Surface Weather Research Articles

Spatiotemporal Analysis of Future Precipitation Changes in the Huaihe River Basin Based on the NEX-GDDP-CMIP6 Dataset and Monitoring Data

This research analyzes extreme precipitation events in the Huaihe River Basin in China, a densely populated region with a history of human settlements and agricultural activities. This study aims to explore the impact of extreme precipitation index changes and provide decision-making suggestions for flood early warning and agricultural development in the Huaihe River Basin. The study utilizes the NEX-GDDP-CMIP6 climate model dataset and daily value dataset (V3.0) from China’s national surface weather stations to investigate temporal and spatial changes in the extreme precipitation indices from 1960 to 2014 and future projections. At the same time, this study adopts the RclimDex model, Taylor diagram, and Sen+Mann–Kendall trend analysis research methods to analyze the data. The results reveal a slight increase in extreme precipitation indices from the northwest to southeast within the basin, except for the CDD, which shows a decreasing trend. Regarding the spatial variation, the future increase in extreme precipitation in the Huaihe River Basin shows a spatial variation characteristic that decreases from the northwest to southeast. These findings suggest that extreme precipitation events are intensifying in the region. Understanding these trends and their implications is vital for adaptation strategy planning and mitigating the risks associated with extreme precipitation events in the Huaihe River Basin.

A Joint Perspective on North American and Euro‐Atlantic Weather Regimes

AbstractWeather regimes are recurrent and quasi‐stationary large‐scale atmospheric circulation patterns, typically linking to surface weather. Two commonly used sets of weather regimes are wintertime North American and Euro‐Atlantic regimes. Notwithstanding recent evidence pointing to a connection between winter weather in North America and Europe, there is little knowledge on the possible relation between North American and Euro‐Atlantic regimes. Here, we find that specific pairs of North American and Euro‐Atlantic regimes show a close visual and statistical correspondence. Moreover, the joint analysis of the two sets of regimes can provide medium‐range statistical predictability for anomalies in their occurrence frequencies. Conditioning on North American weather regimes also results in anomalies in both the large‐scale circulation during specific Euro‐Atlantic regimes, and the associated European surface weather. We conclude that there is a benefit in conducting joint analyses of North American and European weather regimes, as opposed to considering the two in isolation.

The 10‐ to 30‐Day Intraseasonal Weather Process Associated With Wintertime PM2.5 Pollution in Eastern China

AbstractSevere PM2.5 pollution strikes winter eastern China, while its physical process associated with the atmospheric evolution from several days to weeks is not clear. This study uses weather types as intermediate agents between large‐scale atmospheric circulation and regional air pollution, to explore the whole circulation evolutionary chain related to wintertime PM2.5 pollution in eastern China. We extract objectively 6 weather types (WTs) for wintertime surface synoptic patterns in eastern China and analyze quantitatively the impacts of WTs on PM2.5 pollution. Then the preferred transitions between WTs are explored to explain potential weather process. We find a most frequently recurring circulation evolution corresponding to Mongolia high pressure that stretches southward to eastern China, referred as the WTs transition of YSH− > BLH− > MH, with PM2.5 increased in North China and dissipated as the high pressure moves southward. Further to establish intraseasonal connections between large‐scale circulation and regional synoptic patterns, several leading evolutionary chains are discovered among tropical Madden‐Julian Oscillation (MJO), extratropical teleconnections, and WTs of eastern China at an extended period. The positive or negative phase of teleconnections can indicate the WTs change 4–10 days in advance, while MJO further extends that indicative effect to 10–15 days. The intraseasonal oscillations of teleconnections such as conventional Eurasia, Scandinavia, Arctic Oscillation, Polar/Eurasia, Western Pacific and East Atlantic are associated with the transition of YSH− > BLH− > MH, and MJO may act as a precursor of teleconnection phases to extend effectively the prediction time for surface weather conditions and air pollution in eastern China.

