Observational Datasets Research Articles

A dataset of meteorological observations at the National Field Scientific Observation and Research Station of Farmland Ecosystem in Changwu County, Shaanxi Province (2005–2019)

A dataset of meteorological observations at the National Field Scientific Observation and Research Station of Farmland Ecosystem in Changwu County, Shaanxi Province (2005–2019)

A dataset of specific leaf area observations in the Pan-Central Asian Arid Zone

A dataset of specific leaf area observations in the Pan-Central Asian Arid Zone

A dataset of meteorological observations from the Great Wall Station and Zhongshan Station in Antarctica during the past 35 years

A dataset of meteorological observations from the Great Wall Station and Zhongshan Station in Antarctica during the past 35 years

Possible influence of the large-scale environment on extreme waves over the Yellow Sea in boreal spring

The Yellow Sea (YS) is exposed to various weather systems, such as typhoons, monsoon activities, and extratropical cyclones, which can pose a major threat to the adjacent coastal regions through the development of energetic oceanic surface waves. Unusually severe surface wave events in the YS occur with considerable frequency during the boreal spring (March-April-May), but have received less attention compared to winter and summer. This study focuses on the characteristics of spring extreme wave height (EWH) events in the YS during 1979-2022, based on observational and long-term reanalysis datasets. Our analysis shows that extreme waves, i.e. daily maximum heights in the top 5% of all spring days, take about 12 h to build up to the peaks, while they decay more slowly after the peaks. During the extreme events, the Siberian High is found to extend anomalously eastward compared to spring climatology. Such an anomalous extension contributes to the increase of the sea level pressure gradient and the intensification of the surface wind speed in the YS. Meanwhile, in the range of 6 ∼ 24 h following the peaks of the EWH events in the YS, swells induced by strong northerly winds begin to have an impact in the YS. These swells contribute to maintaining higher wave energy levels in the YS for longer after the atmospheric source has been removed. We further explore the large-scale environmental conditions that could provide the predictability of extreme waves in the basin developed by these findings. This study presents implications for assessing the risks associated with extreme waves in coastal regions and for improving coastal management strategies in the YS.

Constraining hybrid potential scalar field cosmological model in Lyra’s geometry with recent observational data

In this study, we investigate a scalar field cosmological model with Lyra’s geometry to explain the present cosmic expansion in a homogeneous and isotropic flat FRW universe. In Einstein’s field equations, we presupposed a variable displacement vector as an element of Lyra’s geometry. In the context of the conventional theory of gravity, we suggest a suitable parametrization of the scalar field’s dark energy density in the hybrid function of redshift [Formula: see text], confirming the essential transition behavior of the universe from a decelerating era to the present accelerated scenario. We present constraints on model parameters using the most recent observational datasets from OHD, BAO/CMB and Pantheon, taking Markov Chain Monte Carlo (MCMC) analysis into account. For the proposed model, the best estimated values of parameters for the combined dataset (OHD, BAO/CMB and Pantheon) are [Formula: see text][Formula: see text]km/s/Mpc, [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text]. The model exhibits a flipping nature, and the redshift transition occurs at [Formula: see text]. The current value of the decelerated parameter for the proposed model is calculated as [Formula: see text] for the combined dataset. Some dynamical properties of the model like energy density ([Formula: see text]), scalar field pressure ([Formula: see text]), EoS parameter of scalar field ([Formula: see text]), and effective EoS parameter ([Formula: see text]) are analyzed and presented. Further, we have also examined the statefinder diagnosis and jerk parameters of the derived model. The total density parameter for the derived model is found to be unity which is in nice agreement with recent standard findings.

