Probability Of Precipitation Research Articles

Extreme rainfall sensitivity in convective‐scale ensemble modelling over Singapore

A convective‐scale ensemble system was developed to predict the occurrence of heavy convective rainfall around Singapore with a focus on the prediction of high‐impact events. The new ensemble SINGV‐EPS has been nested within two global ensembles, MOGREPS‐G (UK Met Office) and EC‐ENS (ECMWF). Predicting the occurrence of convective rainfall in an area such as Singapore is challenging and this article discusses the use of the convection‐permitting ensemble to characterize the uncertainties in the prediction of such localized heavy rainfall. First, verification of wind, temperature, and precipitation is performed for a month‐long period to assess the relative performance of each ensemble. This reveals differences, but no robust signal to say one is better than the other. The results are not statistically significant and not all variables are consistently better with one ensemble or the other. Secondly, the precipitation characteristics of SINGV‐EPS are analysed from probabilities of precipitation and variability among the ensemble members. SINGV‐EPS is sensitive to the choice of the global ensemble providing the initial conditions and boundaries. The results suggest there is benefit, in some cases, from combining the two ensembles. Thirdly, the spread of the ensemble precipitation is analysed using the dispersion Fractions Skill Score (dFSS). We compare the impact of the initial perturbations and the perturbations in lateral boundary conditions in both nesting options. The initial perturbations dominate in the beginning of the forecasts, with influence up to T+24 h, and are associated with an upscale growth of the uncertainties. The impact of the parent ensemble and lateral boundary conditions dominate at the end of the forecast and tend to influence larger scales more.

Prediction of summer precipitation change in the middle 21st century (2021-2050) of Qinghai-Tibet Plateau based on the MPI-ESM-MR/RegCM4 Model

The Regional Climate Model RegCM4.5 driven by the MPI-ESM-MR was used to predict the summer precipitation over the Qinghai-Tibet Plateau in the middle of the 21st century (2021-2050) under the RCP4.5 and RCP8.5 scenarios. Results showed that under the RCP4.5 and RCP8.5 scenarios, the average annual summer precipitation in the southeastern Qinghai-Tibet Plateau and the Qilian Mountains increased significantly, while the western Qinghai-Tibet Plateau decreased significantly, and the interannual variation trend of the summer precipitation was not significant. In the future, the summer precipitation over the Qinghai-Tibet Plateau has significant diurnal variation patterns and spatial differences between east and west regions. Specifically, summer precipitation decreases during daytime, while it increases slightly during night, especially in the eastern Qinghai-Tibet Plateau. At the same time, the increase in the probability of precipitation intensity above 10mm/day in the eastern of the Qinghai-Tibet Plateau means that the probability of extreme precipitation in the future may increase, while the precipitation intensity below 10mm/day is slightly reduced.

Precipitation Characteristics in the Community Atmosphere Model and Their Dependence on Model Physics and Resolution

AbstractPrecipitation amount (A), frequency (F), intensity (I), and duration (D) are important properties of precipitation, but their estimates are sensitive to data resolution. This study investigates this resolution dependence, and the influences of different model physics, by analyzing simulations by the Community Atmospheric Model (CAM) version 4 (CAM4) and version 5 (CAM5) with varying grid sizes from ~0.25 to 2.0°. Results show that both CAM4 and CAM5 greatly overestimate F and D but underestimate I at all resolutions, despite realistic A. These biases partly result from too much parameterized (convective) precipitation with high F and D but low I. Different cloud microphysics schemes contribute to the precipitation differences between CAM4 and CAM5. The A, F, I, and D of convective and nonconvective precipitation react differently to grid‐size decreases, leading to the large decreases in F and D but increases in the I for total precipitation as model resolution increases. This resolution dependence results from the increased probability of precipitation over a larger area (area aggregation effect, which is smaller than in observations) and the varying performance of model physics under changing resolution (model adjustment effect), which roughly enhances the aggregation‐induced dependence. Finer grid sizes not only increase resolved precipitation, which has higher intensity and thus improves overall precipitation intensity in CAM, but also reduce the area aggregation effect. Thus, the long‐standing drizzling problem in climate models may be mitigated by increasing model resolution and modifying model physics to suppress parameterized convective precipitation and enhance resolved nonconvective precipitation.