Modeling the Effects of Local Atmospheric Conditions on the Thermodynamics of Sobradinho Lake, Northeast Brazil

The objective of this work was to study climate variability and its impacts on the temperature of Sobradinho Lake in Northeast Brazil. Surface weather station data and lake measurements were used in this study. The model applied in this work is FLake, which is a one-dimensional model used to simulate the vertical temperature profile of freshwater lakes. First, the climate variability around Sobradinho Lake was analyzed. Observations showed a reduction in precipitation during 1991–2020 compared to 1981–2010. To study climate variability impacts on Sobradinho Lake, the years 2013, 2015, and 2020 were selected to characterize normal, dry, and rainy years, respectively. In addition, the months of January, April, July, and October were analyzed for rainy months, rainy–dry transitions, dry months, and dry–rainy transitions. Dry years showed higher incoming solar radiation at the surface and, consequently, higher 2 m air temperatures. A characteristic of the normal years was more intense surface winds. October presented the highest incoming solar radiation, the highest air temperature, and the most intense winds at the surface. The lowest incoming solar radiation at the surface was observed in January, and the lightest wind was observed in April. To assess the effects of these atmospheric conditions on the thermodynamics of Sobradinho Lake, the FLake model was forced using station observation data. The thermal amplitude of the lake surface temperature (LST) varied by less than 1 °C during the four months. This result was validated against surface lake observations. FLake was able to accurately reproduce the diurnal cycle variation in sensible heat fluxes (H), latent heat fluxes, and momentum fluxes. The sensible heat flux depends directly on the difference between the LST and the air temperature. During daytime, however, Flake simulated negative values of H, and during nighttime, positive values. The highest values of latent heat flux were simulated during the day, with the maximum value was simulated at 12:00 noon. The momentum flux simulated a similar pattern, with the maximum values simulated during the day and the minimum values during the night. The FLake model also simulated the deepest mixing layer in the months of July and October. However, our results have limitations due to the lack of observed data to validate the simulations.

High-resolution WRF forecasts in the SmartAQ system: Evaluation of the meteorological forcing used for PMCAMx predictions in an urban area

High-resolution modeling approaches are becoming a necessary step for enhancing regional air quality monitoring and forecasting. This study first evaluates the meteorological driver of the WRF- PMCAMx 1 × 1 km2 SmartAQ air quality prediction system and then assesses the impact of meteorological errors on the PM2.5 predictions. The comprehensive multi-variable verification on several timescales appraises the suitability of such a high-resolution modeling system in predicting the surface weather parameters in a major coastal urban area while gaining valuable insights into the model performance. The highest agreement between the observed and modelled values has been found for the actinometric and the thermodynamic variables, with the exception of the soil moisture which was systematically overestimated. WRF largely captures the variability of the observed values for the dynamic variables, showing better skill in the warm season, with a systematic overestimation though. The overall ability in predicting the rain events, important for the wet deposition of atmospheric pollutants is approximately 50%. Wind speed forecast errors contribute to the PM2.5 prediction errors, regardless of season and day/night hours. The impact of the wind errors is larger at almost all stations in the cold season when local emissions are elevated.

Physics-Constrained Deep Learning Postprocessing of Temperature and Humidity

Abstract Weather forecasting centers currently rely on statistical postprocessing methods to minimize forecast error. This improves skill but can lead to predictions that violate physical principles or disregard dependencies between variables, which can be problematic for downstream applications and for the trustworthiness of postprocessing models, especially when they are based on new machine learning approaches. Building on recent advances in physics-informed machine learning, we propose to achieve physical consistency in deep learning–based postprocessing models by integrating meteorological expertise in the form of analytic equations. Applied to the postprocessing of surface weather in Switzerland, we find that constraining a neural network to enforce thermodynamic state equations yields physically consistent predictions of temperature and humidity without compromising performance. Our approach is especially advantageous when data are scarce, and our findings suggest that incorporating domain expertise into postprocessing models allows the optimization of weather forecast information while satisfying application-specific requirements. Significance Statement Postprocessing is a widely used approach to reduce forecast error using statistics, but it may lead to physical inconsistencies. This outcome can be problematic for trustworthiness and downstream applications. We present the first machine learning–based postprocessing method intentionally designed to strictly enforce physical laws. Our framework improves physical consistency without sacrificing performance and suggests that human expertise can be incorporated into postprocessing models via analytic equations.

