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This paper describes the structural and photocatalytic studies of TiO 2 nanoparticles prepared by two different precipitation methods using titanium tetrachloride (TiCl 4) and titanium oxalate [Ti(C 2 O 4) 2 as raw materials for the first time. The samples were characterized by TG-DSC, XRD, TEM, EDS, SAED, UV–Vis, Raman, N 2 adsorption-desorption isotherms, and XPS to investigate the effects of salt type and calcination temperature on the crystal structure, morphology, composition, energy band gap and photocatalytic activity of TiO 2 nanoparticles. Results indicated that when TiCl 4 was used as Ti source, the crystallization temperature of amorphous TiO 2 to anatase TiO 2 was as low as 250 °C, but it increased to ~450 °C when Ti(C 2 O 4) 2 was selected. Besides, TiCl 4 was able to dynamically modify the crystalline structure of TiO 2 nanoparticles from anatase into rutile after calcination at ~450 °C, however this temperature was increased to ~850 °C for Ti(C 2 O 4) 2. The optimal photocatalytic property of methyl orange (MO) degradation in two TiO 2 systems was 99.08% and 38.6% with corresponding calcination temperatures of 450 °C and 550 °C, respectively, which was mainly correlated with the different crystal structure and crystallite size. [ABSTRACT FROM AUTHOR]

• Mixing effects on barium sulfate precipitation test process course were investigated. • Comparison of several literature barium sulfate precipitation kinetics is presented. • Two reactor types of three different sizes each are considered. • Mixing in jet reactors was generally found to be faster than precipitation. • Both large eddy simulations and k-ε model can be used for precipitation modeling in jet reactors. The paper presents an application of large eddy simulations (LES) to predict the course of barium sulfate precipitation carried out in jet reactors and basic guidelines for a reactor design. The reactors in question were of different geometries and made in different sizes in order to achieve high mixing intensities and low residence times in a mixing chamber, thus enabling to reflect and understand the effects of process conditions and influence of mixing on the course of precipitation better. The system's behavior is explained using experimental and simulation results. Simulations were validated by comparing LES model predictions with experimental data, as well as the k-ε model supplemented with the multiple-time-scale mixing model. The BaSO 4 precipitation results obtained with a CFD based precipitation model are in a very good agreement with experiments and are used to distinguish the jet reactors that allow obtaining a product with favorable characteristics, i.e., the smallest mean particle size. In general, the smallest studied reactors are found to best meet this criterion. A special attention is given to precipitation kinetics models, the choice of which can significantly affect predictions' accuracy. Several literature precipitation kinetics models were tested and the ones working best in jet reactors were identified. Finally, a time-scale analysis of process controlling mechanisms is highlighted and the assumption of negligibility of the subgrid closure for precipitation in LES is validated. [ABSTRACT FROM AUTHOR]

The use of radar measurements for the space time estimation of precipitation has for many decades been a central topic in hydro-meteorology. In this paper we are interested specifically in daily and sub-daily extreme values of precipitation at gauged or ungauged locations which are important for design. The purpose of the paper is to develop a methodology to combine daily precipitation observations and radar measurements to estimate sub-daily extremes at point locations. Radar data corrected using precipitation-reflectivity relationships lead to biased estimations of extremes. Different possibilities of correcting systematic errors using the daily observations are investigated. Observed gauged daily amounts are interpolated to unsampled points and subsequently disaggregated using the sub-daily values obtained by the radar. Different corrections based on the spatial variability and the subdaily entropy of scaled rainfall distributions are used to provide unbiased corrections of short duration extremes. Additionally a statistical procedure not based on a matching day by day correction is tested. In this last procedure as we are only interested in rare extremes, low to medium values of rainfall depth were neglected leaving a small number of L days of ranked daily maxima in each set per year, whose sum typically comprises about 50% of each annual rainfall total. The sum of these L day maxima is first iterpolated using a Kriging procedure. Subsequently this sum is disaggregated to daily values using a nearest neighbour procedure. The daily sums are then disaggregated by using the relative values of the biggest L radar based days. Of course, the timings of radar and gauge maxima can be different, so the method presented here uses radar for disaggregating daily gauge totals down to 15 min intervals in order to extract the maxima of sub-hourly through to daily rainfall. The methodologies were tested in South Africa, where an S-band radar operated relatively continuously at Bethlehem from 1998 to 2003, whose scan at 1.5 km above ground [CAPPI] overlapped a dense (10 km spacing) set of 45 pluviometers recording in the same 6-year period. This valuable set of data was obtained from each of 37 selected radar pixels [1 km square in plan] which contained a pluviometer not masked out by the radar foot-print. The pluviometer data were also aggregated to daily totals, for the same purpose. The extremes obtained using disaggregation methods were compared to the observed extremes in a cross validation procedure. The unusual and novel goal was not to obtain the reproduction of the precipitation matching in space and time, but to obtain frequency distributions of the point extremes, which we found to be stable. [ABSTRACT FROM AUTHOR]

precipitation and evaporation are two vital components of water cycle, and Hydrogen and oxygen stable isotopes are important tracers in water, which are very sensitive to changes in the climate and the water cycle. Rayleigh Fractionation equation is usually applied to calculate isotope distributions in order to quantify the precipitation and evaporation processes. It is reasonable to explain the evaporation process using the Rayleigh fractionation equation. However, the composition of hydrogen and oxygen stable isotopes in the precipitation will change due to the effects of dynamic fractionation and dilution during raindrop fall. In this case, it is not feasible to rely entirely on the Rayleigh fractionation equation. In this paper, atmospheric precipitation isotopes data recorded during 1996–2016 in Hong Kong from the Global Network of Isotopes in Precipitation (GNIP) are analyzed, including local meteoric water line (LMWL), deuterium-excess (d), temperature effect and rainfall amount effect. The results indicate that precipitation is affected by secondary evaporation and dilution effects. In addition, the stable isotope composition in precipitation is the result of temperature and precipitation. Due to the effects of these two effects, changes of isotope composition in precipitation are not always consistent. When the rainfall density is very large, the temperature effect is covered up and the temperature is inversely related to the isotope composition of precipitation. In contrast, when the rainfall density is small, the temperature is positively related to the isotope composition of precipitation. Therefore, it is suggested to consider the rainfall density while studying the relationship between the isotope composition of precipitation and the local climate. These finds can provide some enlightenments and references on how to investigate the changing pattern of isotope composition in precipitation. [ABSTRACT FROM AUTHOR]

Abstract Precipitation complexity measurement is often overlooked in precipitation time series research. Entropy, as a measure of system complexity, can be used to diagnose the complexity of precipitation. However, it is difficult to judge the applicability of different theoretical entropy models for solving precipitation uncertainty problems. This paper introduces the distinction degree theory and the serial number sum theory to screen the optimal entropy model for precipitation complexity measurement. The optimal entropy model was used to analyze the spatiotemporal differences of monthly precipitation complexity in Heilongjiang Province, China. Possible influencing factors of precipitation complexity were also examined. The results indicated that in the complexity measurement of precipitation based on entropy theory, the stability and reliability of sample entropy was higher than those of fuzzy entropy, wavelet entropy and permutation entropy. The complexity of monthly precipitation in the selected study area significantly increased with time. The average complexity of monthly maximum precipitation, monthly average precipitation and monthly minimum precipitation were 0.665, 0.622 and 0.545, respectively, and their tendency change rates were 0.070/decade, 0.055/decade and 0.038/decade, respectively. The areas with high monthly precipitation complexity were concentrated in the central, eastern and northwest parts of the study area, and the precipitation was less predictable. Monthly precipitation in the southwest was less complex and more predictable. The highest monthly precipitation complexity was 1.012, at Hulin station, and the lowest was 0.510, at Mingshui station. The increasing complexity of monthly precipitation in the province was strongly related to local industrial and agricultural production. The superposition effects of altitude, topographic relief, change in grassland area and agricultural production formed the basic pattern of spatial differences in monthly precipitation complexity. The results may provide a scientific guidance for regional precipitation predictability measurement, effective assessment of droughts and floods, and water resources management. Highlights • The optimal entropy model was selected to diagnose complexity in precipitation time series. • Complex monthly precipitation was concentrated in the central eastern and northwest parts of the study area. • Monthly precipitation complexity had a strong relationship with local industrial and agricultural production. • This study may provide a scientific guidance for regional water resources management. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we analyzed the effect of SST on the precipitation of coastal areas of the eastern China. The relationship between the precipitation and SST of different seasons based on CMDC's monthly precipitation data and NOAA's SST data from 1979 to 2016 was calculated. Analysis on the links between precipitation and SST has shown a significant correlation in winter and spring, with three positive strong oceanic signals and two negatively correlated sea areas. Meanwhile, the precipitation and SST of highly correlated regions have increased during the past 38 years. Considering the seasonal variation, precipitation presents upward trend in summer and winter with appreciable seasonal variations. For one of the highly related areas, such as the East China Sea (ECS) and the north of Philippine Sea (PS), the SST there has been increased in all four seasons. Spectral analysis also shows that the precipitation and the MEI have the same cycles about 3–5 years. Moreover, lead-lag analysis between precipitation time-series and three climate indexes (Niño 1 + 2 SST index, Niño 3 SST index and SIOD SST index) were applied. The results show that when the SSTs lead 2 to 4 months, they are highly related to the precipitation in the coastal areas of the eastern China. Highlights • Precipitation and SST concentrations in the study area show a significant correlation in winter and spring; • Precipitation and SST experienced different rises from 1979–2016. Precipitation presents an upward trend in summer and winter; • In SST correlated areas, they all had been increasing during the period of 1979–2016 about 1.1 °C on average; • Precipitation and three index (Niño 1 + 2 SST index, Niño 3 SST index, and SIOD SST index) indicated lead-lag relationship; • When Niño 1 + 2 SST index leads precipitation by three months, the two variables are significant positive correlated. [ABSTRACT FROM AUTHOR]

