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Abstract Adverse weather conditions can have different effects on different types of road crashes. We quantify the combined effects of traffic volume and meteorological parameters on hourly probabilities of 78 different crash types using generalized additive models. Using tensor product bases, we model non-linear relationships and combined effects of different meteorological parameters. We evaluate the increase in relative risk of different crash types in case of precipitation, sun glare and high wind speeds. The largest effect of snow is found in case of single-truck crashes, while rain has a larger effect on single-car crashes. Sun glare increases the probability of multi-car crashes, in particular at higher speed limits and in case of rear-end crashes. High wind speeds increase the probability of single-truck crashes and, for all vehicle types, the risk of crashes with objects blown on the road. A comparison of the predictive power of models with and without meteorological variables shows an improvement of scores of up to 24%, which makes the models suitable for applications in real-time traffic management or impact-based warning systems. These could be used by authorities to issue weather-dependent driving restrictions or situation-specific on-board warnings to improve road safety.

Abstract Weather has a substantial influence on people’s travel behavior. In this study we analyze if meteorological variables can improve predictions of hourly traffic counts at 1400 stations on federal roads and highways in Germany. Motorbikes, cars, vans and trucks are distinguished. It is evaluated in how far the mean squared error of Poisson regression models for hourly traffic counts is reduced by using precipitation, temperature, cloud cover and wind speed data. It is shown that in particular motorbike counts are strongly weather-dependent. On federal roads the mean squared error is reduced by up to 60% in models with meteorological predictor variables, when compared to models without meteorological variables. A detailed analysis of the models for motorbike counts reveals non-linear relationships between the meteorological variables and motorbike counts. Car counts are shown to be specifically sensitive to weather in touristic regions like seaside resorts and nature parks. The findings allow for several potential applications like improvements of route planning in navigation systems, implementations in traffic management systems, day-ahead planning of visitor numbers in touristic areas or the usage in road crash modelling.

Abstract Artificial intelligence (AI) is widely used to analyze gastrointestinal (GI) endoscopy image data. AI has led to several clinically approved algorithms for polyp detection, but application of AI beyond this specific task is limited by the high cost of manual annotations. Here, we show that a weakly supervised AI can be trained on data from a clinical routine database to learn visual patterns of GI diseases without any manual labeling or annotation. We trained a deep neural network on a dataset of N = 29,506 gastroscopy and N = 18,942 colonoscopy examinations from a large endoscopy unit serving patients in Germany, the Netherlands and Belgium, using only routine diagnosis data for the 42 most common diseases. Despite a high data heterogeneity, the AI system reached a high performance for diagnosis of multiple diseases, including inflammatory, degenerative, infectious and neoplastic diseases. Specifically, a cross-validated area under the receiver operating curve (AUROC) of above 0.70 was reached for 13 diseases, and an AUROC of above 0.80 was reached for two diseases in the primary data set. In an external validation set including six disease categories, the AI system was able to significantly predict the presence of diverticulosis, candidiasis, colon and rectal cancer with AUROCs above 0.76. Reverse engineering the predictions demonstrated that plausible patterns were learned on the level of images and within images and potential confounders were identified. In summary, our study demonstrates the potential of weakly supervised AI to generate high-performing classifiers and identify clinically relevant visual patterns based on non-annotated routine image data in GI endoscopy and potentially other clinical imaging modalities.

Freva – Free Evaluation System Framework for Earth system modeling is an efficient solution to handle evaluation systems of research projects, institutes or universities in the climate community. It is a scientific software framework for high performance computing that provides all its available features both in a shell and web environment. The main system design is equipped with the programming interface, history of evaluations, and a standardized model database. Plugin – a generic application programming interface allows scientific developers to connect their analysis tools with the evaluation system independently of the programming language. History – the configuration sub-system stores every analysis performed with the evaluation system in a database. Databrowser – an implemented meta data system with its advanced but easy-to-handle search tool supports scientists and their plugins to retrieve the required information of the database. The combination of these three core components, increases the scientific outcome and enables transparency and reproducibility for research groups using Freva as their framework for evaluation of Earth system models.