Regulation of oxidation degree for graphene oxide on hydration process and engineering properties of natural hydraulic lime pastes for grout strengthening of stone cultural relics

The stone cultural relics represented by grottoes carry precious historical and cultural information and are important representations of the mutual learning of civilizations. Fissures are the main threat to stone cultural relics. The development of grout strengthening materials for fissure repair is of great significance to restrain or delay other threats, such as instability, water seepage, and surface weathering. In this work, based on the improved Hummers’ method, graphene oxide (GO) with different oxidation degrees was prepared by adjusting the amount of added KMnO4, which was then added to natural hydraulic lime (NHL2) pastes to explore the effect of the GO oxidation degree on the hydration process and engineering properties of NHL2 pastes. The results indicated that the addition of GO significantly promoted the hydration rate of the NHL2 pastes and resulted in the formation of more regular and ordered flower-like hydration products and aggregates. Compared with the reference sample, the engineering properties of the modified NHL2 pastes, including the mechanical properties and the autogenous shrinkage behavior, were significantly improved. The simulated grouting experiment also indicated that NHL2 pastes had good groutability and interface combination statuses. In the hydration process of NHL2, GO provided nucleation sites. The hydration reactions were preferentially carried out on the GO sheets, and regular aggregates formed to fill the cracks and holes in the NHL2 pastes, which improved the mechanical properties of the strengthened material. This work provides an effective strategy for the grout strengthening of stone cultural relics fissures.

PAINTS FORMULATION USING DIISOCYANATES AND 1,4 –DIAMINO ANTHRAQUINONE (1,4 DAQ) AS PIGMENTS AND POLYVINYL ALCOHOL AS BINDER, CHARACTERIZATION AND THEIR APPLICATION ON WOOD SUBSTRATE

The Paint and coatings industry produces a huge variety of products that protect, preserve, and also beautify the objects to which they are applied, however, the components are expensive, therefore there is the need to look for alternative constituents which are cheaper and more durable. Emulsion Paints were formulated using colourants produced from diisocyanates and 1,4 diamino anthraquinone and polyvinyl alcohol as binder. The constituents of the paint was stirred mechanically in a ceramic mortar and pestle for 1 hour, and it was applied on the substrate (ceiling board). The coated samples were subjected to physical characterization, viscosity, surface drying time, weathering and lightfastness and compared with a commercial paint (Emulsion paint). The results indicates a very good light fastness property of 6-7, for paints produced from hexamethylene diisocyanate and where resistant to weathering, while paints produced from 1,4 phenylene diisocyanate showed a poor light fastness of 1-2, however it also indicated resistant to weathering. Hexamethylene diisocyanate, is less expensive and is more durable and economical in emulsion paint production compared to 1,4 phenylene diisocyanate which fades easily and is also expensive.

Photonic crystals built by time in ancient Roman glass

Ancient glass objects typically show distinctive effects of deterioration as a result of environmentally induced physicochemical transformations of their surface over time. Iridescence is one of the distinctive signatures of aging that is most commonly found on excavated glass. In this work, we present an ancient glass fragment that exhibits structural color through surface weathering resulting in iridescent patinas caused by silica reprecipitation in nanoscale lamellae. This archaeological artifact reveals an unusual hierarchically assembled photonic crystal with extremely ordered nanoscale domains, high spectral selectivity, and reflectivity (~90%), that collectively behaves like a gold mirror. Optical characterization paired with nanoscale elemental analysis further underscores the high quality of this structure providing a window into this sophisticated natural photonic crystal assembled by time.