Highlights • Anomaly correlation exhibits persistent patterns at the global scale. • Anomaly correlation tends to be similarly high, or low, across initialization times. • Anomaly correlation tends to improve with initialization time. Abstract Global climate models (GCMs) produce informative seasonal forecasts of global precipitation months ahead of the occurrence for hydrological forecasting. Meanwhile, the skill of GCM forecasts varies by location and initialization time. In this paper, we investigate the anomaly correlation, which indicates the correspondence between forecasts and observations, for 10 sets of global precipitation forecasts in the North American Multi-Model Ensemble (NMME) project. We propose to use principal component analysis to characterize the variation of anomaly correlation. We identify the existence of spatial and temporal patterns at the global scale. The spatial pattern reveals that high (low) anomaly correlation at one initialization time coincides with high (low) anomaly correlation at other initialization times. In other words, for a grid cell, the anomaly correlation at different initialization times tends to be similarly high, or low. It is observed that some of the regions where grid cells are with overall high anomaly correlation tend to exhibit tele-connections with global climate drivers. On the other hand, the temporal pattern suggests that the anomaly correlation tends to improve with initialization time. This pattern is attributable to data assimilation that bases forecasts at a later initialization time on more global observations and simulations. Generally, the two patterns are effective and explain 50% to 70% of the variation of anomaly correlation for the 10 sets of NMME forecasts. The projections of anomaly correlation vectors onto the two patterns help illustrate where and when the NMME precipitation forecasts are skillful. [ABSTRACT FROM AUTHOR]

Abstract The paper summarizes an end-to-end activity connecting the global climate modeling enterprise with users of climate information in Alaska. The effort included retrieval of the requisite observational datasets and model output, a model evaluation and selection procedure, the actual downscaling by the delta method with its inherent bias-adjustment, and the provision of products to a range of users through visualization software that empowers users to explore the downscaled output and its sensitivities. An additional software tool enables users to examine skill metrics and relative rankings of 21 global models for Alaska and six other domains in the Northern Hemisphere. The downscaled temperatures and precipitation are made available as calendar-month decadal means under three different greenhouse forcing scenarios through 2100 for more than 4000 communities in Alaska and western Canada. The visualization package displays the uncertainties inherent in the multi-model ensemble projections. These uncertainties are often larger than the projected changes. Highlights • Downscaled climate data for more than 4000 communities are viewable by users. • A software tool allows users to evaluate skill 22 climate models for eight regions. • Across-model uncertainties often exceed changes of temperature and precipitation. • Projected warming and wetting are largest in winter and summer, respectively. • Emission scenario is a key determinant of future warming of Arctic communities. [ABSTRACT FROM AUTHOR]

This paper investigates the annual and seasonal precipitation variability at 20 stations over upper Indus River basin (UIRB) by using an innovative trend analysis (ITA) method, Mann-Kendall (MK) and Sen's slope estimator tests. Moreover, the annual precipitation time series is investigated for five precipitation intensities i.e. light, low, moderate, high and heavy; and on a seasonal scale for extreme values i.e. light and heavy precipitation intensities. Annual precipitation showed an increasing trend at four stations in the northeast region and a decreasing trend at two stations in the southeast region of UIRB. On a seasonal scale, the results showed that Garidopatta, Peshawar, Astore and Gupis stations are more sensitive to seasonal precipitation variabilities. Changes in heavy precipitation (> 90th percentile) are found more severe in winter and summer seasons in the northern regions suggesting a probable flooding aggravation. However, a reduction in flooding event observed during the spring season in the southern regions of the UIRB indicating a shift in the precipitation regime from south to north. The results of light precipitation (< 10th percentile) intensity showed that winter and spring seasons are more vulnerable to the occurrence of prolonged drought events. Moreover, ITA method has the advantage of detecting the sub-trends in the precipitation series because of its ability to present the results in graphical format. The results of this study could help to understand seasonal and annual precipitation variability over UIRB and will have imperative insinuations for further studies. [ABSTRACT FROM AUTHOR]

Precipitation products with high spatial resolution are important for basin-scale hydrological and meteorological applications. Downscaling techniques commonly used with satellite-derived rainfall data build statistical regression relationships between the precipitation and land surface characteristics to obtain rainfall estimates with improved spatial resolution. However, these relationships tend to be extended mistakenly from the regional scale to the hill slope scale. This paper introduces a quadratic parabolic profile (QPP) model for downscaling precipitation. The proposed technique uses a quadratic parabolic equation to express the rule for changes of precipitation with elevation. It is assumed that precipitation is the primary factor restricting vegetation growth during the growing season. Therefore, an ordinary least square regression method is used to fit an “elevation–normalized difference vegetation index (NDVI)” function to determine the parameters of the QPP model. This method was implemented in the Three-River Headwaters Region (TRHR) during the growing seasons of 2009–2013 for both monthly and total precipitation. The results indicated that the precipitation estimates downscaled using the QPP method had higher accuracies than those of commonly used exponential regression, multiple linear regression, and geographically weighted regression models. The average root mean square errors (RMSEs) and mean absolute percent errors (MAPEs) of total precipitation during the growing season of the commonly used models were 17%–69% and 17%–92% higher, respectively, than those of the QPP model. Meanwhile, the precipitation downscaled using the QPP technique also had lower MAPEs and RMSEs than the PERSIANN-CCS, PERSIANN-CDR, GSMaP-RNL, and GSMaP-RNLG products. Downscaled precipitation estimates from the QPP model exhibited patterns with elevation that were more detailed and more reliable than from the commonly used downscaling methods and another four satellite products. In addition, the QPP model is insensitive to errors in the NDVI or elevation. These findings suggest the proposed approach could be implemented successfully to downscale both monthly and total precipitation of the Tropical Rainfall Measuring Mission (TRMM) 3B43 product throughout the growing season in the TRHR. [ABSTRACT FROM AUTHOR]

Deriving high quality precipitation estimates at high spatial resolution is of prime importance for many hydrological, meteorological, and environmental investigations. Rain gauge observations and satellite-derived precipitation data are two main sources of precipitation estimates. Gauge observations are accurate and reliable, but are heavily point-based and sparse in areas of rugged or complex terrains. Satellite-derived precipitation products can cover large areas, but they are generally characterized by inherent bias. To optimize the use of both datasets, we propose in this paper, a downscaling-integration framework to generate high quality monthly precipitation datasets at 1 km spatial resolution by merging rain gauge observations and TRMM 3B43 products. Firstly, an area-to-point kriging (ATPK) approach is used to downscale the original TRMM product to 1 km, so as to ensure a fair comparison with rain gauge data. Then, the downscaled TRMM precipitation datasets are integrated with the gauge observations using geographically weighted regression kriging (GWRK). The geographical factors (i.e. longitude, latitude and elevation) are also used as auxiliary variables in the GWRK model. Applying this approach to an experiment conducted at the middle and lower reaches of the Yangtze River in China from 2001 to 2014 shows that: (1) the downscaled monthly TRMM precipitation data by ATPK are more accurate than the original TRMM estimates; (2) the GWRK model employing the downscaled TRMM precipitation data and geographical factors provides better monthly precipitation estimates than the conventional ordinary kriging (OK) interpolation and the commonly used merging methods (i.e. geographical difference analysis, GDA and kriging with external drift, KED); (3) the GWRK method reduces the influence of the inaccuracy (bias) of satellite-derived precipitation data on the precipitation estimates compared to GDA. The approach presented in this study has provided an efficient alternative for solving the scale mismatch problem between point-based gauge data and low resolution satellite data, and producing improved precipitation data at high spatial resolution. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP), being an average of surpassing 4000 m above sea level and around 2.5 × 10 6  km 2 , is the highest and largest plateau in the world and also called as the “Third Pole”. Due to its elevated land surface and complex terrain, the TP is subjected to combined regulations of multiple climate systems and associated large-scale atmospheric circulations. In this paper, we comprehensively review the recent studies of atmospheric moisture transport versus precipitation across the TP, with the attempt to link the two, which did not receive much attention previously. This review focuses on the atmospheric moisture transport and associated circulation patterns in this region, widely adopted approaches to identify the atmospheric moisture transport, qualitative and quantitative analyses for the role of water vapor transport on the precipitation, as well as the internal physical mechanism between atmospheric moisture transport and precipitation over the TP. Moreover, directions of future research are discussed based on the following aspects, which include 1) proposing an integrated statistical-physical framework for demonstrating the influence of atmospheric moisture transport and associated circulation patterns on the precipitation, especially the extremes, in the high-cold mountainous region; 2) quantifying the contribution of atmospheric water vapor from the surrounding sources as well as the local moisture recycling on the TP's precipitation; 3) providing higher quality data for atmospheric water vapor and precipitation; 4) emphasizing on the physical mechanism sustaining the atmospheric moisture transport as well as its potential influence on the extreme precipitation, including amount, frequency, intensity and duration. It is expected that this review will be beneficial for exploring the linkage between atmospheric moisture transport versus precipitation across the TP. [ABSTRACT FROM AUTHOR]