Background & Aims: Macrophage migration inhibitory factor (MIF) is an inflammatory cytokine and an important regulator of innate immune responses. We hypothesised that serum concentrations of MIF are associated with disease severity and outcome in patients with decompensated cirrhosis and acute-on-chronic liver failure (ACLF). Methods: Circulating concentrations of MIF and its soluble receptor CD74 (sCD74) were determined in sera from 292 patients with acute decompensation of cirrhosis defined as new onset or worsening of ascites requiring hospitalisation. Of those, 78 (27%) had ACLF. Short-term mortality was assessed 90 days after inclusion. Results: Although serum concentrations of MIF and sCD74 did not correlate with liver function parameters or ACLF, higher MIF (optimum cut-off >2.3 ng/ml) and lower concentrations of sCD74 (optimum cut-off

The seasonal cycle of extreme precipitation in Germany is investigated by fitting statistical models to monthly maxima of daily precipitation sums for 2,865 rain gauges. The basis is a non-stationary generalized extreme value (GEV) distribution variation of location and scale parameters. The negative log-likelihood serves as the forecast error for a cross validation to select adequate orders of the harmonic functions for each station. For nearly all gauges considered, the seasonal model is more appropriate to estimate return levels on a monthly scale than a stationary GEV used for individual months. The 100-year return-levels show the influence of cyclones in the western, and convective events in the eastern part of Germany. In addition to resolving the seasonality, we use a simulation study to show that annual return levels can be estimated more precisely from a monthly-resolved seasonal model than from a stationary model based on annual maxima.

The Antarctic Oscillation (AAO) is the dominant mode of atmospheric variability in the southern hemisphere. It is obtained via a principal component analysis (PCA) for geopotential height anomalies. Being the southern hemisphere's dominant mode, an adequate representation in earth system models is desirable. This paper evaluates to what extent the AAO and related precipitation is represented in the Max Planck Institute's Earth System Model (MPI-ESM). To this end we compare AAO spatial patterns (empirical orthogonal functions, EOFs), spectral properties of the associated principal components (PCs) and AAO-related precipitation patterns of MPI-ESM to three reanalyses: the ECMWF's ERA-40 and ERA-Interim, and the NCEP/NCAR 40-year reanalysis project. Differences between MPI-ESM and ERA-Interim leading EOFs reveal that the three typical centres of action are less pronounced and slightly shifted in the model. Spectral density estimates of the associated PCs show reduced variability in the MPI-ESM for periods between 4 to 5 months. The relation between AAO and southern hemispheric precipitation is assessed via composites and correlation analysis. In both, model and reanalyses, a negative AAO index leads to a general increase of precipitation between 30° S and 50° S and a decrease south of 50° S. Differences between maps of correlation for AAO and precipitation are most prominent near Indonesia and Antarctica probably due to a lack of pressure around Antarctica in the model. Altogether the MPI-ESM underestimates the relation of AAO and southern hemispheric precipitation but gives the correct sign and spatial distribution of correlation values.

We present the evaluation of temperature and precipitation forecasts obtained with the MiKlip decadal climate prediction system. These decadal hindcast experiments are verified with respect to the accuracy of the ensemble mean and the ensemble spread as a representative for the forecast uncertainty. The skill assessment follows the verification framework already used by the decadal prediction community, but enhanced with additional evaluation techniques like the logarithmic ensemble spread score. The core of the MiKlip system is the coupled Max Planck Institute Earth System Model. An ensemble of 10 members is initialized annually with ocean and atmosphere reanalyses of the European Centre for Medium-Range Weather Forecasts. For assessing the effect of the initialization, we compare these predictions to uninitialized climate projections with the same model system. Initialization improves the accuracy of temperature and precipitation forecasts in year 1, particularly in the Pacific region. The ensemble spread well represents the forecast uncertainty in lead year 1, except in the tropics. This estimate of prediction skill creates confidence in the respective 2014 forecasts, which depict less precipitation in the tropics and a warming almost everywhere. However, large cooling patterns appear in the Northern Hemisphere, the Pacific South America and the Southern Ocean. Forecasts for 2015 to 2022 show even warmer temperatures than for 2014, especially over the continents. The evaluation of lead years 2 to 9 for temperature shows skill globally with the exception of the eastern Pacific. The ensemble spread can again be used as an estimate of the forecast uncertainty in many regions: It improves over the tropics compared to lead year 1. Due to a reduction of the conditional bias, the decadal predictions of the initialized system gain skill in the accuracy compared to the uninitialized simulations in the lead years 2 to 9. Furthermore, we show that increasing the ensemble size improves the MiKlip decadal climate prediction system for all lead years.