Weather persistence on sub-seasonal to seasonal timescales: a methodological review

Abstract. Persistence is an important concept in meteorology. It refers to surface weather or the atmospheric circulation either remaining in approximately the same state (quasi-stationarity) or repeatedly occupying the same state (recurrence) over some prolonged period of time. Persistence can be found at many different timescales; however, sub-seasonal to seasonal (S2S) timescales are especially relevant in terms of impacts and atmospheric predictability. For these reasons, S2S persistence has been attracting increasing attention from the scientific community. The dynamics responsible for persistence and their potential evolution under climate change are a notable focus of active research. However, one important challenge facing the community is how to define persistence from both a qualitative and quantitative perspective. Despite a general agreement on the concept, many different definitions and perspectives have been proposed over the years, among which it is not always easy to find one's way. The purpose of this review is to present and discuss existing concepts of weather persistence, associated methodologies and physical interpretations. In particular, we call attention to the fact that persistence can be defined as a global or as a local property of a system, with important implications in terms of methods and impacts. We also highlight the importance of timescale and similarity metric selection and illustrate some of the concepts using the example of summertime atmospheric circulation over western Europe.

Ensemble Forecast Sensitivity to Observations Impact (EFSOI) applied to a regional data assimilation system over south-eastern South America

Observations that are assimilated into numerical weather prediction systems are conformed by numerous data sets and their impact should be objectively evaluated. This can be efficiently achieved by the Forecast Sensitivity to Observation Impact (FSOI) methodology. In this study we explore the application of the ensemble formulation of FSOI (EFSOI) in a regional data assimilation system over south-eastern South America and evaluate the observations that result beneficial or detrimental to the forecast. In this paper we focus on the impact of two types of surface weather stations: human-operated conventional surface weather station networks from the National Meteorological Service and non-conventional automatic surface weather stations from different public and private networks (CSWS and NSWS respectively). To achieve this, the Weather Research and Forecasting model coupled with the Local Ensemble Transform Kalman Filter is used with 20 members. The experiment was carried out during 30 days of the intensive observing period of the RELAMPAGO-CACTI (Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations-Clouds, Aerosols, and Complex Terrain Interaction) field campaign that was conducted during the 2018–2019 austral warm season in the center of Argentina. 20 km resolution analyses were obtained every 6-h, assimilating data from soundings, aircrafts, GOES derived motion winds, AIRS retrievals, CSWS and NSWS. It is shown that, considering the entire period, all the observation sources had a positive impact on the 6-h forecasts. However, when each variable is analyzed individually, surface pressure observations from both CSWS and NSWS show on average a negative impact. Two data denial experiments were carried out in order to support EFSOI results. This paper is the first approximation to quantify the impact of different observation sources and observed variables at a regional scale over south-eastern South America, a data sparse region where unconventional networks can help close large geographical data gaps. The results of this work help identify observation data sources detrimental for the data assimilation system, suggesting data selection criteria to improve the regional forecast accuracy.

The quasi-biennial oscillation (QBO) and global-scale tropical waves in Aeolus wind observations, radiosonde data, and reanalyses

Abstract. The quasi-biennial oscillation (QBO) of the stratospheric tropical winds influences the global circulation over a wide range of latitudes and altitudes. Although it has strong effects on surface weather and climate, climate models have great difficulties in simulating a realistic QBO, especially in the lower stratosphere. Therefore, global wind observations in the tropical upper troposphere and lower stratosphere (UTLS) are of particular interest for investigating the QBO and the tropical waves that contribute significantly to its driving. In our work, we focus on the years 2018–2022 and investigate the QBO and different tropical wave modes in the UTLS region using global wind observations made by the Aeolus satellite instrument and three meteorological reanalyses: the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-5), the Japanese 55-year Reanalysis (JRA-55) of the Japan Meteorological Agency (JMA), and the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). Further, we compare these data with observations of selected radiosonde stations. By comparison with Aeolus observations, we find that, on zonal average, the QBO in the lower stratosphere is well represented in all three reanalyses, with ERA-5 performing best. Averaged over the years 2018–2022, agreement between Aeolus and the reanalyses is better than 1 to 2 m s−1, with somewhat larger differences during some periods. Differently from zonal averages, radiosonde stations provide only local observations and are therefore biased by global-scale tropical waves, which limits their use as a QBO standard. While reanalyses perform well on zonal average, there can be considerable local biases between reanalyses and radiosondes. We also find that, in the tropical UTLS, zonal wind variances of stationary waves and the most prominent global-scale traveling equatorial wave modes, such as Kelvin waves, Rossby-gravity waves, and equatorial Rossby waves, are in good agreement between Aeolus and all three reanalyses (in most cases better than 20 % of the peak values in the UTLS). On zonal average, this supports the use of reanalyses as a reference for comparison with free-running climate models, while locally, certain biases exist, particularly in the QBO wind shear zones and around the 2019–2020 QBO disruption.

