Classification of sea-ice types in SAR imagery

We present a supervised three-stage classification (labeling) scheme applied to SAR images of polar regions for detecting different sea-ice types. The three-stage labeling procedure consists of: 1) a speckle noise filtering stage, based on a sequence of contour detection, segmentation and filtering steps, which removes SAR speckle noise (and texture information as well) without losing spatial details;2) a second stage providing Bayesian, maximum-a-posteriori, hierarchical (coarse-tofine), adaptive (data-driven) and contextual labeling of piecewise constant intensity images featuring little useful texture information;and 3) an output stage providing a many-to-one relationship between second stage output categories (types or clusters) and desired output classes. Modules 1) and 2), which demonstrated their validity in several applications in the existing literature, are briefly recalled in the current paper. The proposed labeling scheme features some interesting functional properties when applied to sea-ice SAR images: it is easy to use, i.e. it requires minor user interaction, is robust to changes in input conditions and performs better than a noncontextual (per-pixel) classifier. Application results are presented and discussed for a pair of SAR images extracted, respectively, from an ERS-1 scene acquired on November 1992 over the Bellingshausen Sea (Antarctica) and from an ERS-2 scene of the East Greenland Sea acquired on March 1997 when a field experiment by the research vessel “Jan Mayen” was conducted in the same area

Analysis of an intense bora event in the Adriatic area

International audienceNumerical simulations of a bora event, recently occurred in the Adriatic area, are presented. Two reference runs at different horizontal resolution (about 20km and 8km) describe the case. Initial conditions for the atmospheric model integration are obtained from ECMWF analyses. Satellite data are used for comparisons. A further run at horizontal resolution of 8km, using initial satellite sea surface temperatures, is performed to evaluate their impact on the low level wind over the Adriatic Sea. All the simulations are carried out with 50 layers in the vertical. Numerous aspects of the simulations are found to be in agreement with the understanding as well as the observational knowledge of bora distinctive characteristics. Satellite data and model results indicate that a more realistic simulation of the bora wind over the sea is achieved using the model with 8km horizontal resolution and that the low level wind in this case is sensitive, though weakly, to the difference between the used sea surface temperature fields. Simulation results also show that both wind intensity and the area around wind peaks tend to increase when relatively higher sea surface temperatures are used

Synergic use of SAR imagery and high resolution atmospheric model to estimate marine wind fields : an application in presence of an atmospheric gravity wave episode.

A study aimed at retrieving sea surface wind fields of semi-enclosed basins from combined use of SAR imagery and a high resolution mesoscale numerical atmospheric model, is presented. Two consecutive ERS-2 SAR frames and a set of NOAA/AVHRR and MODIS images acquired over the North Tyrrhenian Sea on March 30, 2000 were used for the analysis. SAR wind speeds and directions at 10 m above the sea surface were retrieved using the semi-empirical backscatter models CMOD4 and CMOD-IFREMER. Surface wind vectors predicted by the meteorological ETA model were exploited as guess input to SAR wind inversion procedure. ETA is a three-dimensional, primitive equation, grid-point model currently operational at the National Centers for Environmental Prediction of the U.S. National Weather Service. The model was adapted to run with a resolution up to about 4.0 Km. It was found that the inversion methodology was not able to resolve wind speed modulations due to the action of an atmospheric gravity wave, called “lee wave”, which occurred in the analyzed area. A simple atmospheric wave propagation model was thus used to account for the SAR observed surface wind speed modulation. Synergy with ETA model outputs was further exploited in simulations where atmospheric parameters up-wind the atmospheric wave were provided as input to the lee wave propagation model

A Deep Learning Method for AGILE-GRID Gamma-Ray Burst Detection

The follow-up of external science alerts received from gamma-ray burst (GRB) and gravitational wave detectors is one of the AGILE Team's current major activities. The AGILE team developed an automated real-time analysis pipeline to analyze AGILE Gamma-Ray Imaging Detector (GRID) data to detect possible counterparts in the energy range 0.1-10 GeV. This work presents a new approach for detecting GRBs using a convolutional neural network (CNN) to classify the AGILE-GRID intensity maps by improving the GRB detection capability over the Li & Ma method, currently used by the AGILE team. The CNN is trained with large simulated data sets of intensity maps. The AGILE complex observing pattern due to the so-called "spinning mode" is studied to prepare data sets to test and evaluate the CNN. A GRB emission model is defined from the second Fermi-LAT GRB catalog and convoluted with the AGILE observing pattern. Different p-value distributions are calculated, evaluating, using the CNN, millions of background-only maps simulated by varying the background level. The CNN is then used on real data to analyze the AGILE-GRID data archive, searching for GRB detections using the trigger time and position taken from the Swift-BAT, Fermi-GBM, and Fermi-LAT GRB catalogs. From these catalogs, the CNN detects 21 GRBs with a significance of >= 3 sigma, while the Li & Ma method detects only two GRBs. The results shown in this work demonstrate that the CNN is more effective in detecting GRBs than the Li & Ma method in this context and can be implemented into the AGILE-GRID real-time analysis pipeline

