pure and applied geophysics最新文献

Due to the increase in the severity and frequency of cold and heat waves, climate change exacerbates the seasonal or diurnal exposure of humans to heat or cold stress. Human thermal comfort (HTC) is an interdisciplinary spatial concern playing a pivotal role in directly influencing public health and well-being. This paper focuses on assessing the bioclimatic conditions of outdoor environments through the use of bioclimatic indices that characterize human comfort in relation to the thermal environment. Specifically, Thom's Discomfort Index (DI) and the Effective Temperature-Taking Wind Velocity (ETv) indices were used to simulate the climate change scenarios. The objective of the study is to assess the spatial and seasonal changes in temperature, relative humidity, and bioclimatic comfort zones in Erzurum, one of the coldest cities in Turkey by conducting assessments under two IPCC scenarios: SSP 245 and SSP 585. The bioclimatic comfort zones and their short- and long-term changes were modelled using ArcGIS 10.7 software. At present situation, 70.18% of Erzurum province is classified in comfortable zone in summer; however, SSP 585 projects a shift to hot zone at a rate of 91.54% by 2100. Conversely, the ETv index suggests a reduced risk of extreme cold but an increase in the rate of cold zones in winter. By 2100, SSP 245 predicts 29.28% increase in cold zones, while SSP 585 predicts 83.84%. The impact of geographical structure on the seasonal warming and cooling in the zones is noteworthy. The modelling framework and results are critical for shaping national and local health and climate policies. They also help to predict the impact of cold and heat under future climatic and socio-economic scenarios.

Climate change, meant to be changes in temperature and precipitation patterns over a longer period, may be due to natural as well as some anthropogenic factors. The Himalayan region frequently faces extreme events in the form of heat and cold waves, disturbed precipitation patterns and ecological disbalances. This region is facing a 0.2 °C increase in temperature in a year, which is quite higher than the global mean temperature rise. The accelerated pace of global warming has triggered profound impacts on the Western Himalayan Glaciers (WHG) and induced the melting of glaciers, which have affected the well-being of the entire ecosystem for many decades. Glacier retreat causes rapid channelization of freshwater resources into rivers and streams, which disrupts the hydrological patterns because melt water induces changes in river physical properties (change in flow rate, volume, temperature), chemical characteristics (pH disruption, increased turbidity, oxygenation, and mineral enrichment), and ecological system (nutrients dynamics, algal bloom potential, disrupt biodiversity and hydrological processes). To a greater extent, anthropogenic activities are the main driver for these changes because they are responsible for heavy deforestation and an enormous emission of greenhouse gases and aerosols. Therefore, a comprehensive study of available data and scientific literature is made here to investigates the retreat of glaciers in the Western Himalayas regarding the drivers of melting and their ecological, hydrological, and socio-economic impacts. In this study, temperature data from the Climate Research Unit (CRU) and rainfall data from the Tropical Rainfall Measuring Mission (TRMM) are analysed to look over the meteorological pattern over recent decades. However, major findings reveal that rising temperatures associated with global and localized warming, wind patterns, topographic position, and anthropogenic factors have led to melting of the Western Himalayan Glacier (WHG). This research underscores the urgent need for effective adaptation strategies and coordinated global action to mitigate the impacts of climate change on vulnerable communities and ecosystems in the region.

Forecasting seasonal rainfall is challenging but has important socio-economic implications. In Vietnam, despite encouraging results from previous studies, practical application remains difficult. This is because forecast accuracy remains low when studies only evaluate and use the products of global or regional models, or correct forecasts using simplistic traditional statistical methods. Modern statistical models using artificial intelligence show great potential to improve seasonal rainfall forecasting. This study aims to build a highly accurate forecasting tool for rainfall from May to October in seven climatic regions of Vietnam using a hybrid approach that combines convolutional neural networks (CNNs) and the dynamic downscaling product of the climate Weather Research and Forecasting model (clWRF). Three CNN models were developed for lead times of 1, 3, and 5 months. To train the models, the study used monthly data from 1983 to 2011 (29 years), including 5 meteorological variables from clWRF forecasts and monthly rainfall observations. The forecasts from the CNN models were then evaluated against observations using the dataset for 2012–2020 (9 years). The results showed that the distribution of rainfall forecasted using CNN models closely agreed with the observations. In addition, the performance of the CNN models was supported by low Relative Mean Absolute Error (mostly below 30% for the regional average and 50% at each grid point), along with reasonably high spatial correlations with observed patterns (0.4–0.8). These results outperformed the forecasts produced by the clWRF model, with Added Value mostly positive, ranging from 0.2 to 0.8. This study contributes to enhancing the practical application of seasonal rainfall forecast information.

