Measurements of stratospheric volcanic aerosol optical depth from NOAA TIROS Observational Vertical Sounder (TOVS) observations

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Décomposition de mélange de distributions et application à des données climatiques

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Remote sensing of the vertical distribution of atmospheric water vapor from the TOVS observations: Method and validation

International audienceThis paper presents a method to remotely sense the vertical distribution of atmospheric water vapor using spaceborne measurements from the TOVS instrument aboard the NOAA polar satellite series. It describes a new approach to the water vapor retrieval scheme in the improved initialization inversion (3I) method. The technique is based on a neural network scheme, which is analyzed theoretically. Cross‐comparisons of its results with a wide variety of independent observations (in situ measurements or other global data sets, e.g., the special sensor microwave/imager (SSM/I) retrievals, analyses) are then carried out to fully evaluate the method. It is shown that the mean of the differences between total water vapor contents obtained from each data set represents less than 20% of the global mean value of the water vapor content. Different behaviors between TOVS and SSM/I in tropical situations are also highlighted. Concerning the vertical profile, the standard deviation between water vapor content retrieved by 3I and measured by radiosondes varies from 20% in the 1000–850 hPa layer to less than 40% in the 500–300 hPa layer. The vertical increase of the error is linked to the difficulty of measuring weak values by radiosonde instruments, radiometers, or analyses

Relationship between sea surface temperature, vertical dynamics, and the vertical distribution of atmospheric water vapor inferred from TOVS observations

International audienceWith the aim of better understanding the respective role of sea surface temperature (SST) and vertical dynamics on the vertical distribution of atmospheric water vapor, particularly in the tropics, global scale observations from NOAA 10, covering a 31−month period, have been processed using the improved initialization inversion ((3I) [ Chédin and Scott , 1984]) retrieval method and interpreted in terms of tropospheric layered water vapor contents. The method of analysis uses the power law, which expresses the specific humidity q at pressure p as a function of their values at the surface, q 0 and p 0 ; q = q 0 ( p / p 0 ) λ . This description is applied independently to three layers giving three values of λ: λ 1 for surface‐700 hPa, λ 2 for 700–500 hPa, and λ 3 for 500–300 hPa. It is shown that λ 2 is a good indicator of the large‐scale vertical dynamics and gives results equivalent to those obtained using the vertical velocity at 500 hPa issued from a model. Consequently, the role of enhanced upward motion with increased SST for the “super greenhouse effect” situations is confirmed as well as the contribution of externally forced subsidence on the suppression of the deep convection for cases where SSTs exceed about 303 K. In addition, the influence of SST on the vertical distribution of water vapor is analyzed together with the large‐scale vertical dynamics contribution. The results show that the rate of change of water vapor content in the 700‐ to 500‐hPa and 500‐ to 300‐hPa layers with respect to SST increases with decreasing rate of change of λ 2 with respect to SST, that is, with increasing rate of change of upward vertical dynamics with respect to SST

Mixture model-based atmospheric air mass classification: a probabilistic view of thermodynamic profiles

International audienceAir mass classification has become an important area in synoptic climatology, simplifying the complexity of the atmosphere by dividing the atmosphere into discrete similar thermodynamic patterns. However, the constant growth of atmospheric databases in both size and complexity implies the need to develop new adaptive classifications. Here, we propose a robust unsupervised and supervised classification methodology of a large thermodynamic dataset, on a global scale and over several years, into discrete air mass groups homogeneous in both temperature and humidity that also provides underlying probability laws. Temperature and humidity at different pressure levels are aggregated into a set of cumulative distribution function (CDF) values instead of classical ones. The method is based on a Gaussian mixture model and uses the expectation-maximization (EM) algorithm to estimate the parameters of the mixture. Spatially gridded thermodynamic profiles come from ECMWF reanalyses spanning the period 2000-2009. Different aspects are investigated, such as the sensitivity of the classification process to both temporal and spatial samplings of the training dataset. Comparisons of the classifications made either by the EM algorithm or by the widely used k-means algorithm show that the former can be viewed as a generalization of the latter. Moreover, the EM algorithm delivers, for each observation, the probabilities of belonging to each class, as well as the associated uncertainty. Finally, a decision tree is proposed as a tool for interpreting the different classes, highlighting the relative importance of temperature and humidity in the classification process

Copula analysis of mixture models

International audienceContemporary computers collect databases that can be too large for classical methods to handle. The present work takes data whose observations are distribution functions (rather than the single numerical point value of classical data) and presents a computational statistical approach of a new methodology to group the distributions into classes. The clustering method links the searched partition to the decomposition of mixture densities, through the notions of a function of distributions and of multi-dimensional copulas. The new clustering technique is illustrated by ascertaining distinct temperature and humidity regions for a global climate dataset and shows that the results compare favorably with those obtained from the standard EM algorithm method

Retrieving the effective radius of Saharan dust coarse mode from AIRS

International audienceWe show that the effective radius of the dust coarse mode can be retrieved from Aqua/AIRS observations, using a two step process. First, for each AIRS observation, the dust infrared optical depth, the mean altitude of the dust layer and an estimate of the temperature and water vapor profiles are obtained from 8 spectral channels, using a Look‐Up‐Table approach. Second, the effective radius is obtained from an additional AIRS channel (located at 9.32 μm), selected for its sensitivity to dust particle size and its insensitivity to dust particle shape or to other potential contaminants (ozone, for example). The dust coarse mode effective radius is retrieved from AIRS over the Atlantic Ocean for the period April to June 2003. It compares well with in situ measurements, transport model simulations and sun‐photometer retrievals. We find that the coarse mode effective radius decreases slightly with transport distance, from 2.4 μm to about 2 μ

Infrared continental surface emissivity spectra and skin temperature retrieved from IASI observations over the tropics

International audienceLand 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 (308N-308S), covering the period July 2007-March 2011, are interpreted in terms of 18 3 18 monthly mean surface skin temperature and emissivity spectra from 3.7 to 14 mm at a resolution of 0.05 mm. The standard deviation estimated for the surface temperature is about 1.3 K. For the surface emissivity, it varies fromabout 1%-1.5%for the 10.5-14- and 5.5-8-mmwindows to about 4% around 4 mm. 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. © 2012 American Meteorological Society

Une méthode utilisant les techniques neuronales pour le calcul rapide de la distribution verticale du bilan radiatif thermique terrestre.

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