Component Analysis and Identification of Ancient Glass Products Based on Statistical Methods

This paper analyzes its role in the composition analysis and identification of ancient glass products by flexible use of statistical methods, and emphasizes four statistical methods: systematic clustering algorithm, K-means algorithm, logistic regression model and grey correlation analysis. Taking the C project of CUMCM in 2022 as an example, this paper systematically introduces these four common data classification and statistical methods to classify and analyze the given data. In this paper, suitable chemical components of high potassium and lead barium glass were selected for subdivision, and the specific division methods and results w ere given. The chemical composition of glass relics of unknown category was analyzed to identify their type. The grey correlation matrix of surface weathering of high-potassium cultural relics was obtained, and the correlation degree of chemical components was analyzed. This greatly promotes the composition analysis and identification of chemical components in ancient relics.

Identification of ancient glass categories based on distance discriminant analysis

It is crucial for archaeological investigations to identify the category of cultural relics by analyzing their chemical composition. This study analyzed the chemical composition distribution of glass cultural relics and applied distance discriminant analysis methods to classify them into two categories. Through stepwise regression, four key feature factors ({SiO}_{2}, {K}_{2}O, PbO, and the presence of weathering on the artifact's surface) were selected from a total of 15 features, including surface weathering. Aside from using columnar table analysis to determine weathering on the surface of the artifact and correlations between categories, and using Spearman correlation coefficients to select key feature factors such as {SiO}_{2}, {K}_{2}O, PbO, BaO, and ,SrO from 14 total feature factors (excluding weathering on the surface), we established a Mahalanobis distance discriminant model to differentiate unknown glass artifacts. Results indicate that Spearman-Mahalanobis distance discrimination outperformed stepwise regression-Mahalanobis distance discrimination, with an overall accuracy of 99.10% for the former and 98.69% for the latter in identifying high-potassium glass or lead-barium glass.

On the Electrification of Winter Season in Cold Climate Megacities—The Case of New York City

Abstract Cities are accelerating policies to electrify their energy sectors as a key strategy for reducing greenhouse gas emissions. In densely populated cities with cold climates, the building sector often accounts for over 70% of total energy consumption during winter seasons. In such cold climate megacities, the common practice for heating building spaces involves burning oil or gas. A major shift from this conventional approach toward electric-based heating technologies could have far-reaching implications. In this work, we focus on New York City (NYC), where buildings account for over 75% of the total energy consumption used during winter seasons. The city has adopted policies aimed at achieving deep decarbonization by targeting buildings as a primary source of emissions. We evaluate the potential energy infrastructure and environmental impacts of such major shifts by focusing on the adoption of air-source heat pumps from natural gas boilers. The Weather Research and Forecasting model, coupled with a multilayer building environment parameterization and building energy model, is used to perform this analysis. A city-scale case study was performed over the winter month of January 2021. Simulation results show good agreement with surface weather stations. We show that a shift of heating systems from gas to electricity results in an equivalent peak energy demand from 21,500 MW to 5800 MW, while reducing the peak urban heat island (UHI) by 2.5–3 °C. Results highlight potential trade-offs in adaptation strategies for cities, which may be necessary in the context of increasing decarbonization policies.

