Dense Station Network Research Articles

Lithological control of the active Mejillones fault from Triassic to Quaternary, northern Chile

Understanding the lithological control of crustal faults near cities is important for assessing the seismic hazard and characterising complex fault systems. We studied the Mejillones Fault (MF), located near one of the strategic ports in northern Chile. The MF is an active geological fault that extends 40 km in the N-S direction and is part of an extensive complex system of structures known as the Atacama Fault System (AFS). We built an S-wave tomography model using seismic ambient noise data that was recorded continuously in a dense network of stations from 2013 to 2015. The tomographic maps exhibited velocity anomalies that correlate with surface geology. We found lower velocities in the western part of the MF than in the eastern zone, with a shear wave velocity (Vs) of about 2.4 km/s, associated to fractured metamorphic rocks (Triassic) and marine sediments (Neogene-Quaternary). Higher velocities (Vs > 3.0 km/s) in the eastern part of the fault are related to igneous rocks from the Upper Jurassic. The difference in velocities between the western and eastern zones of the MF remained persistent in depth. The potential depth of the MF could be >5 km and delimits the boundary between 2 different geological zones. Intermediate Vs (∼2.8 km/s) at −23.25°, observed up to 4 km depth, can be associated with andesitic rocks deposited in an intra-arc basin (Upper Triassic) and accommodated by the MF over the last ∼200 Myr, evidencing the activity of this fault from Triassic to Quaternary.

元ㆍ安南 간 육상 교통 루트와 驛站

The primary transportation routes of the Yuan Dynasty, known as the postal routes, were developed to facilitate the movement of troops and military supplies. Later, these routes became important for official business and trade between Yuan and neighboring countries. Although land transportation routes connecting the central plains of China with Annam began to be established during the Tang Dynasty, they were not actively used until the Yuan Dynasty. The transportation routes between Yuan and Annam were straightened to shorten the travel time by eliminating detours and to enhance the stability and continuity of the routes by constructing postal stations every 60 li. The primary land transportation routes between the two countries were (1) Chongqing-Mengzi-Thang Long and (2) Chongqing-Yongzhou-Thang Long. The first route, departing from Chongqing in Yunnan Province, went directly through steep mountains to reach Mengzi, and then followed the Hong River into Annam. This was the route used by envoys from both countries before the Southern Song conquest; however, it was time-consuming due to the mountainous terrain. The second route was developed after Yuan took control of Yongzhou, becoming the fastest transportation route between the two countries and mainly used for envoys and trade. This route was established by Toghon, featuring a dense network of postal stations every 60 li within Annam, which intimidated the king of Annam. Since the establishment of these postal stations in 1284, Annamese envoys traveled along this route to Dadu every one to two years to deliver tributes, as well as to transport goods for the Yuan court. Annam’s tributes were used to supply luxuries for the imperial family, while gifts from Yuan were used as ceremonial items to enhance the authority of the king of Annam. During this period, Yongping, a postal station at the entrance to Annam, flourished as a hub for private trade.

Assessing Multi‐Source Precipitation Estimates in Nepal: A Benchmark for Sub‐Seasonal Model Assessment