Winter wind storms related to intense extra-tropical cyclones are meteorological extreme events, often with major impacts on economy and human life, especially for Europe and the mid-latitudes. Hence, skillful decadal predictions regarding the frequency of their occurrence would be of great socio-economic value. The present paper extends the study of Kruschke et al. (2014) in several aspects. First, this study is situated in a more impact oriented context by analyzing the frequency of potentially damaging wind storm events instead of targeting at cyclones as general meteorological features which was done by Kruschke et al. (2014). Second, this study incorporates more data sets by analyzing five decadal hindcast experiments – 41 annual (1961–2001) initializations integrated for ten years each – set up with different initialization strategies. However, all experiments are based on the Max-Planck-Institute Earth System Model in a low-resolution configuration (MPI-ESM-LR). Differing combinations of these five experiments allow for more robust estimates of predictive skill (due to considerably larger ensemble size) and systematic comparisons of the underlying initialization strategies. Third, the hindcast experiments are corrected for model bias and potential drifts over lead time by means of a novel parametric approach, accounting for non-stationary model drifts. We analyze whether skillful probabilistic three-category forecasts (enhanced, normal or decreased) can be provided regarding winter (ONDJFM) wind storm frequencies over the Northern Hemisphere (NH). Skill is assessed by using climatological probabilities and uninitialized transient simulations as reference forecasts. It is shown that forecasts of average winter wind storm frequencies for winters 2–5 and winters 2–9 are skillful over large parts of the NH. However, most of this skill is associated with external forcing from transient greenhouse gas and aerosol concentrations, already included in the uninitialized simulations. Only over East Asia and the Northwest Pacific, the Northwest Atlantic as well as the Eastern Mediterranean the initialized hindcasts perform significantly better than the uninitialized simulations. While no significant differences are evident between anomaly- and full-field-initialization, initializing the model's ocean component from GECCO2-ocean-reanalysis yields slightly better results than from ORA-S4, especially over the Northeast Pacific. Additionally, it is shown that the novel parametric drift-correction approach – estimating potential cubic drifts with parameters linearly changing in time – is more appropriate than the standard procedure – estimating constant model drifts via the lead-time-dependent bias – and, hence, yields higher skill estimates.

Convection-permitting models (CPMs)—the newest generation of high-resolution climate models—have been shown to greatly improve the representation of subdaily and hourly precipitation, in particular for extreme rainfall. Intense precipitation events, however, often occur on subhourly timescales. The distribution of subhourly precipitation, extreme or otherwise, during a rain event can furthermore have important knock-on effects on hydrological processes. Little is known about how well CPMs represent precipitation at the subhourly timescale, compared to the hourly. Here we perform multi-decadal CPM simulations centred over Catalonia and, comparing with a high temporal-resolution gauge network, find that the CPM simulates subhourly precipitation at least as well as hourly precipitation is simulated. While the CPM inherits a dry bias found in its parent model, across a range of diagnostics and aggregation times (5, 15, 30 and 60 min) we find no consistent evidence that the CPM precipitation bias worsens with shortening temporal aggregation. We furthermore show that the CPM excels in its representation of subhourly extremes, extending previous findings at the hourly timescale. Our findings support the use of CPMs for modelling subhourly rainfall and add confidence to CPM-based climate projections of future changes in subhourly precipitation, particularly for extremes.

The link between the indices of twelve atmospheric teleconnection patterns (mostly Northern Hemispheric) and gridded European temperature data is investigated by means of multiple linear regression models for each grid cell and month. Furthermore index-specific signals are calculated to estimate the contribution to temperature anomalies caused by each individual teleconnection pattern. To this extent, an observational product of monthly mean temperature (E-OBS), as well as monthly time series of teleconnection indices (CPC, NOAA) for the period 1951–2010 are evaluated. The stepwise regression approach is used to build grid cell based models for each month on the basis of the five most important teleconnection indices (NAO, EA, EAWR, SCAND, POLEUR), which are motivated by an exploratory correlation analysis. The temperature links are dominated by NAO and EA in Northern, Western, Central and South Western Europe, by EAWR during summer/autumn in Russia/Fenno-Scandia and by SCAND in Russia/Northern Europe; POLEUR shows minor effects only. In comparison to the climatological forecast, the presented linear regression models improve the temperature modelling by 30–40 % with better results in winter and spring. They can be used to model the spatial distribution and structure of observed temperature anomalies, where two to three patterns are the main contributors. As an example the estimated temperature signals induced by the teleconnection indices is shown for February 2010.

