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Abstract: The paper presents the statistical estimation of extreme wind speed using annually r largest order statistics (r-LOS) extracted from the time series of wind data. The method is based on a joint generalized extreme value distribution of r-LOS derived from the theory of Poisson process. The parameter estimation is based on the method of maximum likelihood. The hourly wind speed data collected at 30 stations in Ontario, Canada, are analyzed in the paper. The results of r-LOS method are compared with those obtained from the method of independent storms (MIS) and specifications of the Canadian National Building Code (CNBC-1995). The CNBC estimates are apparently conservative upper bound due to large sampling error associated with annual maxima analysis. Using the r-LOS method, the paper shows that the wind pressure data can be suitably modelled by the Gumbel distribution. [Copyright &y& Elsevier]

This paper extends the results of the "fuzzy" OEN mixture model to estimate the probability densities, autocorrelations and spectra of the individual wind mechanisms within the mixed climates of Rome: Fiumicino and Ciampino, and discusses the implications for synthesising long-term simulation of the surface wind vector and for the assessment of extreme mean wind speeds. The paper confirms, from observations, that the strong diurnal effects observed at these locations are confined to a low range of wind speeds and do not significantly affect the extreme wind speeds for annual, or longer, epochs. A procedure is proposed to simulate exceptionally long timeseries of the mean wind vector from the OEN mixture model to examine convergence of the upper tail of the extreme wind speed distribution to the presumed asymptote. Every observation is assigned a fuzzy probability of belonging to each ellipse mechanism of the OEN model and sorted into nominally diurnal (sea-land breeze, transient coastal jet or nocturnal downslope wind) and nominally non-diurnal (pressure-driven geostrophic wind) sets. All the 200 largest independent daily-maximum 10-min mean wind speeds belong to the non-diurnal set. Diurnal variations have no significant effect on the distribution of annual maximum mean wind speeds. Key: All – All daily maxima. Hard D , Hard ND – Daily maxima of uncorrected Diurnal and Non-diurnal sets. Serial D , Serial ND – Independent maxima separated by at least 4 days of serially-corrected Diurnal and Non-diurnal sets.The paper presents the groundwork necessary to define a procedure to generate very long synthetic timeseries of wind speed and direction that correctly reproduce seasonal-diurnal variations. Image 1 • The fuzzy model extends to estimate the PDFs, ACFs and PSDFs of the OEN model components. • Diurnal variation is confined to low wind speeds and does not contribute to the annual maxima. • The OEN random perturbation PDFs can be represented by a hyperbolic probability distribution. • The OEN random perturbation PSDFs can be represented by a bandwidth-limited pink noise model. • The Harris (2014) simulation method is adapted to generate seasonal/diurnal timeseries of each OEN component. [ABSTRACT FROM AUTHOR]

• A first comprehensive and systematic review on the research of extreme precipitation in China. • Variation and regional characteristics of extreme precipitation under non-stationary conditions due to climate change and human activities. • Supports and basis for engineering application and further research on extreme precipitation and flood in China. Recent years have witnessed global massive property losses and casualties caused by extreme precipitation and its subsequent natural disasters, including floods and landslides. China is one of the countries deeply affected by these casualties. If the statistical characteristics and laws of extreme precipitation could be clearly grasped, then the negative impacts triggered by it may be minimized. China is a vast country and diverse in climate and terrain, hence different regions may be suitable for different analyses and research methods. Therefore, it is necessary to clarify the research progress, methods and current status of extreme precipitation across the country. This paper attempts to provide a comprehensive review of techniques and methods used in extreme precipitation research and engineering practice and their applications. The literature is reviewed focusing on seven aspects: (1) annual maxima method (AM), (2) peaks over threshold method (POT), (3) probable maximum precipitation (PMP), (4) non-stationary analysis of precipitation extremes, (5) intensity-duration-frequency curves (IDF), (6) uncertainty in extreme precipitation frequency analysis, and (7) spatial variability of extreme precipitation. Research on extreme precipitation in China is generally based or centered on the above seven aspects. The current study aims to provide ideas for further research on extreme precipitation frequency analysis and its response to climate change and human activities. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract: This paper provides a contribution to the testing of existing methods of analysis of extreme wind speeds and to the development of better alternatives. A method is developed for synthesising a correlated random time series with a Rayleigh amplitude distribution and an arbitrary auto-correlation. The auto-correlation is selected to be the Von Karman model because the method is then used to generate 20,000 years of simulated hourly mean wind speeds. Annual maxima are extracted and exhibited on Gumbel plots. Familiar problems with convergence to asymptotic forms are confirmed and a new problem is revealed in that the annual rate parameter, previously believed to be constant, is found to vary significantly in the range of the measured data encountered in practical extreme value analyses. With the exception of newly developed penultimate methods, all the existing methods of analysis depend implicitly on either convergence to an asymptotic form, or invariance of the annual rate parameter, or both. This has serious implications for the accuracy of these methods, not only for the analysis of annual maxima, but also for extensions of these methods developed to use more data from each year. [Copyright &y& Elsevier]

