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Nadia Fourrié, Chantal Claud, and Alain Chédin

Abstract

At the end of December 1999, two extremely severe storms only one day apart affected western Europe and caused considerable damage. A variable derived from satellite observations, the so-called temperature of the lower stratosphere (TLS), is used in this study for detecting and tracking the upper-level components of these storms. TLS is computed from a regression over five Television and Infrared Observation Satellite (TIROS-N) Operational Vertical Sounder (TOVS, aboard NOAA satellites) channels, with coefficients calculated from a climatological dataset [thermodynamical initial-guess retrieval (TIGR)], and provides information on the temperature near the tropopause. The objective of this paper is to assess the ability of TLS, in situations such as these two exceptional storms, to track and depict upper-tropospheric precursors of surface lows. After a brief synoptic description of the meteorological situation, TLS fields as well as the Action de Recherche Petite Échelle Grand Échelle (ARPEGE) model fields (mean sea level pressure, temperature, wind velocity, and geopotential height of the dynamical tropopause) are discussed concurrently for the period 23–27 December. Although the upper-level thermal fields are consistent overall, differences appear, especially during the incipient stage of the second storm. The forecast, which was poor in the operational context, is modified when a configuration close to the TLS one is adopted. Qualitative comparisons of TLS with Microwave Sounding Unit (MSU) channel-3 limb-corrected brightness temperatures and with the water vapor imagery are also shown. One advantage of TLS over these two other fields is the earlier detection of the upper-level precursor of the second storm. Because TLS computation is easy and fast, the suitability of TLS as a possible forecasting aid over midoceanic regions is promoted.

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Mathieu Vrac, Alain Chédin, and Edwin Diday

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This work focuses on the clustering of a large dataset of atmospheric vertical profiles of temperature and humidity in order to model a priori information for the problem of retrieving atmospheric variables from satellite observations. Here, each profile is described by cumulative distribution functions (cdfs) of temperature and specific humidity. The method presented here is based on an extension of the mixture density problem to this kind of data. This method allows dependencies between and among temperature and moisture to be taken into account, through copula functions, which are particular distribution functions, linking a (joint) multivariate distribution with its (marginal) univariate distributions. After a presentation of vertical profiles of temperature and humidity and the method used to transform them into cdfs, the clustering method is detailed and then applied to provide a partition into seven clusters based, first, on the temperature profiles only; second, on the humidity profiles only; and, third, on both the temperature and humidity profiles. The clusters are statistically described and explained in terms of airmass types, with reference to meteorological maps. To test the robustness and the relevance of the method for a larger number of clusters, a partition into 18 classes is established, where it is shown that even the smallest clusters are significant. Finally, comparisons with more classical efficient clustering or model-based methods are presented, and the advantages of the approach are discussed.

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Filipe Aires, William B. Rossow, and Alain Chédin

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The Independent Component Analysis (ICA) is a recently developed technique for component extraction. This new method requires the statistical independence of the extracted components—a stronger constraint that uses higher-order statistics—instead of the classical decorrelation (in the sense of “no correlation”), which is a weaker constraint that uses only second-order statistics. This technique has been used recently for the analysis of geophysical time series with the goal of investigating the causes of variability in observed data (i.e., exploratory approach). The authors demonstrate with a data simulation experiment that, if initialized with a Principal Component Analysis (PCA), the ICA performs a rotation of the classical PCA (or EOF) solution. This experiment is conducted using a synthetic dataset, where the correct answer is known, to more clearly illustrate and understand the behavior of the more familiar PCA and less familiar ICA. This rotation uses no localization criterion like other rotation techniques; only the generalization of decorrelation into full statistical independence is used. This rotation of the PCA solution seems to be able to avoid the tendency of PCA to mix several components, even when the signal is just their linear sum.

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Filipe Aires, Alain Chédin, Noëlle A. Scott, and William B. Rossow

Abstract

In this paper, a fast atmospheric and surface temperature retrieval algorithm is developed for the high-resolution Infrared Atmospheric Sounding Interferometer (IASI) spaceborne instrument. This algorithm is constructed on the basis of a neural network technique that has been regularized by introduction of information about the solution of the problem that is in addition to the information contained in the problem (a priori information). The performance of the resulting fast and accurate inverse radiative transfer model is presented for a large diversified dataset of radiosonde atmospheres that includes rare events. Two configurations are considered: a tropical-airmass specialized scheme and an all-airmasses scheme. The surface temperature for tropical situations yields an rms error of 0.4 K for instantaneous retrievals. Results for atmospheric temperature profile retrievals are close to the specifications of the World Meteorological Organization, namely, 1-K rms error for the instantaneous temperature retrieval with 1-km vertical resolution.