A Censored Shifted Mixture Distribution Mapping Method to Correct the Bias of Daily IMERG Satellite Precipitation Estimates

Satellite precipitation estimates (SPE) provide useful input for hydrological modeling. However, hydrological modeling is frequently hindered by large bias and errors in SPE, inducing the necessity for bias corrections. Traditional distribution mapping bias correction of daily precipitation commonly uses Bernoulli and gamma distributions to separately model the probability and intensities of precipitation and is insufficient towards extremes. This study developed an improved distribution mapping bias correction method, which established a censored shifted mixture distribution (CSMD) as a transfer function when mapping raw precipitation to the reference data. CSMD coupled the censored shifted statistical distribution to jointly model both the precipitation occurrence probability and intensity with a mixture of gamma and generalized Pareto distributions to enhance extreme-value modeling. The CSMD approach was applied to correct the up-to-date SPE of Integrated Multi-satelliE Retrievals for Global Precipitation Measurement (GPM) with near-real-time “Early” run (IMERG-E) over the Yangtze River basin. To verify the hydrological response of bias-corrected IMERG-E, the streamflow of the Wujiang River basin was simulated using Ge´nie Rural with 6 parameters (GR6J) and Coupled Routing Excess Storage (CREST) models. The results showed that the bias correction using both BerGam (traditional bias correction combining Bernoulli with gamma distributions) and the improved CSMD could reduce the systematic errors of IMERG-E. Furthermore, CSMD outperformed BerGam in correcting overall precipitation (with the median of mean absolute errors of 2.46 mm versus 2.81 mm for CSMD and BerGam respectively, and the median of modified Nash–Sutcliffe efficiency of 0.39 versus 0.29) and especially in extreme values for uniform format and particular attention paid to extremes. In addition, the hydrological effect that CSMD correction exerted on IMERG-E, driving GR6J and CREST rainfall-runoff modeling, outperformed that of the BerGam correction. This study provides a promising integrated distribution mapping framework to correct the biased daily SPE, contributing to more reliable hydrological forecasts by informing accurate precipitation forcing.

Stochastic Spectral Method for Radar-Based Probabilistic Precipitation Nowcasting

AbstractNowcasts (short-term forecasts) of heavy rainfall causing flash floods are highly valuable in densely populated urban areas. In the Collaborative Adaptive Sensing of the Atmosphere (CASA) project, a high-resolution X-band radar network was deployed in the Dallas–Fort Worth (DFW) metroplex. The Dynamic and Adaptive Radar Tracking of Storms (DARTS) method was developed as a part of the CASA nowcasting system. In this method, the advection field is determined in the spectral domain using the discrete Fourier transform. DARTS was recently extended to include a filtering scheme for suppressing small-scale precipitation features that have low predictability. Building on the earlier work, Stochastic DARTS (S-DARTS), a probabilistic extension of DARTS, is developed and tested using the CASA DFW radar network. In this method, the nowcasts are stochastically perturbed in order to simulate uncertainties. Two novel features are introduced in S-DARTS. First, the scale filtering and perturbation based on an autoregressive model are done in the spectral domain in order to achieve high computational efficiency. Second, this methodology is extended to modeling the temporal evolution of the advection field. The performance and forecast skill of S-DARTS are evaluated with different precipitation intensity thresholds and ensemble sizes. It is shown that S-DARTS can produce reliable probabilistic nowcasts in the CASA DFW domain with 250-m spatial resolution up to 45 min for lower precipitation intensities (below 2 mm h−1). For higher intensities (above 5 mm h−1), adequate skill can be obtained up to 15 min.

Associations between habitat quality, body size and reproductive fitness in the alpine endemic spider Vesubia jugorum