Summary: The analysis of annual extremes of hydrological and meteorological variables is frequently complicated by the presence of gaps in record, and when records are not only fragmented but also short, it is necessary to utilize to the full the information contained in them. One method is to abstract for statistical analysis all extreme events whose peaks exceed a pre-selected threshold value, but the threshold must be carefully chosen if “clumps” of peaks are to be avoided. A common alternative is a statistical analysis of maxima in years that are complete, possibly including in the analysis values from incomplete years according to some empirical rule. A plausible probability distribution has been proposed by [Jones, D.A., 1997. Plotting positions via maximum likelihood for a non-standard situation. Hydrol. Earth Syst. Sci. 1, 357–366] for the extremes observed in incomplete years, which takes into account not only the proportion of record that is missing within an incomplete year, but also the effect of seasonality. As part of a larger study on the hydrology of the Amazon basin, this paper uses 484 records with length not less than 12years from an extensive network of 750 rain gauges, to compare the method proposed by Jones (termed the DAJ method) with the following alternative procedures: (i) using only complete years of record and (ii) including years with less than 20% missing record, as if they were complete. Using the large-sample variance calculated for the annual maximum one-day rainfall with 100-year return period (P 100), the method proposed by Jones is shown to give smaller standard errors than either of the alternatives. Using the number of years in each record to calculate weighted mean variances over the 484 records, the mean standard errors of P 100 obtained by methods (i) and (ii) were 1.25 and 1.06 times the mean standard error given by the DAJ method. The precision of estimates obtained by the latter method was therefore better than either alternative. [Copyright &y& Elsevier]

Abstract: The debate on the enhanced greenhouse effect continues, confusing the climate change impact analysis and the decision makers. Hence, this paper is addressed to the extreme regional-scale evaluation of global climate model (GCM). Given that assessment of models is crucial to consider future scenarios in climate change impact analyses, the exploratory work is essential, as it identifies the strengths and weaknesses of GCM to regional-scale differences in model performance, providing insights on regional-scale climate interactions and processes. That is why this manuscript attempts to compare the spatial pattern of a longer temporal aggregation period (1960/61–1989/90) for extreme temperatures simulated by a particular GCM (HadCM3) to that of NCEP Reanalyses, which are considered as “truth”. Climatic risk analyses and forecasting, concern among other topics, assess an interpretation in intensity and frequency of recurrence of long duration extreme values (heat/cold waves). One study assesses the impact of extreme events analysing the temporal dependence of extreme events. The present study leads to the conclusion that the HadCM3 Simulations do not reproduce reasonably the NCEP Reanalyses over the Modelling the Impact of Climate Extremes (MICE—EU Project) domain, even despite the fact that the climatology of extremes has demonstrated very similar spatial patterns. Consequently, all diagnostics of extremes led support to the fitted Generalised Extreme Value (GEV) model for extrapolation, suggesting that the HadCM3 is not adequate for forecasting, simulating future scenarios of extremes based on the analysis of risk or probability maps for spatial uncertainties. The greatest difference between the two models is in terms of the precision of estimation. [Copyright &y& Elsevier]