The Black Sea Physics Analysis and Forecasting System within the Framework of the Copernicus Marine Service

This work describes the design, implementation and validation of the Black Sea physics analysis and forecasting system, developed by the Black Sea Physics production unit within the Black Sea Monitoring and Forecasting Center as part of the Copernicus Marine Environment and Monitoring Service. The system provides analyses and forecasts of the temperature, salinity, sea surface height, mixed layer depth and currents for the whole Black Sea basin, excluding the Azov Sea, and has been operational since 2016. The system is composed of the NEMO (v 3.4) numerical model and an OceanVar scheme, which brings together real time observations (in-situ temperature and salinity profiles, sea level anomaly and sea surface temperature satellite data). An operational quality assessment framework is used to evaluate the accuracy of the products which set the basic standards for the future upgrades, highlighting the strengths and weaknesses of the model and the observing system in the Black Sea.

On the accuracy of aurora visible boundaries in the OVATION Prime (PC) model

Оценка положения экваториальных границ аврорального овала при разных магнитосферных условиях, несёт в себе информацию о формирующихся плазменных структурах, глубине их проникновения во внутреннюю магнитосферу, движении внутренней границы плазменного слоя и т.д. Развитие алгоритмов определения положения видимой экваториальной границы аврорального овала является важной частью исследований, связанных с разработкой моделей химического состава ионосферы, моделей авроральных высыпаний частиц и оценки точности этих моделей. Немаловажную роль исследования полярных сияний (прогноз, интенсивность, положение) играют и для развития туристического сегмента в Арктике и информационных ресурсов служб мониторинга и прогноза космической погоды. В рамках исследования оценки точности положения видимых границ овала сияний в моделях авроральных высыпаний частиц была выбрана наземная наблюдательная сеть оптических камер всего неба проекта THEMIS, запущенная в 2008 г., и модифицированная модель OVATION Prime (PC), разработанная в отделе Геофизики ФГБУ «ААНИИ использующая в качестве входного параметра наземный индекс полярной шапки (PC-индекс). The location of the equatorial boundaries of the auroral oval under different magnetospheric conditions contains information about the forming plasma structures, the depth of their penetration into the inner magnetosphere, the motion of the inner boundary of the plasma layer, etc. The development of methods and algorithms for determining the position of the visible equatorial boundary of the auroral oval is an important part of research related to the development of models of the chemical composition of the ionosphere, models of auroral particle precipitation, and assessment of the accuracy of these models. Research of aurora borealis (forecast, intensity, position) also plays an important role for the development of the tourist segment in the Arctic and information resources of space weather monitoring and forecasting services.

Understanding of RF Cloud Interference Measurement and Modeling

Importance of spectrum regulation and management was first revealed on May of 1985 after the release of unlicensed ISM bands resulting in emergence of Wi-Fi, Bluetooth and many other wireless technologies that has affected our daily lives by enabling the emergence of the smart world and IoT era. Today, the idea of a liberated spectrum is circulating around, which can potentially direct wireless networking industry into another revolution by enabling a new paradigm in intelligent spectrum regulation and management. The RF signal radiated from IoT devices as well as other wireless technologies create an RF cloud causing co- and cross-interference to each other. Lack of a science and technology for understanding, measurement, and modeling of the RF cloud interference in near real-time results in inefficient utilization of the precious spectrum, a unique natural resource shared among all wireless devices of the universe in frequency, time, and space. Near real time forecasting of the RF cloud interference is essential to pursue the path to the optimal utilization of spectrum and a liberated spectrum management. This paper presents a historical perspective on the evolution of spectrum regulation and management, explains the diversified meanings of interference for different sectors of the wireless industry, and presents a path for implementing a theoretical foundation for interference monitoring and forecasting to enable the emergence of a liberated spectrum industry and a new paradigm in spectrum management and regulations.