Increasing meteorological data availability and quality implies an adaptation of the interpolation methods for data combination. In this paper, we propose a new method to efficiently combine weather radar data with data from two heated rain gauge networks of different quality. The two networks being non-collocated (no common location between the two networks), pseudo cross-variograms are used to compute the linear model of coregionalization for kriging computation. This allows considering the two networks independently in a co-kriging approach. The methodology is applied to the Upper Rhône River basin, an Alpine catchment in Switzerland with a complex topography and an area of about 5300 km 2 . The analysis explores the newly proposed Regression co-kriging approach, in which two independent rain gauge networks are considered as primary and secondary kriging variables. Regression co-kriging is compared to four other methods, including the commonly applied Inverse distance weighting method used as baseline scenario. Incorporation of additional networks located within and around the target region in the interpolation computation is also explored. The results firstly demonstrate the added value of the radar information as compared to using only ground stations. As compared to Regression kriging using only the network of highest quality, the Regression co-kriging method using both networks slightly increases the performance. A key outcome of the study is that Regression co-kriging performs better than Inverse distance weighting even for the data availability scenario when the radar network was providing lower quality radar data over the studied basin. The results and discussion underline that combining meteorological information from different rain gauge networks with different equipments remains challenging for operational purposes. Future research in this field should in particular focus on additional pre-processing of the radar data to account for example for areas of low visibility of the weather radars due to the topography. [ABSTRACT FROM AUTHOR]

This review paper presents the methods that have been used to date for the purification of crude biodiesel obtained by heterogeneous production processes. At first, a typical biodiesel production process using heterogeneous catalysts is shortly described, paying attention to the main processing steps. Then, possible impurities and their effects on the biodiesel quality are pointed out. The main part of this review paper deals with the traditional (wet and dry washing) and novel (membrane extraction, precipitation, complexation, simultaneous ion-exchange and precipitation as well as simultaneous biodiesel synthesis and purification) purification methods. These purification methods are compared with respect of their refining efficiency. A special attention is paid to their comparison regarding the type of raw materials used in biodiesel production. Furthermore, advantages and disadvantages of the crude biodiesel purification methods are emphasized. Finally, environmental aspects of crude biodiesel purification are highlighted. [ABSTRACT FROM AUTHOR]

The oceanic influence on winter precipitation in the Iberian Peninsula has been evidenced in numerous scientific papers. Large-scale forecasting models generally use variables such as Sea Surface Temperature (SST), soil moisture and ice cover, but they are not very accurate yet. Using observational data, this paper analyzes the influence of North Atlantic and Mediterranean SST on winter precipitation in the Iberian Peninsula between October 1951 and September 2011. First, trends of both data sets have been calculated to study their behavior during the past six decades, showing an overall increase of SST and a substantial decrease in winter precipitation in the Iberian Peninsula, except in eastern and south-eastern regions. Then, connection patterns between autumn Sea Surface Temperature Anomalies and winter precipitation have been studied to identify ocean regions that may be used as potential predictors of winter precipitation. After applying a Principal Component Analysis to cluster the information provided by the 1431 measuring points of a SST grid with a small number of variables, the Principal Components extracted were introduced into a Multiple Linear Regression algorithm in order to obtain an estimation of winter precipitation in each river basin. The validation process has shown that the algorithm explains nearly 50% of inter-annual variability of winter precipitation in the basins of the Iberian Peninsula with a strongly oceanic influence; this percentage is somewhat lower in the Mediterranean regions. [ABSTRACT FROM AUTHOR]

This paper advances the physical understanding of the flow duration curve (FDC) regional variation. It provides a process-based analysis of the interaction between climate and landscape properties to explain disparities in FDC shapes. We used (i) long term measured flow and precipitation data over 73 catchments from the eastern US. (ii) We calibrated the Sacramento model (SAC-SMA) to simulate soil moisture and flow components FDCs. The catchments classification based on storm characteristics pointed to the effect of catchments landscape properties on the precipitation variability and consequently on the FDC shapes. The landscape properties effect was pronounce such that low value of the slope of FDC (SFDC)—hinting at limited flow variability—were present in regions of high precipitation variability. Whereas, in regions with low precipitation variability the SFDCs were of larger values. The topographic index distribution, at the catchment scale, indicated that saturation excess overland flow mitigated the flow variability under conditions of low elevations with large soil moisture storage capacity and high infiltration rates. The SFDCs increased due to the predominant subsurface stormflow in catchments at high elevations with limited soil moisture storage capacity and low infiltration rates. Our analyses also highlighted the major role of soil infiltration rates on the FDC despite the impact of the predominant runoff generation mechanism and catchment elevation. In conditions of slow infiltration rates in soils of large moisture storage capacity (at low elevations) and predominant saturation excess, the SFDCs were of larger values. On the other hand, the SFDCs decreased in catchments of prevalent subsurface stormflow and poorly drained soils of small soil moisture storage capacity. The analysis of the flow components FDCs demonstrated that the interflow contribution to the response was the higher in catchments with large value of slope of the FDC. The surface flow FDC was the most affected by the precipitation as it tracked the precipitation duration curve (PDC). In catchments with low SFDCs, this became less applicable as surface flow FDC diverged from PDC at the upper tail (> 40% of the flow percentile). The interflow and baseflow FDCs illustrated most the filtering effect on the precipitation. The process understanding we achieved in this study is key for flow simulation and assessment in addition to future works focusing on process-based FDC predictions. [ABSTRACT FROM AUTHOR]

Climate change impact assessment on meteorological variables involves large uncertainties as a result of incomplete knowledge on the future greenhouse gas concentrations and climate model physics, next to the inherent internal variability of the climate system. Given that the alteration in greenhouse gas concentrations is the driver for the change, one expects the impacts to be highly dependent on the considered greenhouse gas scenario (GHS). In this study, we denote this behavior as GHS sensitivity. Due to the climate model related uncertainties, this sensitivity is, at local scale, not always that strong as expected. This paper aims to study the GHS sensitivity and its contributing role to climate scenarios for a case study in Belgium. An ensemble of 160 CMIP5 climate model runs is considered and climate change signals are studied for precipitation accumulation, daily precipitation intensities and wet day frequencies. This was done for the different seasons of the year and the scenario periods 2011–2040, 2031–2060, 2051–2081 and 2071–2100. By means of variance decomposition, the total variance in the climate change signals was separated in the contribution of the differences in GHSs and the other model-related uncertainty sources. These contributions were found dependent on the variable and season. Following the time of emergence concept, the GHS uncertainty contribution is found dependent on the time horizon and increases over time. For the most distinct time horizon (2071–2100), the climate model uncertainty accounts for the largest uncertainty contribution. The GHS differences explain up to 18% of the total variance in the climate change signals. The results point further at the importance of the climate model ensemble design, specifically the ensemble size and the combination of climate models, whereupon climate scenarios are based. The numerical noise, introduced at scales smaller than the skillful scale, e.g. at local scale, was not considered in this study. [ABSTRACT FROM AUTHOR]

This study proposes a wavelet-based multi-resolution modeling approach for statistical downscaling of GCM variables to mean monthly precipitation for five locations at Krishna Basin, India. Climatic dataset from NCEP is used for training the proposed models (Jan.’69 to Dec.’94) and are applied to corresponding CanCM4 GCM variables to simulate precipitation for the validation (Jan.’95–Dec.’05) and forecast (Jan.’06–Dec.’35) periods. The observed precipitation data is obtained from the India Meteorological Department (IMD) gridded precipitation product at 0.25 degree spatial resolution. This paper proposes a novel Multi-Scale Wavelet Entropy (MWE) based approach for clustering climatic variables into suitable clusters using k-means methodology. Principal Component Analysis (PCA) is used to obtain the representative Principal Components (PC) explaining 90–95% variance for each cluster. A multi-resolution non-linear approach combining Discrete Wavelet Transform (DWT) and Second Order Volterra (SoV) is used to model the representative PCs to obtain the downscaled precipitation for each downscaling location (W-P-SoV model). The results establish that wavelet-based multi-resolution SoV models perform significantly better compared to the traditional Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) based frameworks. It is observed that the proposed MWE-based clustering and subsequent PCA, helps reduce the dimensionality of the input climatic variables, while capturing more variability compared to stand-alone k-means (no MWE). The proposed models perform better in estimating the number of precipitation events during the non-monsoon periods whereas the models with clustering without MWE over-estimate the rainfall during the dry season. [ABSTRACT FROM AUTHOR]

