Search

Melting layer usually exists in precipitation where ice particles gradually melt into liquid particles. Utilizing the detection from the Dual-frequency Precipitation Radar onboard Global Precipitation Measurement Mission Core Observatory during 2018-2022, this paper investigates the quasi-global climatological features of Melting layer over 65°S ~65°N and analyzes the relationships with elevation in the golden case involving the section along 32.5°N across China. The distribution of Melting layer is latitude-dependent: Melting layer is higher in 30°S~30°N and decrease toward the mid-and high-latitude, which are generally lower in 30°S~65°S throughout the year. The height and thickness of Melting layer change more dramatically in mid-and high latitudes than in low latitudes for diurnal variations. The higher elevation terrain has the lower Melting layer top height than other terrains, yet the geometric thickness of Melting layer is also thinner. A golden case is selected to represent the analysis of Melting layer play role in rainfall. When the Melting layer grow thinner with steeply rising elevation due to the thermal change and orographic uplift, there is weak rain with a greater number of smaller raindrops in the golden case. However, Melting layer can get widen and raindrops can get sufficient growth as the elevation declines. [ABSTRACT FROM AUTHOR]

The North China Plain (NCP) is one of the three great plains in China and also serves as a vital region for grain, cotton, and oil production. Under the influence of regional hydrothermal changes, groundwater overexploitation, and seawater intrusion, the vegetation coverage is undergoing continuous alterations. However, a comprehensive assessment of impacts of precipitation, temperature, and groundwater on vegetation in marine sedimentary regions of the NCP is lacking. Heilonggang Basin (HB) is located in the low-lying plain area in the east of NCP, which is part of the NCP. In this study, the HB was chosen as a typical area of interest. We collected a series of data, including the Normalized Difference Vegetation Index (NDVI), precipitation, temperature, groundwater depth, and Total Dissolved Solids (TDS) from 2001 to 2020. Then the spatiotemporal variation in vegetation was analyzed, and the underlying driving mechanisms of vegetation variation were explored in this paper. The results show that NDVI experiences a rapid increase from 2001 to 2004, followed by stable fluctuations from 2004 to 2020. The vegetation in the HB has achieved an overall improvement in the past two decades, with 76% showing improvement, mainly in the central and eastern areas, and 24% exhibiting deterioration in other areas. From 2001 to 2020, NDVI correlates positively with precipitation, whereas its relationship with temperature fluctuates between positive and negative, and is not statistically significant. There is a threshold for the synergistic change of NDVI and groundwater depth. When the groundwater depth is lower than 3.8 m, NDVI increases sharply with groundwater depth. However, beyond this threshold, NDVI tends to stabilize and fluctuate. In the eastern coastal areas, NDVI exhibits a strong positive correlation with groundwater depth, influenced by the surface soil TDS controlled by groundwater depth. In the central regions, a strong negative correlation is observed, where NDVI is primarily impacted by soil moisture under the control of groundwater. In the west and south, a strong positive correlation exists, with NDVI primarily influenced by the intensity of groundwater exploitation. Thus, precipitation and groundwater are the primary driving forces behind the spatiotemporal variability of vegetation in the HB, while in contrast, the influence of temperature is uncertain. This study has elucidated the mechanism of vegetation response, providing a theoretical basis for mitigating adverse factors affecting vegetation growth and formulating rational water usage regulations in the NCP. [ABSTRACT FROM AUTHOR]

There are several standardized meteorological, hydrological, agricultural, and environmental indices for drought classification into a set of dry categories all of which are derived from the basic concept of standardized precipitation index (SPI). Almost all these indices are based on crisp (bivalent) logic where boundary limits between neighboring categories are numbers without transboundary inclusiveness. The SPI procedure depends on the standard normal (Gaussian) probability distribution function (PDF) with zero mean and standard deviation equal to one. This paper proposed fuzzification of the SPI limits among categories for the inclusion of more than one category with different membership degrees. This method is referred to as the fuzzy SPI (FSPI) procedure, which provides drought tracing possibility and categorization. At the end for a numerical value, one can defuzzify the fuzzy result through defuzzification methods. FSPI provides preliminary warning system in terms of two or more fuzzy rule propositions and categories. Thus, one can know the logical alternatives of the drought behavior of a given hydro-meteorology variable. The application of the FSPI is presented for New Jersey State wise and Istanbul annual precipitation records. [ABSTRACT FROM AUTHOR]