AbstractSub‐seasonal to seasonal (S2S) prediction provides an extended range of lead time for decision‐makers across multiple sectors. S2S forecast is crucial for a country that are susceptible to hydro‐meteorological disasters like Nepal. However, S2S forecast requires assessment with reliable datasets before its application. Since, Nepal lacks a dense station network, multi‐source precipitation estimates (MPEs) are the obvious alternatives. Therefore, using classical evaluation metrics and extreme precipitation indices, this study assessed eleven high‐resolution datasets against 159‐gauge stations over Nepal for the 2001–2020 period. These datasets are classified as gauge‐interpolated (APHRODITE), merged (TPMFD, MSWEP, CLDAS, and CHIRPS), satellite‐based (IMERGV7, IMERGV6, CMORPH, and PERSIANN), and reanalysis (ERA5‐L and HAR). Satellite datasets (except IMERGV7) failed to capture the spatial pattern of mean annual precipitation, while others broadly captured it. Most MPEs struggled to accurately estimate wet season precipitation compared to dry season. Furthermore, increasing estimation error from light to extreme precipitation and decreasing skill metrics from flat to complex terrain, demonstrate the intensity and terrain‐specific limitations of MPEs. In terms of precipitation extremes, APHRODITE exhibits the highest skill, followed by MSWEP, TPMFD, HAR, ERA5‐L, IMERGV7, CLDAS, IMERGV6, CHIRPS, CMORPH, and PERSIANN. IMERGV7 exhibits increased skill in detecting extreme precipitation and precipitation over orography against IMERGV6. APHRODITE and TPMFD datasets performed consistently well in all scales, including weekly anomaly, with an average anomaly correlation of 0.84 and 0.66 respectively. However, since APHRODITE is not widely available, TPMFD can serve as a benchmark dataset for evaluating high‐resolution S2S forecasts within the study region.

Directionality characteristics of horizontal response spectra from the 2022 Mw 6.9 Chihshang, Taiwan earthquake

Horizontal earthquake ground motion intensity, and specifically response spectral ordinates, vary with orientation. This phenomenon is usually referred to as ground motion directionality and can be separated into two aspects: the orientation where the maximum spectral response occurs and the variation of response spectral ordinates as the orientation moves away from the orientation of maximum spectral response. This work studies both aspects using the recent 2022 Mw 6.9 Chihshang, Taiwan earthquake, which was recorded by a dense network of strong motion stations with various geological and topographical settings. The mean variation of response spectral ordinates with orientation is found to be slightly more significant than that of previous shallow crustal earthquakes in active tectonic regimes. Moreover, the orientation of maximum spectral response is found to be close to the transverse orientation, which is perpendicular to the orientation at a given site that points to the earthquake epicenter, confirming prior observations made for strike-slip earthquakes. These results suggest that the location of a site relative to the seismic source could be used to modify the outputs of ground motion models to estimate spectral responses at specific horizontal orientations.

Added value of amateur observational network for high-resolution climatological analysis: a case study in the aterno valley, abruzzo, Italy

The ecological transition calls for an increasing need for local climate services. A fine spatial characterization of atmospheric relevant quantities (temperature, precipitation, humidity, wind, solar radiation, etc.) at long-term climatological scales is typically based on observational networks run by public entities such as the European Union (e.g. Copernicus services) and national and regional Agencies (e.g. National Met Office, Hydrographic Offices). The aim of this work is to verify if the density of these networks is adequate to represent the variability over the territory, with particular regard to a complex terrain area such as the Aterno river Valley in Abruzzo, Central Italy. We use a combination of public networks and the available dense amateur network of weather stations. We subject the database to careful data quality check both in terms of temporal and spatial anomalies. We found that the public network is generally adequate to represent the spatial and temporal variability over the area in terms of temperature and precipitation, but this is not the case for wind and relative humidity for the lack of sensors. We suggest that an integration of public and non-institutional observational networks is desirable for a finer climatological characterization of a complex territory and for allowing the description of more phenomena, in order to better inform adaptation measures with respect to climate change.

Modeling Geomagnetically Induced Currents in the Alberta Power Network: Comparison and Validation Using Hall Probe Measurements During a Magnetic Storm

