Jan.  2020

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# A Hybrid Statistical-Dynamical Downscaling of Air Temperature over Scandinavia Using the WRF Model

• An accurate simulation of air temperature at local scales is crucial for the vast majority of weather and climate applications. In this work, a hybrid statistical–dynamical downscaling method and a high-resolution dynamical-only downscaling method are applied to daily mean, minimum and maximum air temperatures to investigate the quality of local-scale estimates produced by downscaling. These two downscaling approaches are evaluated using station observation data obtained from the Finnish Meteorological Institute over a near-coastal region of western Finland. The dynamical downscaling is performed with the Weather Research and Forecasting (WRF) model, and the statistical downscaling method implemented is the Cumulative Distribution Function-transform (CDF-t). The CDF-t is trained using 20 years of WRF-downscaled Climate Forecast System Reanalysis data over the region at a 3-km spatial resolution for the central month of each season. The performance of the two methods is assessed qualitatively, by inspection of quantile-quantile plots, and quantitatively, through the Cramer-von Mises, mean absolute error, and root-mean-square error diagnostics. The hybrid approach is found to provide significantly more skillful forecasts of the observed daily mean and maximum air temperatures than those of the dynamical-only downscaling (for all seasons). The hybrid method proves to be less computationally expensive, and also to give more skillful temperature forecasts (at least for the Finnish near-coastal region).
摘要: 精确模拟局地气温在天气、气候的业务预报和研究中均有重要作用。本文中对比分析了混合动力-统计降尺度和仅动力降尺度两种降尺度方法对局地日平均气温、最高、最低气温的估计能力。文中作为对比的观测资料来源于芬兰气象研究所，关注的区域为西芬兰的近岸区域。动力降尺度方法中采用WRF模型，而混合动力-统计降尺度方法是在WRF模型的基础上，运用了累积分布函数转换法作统计降尺度。在该方法中利用了基于WRF模型的20年气候预测系统再分析数据，空间分辨率为3 km，用每个季度的中间月份来做统计降尺度。作者用分位数图对两种降尺度方法的估计效果做了定性分析，同时用Cramer-von Mises检验、平均绝对误差和均方根误差做了定量比较。结果表明混合动力-统计降尺度的预报效果显著高于仅动力降尺度方法，动力-统计降尺度的这种优势体现在所有季节中。至少对于芬兰近岸区域来说，混合降尺度花费更少的计算资源，且对温度的预报精确性更高。
• Figure 1.  Spatial extent of the model grids used in the (a) 20-year and (b) 1-year WRF simulations with the boundary regions excluded. The spatial resolutions of the grids are 27 km (red), 9 km (green), 3 km (blue) and 1 km (orange).

Figure 2.  Spatial distribution of the seven FMI stations (black dots) in the 3-km WRF grid of the 20-year simulation (boundary regions excluded). The shading is the orography (m) as seen by the model and the star highlights the approximate location of the Honkajoki wind farm. The 1-km grid of the 1-year run has a similar spatial extent.

Figure 3.  Q-Q plots for the daily mean air temperature (K), for the central month of each season (April, July, October 2016 and January 2017), and for the seven stations shown in Fig. 2. The red dots represent the 100 quantiles of the dynamically and statistically downscaled (WRF[3km]+CDF-t) data against that observed (FMI). The blue dots are the same but for the higher-resolution dynamically downscaled (WRF[1km]) data. The main diagonal, which indicates a perfect agreement between each pair of CDFs, is drawn as a black line. The numbers at the top of each panel show the MAE (K) between each set of distributions, with the panels for a given season sorted in ascending order of the hybrid method’s MAE.

Figure 4.  As in Fig. 3 but for the daily maximum air temperature (K).

Figure 5.  As in Fig. 3 but for the daily minimum air temperature (K).

Figure 6.  Daily mean, minimum and maximum air temperature distributions (K) from WRF[3 km]+CDF-t (red curve), WRF[1 km] (blue curve) and that observed as given by the FMI data (green curve) at the location of station 2 and for the summer season.

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## Manuscript History

Manuscript received: 23 April 2019
Manuscript revised: 08 August 2019
Manuscript accepted: 26 August 2019
###### 通讯作者: 陈斌, bchen63@163.com
• 1.

沈阳化工大学材料科学与工程学院 沈阳 110142

## A Hybrid Statistical-Dynamical Downscaling of Air Temperature over Scandinavia Using the WRF Model

###### Corresponding author: Jianfeng WANG, jianfeng.wang@umu.se;
• 1. Department of Mathematics and Mathematical Statistics, Umeå University, SE 901 87, Umeå Sweden
• 2. Group of Atmospheric Science, Division of Space Technology, Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, SE 971 87 Luleå, Sweden
• 3. Novia University of Applied Sciences, PO BOX 6, FI-65201 Vaasa, Finland
• 4. Instituto Andaluz de Ciencias de la Tierra, 18100 Granada, Spain
• 5. The Pheasant Memorial Laboratory for Geochemistry and Cosmochemistry, Institute for Planetary Materials, Okayama University at Misasa, Tottori 682-0193, Japan

Abstract: An accurate simulation of air temperature at local scales is crucial for the vast majority of weather and climate applications. In this work, a hybrid statistical–dynamical downscaling method and a high-resolution dynamical-only downscaling method are applied to daily mean, minimum and maximum air temperatures to investigate the quality of local-scale estimates produced by downscaling. These two downscaling approaches are evaluated using station observation data obtained from the Finnish Meteorological Institute over a near-coastal region of western Finland. The dynamical downscaling is performed with the Weather Research and Forecasting (WRF) model, and the statistical downscaling method implemented is the Cumulative Distribution Function-transform (CDF-t). The CDF-t is trained using 20 years of WRF-downscaled Climate Forecast System Reanalysis data over the region at a 3-km spatial resolution for the central month of each season. The performance of the two methods is assessed qualitatively, by inspection of quantile-quantile plots, and quantitatively, through the Cramer-von Mises, mean absolute error, and root-mean-square error diagnostics. The hybrid approach is found to provide significantly more skillful forecasts of the observed daily mean and maximum air temperatures than those of the dynamical-only downscaling (for all seasons). The hybrid method proves to be less computationally expensive, and also to give more skillful temperature forecasts (at least for the Finnish near-coastal region).

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