Modern optical and infrared astronomical sites are getting used to a flexible way of operation, namely queue
modes, allowing astronomical observations in the most appropriate weather conditions for each specific observing
scientific program. The forecast of weather conditions is then a mandatory issue to plan in advance the
observations queue for each night in order to exploit efficiently the astronomical facilities with the largest high
quality data output for scientific exploitation. The precipitable water vapour is the parameter accounting for
the infrared (IR) quality of an astronomical site. The temporal fluctuation of this parameter drastically affects
the quality of the IR data recorded at ground telescopes. An optical/IR telescope needs the forecasting of the
precipitable water vapour for a proper queue scheduling of IR observations. The Roque de los Muchachos Observatory
(ORM) on the island of La Palma (Spain) presents an abrupt topography which difficult the forecasting
at this astronomical site. We discuss the performance of a mesoscale numerical weather model (WRF, Weather
Research and Forecasting) applied to ORM region including the comparison with local precipitable water vapour
estimations from GPS (Global Positioning System).
Measurements of ground displacement have been carried out on the entire active volcanic island of Tenerife, Canary
Islands, by means of classical and advanced DInSAR techniques. The main limiting factor on the accuracy of DInSAR
measurements is the distribution of the water vapour in the lower troposphere. Hence, it is yet necessary to perform a
detailed spatial and temporal characterization of water vapour to understand, and to be able to carry out a direct
computation of, the effect of the tropospheric delay on DInSAR results. In this sense, satellite and balloon data have
been analysed to infer the variability in the distribution of water vapour and hence, the robustness of DInSAR results on
the island of Tenerife.
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.
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.
In this work a method for determining the micro- and macro-physical properties of oceanic stratocumulus clouds at night-time (when only infrared data are available) is presented. It is based on the inversion of a radiative transfer model that computes the brightness temperatures in NOAA-AVHRR channels 3, 4 and 5. The inversion is performed using an artificial neural network (ANN), which is trained to fit the theoretical computations. A detailed study of the ANN parameters and training algorithms demonstrates the convenience of using the "back propagation with momentum" method. The proposed retrieval, using both uniform and adiabatic models for clouds, was validated using ground data collected in Tenerife (Canary Islands), and a good agreement was obtained in those pixels near the sample site. The convenience of using the adiabatic approximation is discussed.
This paper presents the results of a case study using the dual-view ATSR-2 instrument to retrieve optical and microphysical parameters of cirrus clouds overlaying a variable water cloud. The proposed method utilizes the channels at 0.87 and 1.6μm in both nadir and forward views and the nadir view of the 3.7μm band. A lookup table is generated using a radiative transfer model, which is inverted using an evolutionary scatter search technique. An uncertainty analysis demonstrates the robustness of the retrieved ice cloud optical thickness and effective size, even in the presence of an optically thick water cloud below. A comparison with in-situ aircraft observations from the INCA campaign shows good agreement for these parameters.
Biometric identification based on hand veins subcutaneous network structure appears as a promising technique for personal recognition due to its robustness, low cost implementation and high users acceptability. Two of the most critical stages in these vein check identification systems are the vein pattern segmentation and matching, which extremely depend on the image acquisition process. The acquisition becomes a bottleneck in the performance of the whole system, as it represents the first stage in the recognition process. In this paper, solutions for the segmentation and matching under poor illumination conditions during the image acquisition process and low pixel camera resolution are presented. In particular, exhaustive studies will be presented in order to show that the use of the parametric black tophat transform, instead of the classical tophat mathematical morphology tool, and the utilization of homomorphic filters greatly benefit the performance of the segmentation and matching processes. Furthermore, it will be demonstrated that slight vertical and horizontal hand movements in the image acquisition can be easily corrected by using simple scaling operations. This fact makes unnecessary to fix the hand position and thus, contributes to enhance even more the non-invasive condition of these biometric systems. A first prototype for the image acquisition process has been implemented by only using a very simple CDD camera and LED diodes, obtaining infrared images of 60 people. The application of the mentioned techniques on these images, combined with some typical image processing operations like mean and median filtering, skeletonization and pruning, leads to a reliable vein based identification system, even in poor image acquisition conditions.
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.
In this work, a method to estimate the emissivity distribution is developed for stratocumulus clouds paying special attention to water vapor over these clouds. The main aim is to obtain an approximate distribution of effective radius for optically thick stratocumulus clouds. This method is based on night imagery obtained from the NOAA-AVHRR IR channels, and an atmospheric radiative transfer model that makes use of the discrete ordinate method called DISORT. We have solved the problem of the local estimation, for the Canary Islands, of the influence of the water vapor over the stratocumulus level. Finally we associate the emissivity distribution with the distribution of the droplet effective radius. This allows us to estimate a unique effective radius for the cloud taking advantage of statistic in the image in order to avoid the non-monotonous behavior of emissivity with the droplet effective radius in the 3.7 micrometers band. The results are compared with satellite data from NOAA-14.
In this work, a method for the retrieval of droplet radius, temperature and optical thickness of oceanic stratocumulus is developed.It is based on night imagery obtained from the NOAA-AVHRR IR channels and an atmospheric radiative transfer model that makes use of the discrete ordinate method called DISORT. Using this mode, we have simulated the theoretical radiance that reaches the satellite supposing a planar homogeneous cloud layer. The stratocumulus clouds are assumed to be composed by spherical water droplets with a gamma size distribution that provides a particular effective radius. The single scattering parameters are deduced from Mie's theory. Once evaluated the model behavior, we must invert a non lineal system of three equations to obtain the cloud parameters from the channels, 3,4 and 5 brightness temperatures. The main problem is the behavior of the radiative parameters when the effective radius is varied, because exist several values that provide the same temperatures. That implies that the systems have not a unique solution and, in order to avoid this problem we propose an optimal radii discretization on the basis of the above-mentioned microphysical features.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.