pure and applied geophysics最新文献

This study assesses physical infrastructure vulnerability for infrastructure network components exposed during the 2015 Illapel tsunami in Coquimbo, Chile. We analyse road and utility pole vulnerability to damage, based on interpolated and simulated tsunami hazard intensity (flow depth, flow velocity, hydrodynamic force and momentum flux) and network component characteristics. A Random Forest Model and Spearman’s Rank correlation test are applied to analyse variable importance and monotonic relationships, with respect to damage, between tsunami hazards and network component attributes. These models and tests reveal that flow depth correlates higher with damage, relative to flow velocity, hydrodynamic force and momentum flux. Scour (for roads and utility poles) and debris strikes (for utility poles) are strongly correlated with damage. A cumulative link model methodology is used to fit fragility curves. These fragility curves reveal that, in response to flow depth, Coquimbo roads have higher vulnerability than those analysed in previous tsunami event studies, while utility poles demonstrate lower vulnerability than with previous studies. Although we identify tsunami flow depth as the most important hydrodynamic hazard intensity metric, for causing road and utility pole damage, multiple characteristics correlate with damage and should also be considered when classifying infrastructure damage levels.

Selecting proper parameterization scheme combinations for a particular application is of great interest to Weather Research and Forecasting (WRF) model users. The goal of this research is to create an objective method for identifying a set of scheme combinations to form a Multi-Physics Ensemble (MPE) suitable for short-term precipitation forecasting over Odisha, India’s east coast state. In this study, five member ensembles for Cloud Microphysics (CMP) and Land Surface Model (LSM, conventional ensemble) are created, as well as an ensemble of the top five performing members (optimized ensemble) for 13 Monsoon Depressions (MD) and 8 Deep Depression (DD) cases. There are a total of 30 combinations (5 PBL * 5 CMP, 5 LSM with best PBL and CMP, and one with ISRO Land Use Land Cover data). WRF 4.1 is used to carry out simulations, which are initialized with ERA5 reanalysis data and have a 72-h lead time. Rainfall verification skill scores indicate that ensemble members perform significantly better than any deterministic model. Rainfall characteristics such as location, intensity, and time of occurrence are well predicted in ensemble members as measured by a higher correlation coefficient and a lower RMSE. Neighbourhood ensemble probability also demonstrates that ensemble members have a higher chance of detecting heavy to very heavy rainfall events with more spatial accuracy. The study also concludes that choice of parameterization also affects large-scale dynamical parameters (temperature, humidity, wind, hydrometeors) and thus associated rainfall. Ensemble members exhibited less bias in the composite analysis of large-scale parameters. Furthermore, a composite analysis of moisture budget components revealed that the convergence term is the most important component of moisture accumulation, resulting in rainfall during the monsoon low-pressure system. These findings indicate that the proposed method is an effective method for reducing bias in rainfall forecasts.

Aerosols have a significant impact on solar radiation, air quality deterioration, and climate change. To effectively assess the consequences of aerosols, we have to initially recognize the spatiotemporal distributions of their characteristics. This article uses OMI retrieved UVAI and Aqua MODIS retrieved AE data to analyze the spatiotemporal distributions and inter-annual trends of absorbing aerosol across Nigeria. Significant spatio-temporal variations in UVAI were observed high in northern and low in southern regions. The mean UVAI and AE over Nigeria shows an increasing and decreasing trend of 3.16% year⁻¹ and − 1.62% year⁻¹. Annually, UVAI showed an increasing trend of 7.59%, 9.79%, 5.19%, 6.27%, 7.65%, 4.87%, 7.17%, 4.99%, 4.10%, and 6.73% over Delta, Niger, Edo, Sokoto, Kwara, Osun, Kogi, Ondo, Oyo, and Ogun states respectively whereas AE is decreasing at the rate of − 0.53% year⁻¹, − 2.50% year⁻¹, − 0.84% year⁻¹, − 1.18% year⁻¹, − 2.12% year⁻¹, − 1.19% year⁻¹, − 1.12% year⁻¹, − 0.85% year⁻¹, − 1.77% year⁻¹, and − 1.06% year⁻¹ over Delta, Niger, Edo, Sokoto, Kwara, Osun, Kogi, Ondo, Oyo, and Ogun states respectively. UVAI was observed high during winter associated with dust storm activities. The HYSPLIT cluster analysis reveals the dominance of natural aerosols over northern and southern regions and Urban/industrial pollutants over the central regions of Nigeria. A strong positive correlation of UVAI with precipitation and temperature is observed in the northern regions of Nigeria. In accordance with the outcomes of this study, multiple methods such as planting more trees, improving fuel quality, etc. can be employed to lower the concentration of particle pollution.

In the present study, we have made an effort to examine the influence of dust aerosols on the precipitation over India, and six homogeneous rainfall zones of India during pre-monsoon (March to May) and monsoon (June to September) seasons. In this regard, a set of sensitivity experiments are performed using a regional climate model, namely, RegCM-4.7.0, for 11 years. Extensive analysis has been carried out to evaluate the model’s performance in simulating the mean precipitation, its variability (spatial and temporal), dust aerosol optical depth, and monsoonal wind. It is observed that the model has the potential to capture the distinct features like prevailing north westerlies with less precipitation during pre-monsoon and southwesterlies with more precipitation during monsoon season, with systematic differences in magnitude while compared against observation. High dust aerosol optical depth is found during pre-monsoon season over central northeast, west central and northeast parts of India. Dust significantly increases precipitation parts over southern peninsula, west central and northwest during the monsoon season due to increases in wind at 850 hPa facilitating moisture transport from Arabian Sea. Interestingly, it is also noticed that dust aerosols modulate the magnitude of extreme precipitation indices over India implying importance in including dust aerosols effects in the climate models.