Comparative analysis of global and national systems for observing, monitoring and forecasting natural disasters and hazards with а view to reducing risk

The object of research is modern systems for observing, monitoring and forecasting natural disasters and hazards. Although early warning systems are often used to predict the magnitude, location and time of potentially hazardous events, these systems rarely provide impact estimates, such as the expected amount and distribution of material damage, human consequences, service disruption or financial losses. Supplementing early warning systems with predictions of impact has the dual advantage of providing better information to governing bodies for informed emergency decisions and focusing the attention of various branches of science on the goal of mitigating or preventing negative effects. The publication analyses current trends in the growth of natural risks, taking into account the risks associated with global climate change. The issues related to the growing risks of natural disasters and catastrophes at the present stage of societal development and directions of activities at the international and national levels for their reduction are considered. Disaster risk prevention and mitigation measures are described and areas of work in this area are highlighted. The decision-making sequence model is given, global and regional systems of observation, analysis, detection, forecasting, preliminary warning and exchange of information on natural hazards related to weather, climate and water are described. The factors that «unbalance» the global economy in terms of intensity, magnitude, magnitude of losses due to catastrophic events are analyzed. Addressing disaster prevention requires a structure at the national level in each country that includes policy, institutional, legal, strategic and operational frameworks, as well as at the regional and societal levels. This structure will organize and implement disaster risk reduction activities and establish an organizational system that will understand disaster risk and ensure that it is reduced through public participation.

Molecular Variability and Host Distribution of ‘Candidatus Phytoplasma solani’ Strains from Different Geographic Origins

The knowledge of phytoplasma genetic variability is a tool to study their epidemiology and to implement an effective monitoring and management of their associated diseases. ‘Candidatus Phytoplasma solani’ is associated with “bois noir” disease in grapevines, and yellowing and decline symptoms in many plant species, causing serious damages during the epidemic outbreaks. The epidemiology of the diseases associated with this phytoplasma is complex and related to numerous factors, such as interactions of the host plant and insect vectors and spreading through infected plant propagation material. The genetic variability of ‘Ca. P. solani’ strains in different host species and in different geographic areas during the last two decades was studied by RFLP analyses coupled with sequencing on vmp1, stamp, and tuf genes. A total of 119 strains were examined, 25 molecular variants were identified, and the variability of the studied genes was linked to both geographic distribution and year of infection. The crucial question in ‘Ca. P. solani’ epidemiology is to trace back the epidemic cycle of the infections. This study presents some relevant features about differential strain distribution useful for disease monitoring and forecasting, illustrating and comparing the phytoplasma molecular variants identified in various regions, host species, and time periods.

AI powered IoT based Real-Time Air Pollution Monitoring and Forecasting

Air is one of the most fundamental constituents for the sustenance of life on earth. The consumption of non-renewable energy sources and industrial parameters steadily increases air pollution. These factors affect the welfare and prosperity of life on earth; therefore, the nature of Air Quality in our environment needs to be monitored continuously. This paper presents the execution and plan of Internet-of-Things (IoT) based Air Pollution Monitoring and Forecasting utilising Artificial Intelligent (AI) methods. Also, Online Dashboard was created for real-time monitoring of Air pollutants (both live and forecasted data) through ‘firebase’ from the Google cloud server. The air pollutants like Carbon Mono Oxide (CO), Ammonia (NH3), and Ozone (O3) layer information are collected from IoT-based sensor nodes in Vijayawada Region. Time Series modelling techniques like the Naive Bayes Model, Auto Regression Model (AR), Auto Regression Moving Average Model (ARMA), and Auto-Regression Integrating Moving Average Model (ARIMA) used to forecast the individual air pollutants aforementioned. The data collected from the IoT sensor node with a time frame is fed as input features for training the model, and optimised model parameters are obtained. The obtained model parameters are again verified with new unseen data for time. The performances of various Time Series models are validated with the help of performance indices like Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The machine learning algorithm flashed in Raspberry Pi-3. It acts as an edge computing device. The current air pollutants data and forecasted data are monitored for the next 4 hours through an online dashboard created in an open-source firebase from Google cloud service.