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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]

While political debates on climate change become increasingly heated, our houses and city infrastructure continue to suffer from an increasing trend of damages due to adverse atmospheric events, from heavier-than-usual rainfalls to heat waves, droughts, and floods. Adapting our homes and critical infrastructure to sustain the effects of climate dynamics requires novel data-driven interdisciplinary approaches for efficient risk mitigation. We develop a new systematic framework based on the machinery of statistical and machine learning tools to evaluate water-related home insurance risks and quantify uncertainty due to varying climate model projections. Furthermore, we introduce the concept of data depth to the analysis of weather and climate ensembles, which remains a novel territory for statistical depth methodology as well as the field of environmental risk and ensemble forecasting in general. We illustrate the new data-driven methodology for risk analysis in application to rainfall-related home insurance in the Canadian Prairies over 2002–2011. Supplementary materials accompanying this paper appear online. [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]

Karst spring water dynamic characteristics and its response to atmospheric precipitation are of great significance for water resources utilization under the background of climate change. This paper selects Longzici spring area, North China, as the study area. Based on a long series of spring water flow and precipitation data, the dynamic characteristics of spring flow were analyzed and the numerical simulation of the groundwater flow model was established. The results show that the groundwater kept the sustained decline over the past decades which is in a negative equilibrium state, with a storage variable of − 2.26 million m3/year. The sensitivity of spring flow to precipitation under different precipitation scenarios shows that the water level changes in the recharge and drainage areas are similar about (3–5 cm) and slightly larger than that in the runoff area(1.5 cm) when minimum rainfall (287.24 mm) happens. When the precipitation is at its maximum (867.66 mm), the water level change in the runoff area can reach 95 cm which is much larger than those in the recharge and discharge areas. The results indicate that Longzici karst spring has a relatively good regulation water resource capacity and the runoff area is more sensitive which plays an important role in response to climate change. [ABSTRACT FROM AUTHOR]

The Canadian Cordillera in the province of British Columbia witnesses numerous rock falls every year. Studies on the recorded rock fall data in this area show that rock fall hazard follows weather conditions, especially precipitation and freeze-thaw cycles. This relationship indicates that a weather-based approach can be implemented to estimate possible changes in the rock fall hazard due to climate change. In this paper, we used a statistical approach to quantify the relationship between monthly weather averages and rock fall frequencies for a section of a transportation corridor along the Fraser River in British Columbia, Canada. In this regard, von Mises distributions are used to find the best-fitted models to the monthly precipitation and freeze-thaw cycles, and proper relative weights are applied to the models in order to calibrate them to the rock fall monthly frequency. The calibrated model is used with input data from climatic predictions for 2041–2070 and 2071–2100 to see how rock fall distribution will be affected due to climate change in the future decades. Results show that between 9 and 19%, more rock fall is anticipated in future winters. Rock falls are expected to decrease in other months, especially in October, November, March, and April. This paper presents a method to predict changes in rock fall hazard seasonality due to climate change and illustrates the method with a case study along a section of the Canadian Cordillera. [ABSTRACT FROM AUTHOR]

Concurrent compound dry and hot events (CDHEs) amplified more damange on the ecosystems and human society than individual extremes. Under climate change, compound dry and hot events become more frequent on a global scale. This paper proposes a mathematical method to quantitatively attribute changes of CDHEs to changes of precipitation, change in temperature and change in the dependence between precipitation and temperature. The attribution is achieved by formulating the total differential equation of the return period of CDHEs among Meta-gaussian model. A case study of China is devised based on monthly precipitation and temperature data during the period from 1921 to 2020 for 80 major river basins. It is found that temperature is the main driving factor of increases in CDHEs for 49 major river basins in China, except for the upper and middle reaches of the Yangtze River. In West China, precipitation changes drove the increase in CDHEs in 18 river basins (23%), particularly in parts of North Xinjiang, Qinghai and Gansu. On the other hand, dependence between precipitation and temperature dominated changes of CDHEs in 13 river basins (16%) of China with other factors, including parts of South China, East China and Northwestern China. Furthermore, changes in both the mean and spread of precipitation and temperature can also contribute to changes in CDHEs. [ABSTRACT FROM AUTHOR]

