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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]

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]

Long instrumental records are fundamental to study climate changes, but their reliability and quality have to be checked before any statistical research. Moreover, when metadata are used to solve some problems, data interpretation may change and require further work to refine the series. A thorough revision of the three-century precipitation series in Padua (1713–2018) shows that the results of previous analyses were affected by serious biases. This paper clarifies key features concerning early instruments, exposure, relocation and observational protocols. The daily analysis pointed out a number of problems, bias, irregular sampling and underestimations that have passed unobserved so far. A comparison with the parallel series of Bologna and Venice made it possible to distinguish bias from the climate signal or to reconstruct missing data. The instrumental threshold was recognized to be fundamental to determine the frequency of precipitation, but less important with respect to its amount. This paper provides a methodological example to test the goodness of long instrumental series, in particular to identify problems related to metadata and observations, which is the preliminary step to perform a sound correction and obtain a reliable series. It also includes the set of original raw data, transformed into modern units. [ABSTRACT FROM AUTHOR]

Gridded data products, for example interpolated daily measurements of precipitation from weather stations, are commonly used as a convenient substitute for direct observations because these products provide a spatially and temporally continuous and complete source of data. However, when the goal is to characterize climatological features of extreme precipitation over a spatial domain (e.g., a map of return values) at the native spatial scales of these phenomena, then gridded products may lead to incorrect conclusions because daily precipitation is a fractal field and hence any smoothing technique will dampen local extremes. To address this issue, we create a new "probabilistic" gridded product specifically designed to characterize the climatological properties of extreme precipitation by applying spatial statistical analysis to daily measurements of precipitation from the Global Historical Climatology Network over the contiguous United States. The essence of our method is to first estimate the climatology of extreme precipitation based on station data and then use a data-driven statistical approach to interpolate these estimates to a fine grid. We argue that our method yields an improved characterization of the climatology within a grid cell because the probabilistic behavior of extreme precipitation is much better behaved (i.e., smoother) than daily weather. Furthermore, the spatial smoothing innate to our approach significantly increases the signal-to-noise ratio in the estimated extreme statistics relative to an analysis without smoothing. Finally, by deriving a data-driven approach for translating extreme statistics to a spatially complete grid, the methodology outlined in this paper resolves the issue of how to properly compare station data with output from earth system models. We conclude the paper by comparing our probabilistic gridded product with a standard extreme value analysis of the Livneh gridded daily precipitation product. Our new data product is freely available on the Harvard Dataverse (https://bit.ly/2CXdnuj). [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]

The spatial interpolation of meteorological data have important applications in ecological environment monitoring, such as soil erosion, ecological vulnerability evaluation. However, there are significant differences in the interpolation accuracy of climatic elements under different topographic and geomorphic conditions. Based on data of 810 meteorological stations across the country, five typical interpolation methods, namely ordinary Kriging method, inverse distance weight method, spline function method, natural neighborhood method and trend surface method, were utilized in this paper to analyze and compare the interpolation accuracy of five climate factors, namely temperature, precipitation, accumulated temperature(>10°),wind speed and sunshine hours, under different topographic and geomorphological conditions. The results showed that: (1) Ordinary kriging method of temperature had better applicability in plain, hill and medium-large undulating mountain areas for temperature while the inverse distance weight method and spline function method had higher interpolation accuracy in the platform area and the small undulating mountain area, respectively. (2) The optimal interpolation of the precipitation in plain, platform and medium-large undulating mountain areas was ordinary kriging method while the inverse distance weight method and spline function method had better applicability in small undulating mountain area. (3) For accumulated temperature (>10 °C), the spline function method had higher interpolation accuracy in plain and platform areas, while ordinary kriging method had better applicability in hilly, small and medium-large undulating mountain area al (4) The optimal spatial interpolation of wind speed in plain and hilly areas was the inverse distance weight method. The natural neighborhood method and the spline function method had the best applicability in plateau, medium-large undulating mountainous areas and small undulating mountain areas, respectively. (5) For sunshine hours, the optimal spatial interpolations in plain and hilly areas were natural neighborhood method and spline function method, respectively, while the ordinary kriging method and the inverse distance weighting method had better applicability in platform, large undulating mountainous areas and small undulating mountainous areas, respectively. [ABSTRACT FROM AUTHOR]