This paper analyzes the historical precipitation and temperature trends and the future climate projections with reference to the northern part of Tuscany (Italy). The trends are identified and quantified at monthly and annual scale at gauging stations with data collected for long periods (60–90 years). An ensemble of 13 Regional Climate Models (RCMs), based on two Representative Concentration Pathways (RCP4.5 and RCP8.5), was then used to assess local scale future precipitation and temperature projections and to represent the uncertainty in the results. The historical data highlight a general decrease of the annual rainfall at a mean rate of 22 mm per decade but, in many cases, the tendencies are not statistically significant. Conversely, the annual mean temperature exhibits an upward trend, statistically significant in the majority of cases, with a warming rate of about 0.1 °C per decade. With reference to the model projections and the annual precipitation, the results are not concordant; the deviations between models in the same period are higher than the future changes at medium- (2031–2040) and long-term (2051–2060) and highlight that the model uncertainty and variability is high. According to the climate model projections, the warming of the study area is unequivocal; a mean positive increment of 0.8 °C at medium-term and 1.1 °C at long-term is expected with respect to the reference period (2003–2012) and the scenario RCP4.5; the increments grow to 0.9 °C and 1.9 °C for the RCP8.5. Finally, in order to check the observed climate change signals, the climate model projections were compared with the trends based on the historical data. A satisfactory agreement is obtained with reference to the precipitation; a systematic underestimation of the trend values with respect to the models, at medium- and long-term, is observed for the temperature data. [ABSTRACT FROM AUTHOR]

The present paper addresses driving mechanisms of global monsoon (GM) variability and outstanding issues in GM science. This is the second synthesis of the PAGES GM Working Group following the first synthesis “The Global Monsoon across Time Scales: coherent variability of regional monsoons” published in 2014 (Climate of the Past, 10, 2007–2052). Here we introduce the GM as a planetary scale circulation system and give a brief accounting of why it exhibits regional structure. The primary driver of the GM is solar insolation, and the specific features in the underlying surface, such as land-sea distribution, topography, and oceanic circulations, are mainly responsible for the differences among regional monsoon systems. We then analyze the monsoon formation mechanisms, together with the major processes that drive monsoon variations at various timescales, including external forcings and internal feedbacks. On long time scales, external forcings often induce variability on a global scale, whereas short-term variability in regional monsoon systems is usually caused by internal feedbacks within the climate system. Finally, a number of debatable issues are discussed, with an emphasis on time scales beyond the instrumental record. These include the dual nature of the monsoon as wind and rain, the meaning of oxygen isotope in hydrological cycle, in particular of speleothem δ 18 O, the role of ice-sheet in monsoon variations, etc. In general, the GM as a system comprises a hierarchy of regional and local monsoons with various degrees of similarity, though all show coherent variability driven by a common solar forcing. The goal of the GM concept, therefore, is by no means to replace or diminish research on the regional monsoons, but to help dissect the mechanisms and controlling factors of monsoon variability at various temporal-spatial scales. [ABSTRACT FROM AUTHOR]

Loess Plateau has great uncertainty on drought occurrence due to climate change. This paper analyzes the evolution of precipitation, potential evapotranspiration and standardized precipitation evapotranspiration index (SPEI) based on the Coupled Model Inter-comparison Project Phase 5 (CMIP5) data and regional downscaling model (RegCM4.0). Results indicate that, under RCP2.6 Scenario, the precipitation will increase significantly (5% confidence level) at the rate of 16.40 mm/10 a. However, the potential evapotranspiration is showing non-significant decreasing trend at the rate of 2.16 mm/10 a. Moreover, the SPEI will decrease in the south and northernmost area and increase in the central northern area of Loess Plateau. Under RCP8.5 Scenario, the precipitation will increase significantly (5% confidence level) at the rate of 19.12 mm/10 a. The potential evapotranspiration will non-significantly decrease at the rate of 2.16 mm/10 a and the SPEI is showing increasing trend almost in the whole Loess Plateau. Generally, Loess Plateau is becoming wetter in the central part under RCP2.6 Scenario and the wet area will be enlarged to almost the whole plateau under RCP8.5 Scenario. Based on the results, the water resources will increase under global warming, which may alleviate the water scarcity issue in the Loess Plateau. [ABSTRACT FROM AUTHOR]

In the first part of this paper, monthly precipitation data from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) and Tropical Rainfall Measuring Mission 3B42 algorithm Version 7 (TRMM-3B42V7) are evaluated over Iran using the Generalized Three-Cornered Hat (GTCH) method which is self-sufficient of reference data as input. Climate Data Unit (CRU) is added to the GTCH evaluations as an independent gauge-based dataset thus, the minimum requirement of three datasets for the model is satisfied. To ensure consistency of all datasets, the two satellite products were aggregated to 0.5° spatial resolution, which is the minimum resolution of CRU. The results show that the PERSIANN-CDR has higher Signal to Noise Ratio (SNR) than TRMM-3B42V7 for the monthly rainfall estimation, especially in the northern half of the country. All datasets showed low SNR in the mountainous area of southwestern Iran, as well as the arid parts in the southeast region of the country. Additionally, in order to evaluate the efficacy of PERSIANN-CDR and TRMM-3B42V7 in capturing extreme daily-precipitation amounts, an in-situ rain-gauge dataset collected by the Islamic Republic of the Iran Meteorological Organization (IRIMO) was employed. Given the sparsity of the rain gauges, only 0.25° pixels containing three or more gauges were used for this evaluation. There were 228 such pixels where daily and extreme rainfall from PERSIANN-CDR and TRMM-3B42V7 could be compared. However, TRMM-3B42V7 overestimates most of the intensity indices (correlation coefficients; R between 0.7648–0.8311, Root Mean Square Error; RMSE between 3.29mm/day-21.2mm/5day); PERSIANN-CDR underestimates these extremes (R between 0.6349–0.7791 and RMSE between 3.59mm/day-30.56mm/5day). Both satellite products show higher correlation coefficients and lower RMSEs for the annual mean of consecutive dry spells than wet spells. The results show that TRMM-3B42V7 can capture the annual mean of the absolute indices (the number of wet days in which daily precipitation > 10 mm, 20 mm) better than PERSIANN-CDR. The results of daily evaluations show that the similarity of Empirical Cumulative Density Function (ECDF) of satellite products and IRIMO gauges daily precipitation, as well as dry spells with different thresholds in some selected pixels (include at least five gauges), are significant. The results also indicate that ECDFs become more significant when threshold increases. In terms of regional analyses, the higher SNR of the products on monthly (based on the GTCH method) and daily evaluations (significant ECDFs) is mostly consistent. [ABSTRACT FROM AUTHOR]

The estimation of water resources at basin scale requires modelling of all components of the hydrological system. Because of the great uncertainties associated with the estimation of each water cycle component and the large error in budget closure that results, water budget is rarely carried out explicitly. This paper fills the gap in providing a methodology for obtaining it routinely at daily and subdaily time scales. In this study, we use various strategies to improve water budget closure in a small basin of Italian Prealps. The specific objectives are: assessing the predictive performances of different Kriging methods to determine the most accurate precipitation estimates; using MODIS imagery data to assist in the separation of snowfall and rainfall; combining the Priestley-Taylor evapotranspiration model with the Budyko hypothesis to estimate at high resolution (in time and space) actual evapotranspiration (ET); using an appropriate calibration-validation strategy to forecast discharge spatially. For this, 18 years of spatial time series of precipitation, snow water equivalent, rainfall-runoff and ET at hourly time steps are simulated for the Posina River basin (Northeast Italy) using the JGrass-NewAGE system. Among the interpolation methods considered, local detrended kriging is seen to give the best performances in forecasting precipitation distribution. However, detrended Kriging gives better results in simulating discharges. The parameters optimized at the basin outlet over a five-year period show acceptable performances during the validation period at the outlet and at interior points of the basin. The use of the Budyko hypothesis to guide the ET estimation shows encouraging results, with less uncertainty than the values reported in literature. Aggregating at a long temporal scale, the mean annual water budget for the Posina River basin is about 1269 ± 372 mm (76.4%) runoff, 503.5 ± 35.5 mm (30%) evapotranspiration, and − 50 ± 129 mm ( − 4.2%) basin storage from basin precipitation of 1730 ± 344 mm. The highest interannual variability is shown for precipitation, followed by discharge. Evapotranspiration shows less interannual variability and is less dependent on precipitation. [ABSTRACT FROM AUTHOR]

Despite the tremendous improvement made in numerical weather and climate models over the recent years, the forecasts generated by those models still cannot be used directly for hydrological forecasting. A post-processor like the Ensemble Pre-Processor (EPP) developed by U.S. National Weather Service must be used to remove various biases and to extract useful predictive information from those forecasts. In this paper, we investigate how different designs of canonical events in the EPP can help post-process precipitation forecasts from the Global Ensemble Forecast System (GEFS) and Climate Forecast System Version 2 (CFSv2). The use of canonical events allow those products to be linked seamlessly and then the post-processed ensemble precipitation forecasts can be generated using the Schaake Shuffle procedure. We used the post-processed ensemble precipitation forecasts to drive a distributed hydrological model to obtain ensemble streamflow forecasts and evaluated those forecasts against the observed streamflow. We found that the careful design of canonical events can help extract more useful information, especially when up-to-date observed precipitation is used to setup the canonical events. We also found that streamflow forecasts using post-processed precipitation forecasts have longer lead times and higher accuracy than streamflow forecasts made by traditional Extend Streamflow Prediction (ESP) and the forecasts based on original GEFS and CFSv2 precipitation forecasts. [ABSTRACT FROM AUTHOR]