Mid-latitudinal cyclones are a key factor for understanding regional anomalies in primary meteorological parameters such as temperature or precipitation. Extreme cyclones can produce notable impacts on human society and economy, for example, by causing enormous economic losses through wind damage. Based on 41 annually initialised (1961–2001) hindcast ensembles, this study evaluates the ability of a single-model decadal forecast system (MPI-ESM-LR) to provide skilful probabilistic three-category forecasts (enhanced, normal or decreased) of winter (ONDJFM) extra-tropical cyclone frequency over the Northern Hemisphere with lead times from 1 yr up to a decade. It is shown that these predictions exhibit some significant skill, mainly for lead times of 2–5 yr, especially over the North Atlantic and Pacific. Skill for intense cyclones is generally higher than for all detected systems. A comparison of decadal hindcasts from two different initialisation techniques indicates that initialising from reanalysis fields yields slightly better results for the first forecast winter (month 10–15), while initialisation based on an assimilation experiment provides better skill for lead times between 2 and 5 yr. The reasons and mechanisms behind this predictive skill are subject to future work. Preliminary analyses suggest a strong relationship of the model's skill over the North Atlantic with the ability to predict upper ocean temperatures modulating lower troposphere baroclinicity for the respective area and time scales.

Decadal prediction systems are designed to become a valuable tool for decision making in different sectors of economy, administration or politics. Progress in decadal predictions is also expected to improve our scientific understanding of the climate system. The German Federal Ministry for Education and Research (BMBF) therefore funds the German national research project MiKlip (Mittelfristige Klimaprognosen). A network of German research institutions contributes to the development of the system by conducting individual research projects. This special issue presents a collection of papers with results of the evaluation activities within the first phase of MiKlip. They document the improvements of the MiKlip decadal prediction system which were achieved during the first phase. Key aspects are the role of initialization strategies, model resolution or ensemble size. Additional topics are the evaluation of specific weather parameters in selected regions and the use of specific observational datasets for the evaluation.

Parental harsh disciplining, like corporal punishment, has consistently been associated with adverse mental health outcomes in children. It remains a challenge to accurately assess the consequences of harsh discipline, as researchers and clinicians generally rely on parent report of young children's problem behaviors. If parents rate their parenting styles and their child's behavior this may bias results. The use of child self-report on problem behaviors is not common but may provide extra information about the relation of harsh parental discipline and problem behavior. We examined the independent contribution of young children's self-report above parental report of emotional and behavioral problems in a study of maternal and paternal harsh discipline in a birth cohort. Maternal and paternal harsh discipline predicted both parent reported behavioral and parent reported emotional problems, but only child reported behavioral problems. Associations were not explained by pre-existing behavioral problems at age 3. Importantly, the association with child reported outcomes was independent from parent reported problem behavior. These results suggest that young children's self-reports of behavioral problems provide unique information on the effects of harsh parental discipline. Inclusion of child self-reports can therefore help estimate the effects of harsh parental discipline more accurately.

Different etiologies such as drug toxicity, acute viral hepatitis B or acetaminophen poisoning can cause acute liver injury (ALI) or even acute liver failure (ALF). Excessive cell death of hepatocytes in the liver is known to result in a strong hepatic inflammation. Experimental murine models of liver injury highlighted the importance of hepatic macrophages, so-called Kupffer cells, for initiating and driving this inflammatory response by releasing proinflammatory cytokines and chemokines including tumor necrosis factor (TNF), interleukin-6 (IL-6), IL-1-beta or monocyte chemoattractant protein 1 (MCP-1, CCL2) as well as activating other non-parenchymal liver cells, e.g. endothelial or hepatic stellate cells (HSC). Many of these proinflammatory mediators can trigger hepatocytic cell death pathways, e.g. via caspase activation, but also activate protective signaling pathways, e.g. via nuclear factor kappa B (NF-kB). Recent studies in mice demonstrated that these macrophage actions largely depend on the recruitment of monocytes into the liver, namely of the inflammatory Ly6c+ (Gr1+) monocyte subset as precursors of tissue macrophages. The chemokine receptor CCR2 and its ligand MCP-1/CCL2 promote monocyte subset infiltration upon liver injury. In contrast, the chemokine receptor CX3CR1 and its ligand fractalkine (CX3CL1) are important negative regulators of monocyte infiltration by controlling their survival and differentiation into functionally diverse macrophage subsets upon injury. The recently identified cellular and molecular pathways for monocyte subset recruitment, macrophage differentiation and interactions with other hepatic cell types in the injured liver may therefore represent interesting novel targets for future therapeutic approaches in ALF.

