Giuliano Liuzzi

Infrared atmospheric sounder interferometer radiometric noise assessment from spectral residuals

The problem of characterizing and estimating the radiometric noise of satellite high spectral resolution infrared spectrometers from Earth views is addressed in this paper. A methodology has been devised which is based on the common concept of spectral residuals (Observations-Calculations) obtained after spectral radiance inversion for atmospheric and surface parameters. An in-depth analytical assessment of the statistical covariance matrix of the spectral residuals has been performed which is based on the optimal estimation theory. It has been mathematically demonstrated that the use of spectral residuals to assess instrument noise leads to an effective estimator, which is largely independent of possible departures of the observational covariance matrix from the true covariances. Application to the Infrared Atmospheric Sounder Interferometer has been considered. It is shown that Earth-view-derived observation errors agree with blackbody in-flight calibration. The spectral residuals approach also proved to be effective in characterizing noise features due to mechanical microvibrations of the beam splitter of the IASI instrument.

Validation of H2O continuum absorption models in the wave number range 180–600 cm−1 with atmospheric emitted spectral radiance measured at the Antarctica Dome-C site

This work presents the results concerning the analysis of a set of atmospheric emitted (down welling) spectral radiance observations in the spectral range 180 to 1100 cm(-1) acquired at the Dome-C site in Antarctica during an extensive field campaign in 2011-2012. The work has been mainly focused on retrieving and validating the coefficients of the foreign contribution to the water vapour continuum absorption, within a spectral range overlapping the water vapour rotational band. Retrievals have been performed by using a simultaneous physical retrieval procedure for atmospheric and spectroscopic parameters. Both day (summer) and night (winter) spectra have been used in our analysis. This new set of observations in the far infrared range has allowed us to extend validation and verification of state-of-art water vapour continuum absorption models down to 180 cm(-1). Results show that discrepancies between measurements and models are less than 10% in the interval 350-590 cm(-1), while they are slightly larger at wave numbers below 350 cm(-1). On overall, our study shows a good consistency between observations and state-of-art models and provides evidence toward needing to adjust absorptive line strengths. Finally, it has been found that there is a good agreement between the coefficients retrieved using either summer or winter spectra, which are acquired in far different meteorological conditions.

Demonstration of random projections applied to the retrieval problem of geophysical parameters from hyper-spectral infrared observations.

The random projections statistical technique has been used to reduce the dimensionality of the radiance data space generated from high spectral resolution infrared observations. The mathematical inversion of the physical radiative transfer equation for geophysical parameters has been solved in this space of reduced dimensionality. The great advantage of using random projections is that they provide an unified treatment of instrument noise and forward model error, which can be comprehensively modeled with a single variance term. The result is a novel retrieval approach, which combines computational efficiency to possibly improved accuracy of the retrieval products. The novel approach has been demonstrated through application to the Infrared Atmospheric Sounding Interferometer. We have found that state-of-the-art spectroscopy and related line-mixing treatment for the ν2CO2 absorption band, i.e., the fundamental band for temperature retrieval, show an excellent consistency with satellite observations.

Evaluation of Radiative Transfer Models With Clouds

Data from hyperspectral infrared sounders are routinely ingested worldwide by the National Weather Centers. The cloud-free fraction of this data is used for initializing forecasts which include temperature, water vapor, water cloud, and ice cloud profiles on a global grid. Although the data from these sounders are sensitive to the vertical distribution of ice and liquid water in clouds, this information is not fully utilized. In the future, this information could be used for validating clouds in National Weather Center models and for initializing forecasts. We evaluate how well the calculated radiances from hyperspectral Radiative Transfer Models (RTMs) compare to cloudy radiances observed by AIRS and to one another. Vertical profiles of the clouds, temperature, and water vapor from the European Center for Medium-Range Weather Forecasting were used as input for the RTMs. For nonfrozen ocean day and night data, the histograms derived from the calculations by several RTMs at 900 cm 1 have a better than 0.95 correlation with the histogram derived from the AIRS observations, with a bias relative to AIRS of typically less than 2 K. Differences in the cloud physics and cloud overlap assumptions result in little bias between the RTMs, but the standard deviation of the differences ranges from 6 to 12 K. Results at 2,616 cm 1 at night are reasonably consistent with results at 900 cm . Except for RTMs which use full scattering calculations, the bias and histogram correlations at 2,616 cm 1 are inferior to those at 900 cm 1 for daytime calculations. Plain Language Summary Getting the right clouds of the right type, at the right time and location in Global Circulation Models, is key to getting the local energy balance right. This is key to an accurate forecast. If the clouds are of the wrong type or at the wrong location or time, the accuracy of the forecast is degraded. We evaluate the accuracy of the best currently available cloud description (produced by the European Center for Medium-Range Weather Forecasting) by comparing the radiances calculated using Radiative Transfer Models (RTMs) from six major development teams to cloudy radiances observed by the Atmospheric Infrared Sounder at the same location and time. The better RTMs fit statistically reasonably well in the 11-μm atmospheric window area, with little latitude (zonal) and day/night cloud-type related bias. None of the RTMs fit well in the 4-μm atmospheric window area during daytime, unless the calculations use full scattering. With the current state of art, all major RTMs would be suitable to start the validation of cloud effects in the National Weather Center models using just one 11-μm atmospheric window channel.