This paper presents novel formulations of Mean field bias (MFB) and local bias (LB) correction schemes that incorporate conditional bias (CB) penalty. These schemes are based on the operational MFB and LB algorithms in the National Weather Service (NWS) Multisensor Precipitation Estimator (MPE). By incorporating CB penalty in the cost function of exponential smoothers, we are able to derive augmented versions of recursive estimators of MFB and LB. Two extended versions of MFB algorithms are presented, one incorporating spatial variation of gauge locations only (MFB-L), and the second integrating both gauge locations and CB penalty (MFB-X). These two MFB schemes and the extended LB scheme (LB-X) are assessed relative to the original MFB and LB algorithms (referred to as MFB-O and LB-O, respectively) through a retrospective experiment over a radar domain in north-central Texas, and through a synthetic experiment over the Mid-Atlantic region. The outcome of the former experiment indicates that introducing the CB penalty to the MFB formulation leads to small, but consistent improvements in bias and CB, while its impacts on hourly correlation and Root Mean Square Error (RMSE) are mixed. Incorporating CB penalty in LB formulation tends to improve the RMSE at high rainfall thresholds, but its impacts on bias are also mixed. The synthetic experiment suggests that beneficial impacts are more conspicuous at low gauge density (9 per 58,000 km 2 ), and tend to diminish at higher gauge density. The improvement at high rainfall intensity is partly an outcome of the conservativeness of the extended LB scheme. This conservativeness arises in part from the more frequent presence of negative eigenvalues in the extended covariance matrix which leads to no, or smaller incremental changes to the smoothed rainfall amounts. [ABSTRACT FROM AUTHOR]

This study evaluates the effect on the availability of water resources for agriculture of expected future changes in precipitation and temperature distributions in north-western Africa. It also puts forward some locally derived adaptation strategies to climate change that can have a positive impact on water resources in the Chtouka Aït Baha region. Historical baselines of precipitation and temperature were derived using satellite data respectively from CHIRPS and CRU, while future projections of temperature and precipitation were extracted from the Coordinated Regional Climate Downscaling Experiment database (CORDEX). Projections were also generated for two future periods (2030–2049 and 2080–2099) under two Representative Concentration Pathways: RCP4.5 and RCP8.5. Regional climate models and satellite data outputs were evaluated by calculating their bias and RMSE against historical baseline and observed data. Under the RCP8.5 scenario, temperature in the region shows an increase by 2 °C for the 2030–2049 time period, and by 4 to 5 °C towards the end of the 21st century. According to the RCP4.5 scenario, precipitation shows a reduction of 10 to 30% for the period 2030–2049, up to 60% for 2080–2099. Outputs from the climate change projections were used to force the HEC-HMS hydrological model. Simulation results indicate that water deficit at basin level will likely triple towards 2050 due to increase in water demand and decrease in aquifer recharge and dam storage. This alarming situation, in a country that already suffers from water insecurity, emphasizes the need for more efforts to implement climate change adaptation measures. This paper presents an assessment of 38 climate change adaptation measures according to several criteria. The evaluation shows that measures affecting the management of water resources have the highest benefit-to-efforts ratio, which indicates that decision makers and stakeholders should increasingly focus their efforts on management measures. [ABSTRACT FROM AUTHOR]

Since the 1950s, the population in the arid to hyperarid Tarim River basin has grown rapidly concurrent with an expansion of irrigated agriculture. This threatens the Tarim River basin's natural ecosystems and causes water shortages, even though increased discharges in the headwaters have been observed more recently. These increases have mainly been attributed to receding glaciers and are projected to cease when the glaciers are unable to provide sufficient amounts of meltwater. Under these circumstances water management will face a serious challenge in adapting its strategies to changes in river discharge, which to a greater extent will depend on changes in precipitation. In this paper, we aim to develop accurate seasonal predictions of precipitation to improve water resources management. Possible predictors of precipitation for the Tarim River basin were either downloaded directly or calculated using NCEP/NCAR Reanalysis 1 and NOAA Extended Reconstructed Sea Surface Temperature (SST) V3b data in monthly resolution. To evaluate the significance of the predictors, they were then correlated with the monthly precipitation dataset GPCCv6 extracted for the Tarim River basin for the period 1961 to 2010. Prior to the Spearman rank correlation analyses, the precipitation data were averaged over the subbasins of the Tarim River. The strongest correlations were mainly detected with lead times of four and five months. Finally, an artificial neural network model, namely a multilayer perceptron (MLP), and a multiple linear regression (LR) model were developed each in two different configurations for the Aksu River subbasin, predicting precipitation five months in advance. Overall, the MLP using all predictors shows the best performance. The performance of both models drops only slightly when restricting the model input to the SST of the Black Sea and the Siberian High Intensity (SHI) pointing towards their importance as predictors. [ABSTRACT FROM AUTHOR]

Summary The multisensor Quantitative Precipitation Estimates (MQPEs) created by the US National Weather Service (NWS) are subject to a non-stationary bias. This paper quantifies the impacts of climatological adjustment of MQPEs alone, as well as the compound impacts of adjustment and model calibration, on the accuracy of simulated flood peak magnitude and that in detecting flood events. Our investigation is based on 19 watersheds in the mid-Atlantic region of US, which are grouped into small (<500 km 2 ) and large (>500 km 2 ) watersheds. NWS archival MQPEs over 1997–2013 for this region are adjusted to match concurrent gauge-based monthly precipitation accumulations. Then raw and adjusted MQPEs serve as inputs to the NWS distributed hydrologic model-threshold frequency framework (DHM-TF). Two experiments via DHM-TF are performed. The first one examines the impacts of adjustment alone through uncalibrated model simulations, whereas the second one focuses on the compound effects of adjustment and calibration on the detection of flood events. Uncalibrated model simulations show broad underestimation of flood peaks for small watersheds and overestimation those for large watersheds. Prior to calibration, adjustment alone tends to reduce the magnitude of simulated flood peaks for small and large basins alike, with 95% of all watersheds experienced decline over 2004–2013. A consequence is that a majority of small watersheds experience no improvement, or deterioration in bias (0% of basins experiencing improvement). By contrast, most (73%) of larger ones exhibit improved bias. Outcomes of the detection experiment show that the role of adjustment is not diminished by calibration for small watersheds, with only 25% of which exhibiting reduced bias after adjustment with calibrated parameters. Furthermore, it is shown that calibration is relatively effective in reducing false alarms (e.g., false alarm rate is down from 0.28 to 0.19 after calibration for small watersheds with calibrated parameters); but its impacts on detection rate are mixed. As an example, the detection rate of 2-Y events in fact declines for small watersheds after calibration is performed (from 0.4 to 0.28, and from 0.28 to 0.19 with raw and adjusted MQPE, respectively). These mixed outcomes underscore the complex interplays between errors in MQPEs, conditional bias in the reference gauge-based analysis, and structural deficiencies of the hydrologic model. [ABSTRACT FROM AUTHOR]

Summary Accurate precipitation maps are essential for ecological, environmental, element cycle and hydrological models that have a spatial output component. It is well known that topography has a major influence on the spatial distribution of precipitation and that increasing topographical complexity is associated with increased spatial heterogeneity in precipitation. This means that when mapping precipitation using classical interpolation techniques (e.g. regression, kriging, spline, inverse distance weighting, etc.), a climate measuring network with higher spatial density is needed in mountainous areas in order to obtain the same level of accuracy as compared to flatter regions. In this study, we present a mean total annual precipitation mapping technique that combines topographical information (i.e. elevation and slope orientation) with average total annual rain gauge data in order to overcome this problem. A unique feature of this paper is the identification of the scale at which topography influences the precipitation pattern as well as the direction of the dominant weather circulation. This method was applied for Belgium and surroundings and shows that the identification of the appropriate scale at which topographical obstacles impact precipitation is crucial in order to obtain reliable mean total annual precipitation maps. The dominant weather circulation is determined at 260°. Hence, this approach allows accurate mapping of mean annual precipitation patterns in regions characterized by rather high topographical complexity using a climate data network with a relatively low density and/or when more advanced precipitation measurement techniques, such as radar, aren’t available, for example in the case of historical data. [ABSTRACT FROM AUTHOR]