AbstractAimIn theory, the highest‐quality habitat across a species’ range should support individuals with the highest fitness, making it possible to evaluate the outputs of species distribution models (SDMs) by exploring the relationship between habitat quality and functional traits related to species’ fitness. However, this relationship has been tested almost exclusively in plants. We investigated the degree to which morphological and reproductive traits of an alpine spider varied along a gradient of habitat quality projected via SDMs.LocationSouth‐western Alps (France and Italy).Time period2007–2018.Major taxa studiedVesubia jugorum (Arachnida: Araneae: Lycosidae).MethodsWe used climatic, topographical and geomorphological variables at a resolution of 250 m to model habitat quality for V. jugorum, with multiple algorithms (generalized additive models, boosted regression trees and maximum entropy models). We collected spiders in the field and measured their body size and egg‐case size (a functional trait related to reproductive success). We tested the relationship between functional traits and habitat quality with linear mixed models.ResultsAll SDM algorithms we tested fit the data well, with similar explanatory ability. Models revealed a positive relationship between the probability of presence and cumulative precipitation and percentage of rock. An additional important variable in the SDMs was duration of snow coverage, with optimal projected habitat quality between 40 and 100 days/year with snow. Modelled habitat quality was positively associated with maximum body and egg‐case size.Main conclusionsOur work provides evidence of a positive association between projected habitat quality and traits of a terrestrial invertebrate. A model that fits the data well potentially can be used to predict variations in species’ traits, thus offering an experimental test of SDM projections with field‐collected data. We encourage SDM users to incorporate data on functional traits into their modelling fitting exercises.

Probabilistic Precipitation Forecasting over East Asia Using Bayesian Model Averaging

Abstract Bayesian model averaging (BMA) was applied to improve the prediction skill of 1–15-day, 24-h accumulated precipitation over East Asia based on the ensemble prediction system (EPS) outputs of ECMWF, NCEP, and UKMO from the TIGGE datasets. Standard BMA deterministic forecasts were accurate for light-precipitation events but with limited ability for moderate- and heavy-precipitation events. The categorized BMA model based on precipitation categories was proposed to improve the BMA capacity for moderate and heavy precipitation in this study. Results showed that the categorized BMA deterministic forecasts were superior to the standard one, especially for moderate and heavy precipitation. The categorized BMA also provided a better calibrated probability of precipitation and a sharper prediction probability density function than the standard one and the raw ensembles. Moreover, BMA forecasts based on multimodel EPSs outperformed those based on a single-model EPS for all lead times. Comparisons between the two BMA models, logistic regression, and raw ensemble forecasts for probabilistic precipitation forecasts illustrated that the categorized BMA method performed best. For 10–15-day extended-range probabilistic forecasts, the initial BMA performances were inferior to the climatology forecasts, while they became much better after preprocessing the initial data with the running mean method. With increasing running steps, the BMA model generally had better performance for light to moderate precipitation but had limited ability for heavy precipitation. In general, the categorized BMA methodology combined with the running mean method improved the prediction skill of 1–15-day, 24-h accumulated precipitation over East Asia.

최신 기상예측을 활용한 익일 24시간 전력수요예측 알고리즘

Along with the spread of renewable energy, hourly load is further affected by the hourly weather. In the past, the Korea Meteorological Administration provided only weather forecasts for daily maximum and minimum temperatures and daily average amount of cloud. Currently, Dong-Nae forecasts for weather provided by Korea Meteorological Administration are provided for hourly temperature, amount of cloud, precipitation, precipitation probability, direction of the wind and wind speed by region at 3-hour intervals 8 times per day. Accordingly 24-hours load forecasting algorithm using Dong-Nae forecast is proposed to improve the performance. In the proposed algorithm, the effect of temperature to load is reflected using the weekly load sensitivity to temperature per 3-hours, while day ahead load forecasting is performed using the exponential smoothing model. In addition the effect of small solar photovoltaic generation is considered in the proposed algorithm using daytime load sensitivity to amount of cloud per 3-hours. In the case study, the load forecast is performed for the day ahead except special days in 2017. The accuracy of the proposed algorithm was improved by 27.88%, 9.25%, and 9.29% for the overall average percentage error, on Monday, weekday, and weekend, respectively, in 2017 over the overall average percentage error of the algorithm of the exponential smoothing model that reflects the effects of maximum and minimum temperatures.