The precipitation method is an efficient, economically feasible, and reproducible synthetic route to cathode materials for lithium-ion batteries with attractive performance characteristics, in particular, lithium iron phosphate (LiFePO4). This paper reviews the mechanisms of the key steps of the synthesis, namely, precipitation of iron phosphate FePO4 followed by its sintering with a lithium-containing raw material to give the LiFePO4 phase. The most probable interactions determining the kinetics of the precipitation process are considered using the data on the dissociation degree of the reacting components. The influence of the nature and concentrations of the commonly used sources of iron (FeSO4, FeCl3, Fe(NO3)3) and phosphorus (H3PO4, NH4H2PO4, (NH4)2HPO4), as well as the precipitation conditions (pH, temperature) on the precipitation efficiency of FePO4 is analyzed. The effect of the nature of the lithium-containing raw material (LiOH, Li2CO3, LiNO3) and the sintering (calcination) temperature on the morphology, phase composition, and electrochemical properties of the resulting LiFePO4 is discussed. The possibility is considered of obtaining spherical particles with high bulk density, which provides high specific and volumetric energy density of electrochemical cells. Based on the relationships established, optimal parameters for the synthesis of LiFePO4 with preliminary FePO4 precipitation step are proposed. [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]

Seasonal precipitation and temperature predictions with global climate models that are developed based on the ocean–atmosphere interactions, contribute to the water resources management and hazard mitigation. To date, multi-model ensemble seasonal climate prediction systems, such as North American multi-model ensemble (NMME), are an effective way to provide useful forecast information a few months ahead especially over regions with strong ocean–atmosphere coupling. Previous studies have evaluated the skill of NMME hindcasts worldwide, however, whether the NMME real-time forecasts perform as well as the hindcasts and how the decadal variability in ocean-atmospheric teleconnections affect the prediction skill remain unclear. In this paper, based on precipitation and temperature datasets from nine models of NMME, the evaluation of forecast skills during hindcast (1982–2010) and real-time forecast (2011–2020) periods is carried out in the Yangtze River basin over China. Results show that although selecting an appropriate time frame for the calculation of climatology can reduce errors of real-time prediction, but the real-time prediction skills are lower than hindcast skills in the Yangtze River basin, with anomaly correlation decreased by 14–51% (38–75%) and error increased by 30–31% (51–55%) for seasonal precipitation (temperature) predictions up to the sixth lead-season, and the skill decrease is larger at longer leads. The failure in representing the decadal variations of ocean-atmospheric teleconnection (especially the association with Indian Ocean surface temperature) during the real-time forecast period can partly explain the decline in the prediction skills. Our findings suggest that a better grasp of decadal variability is needed to improve the real-time climate predictions. [ABSTRACT FROM AUTHOR]

The spatial and temporal variability of the main climatic indicators on the territory of the Volga Federal District in the 20th–21st centuries is considered. A general increasing trend in air temperature is revealed, and a heterogeneous pattern of precipitation changes is shown. The temperature variations until the end of the 21st century are analyzed on the example of Kazan using 40 CMIP6 climate models for four anthropogenic scenarios. In the paper, there is an assessment of the dynamics of agroclimatic resources on the territory of the Volga Federal District: the duration of the growing season, the sum of positive temperatures, total precipitation, and photosynthetic radiation. A correlation has been found between the temperature fluctuations in the region and the atmospheric circulation indices (NAO, AO, SCAND, and EAWR). For the territory of Tatarstan, a degree of aridity and waterlogging is evaluated using the agroclimatic indices: the Budyko's dryness index, the Selyaninov's hydrothermal coefficient, the Sapozhnikova's moisture index. It is shown that there is a tendency toward an increase in the duration of the growing season, its heat supply and aridity in the region in summer. A statistical estimation of the dependence of spring wheat yield on the agroclimatic indices on the territory of the Republic of Tatarstan is given. [ABSTRACT FROM AUTHOR]