BACKGROUND:Interleukin-8 (IL-8, CXCL8) is a potent chemoattractant for neutrophils and contributes to acute liver inflammation. Much less is known about IL-8 in chronic liver diseases (CLD), but elevated levels were reported from alcoholic and hepatitis C-related CLD. We investigated the regulation of IL-8, its receptors CXCR1 and CXCR2 and possible IL-8 responding cells in CLD patients. METHODOLOGY:Serum IL-8 levels were measured in CLD patients (n = 200) and healthy controls (n = 141). Intrahepatic IL-8, CXCR1 and CXCR2 gene expression was quantified from liver samples (n = 41), alongside immunohistochemical neutrophil (MPO) and macrophage (CD68) stainings. CXCR1 and CXCR2 expression was analyzed on purified monocytes from patients (n = 111) and controls (n = 31). In vitro analyses explored IL-8 secretion by different leukocyte subsets. PRINCIPAL FINDINGS:IL-8 serum levels were significantly increased in CLD patients, especially in end-stage cirrhosis. Interestingly, patients with cholestatic diseases exhibited highest IL-8 serum concentrations. IL-8 correlated with liver function, inflammatory cytokines and non-invasive fibrosis markers. Intrahepatically, IL-8 and CXCR1 expression were strongly up-regulated. However, intrahepatic IL-8 could only be associated to neutrophil infiltration in patients with primary biliary cirrhosis (PBC). In non-cholestatic cirrhosis, increased IL-8 and CXCR1 levels were associated with hepatic macrophage accumulation. In line, CXCR1, but not CXCR2 or CXCR3, expression was increased on circulating monocytes from cirrhotic patients. Moreover, monocyte-derived macrophages from CLD patients, especially the non-classical CD16⁺ subtype, displayed enhanced IL-8 secretion in vitro. CONCLUSIONS:IL-8 is strongly activated in CLD, thus likely contributing to hepatic inflammation. Our study suggests a novel role of IL-8 for recruitment and activation of hepatic macrophages via CXCR1 in human liver cirrhosis.

BackgroundMonocyte-derived macrophages critically perpetuate inflammatory responses after liver injury as a prerequisite for organ fibrosis. Experimental murine models identified an essential role for the CCR2-dependent infiltration of classical Gr1/Ly6C(+) monocytes in hepatic fibrosis. Moreover, the monocyte-related chemokine receptors CCR1 and CCR5 were recently recognized as important fibrosis modulators in mice. In humans, monocytes consist of classical CD14(+)CD16(-) and non-classical CD14(+)CD16(+) cells. We aimed at investigating the relevance of monocyte subpopulations for human liver fibrosis, and hypothesized that 'non-classical' monocytes critically exert inflammatory as well as profibrogenic functions in patients during liver disease progression.Methodology/principal findingsWe analyzed circulating monocyte subsets from freshly drawn blood samples of 226 patients with chronic liver disease (CLD) and 184 healthy controls by FACS analysis. Circulating monocytes were significantly expanded in CLD-patients compared to controls with a marked increase of the non-classical CD14(+)CD16(+) subset that showed an activated phenotype in patients and correlated with proinflammatory cytokines and clinical progression. Correspondingly, CD14(+)CD16(+) macrophages massively accumulated in fibrotic/cirrhotic livers, as evidenced by immunofluorescence and FACS. Ligands of monocyte-related chemokine receptors CCR2, CCR1 and CCR5 were expressed at higher levels in fibrotic and cirrhotic livers, while CCL3 and CCL4 were also systemically elevated in CLD-patients. Isolated monocyte/macrophage subpopulations were functionally characterized regarding cytokine/chemokine expression and interactions with primary human hepatic stellate cells (HSC) in vitro. CD14(+)CD16(+) monocytes released abundant proinflammatory cytokines. Furthermore, CD14(+)CD16(+), but not CD14(+)CD16(-) monocytes could directly activate collagen-producing HSC.Conclusions/significanceOur data demonstrate the expansion of CD14(+)CD16(+) monocytes in the circulation and liver of CLD-patients upon disease progression and suggest their functional contribution to the perpetuation of intrahepatic inflammation and profibrogenic HSC activation in liver cirrhosis. The modulation of monocyte-subset recruitment into the liver via chemokines/chemokine receptors and their subsequent differentiation may represent promising approaches for therapeutic interventions in human liver fibrosis.

