Juan C. Pérez

Remote Sensing of Water Cloud Parameters Using Neural Networks

Abstract In this work a method for determining the micro- and macrophysical properties of oceanic stratocumulus clouds is presented. It is based on the inversion of a radiative transfer model that computes the albedos and brightness temperatures in the NOAA Advanced Very High Resolution Radiometer (AVHRR) channels. This inversion is performed using artificial neural networks (ANNs), which are trained and optimized by genetic algorithms to fit theoretical computations. A detailed study of the ANN parameters and training algorithms demonstrates the convenience of using the “backpropagation with momentum” method. The proposed retrieval method is applied to daytime and nighttime imagery and was validated using ground data collected in Tenerife (Canary Islands), obtaining a good agreement.

Retrieval of Marine Stratus Cloud Droplet Size from NOAA-AVHRR Nighttime Imagery

Abstract A method for retrieval of the droplet radius and temperature of oceanic stratocumulus is presented. It is based on night imagery obtained from the infrared channels of NOAA–AVHRR and an atmospheric radiative transfer model that makes use of the discrete ordinate method DISORT. It uses the observed satellite brightness temperature differences (BTD) between channels 4 and 5 to obtain the cloud temperature and between channels 3 and 4 to extract the effective radius of the cloud droplets. We also studied the peculiarities of the method, taking into account the behavior of the single scattering parameters, deduced from Mie theory, with droplet size. Results obtained are compared with in situ data collected at the Canary Islands (Spain) during summer 1996.

Using a Mesoscale Meteorological Model to Reduce the Effect of Tropospheric Water Vapour from DInSAR Data: A Case Study for the Island of Tenerife, Canary Islands

Measurements of ground displacement through classical Differential Interferometric SAR (DInSAR) and advanced DInSAR techniques have been carried out over the entire actively volcanic island of Tenerife, Canary Islands. However, a detailed analysis of the effect of tropospheric water vapour on DInSAR at Tenerife should be carried out to evaluate its influence, including correction models that might improve the accuracy of DInSAR derived deformation signals. Unlike water vapour correction models that are based on space platforms (e.g. MODIS and MERIS), we present an alternative approach that is based on precise water vapour estimations derived from mesoscale numerical meteorological models, in particular the Weather Research and Forecasting (WRF) model. The application of this approach to a set of DInSAR observations of the island of Tenerife shows encouraging results.

High-Resolution Future Projections of Temperature and Precipitation in the Canary Islands

AbstractThe complex orography of the Canary Islands favors the creation of microclimates, which cannot be studied using global climate models or regional models with moderate resolution. In this work, WRF is used to perform a dynamic climate regionalization in the archipelago, using the pseudo–global warming technique to compute the initial and boundary conditions from a reanalysis dataset and from results of 14 global climate models. The simulations were performed for three decades, one at present (1995–2004) and two in the future (2045–54 and 2090–99), and for two different greenhouse gas scenarios (RCP4.5 and RCP8.5), defined in phase 5 of the Coupled Model Intercomparison Project. The obtained results, at a 5-km horizontal resolution, show a clear dependence of temperature increase with height and a positive change in diurnal temperature range, which is mainly due to a reduction in soil moisture and a slight decrease in cloud cover. This negative change in soil moisture is mainly a consequence of a de...

Cloud climatology in the Canary Islands region using NOAA-AVHRR data

In this work a threshold technique for cloud detection and classification is applied to 9 years NOAA-AVHRR imagery in order to obtain a cloud climatology of the Canary Islands region (Northeast Atlantic Ocean). Once the clouds are classified, a retrieval method is used to estimate cloud macro- and micro-physical parameters, such as, effective particle size, optical thickness and top temperature. This retrieval method is based on the inversion of the simulated radiances obtained by a numerical radiative transfer model, libRadtran, using artificial neural networks (ANNs). The ANNs, whose architecture was based on Multilayer Perceptron model, were trained with simulated theoretical radiances using backpropagation with momentum method, and their architectures were optimized through genetic algorithms. The global procedure was performed for both day and night overpasses and, from a set of more than 9000 images, maps of relative frequency were calculated. These results were compared with ISCCP data for the 21-year period 1984-2004. The relationships between the retrieved cloud properties and some climate and atmospheric variables were also considered.

