F. Aires - Academia.edu (original) (raw)
Papers by F. Aires
Journal of Geophysical Research: Atmospheres, 2012
ABSTRACT In this paper, synergy refers to a process where the use of multiple satellite observati... more ABSTRACT In this paper, synergy refers to a process where the use of multiple satellite observations makes the retrieval more precise than the best individual retrieval. Two general strategies can be used in order to use multi-wavelength observations in an inversion scheme. First, the multi-wavelength observations are merged in the input of the retrieval scheme. This means that the various satellite observations are used simultaneously and that their possible interactions can be exploited by the retrieval scheme. Second, each multi-wavelength observations are used independently to retrieve a same geophysical variable and then, these independent retrievals are combined a posteriori using for example a simple weighted averaging. In this paper, it is shown that the first approach provides better synergy results: The retrieval is better suited to optimize the use of all the information available because they are provided to the algorithm simultaneously. In particular, the retrieval process is able, in this case, to exploit the possible interactions between the various input information. The two retrieval approaches are tested and compared using an application for the retrieval of atmospheric profiles and integrated column quantities (temperature, water vapor, and ozone) using MetOp-A observations from IASI, AMSU-A and MHS instruments. Although real satellite observations are considered in this analysis, the results are dependent on the correlation structure in the training data set (i.e. ECMWF analysis) used to calibrate the retrieval algorithm. However, it can be seen that the infrared and microwave observations have a good synergy for the retrieval of atmospheric temperature, water vapor, and for ozone thanks to an indirect synergy.
Journal of Geophysical Research, 2012
Journal of Geophysical Research, 2005
1] In part 1 of this study (Prigent et al., 2004), in situ measurements were used to analyze and ... more 1] In part 1 of this study (Prigent et al., 2004), in situ measurements were used to analyze and describe the sensitivities of satellite measurements (i.e., active and passive microwave observations and surface skin temperature diurnal cycle amplitude) to the soil moisture variations to describe the complex relationships that exist between them. Soil moisture was considered in the first 10-cm layer on a 0.25°equal-area grid and a monthly timescale. In this study, the lessons from the first paper are exploited to document the sensitivity of the satellite data to the global large-scale variations of soil moisture. A statistical model based on neural networks is developed to link the satellite observations and soil moisture estimates. Given the lack of available in situ soil moisture measurements on a global basis, National Centers for Environmental Prediction (NCEP) and European Centre for Medium-Range Weather Forecasts (ECMWF) soil moisture reanalyses are used as a realistic global indicator of soil moisture. As a consequence, the statistical model cannot be considered as a retrieval scheme per se, but it shows the feasibility of such an approach. It also quantifies the information content that can be expected from the satellite observations. Applications of such a statistical model include checking the consistency of surface model, and as the basis for variational assimilation of satellite observations into a numerical surface model. Citation: Aires, F., C. Prigent, and W. B. Rossow (2005), Sensitivity of satellite microwave and infrared observations to soil moisture at a global scale: 2. Global statistical relationships,
Journal of Geophysical Research, 2000
With the aim of identifying the physical causes of variability of a given dynamical system, the g... more With the aim of identifying the physical causes of variability of a given dynamical system, the geophysical community has made an extensive use of classical component extraction techniques such as principal component analysis (PCA) or rotational techniques (RT). We introduce a recently developed algorithm based on information theory: independent component analysis (ICA). This new technique presents two major advantages over classical methods. First, it aims at extracting statistically independent components where classical techniques search for decorrelated components (i.e., a weaker constraint). Second, the linear hypothesis for the mixture of components is not required. In this paper, after having briefly summarized the essentials of classical techniques, we present the new method in the context of geophysical time series analysis. We then illustrate the ICA algorithm by applying it to the study of the variability of the tropical sea surface temperature (SST), with a particular emphasis on the analysis of the links between E1 Nifio Southern Oscillation (ENSO) and Atlantic SST variability. The new algorithm appears to be particularly efficient in describing the complexity of the phenomena and their various sources of variability in space and time.
Journal of Geophysical Research, 2001
JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 106, NO. D14, PAGES 14,887-14,907, JULY 27, 2001 A new neur... more JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 106, NO. D14, PAGES 14,887-14,907, JULY 27, 2001 A new neural network approach including first guess for retrieval of atmospheric water vapor, cloud liquid water path, surface temperature, and emissivities over land from ...
Journal of Geophysical Research, 2004
1] A technique to estimate the uncertainties of the parameters of a neural network model, i.e., t... more 1] A technique to estimate the uncertainties of the parameters of a neural network model, i.e., the synaptic weights, was described in the work of Aires [2004]. Using these weight uncertainty estimates, we compute the uncertainties in the network outputs (i.e., error bars and correlation structure of these errors). Such quantities are very important for evaluating any application of the neural network technique. The theory is applied to the same remote sensing problem as in the work of Aires [2004] concerning the retrieval of surface skin temperature, microwave surface emissivities and integrated water vapor content from a combined analysis of microwave and infrared observations over land.