Changes in patterns of meteorological parameters, like precipitations, temperature, wind, etc., are causing significant increases in various extreme events. And these extreme events, i.e., floods, heatwaves, hurricanes, droughts, etc., lead to a shortage of water resources, crop failures, wildfires, and economic losses. However, Global Circulation Models (GCMs) are considered the most important tools for quantifying climate change. Therefore, we selected 20 different GCMs of precipitation in our research, as the frequency of extreme events, like drought and flood, is highly related to changes in precipitation patterns. However, this research introduced a new weighting scheme — MCFSAWS-Ensemble: Monte Carlo Feature Selection Adaptive Weighting Scheme to Ensemble multiple GCMs, whereas, Monte Carlo Feature Selection (MCFS) is one of the most popular algorithms for discovering important variables. However, the proposed weighting scheme (MCFSAWS-Ensemble) is mainly based on two sources. Initially, it evaluates the prior performance of each GCM model to define their relative importance using MCFS. Then, it computes value by value difference between the observed and simulated model. In addition, the application of this paper is based on the monthly time series data of precipitation in the Tibet Plateau region of China. In addition, we used twenty GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to analyze the implications of the MCFSAWS-Ensemble. Further, we compared the performance of the MCFSAWS-Ensemble scheme with Simple Model Averaging (SMA) through Mean Average Error (MAE) and correlation statistics. The results of this research indicate that the proposed weighting scheme (MCFSAWS-Ensemble) is more accurate than the SMA approach. Consequently, we recommend the use of advanced machine learning algorithms such as MCFS for making accurate multi-model ensembles. [ABSTRACT FROM AUTHOR]

In this paper, we describe and analyze two datasets entitled "Homogenised monthly and daily temperature and precipitation time series in China during 1960–2021" and "Homogenised monthly and daily temperature and precipitation time series in Greece during 1960–2010". These datasets provide the homogenised monthly and daily mean (TG), minimum (TN), and maximum (TX) temperature and precipitation (RR) records since 1960 at 366 stations in China and 56 stations in Greece. The datasets are available at the Science Data Bank repository and can be downloaded from https://doi.org/10.57760/sciencedb.01731 and https://doi.org/10.57760/sciencedb.01720. For China, the regional mean annual TG, TX, TN, and RR series during 1960–2021 showed significant warming or increasing trends of 0.27°C (10 yr)−1, 0.22°C (10 yr)−1, 0.35°C (10 yr)−1, and 6.81 mm (10 yr)−1, respectively. Most of the seasonal series revealed trends significant at the 0.05 level, except for the spring, summer, and autumn RR series. For Greece, there were increasing trends of 0.09°C (10 yr)−1, 0.08°C (10 yr)−1, and 0.11°C (10 yr)−1 for the annual TG, TX, and TN series, respectively, while a decreasing trend of −23.35 mm (10 yr)−1 was present for RR. The seasonal trends showed a significant warming rate for summer, but no significant changes were noted for spring (except for TN), autumn, and winter. For RR, only the winter time series displayed a statistically significant and robust trend [−15.82 mm (10 yr)−1]. The final homogenised temperature and precipitation time series for both China and Greece provide a better representation of the large-scale pattern of climate change over the past decades and provide a quality information source for climatological analyses. [ABSTRACT FROM AUTHOR]

Selecting the appropriate climate models and determining their strengths and weaknesses are the key steps in examining climate change impacts on water resources. In this paper, the efficiency of monthly, seasonal, and annual precipitation estimations by 21 Global Climate Models (GCMs) provided by the NASA Earth Exchange Global Daily Downscaled Projections dataset (NEX-GDDP) was evaluated against the Observational Precipitation (OP) of 50 synoptic stations located in eight Precipitation Zones (PZs) of Iran. First, the average of GCMs precipitation for the period 1976–2005 was evaluated vs the OP data at both PZ and national levels. Then the efficiency of the GCMs and their Ensemble Average (EA) in estimating monthly, seasonal, and annual precipitation were determined using NSE, NRMSE, KGE, and PBIAS statistics, and subsequently, the GCMs were ranked according to their statistics. The results show that the time variability of the GCMs' average annual precipitation agrees with that of the OP, but it underestimates the extreme values. The agreement of the models' estimations with observation is generally lowest in summer and the highest in winter and spring. Most of the GCMs show a lower ability in precipitation estimation in dry seasons than in wet seasons. EA of the models for all individual months shows the highest errors in the coastal areas of the Caspian Sea, especially for the spring and winter seasons. The examination of the GCMs' efficiency at the national level shows that the EA and GFDL-ESM2G models have the least errors at monthly and annual time scales, respectively. [ABSTRACT FROM AUTHOR]