AbstractDuring space weather events, geomagnetic disturbances (GMDs) induce geoelectric fields which drive geomagnetically induced currents (GICs) through electrically‐grounded power transmission lines. Alberta, Canada—located near the auroral zone and thus prone to large GMDs—has a dense network of magnetometer stations and surface impedance measurements to better characterize the GMD and ground conductivity, respectively. GIC monitoring devices were recently installed at five substation transformer neutrals, providing a unique opportunity to compare data to modeled GICs. GICs are modeled across the >240 kV provincial power transmission network during a moderate GMD event on 24 April 2023. GIC monitoring devices measured larger neutral‐to‐ground currents than expected up to 117 Amps during peak storm time, providing unequivocal evidence linking network GICs with GMDs. The model performs reasonably well (correlation coefficients >0.5; performance parameter >0.15) at four of five substations, but generally underestimates peak GIC values (sometimes by a factor >2), suggesting that the present model underrepresents overall network risk. The model performs poorly at one of the five substations (correlation = 0.46; performance parameter = 0.10), the reasons for which may be due to simplifications and/or unknowns in network parameters. Despite these underestimates, during this GMD, the model predicts the largest GIC at substations located in the northeastern part of the province (240 kV) or around Edmonton (500 kV)—regions which have significant electrical and industrial infrastructure. Further refinement of the network model with transformer resistances, more line and earthing resistances, and/or including lower voltage levels is necessary to improve data fit.

SolarET: A generalizable machine learning approach to estimate reference evapotranspiration from solar radiation

Irrigation is the most significant consumer of freshwater worldwide. Deciding on the right amount of irrigation is crucial for sustainable water management and food production. The Penman-Monteith (P-M) reference crop evapotranspiration (ETO) is the gold standard in irrigation management and scheduling; however, its calculation requires measurements from multiple sensors over an extended reference grass surface. The cost of land, sensors, maintenance, and water to keep the grass surface green impedes having a dense network of ETO stations. To solve this challenge, this research aims to develop an input-limited ETO estimation approach based on historical weather data and machine learning (ML) algorithms to relax the need for a reference grass surface. This approach, called "SolarET," takes solar radiation (RS) data as its sole input. RS is the only meteorological driving factor of ETO that does not rely on the measuring surface. To test the generalizability of SolarET, we test its performance over unseen arbitrary locations across California. California is chosen as the case study since it is one of the world's most hydrologically altered and agriculturally productive regions. In total, 19,088,736 hourly data samples from 131 automated weather stations have been used in this study. The ML models have been trained over 114 stations and tested over 17 unseen stations, each representing a California climatic zone. Our findings point to the superiority of decision tree-based algorithms versus neural networks. SolarET works best in irrigation-oriented regions of California (e.g., Central Valley) and is less accurate in coastal and desert zones. Our results demonstrate the higher accuracy of SolarET using hourly (RMSE = 0.93 mm/day) and daily (RMSE = 0.97 mm/day) RS data in comparison to well-known empirical alternatives like Priestley-Taylor (PT) (RMSE = 1.35 mm/day) and Hargreaves-Samani (HS) (RMSE = 2.69 mm/day).

Improving the operational forecasts of outdoor Universal Thermal Climate Index with post-processing

The Universal Thermal Climate Index (UTCI) is a thermal comfort index that describes how the human body experiences ambient conditions. It has units of temperature and considers physiological aspects of the human body. It takes into account the effect of air temperature, humidity, wind, radiation, and clothes. It is increasingly used in many countries as a measure of thermal comfort for outdoor conditions, and its value is calculated as part of the operational meteorological forecast. At the same time, forecasts of outdoor UTCI tend to have a relatively large error caused by the error of meteorological forecasts. In Slovenia, there is a relatively dense network of meteorological stations. Crucially, at these stations, global solar radiation measurements are performed continuously, which makes estimating the actual value of the UTCI more accurate compared to the situation where no radiation measurements are available. We used seven years of measurements in hourly resolution from 42 stations to first verify the operational UTCI forecast for the first forecast day and, secondly, to try to improve the forecast via post-processing. We used two machine-learning methods, linear regression, and neural networks. Both methods have successfully reduced the error in the operational UTCI forecasts. Both methods reduced the daily mean error from about 2.6∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{\\circ }$$\\end{document}C to almost zero, while the daily mean absolute error decreased from 5∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{\\circ }$$\\end{document}C to 3∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{\\circ }$$\\end{document}C for the neural network and 3.5∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{\\circ }$$\\end{document}C for linear regression. Both methods, especially the neural network, also substantially reduced the dependence of the error on the time of the day.

