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Tello Alonso M.,Polytechnic University of Catalonia | Tello Alonso M.,Institute Catala Of Ciencies Del Clima | Lopez-Dekker P.,German Aerospace Center | Mallorqui J.J.,Polytechnic University of Catalonia
IEEE Transactions on Geoscience and Remote Sensing | Year: 2010

Radar data have already proven to be compressible with no significant losses for most of the applications in which it is used. In the framework of information theory, the compressibility of a signal implies that it can be decomposed onto a reduced set of basic elements. Since the same quantity of information is carried by the original signal and its decomposition, it can be deduced that a certain degree of redundancy exists in the explicit representation. According to the theory of compressive sensing (CS), due to this redundancy, it is possible to infer an accurate representation of an unknown compressible signal through a highly incomplete set of measurements. Based on this assumption, this paper proposes a novel method for the focusing of raw data in the framework of radar imaging. The technique presented is introduced as an alternative option to the traditional matched filtering, and it suggests that the new modes of acquisition of data are more efficient in orbital configurations. In this paper, this method is first tested on 1-D simulated signals, and results are discussed. An experiment with synthetic aperture radar (SAR) raw data is also described. Its purpose is to show the potential of CS applied to SAR systems. In particular, we show that an image can be reconstructed, without the loss of resolution, after dropping a large percentage of the received pulses, which would allow the implementation of wide-swath modes without reducing the azimuth resolution. © 2010 IEEE.

Hantson S.,University of Alcala | Pueyo S.,Institute Catala Of Ciencies Del Clima | Chuvieco E.,University of Alcala
Global Ecology and Biogeography | Year: 2015

Aim: In order to understand fire's impacts on vegetation dynamics, it is crucial that the distribution of fire sizes be known. We approached this distribution using a power-law distribution, which derives from self-organized criticality theory (SOC). We compute the global spatial variation in the power-law exponent and determine the main factors that explain its spatial distribution. Location: Global, at 2° grid resolution. Methods: We use satellite-derived MODIS burned-area data (MCD45) to obtain global individual fire size data for 2002-2010, grouped together for each 2° grid. A global map of fire size distribution was produced by plotting the exponent of the power law. The drivers of the spatial trends in fire size distribution, including vegetation productivity, precipitation, population density and net income, were analysed using a generalized additive model (GAM). Results: The power law gave a good fit for 93% of the global 2° grid cells with important fire activity. A global map of the fire size distribution, as approached by the power law shows strong spatial patterns. These are associated both with climatic variables (precipitation and evapotranspiration) and with anthropogenic variables (cropland cover and population density). Main conclusions: Our results indicate that the global fire size distribution changes over gradients of precipitation and aridity, and that it is strongly influenced by human activity. This information is essential for understanding potential changes in fire sizes as a result of climate change and socioeconomic dynamics. The ability to improve SOC fire models by including these human and climatic factors would benefit fire projections as well as fire management and policy. © 2014 John Wiley & Sons Ltd.

Philippon N.,University of Burgundy | Doblas-Reyes F.J.,Institute Catala Of Ciencies Del Clima | Ruti P.M.,Ente por le Nuove Tecnologie
Climate Dynamics | Year: 2010

In the framework of the ENSEMBLES FP6 project, an ensemble prediction system based on five different state-of-the-art European coupled models has been developed. This study evaluates the performance of these models for forecasting the West African monsoon (WAM) at the monthly time scale. From simulations started the 1 May of each year and covering the period 1991-2001, the reproducibility and potential predictability (PP) of key parameters of the WAM-rainfall, zonal and meridional wind at four levels from the surface to 200 hPa, and specific humidity, from July to September-are assessed. The Sahelian rainfall mode of variability is not accurately reproduced contrary to the Guinean rainfall one: the correlation between observations (from CMAP) and the multi-model ensemble mean is 0.17 and 0.55, respectively. For the Sahelian mode, the correlation is consistent with a low PP of about ~6%. The PP of the Guinean mode is higher, ~44% suggesting a stronger forcing of the sea surface temperature on rainfall variability over this region. Parameters relative to the atmospheric dynamics are on average much more skillful and reproducible than rainfall. Among them, the first mode of variability of the zonal wind at 200 hPa that depicts the Tropical Easterly Jet, is correlated at 0.79 with its "observed" counterpart (from the NCEP/DOE2 reanalyses) and has a PP of 39%. Moreover, models reproduce the correlations between all the atmospheric dynamics parameters and the Sahelian rainfall in a satisfactory way. In that context, a statistical adaptation of the atmospheric dynamic forecasts, using a linear regression model with the leading principal components of the atmospheric dynamical parameters studied, leads to moderate receiver operating characteristic area under the curve and correlation skill scores for the Sahelian rainfall. These scores are however much higher than those obtained using the modelled rainfall. © 2010 Springer-Verlag.

Isern-Fontanet J.,Institute Catala Of Ciencies Del Clima | Hascoet E.,French Research Institute for Exploitation of the Sea
Journal of Geophysical Research: Oceans | Year: 2014

The noise present in infrared satellite measurements of sea surface temperature (SST) hampers the use of surface quasi-geostrophic (SQG) equations to diagnose ocean dynamics at high resolutions. Here we propose a methodology to reduce the contribution of noise when diagnosing surface vorticity, divergence, and vertical velocity from SST able to retain the dynamics at scales of a few kilometers. It is based on the use of denoising techniques with curvelets as basis functions and the application of a selective low-pass filters to improve the reconstruction of surface upwelling/downwelling patterns. First, it is tested using direct numerical simulations of SQG turbulence and then applied to diagnose lowfrequency vertical velocity patterns from real MODIS (Moderate Resolution Imaging Spectroradiometer) images. The methodology here presented, which is not tied to the validity of SQG equations nor to the use of SST, is quite general and can be applied to a wide range of measurements and dynamical frameworks. © 2013. American Geophysical Union. All Rights Reserved.

Garcia-Diez M.,Institute Catala Of Ciencies Del Clima | Garcia-Diez M.,University of Cantabria | Fernandez J.,University of Cantabria | Vautard R.,CEA Saclay Nuclear Research Center
Climate Dynamics | Year: 2015

Regional Climate Models are widely used tools to add detail to the coarse resolution of global simulations. However, these are known to be affected by biases. Usually, published model evaluations use a reduced number of variables, frequently precipitation and temperature. Due to the complexity of the models, this may not be enough to assess their physical realism (e.g. to enable a fair comparison when weighting ensemble members). Furthermore, looking at only a few variables makes difficult to trace model errors. Thus, in many previous studies, these biases are described but their underlying causes and mechanisms are often left unknown. In this work the ability of a multi-physics ensemble in reproducing the observed climatologies of many variables over Europe is analysed. These are temperature, precipitation, cloud cover, radiative fluxes and total soil moisture content. It is found that, during winter, the model suffers a significant cold bias over snow covered regions. This is shown to be related with a poor representation of the snow-atmosphere interaction, and is amplified by an albedo feedback. It is shown how two members of the ensemble are able to alleviate this bias, but by generating a too large cloud cover. During summer, a large sensitivity to the cumulus parameterization is found, related to large differences in the cloud cover and short wave radiation flux. Results also show that small errors in one variable are sometimes a result of error compensation, so the high dimensionality of the model evaluation problem cannot be disregarded. © 2015, Springer-Verlag Berlin Heidelberg.

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