer (NAST-I).

Hyperspectral remote sounding was introduced with the High spectral resolution Interferometer Sounder (HIS) that flew on the NASA ER-2 aircraft in the mid-1980s. The results from the HIS demonstrated that high vertical resolution sounding information could be achieved using quasi-continuous spectra of the atmosphere's radiance to space. This has led to a series of research and operational satellite instruments designed to exploit the hyperspectral resolution sounding approach. The experimental versions, the ADEOS IMG (Interferometer for the Measurement of trace Gases) and the Aqua AIRS (Atmospheric InfraRed Sounder) have already been orbited. The IASI (Infrared Atmospheric Sounding Interferometer) and the CrIS (Cross-track Infrared Sounder) instruments are soon to be orbited on the METOP and the NPP/NPOESS operational series of polar orbiting satellites, respectively. Geostationary satellite hyperspectral resolution sounding instrumentation was initiated with the experimental GIFTS (Geostationary Imaging Fourier Transform Spectrometer) instrument whose development is providing risk reduction for the next generation of operational geostationary satellite instruments (e.g., the GOES-R Hyperspectral Environmental Suite, HES).This presentation traces the evolution of the hyperspectral resolution sounding program. Intercomparisons of the different satellite instrument approaches are discussed. Experimental results from the current aircraft and experimental satellite systems are presented to demonstrate the power of the hyperspectral resolution sounding technique.

The Moderate Resolution Imaging Spectroradiometer (MODIS) and the Atmospheric Infrared Sounder (AIRS) measurements from the NASA Earth Observing System Aqua satellite enable global monitoring of the distribution of clouds during day and night. The MODIS is able to provide a high-spatial-resolution (1–5 km) cloud mask, cloud classification mask, cloud-phase mask, cloud-top pressure (CTP), and effective cloud amount during both the daytime and the nighttime, as well as cloud particle size (CPS) and cloud optical thickness (COT) at 0.55 m during the daytime. The AIRS high-spectral-resolution measurements reveal cloud properties with coarser spatial resolution (13.5 km at nadir). Combined, MODIS and AIRS provide cloud microphysical properties during both the daytime and nighttime. A fast cloudy radiative transfer model for AIRS that accounts for cloud scattering and absorption is described in this paper. Onedimensional variational (1DVAR) and minimum-residual (MR) methods are used to retrieve the CPS and COT from AIRS longwave window region (790–970 cm 1 or 10.31–12.66 m, and 1050–1130 cm 1 or 8.85–9.52 m) cloudy radiance measurements. In both 1DVAR and MR procedures, the CTP is derived from the AIRS radiances of carbon dioxide channels while the cloud-phase information is derived from the collocated MODIS 1-km phase mask for AIRS CPS and COT retrievals. In addition, the collocated 1-km MODIS cloud mask refines the AIRS cloud detection in both 1DVAR and MR procedures. The atmospheric temperature profile, moisture profile, and surface skin temperature used in the AIRS cloud retrieval processing are from the European Centre for Medium-Range Weather Forecasts forecast analysis. The results from 1DVAR are compared with the operational MODIS products and MR cloud microphysical property retrieval. A Hurricane Isabel case study shows that 1DVAR retrievals have a high correlation with either the operational MODIS cloud products or MR cloud property retrievals. 1DVAR provides an efficient way for cloud microphysical property retrieval during the daytime, and MR provides the cloud microphysical property retrievals during both the daytime and nighttime.

The Atmospheric Infrared Sounder (AIRS) on National Aeronautics and Space Administration’s Earth Observing System (EOS) Aqua satellite is a high spectral resolution (ν / ν ∆ =1200, where ν is the wavenumber and ν ∆ is full-width half maximum of a channel) infrared (IR) sounder with 2378 channels. AIRS measures radiances in the IR range 3.74-15.4 μ m, which can be used to estimate the atmospheric vertical temperature and water vapor profiles from the Earth’s surface to an altitude of 40km with a horizontal spatial resolution of 13.5km at nadir. Due to its relatively poor spatial resolution, the chance of an AIRS footprint to be completely clear is less than 10% statistically (Huang and Smith, 2004). One of the important questions is how to effectively perform cloud-clearing (CC) for the AIRS cloudy footprints, while still retaining the single footprint sounding gradient information for numerical weather prediction. Smith et al. (2004) combine Moderate Resolution Imaging Spectroradiometer (MODIS) IR clear radiances and AIRS cloudy for CC using the traditional single band N approach. In our study, an optimal CC method is developed to retrieve the AIRS clear column radiances by combing the multiband MODIS IR clear radiance observations and the AIRS cloudy radiances on a single footprint basis. The definition of N (Smith, 1968) is used as the basis in the optimal MODIS/AIRS CC formulation, while nine IR