Probabilistic location of early instrumental earthquakes based on macroseismic and instrumental data

There are regions of moderate seismicity that have long lacked a dense network of seismic stations due to their sparse population, inaccessibility, and low economic levels. Characterization of earthquakes in such regions is typically limited to the small amount of macroseismic information available and data from only one or two seismic stations. Most often, the location of such earthquakes was based on the use of macroseismic information alone. We propose a method for probabilistic estimation of the location of such earthquakes based on a joint analysis of their macroseismic and instrumental data. The technique is implemented in the ProLoM program (Probabilistic Locator by Macroseismics). In this study, we test the performance of the method on a test earthquake of May 20, 1967 and present the results of the analysis of the earthquakes that occurred in the north of the East European Platform on June 30, 1911 and January 13, 1939.

Toroidal mantle flow beneath the NE termination of the Kuril–Kamchatka subduction zone from seismic anisotropy

SUMMARY This study presents the findings of a splitting analysis conducted on core-refracted teleseismic shear waves (SKS, SKKS and PKS, called together as XKS) and local shear waves, obtained from a dense seismological network spanning the Kamchatka Peninsula. The objective of the study is to examine the pattern of mantle flow beneath the study area through the investigation of seismic anisotropy. The peninsula is situated at the northeastern end of the Kuril–Kamchatka subduction zone, where the Kuril trench intersects with the western boundary of the Aleutian trench. The data set utilized in this study comprises waveform data from a dense network of seismic stations (99 broad-band and short-period stations for the local shear wave splitting analysis and 69 broad-band stations for the SKS splitting analysis). The seismograms were downloaded from publicly available data repositories including the IRIS Data Management Center and the GFZ Data Services (GEOFON program). The dense station coverage allows us to investigate the lateral variations in anisotropy, providing insights into the flow patterns within the mantle. The processing of the combined data sets of local shear wave and teleseismic XKS waves allowed us to partially decipher the source of anisotropy in the mantle. Small delay (splitting) times (∼0.35 s) observed from the local-S data suggest that anisotropy in the mantle wedge is relatively weak with lateral variations. Larger splitting times (∼1.1 s) observed for the XKS waves relative to local S suggest that the main part of splitting on the XKS waves occurs in the subslab mantle. On the other hand, the rotational pattern of seismic anisotropy observed by both the local S and XKS waves suggests the presence of a toroidal flow at the NE edge of the subducting slab, which affects both the mantle wedge and subslab mantle. For the regions away from the edge of the slab, the mantle flow seems to be governed mainly by the drag of the lithospheric plate over the underlying asthenosphere.

Energy Consumption Characteristics Study of Surface Meteorological Observation Station in North and South China

Surface meteorological observation is one of the most important sources of primary data for the meteorological sector and are crucial to the accuracy of weather forecasting. China’s meteorological authorities have built a dense network of surface observation stations, which require a considerable amount of electrical energy to keep the network running normally. There has been a lack of analytical studies dedicated to the energy consumption of surface meteorological observation stations domestically and internationally. This paper selects measured energy consumption data of 4 surface meteorological observation stations in two typical provinces (autonomous regions) in the north and south China over the past two years, and proposes, for the first time, to use the average power of the surface meteorological observation station, adopts the electrical energy consumption calculation method, carries out energy consumption calculation and analysis by month, sub-region and season, derives the characteristics of energy consumption changes of the surface meteorological observation station, and proposes energy saving and consumption reduction measures.

