Development of Cave Tourism in the Domica Area in Slovakia

The purpose of the study. The main goal of the study is to determine the most effective solutions for the development of cave tourism and medical tourism, as well as the standard of living and infrastructure of the region, based on the used literature and the questionnaire research on tourism development. Applied methods. The study starts with information about the natural and geological heritage found in the Domica region in Slovakia. It continues with the discovery, construction and characterization of the most significant caves from a tourist and economic point of view. Following the professional characterization, the questionnaire research developed and evaluated in the last stage of the study analyzes the possibility of the development of cave tourism and health tourism in the immediate vicinity of the Domica Cave based on the opinions, remarks and experiences of the service providers in the area. Outcomes. Caves are called natural underground cavities that have formed as a result of geomorphological and geophysical processes under different natural conditions. The caves in the karst are dissolved or are created by the weathering of the bedrock, while after the leakage of gases, caves form as cavities in the volcanic rocks. Few countries have as many different underground karst formations as Slovakia, with 7,014 known caves, of which only 18 can be visited. Discovering these underground wonders is a new challenge for hikers. Interest in caves peaked in the 20th century, when the desire to return to nature and improve the health of patients with respiratory diseases (speleotherapy) became the leading motivation. Today, caves are most often used for recreation. Nevertheless, within geotourism a popular way to explore caves is caving and the associated extreme or less extreme sports that only came to the fore in the 21st century. The results of the research of this study are sufficient evidence that the region is suitable for the development of cave tourism and medical tourism, for which the most obvious solution is to create an international geopark.

An assessment of tropopause characteristics of the ERA5 and ERA-Interim meteorological reanalyses

The tropopause layer plays a key role in manifold processes in atmospheric chemistry and physics. Here we compare the representation and characteristics of the lapse rate tropopause according to the definition of the World Meteorological Organization (WMO) as estimated from European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data. Our study is based on ten-year records (2009 to 2018) of ECMWF's state-of-the-art reanalysis ERA5 and its predecessor ERA-Interim. The intercomparison reveals notable differences between ERA5 and ERA-Interim tropopause data, in particular on small spatiotemporal scales. The monthly mean differences of ERA5 minus ERA-Interim tropopause heights vary between −300 m at the transition from the tropics to the extratropics (near 30° S and 30° N) to 150 m around the equator. Mean tropopause temperatures are mostly lower in ERA5 than in ERA-Interim, with a maximum difference of up to −1.5 K in the tropics. Monthly standard deviations of tropopause heights of ERA5 are up to 350 m or 60 % larger than for ERA-Interim. Monthly standard deviations of tropopause temperatures of ERA5 exceed those of ERA-Interim by up to 1.5 K or 30 %. The occurrence frequencies of double tropopause events in ERA5 exceed those of ERA-Interim by up to 25 percentage points at mid latitudes. We attribute the differences between the ERA5 and ERA-Interim tropopause data and the larger, more realistic variability of ERA5 to improved spatiotemporal resolution and better representation of geophysical processes in the forecast model as well as improvements in the data assimilation scheme and the utilization of additional observations in ERA5. The improved spatiotemporal resolution of ERA5 allows for a better representation of mesoscale features, in particular of gravity waves, which affect the temperature profiles in the upper troposphere and lower stratosphere and thus the tropopause height estimates. We evaluated the quality of the ERA5 and ERA-Interim reanalysis tropopause data by comparisons with COSMIC and MetOp Global Positioning System (GPS) satellite observations as well as high-resolution radiosonde profiles. The comparison indicates an uncertainty of the first tropopause for ERA5 (ERA-Interim) of about ±150 m to ±200 m (±250 m) based on radiosonde data and ±120 m to ±150 m (±170 to ±200 m) based on the coarser resolution GPS data at different latitudes. Consequently, ERA5 will provide more accurate information than ERA-Interim for future tropopause-related studies.

Analysis of time series by the example of registration of variations in the gravitational field

In this work, a time series model is used to study the structure of gravimetric data series to identify patterns in the change in the levels of the series and build its model in order to predict and study the relationships between the levels of gravimetric data. Observations of the activity of geophysical processes showed that the periods of variations in geophysical processes are scattered chaotically on the time axis. According to their schedule, it is impossible to definitely speak about the regularity in the duration of the periods of variations, and in the alternation of periods of seismic calm with a period of high seismic activity. The impetus for this study was the desire to analyze the structure of a number of formal methods to search for statistical patterns in the variations of geophysical parameters over time. Time series models were used to study the dynamics of geophysical events. Forecasting was carried out using the SPSS 20 package and EXCEL 2016. The accuracy of the forecast is indicated by the comparison of the forecast series with the actual data. The predicted values of the gravimetric data are within the confidence intervals. If you start forecasting too early, the forecast may differ from the forecast based on all statistical data. If the data shows seasonal trends, it is recommended to start forecasting from the date before the last point of the statistical data. Spatial and time series models can be used to study the dynamics of geophysical events. A spatial model describes a set of geophysical parameters at a given point in time. A time series is a series of regular observations of a certain parameter at successive points in time or at intervals of time. In this work, the time series model is used: to identify the statistical relationship between the frequency and depth of occurrence of earthquakes, as well as to identify the statistical dependence of these data on gravimetric variations; determination of patterns in the change in the levels of the series and the construction of its model in order to predict and study the relationships between geophysical phenomena.