The Agile Alert System For Gamma-Ray Transients

In recent years, a new generation of space missions offered great opportunities of discovery in high-energy astrophysics. In this article we focus on the scientific operations of the Gamma-Ray Imaging Detector (GRID) onboard the AGILE space mission. The AGILE-GRID, sensitive in the energy range of 30 MeV-30 GeV, has detected many gamma-ray transients of galactic and extragalactic origins. This work presents the AGILE innovative approach to fast gamma-ray transient detection, which is a challenging task and a crucial part of the AGILE scientific program. The goals are to describe: (1) the AGILE Gamma-Ray Alert System, (2) a new algorithm for blind search identification of transients within a short processing time, (3) the AGILE procedure for gamma-ray transient alert management, and (4) the likelihood of ratio tests that are necessary to evaluate the post-trial statistical significance of the results. Special algorithms and an optimized sequence of tasks are necessary to reach our goal. Data are automatically analyzed at every orbital downlink by an alert pipeline operating on different timescales. As proper flux thresholds are exceeded, alerts are automatically generated and sent as SMS messages to cellular telephones, e-mails, and push notifications of an application for smartphones and tablets. These alerts are crosschecked with the results of two pipelines, and a manual analysis is performed. Being a small scientific-class mission, AGILE is characterized by optimization of both scientific analysis and ground-segment resources. The system is capable of generating alerts within two to three hours of a data downlink, an unprecedented reaction time in gamma-ray astrophysics.Comment: 34 pages, 9 figures, 5 table

The AGILE real-time analysis pipelines in the multi-messenger era

In the multi-messenger era, space and ground-based observatories usually develop real-time analysis (RTA) pipelines to rapidly detect transient events and promptly share information with the scientific community to enable follow-up observations. These pipelines can also react to science alerts shared by other observatories through networks such as the Gamma-Ray Coordinates Network (GCN) and the Astronomer's Telegram (ATels). AGILE is a space mission launched in 2007 to study X-ray and gamma-ray phenomena. This contribution presents the technologies used to develop two types of AGILE pipelines using the RTApipe framework and an overview of the main scientific results. The first type performs automated analyses on new AGILE data to detect transient events and automatically sends AGILE notices to the GCN network. Since May 2019, this pipeline has sent more than 50 automated notices with a few minutes delay since data arrival. The second type of pipeline reacts to multi-messenger external alerts (neutrinos, gravitational waves, GRBs, and other transients) received through the GCN network and performs hundreds of analyses searching for counterparts in all AGILE instruments' data. The AGILE Team uses these pipelines to perform fast follow-up of science alerts reported by other facilities, which resulted in the publishing of several ATels and GCN circulars.Comment: 8 pages, 3 figures, Proceedings of the 37th International Cosmic Ray Conference (ICRC 2021), Berlin, German

AGILE Observations of the LIGO-Virgo Gravitational-wave Events of the GWTC-1 Catalog

We present a comprehensive review of AGILE follow-up observations of the Gravitational Wave (GW) events and the unconfirmed marginal triggers reported in the first LIGO-Virgo (LV) Gravitational Wave Transient Catalog (GWTC-1). For seven GW events and 13 LV triggers, the associated 90% credible region was partially or fully accessible to the AGILE satellite at the T 0; for the remaining events, the localization region was not accessible to AGILE due to passages into the South Atlantic Anomaly, or complete Earth occultations (as in the case of GW170817). A systematic search for associated transients, performed on different timescales and on different time intervals about each event, led to the detection of no gamma-ray counterparts. We report AGILE MCAL upper limit fluences in the 400 keV-100 MeV energy range, evaluated in a time window of T 0 ± 50 s around each event, as well as AGILE GRID upper limit (UL) fluxes in the 30 MeV-50 GeV energy range, evaluated in a time frame of T 0 ± 950 s around each event. All ULs are estimated at different integration times and are evaluated within the portions of GW credible region accessible to AGILE at the different times under consideration. We also discuss the possibility of AGILE MCAL to trigger and detect a weak soft-spectrum burst such as GRB 170817A