Exceedance probability of precipitation for the Shuhe to Futuan Water Transfer Project in China

Influenced by the uncertainties and differences of the variations of precipitation abundance, the water source area of a water transfer project is different from the water receiving area, which will lead to risks for the operation of the water transfer project. In this article, expressions for the exceedance probabilities of wet vs. dry conditions were constructed on the basis of the copula joint distribution function. The flood season, non-flood season, and annual precipitation data from the Jvxian and Rizhao stations of the Shuhe to Futuan Water Transfer Project (SFWTP) were used for the analysis of synchronous and asynchronous exceedance probabilities. The computed results demonstrate that synchronous exceedance probabilities were greater than asynchronous exceedance probabilities via the analysis of the flood season, non-flood season, and annual precipitation, and the favorable probability is largest during the flood season. In order to maximize the SFWTP benefit, it is essential to increase urban water supply in the non-flood season, and transport water in the flood season to Rizhao reservoir. To further analyze the impacts of climate change on exceedance probability, the precipitation data were divided into two sub-periods by Pettitt test. In last sub-period, favorable probabilities increased with the decrease of synchronous exceedance probability in all timescales, which suggests that there are positive impacts of climate change on the exceedance probabilities.

Extreme Rainfall Associated With Hurricane Maria Over Puerto Rico and Its Connections to Climate Variability and Change

AbstractHurricane Maria was associated with record‐breaking rainfall over Puerto Rico and caused unprecedented flooding and landslides. Here we analyze the extreme rainfall produced by Hurricane Maria using 35 stations with daily precipitation data from 1956–2016. A covariate‐based extreme value analysis point process approach that accounts for natural climate variability and long‐term climate change influences on extreme rainfall is applied. Hurricane Maria produced the single largest maximum rainfall event since 1956 and had the highest total averaged precipitation of 129 storms that have impacted the island since 1956. Return periods for an event of Hurricane Maria's precipitation magnitude decreased in 48.6% of stations across Puerto Rico and at least halved when averaged across the island. Within the most affected areas it is likely that the probability of precipitation of Maria's magnitude has increased by a factor greater than 1 (best estimate 4.85) as a result of long‐term climate trends.

Using climate change models to assess the probability of weather extremes events: a local scale study based on the generalized extreme value distribution

ABSTRACT Regional climate models (e.g. Eta) nested to global climate models (e.g. HadGEM2-ES and MIROC5) have been used to assess potential impacts of climate change at regional scales. This study used the generalized extreme value distribution (GEV) to evaluate the ability of two nested models (Eta-HadGEM2-ES and Eta-MIROC5) to assess the probability of daily extremes of air temperature and precipitation in the location of Campinas, state of Sao Paulo, Brazil. Within a control run (1961-2005), correction factors based on the GEV parameters have been proposed to approach the distributions generated from the models to those built from the weather station of Campinas. Both models were also used to estimate the probability of daily extremes of air temperature (maximum and minimum) and precipitation for the 2041-2070 period. Two concentration paths of greenhouse gases (RCP 4.5 and 8.5) have been considered. Although both models project changes to warmer conditions, the responses of Eta-Hadgem2-ES to both RCPs are significantly larger than that of Eta-Miroc5. While Eta-Hadgem2-ES suggests the location of Campinas will be free from agronomic frost events, Eta-Miroc5 indicates that air temperature values equal to or lower than 5 and 2 °C are expected to present a cumulative probabilityof ~0.20 and ~0.05, respectively (RCP 8.5). Moreover, while the Eta-Miroc5 projected a reduction in the extreme-precipitation amounts, the Eta-Hadgem2-ES projected implausible large daily precipitation amounts. The Eta-Miroc5 performed better than the Eta-Hadgem2-ES for assessing the probability of air temperature and precipitation in Campinas. This latter statement holds particularly true for daily-extreme precipitation data.

Use of Daily Station Observations to Produce High-Resolution Gridded Probabilistic Precipitation and Temperature Time Series for the Hawaiian Islands

AbstractIt is a major challenge to develop gridded precipitation and temperature estimates that adequately resolve the extreme spatial gradients present in the Hawaiian Islands. The challenge is particularly pronounced because the available station networks are irregularly spaced and sparse, creating large uncertainties in gridded spatial meteorological estimates. Here a 100-member, daily ensemble of precipitation and temperature estimates over the Hawaiian Islands for the period 1990–2014 at 1-km grid resolution is developed. First, an intermediary ensemble estimate of the monthly climatological precipitation and temperature is created, and those climatological surfaces are used to inform daily anomaly interpolation. This climatologically aided interpolation (CAI) method extends our initial ensemble system developed for the continental United States. This study demonstrates that direct interpolation of daily precipitation values is inferior to the CAI methodology, particularly over longer time periods (from years to decades). Daily interpolation performs better for short time periods (e.g., 1 month or less) or when the precipitation distribution substantially diverges from climatology. The CAI ensemble is able to reproduce observed precipitation and temperature patterns, including precipitation occurrence. Leave-one-out cross-validation results illustrate that the ensemble has 1) minimal bias for precipitation and temperature; 2) a mean absolute error of 2.5 mm day−1, 1.0 K, and 2.2 K for precipitation and mean and diurnal temperature, respectively; 3) a mean absolute error of 3.3 mm day−1 for the standard deviation of precipitation; and 4) nearly unbiased probability distributions across multiple thresholds of precipitation intensity. Additionally, the ensemble provides estimates of uncertainty across the distributions with increasing uncertainty for higher percentiles.