An Investigation into the Composition Analysis and Identification of Ancient Glass Artifacts

This study employs association analysis and chi-square tests to examine the relationship between weathering and glass type, color, and patterns in ancient glass artifacts. The findings reveal a stronger association between lead-barium glass and weathering, with weaker associations for color and patterns. Combining both methods demonstrates that glass type exhibits the strongest correlation with weathering, followed by color, while patterns remain unrelated. Utilizing kernel density plot analysis, the research investigates variations in chemical composition pre- and post-weathering, predicting component content before weathering. The classification of glass is based on intrinsic and extrinsic characteristics, highlighting notable differences in potassium oxide, lead oxide, and barium oxide between high-potassium and lead-barium glass. Scatter plots further illustrate these compositional disparities. Applying a decision tree, glass is classified according to patterns, color, and surface weathering, determining the influence of various factors. Hierarchical clustering is then used to categorize chemical compositions, identifying five categories for lead-barium glass and nine for high-potassium glass.

Deforestation and Its Effect on Surface Albedo and Weather Patterns

Deforestation is an important environmental problem and a key promoter of regional climate change through modifying the surface albedo. The objective of this research was to characterize the impact of deforestation and land use changes on surface albedo (α) and climate patterns in a tropical highland region of Mexico, between the years 2014 and 2021. The main land cover types are coniferous forests (CF), oak and gallery woodlands (OGW), and annual agriculture (AA), which represent more than 88% of the regional territory. We used 2014 and 2021 Landsat 8 OLI images with topographic and atmospheric correction in order to develop an inventory of albedo values for each land cover type in both time scenarios. Albedo images were generated by using the equation proposed by Liang in 2001, which is based on the reflectance of the bands 2, 3, 4, 5, and 7. Differences in albedo values were calculated between the years 2014 and 2021, and those differences were correlated with variations in climate parameters, for which we used climate data derived from the WRF model. In addition, the different land use changes found were classified in terms of triggers for increasing or decreasing surface albedo. We used the Mann–Whitney U Test to compare the 2021 − 2014 climatic deviations in two samples: Sample A, which included sites without albedo change in 2021; and Sample B, including sites with albedo change in 2021. Results showed that between 2014 and 2021, at least 38 events of land use change or deforestation occurred, with albedo increments between 1 and 11%, which triggered an average increment of 2.16% (p < 0.01; Mann–Whitney U Test) of the regional surface albedo in comparison to the 2014 scenario. In this period, the albedo for CF, OGW, and AA also increased significantly (p < 0.001; Mann–Whitney U Test) by +79, +12, and +9%, respectively. In addition, the regional albedo increment was found to be significant and negatively correlated (p < 0.01 Spearman’s coefficient) with relative humidity (RH), maximum temperature (Tmax), and minimum temperature (Tmin), and correlated (p < 0.01) positively with diurnal temperature range (DTR). The Mann–Whitney U Test revealed that 2021 climatic variations in Sample B sites are statistically different (p < 0.05) to 2021 climatic variations in Sample A sites, which demonstrates that albedo changes are linked to a decrease in minimum temperature and relative humidity and an increase in DTR. Conversion of CF and OGW into AA, perennial agriculture (PA), or grassland (GR) always yielded an albedo increment, whilst the conversion of AA to irrigation agriculture (IA) or PA triggered a decrease in albedo, and finally, the pass from GR or AA to protected agriculture (PA) caused albedo to increase or decrease, depending on the greenhouse covers materials. Reducing deforestation of CF and OGW, conversion of AA or GR into PA, and selecting adequate greenhouse covers could help to mitigate regional climate change.

Integration of photogrammetry and laser scanning for enhancing scan-to-HBIM modeling of Al Ula heritage site

The study highlights the significance of combining imaging and laser scanning techniques to enrich point clouds used for reliable documentation and Heritage Building Information Modeling (HBIM). The fusion-based approach was used to document Al Ula Heritage site in Saudi Arabia, a UNESCO World Heritage Site with well-preserved tombs from the first century BC. The HBIM parametric modeling process requires a detailed survey to collect all geometric data. Although terrestrial laser scanners (TLS) are an efficient tool for 3D recording of heritage scenes in a short period of time, the data resolution is insufficient for identifying and evaluating the spatial distribution of surface weathering forms. Furthermore, combining scans collected at different times may pose difficulties in recording the texture. These issues have an impact on the subsequent 3D modeling phase as well as the efficiency of interpreting and tracing surface features in the Scan-to-BIM process. The proposed workflow using imagery data to enhance both the geometry and coloring of laser point clouds. In addition to texture mapping, the high-resolution imagery is used to densify the laser data using dense image matching, allowing for a clear reading of the surface features, and serving as a useful tool to identify the type, extent, and severity of façade damage. TLS and imagery data were collected separately, with images taken at the best time and location for realistically recording of surface details. Finally, the captured images are orthorectified by TLS geometric information and used for HBIM texturing to provide realistic decay mapping. The results showed that the combination of TLS and photogrammetry allowed for the efficient collection of 3D data, which improved tracing and digitizing HBIM with complete mapping information. The research findings will be greatly useful in the management and planning of historic building conservation and restoration projects.