In the paper, the frequency and spatial distribution of atmospheric and hydrological droughts over the territory of Belarus in 1945–2020 is assessed. Aridity is evaluated using the standardized precipitation and streamflow indices (SPI and SSFI). Trends in monthly total precipitation are determined, the features of the streamflow formation during the open-channel period are revealed. The frequency of anticyclones that determines hydrometeorological conditions on the territory of Belarus is calculated. The statistically significant increase in the number of the days with anticyclonic weather over the country during the warm season is found. The increasing frequency of dry conditions in summer months against the background of a rise in the precipitation intensity is highlighted. A simultaneous increase in the frequency of very-low-flow periods on rivers and an increase in the height of warm rainfall floods in some river basins are revealed. [ABSTRACT FROM AUTHOR]

The overview of papers dealing with the analysis of current and future climate change on the territory of Russia and their impact on the crop productivity is presented. Using the reanalysis data for 1950–2020, trends in air temperature and precipitation are estimated for different regions of Russia. A correlation was found between changes in temperature and atmospheric circulation indices. [ABSTRACT FROM AUTHOR]

More than 85% of the Ethiopian population's livelihood depends on rain-fed agriculture. Therefore, accurate climate estimation is quite useful to improve food security. However, the poor spatial resolution of satellite open access Outgoing Long-wave Radiation (OLR) data and the complicated relationship with the temperature and precipitation over variable topography features, the climate estimation is below the satisfactory level. Hence, to address this problem, this paper aimed to model OLR based on moisture parameters related to the air temperature in Ethiopia. European Center for Medium-Range Weather Forecast (ECMWF) and satellite data from 1981 to 2020 has been used to develop a new OLR model. The atmospheric moisture budget equation was applied for the new OLR mode development. The results indicated that the seasonal variation moisture coefficient (α) for winter, spring, summer, and autumn is found 0.99, − 0.29, 0.91, and 0.98 m3w−1 which is satisfied the model requirement. During winter, spring, summer, and autumn, the average value of OLR are found 269.60, 246.10, 247.90, and 255.60 Wm−2 from satellite direct observation respectively; while from the developed model is found at 268.79, 244.01, 248.97 and 254.93 Wm−2 respectively. Based on error matrices the developed mode is well agreed with satellite direct observation at a specific point and it is helpful to estimate OLR in the required spatial resolution. Generally the increment of the local moisture which is decreased OLR and increases the air temperature. Hence, stakeholders must pay high attention to the new OLR model adaptations for flood prevention and drought monitoring in Ethiopia. [ABSTRACT FROM AUTHOR]

Anti-slide piles have been widely applied in many practical projects around the world. In this work, a novel upper-hollow drainage anti-slide pile (UDAP) is selected to evaluate its working performance and mechanism by means of a physical model test and a numerical simulation. The physical model test indicates that UDAPs exhibit an excellent drainage performance, and the earth pressure and the pore water pressure around them dramatically decrease. The catchment holes placed on the lateral and upslope sides of the UDAPs have the same effects, and their drainage capacity improves with depth. The numerical simulation results of the UDAP arranged in the Quchi landslide (China) indicates that the retaining effect of the UDAPs is significantly better than that of the conventional anti-slide piles (CAPs). The landslide thrust and deformation are reduced due to the decrease of groundwater table and the consequent pore water pressure reduction due to the drainage effect of the UDAPs. Furthermore, the deflection and internal force of the UDAPs are significantly lower than those of the CAPs. The retaining effect of the continuous and interval arrangement scheme of the UDAPs is almost identical. Furthermore, the value of the internal force and the law of variation of the UDAPs and CAPs are basically the same. Therefore, the results of this paper can provide an important basis for the future research, design, popularization and application of the UDAPs. [ABSTRACT FROM AUTHOR]