The adipocyte-derived cytokine leptin was implicated to link inflammation and metabolic alterations. We investigated the potential role of leptin components in critically ill patients, because systemic inflammation, insulin resistance, and hyperglycemia are common features of critical illness. Upon admission to Medical Intensive Care Unit (ICU), free leptin and soluble leptin-receptor serum concentrations were determined in 137 critically ill patients (95 with sepsis, 42 without sepsis) and 26 healthy controls. Serum leptin or leptin-receptor did not differ between patients or controls and were independent of sepsis. However, serum leptin was closely associated with obesity and diabetes and clearly correlated with markers of metabolism and liver function. Leptin-receptor was an unfavourable prognostic indicator, associated with mortality during three years follow-up. Our study indicates a functional role of leptin in the pathogenesis of severe illness and emphasizes the impact of complex metabolic alterations on the clinical outcome of critically ill patients.

Epigenetic modifications, including DNA methylation, represent a potential mechanism for environmental impacts on human disease. Maternal smoking in pregnancy remains an important public health problem that impacts child health in a myriad of ways and has potential lifelong consequences. The mechanisms are largely unknown, but epigenetics most likely plays a role. We formed the Pregnancy And Childhood Epigenetics (PACE) consortium and meta-analyzed, across 13 cohorts (n = 6,685), the association between maternal smoking in pregnancy and newborn blood DNA methylation at over 450,000 CpG sites (CpGs) by using the Illumina 450K BeadChip. Over 6,000 CpGs were differentially methylated in relation to maternal smoking at genome-wide statistical significance (false discovery rate, 5%), including 2,965 CpGs corresponding to 2,017 genes not previously related to smoking and methylation in either newborns or adults. Several genes are relevant to diseases that can be caused by maternal smoking (e.g., orofacial clefts and asthma) or adult smoking (e.g., certain cancers). A number of differentially methylated CpGs were associated with gene expression. We observed enrichment in pathways and processes critical to development. In older children (5 cohorts, n = 3,187), 100% of CpGs gave at least nominal levels of significance, far more than expected by chance (p value < 2.2 × 10(-16)). Results were robust to different normalization methods used across studies and cell type adjustment. In this large scale meta-analysis of methylation data, we identified numerous loci involved in response to maternal smoking in pregnancy with persistence into later childhood and provide insights into mechanisms underlying effects of this important exposure.

Annual maxima of daily precipitation sums can be typically described well with a stationary generalized extreme value (GEV) distribution. In many regions of the world, such a description does also work well for monthly maxima for a given month of the year. However, the description of seasonal and interannual variations requires the use of non-stationary models. Therefore, in this paper we propose a non-stationary modeling strategy applied to long time series from rain gauges in Germany. Seasonal variations in the GEV parameters are modeled with a series of harmonic functions and interannual variations with higher-order orthogonal polynomials. By including interactions between the terms, we allow for the seasonal cycle to change with time. Frequently, the shape parameter ξ of the GEV is estimated as a constant value also in otherwise instationary models. Here, we allow for seasonal–interannual variations and find that this is beneficial. A suitable model for each time series is selected with a stepwise forward regression method using the Bayesian information criterion (BIC). A cross-validated verification with the quantile skill score (QSS) and its decomposition reveals a performance gain of seasonally–interannually varying return levels with respect to a model allowing for seasonal variations only. Some evidence can be found that the impact of climate change on extreme precipitation in Germany can be detected, whereas changes are regionally very different. In general, an increase in return levels is more prevalent than a decrease. The median of the extreme precipitation distribution (2-year return level) generally increases during spring and autumn and is shifted to later times in the year; heavy precipitation (100-year return level) rises mainly in summer and occurs earlier in the year. [ABSTRACT FROM AUTHOR]