Morphology and growth of convective cold pools observed by a dense station network in Germany

AbstractThis study explores the morphology of convective cold pools, that is, their size, shape, and structure, as well as factors controlling their growth using surface‐based observations of the Field Experiment on Sub‐mesoscale Spatio Temporal Variability in Lindenberg (FESSTVaL). FESSTVaL featured a dense network of 99 custom‐built, low‐cost measurement stations covering a circular area of 30 km in diameter at sub‐mesoscale resolution (distances between 0.1 and 4.8 km) and was held at the Lindenberg Observatory near Berlin (Germany) from May–August 2021. The station network sampled 42 cold‐pool events during the 103‐day measurement period. The morphological properties of cold pools are derived by interpolating the temperature observations spatially to a Cartesian grid and defining cold pools as individual objects at a given time with a temperature perturbation stronger than K. The sample of 1232 cold‐pool objects with extents sufficiently captured by the network has a median equivalent diameter of 8.5 km. The objects exhibit aspect ratios between 1.5 and 1.6 independent of their size and strength, meaning they are generally not circularly shaped. On average, is strongest at the cold‐pool center and decreases linearly towards the edge. For the growth phase of four selected events, the cold‐pool object area scales linearly with the radar‐observed, area‐integrated rainfall accumulation, while the object‐mean temperature perturbation strengthens most efficiently early in the life cycle. The global, radial expansion velocity decreases as the cold pool gets stronger and larger, in contradiction of density‐current theory. Instead, is a better predictor of the expansion rate. These findings identify cold‐air import by precipitation, through both evaporative cooling and convective downdrafts, as the dominant driver of the observed growth.

High Resolution WRF Modelling of Extreme Heat Events and Mapping of the Urban Heat Island Characteristics in Athens, Greece

In recent decades, large-scale urbanisation has developed rapidly, resulting in significant changes in the local and regional environment and climate. Large metropolitan areas worldwide induce significant changes in local atmospheric circulation and boundary layer meteorology by modifying the underlying surface characteristics and through the emission of anthropogenic heat and pollutants into the atmosphere. We investigate the urban heat island (UHI) characteristics in the city of Athens, Greece, which is one of Europe’s largest metropolitan complexes with a population of approximately 3.7 million inhabitants. The UHI effect is intense due to the city’s size, dense construction, high incident solar radiation, and almost complete lack of natural vegetation, with previous studies suggesting a temperature rise of 4 °C on average in the city centre compared to summer background conditions. We used high-resolution WRF simulations (1-km horizontal grid) driven with ERA5 reanalysis data to produce surface temperature maps in the city of Athens and the surrounding areas (Region of Attiki) during the summer period of 1 July–20 August 2021. Different model parameterizations were tested, both with respect to urban characteristics and physical parameters. The daily minimum and maximum temperatures (Tmin and Tmax) derived from the model were validated against observational data from a dense network of weather stations covering metropolitan Athens and surrounding locations. We further investigate the influence of different meteorological conditions on the UHI gradients as produced by the model and the observational dataset, including the extreme heat wave of 28 July–5 August 2021, during which persistent maximum temperatures of >40 °C were recorded for nine consecutive days. The results indicate a strong correlation between WRF output and recorded minimum and maximum temperatures throughout the test period (R ranges from 0.80 to 0.93), with an average mean absolute bias (MAB) of 1.5 °C, and reveal the intensity and spatiotemporal variability of the UHI phenomenon in the city of Athens, with UHI magnitude reaching 8–9 °C at times. Our work aims to maximise the potential of using high-resolution WRF modelling for simulating extreme heat events and mapping the UHI effect in large metropolitan complexes.

Subsurface imaging dataset acquired at the Garner Valley Downhole Array site using a dense network of three-component nodal stations