Search for Martian methane with TES data: development of a dedicated radiative transfer code: first results

In the present work we describe a dedicated and fast monochromatic radiative transfer code, developed for computing Martian radiance spectra as seen by the Thermal Emission Spectrometer (TES), and its Jacobians with respect to gas, dust aerosol and ice concentrations, atmospheric temperatures, and surface emissivity. The code accuracy has been tested comparing its results with a state-of-art line-by-line radiative transfer model, and it has been optimized for simulating nadir-viewing spectra. The model is well-suited to simulate spectra with di erent amounts of methane in the atmosphere, whose detection is currently one of the most fascinating issues concerning the Martian atmospheric chemistry and planetary dynamics.

Physical Retrieval of Land Surface Emissivity Spectra from Hyper-Spectral Infrared Observations and Validation with In Situ Measurements

A fully physical retrieval scheme for land surface emissivity spectra is presented, which applies to high spectral resolution infrared observations from satellite sensors. The surface emissivity spectrum is represented with a suitably truncated Principal Component Analysis (PCA) transform and PCA scores are simultaneously retrieved with surface temperature and atmospheric parameters. The retrieval methodology has been developed within the general framework of Optimal Estimation and, in this context, is the first physical scheme based on a PCA representation of the emissivity spectrum. The scheme has been applied to IASI (Infrared Atmospheric Sounder Interferometer) and the retrieved emissivities have been validated with in situ observations acquired during a field experiment carried out in 2017 at Gobabeb (Namib desert) validation station. It has been found that the retrieved emissivity spectra are independent of background information and in good agreement with in situ observations.

Hyper fast radiative transfer for the physical retrieval of surface parameters from SEVIRI observations

This paper describes the theoretical aspects of a fast scheme for the physical retrieval of surface temperature and emissivity from SEVIRI data, their implementation and some sample results obtained. The scheme is based on a Kalman Filter approach, which effectively exploits the temporal continuity in the observations of the geostationary Meteosat Second Generation (MSG) platform, on which SEVIRI (Spinning Enhanced Visible and InfraRed Imager) operates. Such scheme embodies in its core a physical retrieval algorithm, which employs an hyper fast radiative transfer code highly customized for this retrieval task. Radiative transfer and its customizations are described in detail. Fastness, accuracy and stability of the code are fully documented for a variety of surface features, showing a peculiar application to the massive Greek forest fires in August 2007.

SEVIRI Cloud mask by Cumulative Discriminant Analysis

In the context of cloud detection for satellite observations we want to use the method of Cumulative Discriminant Analysis (CDA) as a tool to distinguish between clear and cloudy sky applied to Spinning Enhanced Visible and Infrared Imager (SEVIRI) data. The methodology is based on the choice of several statistics related to the cloud properties, whose correlation has been analyzed by Principal Component Analysis (PCA). Results have been compared with the SEVIRI reference cloud mask provided by the European Centre for the Exploitation of Meteorological Satellite (EUMETSAT), in order to find suitable thresholds able to discriminate between clear or cloudy conditions. We trained the statistics on a selected region, the Basilicata area, located in the south of Italy, in different periods of the year 2012, in order to take into account the seasonal variability. Moreover we separated land and sea surface and distinguished between day-time or night-time. The validation of thresholds, obtained through SEVIRI observations analysis, shows a good agreement with the reference cloud mask.

Simultaneous physical retrieval of Martian geophysical parameters using Thermal Emission Spectrometer spectra: the φ-MARS algorithm

In this paper, we present a new methodology for the simultaneous retrieval of surface and atmospheric parameters of Mars. The methodology is essentially based on similar codes implemented for high-resolution instruments looking at Earth, supported by a statistical retrieval procedure used to initialize the physical retrieval algorithm with a reliable first guess of the atmospheric parameters. The methodology has been customized for the Thermal Emission Spectrometer (TES), which is a low-resolution interferometer. However, with minor changes to the forward and inverse modules, it is applicable to any instrument looking at Mars, and with particular effectiveness to high-resolution instruments. The forward module is a monochromatic radiative transfer model with the capability to calculate analytical Jacobians of any desired geophysical parameter. In the present work, we describe the general methodology and its application to a large sample of TES spectra. Results are drawn for the case of surface temperature and emissivity, atmospheric temperature profile, water vapor, and dust and ice mixing ratios. Comparison with climate models and other TES data analyses show very good agreement and consistency.

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.