The Atmospheric Infrared Sounder (AIRS) onboard the National Aeronautics and Space Administration's Earth Observing System's (EOS) Aqua spacecraft, with its high spectral resolution and radiometric accuracy, provides atmospheric vertical temperature and moisture sounding information with high vertical resolution and accuracy for numerical weather prediction (NWP). Due to its relatively coarse spatial resolution (13.5 km at nadir), the chance for an AIRS footprint to be completely cloud free is small. However, the Moderate Resolution Imaging Spectroradiometer (MODIS), also on the Aqua satellite, provides colocated clear radiances at several spectrally broad infrared (IR) bands with 1-km spatial resolution; many AIRS cloudy footprints contain clear MODIS pixels. An optimal cloud-correction or cloud-clearing (CC) algorithm, an extension of the traditional single-band N/sup */ technique, is developed. The technique retrieves the hyperspectral infrared sounder clear column radiances from the combined multiband imager IR clear radiance observations with high spatial resolution and the hyperspectral IR sounder cloudy radiances on a single-footprint basis. The concurrent AIRS and MODIS data are used to verify the algorithm. The AIRS cloud-removed or cloud-cleared radiance spectrum is convolved to all the possible MODIS IR spectral bands with spectral response functions (SRFs). The convoluted cloud-cleared brightness temperatures (BTs) are compared with MODIS clear BT observations within AIRS cloud-cleared footprints passing our quality tests. The bias and the standard deviation between the convoluted BTs and MODIS clear BT observations is less than 0.25 and 0.5 K, respectively, over both water and land for most MODIS IR spectral bands. The AIRS cloud-cleared BT spectrum is also compared with its nearby clear BT spectrum, the difference, accounting the effects due to scene nonuniformity, is reasonable according to the analysis. The multiband optimal cloud-clearing is also compared with the traditional single-band N/sup */ cloud-clearing; the performance enhancement of the optimal cloud-clearing over the single-band traditional N/sup */ cloud-clearing is demonstrated and discussed. It is found that more than 30% of the AIRS cloudy (partly and overcast) footprints in this study have been successfully cloud-cleared using the optimal cloud-clearing method, revealing the potential application of this method to the operational processing of hyperspectral IR sounder cloudy radiance measurements when the collocated imager IR data are available. The use of a high spatial resolution imager, along with information from a high spectral resolution sounder for cloud-clearing, is analogous to instruments planned for the next-generation Geostationary Operational Environmental Satellite (GOES-R) instruments-the Advanced Baseline Imager and the Hyperspectral Environmental Suite. Since no microwave instruments are being planned for GOES-R, the cloud-clearing methodology demonstrated in this paper will become the most practical approach for obtaining the reliable clear-column radiances.

The unprecedented size of ultraspectral sounder data makes its compression a challenging task. Ultraspectral sounder data features strong correlations in disjoint spectral regions affected by the same type of absorbing gases. Previously, we proposed a reordering scheme to better explore these correlations of the ultraspectral sounder data. With this preprocessing scheme, the state-of-the-art compression algorithms such as CALIC, JPEG-LS and JPEG2000 significantly improve the compression ratios up to 15% on average. In this paper, we investigate the effects of different starting channels for spectral reordering on the lossless compression of 3D ultraspectral sounder data obtained from Atmospheric Infrared Sounder (AIRS) observations. It is shown that the compression ratios and reordering indices are dependent on the choice of the starting channel for reordering.

Improvements in weather and climate observation, analysis, and prediction will be achieved through advances of contemporary and future ultraspectral infrared sounders such as Atmospheric Infrared Sounder (AIRS), Tropospheric Emission Spectrometer (TES), Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS), and Hyperspectral Environmental Suite (HES). Given their unprecedented 3D data sizes to be generated each day, the use of robust data compression techniques will be beneficial to data transfer and archive. Lossless or near-lossless compression of this ultraspectral sounder data is desired to avoid potentially significant degradation of the geophysical parameter retrieval in an associated ill-posed inverse problem. In this paper we investigate various 2D and 3D compression techniques applicable to ultraspectral sounder data. These techniques include transform-based (JPEG2000, 3D-SPIHT), prediction-based (JPEG-LS, CALIC), and clustering-based (PVQ, DPVQ, PPVQ) compression methods. Data preprocessing schemes for compression gains are also illustrated.

The single-scattering properties of ice particles in the near- through far-infrared spectral region are computed from a composite method that is based on a combination of the finite-difference time-domain technique, the T-matrix method, an improved geometrical-optics method, and Lorenz-Mie theory. Seven nonspherical ice crystal habits (aggregates, hexagonal solid and hollow columns, hexagonal plates, bullet rosettes, spheroids, and droxtals) are considered. A database of the single-scattering properties for each of these ice particles has been developed at 49 wavelengths between 3 and 100 microm and for particle sizes ranging from 2 to 10,000 microm specified in terms of the particle maximum dimension. The spectral variations of the single-scattering properties are discussed, as well as their dependence on the particle maximum dimension and effective particle size. The comparisons show that the assumption of spherical ice particles in the near-IR through far-IR region is generally not optimal for radiative transfer computation. Furthermore, a parameterization of the bulk optical properties is developed for mid-latitude cirrus clouds based on a set of 21 particle size distributions obtained from various field campaigns.