Summary: In this paper, the trends of the annual streamflow and precipitation and cross correlations between them were analyzed in nine large river basins of China during 1956–2005. The results indicate that: (1) the annual mean streamflow decreases in arid and semi-arid regions of north China; however, increasing trends occur in south and Southwest China; (2) the annual streamflow and precipitation exhibit reasonable correlation in nine large river basins except those located in inland areas. The annual streamflow over most areas of China is fed by precipitation; however, the decline in streamflow is faster than the decreases of precipitation since 1970s in the arid and semi-arid regions of north China. The relationship between the annual precipitation and streamflow presents a non-stationary state in north China. This non-stationary relationship is strongly influenced by both human activities and precipitation changes; (3) a significant increase of water use might be the major factor responsible for the steeper decline in streamflow than in precipitation in Haihe River, Yellow River and Songliao River basins in north China. In inland river areas, increase of water use and actual evapotranspiration might result in decline in streamflow although precipitation has an increase tendency. This paper sheds light on the non-stationary relationship between annual precipitation and streamflow and possible underlying causes, which will be helpful for a better understanding of the changes of precipitation and streamflow in China at large scale and in other regions of the world. [Copyright &y& Elsevier]

Abstract: A Local Area Weather Radar (LAWR) is an X-band weather radar developed to meet the needs of high resolution rainfall data for hydrological applications. The LAWR system and data processing methods are reviewed in the first part of this paper, while the second part of the paper focuses on calibration. The data processing for handling the partial beam filling issue was found to be essential to the calibration. LAWR uses a different calibration process compared to conventional weather radars, which use a power-law relationship between reflectivity and rainfall rate. Instead LAWR uses a linear relationship of reflectivity and rainfall rate as result of the log transformation carried out by the logarithmic receiver as opposed to the linear receiver of conventional weather radars. Based on rain gauge data for a five month period from a dense network of nine gauges within a 500×500m area and data from a nearby LAWR, the existing calibration method was tested and two new methods were developed. The three calibration methods were verified with three external gauges placed in different locations. It can be concluded that the LAWR calibration uncertainties can be reduced by 50% in two out of three cases when the calibration is based on a factorized 3 parameter linear model instead of a single parameter linear model. [Copyright &y& Elsevier]

Abstract: This paper presents a climatological validation of atmospheric circulation type catalogues included in the COST 733 dataset (COST733CAT). The circulation type classifications ability to describe patterns of the Norwegian precipitation climate is analysed. For all 73 classifications included in the COST733CAT several statistical measures are calculated for each individual circulation type. These statistics include both frequencies of precipitation occurrence and statistical moments used to fit γ-distributions for each circulation type at precipitation stations. In this paper results from four stations in different precipitation regions and classifications with ∼9 types are emphasized. The circulation types employed represent the COST 733 domain 02 (57–72°N, 6°W–25°E). Precipitation occurrence frequency statistics and probability density functions are used to analyse if the types within the circulation type classifications can describe different precipitation conditions. Simple measures are developed in order to compare classifications. The similarity of probability density functions of type specific precipitation within classifications are analysed applying Kolmogorov–Smirnov tests. The type-specific distribution functions are also compared with the probability function of all precipitation events. The results reveal that some classifications, particularly those associated with the neural network methods does not discriminate precipitation distributions very well. On the other side many classifications performs well and get high scores. Evaluating all 73 catalogues the results indicate best results for classifications with 27–30 types. [Copyright &y& Elsevier]

Abstract: Precipitation is one of the most important elements of the hydrological cycle, and extreme events associated with precipitation are considered a key factor in several types of human activities, including agriculture, for instance. Therefore, the main objective of this paper is to evaluate extreme precipitation indices for the past century, and to analyse the possible tendency of future precipitation conditions for this century for the Carpathian Basin. Several climate extreme indices have been analysed according to the guidelines suggested by the joint WMO-CCl/CLIVAR Working Group (formed at the end of the 1990s) on climate change detection. These precipitation indices include the number of wet days using several threshold values, e.g., 20mm (RR20), 10mm (RR10), 5mm (RR5), 1mm (RR1), 0.1mm (RR0.1), the upper quartile and the 95th percentile of daily precipitation in the base-period 1961–1990 (R75 and R95); the maximum number of consecutive dry days (CDD); the highest 1-day precipitation amount (Rx1); the greatest 5-day rainfall total (Rx5); the annual fraction due to extreme precipitation events (R95T); simple daily intensity index (SDII), etc. Our results suggest that regional intensity and frequency of extreme precipitation increased in the Carpathian Basin during the second half of the 20th century, while the total precipitation decreased and the mean climate became slightly drier during the whole 20th century. In the second part of this paper, several IPCC emission scenarios have been compared and GCM outputs have been analysed in order to project precipitation conditions in the Carpathian Basin for the 21st century. These climate simulations suggest that climate of this region may become drier in summer and wetter in winter, which highlight the importance of hydrological and agricultural planning in Hungary. [Copyright &y& Elsevier]

Abstract: This paper presents first exploratory results and analyses performed on a 30-year (1978–2007) homogeneous precipitation database that has been created for the Calabria peninsula, southern Italy. The database consists of complete time series of daily values of precipitation from 88 stations distributed over the region. The database temporal resolution is one day while its spatial resolution is about 10 km. Calabria offers an interesting scenario for mesoclimatic studies in spatial and temporal rainfall variability. The region is located in the Central Mediterranean Basin, which has a strong seasonal variability that mirrors in large precipitation differences among seasons. The complex orography of the region leads to different exposures of the territory to the rain-bearing air masses and large differences in rainfall can occur in few tens of kilometers. Different kinds of Mediterranean storms are important for Calabria: a) storms originating in the lee of the Alps; b) storms that enter the Basin or develop in the western Mediterranean (Gulf of Lyon, Rhone valley, Iberia, Gibraltar); c) storms from northern Africa; d) storms moving over the central Mediterranean from Balkans and eastern Europe. Again, their interaction with local orography determines complex rainfall patterns and variability. In this paper, the maps of average values of precipitation, rain frequency, rain rate, persistence of rainfall and length of dry periods are presented. Precipitation spatial structure shows that the orography and the sea have a major role, while the seasonal dependence of rainfall unequivocally reveals the influence of the synoptic scale conditions. A large number of cast-studies of heavy precipitation events have been investigated in the past and several of them refer to the east side of Calabria. The results shown here confirm this issue in a climatological sense: despite the fact that yearly precipitation is larger on the west side of the peninsula, most intense rainstorms affect mainly the east side, which is favorably exposed to these storms. The current database represents a foundation for further investigations. [Copyright &y& Elsevier]

Abstract: The WMO Field Intercomparison of Rainfall Intensity (RI) Gauges started on October, 1st 2007 at Vigna di Valle (Italy) and was concluded in May 2009. Those catching type instruments, out of the selected rain gauges based on various measuring principles, and the four rain gauges selected as reference instruments to be installed in a pit, were preliminarily calibrated in the laboratory before their final installation at the Field Intercomparison site. The recognized WMO laboratory at the University of Genoa was involved in this task, using the same standard tests adopted for the previously held WMO Laboratory Intercomparison of RI gauges. Further tests were performed to investigate the one-minute performance of the involved instruments. The present paper deals with basically Tipping-Bucket Rain gauges (TBRs) and Weighing Gauges (WGs), using results from tests performed under constant flow rates in laboratory conditions. The objective of this initial phase of the Intercomparison was to single out the counting errors associated with each instrument, so as to help the understanding of the measured differences between instruments in the field during the second phase. Results and comments on the preliminary laboratory calibration exercise are reported in this paper together with their implications for the analysis of the outcome of the Intercomparison in the Field. [Copyright &y& Elsevier]

Summary: Climate change impact studies are often based on coarse scale projections of general circulation models (GCMs) under greenhouse gases emissions scenarios. Outputs from GCMs have then to be downscaled to obtain information relevant to hydrologic studies. This paper investigates the uncertainty in catchment-scale precipitation scenarios due to (1) the emissions scenario, (2) the configuration of the GCM, and (3) the downscaling method. Two emissions scenarios, six global climate models and four downscaling methods are used to build climate change scenarios for three catchment case studies in the UK. Missing combinations are reconstructed by using a variance decomposition algorithm. The highly increasing variance towards the end of the century is shown to be largely due to the spread of results from different GCMs. Multimodel averaged results at the catchment scale generally show an increase in the amplitude of the annual cycle of precipitation, with wetter winters and drier summers. Hydrologists and water resources planners make use of downscaled climate scenarios, often with little regard for the performance of scenario construction methods, for informing decisions on water resources and flood risk management policies and projects. This paper shows that both the downscaling method and the multimodel building scheme applied have a significant impact on the seasonal precipitation regime that may in turn lead to quite different conclusions in impacts assessments. [Copyright &y& Elsevier]

Abstract: The hydro-meteorological evaluation of a flood event in July 1997 (the Odra flood in Central Europe) demonstrated that new procedures to estimate design floods for the reservoir outflow structures in the Czech Republic (CR) were needed. Therefore, the techniques of the estimation of Probable Maximum Precipitation (PMP) were developed in a national research project (1998–2000), and the activity focused on the improvement of the area related PMP estimates was going on within a present national project. In the frame of the evaluation of the next extreme precipitation event in August 2002 (the Labe flood in the CR and Germany), we compared the catchments related precipitation with the PMP estimates. In this paper, an outline of the PMP estimation techniques is given and the use of data from the Czech gauges is described, the aim being the statistical derivation of the point and area PMP estimates for precipitation duration of 1 to 5 days. The use of radar data in assessing the maximized area reduction factor is discussed and the relationship resulting from the radar measurements over the CR territory is presented. An evaluation of the radar-based area rainfall enabled us to transform the point PMP to the area PMP estimate designed for the river basins in CR. In the last part of the paper, the results obtained by comparing the rainfalls in 1997 and 2002 flood events with the PMP estimates are presented. The comparison showed that the maximum area rainfalls over small Czech catchments (the 3rd order river basins) did not exceed 63% of the corresponding PMP values. [Copyright &y& Elsevier]