This paper presents results from high-resolution climate change simulations that permit convection and resolve mesoscale orography at 3-km grid spacing over Fenno-Scandinavia using the HARMONIE-Climate (HCLIM) model. Two global climate models (GCMs) have been dynamically down-scaled for the RCP4.5 and RCP8.5 emission scenarios and for both near and far future periods in the 21st century. The warmer and moister climate conditions simulated in the GCMs lead to changes in precipitation characteristics. Higher precipitation amounts are simulated in fall, winter and spring, while in summer, precipitation increases in northern Fenno-Scandinavia and decreases in the southern parts of the domain. Both daily and sub-daily intense precipitation over Fenno-Scandinavia become more frequent at the expense of low-intensity events, with most pronounced shifts in summer. In the Scandinavian mountains, pronounced changes occur in the snow climate with a shift in precipitation falling as snow to rain, reduced snow cover and less days with a significant snow depth. HCLIM at 3-km grid spacing exhibits systematically different change responses in several aspects, e.g. a smaller shift from snow to rain in the western part of the Scandinavian mountains and a more consistent decrease in the urban heat island effect by the end of the 21st century. Most importantly, the high-resolution HCLIM shows a significantly stronger increase in summer hourly precipitation extremes compared to HCLIM at the intermediate 12-km grid spacing. In addition, an analysis of the statistical significance of precipitation changes indicates that simulated time periods of at least a couple of decades is recommended to achieve statistically robust results, a matter of important concern when running such high-resolution climate model experiments. The results presented here emphasizes the importance of using "convection-permitting" models to produce reliable climate change information over the Fenno-Scandinavian region. [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]

Spatial prediction(SP) based on machine learning(ML) has been applied to soil water quality, air quality, marine environment, etc. However, there are still deficiencies in dealing with the problem of small samples. Normally, ML requires large amounts of training samples to prevent underfitting. And the data augmentation(DA) methods of mixup and synthetic minority over-sampling technique(SMOTE) ignore the similarity of geographic information. Therefore, this paper proposes a modified upsampling method and combines it with the random forest spatial interpolation(RFSI) to deal with the small sample problem in geographical space. The modified upsampling is mainly reflected in the following two aspects. Firstly, in the process of selecting the nearest points, it is to select points with similar geographic information in some aspects of the category after classification. Secondly, the selected difference is the difference of each category. In order to verify the effectiveness of the proposed method, we use daily precipitation data for January 2018 in Chongqing. The experimental results show that the combination of the modified upsampling method and RFSI effectively improves the accuracy of SP. [ABSTRACT FROM AUTHOR]

Research into weather circulation changes over the UK for future climate has mainly focused on changes in the Summer and Winter seasons, with less analysis on seasonality and the transition seasons. Using the 30 Met Office weather patterns we examine the influence of climate change on seasonality through atmospheric circulation using a number of climate models. Changes in seasonality are important as they can have large impacts on many sectors including agriculture, energy and tourism. This paper finds a noticeable increase in Autumn over the UK in the frequency of drier summer-type regimes and a decrease in stormy winter types that emerge as early as the 2020s. The change in circulation signal once isolated from the overall signal is responsible for a 4–12% decrease in Autumn mean rainfall on average for England by the end of this century (where the values in the range are dependent on the emissions scenario). This change is projected over English regions that are already experiencing water stress, and with predictions of drier summers over the UK in future, this could further increase drought risk. The change in circulation in Autumn also moderates the large increase in the number of large-scale extreme daily rainfall events over the same regions predicted due to climate change. While this future circulation change is replicated across all the climate models used, large differences remain in the strength of the signal between models. The climate models used replicate the frequency of the 30 weather patterns well for all seasons. [ABSTRACT FROM AUTHOR]

The examination and integration of numerical forecast products are essential for using and developing numerical forecasts and hydrological forecasts. In this paper, the control forecast products from 2010 to 2014 of four model data (China Meteorological Administration (CMA), the National Centers for Environmental Prediction (NCEP), the European Centre for Medium-Range Weather Forecasts (ECMWF), and the United Kingdom Meteorological Office (UKMO)) from The Interactive Grand Global Ensemble (TIGGE) data center were evaluated comprehensively. On this basis, a study of runoff forecasting based on multi-model (multiple regression (MR), random forest (RF), and convolutional neural network-gradient boosting decision tree (CNN-GBDT)) precipitation integration is carried out. The results show that the CMA model performs the worst, while the other models have their advantages and disadvantages in different evaluation indexes. Compared with the single-index optimal model, CMA model had a higher root-mean-square error (RMSE) of 18.4%, and a lower determination coefficient (R2) of 14.7%, respectively. The integration of multiple numerical forecast information is better than that of a single model, and CNN-GBDT method is superior to the multiple regression method and random forest method in improving the precision of rainfall forecast. Compared with the original model, the RMSE decreases by 13.1 ~ 27.9%, PO decreases to 0.538 at heavy rainfall, and the R2 increases by 4 ~ 15.2%, but the degree of improvement decreases gradually with the increase in rainfall order. The method of multi-model ensemble rainfall forecasting based on a machine learning model is feasible and can improve the accuracy of short-term rainfall forecasting. The runoff forecast based on multi-model precipitation integration has been improved, and NSE increases from 0.88 to 0.935, but there is still great uncertainty about flood peaks during the flood season. [ABSTRACT FROM AUTHOR]

