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Stochastic daily weather generators are often used to generate long time series of weather variables to drive hydrological and agricultural models. More recently, they have also been used as a downscaling tool for studying the impacts of climate change. This article describes a versatile stochastic weather generator (WeaGETS) for producing daily precipitation, and maximum and minimum temperatures (Tmax and Tmin). WeaGETS regroups several options of other weather generators into one package, such as three Markov models to produce precipitation occurrence, four distributions to generate wet day precipitation amount, and two methods to simulate Tmax and Tmin. More importantly, a spectral correction approach is included in WeaGETS for correcting the underestimation of interannual variability, which is a problem common to all weather generators. The performance of WeaGETS is demonstrated through a comparison against two well-known weather generators (WGEN and CLIGEN) with respect to the generation of precipitation, Tmax, and Tmin for two Canadian meteorological stations. The results show that the widely used first-order Markov model is adequate for producing precipitation occurrence, but it underestimates the longest dry spell for the low-precipitation station. The higher-order models have positive effects. The mixed exponential and skewed normal Pearson III distributions are consistently better than the exponential and gamma distributions at generating precipitation amounts. The two-component mixed exponential distribution is better at representing extreme precipitation events than the other three distributions. WeaGETS is consistently better than WGEN and CLIGEN at producing Tmax and Tmin. Both WGEN and CLIGEN underestimate the monthly and interannual variances of precipitation and temperatures. However, WeaGETS successfully preserves the observed low-frequency variability and autocorrelation functions of precipitation and temperatures. Overall, WeaGETS is consistently better than the other two weather generators (WGEN and CLIGEN) for producing precipitation, Tmax and Tmin. The Mat lab freeware allows for easy modification of all routines, making it easy to add additional weather variables to simulate. [ABSTRACT FROM AUTHOR]

Stochastic weather generators are commonly used to generate time series of weather variables to drive agricultural and hydrologic models. One of their most appealing features is the ability to rapidly generate the very long time series used in agricultural and hydrological impact studies. However, they also have various problems, such as the inability to represent the interannual variability of the climate system, and it is difficult for them to accurately preserve the auto- and cross-correlation of maximum and minimum temperatures (Tmax and Tmin). This research aims to merge two widely used weather generators (CLIGEN (v5.22564) and WGEN) into a hybrid method that combines the strengths of each (referred to as the conditional method) for generating Tmax and Tmin and apply an approach to correct the interannual variability of Tmax and Tmin (referred to as the spectral correction method). The results show that CLIGEN reproduced mean daily Tmax and Tmin very well. WGEN also produced mean daily Tmax reasonably well but slightly underestimated mean daily Tmin. Moreover, CLIGEN was better than WGEN at producing standard deviations of daily Tmax and Tmin. The conditional and spectral correction methods resulted in a weather generator that accurately produced means, standard deviations, and extremes of daily Tmax and Tmin. The auto- and cross-correlations of and between daily Tmax and Tmin were well reproduced and much better than those of CLIGEN- and WGEN-generated data. Moreover, the spectral correction approach successfully reproduced the observed interannual variability of Tmax and Tmin. [ABSTRACT FROM AUTHOR]

The potential of remote sensing for providing information to hydrologists and water resources practitioners has been recognized since the1970s. The variety of satellite and airborne platforms and the greater ease of access to imagery now make it possible to evaluate and quantify an increasingly large number of watershed physical characteristics and state variables. Canadian scientists have been very active over the last 5 years creating algorithms to extract hydrological information from remotely sensed data and to develop new, or adapt existing, hydrological methods capable of making efficient use of this new information. Over the years, research and applications of remote sensing in Canadian hydrology have embraced a variety of topics and recentresearch has placed significant emphasis on radar remote sensing as the Canadian RADARSAT satellite was launched successfully on 4 November 1995. This paper reviews recent (1995-99) remote sensing contributions in hydrology by Canadians, specifically focusing on the usefulness and applicability of current remote sensing technology for water management purposes. A very brief description of the theory underlyingeach application as well as relevant sensors is presented. [ABSTRACT FROM AUTHOR]

The propagation of sonic boom through kinematic turbulence is known to have an important impact on the noise perceived at the ground. In this work, a recent numerical method called FLHOWARD3D based on a one-way approach is used to simulate the propagation of classical and low-boom waveforms. Kinematic turbulence is synthesized following a von Kármán energy spectrum. Two- and three-dimensional (2D and 3D) simulations are compared to experimental measurements, and 2D simulations are found to be slightly less accurate than 3D ones but still consistent with experimental levels around 98% of the time. A stochastic study is carried out on the 2D simulation using the generalized polynomial chaos method with parameters of the von Kármán spectrum as uncertain parameters. Differences between the propagation of a classical N-wave and low booms are observed: the classical N-wave shows higher peak pressure and variations than low-boom signatures. The standard deviation for the peak pressure, the D-weighted sound exposure level (D-SEL), and the perceived level in dB (PLdB) metrics all show a linear increase with the distance, with a faster increase for the classical N-wave for the peak pressure and D-SEL and a similar increase between the different booms for PLdB. In general, it is found that low-boom waveforms show less sensitivity to turbulence.

This study proposes a geopolitical analysis of opinion dynamics based on a statistical exploration of a press dataset covering 2014-2019. This exploration questions three case studies of geopolitical and international interest: international migration, political borders, and pandemics. Through the framework of geopolitical agenda , the aim of this study is to question the "crisis" status of changes in the media coverage of the three topics in a cross-analysis and multilingual analysis of 20 western European newspapers. It concludes that there is a prevalence of national agendas., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Grasland , Toureille, Leconte  and Severo .)