Guido Masiello

Spectrally resolved observations of atmospheric emitted radiance in the H2O rotation band

[1]xa0This paper presents the project Earth Cooling by Water Vapor Radiation, an observational programme, which aims at developing a database of spectrally resolved far infrared observations, in atmospheric dry conditions, in order to validate radiative transfer models and test the quality of water vapor continuum and line parameters. The project provides the very first set of far-infrared spectral downwelling radiance measurements, in dry atmospheric conditions, which are complemented with Raman Lidar-derived temperature and water vapor profiles.

Qualifying IMG tropical spectra for clear sky

The problem of cloud detection for the Interferometric Monitoring of Greenhouse Gases spectrometer has been addressed by considering a set of thresholding tests which takes full advantage of the high spectral resolution of the sensor. The methodology has been applied to a case study consisting of spectra recorded in the tropics on sea surface, although the scheme may be easily extended to other latitudes. The algorithm is very efficient because it uses only the observed spectrum and no on-line radiative transfer calculation is needed. Based on this cloud detection scheme a set of clear-sky tropical spectra have been identified to be used by the scientific community for further studies such as retrieval of atmospheric properties and high spectral resolution radiative transfer modeling.

Dimensionality‐reduction approach to the thermal radiative transfer equation inverse problem

[1]xa0An original algorithm is illustrated for the inversion of geophysical parameters from spectral observations in the thermal band. The algorithm exploits the Hotelling transform and projects the linearized version of the radiative transfer equation in a space of reduced dimensionality. The inversion is performed in this latter space, which speeds up the computations and makes the method attractive for real-time retrieval from high spectral resolution infrared observations.

Homomorphism between cloudy and clear spectral radiance in the 800–900-cm -1 atmospheric window region

The sensitivity of a new algorithm for cloud detection over a sea surface has been assessed on the basis of extensive simulations of clear and cloudy radiance spectra, including water and ice and low- and high-altitude clouds. The new algorithm makes use of autocorrelation and cross correlation between an observed spectrum and either a synthetic or a laboratory spectrum and can be used to determine quantitatively the degree of homogeneity of two spectra in the 800-900-cm(-1) region (11.11-12.5 microm). The scheme is intended for high-spectral-resolution observations and could form the basis for an operational stand-alone cloud-detection algorithm for next-generation sounding spectrometers. Application of the scheme to real observations is presented and discussed.

Retrieving N2O from nadir-viewing infrared spectrometers

The paper describes and demonstrates a methodology for the physical retrieval of nitrous oxide that uses the spectral radiance measured by the next generation of high-resolution satellite-borne infrared sensors. The performance of the retrieval scheme has been assessed on the basis of numerical exercises. Examples of retrievals based on Interferometric Monitoring of Greenhouse Gases (IMG) spectra measured over the sea surface are given to demonstrate the ability of the scheme to obtain accurate N2O concentration values.

IMG evidence of chlorofluorocarbon absorption in the atmospheric window region 800–900 cm−1

Abstract Infrared spectra recorded by the interferometric monitoring of greenhouse gases sensor have been analysed to assess the effect of the molecule CCl3F in the atmospheric window region 800–900 cm −1 . The analysis has been carried out at a sampling rate of 0.25 cm −1 which is typical of next generation space borne infrared sensors such as the European infrared atmospheric sounding interferometer. It has been found that at this spectral resolution absorption by CCl3F is clearly evident and mostly effective in the spectral range 840–860 cm −1 where it may account for up to 1K depletion in the brightness temperature spectrum. It will be shown that a CCl3F concentration of 270pptv (mixing ratio) fits very well to the spectral observations. Our findings show that chlorofluorocarbons may play an interfering role in determining surface or close to surface geophysical parameters. On the other hand, the fact that these compounds are clearly identifiable in infrared spectra opens the way to an effective monitoring of their presence and abundance from space.

Fourier Transform Spectroscopy with Partially Scanned Interferograms as a Tool to Retrieve Atmospheric Gases Concentrations from High Spectral Resolution Satellite Observations - Methodological Aspects and Application to IASI

In the context of infrared remote sensing, the idea of using partially scanned interferograms for the retrieval of atmospheric parameters dates back to Kyle (1977) who argued that large portions of the spectrum (the Fourier transform of the interferogram and vice versa) could bring poor or no information for a given atmospheric parameter, whereas small ranges in the interferogram domain could concentrate much information about the parameter at hand. Kyle (1977) exemplified the technique for temperature, whereas a correlation interferometer was proposed for the observation of atmospheric trace gases by Goldstein et al. (1978). The direct inversion of small segments of interferometric radiances for the purpose of temperature retrieval was further analyzed and exemplified in Smith et al. (1979).