A measurement scheme aimed at investigating precipitation properties based on collocated disdrometer and profiling instruments is used in many experimental campaigns. Raindrop size distribution (RSD) estimated by disdrometer is referred to the ground level; the collocated profiling instrument is supposed to provide complementary estimation at different heights of the precipitation column above the instruments. As part of the Special Observation Period 1 of the HyMeX (Hydrological Cycle in the Mediterranean Experiment) project, conducted between 5 September and 6 November 2012, a K-band vertically pointing micro rain radar (MRR) and a 2D video disdrometer (2DVD) were installed close to each other at a site in the historic center of Rome (Italy). The raindrop size distributions collected by 2D video disdrometer are considered to be fairly accurate within the typical sizes of drops. Vertical profiles of raindrop sizes up to 1085 m are estimated from the Doppler spectra measured by the micro rain radar with a height resolution of 35 m. Several issues related to vertical winds, attenuation correction, Doppler spectra aliasing, and range-Doppler ambiguity limit the performance of MRR in heavy precipitation or in convection, conditions that frequently occur in late summer or in autumn in Mediterranean regions. In this paper, MRR Doppler spectra are reprocessed, exploiting the 2DVD measurements at ground to estimate the effects of vertical winds at 105 m (the most reliable MRR lower height), in order to provide a better estimation of vertical profiles of raindrop size distribution from MRR spectra. Results show that the reprocessing procedure leads to a better agreement between the reflectivity computed at 105 m from the reprocessed MRR spectra and that obtained from the 2DVD data. Finally, vertical profiles of MRR-estimated RSDs and their relevant moments (namely median volume diameter and reflectivity) are presented and discussed in order to investigate the microstructure of rain both in stratiform and convective conditions. [ABSTRACT FROM AUTHOR]

The assessment of future precipitation variations prevailing in an area is essential for the research regarding climate and climate change. The current paper focuses on 3 selected areas in Greece that present different climatic characteristics due to their location and aims to assess and compare the future variation of annual and seasonal precipitation. Future precipitation data from the ENSEMBLES anthropogenic climate-change (ACC) global simulations and the Climate version of the Local Model (CLM) were obtained and analyzed. The climate simulations were performed for the future periods 2021–2050 and 2071–2100 under the A1B and B1 scenarios. Mann–Kendall test was applied to investigate possible trends. Spatial distribution of precipitation was performed using a combination of dynamic and statistical downscaling techniques and Kriging method within ArcGIS 10.2.1. The results indicated that for both scenarios, reference periods and study areas, precipitation is expected to be critically decreased. Additionally, Mann–Kendall test application showed a strong downward trend for every study area. Furthermore, the decrease in precipitation for the Ardas River basin characterized by the continental climate will be tempered, while in the Sperchios River basin it will be smoother due to the influence of some minor climatic variations in the basins' springs in the highlands where milder conditions occur. Precipitation decrease in the Geropotamos River basin which is characterized by Mediterranean climate will be more vigorous. B1 scenario appeared more optimistic for the Ardas and Sperchios River basins, while in the Geropotamos River basin, both applied scenarios brought similar results, in terms of future precipitation response. [ABSTRACT FROM AUTHOR]

This paper focuses on analysis of the time series of 7 Be and 210 Pb activity measured in moss, and the amount, as well as duration of precipitation, to gain a better understanding of the possible relationships between airborne radionuclide deposition and precipitation. Here we consider whether the amount of these airborne radionuclides in moss samples is a cumulative measure of radionuclide deposition and decay, and a new approach for analyses of the relationships between precipitation and moss activity concentrations is suggested. Through these analyses it was shown that comparison of cumulative activity measured at one location using moss, normalized by values of cumulative amount or duration of precipitation, showed different regimes of airborne radionuclide deposition. [ABSTRACT FROM AUTHOR]

A young Mars may well have possessed a global dipolar magnetic field that provided protection for the planet׳s atmosphere from the space weather environment. Against this background, we study in the present paper the effect of various dipole magnetic fields on particle precipitation (range 10 keV–4.5 MeV) on the upper Martian atmosphere as the magnetosphere gradually declined to become an induced magnetosphere. We utilized a hybrid plasma model to provide, in a self-consistent fashion, simulations (that included ion-kinetic effects) of the interaction between the Martian obstacle (magnetized or otherwise) and the solar wind. Besides the intrinsic dipole, with field strengths of ~100 nT and below, we assume modern solar and atmospheric parameters to examine the effect of the single variable, that is the dipole strength. We thereby investigated the precipitation of solar energetic particles on the upper atmosphere of the planet in circumstances characterized by the evolution of a diminishing Martian dynamo that initially generated an ideal dipolar field. It is demonstrated that an assumed Martian dipole would have provided, in the energy range investigated, significant shielding against proton impingement and that the interaction between the solar wind and the assumed Martian magnetic dipole would have been responsible for generating the shielding effect identified. [ABSTRACT FROM AUTHOR]

The decomposition sequence of the supersaturated solid solution leading to the formation of the equilibrium S (Al 2 CuMg) phase in Al Cu Mg alloys has long been the subject of ambiguity and debate. Recent high-resolution synchrotron powder diffraction experiments have shown that the decomposition sequence does involve a metastable variant of the S phase (denoted S1), which has lattice parameters that are distinctly different to those of the equilibrium S phase (denoted S2). In this paper, the difference between these two phases is resolved using high-resolution synchrotron and neutron powder diffraction and atom probe tomography, and the transformation from S1 to S2 is characterised in detail by in situ synchrotron powder diffraction. The results of these experiments confirm that there are no significant differences between the crystal structures of S1 and S2, however, the powder diffraction and atom probe measurements both indicate that the S1 phase forms with a slight deficiency in Cu. The in situ isothermal aging experiments show that S1 forms rapidly, reaching its maximum concentration in only a few minutes at high temperatures, while complete conversion to the S2 phase can take thousands of hours at low temperature. The kinetics of S phase precipitation have been quantitatively analysed for the first time and it is shown that S1 phase forms with an average activation energy of 75 kJ/mol, which is much lower than the activation energy for Cu and Mg diffusion in an Al matrix (136 kJ/mol and 131 kJ/mol, respectively). The mechanism of the replacement of S1 with the equilibrium S2 phase is discussed. [ABSTRACT FROM AUTHOR]

Systematic high quality oceanic in-situ precipitation measurements are requested on an international science level and are essential for improved understanding and validation of hydrological processes in satellite, re-analysis and model data. OceanRAIN, the shipboard “Ocean Rainfall And Ice-phase precipitation measurement Network” for surface validation is, to date, the only systematic long-term disdrometer-based oceanic shipboard precipitation data collection effort to establish a comprehensive statistical basis of precipitation for all climate related hotspots over the global oceans. OceanRAIN utilizes automated disdrometer systems (ODM470) capable of measuring precipitation occurrence, intensity and accumulation and discriminates for rain, snow and mixed-phase precipitation through minute-based particle size distributions. The ODM470 was especially designed for shipboard operation under high and frequently varying wind speeds and rough sea states. This paper provides an overview on the OceanRAIN project, the instrumentation, algorithms, methodology, and data products. The procedure of the data processing chain is outlined, including calibration, shipboard operation, data ingest and quality control. The selected research ships do not circumvent high impact weather, allowing for a collection of the full precipitation spectrum including extremes. By October 2014 the fast growing OceanRAIN database comprised more than 3.7 million minutes of precipitation measurements (including true zeros) since its start in 2010. OceanRAIN aims at increasing knowledge about oceanic precipitation, improving error characterization of GPM (Global Precipitation Measurement) era precipitation retrievals, adding to the continual improvement of the satellite retrieval algorithms, as well as benchmarking existing satellite-based climatologies, re-analysis and model data. The accumulating data volume can be utilized for statistical and process study applications on different temporal and spatial scales, microphysical studies of rain and snow formation, and yields insight to the point-to-area problem of precipitation. Information on OceanRAIN including data products is available via http://www.oceanrain.org . [ABSTRACT FROM AUTHOR]

This paper examines differences between Version 6 (V6) and Version 7 (V7) 3-hourly TRMM (Tropical Rainfall Measuring Mission) Multi-Satellite Precipitation Analysis (TMPA 3B42) research products in JJA (June, July and August) and DJF (December, January and February) over a 13-year period from 1998 to 2010 on a global scale. Different surface types and rain regimes are considered in the comparison. The study finds that more rain events are found in V7 than those in V6 in both JJA and DJF, especially over oceans. Overall, both versions show a good agreement in moderate and heavy rain regimes. High Pearson’s correlation coefficients are found in tropical rain band regions. Histograms of both versions are very similar; however higher frequencies of rain events are found in V7 in light rain regime, especially over oceans, than those in V6. For light rain, rainfall estimates in V6 are less than those in V7 over land and oceans in both seasons. For moderate rain, rainfall estimates in V6 are larger than those in V7 over land in most years. Over oceans, it is a mixed situation in which V6 > V7 for some years and V6 < V7 for the other years. For heavy rain, rainfall estimates in V6 are larger than those in V7 throughout all JJA and DJF seasons for both land and oceans, which is also shown in a case study. Large variance in the individual differences is found in light rain and less in heavy rain. No apparent trends are observed. For light rain, all statistics support that there is an uncertainty issue in both versions. [ABSTRACT FROM AUTHOR]