Meteorological ensemble members are a collection of scenarios for future weather issued by a meteorological center. Such ensembles nowadays form the main source of valuable information for probabilistic forecasting which aims at producing a predictive probability distribution of the quantity of interest instead of a single best guess point-wise estimate. Unfortunately, ensemble members cannot generally be considered as a sample from such a predictive probability distribution without a preliminary post-processing treatment to re-calibrate the ensemble. Two main families of post-processing methods, either competing such as the BMA or collaborative such as the EMOS, can be found in the literature. This paper proposes a mixed-effect model belonging to the collaborative family. The structure of the model is formally justified by Bruno de Finetti's representation theorem which shows how to construct operational statistical models of ensemble based on judgments of invariance under the relabeling of the members. Its interesting specificities are as follows: (1) exchangeability contributes to parsimony, with an interpretation of the latent pivot of the ensemble in terms of a statistical synthesis of the essential meteorological features of the ensemble members, (2) a multiensemble implementation is straightforward, allowing to take advantage of various information so as to increase the sharpness of the forecasting procedure. Focus is cast onto normal statistical structures, first with a direct application for temperatures, then with its very convenient Tobit extension for precipitation. Inference is performed by expectation maximization (EM) algorithms with both steps leading to explicit analytic expressions in the Gaussian temperature case, and recourse is made to stochastic conditional simulations in the zero-inflated precipitation case. After checking its good behavior on artificial data, the proposed post-processing technique is applied to temperature and precipitation ensemble forecasts produced for lead times from 1 to 9 days over five river basins managed by Hydro-Québec, which ranks among the world's largest electric companies. These ensemble forecasts, provided by three meteorological global forecast centers (Canadian, USA and European), were extracted from the THORPEX Interactive Grand Global Ensemble (TIGGE) database. The results indicate that post-processed ensembles are calibrated and generally sharper than the raw ensembles for the five watersheds under study. Supplementary materials accompanying this paper appear on-line. [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]

In this paper, we evaluated a dynamical downscaling produced for Central South Chile (32°S–38°S) relative to climatic conditions between 1980 and 2005. Assessing the skill of dynamical downscaling relative to the present climate is key to determine the degree of confidence on regional climatic projections. We used the Weather Research and Forecasting model to simulate that period at ~ 9 km grid-cell size, forced by the bias-corrected Community Earth System Model. Results indicated that the dynamical downscaling adequately reproduced spatio-temporal features of the climate within the region. Temperature showed a positive bias at the annual scale while the opposite occurred for precipitation. The bias varied when the comparison was performed relative to a gridded product or instrumental records from weather stations. At the monthly scale, the model failed to capture long-term trends relative to the gridded dataset while reproducing spatial patterns, especially for temperature. We found a generally statistically significant spatial clustering of the monthly mean bias that can support implementation and application of dynamical downscaling and bias-correction methods that account for the distinct climatic features of the study area. In particular, the strip 34°S–35°S presented features that are coincident with previous findings suggesting this latitude to be a boundary between different climate regimes north and south. According to our results, we assert that this dynamical downscaling is comparable with other available databases and thus can be utilized in future studies as an additional and independent source of analysis, contributing to a balanced appraisal of climate scenarios for policymaking within the region. [ABSTRACT FROM AUTHOR]

Changing climate and precipitation patterns make the estimation of precipitation, which exhibits two-dimensional and sometimes chaotic behavior, more challenging. In recent decades, numerous data-driven methods have been developed and applied to estimate precipitation; however, these methods suffer from the use of one-dimensional approaches, lack generality, require the use of neighboring stations and have low sensitivity. This paper aims to implement the first generally applicable, highly sensitive two-dimensional data-driven model of precipitation. This model, named frequency based imputation (FBI), relies on non-continuous monthly precipitation time series data. It requires no determination of input parameters and no data preprocessing, and it provides multiple estimations (from the most to the least probable) of each missing data unit utilizing the series itself. A total of 34,330 monthly total precipitation observations from 70 stations in 21 basins within Turkey were used to assess the success of the method by removing and estimating observation series in annual increments. Comparisons with the expectation maximization and multiple linear regression models illustrate that the FBI method is superior in its estimation of monthly precipitation. This paper also provides a link to the software code for the FBI method. [ABSTRACT FROM AUTHOR]