Polar stratospheric chemistry is highly sensitive to changes in water vapor content and temperature. We identified an unusual behavior of water vapor and temperature in the southern polar winter stratosphere in 2023. The relationships between the Hunga-Tonga eruption injection of water vapor (detected in the tropics) and its transport to SH high latitudes, temperature changes and ozone anomalies at southern high latitudes are discussed, as well as the roles of zonal wind and the meridional flux of zonal mean zonal momentum. These parameters exhibit a consistent pattern in anomalous year 2023. In the winter of 2023 in the Southern Hemisphere, an unexpected decrease in ozone levels and the emergence of an excessive ozone hole were observed. This event marked one of the deepest Antarctic ozone holes with the largest area since 2011. This appears to be associated with the Hunga Tonga eruption anomalous water vapor injection. This study highlights importance of water vapor for evolution of the Antarctic stratosphere.

Daily gridded rainfall data from 1901 to 2020, were used to study the seasonal variation of the Extreme Rainfall Events (EREs) and Cumulative Exceedance (CumExc) at the 95th and 99th percentile thresholds in the North-Western Indian Himalayan Region (NW-IHR). The Mann–Kendall and Modified Mann–Kendall tests were used to assess the trends in EREs across seasons, besides comparing the CumExc and frequency of EREs between 1961–1990 and 1991–2020. Additionally, return periods of CumExc were estimated to predict future rainfall intensities. Among the 49 NW-IHR districts, Bandipore (J&K) reported the highest EREs (> 95th percentile) during winter and pre-monsoon, Baramulla (J&K) during post-monsoon, and Pithoragarh (UK) during monsoon. At the 99th percentile, Lahaul & Spiti (HP) recorded the most number of EREs in winter, Bandipore (J&K) in pre-monsoon, Pithoragarh (UK) in monsoon, and Baramulla (J&K) in post-monsoon. The trends of EREs indicate a substantial increase (at > 99th percentile) for all seasons in most of the region. EREs intensity between 1991–2020 is significantly higher than 1961–1990, particularly in the northern region during winter. Nevertheless, during pre- and post-monsoon seasons, most areas experienced a decline in EREs and CumExc. Estimates of CumExc suggest that UK is likely to encounter higher intensities of rainfall extremes compared to HP, J&K, and Ladakh within a 5-year and 10-year return period. This study is crucial for understanding extreme weather events in a climate change-sensitive region, providing insights into EREs frequency, trends, and return periods, which can aid in predicting flash floods and facilitating effective planning and mitigation strategies.

Thermal Thellier type palaeointensity (PI) experiments are the most popular technique in determining strength of the past geomagnetic field (GMF). However, the method often encounters high failure rate. Mineral magnetic properties can provide useful information to enhance the success rate. We conducted the first ever detailed mineral magnetic and Thellier type PI experiments on lava flows within a 1250 m stratigraphic section of the largest end-Cretaceous (~ 65–66 Ma) Deccan flood basalt. The lava flows mostly contain pseudo single domain (PSD) titano-magnetite/magnetite with, low viscosity index (≤ 5), high/moderate Koenigsberger ratio (Qn > 10/ < 10), isothermal remanent magnetisation parameters typical of ferromagnetic minerals, saturation remanence magnetization to saturation magnetization ratio (Mrs/Ms: 0.05–0.31), coercivity of remanence to coercive force ratio (Hcr/Hc: 1.53–3.72) and a single magnetic component pointing to origin or a minor viscous component during alternating field (AF) demagnetizations,. Their thermomagnetic responses are broadly group into 3 categories. The type 1 displays near perfect reversible heating/cooling curves with high Curie temperatures of 570–600 °C; other two types have either widely separated (type2) or quite dissimilar (type 3) heating/cooling curves. 76 samples from 19 flows were subjected to ZI (Zero field-Infield) experiments with pTRM and pTRM tail checks. 24 samples from eight flows display reasonable NRM-TRM linearity and positive pTRM checks and satisfy strict reliability criteria. 42 samples from 11 flows do not satisfy the reliability criteria. Samples of reliable PI results are all characterized by reversible thermomagnetic behaviour with excellent stability during AF/thermal treatment. Samples that failed to meet reliability criteria possess irreversible/widely separated thermomagnetic heating/cooling curves or poor stability to AF/thermal treatment but not significantly different from the successful samples in other magnetic properties. Thus, thermomagnetic behaviour and stability to AF/thermal treatment appear to be the overweighing factors than other mineral magnetic characteristics that govern the success rate of thermal PI experiments. The PI values of upper normal (29N) flows are relatively higher than the lower reverse (29R) flows; this suggests minor time lag in cooling of successive flows. The time averaged PI estimate is an important end-Cretaceous contribution from the Indian subcontinent to the poorly represented database of southern hemisphere.

In this study, we present a new methodology for 2D joint inversion, or data fusion, of DC electro-resistivity and Transient Electromagnetic methods. These geophysical techniques have traditionally been used separately, but by combining them, we aim to decrease ambiguities and increase the robustness of the resulting subsurface model. The inversion process was conducted using the classical Occam method with smooth models and synthetic studies were also conducted to understand the limitations and advantages of the method. We also applied the algorithm to data obtained in groundwater exploration in Brazil, and the results showed that the 2D joint inversion is promising in increasing accuracy and reducing ambiguity in subsurface imaging.