This paper discussed the use of stable isotope compositions of precipitation (δ2H and δ18O) to understand its characteristics and the parameters controlling meteorological conditions in Malaysia. The meteoric water line for Malaysia, or MMWL established in this study are δ2H = (7.43 ± 0.05)δ18O + (7.33 ± 0.11) (n = 595, r2 = 0.97) for individual values, and δ2H = (7.07 ± 0.20)δ18O + (4.90 ± 0.75) (n = 51, r2 = 0.96) for annual precipitation weighted means. No major variations in local meteoric water lines established between the stations, except for one (i.e., Cameron Highlands), which was found to be significantly different due to its high deuterium excess, or d value, attributed to moisture recycling (re–evapotranspiration) from the surrounding dense rainforest area. The precipitation moisture came from the two monsoon systems; the Northeast monsoon (NEM) and Southwest monsoon (SWM). They differ only slightly in their isotope signatures, where the NEM (winter monsoon) is 0.38‰ more negative than the SWM (summer monsoon), and the rainfall amount and temperature have a relatively weak influence on the stable isotopes in precipitation. Rainfall δ18O is poorly correlated with the monthly rainfall amount and temperature, with very low r2 values (–0.4‰/100 mm; 0.09 and 0.7‰/°C; 0.06, respectively). The seasonal variability of isotopic compositions of precipitation in this region is controlled by the insubstantial but conspicuous inverse correlation amount effect. The seasonal patterns of δ2H and δ18O are evident, with higher values observed during the NEM and lower values observed during SWM. However, the temporal variations of rainfall δ2H and δ18O during NEM and SWM are almost indistinguishable due to local climatic conditions, prevailing atmospheric circulations, and sources of precipitation. The apparent sign of El Niño and La Niña events (2015 and 2017, respectively) of the El Niño–Southern Oscillation (ENSO) cycle were successfully identified from this study, indicated by the heavier δ18O from the peaks in the annual precipitation amount versus δ18O annual precipitation weighted mean plot, while La Niña corresponds to the lighter values of the rainfall isotopes. [ABSTRACT FROM AUTHOR]

Long-term and short-term changes in ambient temperature can cause stress in birds, leading to changes in the level of hematological parameters. The H:L ratio (heterophil-to-lymphocyte ratio) is a hematological index that allows for the assessment of the stress induced by environmental changes, including weather conditions. In this paper, we examined the influence of temperatures and the sum of precipitation on the health of nestling pied flycatchers (Ficedula hypoleuca) by using the H:L ratio reflecting the body's response to stress. All examined temperature indicators influenced the H:L ratio, yet the average value of daily minimum temperature during the first 12 days of nestling life had the strongest influence, maximum temperature had the weakest effect, while precipitation had no significant influence. Our research indicates that even a small increase in temperature caused a stress reaction in nestling pied flycatchers, which was reflected by an increase in the H:L ratio. The increase in the stress index (H:L ratio) was probably a result of poor weather conditions (precipitation, low temperature), which prevented the adult birds from actively foraging and properly feeding the nestlings. [ABSTRACT FROM AUTHOR]

Calcium carbonate micro- and nanoparticles are considered as chemically inert materials. Therefore, they are widely considered in the field of biosensing, drug delivery, and as filler material in plastic, paper, paint, sealant, and adhesive industries. The unusual properties of calcium carbonate-based nanomaterials, such as biocompatibility, high surface-to-volume ratio, robust nature, easy synthesis, and surface functionalization, and ability to exist in a variety of morphologies and polymorphs, make them an ideal candidate for both industrial and biomedical applications. Significant research efforts have been devoted for developing novel synthesis methods of calcium carbonate particles in micrometer and nanometer dimensions. This review highlights different approaches of the synthesis of calcium carbonate micro- and nanoparticles, such as precipitation, slow carbonation, emulsion, polymer-mediated method, including in-situ polymerization, mechano-chemical, microwave-assisted method, and biological methods. The applications of these versatile calcium carbonate micro- and nanoparticles in the biomedical field (such as in drug delivery, therapeutics, tissue engineering, antimicrobial activity, biosensing applications), in industries, and environmental sector has also been comprehensively covered. [ABSTRACT FROM AUTHOR]