The consequence of higher-order curvature-based constraints on [formula omitted] gravity

In this investigation, we perform an observational statistical analysis in the theory of f(R,Lm) gravity. The proposed theoretical model is based on the Ricci scalar’s non-linear contribution. We use a distinct parametrization for the deceleration parameter and constrain the model parameters by using various observational data. To determine the best-fit model for the cosmological parameters, we use different observational datasets such as the Hubble Space Telescope, the Pantheon Supernova Survey, the Gold dataset, the Gamma-Ray Burst (GRB), and the Baryon Acoustic Oscillations (BAO). Furthermore, we study the late-time cosmic evolution of the Universe in detail and examine the implications of the constraint values on cosmological parameters. Additionally, we conduct a thorough comparison with the standard cosmological model ΛCDM and other standard models obtained by Odintsov et al. (2024); Odintsov et al. (2023) to examine the validity of our proposed model in the low-redshift regimes. Finally, we find that the proposed model encapsulates an intriguing transition from early deceleration at high redshift to acceleration at low redshift, a quintessence dark energy scenario, and convergence towards the well-established ΛCDM model in late-time Universe’s evolution.

Increasing heat waves frequencies over India during post-El Niño spring and early summer seasons

The increasing frequency of extreme Heat Waves (HWs) has generated significant societal impacts in recent years. This study used different observational datasets to investigate the HW characteristics over India during the post-El Niño spring and early summer seasons (April to June; AMJ). Analysis suggests that HW days are more prevalent over India, predominantly increased in south-central and northwest India, during the decaying phase of strong El Niño years. It is found that anomalous anticyclone circulation accompanied by high pressure extending from the Western North Pacific region towards the Bay of Bengal and India is responsible for enhanced HW days and intensity during the AMJ of strong El Niño decay years. This anomalous anticyclone-induced downdraft reduces the specific humidity in the lower troposphere, leading to decreased cloud cover over India. As a result, shortwave radiation is enhanced at the surface, which causes abnormal HWs over India. During the decaying phase of strong El Niño years, the HW days over India contributed to an increase in the frequency of Discomfort Index hours (above 28 °C), maximum temperatures exceeding 40 °C (hours per day), and Universal Thermal Climate Index days above 38 °C and 46 °C during the spring and early summer months, especially in the East Coast, central, and northwestern parts of India. Thus, proper prediction of large-scale atmospheric circulation over the Indo-western Pacific region during El Niño can help to predict the HW conditions three months in advance. This would help to implement suitable adaptation measures and put into practice strong mitigation policies to limit the increased risk of such events during AMJ of El Niño decay years.

Analysis of Spatial Divergence in Bird Diversity Driven by Built Environment Characteristics of Ecological Corridors in High-Density Urban Areas

Urban ecological corridors play an important role in facilitating bird migration and maintaining biodiversity in urban landscapes as key connections between habitat patches. However, the effects of built environment characteristics of urban ecological corridors on bird diversity have not been well understood. In this study, we used Minhang District, Shanghai, as an example to describe the built environment of urban ecological corridors through three dimensions (habitat characteristics, degree of surrounding urbanization, and degree of slow-traffic connectivity). We calculated species richness, abundance, Shannon–Wiener index, and Simpson Index to assess bird diversity based on the bird observation dataset from the Citizen Science Data Sharing Platform. The effects of built environment characteristics of urban ecological corridors on bird diversity were quantified by the Generalized Linear Model. The results showed that: (1) There were significant differences in the built environment characteristics of urban ecological corridors, which formed the spatial differentiation pattern of bird diversity. (2) Different built environment features of urban ecological corridors have different impacts on bird diversity. Habitat suitability of urban ecological corridors was positively correlated with bird diversity, with birds preferring to inhabit waters with an area of more than 1 ha. The degree of urbanization was negatively correlated with bird diversity, with distance to the center of the area proving to have the strongest positive effect. The degree of slow-traffic connectivity proved that low-intensity human activities in urban ecological corridors had a lower impact on bird diversity. The above findings can provide scientific reference for the construction of urban and regional ecological networks in the future.