Simple, low-cost and accurate data-driven geophysical forecasting with learned kernels

Modelling geophysical processes as low-dimensional dynamical systems and regressing their vector field from data is a promising approach for learning emulators of such systems. We show that when the kernel of these emulators is also learned from data (using kernel flows, a variant of cross-validation), then the resulting data-driven models are not only faster than equation-based models but are easier to train than neural networks such as the long short-term memory neural network. In addition, they are also more accurate and predictive than the latter. When trained on geophysical observational data, for example the weekly averaged global sea-surface temperature, considerable gains are also observed by the proposed technique in comparison with classical partial differential equation-based models in terms of forecast computational cost and accuracy. When trained on publicly available re-analysis data for the daily temperature of the North American continent, we see significant improvements over classical baselines such as climatology and persistence-based forecast techniques. Although our experiments concern specific examples, the proposed approach is general, and our results support the viability of kernel methods (with learned kernels) for interpretable and computationally efficient geophysical forecasting for a large diversity of processes.

Geostatistical Learning: Challenges and Opportunities

Statistical learning theory provides the foundation to applied machine learning, and its various successful applications in computer vision, natural language processing and other scientific domains. The theory, however, does not take into account the unique challenges of performing statistical learning in geospatial settings. For instance, it is well known that model errors cannot be assumed to be independent and identically distributed in geospatial (a.k.a. regionalized) variables due to spatial correlation; and trends caused by geophysical processes lead to covariate shifts between the domain where the model was trained and the domain where it will be applied, which in turn harm the use of classical learning methodologies that rely on random samples of the data. In this work, we introduce the geostatistical (transfer) learning problem, and illustrate the challenges of learning from geospatial data by assessing widely-used methods for estimating generalization error of learning models, under covariate shift and spatial correlation. Experiments with synthetic Gaussian process data as well as with real data from geophysical surveys in New Zealand indicate that none of the methods are adequate for model selection in a geospatial context. We provide general guidelines regarding the choice of these methods in practice while new methods are being actively researched.

On the geophysical processes impacting palaeo-sea-level observations

Past sea-level change represents the large-scale state of global climate, reflecting the waxing and waning of global ice sheets and the corresponding effect on ocean volume. Recent developments in sampling and analytical methods enable us to more precisely reconstruct past sea-level changes using geological indicators dated by radiometric methods. However, ice-volume changes alone cannot wholly account for these observations of local, relative sea-level change because of various geophysical factors including glacio-hydro-isostatic adjustments (GIA). The mechanisms behind GIA cannot be ignored when reconstructing global ice volume, yet they remain poorly understood within the general sea-level community. In this paper, various geophysical factors affecting sea-level observations are discussed and the details and impacts of these processes on estimates of past ice volumes are introduced.

Macroscopic Nonlocal Correlations in the Data Obtained in New Deep-Water Measurements

Macroscopic nonlocal correlations of random dissipative processes manifest at extremely low frequencies, meaning that observing them involves long-term experiments that maintain highly stable conditions in the detectors. This motivated the Baikal experiment, which investigates correlations between helio-geophysical processes featuring a high random component and test random processes in the detectors installed at various depths in the lake and at a remote land observatory. In the latest year-long experiment series, we focused on the data coming from the bottom detector, the one best protected from classical local interference. The results confirm that correlation with solar activity dominates the detector signal and, at the same time, it is easy to distinguish a forward correlation with thermodynamic activity in the upper active layer of Lake Baikal. The presence of this significant forward nonlocal correlation made it possible to simulate a realistic forecast of the active layer temperature a month ahead. We also detected an unusual diurnal variation in the relatively short-period spectrum of deep-water detector signals, presumably associated with the reemission of solar radiation by the Earth surface

The Role of Space Weather and Geophysical Processes in Behavioral Studies

Living organisms with developed endocrine systems react in a complex way to environmental changes. Changing atmospheric pressure causes different blood pressures or hormone levels; carcinogenic radiation modifies the structure of DNA. Charged particles and ions act as neurotransmitters and block certain types of protein channels and receptors. A high concentration of carbon dioxide has an indirect effect on both blood pressure and neuron activity. The bioelectric nature of living tissues highlights the complexity of the connection between the dynamic physical environment and biological systems. Recent results from studies on the interactions and connections mentioned above are reviewed in this paper.

Microseismicity in the Eastern Alps: Preliminary Results From the Swath-D Network

<p>The AlpArray seismological experiment is an international and interdisciplinary project to advance our understanding of geophysical processes in the greater Alpine region. The heart of the project consists of a large seismological array that covers the mountain range and its surrounding areas. To understand how the Alps and their neighbouring mountain belts evolved through time, we can only study its current structure and processes. The Eastern Alps are of prime interest since they currently demonstrate the highest crustal deformation rates. A key question is how these surface processes are linked to deeper structures. The Swath-D network is an array of temporary seismological stations complementary to the AlpArray network located in the Eastern Alps. This creates a unique opportunity to investigate high resolution seismicity on a local scale.</p><p>In this study, a combination of waveform-based detection methods was used to find small earthquakes in the large data volume of the Swath-D network. Methods were developed to locate the seismic events using semi-automatic picks, and estimate event magnitudes. We present an overview of the methods and workflow, as well as a preliminary overview of the seismicity in the Eastern Alps.</p>