Perception of Climate Change Effects over Time and the Contribution of Different Areas of Knowledge to Its Understanding and Mitigation

Climate change is a current subject that is attracting more and more attention, whether from academics or the public. This public attention is mainly due to the frequently published news in the media, reporting consequences caused by extreme weather events. On the other hand, scientists are looking into the origins of the phenomenon, seeking answers that will somehow help to mitigate the effects of climate change. This article presents a review of some of the different possible approaches taken on climate change, to demonstrate the need to build a multidisciplinary perspective of the problem. It is understood that only the integration of different perspectives, presented by different areas of knowledge, such as natural sciences, social and economic sciences and human sciences, will make it possible to build modeling and predictive scenarios, which realistically may represent the development of the earth system under the influence of climate change. In this way, with the support of all areas of knowledge, the creation of forecast models where all possible changes to the different variables of the earth system may be simulated will allow for the mitigation measures presented to be analyzed in advance and, thus, prioritized. This review shows that a multi and interdisciplinary approach, based on the knowledge acquired from different knowledge and science fields, presents itself as the way to solve this global and complex problem caused by climate change.

Using the Residual Network Module to Correct the Sub-Seasonal High Temperature Forecast

The high temperature forecast of the sub-season is a severe challenge. Currently, the residual structure has achieved good results in the field of computer vision attributed to the excellent feature extraction ability. However, it has not been introduced in the domain of sub-seasonal forecasting. Here, we develop multi-module daily deterministic and probabilistic forecast models by the residual structure and finally establish a complete set of sub-seasonal high temperature forecasting system in the eastern part of China. The experimental results indicate that our method is effective and outperforms the European hindcast results in all aspects: absolute error, anomaly correlation coefficient, and other indicators are optimized by 8–50%, and the equitable threat score is improved by up to 400%. We conclude that the residual network has a sharper insight into the high temperature in sub-seasonal high temperature forecasting compared to traditional methods and convolutional networks, thus enabling more effective early warnings of extreme high temperature weather.

Efficiency of earthquake forecast models based on earth tidal correlation with background seismicity along the Tonga–Kermadec trench

The correlation between Earth’s tides and background seismicity has been suggested to become stronger before great earthquakes and weaker after. However, previous studies have only retrospectively analyzed this correlation after individual large earthquakes; it thus remains vague (i) whether such variations might be expected preceding future large earthquakes, and (ii) the strength of the tidal correlation during interseismic periods. Therefore, we retrospectively investigated whether significant temporal variations of the tidal correlation precede large interplate earthquakes along the Tonga–Kermadec trench, where Mw 7-class earthquakes frequently occurred from 1977 to 31 December 2020. We evaluated a forecast model based on the temporal variations of the tidal correlation via Molchan’s error diagram, using the tidal correlation value itself as well as its rate of change as threshold values. For Mw ≥ 7.0 earthquakes, this model was as ineffective as random guessing. For Mw ≥ 6.5, 6.0, or 5.5 earthquakes, the forecast model performed better than random guessing in some cases, but even the best forecast only had a probability gain of about 1.7. Therefore, the practicality of this model alone is poor, at least in this region. These results suggest that changes of the tidal correlation are not reliable indicators of large earthquakes along the Tonga–Kermadec trench. Graphical Abstract

Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts

Major disruptions of the winter season, high-latitude, stratospheric polar vortices can result in stratospheric anomalies that persist for months. These sudden stratospheric warming events are recognized as an important potential source of forecast skill for surface climate on subseasonal to seasonal timescales. Realizing this skill in operational subseasonal forecast models remains a challenge, as models must capture both the evolution of the stratospheric polar vortices in addition to their coupling to the troposphere. The processes involved in this coupling remain a topic of open research. We present here the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project. SNAPSI is a new model intercomparison protocol designed to study the role of the Arctic and Antarctic stratospheric polar vortices in sub-seasonal to seasonal forecast models. Based on a set of controlled, subseasonal, ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. First, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. Second, to attribute specific extreme events to stratospheric variability. Third, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models, and fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. Although not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. The output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of subseasonal forecasts.

