气候变化情景和极端风暴增强的合成每日天气生成

J. Garbrecht, X. Zhang, David Brown, P. Busteed
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引用次数: 2

摘要

流域水文学、土壤和养分运输以及农业生产系统的可持续性的长期模拟需要长期的天气记录,而这些记录往往无法在相关地点获得。生成合成的每日天气数据是增加有限的天气观测的常用方法。这里描述了一个合成的每日天气生成模型(称为SYNTOR)。SYNTOR完成了生成替代天气实现的传统角色,这些替代天气实现具有与其拟模拟的父历史天气相似的统计属性。此外,它还具有模拟气候变化情景和由于气候变化而引起的风暴增强的每日天气记录的能力。简要总结了各种模式分量,并介绍了在俄克拉何马州中西部半干旱气候条件下的应用。SYNTOR生成的每日天气与观测到的天气值比较良好。通过调整天气生成参数来模拟气候变化,以反映气候预估的月平均天气值的变化。风暴强度是通过将风暴分布的前10%增加一个预先确定的量来估计的,这个量是基于以前对美国降水趋势的研究。建议进一步评价已公布的风暴强度值及其相关的不确定性和空间变异性。
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Generation of Synthetic Daily Weather for Climate Change Scenarios and Extreme Storm Intensification
Long-term simulations in watershed hydrology, soil and nutrient transport, and sustainability of agricultural production systems require long-term weather records that are often not available at the location of interest. Generation of synthetic daily weather data is a common approach to augment limited weather observations. Here a synthetic daily weather generation model (called SYNTOR) is described. SYNTOR fulfills the traditional role of generating alternative weather realizations that have statistical properties similar to those of the parent historical weather it is intended to simulate. In addition, it has the capability to simulate daily weather records for climate change scenarios and storm intensification due to climate change. The various model components are briefly summarized and an application is presented for semi-arid climate conditions in west-central Oklahoma. SYNTOR generated daily weather compared well with observed weather values. Climate change is simulated by adjusting weather generation parameters to reflect the changed mean monthly weather values of climate projections. Storm intensification is approximated by increasing the top 10 percentile of storm distribution by a predefined amount based on previous studies of trends in United States precipitation. Further evaluation of published storm intensification values and associated uncertainties and spatial variability is recommended.
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