Abstract
<p>The Dansgaard-Oeschger (DO) events are one of the most striking examples of abrupt climate change in the Earth's history, representing temperature oscillations of about 8 to 16 degrees Celsius within a few decades. DO events have been studied extensively in paleoclimatic records, particularly in ice core proxies. Examples include the Greenland NGRIP record of oxygen isotopic composition.<br>This work addresses the anticipation of DO events using machine learning algorithms. We consider the NGRIP time series from 20 to 60 kyr b2k with the GICC05 timescale and 20-year temporal resolution. Forecasting horizons range from 0 (nowcasting) to 400 years. We adopt three different machine learning algorithms (random forests, support vector machines, and logistic regression) in training windows of 5 kyr. We perform validation on subsequent test windows of 5 kyr, based on timestamps of previous DO events' classification in Greenland by Rasmussen et al. (2014). We perform experiments with both sliding and growing windows.<br>Results show that predictions on sliding windows are better overall, indicating that modelling is affected by non-stationary characteristics of the time series. The three algorithms' predictive performance is similar, with a slightly better performance of random forest models for shorter forecast horizons. The prediction models' predictive capability decreases as the forecasting horizon grows more extensive but remains reasonable up to 120 years. Model performance deprecation is mostly related to imprecision in accurately determining the start and end time of events and identifying some periods as DO events when such is not valid.</p>

Abstract
<p>Air ions are ubiquitous in the atmosphere. These charge carriers can be found in various forms as charged molecules, nanoclusters as well as aerosol particles. The population of air ions normally concentrates in the cluster size range (0.8 – 1.7 nm in mobility equivalent diameters) in the absence of particle formation processes. A concentration burst in the intermediate size range (1.7 – 7 nm) can be typically observed during atmospheric new particle formation (NPF) and in precipitation episodes <sup>1</sup>. Contrary to the intermediate ions formed during NPF that favour growth to larger sizes, intermediate ion bursts resulting from precipitation tend to shrink <sup>2,3</sup>. The production of intermediate ions during precipitation has been attributed to the Lenard effect and they are usually referred to as the balloelectric ions <sup>3</sup>.</p><p>During precipitation the rain-out and wash-out of radon progeny increase the gamma dose at ground level <sup>4</sup>. Being a type of ionising radiation, gamma creates positive and negative charges in the air. These charges are either lost in recombination or transformed into air ions. It is therefore interesting to understand whether the precipitation-associated elevation in gamma radiation plays any role in forming or neutralising the balloelectric ions. At SMEAR II station in Hyytiälä, Finland <sup>5</sup>, we have conducted measurements of air ions, gamma radiation, precipitation together with other meteorological parameters. A similar establishment of the measurement set stands also at SMEAR Estonia station in Jarvseljä, Estonia <sup>6</sup>. The data collected at Hyytiälä from 2017.7 to 2018.8 show that the intermediate ion concentration correlates with rainfall only when the precipitation intensity is greater than 1 mm/h. For milder rainfall with the precipitation intensity being 0.1-1 mm/h, the intermediate ion concentration increases with an increase in the gamma counts. The work is under progress and we intend to extend the analysis to Jarvseljä data for a comprehensive understanding of the observations.</p><p>Acknowledgements: This work received financial supports from European Regional Development Fund (project MOBTT42) under the Mobilitas Pluss programme and from Estonian Research Council project PRG714.</p><p>References:</p><p>1. Tammet, H., Komsaare, K. & Hõrrak, U. Intermediate ions in the atmosphere. Atmospheric Research <strong>135-136</strong>, 263-273, doi:10.1016/j.atmosres.2012.09.009 (2014).</p><p>2. Hõrrak, U. et al. Formation of Charged Nanometer Aerosol Particles Associated with Rainfall: Atmospheric Measurements and Lab Experiment. Report Series in Aerosol Science <strong>80</strong>, 180-185 (2006).</p><p>3. Tammet, H., Hõrrak, U. & Kulmala, M. Negatively charged nanoparticles produced by splashing of water. Atmos. Chem. Phys. <strong>9</strong>, 357–367 (2009).</p><p>4. Paatero, J. & Hatakka, J. Wet deposition efficiency of short-lived radon-222 progeny in central Finland. Boreal Env. Res. <strong>4</strong>, 285-293 (1999).</p><p>5. Hari, P. & Kulmala, M. Station for measuring ecosystem-atmosphere relations (SMEAR II). Boreal Environ. Res. <strong>10</strong>, 315-322 (2005).</p><p>6. Noe, S. M. et al. SMEAR Estonia: Perspectives of a large-scale forest ecosystem – atmosphere research infrastructure. Forestry Studies <strong>63</strong>, doi:10.1515/fsmu-2015-0009 (2015).</p>

