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A versatile weather generator for daily precipitation and temperature.

机译:一个多功能的天气发生器,用于每天的降水量和温度。

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摘要

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 Matlab freeware allows for easy modification of all routines, making it easy to add additional weather variables to simulate.
机译:每日随机天气生成器通常用于生成长时间的天气变量序列,以驱动水文和农业模型。最近,它们还被用作研究气候变化影响的缩减规模工具。本文介绍了一种多功能随机天气生成器(WeaGETS),用于产生每日降水以及最高和最低温度(T max 和T min )。 WeaGETS将其他天气生成器的几个选项重新组合到一个程序包中,例如使用三种马尔可夫模型来产生降水,使用四种分布来生成湿日降水量,以及两种模拟T max 和T 的方法。分钟。更重要的是,WeaGETS中包含了一种光谱校正方法,用于校正对年际变化的低估,这是所有天气产生器都普遍存在的问题。通过与两个著名的天气生成器(WGEN和CLIGEN)进行比较,证明WeaGETS的性能在降水产生方面,T max 和T min 两个加拿大气象站。结果表明,广泛使用的一阶马尔可夫模型足以产生降水,但它低估了低降水站的最长干旱期。高阶模型具有积极作用。在产生降水量时,混合的指数和偏正态Pearson III分布始终优于指数和伽马分布。两组分混合指数分布比其他三个分布更能代表极端降水事件。在产生T max 和T min 方面,WeaGETS始终优于WGEN和CLIGEN。 WGEN和CLIGEN都低估了降水和温度的月度和年际变化。但是,WeaGETS成功地保留了观测到的低频变化和降水与温度的自相关函数。总体而言,WeaGETS的降水量T max 和T min 始终优于其他两个天气生成器(WGEN和CLIGEN)。 Matlab免费软件可轻松修改所有例程,从而轻松添加其他天气变量以进行仿真。

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