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Alain Chédin, Soumia Serrar, Raymond Armante, Noëlle A. Scott, and Anthony Hollingsworth

Abstract

Since 1979, sensors on board the National Oceanic and Atmospheric Administration (NOAA) series of polar meteorological satellites have provided continuous measurements of the earth's surface and atmosphere. One of these sensors, the Television Infrared Observational Satellite (TIROS-N) Operational Vertical Sounder (TOVS), observes earth-emitted radiation in the infrared—with the High-Resolution Infrared Sounder (HIRS)—and in the microwave—with the Microwave Sounding Unit (MSU)—portions of the spectrum. The NOAA and National Aeronautics and Space Administration (NASA) Pathfinder program was designed to make these data more readily accessible to the community in the form of processed geophysical variables (temperature, water vapor, cloud characteristics, and so on) through the “interpretation” of the infrared and microwave radiances. All presently developed interpretation algorithms more or less directly rely on the comparison between a set of observed and a set of simulated radiances. For that reason, the accuracy of the simulation directly influences that of the interpretation of radiances in terms of thermodynamic variables. Comparing simulations to observations is the key to a better knowledge of the main sources of errors affecting either the former or the latter. Instrumental radiometric problems, radiosonde, surface data, and forward radiative transfer model limitations as well as difficulties raised by differences in space and in time of satellite and radiosonde observations (collocations) have long been studied in detail. Less attention has been paid to errors, presumed negligible, generated by the absence of consideration of main absorbing gases (CO2, N2O, CO, O3, and so on) atmospheric seasonal cycles and/or annual trends. In this paper, all important sources of variability of the observations and of the simulations are first reviewed. Then it is shown that analyzing, at different timescales (seasonal, annual), the departures between simulated and observed NOAA TOVS brightness temperatures reveals signatures of these greenhouse gases' concentration variations. Not only the shape of the seasonal variations (locations of the peaks) is in good agreement with what is presently known, but also their amplitude (peak-to-peak) matches relatively well the values predicted from a line-by-line radiative transfer model. Moreover, annual trends correspond very well with the known increase in concentration of gases such as CO2 or N2O, as a result of human activities. Limits of such an analysis are discussed: the most significant one finds its origin in the modest spectral resolution of the TOVS channels that integrate signatures from several absorbers and from many atmospheric layers. However, results from this work leave some hope to extract from these channels interesting information on CO2, N2O, and CO distributions. These results also strengthen the hope to improve greatly the knowledge of the global distribution of a variety of radiatively active gases with the coming second generation of vertical sounders such as NASA's Advanced Infrared Radiation Sounder (AIRS) or the CNES/Eumetsat Infrared Atmospheric Sounder Interferometer (IASI), both characterized by a much higher spectral resolution.

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Virginie Capelle, Alain Chédin, Eric Péquignot, Peter Schlüssel, Stuart M. Newman, and Noelle A. Scott

Abstract

Land surface temperature and emissivity spectra are essential variables for improving models of the earth surface–atmosphere interaction or retrievals of atmospheric variables such as thermodynamic profiles, chemical composition, cloud and aerosol characteristics, and so on. In most cases, emissivity spectral variations are not correctly taken into account in climate models, leading to potentially significant errors in the estimation of surface energy fluxes and temperature. Satellite infrared observations offer the dual opportunity of accurately estimating these properties of land surfaces as well as allowing a global coverage in space and time. Here, high-spectral-resolution observations from the Infrared Atmospheric Sounder Interferometer (IASI) over the tropics (30°N–30°S), covering the period July 2007–March 2011, are interpreted in terms of 1° × 1° monthly mean surface skin temperature and emissivity spectra from 3.7 to 14 μm at a resolution of 0.05 μm. The standard deviation estimated for the surface temperature is about 1.3 K. For the surface emissivity, it varies from about 1%–1.5% for the 10.5–14- and 5.5–8-μm windows to about 4% around 4 μm. Results from comparisons with products such as Moderate Resolution Imaging Spectroradiometer (MODIS) low-resolution emissivity and surface temperature or ECMWF forecast data (temperature only) are presented and discussed. Comparisons with emissivity derived from the Airborne Research Interferometer Evaluation System (ARIES) radiances collected during an aircraft campaign over Oman and made at the scale of the IASI field of view offer valuable data for the validation of the IASI retrievals.

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Noëlle A. Scott, Alain Chédin, Raymond Armante, Jennifer Francis, Claudia Stubenrauch, Jean-Pierre Chaboureau, Frederic Chevallier, Chantal Claud, and Frédérique Cheruy

From 1979 to present, sensors aboard the NOAA series of polar meteorological satellites have provided continuous measurements of the earth's surface and atmosphere. One of these sensors, the TIROS-N Operational Vertical Sounder (TOVS), observes earth-emitted radiation in 27 wavelength bands within the infrared and microwave portions of the spectrum, thereby creating a valuable resource for studying the climate of our planet. The NOAA–NASA Pathfinder program was conceived to make these data more readily accessible to the community in the form of processed geophysical variables. The Atmospheric Radiation Analysis group at the Laboratoire de Météorologie Dynamique of the Centre National de la Recherche Scientifique of France was selected to process TOVS data into climate products (Path-B). The Improved Initialization Inversion (3I) retrieval algorithm is used to compute these products from the satellite-observed radiances. The processing technique ensures internal coherence and minimizes both observational and computational biases. Products are at a 1° × 1° latitude–longitude grid and include atmospheric temperature profiles (up to 10 hPa); total precipitable water vapor and content above four levels up to 300 hPa; surface skin temperature; and cloud properties (amount, type, and cloud-top pressure and temperature). The information is archived as 1-day, 5-day, and monthly means on the entire globe; a.m. and p.m. products for each satellite are stored separately. Eight years have been processed to date, and processing continues at the rate of approximately two satellite-months per day of computer time. Quality assessment studies are presented. They consist of comparisons to conventional meteorological data and to other remote sensing datasets.

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