This study aims to contribute to developing scientific decision support systems by evaluating the effectiveness of BAT-ANN, CatBoost, LightGBM, Bi-LSTM, RF-Bi-LSTM, and SVM-Bi-LSTM models to determine the most suitable machine learning model for air quality prediction in Istanbul. Among the models trained with air quality and meteorological data collected between 2013 and 2024, CatBoost provided the most successful predictions with the lowest error rates (RMSE: 2.2781, MAE: 1.3708, AIC: 3924.774) and the highest performance metrics (R²: 0.9959, NSE: 0.9959). RF-Bi-LSTM and LightGBM models ranked second and third, respectively. SHAP analysis revealed that PM10 and PM2.5 are the most decisive factors in air quality predictions, and the synergistic effect of these variables leads to a significant increase in AQI predictions. However, it was observed that this effect reached saturation after a certain PM10 threshold. In addition, it was found that NOx showed a strong correlation with PM2.5 levels and increased air pollution by accumulating, especially at low wind speeds. The effect of CO levels on AQI is significant at low concentrations but becomes saturated at high levels. It was determined that the impact of O3 on AQI varies with factors such as temperature and solar radiation and causes sudden AQI increases at high temperatures. This situation shows that time series-based models (Bi-LSTM, RF-Bi-LSTM) can generalize better thanks to their ability to take meteorological variables into account. The CatBoost model provided high accuracy in air pollution prediction by processing categorical data naturally, and the explainability of model estimates was increased through SHAP analysis.

Understanding and applying the performance of the global climate models is crucial to studies in future climate, water resources, and environmental assessment. In this paper, we evaluate a new phase of the Coupled Model Intercomparison Project as CMIP6 for the end of the century (2071–2100) under the pathways SSP1-2.6, SSP2-4.5 and SSP5-8.5 and correct them using a weighing technique. Daily and monthly air temperature and precipitation datasets from sixteen CMIP6 models were evaluated with observed values using Pearson’s correlation (r), Root mean square error (RMSE), L-Infinity norm error (LINE), and exponentially weighted error (EWE). The monthly data sets of the projected SSP1-2.6, SSP2-4.5 and SSP5-8.5 climate scenarios for the end of the century were in good agreement with the observed data. However, daily precipitation performs poorly except for daily air temperature. The weighted sum approach was used to ensemble the five best of sixteen CMIP6 models for daily precipitation data based on the baseline period 1995–2014 from the evaluations. To evaluate the performance of the best weights for the models, we have chosen the least squares objective function, which minimizes the sum of squared differences between the observed values and the predicted values. The optimization problem involves finding the best weights that minimize the least square’s objective function. The weighting approach significantly enhances model performance by effectively combining the strengths of individual models. The hybrid model benefits from a balanced integration of its components by assigning higher weights to more accurate models and lower weights to less accurate ones. The results show that the weighting of the ensemble of five models improved the RMSE by 25.8–35.3% over the single models. The outcome of the study will contribute to future assessment of climate change in hydrology, water resources, and related modelling purposes.