Changes in Future Drought with HadGEM2-AO Projections

The standardized precipitation index (SPI)—a meteorological drought index—uses various reference precipitation periods. Generally, drought projections using future climate change scenarios compare reference SPIs between baseline and future climates. Here, future drought was projected based on reference precipitation under the baseline climate to quantitatively compare changes in the frequency and severity of future drought. High-resolution climate change scenarios were produced using HadGEM2-AO General Circulation Model (GCM) scenarios for Korean weather stations. Baseline and future 3-month cumulative precipitation data were fitted to gamma distribution; results showed that precipitation of future climate is more than the precipitation of the baseline climate. When future precipitation was set as that of the baseline climate instead of the future climate, results indicated that drought intensity and frequency will decrease because the non-exceedance probability for the same precipitation is larger in the baseline climate than in future climate. However, due to increases in regional precipitation variability over time, some regions with opposite trends were also identified. Therefore, it is necessary to understand baseline and future climates in a region to better design resilience strategies and mechanisms that can help cope with future drought.

A Method to Contrast the Impact of Extreme Precipitation: A Case Study from Central Italy

Climate change, which is affecting all over the world, leads to different impacts on the environment and therefore on human life. One of the most important impact is the increasing of extreme precipitation. Increases in heavy precipitation may not always lead to an increase in total precipitation, over a season or over the year. Heavy precipitations increasing has also been documented even when mean total precipitation decreases. This may occur when the probability of precipitation (the number of events) decreases, or if the shape of the precipitation distribution changes, but this latter situation is less. Some climate models forecast a decrease in moderate rainfall, and an increase in the length of dry periods, which offsets the increased precipitation falling during heavy events. Climate impacts are compounded by urban development, which removes the vegetation and soil that slow and filter water, coming from rainfall. Urban sprawl also increases impervious surfaces, which move water over the land and put them, directly, into receiving lakes, rivers and estuaries. To contrast these negative impacts, a solution may be to limit the use of non-permeable surfaces, like concrete, in the urban areas. In this regard, the authors, as researchers of “Sapienza University of Rome”, are studying the possibility of using, in the parking areas of a central Italian city (Rieti), pavement with “green infrastructure”, that can reduce runoff and flood risks during storms. The construction of the pavement with “green infrastructure”, can first mitigate the effect of exceptional rains, secondly reduce the soil consumption. Permeable, or pervious, pavements reduce runoff by allowing rain and melting snow to infiltrate. A first study of the materials, that can be used as parking paving, has led to estimate a permeability coefficient of 2,70 ⋅10−5 m.s−1. In the case study, object of this paper, the authors have first calculated the area, in square meters, of the municipal parking areas of Rieti city. Secondly, they have valuated the possibility of change the concrete pavement, today present in this areas, with a “green infrastructure” pavement, and they have estimated volumes of water that will be infiltrate, in case of extreme precipitations. This estimation was made through the real precipitations data of the last 7 years, measured by the National Hydrographic Service of Italy.