Skewness preferences: Evidence from online poker

We test for skewness preferences in a large set of observational panel data on online poker games (n=4,450,585). Each observation refers to a choice between a safe option and a binary risk of winning or losing the game. Our setting offers a real-world choice situation with substantial incentives where probability distributions are simple, transparent, and known to the decision-makers. Individuals reveal a strong and robust preference for skewness, which is inconsistent with expected utility theory. The effect of skewness is most pronounced among experienced and unsuccessful players but remains significant in all subsamples that we investigate, in contrast to the effect of variance.

Complex interplay of sulfate aerosols and meteorology conditions on precipitation and latent heat vertical structure

An eight-year satellite observation dataset reveals that sulfate aerosols significantly influence the vertical structure of precipitation and latent heat (LH) in the Beijing-Tianjin-Hebei (BTH) region during summer. In this period, prevalent sulfate aerosols combine with warm, humid southerly winds and elevated convective available potential energy (CAPE), influencing precipitation dynamics. Under polluted conditions with specific CAPE and precipitation top temperature (PTT) ranges, precipitation particles experience accelerated growth within the mixed-phase layer, delineated by the −5 °C to 2 °C isotherms, compared to pristine environments. This results in a marked increase in both the intensity and height at which the maximum LH is released. Subsequent analysis reveals that hygroscopic sulfate aerosols, acting as cloud condensation nuclei (CCN), amplify the collision-coalescence process within the mixed layer amid high cloud water content, propelling rapid precipitation particle growth and elevating the PTT. This warming effect surpasses the cooling contribution from robust CAPE, culminating in a net elevation of PTT under polluted scenarios compared to pristine ones. Additionally, quantification of PTT sensitivity to both CAPE and aerosol optical depth (AOD) unveils a high consistency between satellite-detected PTT responses to CAPE and those predicted by cloud-resolving model simulations. The study deduces that the role of aerosols as CCN in either invigorating or diminishing the collision-coalescence process is contingent on the available cloud water.

Evaluation of WRF-Chem air quality forecasts during the AEROMMA and STAQS 2023 field campaigns

ABSTRACT A real-time air quality forecasting system was developed using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to provide support for flight planning activities during the NOAA Atmospheric Emissions and Reactions Observed from Megacities to Marine Areas (AEROMMA) and NASA Synergistic TEMPO Air Quality Science (STAQS) 2023 field campaigns. The forecasting system operated on two separate domains centered on Chicago, IL, and New York City, NY, and provided 72-hour predictions of atmospheric composition, aerosols, and clouds. This study evaluates the Chicago-centered forecasting system’s 1-, 2-, and 3-day ozone (O3) forecast skill for Chiwaukee Prairie, WI, a rural area downwind of Chicago that often experiences high levels of O3 pollution. Comparisons to vertical O3 profiles collected by a Tropospheric Ozone Lidar Network (TOLNet) instrument revealed that forecast skill decreases as forecast lead time increases. When compared to surface measurements, the forecasting system tended to underestimate O3 concentrations on high O3 days and overestimate on low O3 days at Chiwaukee Prairie regardless of forecast lead time. Using July 25, 2023, as a case study, analyses show that the forecasts underestimated peak O3 levels at Chiwaukee Prairie during this regionwide bad air quality day. Wind speed and direction data indicates that this underestimation can partially be attributed to lake breeze simulation errors. Surface fine particulate matter (PM2.5) measurements, Geostationary Operational Environmental Satellite-16 (GOES-16) aerosol optical depth (AOD) data, and back trajectories from the NOAA Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model show that transported Canadian wildfire smoke impacted the Lake Michigan region on this day. Errors in the forecasted chemical composition and transport of the smoke plumes also contributed to underpredictions of O3 levels at Chiwaukee Prairie on July 25, 2023. The results of this work help identify improvements that can be made for future iterations of the WRF-Chem forecasting system. Implications: Air quality forecasting is an important tool that can be used to inform the public about upcoming high pollution days so that individuals may plan accordingly to limit their exposure to health-damaging air pollutants. Forecasting also helps scientists make decisions about where to make observations during air quality field campaigns. A variety of observational datasets were used to evaluate the accuracy of an air quality forecasting system that was developed for NOAA and NASA field campaigns that occurred in the summer of 2023. These evaluations inform areas of improvement for future development of this air quality forecasting system.