There is a growing need to characterize the engineering material properties of the shallow subsurface in three dimensions for advanced engineering analyses. However, imaging the near-surface in three dimensions at spatial resolutions required for such purposes remains in its infancy and requires further study before it can be adopted into practice. To enable and accelerate research in this area, we present a large subsurface imaging data set acquired using a dense network of three-component (3C) nodal stations acquired in 2019 at the Garner Valley Downhole Array (GVDA) site. Acquisition of this data set involved the deployment of 196 stations positioned on a 14 × 14 grid with a 5 m spacing. The array was used to acquire active-source data generated by a vibroseis truck and an instrumented sledgehammer, and passive-wavefield data containing ambient noise. The active-source acquisition included 66 vibroseis and 209 instrumented sledgehammer source locations. Multiple source impacts were recorded at each source location to enable stacking of the recorded signals. The active-source recordings are provided in terms of both raw, uncorrected units of counts and corrected engineering units of meters per second. For each source impact, the force output from the vibroseis or instrumented sledgehammer was recorded and is provided in both raw counts and engineering units of kilonewtons. The passive-wavefield data include 28 h of ambient noise recorded over two nighttime deployments. The data set is shown to be useful for active-source and passive-wavefield three-dimensional imaging and other subsurface characterization techniques, which include horizontal-to-vertical spectral ratios (HVSRs), multichannel analysis of surface waves (MASW), and microtremor array measurements (MAM).

Extraction of Spatiotemporal Distribution Characteristics and Spatiotemporal Reconstruction of Rainfall Data by PCA Algorithm

Scientific analyses of urban flood risks are essential for evaluating urban flood insurance and designing drainage projects. Although the current rainfall monitoring system in China has a dense station network and high-precision rainfall data, the time series is short. In contrast, historical rainfall data have a longer sample time series but lower precision. This study introduced a PCA algorithm to reconstruct historical rainfall data. Based on the temporal and spatial characteristics of rainfall extracted from high-resolution rainfall data over the past decade, historical (6 h intervals) rainfall spatial data were reconstructed into high-resolution (1 h intervals) spatial data to satisfy the requirements of the urban flood risk analysis. The results showed that the average error between the reconstructed data and measured values in the high-value area was within 15% and in the low-value area was within 20%, representing decreases of approximately 65% and 40%, respectively, compared to traditional interpolation data. The reconstructed historical spatial rainfall data conformed to the temporal and spatial distribution characteristics of rainfall, improved the granularity of rainfall spatial data, and enabled the effective and reasonable extraction and summary of the fine temporal and spatial distribution characteristics of rainfall.

DEVELOPMENT OF AN IOT DEVICE BASED ON A GEIGER COUNTER

The threat of radiation exposure is always present in modern world. In recent years, this threat has become even more pronounced due to Russian aggression on Ukrainian territory. Therefore, the need for remote data collection and analysis of radiation background information has become even more important, and research in the field of creating small, low-cost devices that can monitor radiation levels of a certain area in real-time and provide information to local government agencies responsible for the safety of populated areas can be considered relevant. Radiation background level monitoring is essential to ensuring that people are informed of any potential threats to their health. It is proposed to develop a dense network of monitoring stations that can collect and transmit data about background radiation in real time. Measurement results will be processed and stored on a resource that is available to both local authorities and public. The Message Queue Telemetry Transport (MQTT) protocol is used to transmit information, as it is durable, lightweight, and efficient. A hardware and software system for collecting and transmitting radioactivity values has been developed. The system is based on a module with Geiger counter tube, which is used to measure the level of radiation in the area. The module is connected to a microcontroller, which processes the data and transmits it to a central server. To summarize and process the measurement results, the Majordomo broker is used. It is installed on a remotely accessible network storage. In conclusion, the proposed system can ensure the safety of the public, allowing authorities, experts and trained personnel to quickly and efficiently respond to any potential radiation threats, protecting people from radiation exposure.

Application of K-Nearest Neighbor Classification for Static Webcams Visibility Observation

Visibility observations and accurate forecasts are essential in meteorology, requiring a dense network of observation stations. This paper investigates image processing techniques for object detection and visibility determination using static cameras. It proposes a comprehensive method that includes image preprocessing, landmark identification, and visibility estimation, mirroring the observation process of professional meteorological observers. This study validates the visibility observation procedure using the k-nearest neighbors machine learning method across six locations, including four in the Czech Republic, one in the USA, and one in Germany. By comparing our results with professional observations, the paper demonstrates the suitability of the proposed method for operational application, particularly in foggy and low visibility conditions. This versatile method holds potential for adoption by meteorological services worldwide.