Global NDVI Patterns in Response to Atmospheric Water Vapor Anomalies over the Indo-Pacific Warm Pool during April–June

Vertically integrated atmospheric water vapor (VIWV) over the Indo-Pacific warm pool (IPWP) indirectly affects terrestrial vegetation growth (TVG) patterns through atmospheric water vapor transmission. However, their linkages and mechanisms are poorly understood. This study intends to understand the contributions of VIWVIPWP to TVG and the mechanisms by which VIWVIPWP impacts TVG. Combining monthly SST, VIWV, and NDVI data from 1982 to 2015, the linkage between VIWVIPWP and NDVI is investigated during April–June (AMJ). A strong correlation between VIWVIPWP and NDVI suggests that VIWVIPWP is an important factor affecting TVG. A composite analysis of VIWVIPWP anomalies and their relation to NDVI patterns shows that VIWVIPWP positively influences the NDVI of 68.1% of global green land during high-VIWVIPWP years but negatively influences 74.7% in low years. Corresponding to these results, during high-VIWVIPWP years, the warm and humid terrestrial climate conditions improved TVG by 9% and 2% in the Northern and Southern Hemispheres, respectively, but cold and dry conditions inhibited TVG for both hemispheres during the low years. Additionally, strong spatial correlations between VIWVIPWP and precipitation imply that VIWVIPWP affects the spatial–temporal pattern of precipitation. There is a stronger interaction between the Pacific north–south ridge and the two land troughs during high-VIWVIPWP years than during low-VIWVIPWP years. The zonally averaged wind at 850 hPa and VIWV results indicate that, during high-VIWVIPWP years, the enhanced wind from the ocean brings more atmospheric water vapor to land, increasing the probability of precipitation and resulting in moist climate conditions that promote AMJ vegetation growth. In brief, VIWVIPWP indirectly induces vegetation growth by affecting the distributions of terrestrial VIWV and precipitation.

An evaluation of the consistency of extremes in gridded precipitation data sets

Noting a strong imperative to understand precipitation extremes, and that considerable uncertainty affects observational data sets, this paper compares the representation of extremes in a number of widely used daily gridded products, derived from rain gauge data, satellite retrieval and reanalysis for the conterminous United States. Analysis is based upon the concept of “tail dependence” arising in multivariate extreme value theory, and we infer the level of temporal dependence in the joint tail of the precipitation probability distribution for pairwise comparisons of products. In this way, we consider the range of products more like an ensemble and examine the relationships between members, and do not attempt to define, or compare products to, some ground truth. Linear correlation between products is also computed. Considerable discrepancy between groups of products, both annually and seasonally, is linked to source data and complex terrain. In particular, products based on rain gauge data showed remarkable similarity, but differed considerably, showing almost total loss of extremal dependence during DJF in mountainous regions, when compared with satellite products. Additionally, simulated re-forecasts revealed reasonable temporal agreement with large scale generated extremes. The diversity and extent of discrepancies identified across all products raises important questions about their use, and we urge caution, particularly for products derived from satellite data.

Evaluation of Satisfaction Survey of Decision-Making Meteorological Services in Shaanxi Province, China in 2017

In China, decision-making meteorological services provide meteorological information for the production organization, disaster prevention and mitigation by the CPC committee, government, military leaders and decision-making departments at all levels, as well as scientific decision-making in the areas of rational development and utilization of climate resources and environmental protection. In order to understand the user’s satisfaction with the decision-making meteorological service, the Shaanxi Provincial Meteorological Bureau conducted a statistical survey, and the results showed that: 1) In 2017, the satisfaction level of provincial-level decision-making meteorological services in Shaanxi, China was 92.77%. Among them, the satisfaction index of “Ministry Department Service Personnel Professional Image and Service Awareness” was 94.12%, and the “Weather Forecast Warning Accuracy” satisfaction index was 90.18%. 2) Decision-making users have become an important channel for obtaining meteorological information through meteorological websites, televisions, mobile phone text messages, APP mobile applications, broadcasting, and Meteorological Information Express. Rainstorms, floods, high temperature heat, cold winds, hail, precipitation, and lightning are still the main concerns of decision-makers. 3) The focus on haze and UV intensity is 15% and 8% higher than that of 2016. The next 1 - 3 days weather forecast and 0 - 6 hours short-term forecast are still the most valuable forecast products for decision-making users. Compared with 2016, the next 1 - 3 days weather forecast, future 0 - 6 hours forecast, traffic meteorology, precipitation probability, and air quality forecast increase by 1% to 14% in the year of 2017.

