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Ensemble-tailored pattern analysis of high-resolution dynamically downscaled precipitation fields: Example for climate sensitive regions of South America
Frontiers in Earth Science ( IF 2.0 ) Pub Date : 2021-04-29 , DOI: 10.3389/feart.2021.669427
Tanja C. Portele , Patrick Laux , Christof Lorenz , Annelie Janner , Natalia Horna , Benjamin Fersch , Maylee Iza , Harald Kunstmann

For climate adaptation and risk mitigation, decision makers in water management or agriculture increasingly demand for regionalized weather and climate information. To provide these, regional atmospheric models, such as the Weather Research and Forecasting (WRF) model, need to be optimized in their physical setup to the region of interest. The objective of this study is to evaluate four cumulus physics (CU), two microphysics (MP), two planetary boundary layer physics (PBL), and two radiation physics (RA) schemes in WRF according to their performance in dynamically downscaling the precipitation over two typical South American regions: one orographically complex area in Ecuador/Peru (horizontal resolution up to 9 and 3~km), and one area of rolling hills in Northeast Brazil (up to 9~km). For this, an extensive ensemble of 32 simulations over two continuous years was conducted. Including the reference uncertainty of three high-resolution global datasets (CHIRPS, MSWEP, ERA5-Land), we show that different parameterization setups can produce up to four times the monthly reference precipitation. This underscores the urgent need to conduct parameterization sensitivity studies before weather forecasts or input for impact modeling can be produced. Contrarily to usual studies, we focus on distributional, temporal and spatial precipitation patterns and evaluate these in an ensemble-tailored approach. These ensemble characteristics such as ensemble Structure-, Amplitude-, and Location-error (eSAL), allow us to generalize the impacts of combining one parameterization scheme with others. We find that varying the CU and RA schemes stronger affects the WRF performance than varying the MP or PBL schemes. This effect is even present in the convection-resolving 3-km-domain over Ecuador/Peru where CU schemes are only used in the parent domain of the one-way nesting approach. The G3D CU physics ensemble best represents the CHIRPS probability distribution in the 9-km-domains. However, spatial and temporal patterns of CHIRPS are best captured by Tiedtke or BMJ CU schemes. Ecuadorian station data in the 3-km-domain is best simulated by the ensemble whose parent domains use the KF CU scheme. Accounting for all evaluation metrics, no general-purpose setup could be identified, but suited parameterizations can be narrowed down according to final application needs.

中文翻译:

高分辨率动态缩减降水场的集合定制模式分析:以南美气候敏感地区为例

为了适应气候变化和降低风险,水管理或农业领域的决策者对区域天气和气候信息的需求日益增加。为了提供这些,需要对区域大气模型(例如天气研究和预报(WRF)模型)的物理设置进行优化,使其适合感兴趣的区域。这项研究的目的是根据WRF中动态降低降水的性能来评估WRF中的四个积云物理学(CU),两个微物理学(MP),两个行星边界层物理学(PBL)和两个辐射物理学(RA)方案。南美有两个典型的地区:厄瓜多尔/秘鲁的一个地形复杂的区域(水平分辨率高达9和3公里),巴西东北部的一个连绵起伏的丘陵地区(高达9公里)。为了这,连续两年进行了32次模拟的广泛合奏。包括三个高分辨率全球数据集(CHIRPS,MSWEP,ERA5-Land)的参考不确定性,我们表明,不同的参数设置可以产生多达每月参考降水量的四倍。这突显了在产生天气预报或影响模型输入之前迫切需要进行参数化敏感性研究。与通常的研究相反,我们专注于分布,时间和空间降水模式,并采用整体定制的方法对其进行评估。这些合奏特性(例如合奏结构,幅度和位置误差(eSAL))使我们可以概括将一种参数化方案与其他参数化方案组合在一起所产生的影响。我们发现,改变CU和RA方案比改变MP或PBL方案对WRF性能的影响更大。这种效果甚至出现在厄瓜多尔/秘鲁的对流解析3 km区域中,其中CU方案仅在单向嵌套方法的父域中使用。G3D CU物理合奏最能代表9 km域中的CHIRPS概率分布。但是,通过Tiedtke或BMJ CU方案可以最好地捕获CHIRPS的时空格局。3公里范围内的厄瓜多尔站数据最好由其父域使用KF CU方案的合奏模拟。考虑到所有评估指标,无法确定通用设置,但是可以根据最终应用程序的需求来缩小合适的参数设置范围。这种效果甚至出现在厄瓜多尔/秘鲁的对流解析3 km区域中,其中CU方案仅在单向嵌套方法的父域中使用。G3D CU物理合奏最能代表9 km域中的CHIRPS概率分布。但是,通过Tiedtke或BMJ CU方案可以最好地捕获CHIRPS的时空格局。3公里范围内的厄瓜多尔站数据最好由其父域使用KF CU方案的合奏模拟。考虑到所有评估指标,无法确定通用设置,但是可以根据最终应用程序的需求来缩小合适的参数设置范围。这种效果甚至出现在厄瓜多尔/秘鲁的对流解析3 km区域中,其中CU方案仅在单向嵌套方法的父域中使用。G3D CU物理合奏最能代表9 km域中的CHIRPS概率分布。但是,通过Tiedtke或BMJ CU方案可以最好地捕获CHIRPS的时空格局。3公里范围内的厄瓜多尔站数据最好由其父域使用KF CU方案的合奏模拟。考虑到所有评估指标,无法确定通用设置,但是可以根据最终应用程序的需求来缩小合适的参数设置范围。G3D CU物理合奏最能代表9 km域中的CHIRPS概率分布。但是,通过Tiedtke或BMJ CU方案可以最好地捕获CHIRPS的时空格局。3公里范围内的厄瓜多尔站数据最好由其父域使用KF CU方案的合奏模拟。考虑到所有评估指标,无法确定通用设置,但是可以根据最终应用程序的需求来缩小合适的参数设置范围。G3D CU物理合奏最能代表9 km域中的CHIRPS概率分布。但是,通过Tiedtke或BMJ CU方案可以最好地捕获CHIRPS的时空格局。3公里范围内的厄瓜多尔站数据最好由其父域使用KF CU方案的合奏模拟。考虑到所有评估指标,无法确定通用设置,但是可以根据最终应用程序的需求来缩小合适的参数设置范围。
更新日期:2021-04-29
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