Quantifying uncertainties related to observational datasets used as reference for regional climate model evaluation over complex topography — a case study for the wettest year 2010 in the Carpathian region

Gridded observational datasets are often used for the evaluation of regional climate model (RCM) simulations. However, the uncertainty of observations affects the evaluation. This work introduces a novel method to quantify the uncertainties in the observational datasets and how these uncertainties affect the evaluation of RCM simulations. Besides precipitation and temperature, our method uses geographic variables (e.g. elevation, variability of elevation, effect of station), which are considered as uncertainty sources. To assess these uncertainties, a complex analysis based on various statistical tools, e.g. correlation analysis and permutation test, was carried out. Furthermore, we used a special metric, the reduction of error (RE) to identify where the RCM shows improvement compared to the lateral boundary conditions (LBCs). We focused on the Carpathian region, because of its unique orographic and climatic conditions. The method is applied to two observational datasets (CarpatClim and E-OBS) and to RegCM simulations for 2010, the wettest year in this region since 1901.The results show that CarpatClim is wetter than E-OBS, while temperature is similar over the lowland; however, E-OBS is significantly warmer than CarpatClim over the mountains. By the RE metric, RegCM has improvement against the LBCs over mountains for temperature and areas with dense station network for precipitation. Nevertheless, there are significant differences in the results depending on which observational dataset was used concerning precipitation. The evaluation method can be applied to other datasets, different time periods and areas. It is also suitable to find dataset errors, which is also exemplified in this paper.

Surface water load and earthquake stress interactions near the Pirrís reservoir, Costa Rica

This study analyzes the relationships between the stress changes produced by the water load variations due to the filling operations at the Pirrís Reservoir, Costa Rica, and the stress changes generated by three relevant earthquakes after the first impoundment. The earthquakes differ in their magnitude, distance from reservoir and depth. Two of them correspond to a shallow crustal faulting, and the third earthquake corresponds to a subduction thrust-fault. The first filling operations began in March 2011 and the daily time history of water levels was available until December 2017. The seismicity has been recorded by a dense local network of seismological stations operating since January 2008. Effects of both types of sources (water loads and earthquakes) were combined to obtain the total Coulomb Failure Stress in the reservoir area, analyzing which of them was dominant in each case and time. We then analyzed the effects of the pore pressure diffusion due to the water loads on the nearby seismicity including two of the studied earthquakes. Results lead to different conclusions depending on the characteristics of each earthquake. An important result is observed after the occurrence of the subduction earthquake, where the coseismic effects become dominant along the entire study area, eclipsing the effect of water loading at all depths. This shows that in a regime stressed by a regional earthquake, albeit of its moderate magnitude, and regional distance, the effect of water loads is barely perceptible. In the absence of tectonic activity, however, the impact of water loading is notable.

Machine Learning-Based Modeling of Air Temperature in the Complex Environment of Yerevan City, Armenia

Machine learning (ML) was used to assess and predict urban air temperature (Tair) considering the complexity of the terrain features in Yerevan (Armenia). The estimation was performed based on the Partial Least-Squares Regression (PLSR) model with a high number (30) of input variables. The relevant parameters include a newly purposed modification of spectral index IBI-SAVI, which turned out to strongly impact Tair prediction together with land surface temperature (LST). Cross-validation analysis on temperature predictions across a station-centered 1000 m circular area revealed quite a high correlation (R2Val = 0.77, RMSEVal = 1.58) between the predicted and measured Tair from the test set. It was concluded the remote sensing is an effective tool to estimate Tair distribution where a dense network of weather stations is not available. However, further developments will include incorporation of additional weather parameters from the weather stations, such as precipitation and wind speed, as well as the use of non-parametric ML techniques.