Dynamics of monthly amount of rainfall in the territory of Kharkiv region

The purpose of this article is the spatio-temporal analysis of the monthly rainfall in the territory of Kharkiv region during the year. To determine the dynamics of the territory humidity y, the time series of the monthly amount of precipitation at ten meteorological stations of the oblast were used for two periods: 1961-1990 and 1961-2016. Using statistical methods, features of the monthly precipitation amount distribution for twelve months were determined. The main material. Modern climatic changes, expressed in high air temperatures, are crucial for the formation of moisture within the country or region. Humidification regime is formed under the influence of both large-scale atmospheric processes and physical and geographical factors of the investigated area: landscape, presence of water objects, vegetation cover. The value of the monthly rainfall in winter indicates that in the decade of 1961-1970 it was characterized by an increase of up to 13 mm at all stations in the region. During the next decades arid conditions were observed at the rest of meteorological stations. Significant decrease in the amount of precipitation was observed in the period between 1991-2000at the station Velykyi Burluk (13 mm). In the spring period, there is a general trend towards a slight increase in the amount of precipitation. The value of monthly rainfall in the summer and autumn has a tendency to growth. The analysis of the calculated average parameters and the probability of atmospheric precipitation have showed that changes in the amount of precipitation in the region are characterized by high temporal variability. The change in rainfall is volatile in 2-3 years. Conclusions and further research. By comparing the data for two periods (1961-1990, 1961-2016), the peculiarities of changes in the amount of precipitation during the second half of the last century and the beginning of this century were revealed in the region. It has been established that the precipitation is characterized by high variability over the territory, which is reflected in the humidification regime. In general, there is a tendency to a decrease in the amount of precipitation in November-March periods. The spatial-temporal changes in the amount of atmospheric precipitation in the territory of Kharkiv region by seasons have been revealed: their changes at stations have the greatest amplitudes in winter; maximum values are fixed in the summer. The unstable moisture regime remains in the spring and autumn.

A mixed application of an objective synoptic classification and spatial regression models for deriving winter precipitation regimes in the Eastern Pyrenees

Management of hydric resources in alpine mountains requires spatial knowledge of precipitation as a variable of noteworthy importance in the study of hydrological hazards (avalanches, landslides, floods, etc.), especially during the winter season. We therefore study the spatial distribution of mean daily precipitation (MDP) and daily precipitation probability (DPP) in the Eastern Pyrenees, based on a previous objective synoptic classification defining the most frequent atmospheric patterns during the winter season (November–May) between 1990 and 2015. The synoptic classification provided 12 circulation weather types. For each of these, we obtained MDP maps and DPP maps possessing a threshold equal to, or greater than, 2, 5, 10, 20 and 50 mm. The best fit of the models was obtained in the DPP ≥ 2 mm, with an adjusted R2 of around 0.8, followed by the models showing MDP and DPP ≥ 5 mm, with an adjusted R2 generally between 0.7 and 0.8. Finally, we performed an unsupervised classification of the 12 MDP models in order to obtain a categorical and simplified cartography explaining the winter precipitation regimes in the Eastern Pyrenees.

Mixing of Crude Oil with Organic ZnO Nano-Particles from Rice Bran to Improve Physical Properties of Crude Oil: A Novel Agent for Enhanced Oil Recovery

In this study, ZnO nanoparticles were modified with rice bran. Synthesis and production of ZnO nano-particles is highly important due to the use of rice bran. In addition, XRD and SEM analyses were used to ensure the production quality of the nano-particles. The images clearly showed surface uniformity of the synthesized organic ZnO nano-particles. Afterward, the modified nano-particles were injected into crude oil in different weight percentages according to their properties (heavy and light crude oil). The injection was done at a temperature range of 30–150 °C with operating pressures varying from 10 bar to 300 bar. The adhesion force created between heavy or light crude oil molecules and organic ZnO nano-particles was modified with rice bran. Furthermore, an increase in the operating temperature increased the thermal conductivity of oil samples from 0.21 to 2.54 W/m °C for the light crude oil sample and 1.56–6.3 W/m °C for the heavy crude oil sample. The results showed that the percentage of the asphaltene precipitation decreased with the increased API of the crude oil. In addition, the percentage of asphaltene precipitation for nano-light and heavy crude oil was considerably better than the simple light and heavy crude oil samples, respectively. The nano-particles improved the crude oil recovery from reservoirs. The results indicate that the probability of asphaltene precipitation in the case of light crude oil nano-particles is less than the simple light crude oil by 28.3%. This is 8.1% for the heavy crude oil compared to the simple heavy crude oil.