Removing the thermal component of the NOAA-AVHRR Channel 3 in cloudy conditions

In this paper, an operational method for the extraction of the solar component from the AVHRR-Channel 3 radiance, is addressed. This is based on the implementation of a simple radiative transfer model that explains the radiative behavior of each pixel in the image. The outputs of the model are expressed in terms of brightness temperature differences between Channels 3 and 4 (BTD34). As a first step, the model behavior is proved for night images and then it is inverted for daylight ones, supplying the two contributions from the total radiance detected by the sensor.

Stratocumulus parameter retrieval using MODIS nighttime imagery: a theoretical approach

This work is a preliminary study of the viability of retrieving macro physical and micro physical cloud parameters from nighttime radiances provided by MODIS sensor, onboard Terra spacecraft. It is based on the analysis of the sensitivity of every MODIS IR band to each of the parameters that describe the different layers composing the earth-cloud-atmosphere system. IN order to make this analysis, an atmospheric radiative transfer model that makes use of the discrete ordinates method DISORT is employed. Multiple simulations are performed for a great variety of clouds and atmospheric conditions, taking into account the main absorbers in each band. As a first result, the more adequate bands for our purpose are select and, using these channels, the proposed method extracts the parameters characterizing the different layers through a numerical inversion of the radiative model based on an evolutionary method for solving optimization problems called scatter search. In addition, a sensitivity analysis is carried out in order to estimate the impact on the retrieved values of the uncertainties in model inputs and assumptions.

Nonparametric segmentation of clouds from multispectral MSG-SEVIRI imagery

Separating and classifying clouds in remote sensing multispectral imagery is a complex task, especially when optically thin clouds and multilayer systems are present in the images. Many methods, based on both supervised and unsupervised techniques, have been developed previously, but most of them are based on independent pixel processing, using their spectral and textural features. In this work a procedure for segmentation of clouds from multispectral MSG-SEVIRI (Meteosat Second Generation - Spinning Enhanced Visible and Infrared Imager) images is developed. It is based on a nonparametric clustering method, mean shift, which is able to delineate arbitrarily shaped clusters in the feature space. This is an important property, because the clusters that correspond to different kinds of clouds follow complex shapes in the spectral feature space and they cannot be separated by parametric models, usually assuming spherical or elliptical clusters. Some variations of mean shift technique have been also analyzed, and the adaptive version of the algorithm, where the density estimator for every point takes into account the nearest neighbours in the feature space, provided the best performance. Segmentation results were evaluated using different ground true data: MSG SEVIRI cloud data provided by an operational EUMETSAT product and manual human expert segmentation based on the visual inspection and other related information.

Neural Network based Retrieval of Cirrus Properties

In this work, a method for determining the micro- and macro-physical properties of cirrus clouds from NOAA- AVHRR daylight imagery is presented. The combined use of the radiances measured by satellites and an atmospheric radiative transfer model makes possible to obtain cirrus properties, such as optical thickness, mean effective ice crystal size and temperature, through an inversion method. Due to the complexity of this theoretical model, numerical techniques must be used. In this case, the inversion is performed using an artificial neural network (ANN), which is trained and optimized by genetic algorithms. After the training stage, the ability of generalization of this network and the errors introduced in the procedure are analyzed.

Assessment of the Structural Integrity of the Roman Bridge of Alcántara (Spain) Using TLS and GPR

The Roman bridge of Alcantara is the largest in Spain. Its preservation is of the utmost importance and to this end different aspects must be studied. The most prominent is the assessment of its structure, and this is especially important as the bridge remains in use. This paper documents the way the assessment of structural safety was carried out. The assessment methodology of existing structures was applied. The preliminary assessment was based on bibliographic data and non-destructive techniques. The geometric data of the bridge were obtained by Terrestrial Laser Scanning (TLS), which made possible the analysis of its deformations and assessment of its structure. Ground-Penetrating Radar (GPR) was also used with different antennae to work at different depths and spatial resolutions with the aim of analysing structural elements. From the above information, the assessment of structural safety was made using the limit analysis method by applying the historical works carried out on it and those described in the regulation of obligatory compliance in Spain (IAP11), studying the sensitivity of safety to the most relevant parameters. The state of preservation and structural integrity of the bridge is discussed and conclusions are drawn on the areas of greatest risk and the bases for the following assessment phase of preservation of the bridge.