A. Anzalone

The Atmospheric Monitoring System of the JEM-EUSO space mission

An Atmospheric Monitoring System (AMS) is a mandatory and key device of a space-based mission which aims to detect Ultra-High Energy Cosmic Rays (UHECR) and Extremely-High Energy Cosmic Rays (EHECR) from Space. JEM-EUSO has a dedicated atmospheric monitoring system that plays a fundamental role in our understanding of the atmospheric conditions in the Field of View (FoV) of the telescope. Our AMS consists of a very challenging space infrared camera and a LIDAR device, that are being fully designed with space qualification to fulfil the scientific requirements of this space mission. The AMS will provide information of the cloud cover in the FoV of JEM-EUSO, as well as measurements of the cloud top altitudes with an accuracy of 500 m and the optical depth profile of the atmosphere transmittance in the direction of each air shower with an accuracy of 0.15 degree and a resolution of 500 m. This will ensure that the energy of the primary UHECR and the depth of maximum development of the EAS ( Extensive Air Shower) are measured with an accuracy better than 30% primary energy and 120 g=cm 2 depth of maximum development for EAS occurring either in clear sky or with the EAS depth of maximum development above optically thick cloud layers. Moreover a very novel radiometric retrieval technique considering the LIDAR shots as calibration points, that seems to be the most promising retrieval algorithm is under development to infer the Cloud Top Height (CTH) of all kind of clouds, thick and thin clouds in the FoV of the JEM-EUSO space telescope.

A system for the automatic measurement of the nuchal translucency thickness from ultrasound video stream of the foetus

Nowadays the measurement of the nuchal translucency thickness is being used as part of routine ultrasound scanning during the end of the first trimester of pregnancy, for the screening of chromosomal defects, as trisomy 21. Currently, the measurement is being performed manually by physicians. The measurement can take a long time for being accomplished, needs to be performed by highly skilled operators, and is prone to errors. Semi-automated methods requires that the user manually selects a region of the image containing the nuchal translucency, procedure that is somewhat time consuming. In this paper we present a complete system prototype that is able to perform the measurement of the nuchal translucency thickness without any manual intervention from the operator, operating on the video stream coming out from the ultrasound machine.

Combining Fuzzy C-Mean and Normalized Convolution for Cloud Detection in IR Images

An important task for the cloud monitoring in several frameworks is providing maps of the cloud coverage. In this paper we present a method to detect cloudy pixels for images taken from ground by an infra-red camera. The method is a three-steps algorithm mainly based on a Fuzzy C-Mean clustering, that works on a feature space derived from the original image and the output of the reconstructed image obtained via normalized convolution. Experiments, run on several infra-red images acquired under different conditions, show that the cloud maps returned are satisfactory.

A Multi-spectral Stereo Method to Retrieve Cloud top Height applied to Geostationary Satellite images

In this paper we present a method to retrieve the Cloud Top Height (CTH), that is a refined version of a stereoscopic method present in literature. It is applied to stereo image pairs obtained by observations of the Meteosat Second Generation (MSG) geostationary satellites in a stereo setup. The performance of the method is tested both as mono band and multi spectral stereo. The estimated CTH are compared with the cloud altitude maps provided by the MODerate resolution Imaging Spectroradiometer (MODIS) on Terra-Aqua polar satellites. The results show that the new version of the method, performs better in comparison with the original algorithm despite the not proper stereo features of the system.

An experimental comparison of standard stereo matching algorithms applied to cloud top height estimation from satellite IR images

The JEM-EUSO (Japanese Experiment Module-Extreme Universe Space Observatory) telescope will measure Ultra High Energy Cosmic Ray properties by detecting the UV fluorescent light generated in the interaction between cosmic rays and the atmosphere. Cloud information is crucial for a proper interpretation of these data. The problem of recovering the cloud-top height from satellite images in infrared has struck some attention over the last few decades, as a valuable tool for the atmospheric monitoring. A number of radiative methods do exist, like C02 slicing and Split Window algorithms, using one or more infrared bands. A different way to tackle the problem is, when possible, to exploit the availability of multiple views, and recover the cloud top height through stereo imaging and triangulation. A crucial step in the 3D reconstruction is the process that attempts to match a characteristic point or features selected in one image, with one of those detected in the second image. In this article the performance of a group matching algorithms that include both area-based and global techniques, has been tested. They are applied to stereo pairs of satellite IR images with the final aim of evaluating the cloud top height. Cloudy images from SEVIRI on the geostationary Meteosat Second Generation 9 and 10 (MSG-2, MSG-3) have been selected. After having applied to the cloudy scenes the algorithms for stereo matching, the outcoming maps of disparity are transformed in depth maps according to the geometry of the reference data system. As ground truth we have used the height maps provided by the database of MODIS (Moderate Resolution Imaging Spectroradiometer) on-board Terra/Aqua polar satellites, that contains images quasi-synchronous to the imaging provided by MSG.

Comparing different methods to retrieve cloud top height from Meteosat satellite data

Cloud parameters such as the Cloud Top Height (CTH), Cloud Top Temperature (CTT), emissivity, particle size and optical depth have always been matter of interest for the atmospheric community. Particularly the CTH provides information leading to better understand the cloud radiative effects. Although there are many meteorological satellites providing the CTH, there are other sensors, not devoted to this purpose, that give some information from which this crucial parameter can be estimated. In this contribution we will describe three different methodologies to retrieve the CTH. The first technique is based on stereo-vision algorithms and requires two different views of the same scene and does not need of extra atmospheric information. In the second one, brightness temperatures in two IR spectral bands are converted to real cloud temperature by means of the proposed algorithms. From the CTT, the CTH is estimated using temperature vertical profiles (measured or modeled). The third technique retrieves the CTH from the output parameters of post event simulations performed by a Numerical Weather Prediction (NWP) model that in this work will be the mesoscale model WRF (Weather Research Forecast). This article presents a preliminary work, in which the heights retrieved by the three methodologies applied to the geostationary satellite Meteosat 10 are compared with the heights given by MODIS sensor installed on the polar satellite AQUA. This promising results show that valuable information about CTH can be retrieved from Meteosat which provide high frequency and large scale data useful for weather and climate research.

Stereo matching techniques for cloud-top height retrieval

This paper presents an ongoing study for the estimation of the cloud-top height by using only geometrical methods. It is based on the hypothesis that an infra-red camera is on board a satellite and pairs of images concern nearly the same scene. Stereo-vision techniques are therefore explored in order to test the methodology for height retrieval and in particular results of several techniques of stereo matching are evaluated. This study includes area-based matching algorithms by implementing the basic versions, without considering any further steps of optimisation to improve the results. Dense depth maps are the final outputs whose reliability is verified by computing error statistics with respect to a set of Digital Terrain Elevation Data, used as ground truth for a set of nearly cloud free images. A set of real pairs of images from the Along-Track Scanning Radiometer2 11μm data set, has been considered. The evaluated errors range between .75 and .80 km, that is not a particularly bad result if it is compared to the resolution of the ATSR2 pixel (1 km resolution).