High-frequency precipitation variance is calculated in 12 different free-running (non-data-assimilative) coupled high resolution atmosphere–ocean model simulations, an assimilative coupled atmosphere–ocean weather forecast model, and an assimilative reanalysis. The results are compared with results from satellite estimates of precipitation and rain gauge observations. An analysis of irregular sub-daily fluctuations, which was applied by Covey et al. (Geophys Res Lett 45:12514–12522, 2018. https://doi.org/10.1029/2018GL078926) to satellite products and low-resolution climate models, is applied here to rain gauges and higher-resolution models. In contrast to lower-resolution climate simulations, which Covey et al. (2018) found to be lacking with respect to variance in irregular sub-daily fluctuations, the highest-resolution simulations examined here display an irregular sub-daily fluctuation variance that lies closer to that found in satellite products. Most of the simulations used here cannot be analyzed via the Covey et al. (2018) technique, because they do not output precipitation at sub-daily intervals. Thus the remainder of the paper focuses on frequency power spectral density of precipitation and on cumulative distribution functions over time scales (2–100 days) that are still relatively "high-frequency" in the context of climate modeling. Refined atmospheric or oceanic model grid spacing is generally found to increase high-frequency precipitation variance in simulations, approaching the values derived from observations. Mesoscale-eddy-rich ocean simulations significantly increase precipitation variance only when the atmosphere grid spacing is sufficiently fine (< 0.5°). Despite the improvements noted above, all of the simulations examined here suffer from the "drizzle effect", in which precipitation is not temporally intermittent to the extent found in observations. [ABSTRACT FROM AUTHOR]

Global warming and climate change impacts intensify hydrological cycle and consequently unprecedented drought and flood appear in different parts of the world. Meteorological drought assessments are widely evaluated by the concept of standardized precipitation index (SPI), which provides drought classification. Its application is based on the probabilistic standardization procedure, but in the literature, there is a confusion with the statistical standardization procedure. This paper provides distinctive differences between the two approaches and provides the application of a better method. As a novel approach, SPI classification is coupled with fuzzy clustering procedure, which provides drought evaluation procedure based on two variables jointly, precipitation and temperature, which is referred to as the standard precipitation-temperature index (SPTI). The final product is in the form of fuzzy c-means clustering in five clusters with exposition of annual drought membership degrees (MDs) for each cluster and resulting objective function. The application of the proposed fuzzy methodology is presented for the long-term annual precipitation and temperature records from New Jersey Statewide records. [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]

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]

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]

Modern methods and technologies for operational automatic classification of cloud cover and precipitation parameters using visible and infrared data from satellite multi-channel scanning radiometers are considered. The paper analyzes threshold classification techniques and algorithms based on machine learning and artificial neural networks and the results of validation of satellite products obtained from the comparison with ground-based meteorological observations and independent satellite estimates. Problems and prospects of further development and application of cloud clover and precipitation monitoring are formulated. [ABSTRACT FROM AUTHOR]