Characterizing lithological, weathering, and hydrothermal alteration influences on volcanic rock properties via spectroscopy and laboratory testing: a case study of Mount Ruapehu volcano, New Zealand

The geomechanical characterization of volcanic material has important implications for geothermal and mineral exploration, engineering design, geophysical signals of volcano unrest, and models of instability and mass flows. Chemical weathering and hydrothermal systems can alter the host rock, leading to changes in mechanical behavior and failure mode. Here, we compare the physical and mechanical properties of lava, autoclastic breccia, and pyroclastic (scoria) rocks from Mount Ruapehu volcano (Ruapehu) in New Zealand to mineralogical composition determined via infrared spectroscopy and scanning electron microscopy (SEM) with energy-dispersive X-ray spectroscopy (EDS). We use correlation matrices, principal component analysis, and parametric analysis to determine which parameters can be used to predict physical and mechanical properties and form the basis for transfer functions. Laboratory-based spectroscopy shows that the samples contain absorption features indicative of Al- and Mg-rich hydrous phyllosilicates (e.g., kaolinite, halloysite, montmorillonite), Fe- oxides (e.g., goethite), and sulfates attributed to surface weathering, supergene, and steam-heated alteration. We find that porosity and primary lithology are the predominant control on physical and mechanical properties, followed by the pervasiveness of weathering/alteration, and then mineralogical composition. Several properties, such as porosity, uniaxial compressive strength, P-wave seismic velocity, density, and Young’s modulus, show strong correlations with other properties, indicating the potential for transfer functions between these properties. Hydrothermally altered rocks near the vent complex (up to ~ 400 m depth beneath the crater lake) with high-intensity hydrothermal alteration do not follow typical physical and mechanical property trends due to high clay content, low permeability, and low strength. The presence of these rocks within the edifice at Ruapehu implies local barriers to fluid flow and subsequent pore pressure variations. Additionally, they may have less than half the strength than would be dictated by typical porosity-strength trends for surface rocks, increasing the likelihood of structural failure. Trends in the pervasiveness of weathering with physical and mechanical properties, along with shifts in the position of spectral absorption peaks as hydrothermal/weathering alteration increases, suggest that it may be possible to extrapolate properties from imaging spectroscopy.

Analysis of glass artifacts based on hierarchical clustering and regression models

Glass is a valuable evidence of the early friendly trade between China and the Western countries. In this study, the rationality and sensitivity of the developed model were first evaluated by analyzing the definitive and quantitative data of each type of glass artifacts and establishing a model to classify the glass artifact categories into subcategories, as well as categorizing unknown types of glass. Considering the discrete and discontinuous nature of the data, the chi-square test was chosen to investigate the correlation between glass type, decoration and color and surface weathering. Secondly, descriptive statistics of high potassium and lead-barium glass artifacts before and after weathering were conducted to obtain statistical patterns about the chemical composition content, and then the chemical composition content of glass artifacts before weathering was predicted based on the mean rate of change of the chemical composition content of high potassium and lead-barium glass before and after weathering, respectively. Finally, this study further explored the relationship between glass artifacts and the content of each chemical composition using Spearman's correlation coefficient to derive the basis for the classification of glass types, based on the hierarchical clustering method to classify the subclasses of high potassium glass and lead-barium glass with and without weathering, respectively, and used the elbow rule to derive the most appropriate number of subclasses for the above four types of glass, which endowed the model with rationality.