Economic assessment and forecast models for the development of the agri-food sector of the Republic of Belarus

Dialectical method of obtaining knowledge is the general methodological basis of economics and organization, like other sciences, which considers all phenomena in development and interrelated to each other. Guided by this provision, development in the economy of the agri-food sector is studied not on an individual basis, but integrally and in conjunction with the economy and primarily with industry. The use of this method excludes a one-sided approach to the analysis of economic phenomena occurring in the agri-food sector, the maximum economic effect is ensured on the basis of the integral use of factors and conditions of agro-industry. The scientific study is based on the materials (proceedings) of foreign and domestic authors, data from the National Statistical Committee of the Republic of Belarus, the content of national programs for the development of the agro-industrial complex of the Republic of Belarus, as well as materials posted on official electronic media. The methods of systemic and comparative analysis, EMM (economic and mathematical methods) were used in the course of the study.

Financial Data Processing in Big Data Platforms

Today’s digital society generates more and more data on a daily basis in all areas of human activities, especially in the financial sector. Such data can be collected, stored, processed, and analyzed, providing serious analytical opportunities for the end users. A lot of such systems are implemented and work using cloud technologies, which have a number of advantages, but they use a pay-per-use model and thus are not very suitable for medium and small organizations, non-profit and academic institutions. In this paper, a system, capable of fetching, storing, and processing big data is proposed and tested with financial data. It uses an open-source component-based approach and can be custom-built and implemented in national universities or centers of competence/excellence. That can present unique opportunities to researchers and developers to use and work with Big data on economic and financial problems, to investigate dependencies, use large simulation and forecast models and analyze results, using the new technologies and Big data provided by them.

A review of level ice and brash ice growth models

Brash ice forms in harbours and ship channels from frequent ship passages and the resulting freezing–breaking cycles create a unique ice formation. The brash ice accumulation over the winter season is a result of meteorological, thermodynamical and mechanical processes. A reliable brash ice growth model is an important asset when determining navigation routes through ice conditions and when establishing port ice management solutions. This review aims to describe the brash ice development and its modelling as well as the key parameters that influence the brash ice growth and its estimation. This paper summarises the brash ice growth models and the fundamental theories of level ice growth upon which these models are based, and outlines the main knowledge gaps. The results highlight the importance of porosity and piece size distribution and their effect on the consolidation process. The inclusion of the brash ice lateral movement and the side ridge formation would improve the accuracy of forecast models. Furthermore, the findings of the study identify the effect of omitting meteorological parameters such as snow and radiation, from the brash ice growth models. Their contribution to the level ice thickness suggests a significant influence on the brash ice consolidation process.

Weather based forecast models for diseases in mustard crop

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Studying the regularities in public transport vehicle dwell time required for passenger boarding and deboarding

The review of the literature devoted to the research of public transport vehicle (PTV) dwell time required for passenger boarding-deboarding at a stop confirms the importance of taking this time into account when modelling passenger transportation. The data about the dwell time were collected in different time periods for different PTVs in various cities and countries. Thus, there is no general model allowing to define the distribution parameters of dwell time variable and answer the question on the regularities in time values. So, the research of the PTV dwell time at the public transport stops in Kharkiv remains actual. Goal. The search of the regularities in transport system performance indicators including PTV dwell time at a stop will allow to apply relevant mathematical methods for the development of the forecast models which are valuable in the field of organization of passenger transportation and servicing. Methodology. The developed methodology to collect data about PTV dwell time at the stops will allow receiving high-quality survey data. The designed survey sheets enable a surveyor to record all needed information and prepare it for processing. The collected data will allow to define the distribution of the PTV dwell time at a stop. Results. The conducted PTV dwell time survey allowed to collect the sufficient amount of data to estimate the distribution of this variable. During the research, it was determined that the empirical dwell time distribution can be well described with the theoretical gamma distribution. The latter distribution appeared to be applicable for all surveyed PTVs. Originality. The defined distribution of the PTV dwell time at a stop for passenger boarding-deboarding allows receiving the results of passenger flows modelling which are more precise compared to the modelling with no dwell time consideration. The use of the dwell time regularities in the procedure of passenger flows assignment results in the increased precision of flow volumes estimation by up to 14.9 % – from 2.28 % to 1.94 %. Practical value. The received results support the fact that the research of PTV dwell time at a stop is actual, and the solution of the task of dwell time distribution estimation will make it possible to improve passenger flows modelling in public transport route systems.