Diurnal cycles of rain storms over South China during the presummer rainy season

The variation characteristics of rain storms (RSs) are closely related to complex internal physical processes and external dynamic and thermodynamic conditions. Using a 17-year observational rainfall dataset from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR), the diurnal cycles of various RS characteristics and their responses to the South China Sea Summer Monsoon (SCSSM) are investigated in South China (SC) during the presummer rainy season (PSRS). Before SCSSM outbreaks, the RSs in the western inland region of SC (WSC) have stronger precipitation intensities and convective features due to orographic lifting, especially at night, while those in the eastern inland region of SC (ESC) have higher RS occurrence frequencies with higher moisture transport, especially in the afternoon. The onset of the SCSSM leads to abundant moisture and sufficient convective available potential energy over the entire SC area, resulting in significant increases in the occurrence frequency, convective fraction, and precipitation top height of RSs. In the WSC, the weakened upward motion and smaller convective inhibition limit RS development, resulting in weaker rainfall intensity. In the ESC, a particularly unstable atmosphere and enhanced low-level moisture transport in the afternoon lead to a larger three-dimensional size, more active convection, and greater precipitation intensity in the RSs. These results offer fresh insight into the diurnal variation in rainfall during the PSRS and can be effectively used to evaluate numerical model simulations.

Identification of critical drought thresholds affecting vegetation on the Mongolian Plateau

The increasing frequency of drought events due to global climate change has significant impacts on ecosystems, often resulting in reduced vegetation productivity. However, vegetation responses to drought often exhibit non-linear threshold phenomena, where vegetation growth sharply decreases once water stress reaches a certain level. Our study, conducted on the Mongolian Plateau, focuses on revealing the drought response thresholds and distribution patterns of different vegetation types. These thresholds mark critical turning points from high drought resistance to high vulnerability in vegetation response and serve as early warning indicators, crucial for accurately assessing how terrestrial ecosystems adapt to climate change. We utilized remote sensing observation datasets and inversion datasets spanning from 2001 to 2020, including six representative vegetation indices. The results indicate that: (1) Over 70 % of the Mongolian Plateau experiences low vegetation growth (<10th) predominantly influenced by drought, with low vegetation growth often occurring when water availability falls below the 30th percentile; (2) Areas with higher vegetation coverage and relatively moist climates have lower proportions of drought thresholds, implying that more complex ecosystems can better mitigate water stress, while background climatic conditions also regulate vegetation response to water availability; (3) According to multi-model climate predictions, the overall probability of drought risk on the Mongolian Plateau is projected to significantly increase during 2080–2100, especially in drought-prone regions. These findings provide important insights into better understanding the impacts of drought on ecosystems. Additionally, they emphasize the urgency of taking early measures to adapt to future drought risks.

Natural gas leakage: Forward and inverse method of leakage field based on multipath concentration monitoring

Natural gas leakage can easily lead to safety accidents and environmental pollution, affecting normal safe production and sustainable energy development. Among them, the leakage accident caused by the integrity failure of the process equipment is particularly noteworthy. To improve the efficiency of methane leakage monitoring and the accuracy of leakage source location, the forward prediction model of leakage concentration field, the optimization model of monitoring equipment layout, and the inversion model of leakage source location were established. To evaluate the model’s applicability, an open-air oil–gas joint station was selected for experimental simulation. The monitoring data of the open circuit laser methane sensor and the forward simulation results of the leakage concentration field were compared. It was found that at 30 % methane concentration, the monitoring distance was 0–6 m, with high monitoring accuracy. Then, by eliminating redundant monitoring sensors, three leakage field concentration monitoring paths are formed. A representative leakage source scenario set is selected for inversion localization. In the simulation of the single-point leakage experiment, the leakage location error is 1.824 m, and the average relative error is 3.6 %. Through error analysis, the results show that the inversion positioning model can also have a good adaptability to the parameter estimation of the leakage source when there are only 3 sets of observation data.