Abstract
<p>Ambient radioactivity reflects a wide range of physical processes, including atmospheric and geological processes, as well as space weather and solar conditions. Gamma radiation near the Earth’s surface comes from diverse sources, including space (cosmic radiation), the earth’s atmosphere, and solid earth. In addition to the terrestrial gamma radiation originating from the radioactive decay of primordial radionuclides present in every soil and rock, gamma radiation is also continuously produced in the atmosphere from the interaction of secondary cosmic rays and upper-atmosphere gases, as well as from the decay of airborne radon (Rn-222) progeny. Therefore the temporal variability of gamma radiation contains information on a wide range of physical processes and space-earth interactions, but disentangling the different contributions remains a challenging endeavor. Continuous monitoring of gamma radiation at sea enables to remove both the terrestrial and radon exhalation contributions, allowing to examine in detail the space and atmospheric sources of ambient gamma radiation.</p><p>Gamma radiation over the Atlantic Ocean was measured on board the ship-rigged sailing ship NRP Sagres in the framework of the SAIL (Space-Atmosphere-Ocean Interactions in the marine boundary Layer) project. The measurements were performed continuously (every 1-second) with a NaI(Tl) scintillator counting all the gamma rays from 475 keV to 3 MeV. The casing of the instrument was adapted in order to endure the harsh oceanic conditions and installed in the mizzen mast of the ship. The counts were linked to a rigorous temporal reference frame and precise positioning through GNSS.</p><p>Here preliminary results based on the gamma radiation measurements performed from January 5<sup>th</sup> to May 9<sup>th </sup>2020 are presented, corresponding to the journey of the ship from Lisboa to Cabo Verde, Rio de Janeiro, Montevideu, Cape Town, and back to Lisboa. The data exhibit a clear transition from the coastal to the marine environment, enabling to study in detail the temporal variation of gamma radiation in the marine boundary layer, as well as the interface between land and marine conditions in terms of environmental radioactivity.</p>

Abstract
<p>The marine boundary layer offers a unique opportunity to investigate the electrical properties of the atmosphere, as the effect of natural radioactivity in driving near surface ionization is significantly reduced over the ocean, and the concentration of aerosols is also typically lower than over land. This work addresses the temporal variability of the atmospheric electric field in the South Atlantic marine boundary layer based on measurements from the SAIL (Space-Atmosphere-Ocean Interactions in the marine boundary Layer) project. The SAIL monitoring campaign took place on board the Portuguese navy tall ship NRP Sagres during its circumnavigation expedition in 2020.  Two identical field mills (CS110, Campbell Scientific) were installed on the same mast but at different heights (about 5 and 22 meters), recording the atmospheric electric field every 1-second. Hourly averages of the atmospheric electric field are analyzed for the ship’s leg from 3<sup>rd</sup> to 25<sup>th</sup> March, between Buenos Aires (South America) and Cape Town (South Africa). The median daily curve of the electric field has a shape compatible with the Carnegie curve, but significant variability is found in the daily pattern of individual days, with only about 30% of the days exhibiting a diurnal pattern consistent with the Carnegie curve.</p>

Abstract
Automated Program Repair (APR) is an area of research focused on the automatic generation of bug-fixing patches. Current APR approaches present some limitations, namely overfitted patches and low maintainability of the generated code. Several works are tackling this problem by attempting to come up with algorithms producing higher quality fixes. In this experience paper, we explore an alternative. We believe that by using existing low-cost APR techniques, fast enough to provide real-time feedback, and encouraging the developer to work together with the APR inside the IDE, will allow them to immediately discard proposed fixes deemed inappropriate or prone to reduce maintainability. Most developers are familiar with real-time syntactic code suggestions, usually provided as code completion mechanisms. What we propose are semantic code suggestions, such as code fixes, which are seldom automatic and rarely real-time. To test our hypothesis, we implemented a Visual Studio Code extension (named pAPRika), which leverages unit tests as specifications and generates code variations to repair bugs in JavaScript. We conducted a preliminary empirical study with 16 participants in a crossover design. Our results provide evidence that, although incorporating APR in the IDE improves the speed of repairing faulty programs, some developers are too eager to accept patches, disregarding maintenance concerns.

Abstract
A non-parametric identification algorithm is proposed to identify Linear Time Periodic (LTP) systems. The period is unknown and can be any real positive number. The system is modelled as an ARX Linear Parameter Varying (LPV) system with a virtual scheduling signal consisting of two orthogonal sinusoids (a sine and a cosine) with a period equal to the system period. Hence, the system parameters are polynomial functions of the scheduling vector. As these polynomials may have infinite degree, a non-parametric model is adopted to describe the LPV system. This model is identified by a Gaussian Process Regression (GPR) algorithm where the system period is a hyperparameter. The performance of the proposed identification algorithm is illustrated through the identification of a simulated LTP continuous system described by a state-space model. The ARX-LTP discrete-time model estimated in the noiseless case was taken as the true model. Copyright (C) 2021 The Authors.