Accurate gridded precipitation data with high spatial and temporal scales are required for diverse studies such as climatology, meteorology, and hydrology. Currently, one of the sources of global precipitation estimation is the satellite-based precipitation estimate products. Nonetheless, their spatial resolution is often too coarse for usage in local region and basin scales or for parameterizing of meteorological and hydrological models at regional scales. In the present paper, a reconstruction method of satellite-based monthly precipitation was developed to attain improved pixel-based precipitation data with high spatial resolution on Golestan province in Northern Iran. In this endeavor, we considered the spatially heterogeneous relationships between tropical rainfall measuring mission (TRMM) precipitation and environmental variables utilizing the moving-window regression methods, the geographically weighted regression (GWR) and the mixed geographically weighted regression (MGWR) models. By in situ observations from rain gauges in the study area, the calibration and validation were performed, and the following conclusions were derived: (1) the proposed procedure had the ability to enhance both the spatial resolution and accuracy of satellite-based precipitation estimates; (2) the monthly reconstructed precipitation using the GWR model (CC = 0.69, bias = 0.75) and using the MGWR model (CC = 0.72, bias = 0.64) outperformed the TRMM-3B43V7 data (CC = 0.58, bias = 0.84) against ground observations; (3) this research offered a potential solution for producing gridded precipitation estimates at high spatial resolution. [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]

In this research paper, the effect of the addition of copper (0.02 to 3.32 wt.%) on microstructure, mechanical and thermal properties of Al-Zn-Mg alloy are studied using an optical microscope, scanning electron microscope (SEM), X-ray diffractometer (XRD), Vicker’s microhardness tester, universal testing machine (UTM) and differential scanning calorimeter (DSC). Precipitates (η-MgZn2 phases) are present in as-cast Al-Zn-Mg alloy. SEM and XRD analyses have shown the presence of η (MgZn2) and θ (Al2Cu) precipitate in the as-cast Al-Zn-Mg-Cu alloy. Micro-Vickers hardness increases from 121.3 HV1 to 153.1 HV1 for the initial increase in copper content (0.02 to 1.54 wt.%) and then decreases to 137.3 HV1 (3.32 wt.% copper). The melting point decreases continuously with increased copper content from 619.8 °C (0.02 wt.% Cu) to 608.5 °C (3.32 wt.% Cu). Peak compressive stress, absorbed energy per unit volume, yield stress is observed to increase by 43.6%, 75.4%, and 59.3%, respectively, for the rise in copper content (0.02 wt.% to 1.54 wt.%) in the alloy. [ABSTRACT FROM AUTHOR]

Southern Indian Ocean dipole (SIOD) is a dipole SST anomaly in the Southern Indian Ocean that plays an important role in tropical climate variability. However, previous studies focused on the interannual and interdecadal scales. In this paper, the characteristics on sub-seasonal scale of SIOD are revealed from December to February (DJF) based on the ERA-Interim reanalysis dataset, and the excitation mechanisms of SIOD are also discussed. The results show that the two dominant modes of SST in the Southern Indian Ocean are the spatial distribution of Southwest-Northeast direction dipole (SIOD) and triple (SIOT) respectively, which has obvious period of quasi 50–60 days. Moreover, the intensity and zonal oscillation of the Mascarene high are conducive to the formation of SIOD (SIOT). The air–sea interaction during the formation process is composed of three stages. In the first stage the atmospheric forces the ocean to result in the Mascarene high westward (eastward) and enhances the abnormal anticyclone in the Southern Indian Ocean. There are abnormal northerly (southerly) flows on the west (east) sides of the abnormal anticyclone respectively, which weakens (enhances) the southeast trade wind in the climatology. The surface latent heat flux release decreases (increases) and SST is warmed (cooled). During the following stage, the ocean feedback the atmosphere. The warm SST continues to increase, resulting in low-level convective enhancement, which weakens the abnormal anticyclone. The third stage is again the atmosphere forcing the ocean. The abnormal anticyclone gradually turns into an abnormal cyclone and the meridional wind direction is reversed. The release of the latent heat flux increases (decreases) significantly which leads to the cooling (warming) of SST on the west (east) sides of an abnormal cyclone. In addition, the formation and extinction of SIOT are easier affected by the southern annular mode (SAM) than SIOD. The abnormal zonal wave train with wavenumber 4 (3) propagates the Southern Indian Ocean by the westerly jet waveguide and results in an SST anomaly of SIOD (SIOT), accompanied by an obvious sub-seasonal meridional variation of the precipitation in Southern Africa. [ABSTRACT FROM AUTHOR]