Calcium carbonate micro- and nanoparticles are considered as chemically inert materials. Therefore, they are widely considered in the field of biosensing, drug delivery, and as filler material in plastic, paper, paint, sealant, and adhesive industries. The unusual properties of calcium carbonate-based nanomaterials, such as biocompatibility, high surface-to-volume ratio, robust nature, easy synthesis, and surface functionalization, and ability to exist in a variety of morphologies and polymorphs, make them an ideal candidate for both industrial and biomedical applications. Significant research efforts have been devoted for developing novel synthesis methods of calcium carbonate particles in micrometer and nanometer dimensions. This review highlights different approaches of the synthesis of calcium carbonate micro- and nanoparticles, such as precipitation, slow carbonation, emulsion, polymer-mediated method, including in-situ polymerization, mechano-chemical, microwave-assisted method, and biological methods. The applications of these versatile calcium carbonate micro- and nanoparticles in the biomedical field (such as in drug delivery, therapeutics, tissue engineering, antimicrobial activity, biosensing applications), in industries, and environmental sector has also been comprehensively covered. [ABSTRACT FROM AUTHOR]

This paper compares the synthesis of tricalcium phosphate (TCP) (β-Са3(РО4)2) and Са3–хNa2х(PO4)2 substituted calcium phosphates (x = 0.5 and 1: solid solution A and β-CaNaPO4 sodium rhenanite, respectively) via precipitation and by a sol–gel process in ethylene glycol (EG). We have determined the phase composition of the synthesized phosphate powders and studied their micromorphology. Slow addition of ammonium (di)hydrogen phosphate to calcium ethylene glycolate at temperatures above 110°C leads to precipitation of crystalline β-TCP (with an average particle size under 200 nm) from the solution. In the synthesis of sodium calcium substituted phosphates, solvolysis of the sodium phosphates prevents congruent precipitation and requires correction of solution composition. The powder precipitated from a solution of sodium ethylate and calcium ethylene glycolate in EG (molar ratio above 2) crystallizes at 800°C in the rhenanite structure (particle size under 500 nm). In sol–gel synthesis, triethyl phosphate (TEP) was used as a phosphate component. For gelation, we used TEP solvolysis in EG and the formation of ester polymers as a result of reaction between citric acid and EG (Pechini process). The gel thermolysis products obtained between 600 and 800°C contained considerable amounts of residual carbon. The particle size characteristics and sinterability of the calcium phosphate powders precipitated from solutions in EG allow them to be used for filling photosuspensions that are employed in the fabrication of osteoconductive bioceramics with the use of stereolithographic 3D printing. [ABSTRACT FROM AUTHOR]

This paper examines monthly, seasonal, and annual trends in temperature and precipitation time series in Osijek during the period between 1900 and 2018. Two new methods, innovative trend analysis (ITA) and successive average methodology (SAM), together with the classic Mann–Kendall (M–K) and Sen's slope methods, have been applied to determine potential trends in the variables at different time scales. Moreover, time series decomposition using locally estimated scatterplot smoothing (STL) was conducted to determine trend, seasonality, and the relationship between the components of each variable. Regarding the air temperature, ITA showed a monotonic positive trend at relatively low (T ≤ 10 °C) and high (T ≥ 13 °C) temperature ranges in all seasons, excluding spring. A positive trend was also found in the medium temperature range in this season, which agrees with the results of M–K test. The highest Sen's slope was obtained in January, with the second highest in April. According to the results acquired for the observed precipitation time series, it was discovered that Osijek has experienced a decreasing trend in spring precipitation. However, there is no trend in annual precipitation at a 5% significance level. Differing from the M–K results, the ITA shows a decreasing trend in both spring and autumn seasons. Summer precipitation increases with a significant change in the high precipitating levels (p ≥ 100 mm). Comparing successive pairs of partial trends in both historical temperature and precipitation, our results show that trends in peak and trough change-points are very close to each other, indicating a slight positive trend in temperature and a negative change in precipitation over the past century. [ABSTRACT FROM AUTHOR]