半参数小时时空天气发生器

IF 5.7 1区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
Ross Pidoto, Uwe Haberlandt
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引用次数: 1

摘要

摘要长期连续时间序列的气象变量(即降雨、温度和辐射)需要应用,如推导洪水频率分析。然而,观测到的时间序列通常太短,空间太稀疏或不完整,特别是在次日时间步长。随机天气发生器通过产生任意长度的时间序列来克服这个问题。本研究提出了一种基于点交替更新过程的时空时雨量模型的重大修正,现在将其与k-NN重采样模型相结合,用于非降雨气候变量的条件模拟。通过模拟退火优化方法对模拟降雨事件进行重采样,将基于点的降雨模型扩展到空间。这种方法强制执行由三个二元空间降雨标准所描述的观测到的空间依赖性。介绍了一种新的非顺序分支洗牌方法,该方法允许在空间依赖结构中没有显着损失的情况下对大型台站网络(N>50)进行建模。非降雨气候变量(即温度、湿度和辐射)的建模使用非参数k近邻(k-NN)重采样方法实现,并通过每日集水区降雨状态与时空降雨模型耦合。作为输入,使用网格日观测数据集(HYRAS)。然后对所有非降雨气候变量执行最后的确定性分解步骤,以获得每小时输出的时间分辨率。提议的天气发生器在德国400个不同大小的集水区(50 - 20,000平方公里)进行了测试,包括699个次日降雨量记录站。结果表明,随着流域面积的增加,模式性能没有重大损失,并且观测到的气候和降雨统计数据的再现总体上很好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A semi-parametric hourly space–time weather generator
Abstract. Long continuous time series of meteorological variables (i.e. rainfall, temperature and radiation) are required for applications such as derived flood frequency analyses. However, observed time series are generally too short, too sparse in space or incomplete, especially at the sub-daily timestep. Stochastic weather generators overcome this problem by generating time series of arbitrary length. This study presents a major revision to an existing space–time hourly rainfall model based on a point alternating renewal process, now coupled to a k-NN resampling model for conditioned simulation of non-rainfall climate variables. The point-based rainfall model is extended into space by the resampling of simulated rainfall events via a simulated annealing optimisation approach. This approach enforces observed spatial dependency as described by three bivariate spatial rainfall criteria. A new non-sequential branched shuffling approach is introduced which allows the modelling of large station networks (N>50) with no significant loss in the spatial dependence structure. Modelling of non-rainfall climate variables, i.e. temperature, humidity and radiation, is achieved using a non-parametric k-nearest neighbour (k-NN) resampling approach, coupled to the space–time rainfall model via the daily catchment rainfall state. As input, a gridded daily observational dataset (HYRAS) was used. A final deterministic disaggregation step was then performed on all non-rainfall climate variables to achieve an hourly output temporal resolution. The proposed weather generator was tested on 400 catchments of varying size (50–20 000 km2) across Germany, comprising 699 sub-daily rainfall recording stations. Results indicate no major loss of model performance with increasing catchment size and a generally good reproduction of observed climate and rainfall statistics.
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来源期刊
Hydrology and Earth System Sciences
Hydrology and Earth System Sciences 地学-地球科学综合
CiteScore
10.10
自引率
7.90%
发文量
273
审稿时长
15 months
期刊介绍: Hydrology and Earth System Sciences (HESS) is a not-for-profit international two-stage open-access journal for the publication of original research in hydrology. HESS encourages and supports fundamental and applied research that advances the understanding of hydrological systems, their role in providing water for ecosystems and society, and the role of the water cycle in the functioning of the Earth system. A multi-disciplinary approach is encouraged that broadens the hydrological perspective and the advancement of hydrological science through integration with other cognate sciences and cross-fertilization across disciplinary boundaries.
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