This paper describes the relationships between rainfall in Spain (1961–2010) and four teleconnection patterns on three time scales (monthly, seasonal and annual) using data from 144 meteorological stations. Spearman's test was used to carry out partial correlations, and spatial distribution maps of the correlation coefficients were generated with kriging method. We regionalise the study area into homogeneous areas containing weather stations with a similar precipitation response to the patterns. The links between precipitation and the NAO, AO and EA are generally positive in the east and negative in the west of Spain, whereas WeMO correlates positively in the north and negatively in the rest. As a general result, the winter months and winter season show the highest percentage of weather stations with statistically significant correlations. Spearman coefficients of up to 0.62 were found for the NAO, AO and EA. WeMO strongly correlates (values up to 0.55) from October to April. Five areas have been defined: centre/southwest, northwest, north, northeast, and southeast. The mode which best explains rainfall in the two western areas is the NAO; in the north, the WeMO exerts a notable influence; EA correlates better with precipitation in the two eastern zones, especially in spring, autumn and part of winter. They are better controllers of rainfall in these areas than in the whole of the study area. [ABSTRACT FROM AUTHOR]

This paper studied the behavior of 94-GHz radar reflectivity ( Z e ) with variation in the properties of low-level water clouds, such as the effective droplet radius ( r e ), geometrical thickness ( D cld ), and liquid water path (LWP), over the northwest Pacific and China. The changes in the distribution of max Z e (the largest Z e within a cloud layer) were examined in terms of variation in the cloud parameters such as small, mid and large categories, while max Z e had monomodal distributions regarding variation in r e and D cld , that appeared bimodal in the small category of LWP. It was confirmed that the small category of LWP contained both non-precipitating clouds in the incipient stage and raining clouds in the dissipating stage. Next, optically measured particle size was combined with LWP derived from the microwave measurement to classify the precipitation type. Applying max Z e and D cld to the analysis of classified precipitation types corroborated the importance of D cld for examining the occurrence of precipitation. Finally, the position of max Z e relative to the cloud top was investigated using a measure of the probability of precipitation (POP) according to variation in r e . The results showed that the Pacific and China had ‘bow’ and ‘funnel’ shapes, respectively. The emergence of these shapes according to the variation in r e was interpreted as the enhancement of Z e due to droplet collisional growth and the attenuation of Z e by the presence of large particles. Furthermore, a detailed analysis of smaller particles (<10 μm in radius) reinforced the idea of rapid, efficient particle growth in the lower part of the cloud. [ABSTRACT FROM AUTHOR]

As high-resolution climate models become increasingly complex, it should be carefully assessed to what extent the improved physics in such models justifies the large computational cost that the complexity imposes. This paper presents a detailed sensitivity study of convective precipitation characteristics to the number of prognostic moments in bulk microphysics schemes using a high-resolution convection-resolving climate model. It is shown that 1-moment and 2-moment microphysics schemes produce more similar surface precipitation characteristics for a composite of 20 real-case convective simulations in Belgium than for many idealized studies conducted before. In the baseline 2-moment scheme, size sorting of particles is counteracted by collisional drop breakup to produce mean drop sizes that are similar to those in the 1-moment version of the scheme. Hence, fallout, evaporation and surface rain rates are very similar between the two versions of the scheme. Conversely, larger sensitivities of precipitation extremes were found to the treatment of drop breakup and the shape of the particle size distributions. Consistent with previous studies, domain-averaged and peak precipitation increased monotonically with increasing breakup equilibrium diameter Deq. Further, it is shown that a negative exponential size distribution results in excessive radar reflectivities for light rain rates. Surface precipitation and the joint distribution of reflectivity and rain rate are best reproduced by a 2-moment version of the scheme that applies gamma distributions with a diagnostic shape parameter for all particles and a large Deq. However, given the large sensitivities and uncertainties associated with collisional drop breakup and size sorting, it is likely that the full potential of improved physics in a 2-moment scheme will remain underexposed as long as these processes are not better understood. [ABSTRACT FROM AUTHOR]

Summary The climate elasticity of runoff is an important indicator that is used to quantify the relationship between changes in runoff and changes in climate variables. It is a function of both climate and catchment characteristics. Recently, Yang and Yang (2011) proposed an analytical derivation of climate elasticity (YY2011), in which a parameter n was used to represent the impact of the catchment characteristics. In China, both climate and catchment characteristics have large spatial variations. To understand the spatial variation of hydrologic response to climate change, this paper divided China into 210 catchments, further calculated the parameter n , and then estimated the climate elasticity and evaluated the contribution of climate change to runoff for each catchment. The results show that n ranges from 0.4 to 3.8 (with a mean of 1.3 and a standard deviation of 0.6), which has a logarithmic relationship with catchment slope; the precipitation elasticity ranges from 1.1 to 4.8 (with a mean of 1.9 and a standard deviation of 0.6), which shows a large regional variation, smaller values (1.1–2.0) mainly appearing in Southern China, the Songhua River basin and the Northwest, and larger values (2.1–4.8) mainly appearing in the Hai River basin, the Liao River basin and the Yellow River basin. In addition, climate contribution to runoff exhibits a large regional variation, the largest positive values (1.1–3.1%/a) occurring in the Northwest, the largest negative values (−1.0 to −0.5%/a) occurring in the Hai River basin and the middle reach of the Yellow River basin. In theory, the YY2011 method is a first-order approximation. The approximation underestimates the precipitation ( P ) contribution to runoff when P increases and overestimates that when P decreases, and the relative error has a median of ∼3% and a maximum of ∼20% when 10% precipitations change in those catchments of China. [ABSTRACT FROM AUTHOR]

The total glacial area of the Cordillera Blanca, Peru, has shrunk by more than 30% in the period of 1930 to the present with a marked glacier retreat also in the recent decades. The aim of this paper is to assess local air temperature and precipitation changes in the Cordillera Blanca and to discuss how these variables could have affected the observed glacier retreat between the 1980s and present. A unique data set from a large number of stations in the region of the Cordillera Blanca shows that after a strong air temperature rise of about 0.31°C per decade between 1969 and 1998, a slowdown in the warming to about 0.13°C per decade occurred for the 30years from 1983 to 2012. Additionally, based on data from a long-term meteorological station, it was found that the freezing line altitude during precipitation days has probably not increased significantly in the last 30years. We documented a cooling trend for maximum daily air temperatures and an increase in precipitation of about 60mm/decade since the early 1980s. The strong increase in precipitation in the last 30years probably did not balance the increase of temperature before the 1980s. It is suggested that recent changes in temperature and precipitation alone may not explain the glacial recession within the thirty years from the early 1980s to 2012. Glaciers in the Cordillera Blanca may be still reacting to the positive air temperature rise before 1980. Especially small and low-lying glaciers are characterised by a serious imbalance and may disappear in the near future. [ABSTRACT FROM AUTHOR]

Abstract: Currently, several satellite missions are employing X-band synthetic aperture radars (SAR) with polarimetric capabilities. In images collected over land by X-band SAR, precipitation results mainly in evident attenuation of the surface returns. Effects of precipitation in polarimetric SAR images and how to exploit them for precipitation studies are emerging topics of interest. This paper investigates polarimetric signatures of precipitation in images collected by the X-band SARs of the Italian Space Agency COSMO SkyMed constellation using the HH-VV alternate polarimetric mode. Analyzed images were collected in 2010 when the constellation was composed of three satellites and operated in the “tandem like” interferometric configuration, which allowed acquisition of the same scene with the same viewing geometry and a minimum decorrelation time of one day. Observations collected in Piedmont (Italy) and Tampa Bay (Florida, US) have been analyzed along with coincident observations collected by operational weather radars, used to reconstruct the component of SAR returns due to precipitation at horizontal and vertical polarization states. Different techniques are used depending on the different characteristics of terrestrial radars. SAR observations reconstructed from terrestrial measurements are in fairly good agreement with actual SAR observations. Results confirm that the attenuation signature in SAR images collected over land is particularly pronounced in the presence of precipitation cells and can be related to the radar reflectivity integrated along the same path. The difference between copolar HH and VV power measurements reveals a differential attenuation due to anisotropy of precipitation, whose range is limited when the SAR incidence angle is low. A specific feature observed in the CosmoSkyMed alternate polarization implementation is the presence of the scalloping effect, a periodic effect along the azimuth direction that cannot always be removed by standard de-scalloping techniques. Amplitude of scalloping between the two polarizations can reach a couple of decibels, close to the maximum range of differential path integrated attenuation. [Copyright &y& Elsevier]