The paper discusses the multipurpose use of technogenic and natural raw aluminous materials for extracting valuable components, while solving environmental issues. The authors analyzed the process of granulating kaolin, obtained from the Ural region, which involves the addition of aqueous sulfuric acid solution and subsequent firing of the granules. Sulfuric-acid leaching of kaolin-containing raw materials at various acid concentrations and precipitate production times was considered. A certification of the obtained aluminum hydroxide powders was performed. [ABSTRACT FROM AUTHOR]

The Dawu water source is the most important urban water supply source in Zibo City, Shandong Province, China. In this paper, the karst water level, precipitation, exploitation and flood discharge data during 2012–2018 were used to study and explore the characteristics and factors that control and influence the dynamic evolution of the karst water level in the Dawu water source area. The results showed that the dynamic evolution of the karst water level from 2012 to 2018 can be divided into three stages: (1) from 2012 to 2013, the karst water levels were relatively stable and maintained at approximately 36 m. (2) However, the karst water levels decreased from 30.5 to 4.2 m with a decline rate of 6.58 m/a from 2014 to 2017, and the rapid decrease caused a change in the runoff of the karst water flow field. (3) The karst water flow was restored to the original mode of south to north flow with the increase in the karst water level in 2018. The results of the correlation analysis and hierarchical cluster analysis showed that flood discharge, precipitation and exploitation all influence karst water level dynamic changes in the Dawu water source area. Among them, the increase in exploitation was the main reason for the karst water level decline, the flood discharge from the Taihe reservoir is the most direct and effective recharge source. The results of the principal component analysis showed that the influencing intensity of the three factors was constantly changing with time. The main factor that influenced the karst water level dynamic changes in the Dawu water source area was flood discharge in 2012 and 2013, exploitation in 2015 and 2016 and precipitation in 2018. Although in 2014 and 2017, the main factors were the combination of precipitation, exploitation and flood discharge. [ABSTRACT FROM AUTHOR]

This paper continues the research started in [4] that investigates the thundercloud with hail observed over Saint Petersburg on July 22, 2017. The results of numerical simulation of the cumulonimbus cloud using a small-dimensional model are presented. The spatiotemporal variations in the updraft velocity, liquid water content, ice water content, volume charge density, electric field strength, and other parameters are studied. Two cases are simulated and intercompared: without and with the heterogeneous cloud droplet freezing. It is shown that the formation of precipitation including hail occurs more intensively in the second case. Special attention is paid to the simulation of cloud electric structure, which was performed in combination with the modeling of its dynamics and microstructure. It is demonstrated that the most powerful cloud electrification processes occur due to the interaction between cloud and precipitation particles (hailstone–ice crystal, melting hailstone–cloud droplet, raindrop–cloud droplet). The dynamics of the cloud electric structure at different life stages is modeled. [ABSTRACT FROM AUTHOR]

Xinjiang is located in the core China's 'Belt and Road' development, and northern Xinjiang is an important region for economic development. In recent years, due to the strong influence of global climate change and human disturbance, regional climate instability and ecological-economic-social system sensitivity have grown. In this paper, seasonal, interannual, interdecadal, spatial, abrupt, and periodic variations of temperature and precipitation in northern Xinjiang were analyzed using daily surface air temperature and precipitation data from 49 meteorological stations during 1961–2017. At the same time, the driving factors of climate change are discussed. Methods included linear regression, cumulative anomaly, the Mann-Kendall test, and Morlet wavelet analysis. The results indicated that during the study period, annual mean temperature and annual precipitation increased significantly at rates of 0.35°C/10 yr and 13.25 mm/10 yr, respectively, with abrupt changes occurring in 1994 and 1986. Annual mean temperature and annual precipitation in all four seasons showed increasing trends, with the maximum increases in winter of 0.42°C/10 yr and 3.95 mm/10 yr, respectively. The general climate in northern Xinjiang showed a trend towards increasingly warm and humid. In terms of spatial distribution, the temperature and precipitation in high mountainous areas increased the most, while basins areas increased only slightly. Periodic change analysis showed that annual mean temperature and annual precipitation experienced two climatic shifts from cold to warm and dry to wet, respectively. Population change, economic development and land use change are important factors affecting climate change, and more research should be done in this field. [ABSTRACT FROM AUTHOR]

