A New Model of Solar Illumination of Earth’s Atmosphere during Night-Time

In this work, a solar illumination model of the Earth’s atmosphere is developed. The developed model allows us to determine with extreme accuracy how the atmospheric illumination varies during night hours on a global scale. This time-dependent variation in illumination causes a series of sudden changes in the entire Earth-atmosphere-ionosphere system of considerable interest for various research sectors and applications related to climate change, ionospheric disturbances, navigation and global positioning systems. The use of the proposed solar illumination model to calculate the time-dependent Solar Terminator Height (STH) at the global scale is also presented. Time-dependent STH impact on the measurements of ionospheric Total Electron Content (TEC) is, for the first time, investigated on the basis of 20 years long time series of GPS-based measurements collected at the ground. The correlation analysis, performed in the post-sunset hours, allows new insights into the dependence of TEC–STH relation on the different periods (seasons) of observation and solar activity condition

EVIDENCE OF WEAK CHAOS WITHIN PLUG-SLUG TRANSITION IN HORIZONTAL TWO PHASE FLOW

Intermittent behaviour has been observed in gas-liquid flows in a horizontal pipe and a weak sign of deterministic chaos has been diagnosed within a transition from plug to slug flow. The analysis has been performed on the basis of an algorithm which exploits the concept of short-term predictability of chaotic motions. The method is completely non-parametric and works whatever the distribution function of the data points may be. The weak sign of chaos is in contrast with the Lorenz-type systems (strong chaos) and supports the idea of Kolmogorov about irregular motion in hydrodynamical systems

Implementation of Robust Satellite Techniques for Volcanoes on ASTER Data under the Google Earth Engine Platform

The RST (Robust Satellite Techniques) approach is a multi-temporal scheme of satellite data analysis widely used to investigate and monitor thermal volcanic activity from space through high temporal resolution data from sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Spinning Enhanced Visible and Infrared Imager (SEVIRI). In this work, we present the results of the preliminary RST algorithm implementation to thermal infrared (TIR) data, at 90 m spatial resolution, from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Results achieved under the Google Earth Engine (GEE) environment, by analyzing 20 years of satellite observations over three active volcanoes (i.e., Etna, Shishaldin and Shinmoedake) located in different geographic areas, show that the RST-based system, hereafter named RASTer, detected a higher (around 25% more) number of thermal anomalies than the well-established ASTER Volcano Archive (AVA). Despite the availability of a less populated dataset than other sensors, the RST implementation on ASTER data guarantees an efficient identification and mapping of volcanic thermal features even of a low-intensity level. To improve the temporal continuity of the active volcanoes monitoring, the possibility of exploiting RASTer is here addressed, in the perspective of an operational multi-satellite observing system. The latter could include mid-high spatial resolution satellite data (e.g., Sentinel-2/MSI, Landsat-8/OLI), as well as those at higher-temporal (lower spatial) resolution (e.g., EOS/MODIS, Suomi-NPP/VIIRS, Sentinel-3/SLSTR), for which RASTer could provide useful algorithm’s validation and training dataset

A self-sufficient approach for GERB cloudy radiance detection.

Geostationary Earth Radiation Budget (GERB) is the broadband radiometer onboard the Meteosat Second Generation (MSG) platform, launched at the end of August 2002 and still in commissioning phase. GERB data is planned to be used in many applications concerning Earth Radiation Budget (ERB) calculation. In order to evaluate the impact of clouds on ERB, a cloud detection is required and, at present, a cloud mask based on higher spatial and spectral resolution data acquired by Spinning Enhanced Visible and Infrared Imager (SEVIRI), the imager onboard the same MSG platform, is planned to be used in order to identify cloudy GERB soundings. As an alternative, a self-sufficient (only based on GERB data) method (OCA, the One-channel Cloudy-radiance-detection Approach) is proposed, as a time-saving and, probably, more suitable solution than the planned co-location approach. In this paper, preliminary results obtained by using several years of Meteosat data as well as GERB synthetic radiances (produced from Meteosat-7 observations) are presented. It is shown how results obtained by using GERB data alone can be comparable (and better in terms of number and spatial distribution of clear-sky GERB soundings identified) to the ones achieved if the co-location of a higher resolution cloud mask is use

Advanced Satellite Technique for Volcanic Activity Monitoring and Early Warning.