FLRW cosmology in metric-affine F(R,Q) gravity* *Supported by the Ministry of Science and Higher Education of the Republic of Kazakhstan (AP14870191)

We investigated some Friedmann-Lemaître-Robertson-Walker (FLRW) cosmological models in the context of metric-affine gravity, as proposed in [arXiv: 1205.5266v6]. Here, R and Q are the curvature and nonmetricity scalars using non-special connections, respectively. We obtained the modified field equations using a flat FLRW metric. We then found a connection between the Hubble constant , density parameter , and other model parameters in two different situations involving scalars u and w. Next, we used new observational datasets, such as the cosmic chronometer (CC) Hubble and Pantheon SNe Ia datasets, to determine the optimal model parameter values through a Markov chain Monte Carlo (MCMC) analysis. Using these best-fit values of the model parameters, we discussed the results and behavior of the derived models. Further, we discussed the Akaike information criterion (AIC) and Bayesian information criterion (BIC) for the derived models in the context of the Lambda cold dark matter (ΛCDM). We found that the geometrical sector dark equation of state parameter behaves just like a dark energy candidate. We also found that both models are transit phase models. Model-I approaches the ΛCDM model in the late-time universe, whereas Model-II approaches quintessence scenarios.

Trend and inter-annual variability in regional climate models – Validation and hydrological implications in southeast Australia

We assessed the ability of Regional Climate Models (RCMs) to reproduce observed means, inter-annual variance and trends for rainfall indices that were important for runoff generation over southeast Australia. To establish the benefit of the RCM without being penalized or rewarded by the performance of the forcing GCM, we used ECMWF Re-Analysis-interim (ERA-Interim) to force the Weather Research and Forecasting model (WRF) and the Conformal Cubic Atmospheric Model (CCAM). The performance of two different configurations of each RCM was evaluated against an observational dataset (Australian Gridded Climate Data, AGCD) and compared with that of ERA-Interim. The assessments were carried out at both observational and ERA-Interim grid resolutions: ∼5 km and ∼80 km, respectively. As hypothesised, the RCMs outperformed ERA-Interim in representing spatial patterns and magnitude of mean rainfall, because of higher spatial resolution. RCMs also accurately represented the general spatial patterns of variance, but systematically underestimated the inter-annual variability over much of the domain. RCMs performed better than ERA-Interim in reproducing the magnitude of trends in some cases, especially in the decline of cool season rainfall in southeast Australia, which has had significant impacts on water resources. One of the two CCAM runs generally performed best across all rainfall indices, in part because of atmospheric spectral nudging and an improved land-surface model. Based on the findings, we have provided suggestions on where new research on the development of RCMs could be focused and recommendations relevant to the next generation of Australian hydro-climate projections generated from the sixth phase of the Coupled Model Intercomparison Project (CMIP6).

Meridional shift of climatological tropical cyclone genesis location in the western North Pacific

AbstractClimatological monthly mean tropical cyclone (TC) genesis location in the western North Pacific exhibits a marked meridional shift from June to November and the physical cause of such a shift was investigated through the diagnosis of observational and reanalysis datasets. Two genesis potential indices were used to examine key environmental parameters affecting the meridional shift. The diagnosis results indicate that dynamic parameters such as vertical and meridional wind shears play a dominant role in promoting both the northward advance and southward retreat of the mean genesis latitude, while the effect of vertical velocity is negligible. The thermodynamic parameters such as relative humidity and potential intensity in general play a minor role, except that the latter helps promote northward advance during the early TC season. The change of the environmental parameters is closely linked to the sub‐seasonal evolution of the monsoon trough and subtropical high in the western North Pacific. Maximum synoptic‐scale variability also experiences a similar meridional shift, contributing to the TC genesis location shift.