The dynamic structure of climate is governed by changes in precipitation and temperature and can be studied by time series analysis of these factors. This paper describes investigation of time series and seasonal analysis of the monthly mean minimum and maximum temperatures and the precipitation for the Bhagirathi river basin situated in the state of Uttarakhand, India. The data used is from the year 1901–2000 (100 years). The seasonal ARIMA (SARIMA) model was used and forecasting was done for next 20 years (2001–2020). The auto-regressive (p) integrated (d) moving average (q) (ARIMA) model is based on Box Jenkins approach which forecasts the future trends by making the data stationary and removing the seasonality. It was found that the most appropriate model for time series analysis of precipitation data was SARIMA(0,1,1) (0,1,1)12 (with constant) and of temperature data was SARIMA(0,1,0) (0,1,1)12 (with constant). The model prediction results show that the forecast data fits well with the trend in the data. However, over-predictions are found in extreme rainfall events and temperature results. The information of pattern and trends can assist as a prediction tool for development of better water management practices in the area. [ABSTRACT FROM AUTHOR]

The paper studies the connection between the precipitation regime over the northern Baikal mountain region during the warm season (April–October) and atmospheric macrocirculation processes, the results are presented. Based on the data of ten weather stations, spatiotemporal heterogeneities in the precipitation regime are revealed for the period of 2000–2018. The long-term (since the middle of the 1960s) trends in total precipitation are evaluated. According to the analysis of HYSPLIT five-day backward trajectories (for days with precipitation), the main directions of moisture transport to the study area are identified, and the trajectories are typed for each month. Three groups of atmotpheric macroprocesses which induce precipttation are identified, and the contribution of each group to total precipitation is estimated. The spatial distinction between different parts of the study area in the precipitation regime and predominant moisture sources is found. [ABSTRACT FROM AUTHOR]

The paper analyzes long-term changes in the water inflow to Lake Baikal which have occurred since the early 20th century and have been transformed due to the global warming intensification since the 1970s. The differences in distribution parameters and in the values of the inflow of estimated probabilities are studied for the full series and for its two parts divided as of1971. The revealed increase in the wintertime inflow is caused by the cold period duration reduction and by the permafrost degradation resulting from the air temperature rise. Since 1996 inflow has decreased, which is associated not only with precipitation reduction but also with evaporation increase. The Selenga River, the largest tributary of Lake Baikal, demonstrates the greatest decrease in runoff among the Baikal rivers. [ABSTRACT FROM AUTHOR]

Hot stamping (HS) process is a new technology for fabricating high-strength aluminum alloy. In order to improve the formability of materials in the forming process, a new hot stamping process with pre-cooling (HSPC) is proposed in this paper. By adding a pre-cooling stage between the solid solution and the forming, the strain hardening exponent (SHE) could be increased due to the decreasing of the forming temperature, and the uniform deformation ability could be improved. In the HSPC of 7075 aluminum alloy, the blanks exhibited two pre-cooling modes: air cooling pre-cooling (ACPC) and cold iron pre-cooling (CIPC). The effects of pre-cooling rate and temperature on the microstructure and mechanical properties of formed parts were studied. The results showed that when the pre-cooling temperature was 300 °C by CIPC, the formed parts had the smallest thickness thinning and the best mechanical properties after aging. Compared with ACPC, CIPC had less precipitation during quenching, which ensured good subsequent aging strengthening effect. [ABSTRACT FROM AUTHOR]