Nowadays, satellite remote sensing is an important tool for volcanic activity monitoring, thanks to several operational satellite platforms providing data everywhere with high observational frequencies and generally at low cost. Among different techniques available, an advanced satellite method, named RST (Robust Satellite Technique). based on the multitemporal analysis of satellite data, has shown a high capability in volcanic activity monitoring. This approach has proved capable of identifyimg and tracking volcanic ash Cloud and of correctly detecting and monitoring volcanic thermal anomalies. This paper analyzes some recent results, obtained applying this approach to the last eruptive events of Mt. Etna using both polar and geostationary satellites. In particular, for the first time, this approach is implemented on the present geostationary platform MSG-SEVIRI, with 15 min of temporal resolution. Preliminary results, together with a future potential of this implementation, are shown and discussed. Moreover, a differential RST index in time domain is also proposed for near real-time application, as a possible contribution to the development of an efficient early warning satellite system for volcanic hazard mitigation

AVHRR Automated detection of volcanic clouds.

A new satellite‐based technique has recently been proposed which seems suitable for an automatic detection of volcanic clouds in daytime conditions. In this paper the robustness of such a new approach, in particular in detecting early eruptive clouds, is evaluated, on several eruptive events at Mt Etna, by using five years of Advanced Very High Resolution Radiometer (AVHRR) data. The detection scheme is discussed together with its possible extension to night‐time monitoring and the improvements expected by its application to the next generation of satellite sensors (in particular Spinning Enhanced Visible and Infrared Imager (SEVIRI)) with enhanced spectral and temporal resolution. The proposed approach seems to overcome the limitations related to other proposed methods which, in some conditions (very fresh eruptive clouds, cold‐backgrounds, etc.), give false or missed detection and will no longer be applicable to the next generation of Geostationary Operational Environmental Satellites (GOES) due to the planned reduction of their thermal infrared channels until 2010

A Multi-Sensor Exportable Approach for Automatic Flooded Areas Detection and Monitoring by a Composite Satellite Constellation

Timely and frequently updated information about flood-affected areas and their space-time evolution are often crucial in order to correctly manage the emergency phases. In such a context, optical data provided by meteorological satellites, offering the highest available temporal resolution (from hours to minutes), could have a great potential. As cloud cover often occurs reducing the number of usable optical satellite images, an appropriate integration of observations coming from different satellite systems will surely improve the probability to find cloud-free images over the investigated region. To make this integration effective, appropriate satellite data analysis methodologies, suitable for providing congruent results, regardless of the used sensor, are envisaged. In this paper, a sensor-independent approach (RST, Robust Satellites Techniques-FLOOD) is presented and applied to data acquired by two different satellite systems (Advanced Very High Resolution Radiometer (AVHRR) onboard National Oceanic and Atmospheric Administration platforms and Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Earth Observing System satellites) at different spatial resolutions (from 1 km to 250 m) in the case of Elbe flood event occurred in Germany on August 2002. Results achieved demonstrated as the full integration of AVHRR and MODIS RST-FLOOD products allowed us to double the number of satellite passes daily available, improving continuity of monitoring over flood-affected regions. In addition, the application of RST-FLOOD to higher spatial resolution MODIS (250 m) data revealed to be crucial not only for mapping purposes but also for improving RST-FLOOD capability in identifying flooded areas not previously detected at lower spatial resolution

Optimal Setting of Earthquake-Related Ionospheric TEC (Total Electron Content) Anomalies Detection Methods: Long-Term Validation over the Italian Region