Journal of Geophysical Research: Atmospheres, 2012
ABSTRACT In this paper, synergy refers to a process where the use of multiple satellite observati... more ABSTRACT In this paper, synergy refers to a process where the use of multiple satellite observations makes the retrieval more precise than the best individual retrieval. Two general strategies can be used in order to use multi-wavelength observations in an inversion scheme. First, the multi-wavelength observations are merged in the input of the retrieval scheme. This means that the various satellite observations are used simultaneously and that their possible interactions can be exploited by the retrieval scheme. Second, each multi-wavelength observations are used independently to retrieve a same geophysical variable and then, these independent retrievals are combined a posteriori using for example a simple weighted averaging. In this paper, it is shown that the first approach provides better synergy results: The retrieval is better suited to optimize the use of all the information available because they are provided to the algorithm simultaneously. In particular, the retrieval process is able, in this case, to exploit the possible interactions between the various input information. The two retrieval approaches are tested and compared using an application for the retrieval of atmospheric profiles and integrated column quantities (temperature, water vapor, and ozone) using MetOp-A observations from IASI, AMSU-A and MHS instruments. Although real satellite observations are considered in this analysis, the results are dependent on the correlation structure in the training data set (i.e. ECMWF analysis) used to calibrate the retrieval algorithm. However, it can be seen that the infrared and microwave observations have a good synergy for the retrieval of atmospheric temperature, water vapor, and for ozone thanks to an indirect synergy.
Journal of Geophysical Research, 2012
Journal of Geophysical Research, 2005
1] In part 1 of this study (Prigent et al., 2004), in situ measurements were used to analyze and ... more 1] In part 1 of this study (Prigent et al., 2004), in situ measurements were used to analyze and describe the sensitivities of satellite measurements (i.e., active and passive microwave observations and surface skin temperature diurnal cycle amplitude) to the soil moisture variations to describe the complex relationships that exist between them. Soil moisture was considered in the first 10-cm layer on a 0.25°equal-area grid and a monthly timescale. In this study, the lessons from the first paper are exploited to document the sensitivity of the satellite data to the global large-scale variations of soil moisture. A statistical model based on neural networks is developed to link the satellite observations and soil moisture estimates. Given the lack of available in situ soil moisture measurements on a global basis, National Centers for Environmental Prediction (NCEP) and European Centre for Medium-Range Weather Forecasts (ECMWF) soil moisture reanalyses are used as a realistic global indicator of soil moisture. As a consequence, the statistical model cannot be considered as a retrieval scheme per se, but it shows the feasibility of such an approach. It also quantifies the information content that can be expected from the satellite observations. Applications of such a statistical model include checking the consistency of surface model, and as the basis for variational assimilation of satellite observations into a numerical surface model. Citation: Aires, F., C. Prigent, and W. B. Rossow (2005), Sensitivity of satellite microwave and infrared observations to soil moisture at a global scale: 2. Global statistical relationships,
Journal of Geophysical Research, 2000
With the aim of identifying the physical causes of variability of a given dynamical system, the g... more With the aim of identifying the physical causes of variability of a given dynamical system, the geophysical community has made an extensive use of classical component extraction techniques such as principal component analysis (PCA) or rotational techniques (RT). We introduce a recently developed algorithm based on information theory: independent component analysis (ICA). This new technique presents two major advantages over classical methods. First, it aims at extracting statistically independent components where classical techniques search for decorrelated components (i.e., a weaker constraint). Second, the linear hypothesis for the mixture of components is not required. In this paper, after having briefly summarized the essentials of classical techniques, we present the new method in the context of geophysical time series analysis. We then illustrate the ICA algorithm by applying it to the study of the variability of the tropical sea surface temperature (SST), with a particular emphasis on the analysis of the links between E1 Nifio Southern Oscillation (ENSO) and Atlantic SST variability. The new algorithm appears to be particularly efficient in describing the complexity of the phenomena and their various sources of variability in space and time.
Journal of Geophysical Research, 2001
JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 106, NO. D14, PAGES 14,887-14,907, JULY 27, 2001 A new neur... more JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 106, NO. D14, PAGES 14,887-14,907, JULY 27, 2001 A new neural network approach including first guess for retrieval of atmospheric water vapor, cloud liquid water path, surface temperature, and emissivities over land from ...
Journal of Geophysical Research, 2004
1] A technique to estimate the uncertainties of the parameters of a neural network model, i.e., t... more 1] A technique to estimate the uncertainties of the parameters of a neural network model, i.e., the synaptic weights, was described in the work of Aires [2004]. Using these weight uncertainty estimates, we compute the uncertainties in the network outputs (i.e., error bars and correlation structure of these errors). Such quantities are very important for evaluating any application of the neural network technique. The theory is applied to the same remote sensing problem as in the work of Aires [2004] concerning the retrieval of surface skin temperature, microwave surface emissivities and integrated water vapor content from a combined analysis of microwave and infrared observations over land.