Methods to Retrieve the Cloud-Top Height in the Frame of the JEM-EUSO Mission

The Japanese Experiment Module-Extreme Universe Space Observatory (JEM-EUSO) telescope will measure ultrahigh-energy cosmic ray properties by detecting the UV fluorescence light generated in the interaction between cosmic rays and the atmosphere. Therefore, information on the state of clouds in the atmosphere is crucial for a proper interpretation of the data. For a real-time observation of the clouds in the telescope field of view, the JEM-EUSO will use an atmospheric monitoring system composed of a light detection and ranging and an infrared (IR) camera. In this paper, the focus is on the IR camera data. To retrieve the cloud-top height (CTH) from IR images, three different methods are considered here. The first one is based on bispectral stereo vision algorithms and requires two different views of the same scene in different spectral bands. For the second one, brightness temperatures provided by the IR camera are converted to effective cloud-top temperatures, from which the CTH is estimated using the vertical temperature profiles. A third method that uses the primary numerical weather prediction model output parameters, such as the cloud fraction, has also been considered to retrieve the CTH. This paper presents a first analysis, in which the heights retrieved by these three methodologies are compared with the heights given by the Moderate Resolution Imaging Spectroradiometer sensor installed on the polar satellite Terra. Since all these methods are suitable for the JEM-EUSO mission, they could be used in the future in a complementary way to improve the accuracy of the CTH retrieval

Design of the Front End Electronics for the Infrared Camera of JEM-EUSO, and manufacturing and verification of the prototype model

The Japanese Experiment Module (JEM) Extreme Universe Space Observatory (EUSO) will be launched and attached to the Japanese module of the International Space Station (ISS). Its aim is to observe UV photon tracks produced by ultra-high energy cosmic rays developing in the atmosphere and producing extensive air showers. The key element of the instrument is a very wide-field, very fast, large-lense telescope that can detect extreme energy particles with energy above $10^{19}$ eV. The Atmospheric Monitoring System (AMS), comprising, among others, the Infrared Camera (IRCAM), which is the Spanish contribution, plays a fundamental role in the understanding of the atmospheric conditions in the Field of View (FoV) of the telescope. It is used to detect the temperature of clouds and to obtain the cloud coverage and cloud top altitude during the observation period of the JEM-EUSO main instrument. SENER is responsible for the preliminary design of the Front End Electronics (FEE) of the Infrared Camera, based on an uncooled microbolometer, and the manufacturing and verification of the prototype model. This paper describes the flight design drivers and key factors to achieve the target features, namely, detector biasing with electrical noise better than $100 \mu$V from $1$ Hz to $10$ MHz, temperature control of the microbolometer, from $10^{\circ}$C to $40^{\circ}$C with stability better than $10$ mK over $4.8$ hours, low noise high bandwidth amplifier adaptation of the microbolometer output to differential input before analog to digital conversion, housekeeping generation, microbolometer control, and image accumulation for noise reduction

A method for Cloud Mapping in the Field of View of the Infra-Red Camera during the EUSO-SPB1 flight

EUSO-SPB1 was released on April 24th, 2017, from the NASA balloon launch site in Wanaka (New Zealand) and landed on the South Pacific Ocean on May 7th. The data collected by the instruments onboard the balloon were analyzed to search UV pulse signatures of UHECR (Ultra High Energy Cosmic Rays) air showers. Indirect measurements of UHECRs can be affected by cloud presence during nighttime, therefore it is crucial to know the meteorological conditions during the observation period of the detector. During the flight, the onboard EUSO-SPB1 UCIRC camera (University of Chicago Infra-Red Camera), acquired images in the field of view of the UV telescope. The available nighttime and daytime images include information on meteorological conditions of the atmosphere observed in two infra-red bands. The presence of clouds has been investigated employing a method developed to provide a dense cloudiness map for each available infra-red image. The final masks are intended to give pixel cloudiness information at the IR-camera pixel resolution that is nearly 4-times higher than the one of the UV-camera. In this work, cloudiness maps are obtained by using an expert system based on the analysis of different low-level image features. Furthermore, an image enhancement step was needed to be applied as a preprocessing step to deal with uncalibrated data

Bi-spectral infrared algorithm for cloud coverage over oceans by the jem-euso mission program

The need to monitor specific areas for different applications requires high spatial and temporal resolution. This need has led to the proliferation of ad hoc systems on board nanosatellites, drones, etc. These systems require low cost, low power consumption, and low weight. The work we present follows this trend. Specifically, this article evaluates a method to determine the cloud map from the images provided by a simple bi-spectral infrared camera within the framework of JEM-EUSO (The Joint Experiment Missions-Extrem Universe Space Observatory). This program involves different experiments whose aim is determining properties of Ultra-High Energy Cosmic Ray (UHECR) via the detection of atmospheric fluorescence light. Since some of those projects use UV instruments on board space platforms, they require knowledge of the cloudiness state in the FoV of the instrument. For that reason, some systems will include an infrared (IR) camera. This study presents a test to generate a binary cloudiness mask (CM) over the ocean, employing bi-spectral IR data. The database is created from Moderate-Resolution Imaging Spectroradiometer (MODIS) data (bands 31 and 32). The CM is based on a split-window algorithm. It uses an estimation of the brightness temperature calculated from a statistical study of an IR images database along with an ancillary sea surface temperature. This statistical procedure to obtain the estimate of the brightness temperature is one of the novel contributions of this work. The difference between the measured and estimation of the brightness temperature determines whether a pixel is cover or clear. That classification requires defining several thresholds which depend on the scenarios. The procedure for determining those thresholds is also novel. Then, the results of the algorithm are compared with the MODIS CM. The agreement is above 90%. The performance of the proposed CM is similar to that of other studies. The validation also shows that cloud edges concentrate the vast majority of discrepancies with the MODIS CM. The relatively high accuracy of the algorithm is a relevant result for the JEM-EUSO program. Further work will combine the proposed algorithm with complementary studies in the framework of JEM-EUSO to reinforce the CM above the cloud edges.This research was funded by MADRID GOVERNMENT (Comunidad de Madrid;Spain), PEJ-2018-AI_TIC-11476 and by the SPANISH MINISTRY (MICINN), RTI2018-099825-B-C33. The APC was funded by the MADRID GOVERNMENT, EPUC3M14