This paper proposes the use of teaching–learning-based optimization on the Kausel-Roesset stiffness matrix (TLBO-KRSM) for estimating the site-specific shear wave velocity profile (({V}_{s})) in the active multichannel analysis of surface waves (MASW) test. Global search operations are carried out by teaching factor through the teaching–learning process in the TLBO algorithm to obtain converged solutions. The TLBO algorithm (free of control parameter) is expected to predict the ({V}_{s}) profile more accurately than traditional optimization techniques (e.g., genetic algorithm (GA), differential evolution (DE), artificial bee colony optimization (ABCO), and particle swarm optimization (PSO)). The control parameters of GA, DE, ABCO, and PSO are not sufficiently calibrated, so there is a high possibility of misinterpreting the site-specific ({V}_{s}) profile. It utilizes the wavelengths and phase velocities of the experimental fundamental mode dispersion curve to fix the search space of soil layer thicknesses and ({V}_{s}). The proposed TLBO-KRSM algorithm and the other optimization techniques are examined on real field data sets by performing the MASW tests at the IIT Gandhinagar campus. The MASW test results are validated with downhole seismic tests along with the four datasets from various literature studies. When comparing the misfit error and the site-specific ({V}_{s}) profiles, the TLBO-KRSM algorithm has been found to be superior and requires less computational effort to locate the low misfit region to other optimization algorithms for estimating the ({V}_{s}) profiles.

Precipitation forecasting is a challenge in general for any part of the world, but in Lima it is particularly difficult due to its unusual nature and the mechanisms that can generate it. So, it is of great interest to study the mechanisms that generate it when it exceeds historical averages. This work analyzes the synoptic and local circulation conditions that gave rise two precipitation events over the Rimac river basin, in order to characterize the physical processes related to those events. In the first case, the rain affected the city of Lima, while in the second case the precipitation occurred mainly in the upper part of the basin. In the investigation, surface precipitation measurements, weather radar and satellite information, as well as the WRF (Weather Research and Forecasting) model outputs were used. For the analysis of the synoptic-scale general circulation prevailing during both events, data from the Global Forecast System were used (GFS). As a result, the role played by the humid Eastern Amazon flow was confirmed, but in this case, the important role played by the local circulation of sea daytime breezes and its interaction with Amazon flow. Associated with this interaction, the presence of gravity waves and their importance in strengthening cloud systems was observed. At the same time, it was detected that the daytime sea breeze does not change direction during the night, as it generally does, but it stays from the sea towards the land, although somewhat weaker. The weakening of the Eastern flow from the Amazon was observed to be related to the retreat to the east of the ridge of the South Atlantic Anticyclone. Also, the importance of anticyclonic circulation at high levels over the region was confirmed. At the same time, it was found that the WRF model acceptably describes the mechanisms of formation of these events.

We investigate the influence of a fine-scale (FS) layered structure in the atmosphere on the propagation of infrasound signals generated by fragmenting meteoroids. Using a pseudo-differential parabolic equation (PPE) approach, we model broadband acoustic signals from point sources at altitudes of 35–100 km. The presence of FS fluctuations in the stratosphere (37–45 km) and the lower thermosphere (100–120 km) modifies ray trajectories, causing multiple arrivals and prolonged signal durations at ground stations. In particular, meteoroids fragmenting at 80–100 km can produce two distinct thermospheric arrivals beyond 150 km range, while meteoroids descending to 50 km or below yield weak, long-lived arrivals within the acoustic shadow zone via antiguiding propagation and diffraction. Comparison with observed infrasound data confirms that FS-layered inhomogeneities can account for multi-arrival “N-waves,” broadening potential interpretations of meteoroid signals. The results also apply to other atmospheric-entry objects, such as sample return capsules, emphasizing how FS structure impacts shock wave propagation. Our findings advance understanding of wavefield evolution in a layered atmosphere and have broad relevance for global infrasound monitoring of diverse phenomena (e.g., re-entry capsules, rocket launches, and large-scale explosions).

Random adiabatic pressure fluctuations in a turbulent medium are related to vortex fluctuations described by the classical theory of incompressible turbulence. These two types of pressure fluctuations have the same order of amplitude but different dispersion relations and different frequency spectra. Based on measurements in the atmospheric boundary layer and known results from measurements in wind tunnels, the equilibrium form of the spectrum of adiabatic noise in a turbulent medium is proposed. Calculations confirming the relationship between its energy and spectral width and the intensity of turbulent mixing in the ABL are performed. A hypothesis is proposed that allows us to represent Kolmogorov spectra of velocity and temperature fluctuations in the form of series of spectral densities that have no features in the low and high frequency regions of the spectrum.