Interdecadal change in the relationship between the South China late rainy season rainfall and equatorial Pacific SSTs

Abstract The South China (SC) late rainy season rainfall (SCLR) is important to people’s livelihoods and properties of this densely populated and economically developed region. Based on observation and reanalysis datasets, this study identifies that the relationship between the SCLR and equatorial Pacific SSTs (EPSSTs) experiences an interdecadal change around the late 1970s. The SCLR is negatively correlated with the eastern EPSSTs during 1948–1977 (the previous epoch) but positively correlated with the central EPSSTs during 1978–2022, especially during the last three decades 1993–2022 (the post epoch). This is due to the interdecadal change in El Niño flavors and EPSSTs-tropical Atlantic SSTs (TASSTs) relationship. With the increasing frequency of central Pacific El Niños in the post epoch, the El Niño-related warm SST anomaly center shifts from the eastern equatorial Pacific (EEP) to the central equatorial Pacific (CEP). Correspondingly, the anomalous cyclone exerted by the CEP warming reaches SC and causes rainfall surplus there (westward propagation pathway). In contrast, the EEP warming in the previous epoch can’t influence the SCLR through the westward propagation pathway, but through shifting the westerly jet southward via heating the tropical troposphere. The southward shifted westerly jet crashes the Qinghai-Tibet Plateau, triggering cyclonic circulations across the northern East Asia and North Pacific, which in turn cause anticyclonic circulations over the western SC and reduce SC rainfall (eastward propagation pathway). On the other hand, the CEP warming in the post epoch barely influence the SCLR through the eastward propagation pathway because of the enhanced negative relationship between EPSSTs and TASSTs.&amp;#xD;

A performance neural network model for conventional solar stills via transfer learning

Predictive solar-desalination models are becoming more widespread using ML and AI. However, forecasting solar still water productivity based on numerous designs still needs to be improved. Herein, we used transfer learning to create precise supervised predictive ANN regression models for water productivity (L/m2.day) predictions based on literature findings. Such observation datasets from single-basin solar stills were utilized to build the random initialization ANN model. The transfer learning method was applied to the latter model by taking the learned network (weights) for fine-tuning the hyperparameters from the earlier developed novel hybrid solar still known as the source (pre-trained) ANN model, to predict the target ANN model. Based on most minor statistical errors, the pre-trained model with 5–64–64-1 architecture and ReLU activation function was the most appropriate for water productivity prediction. All created ANN models were compared to the MLR model. The results revealed that the generated target ANN model outperformed the ANN RI and MLR with OI values of 0.872, 0.834, and 0.803, respectively, in all modeling stages. The target ANN model's accuracy and generalization were sufficient. The target ANN model had residuals of forecasted distillate values of around 1%. This work discusses the significance of transfer learning to generate accurate target ANN models for predicting freshwater outputs in single-slope solar stills, which can be integrated with established theoretically tuned parameters to enhance performance and maximize distillate water yields.

Running to warmer-drier springs in the Greater Mekong Subregion as climate warms

Using the multivariate EOF analysis performed on the observation datasets, this study reveals that the Greater Mekong Subregion (GMS) has been toward warm-dry springs over most regions especially after the 2000s, accompanied by increased surface temperature and decreased precipitation anomalies. Such climate condition provides favourable environment for compound heat and drought events, raising questions about how the spring climate in GMS will change as climate warms. Based on a multi-model weighting scheme among CMIP6 models, we project the regional climate changes in GMS in a warmer world under the SSP5–8.5 scenario. Results show that the southern GMS including Vietnam, Laos, Thailand, and Cambodia will become warmer-drier throughout the 21st century in response to global warming. In contrast, the climate condition over the northern GMS including Myanmar and Yunnan province of Southwest China is expected to experience a transition from dry to wet springs in a warmer world, resulting in a tendency toward the warm-wet springs particularly in the long-term future. This knowledge indicates the spatial non-uniformity of regional climate under global warming and will aid in advance to plan for water management and agricultural adaptation.