Deployment and surface operations of the SEIS instrument onboard the InSight mission

On November 26th, 2018, the InSight spacecraft successfully landed on Mars after a 6-month journey. After a long deployment and commissioning phase, the SEIS (Seismic Experiment for Interior Structure) instrument was ready to monitor seismic events on the Elysium Planitia plain on the surface of Mars, coupled with the APSS (Auxiliary Payload Sensor Suite) weather station equipped with a magnetometer, wind sensors, and a pressure sensor. The InSight mission goal is to characterize the deep interior structure of Mars, including the thickness and structure of the crust, the composition and structure of the mantle, and the size of the core. Its nominal duration of two years (2019–2020) has yielded unprecedented results with the detection of the first Martian seismic events ever recorded, and the in-depth characterization of its atmosphere with the best weather station ever deployed on Mars. InSight has collected an outstanding amount of high-quality measurements that the scientific community will spend many years analyzing. The extended mission has started and covers the years 2021 and 2022. This paper will describe the operations of the SEIS experiment on Mars since landing, as well as the challenges of operating this instrument. Energy becomes increasingly limited for payloads on Mars due to a significant amount of dust accumulated on the solar panels and the many dust storms in the Martian atmosphere. A new activity was decided for the extended mission in 2021 which consisted in burying the seismometer cable (or tether) with Martian regolith collected locally using the robotic arm, in order to reduce the seismic noise from that subsystem. Preparation activities, testing, results, associated challenges and lessons learned will be presented. Moreover, the paper will address the challenges faced in carrying out operations with COVID-related constraints, as finding oneself operating a seismometer on Mars from home can be challenging. Finally, management of periods of solar conjunctions, during which communication between Earth and Mars is unavailable, will be addressed. © 2022 IAA

Comparison of Nitrogen Dioxide Predictions During a Pandemic and Non-pandemic Scenario in the City of Madrid using a Convolutional LSTM Network

Traditionally, machine learning technologies with the methods and capabilities available, combined with a geospatial dimension, can perform predictive analyzes of air quality with greater accuracy. However, air pollution is influenced by many external factors, one of which has recently been caused by the restrictions applied to curb the relentless advance of COVID-19. These sudden changes in air quality levels can negatively influence current forecasting models. This work compares air pollution forecasts during a pandemic and non-pandemic period under the same conditions. The ConvLSTM algorithm was applied to predict the concentration of nitrogen dioxide using data from the air quality and meteorological stations in Madrid. The proposed model was applied for two scenarios: pandemic (January–June 2020) and non-pandemic (January–June 2019), each with sub-scenarios based on time granularity (1-h, 12-h, 24-h and 48-h) and combination of features. The Root Mean Square Error was taken as the estimation metric, and the results showed that the proposed method outperformed a reference model, and the feature selection technique significantly improved the overall accuracy.

Investigation of the Impacts of COVID-19 on the Electricity Consumption of a University Dormitory Using Weather Normalization

This study investigated the impacts of the COVID-19 pandemic on the electricity consumption of a university dormitory building in the southern US. The historical electricity consumption data of this university dormitory building and weather data of an on-campus weather station, which were collected from January 1st, 2017 to July 31st, 2020, were used for analysis. Four inverse data-driven prediction models, i.e, Artificial Neural Network, Long Short-Term Memory Recurrent Neural Network, eXtreme Gradient Boosting, and Light Gradient Boosting Machine, were exploited to account for the influence of the weather conditions. The results suggested that the total electricity consumption of the objective building decreased by nearly 41% (about 276,000 kWh (942 MMBtu)) compared with the prediction value during the campus shutdown due to the COVID-19. Besides, the daily load ratio (DTR) varied significantly as well. In general, the DTR decreased gradually from 80% to nearly 40% in the second half of March 2020, maintained on a relatively stable level between 30% to 60% in April, May, and June 2020, and then slowly recovered to 80% of the normal capacity in July 2020.

Sub-mesoscale observations of convective cold pools with a dense station network in Hamburg, Germany

From June to August 2020, an observational network of 103 meteorological ground-based stations covered the greater area (50 km × 35 km) of Hamburg (Germany) as part of the Field Experiment on Sub-mesoscale Spatio-Temporal variability at Hanseatic city of Hamburg (FESST@HH). The purpose of the experiment was to shed light on the sub-mesoscale (O(100) m–O(10) km) structure of convective cold pools that typically remain under-resolved in operational networks. During the experiment, 82 custom-built, low-cost APOLLO (Autonomous cold POoL LOgger) stations sampled air temperature and pressure with fast-response sensors at 1 s resolution to adequately capture the strong and rapid perturbations associated with propagating cold pool fronts. A secondary network of 21 weather stations with commercial sensors provided additional information on relative humidity, wind speed, and precipitation at 10 s resolution. The realization of the experiment during the COVID-19 pandemic was facilitated by a large number of volunteers who provided measurement sites on their premises and supported station maintenance. This article introduces the novel type of autonomously operating instruments, their measurement characteristics, and the FESST@HH data set10.25592/UHHFDM.10172;. A case study demonstrates that the network is capable of mapping the horizontal structure of the temperature signal inside a cold pool, and quantifying a cold pool's size and propagation velocity throughout its life cycle. Beyond its primary purpose, the data set offers new insights into the spatial and temporal characteristics of the nocturnal urban heat island and variations of turbulent temperature fluctuations associated with different urban and natural environments.

Mayflower & The Seven Seas: Sonification of The Ocean

Created in conjunction with the Marine Institute at the University of Plymouth, the intention of this project was to use data transmitted by the on-board sensors of the Mayflower Autonomous Ship (MAS), to manipulate specially created pieces of music, based on sea shanties and folk ballads. Technical issues and Covid delays forced a late change, and the project was switched to using data from the university’s weather stations. This paper will illustrate how the music was produced and recorded, and the software configured to make the musical pieces vary and evolve in real-time, according to the changing sea conditions, so that the public will be able to view the current conditions and listen to the music evolve in real-time. © 151st Audio Engineering Society Convention 2021.