σ-IASI-β: a hyperfast radiative transfer code to retrieve surface and atmospheric geophysical parameters

This paper describes an improved, faster, implementation of the σ-IASI model, with a new parameterization of radiative transfer in cloudy atmosphere. The model can compute up and/or downwelling spectral radiances, emitted from the Earth’s system and their analytical Jacobians with respect to a set of geophysical parameters and the water vapour and carbon dioxide continua absorbing coefficients. The paper presents also its software implementation and a retrieval exercise of the tropospheric content of CO2, CO, N2O and CH4 on the Mediterranean Sea. The content of the gases is compared with the ground-based measurements of the Global Atmosphere Watch network. The innovation introduced in the model is the down-sampling of the look-up table by means of a spectral averaging of the layer optical depths on bins of 10 −2 cm −1 width before they are parameterized as a low order polynomial of temperature and, only for water vapour, of water vapour concentration itself to take into account the self-broadening effect. The down-sampling of the look-up table is responsible for an additional speed-up which makes the code useful for almost real time retrieval applications and thus useful for operational purposes. This code is a powerful tool also to check the validity of the molecular spectroscopic parameters. It is an evolution of the well-known code σ-IASI. It has been developed in the context of the Infrared Atmospheric Sounding Interferometer (IASI) of the European Space Agency EUMETSAT, but it is well suited for every nadir viewing satellite, airplane sensor or ground-based sensor with a sampling rate in the range 0.1-2 cm -1 .

Four years of IASI CO2, CH4, N2O retrievals: validation with in situ observations from the Mauna Loa station

IASI (Infrared Atmospheric Sounder Interferometer) soundings for the years 2014 to 2017 over sea surface for the Hawaii region have been used to retrieve column amount of CO2, CH4, N2O. The analysis allowed us to derive CO2, CH4 and N2O growth rates, trend and seasonality, which have been compared to in situ observations from the Mauna Loa validation station. Day and night soundings have been used. During the day, for CO2 and N2O we make specifically use of the IASI short wave band (2000 to 2250 cm-1), which is sensitive to sun radiation. Our forward/inverse module deals with sun radiation using a Cox-Munck model for the bidirectional reflectance distribution function. This makes it possible to exploit IASI soundings in sun-glint or close to sun-glint mode, which improves sensitivity of retrievals close to the surface. The analysis has been performed with our total IASI level 2 processor or τ2IP, which uses the whole IASI spectral coverage, therefore making it possible to exploit the whole information content of data. The code τ2IP also uses a random projection approach to reduce the dimensionality of the data space. Our analysis show that growth rate, trend and seasonality are extracted with high accuracy (we observe correlation with in situ data close or higher than 0.90). After validation, we have applied τ2IP to seven years of data over the Arctic sea basin and computed summer maps (July to September) of CO2 and sea skin temperature. The results show that the increase of skin temperature parallels the increase of CO2 column amount over the Arctic basin.

Dimensionality reduction through random projection for application to the retrieval of atmospheric parameters from hyperspectral satellite sensors

Physical inverse problems found on appropriate forward models, which can have highly systematic errors. As an example, in remote sensing from satellite observations, the forward model depends on spectroscopy of atmospheric gas molecules and radiative transfer modelling, whose accuracy is not perfect. The problem of correctly addressing both error components (instrument and forward model) is one of major concern in retrieval methodology. Until now, the treatment has relied on ad-hoc strategies, which makes the retrieval algorithms sub-optimal or nonoptimal at all. Optimal estimation is based on the Gaussian assumption for noise, which is normally not satisfied in presence of forward model error. In this paper, we will show that a proper Random Projections approach can provide a) an unified and coherent treatment of systematic and random errors; b) a compression tool, which can reduce the dimensionality of the data space; c) a noise model which is truly Gaussian therefore, making it possible to apply rigorously Optimal Estimation and derive the correct retrieval error; d) a simplified treatment of the inverse algebra to get the final solution. The present paper addresses the specific point of how to fully exploit the compression capability of random projections to develop an inverse algorithm able to deal with big data, and minimal loss of information content. The approach will be exemplified for IASI (Infrared Atmospheric Sounder Interfermoter) and we will show the very first physical retrieval scheme, which exploits the full IASI spectral coverage for the simultaneous retrieval of surface and atmospheric parameters. The methodology can be applied to any inverse physical problem dealing with high-dimensionality data space, how normally arises in astrophysical and Earth remote sensing science. The performance of the methodology for the retrieval of temperature and water profiles has been assessed through comparison with radiosonde observations. The retrieval accuracy, for a tropical atmosphere, is better than ± 1.25 K and ± 1.5 g/kg for temperature and water vapour, respectively. We have also performed a retrieval exercise for the Eastern China and we have shown that air quality gases, such as CO, SO2 and NH3 can be simultaneously and confidently retrieved, meaning that Random Projections preserve information content of data.