The Second AGILE MCAL Gamma-Ray Burst Catalog: 13 yr of Observations

We present the results of a systematic search and analysis of GRBs detected by the Astrorivelatore Gamma ad Immagini LEggero (AGILE) MiniCALorimeter (MCAL; 0.4–100 MeV) over a time frame of 13 yr, from 2007 to 2020 November. The MCAL GRB sample consists of 503 bursts triggered by MCAL, 394 of which were fully detected onboard with high time resolution. The sample consists of about 44% short GRBs and 56% long GRBs. In addition, 109 bursts triggered partial MCAL onboard data acquisitions, providing further detections that can be used for joint analyses or triangulations. More than 90% of these GRBs were also detected by the AGILE Scientific RateMeters (RMs), providing simultaneous observations between 20 keV and 100 MeV. We performed spectral analysis of these events in the 0.4–50 MeV energy range. We could fit the time-integrated spectrum of 258 GRBs with a single power-law model, resulting in a mean photon index 〈β〉of−2.3. Among them, 43 bursts could also be fitted with a Band model, with peak energy above 400 keV, resulting in a mean low-energy photon index 〈α〉 = −0.6, a mean high-energy photon index 〈β〉 = −2.5, and a mean peak energy 〈Ep〉 = 640 keV. The AGILE MCAL GRB sample mostly consists of hard-spectrum GRBs, with a large fraction of short-duration events. We discuss properties and features of the MCAL bursts, whose detections can be used to perform joint broad-band analysis with other missions, and to provide insights on the high-energy component of the prompt emission in the tens of mega electron volt energy range.publishedVersio

AGILE Observations of GRB 220101A: A "new Year's Burst" with an Exceptionally Huge Energy Release

We report the AGILE observations of GRB 220101A, which took place at the beginning of 2022 January 1 and was recognized as one of the most energetic gamma-ray bursts (GRBs) ever detected since their discovery. The AGILE satellite acquired interesting data concerning the prompt phase of this burst, providing an overall temporal and spectral description of the event in a wide energy range, from tens of kiloelectronvolts to tens of megaelectronvolts. Dividing the prompt emission into three main intervals, we notice an interesting spectral evolution, featuring a notable hardening of the spectrum in the central part of the burst. The average fluxes encountered in the different time intervals are relatively moderate, with respect to those of other remarkable bursts, and the overall fluence exhibits a quite ordinary value among the GRBs detected by MCAL. However, GRB 220101A is the second farthest event detected by AGILE, and the burst with the highest isotropic equivalent energy of the entire MCAL GRB sample, releasing Eiso = 2.54 × 1054 erg and exhibiting an isotropic luminosity of Liso = 2.34 × 1052 erg s−1 (both in the 400 keV–10 MeV energy range). We also analyzed the first 106 s of the afterglow phase, using the publicly available Swift-XRT data, carrying out a theoretical analysis of the afterglow, based on the forward shock model. We notice that GRB 220101A is with high probability surrounded by a wind-like density medium, and that the energy carried by the initial shock shall be a fraction of the total Eiso, presumably near ∼50%.publishedVersio

Satellite observations for detecting and forecasting sea-ice conditions: A summary of advances made in the SPICES Project by the EU's Horizon 2020 Programme

The detection, monitoring, and forecasting of sea-ice conditions, including their extremes, is very important for ship navigation and offshore activities, and for monitoring of sea-ice processes and trends. We summarize here recent advances in the monitoring of sea-ice conditions and their extremes from satellite data as well as the development of sea-ice seasonal forecasting capabilities. Our results are the outcome of the three-year (2015-2018) SPICES (Space-borne Observations for Detecting and Forecasting Sea-Ice Cover Extremes) project funded by the EU's Horizon 2020 programme. New SPICES sea-ice products include pancake ice thickness and degree of ice ridging based on synthetic aperture radar imagery, Arctic sea-ice volume and export derived from multisensor satellite data, and melt pond fraction and sea-ice concentration using Soil Moisture and Ocean Salinity (SMOS) radiometer data. Forecasts of July sea-ice conditions from initial conditions in May showed substantial improvement in some Arctic regions after adding sea-ice thickness (SIT) data to the model initialization. The SIT initialization also improved seasonal forecasts for years with extremely low summer sea-ice extent. New SPICES sea-ice products have a demonstrable level of maturity, and with a reasonable amount of further work they can be integrated into various operational sea-ice services