There have been numerous statistical and dynamical downscaling model comparisons. However, differences in model skill can be distorted by inconsistencies in experimental set-up, inputs and output format. This paper harmonizes such factors when evaluating daily precipitation downscaled over the Iberian Peninsula by the Statistical DownScaling Model (SDSM) and two configurations of the dynamical Weather Research and Forecasting Model (WRF) (one with data assimilation (D) and one without (N)). The ERA-Interim reanalysis at 0. 75 ∘ resolution provides common inputs for spinning-up and driving the WRF model and calibrating SDSM. WRF runs and SDSM output were evaluated against ECA&D stations, TRMM, GPCP and EOBS gridded precipitation for 2010–2014 using the same suite of diagnostics. Differences between WRF and SDSM are comparable to observational uncertainty, but the relative skill of the downscaling techniques varies with diagnostic. The SDSM ensemble mean, WRF-D and ERAI have similar correlation scores ( r = 0.45 –0.7), but there were large variations amongst SDSM ensemble members ( r = 0.3 –0.6). The best Linear Error in Probability Space ( LEPS = 0.001 –0.007) and simulations of precipitation amount were achieved by individual members of the SDSM ensemble. However, the Brier Skill Score shows these members do not improve the prediction by ERA-Interim, whereas precipitation occurrence is reproduced best by WRF-D. Similar skill was achieved by SDSM when applied to station or gridded precipitation data. Given the greater computational demands of WRF compared with SDSM, clear statements of expected value-added are needed when applying the former to climate impacts and adaptation research. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to elucidate a potential use of the large samples of seasonal means that hindcasts provide for investigating different aspects of climate variability. This use of hindcasts complements their traditional uses in bias correction, real-time forecast calibration, and prediction skill assessment. For seasonal hindcast data from NCEP CFSv2 we show that a sample size 5208 for each target season is achievable. To demonstrate the utility of the proposed concept, we use this large sample dataset to illustrate how it could be used in documenting spatial variability in various moments of seasonal mean precipitation PDF over the US, and further, quantify nuances in the variations in precipitation PDF at different geographical locations with the amplitude of ENSO SSTs. It is our hope that analysis presented in this paper will accelerate utilization of seasonal hindcast datasets in furthering our understanding of different aspects of climate variability. With the advantage of the large sample size, we demonstrated that the precipitation PDF at the each grid of the CONUS can be represented by gamma distribution for a more concise and effective way to summarize precipitation variability. The availability of the large sample dataset also allowed us to analyze the statistical characteristic of the precipitation responses to the different amplitudes of ENSO SSTs. The results show that for strong warm events, enhancement in precipitation has larger amplitude than decrease in precipitation for cold events in the regions of Southern California and southeastern US. The variation of the precipitation signal over the other sub-regions including the southwestern US, mid-northwest, and mid-east shows more linear relationship with the ENSO SSTs. In response to anomalous ENSO SSTs, although the PDF of December-January-February seasonal mean precipitation anomaly is shifted from its climatological PDF, there is still a large overlap between precipitation PDFs for ENSO and its climatological counterpart. This uncertainty in seasonal mean outcomes of precipitation, therefore, limits the seasonal prediction skill. [ABSTRACT FROM AUTHOR]

This paper points out two procedures for verifying the statistical independence between the historical and actual data series. They are based respectively on the computation of an empirical distance correlation coefficient, and the use of Hamming distance between binary encoded phase signals corresponding to Log-Gabor filtered time-series. Both procedures are applied to the monthly precipitation series recorded during 480 successive months at 49 meteorological stations in Dobrogea region (Romania) and on 4 and 5-valued fuzzified versions of the initial data represented using linguistic labels. The results show that the crisp data and their fuzzified versions cannot support the hypothesis of history-based predictability from their history. Hence, the two statistical independence tests are robust with respect to the k-means fuzzification of data and cross-validate each other, being applicable to any long-term analysis of precipitation series, to any other signals in general, and to their fuzzified versions, as well. [ABSTRACT FROM AUTHOR]

Translating changes in land surface conditions and climate into changes in precipitation, runoff and flood frequencies over a range of catchment scales is a pressing challenge for hydrologic design and flood risk management today. In this paper, we describe a novel approach for modeling areal runoff frequency from heavy-to-extreme precipitation in large urban areas using a simple but general stochastic model for runoff at point scale and bi- and univariate parametric probability distributions for positive point precipitation and areal runoff, respectively. We apply the approach to the Dallas–Fort Worth area using a 22-year historical multisensor precipitation dataset from the National Weather Service to characterize how the different factors that specify the second-order statistics of precipitation, imperviousness and soil water holding capacity at point scale may shape areal runoff frequency, and to assess how changes in precipitation climatology and land surface conditions may change areal runoff frequency as a function of catchment scale and magnitude of precipitation. The results indicate that areal runoff frequency is impacted most significantly by changes in climatological mean and coefficient of variation of positive point precipitation, water holding capacity of soil, imperviousness, and spatial correlation scale of positive point precipitation given the probability of occurrence of heavy-to-extreme precipitation, and that a very small number of low-order statistics of point precipitation may describe areal runoff frequency given the conditional probability distribution models for point precipitation and areal runoff. The approach presented hence offers a parsimonious physically-based alternative to purely numerical approaches based on integrated modeling, or empirical approaches based solely on statistical modeling toward predictive modeling of areal precipitation and runoff frequencies under changing hydroclimatological conditions. [ABSTRACT FROM AUTHOR]