Over the last decade, thanks to the availability of historical satellite observations that have begun to be significantly large and thanks to the exponential growth of artificial intelligence techniques, many advances have been made in the detection of geophysical parameters such as seismic-related anomalies. In this study, the variations of the ionospheric Total Electron Content (TEC), one of the main parameters historically proposed as a seismic-connected indicator, are analyzed. To make a statistically robust analysis of the complex phenomena involved, we propose a completely innovative machine-learning approach developed in the R programming language. Through this approach, an optimal setting of the multitude of methodological inputs currently proposed for the detection of ionospheric anomalies is performed. The setting is optimized by analyzing, for the first time, multi-year—mostly twenty-year—time series of TEC satellite data measured by global navigation satellite systems (GNSS) over the Italian region, matched with the corresponding multi-year time series of seismic events. Seismic events including all the countries of the Mediterranean area, up to Turkey, are involved in the analysis. Tens of thousands of possible combinations of input methodological parameters are simulated and classified according to pre-established criteria. Several inputs examined return clear results. These results combined with each other highlight the presence of anomalous seismic-related sequences that have an extremely low probability of having been detected randomly (up to 2 out of 1 million). The anomalies identified represent the most anomalous behaviors of the TEC recorded during the entire period under investigation (e.g., 20 years). Some of the main conclusions are that, at mid-latitudes, ① the detection of seismic-TEC anomalies can be more efficient looking for punctual rather than persistent phenomena; ② the optimal thresholds for the identification of co-seismic anomalies can assume different values depending on type of anomaly (positive or negative) and type of observation; ③ single GNSS receiver data can be useful for capturing local earthquake-ionospheric effects and Global Ionospheric Maps (GIM) data can be functional in detecting large-scale earthquake-ionospheric effects; ④ earthquakes deeper than 50 km are less likely to affect the ionosphere

Landslides Detection and Mapping with an Advanced Multi-Temporal Satellite Optical Technique

Landslides are catastrophic natural phenomena occurring as a consequence of climatic, tectonic, and human activities, sometimes combined among them. Mostly due to climate change effects, the frequency of occurrence of these events has quickly grown in recent years, with a consequent increase in related damage, both in terms of loss of human life and effects on the involved infrastructures. Therefore, implementing properly actions to mitigate consequences from slope instability is fundamental to reduce their impact on society. Satellite systems, thanks to the advantages offered by their global view and sampling repetition capability, have proven to be valid tools to be used for these activities in addition to traditional techniques based on in situ measurements. In this work, we propose an advanced multitemporal technique aimed at identifying and mapping landslides using satellite-derived land cover information. Data acquired by the Multispectral Instrument (MSI) sensor aboard the Copernicus Sentinel-2 platforms were used to investigate a landslide affecting Pomarico city (southern Italy) in January 2019. Results achieved indicate the capability of the proposed methodology in identifying, with a good trade-off between reliability and sensitivity, the area affected by the landslide not just immediately after the event, but also a few months later. The technique was implemented within the Google Earth Engine Platform, so that it is completely automatic and could be applied everywhere. Therefore, its potential for supporting mitigation activities of landslide risks is evident

Integration of optical and passive microwave satellite data for flooded area detection and monitoring

Flooding represents a serious threat to millions of people around the world and its hazard is rising as a result of climate changes. From this perspective, flood risk management is a key focus of many governments, whose priority is to have frequently updated and accurate information about the flood state and evolution to promptly react to the disaster and to put in place effective countermeasures devoted to limit damages and human lives losses. Remote sensing technology allows for flood monitoring at different spatial and temporal resolutions with an adequate level of accuracy. In particular, for emergency response purposes, an integrated use of satellite data, acquired by both optical and passive or active microwave instruments, has to be preferred to have more complete and frequently updated information on soil conditions and to better support decision makers. In this framework, multi-year time series of MODIS (Moderate Resolution Imaging Spectroradiometer) and AMSR-E (Advanced Microwave Scanning Radiometer for Earth Observing System) data were processed and analyzed. In detail, the Robust Satellite Techniques (RST), a multi-sensor approach for satellite data analysis, has been implemented for studying the August 2002 Elbe river flood occurred in Germany, trying to assess the potential of such an integrated system for the determination of soil status and conditions (i.e. moisture variation, water presence) as well as for a timely detection and a near real time monitoring of critical soil conditions