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High-resolution precipitation data derived from dynamical downscaling using the WRF model for the Heihe River Basin, northwest China

机译：使用WRF模型从动态降尺度获得的高分辨率降水数据，用于中国西北黑河流域

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摘要

The community of climate change impact assessments and adaptations research needs regional high-resolution (spatial) meteorological data. This study produced two downscaled precipitation datasets with spatial resolutions of as high as 3 km by 3 km for the Heihe River Basin (HRB) from 2011 to 2014 using the Weather Research and Forecast (WRF) model nested with Final Analysis (FNL) from the National Center for Environmental Prediction (NCEP) and ERA-Interim from the European Centre for Medium-Range Weather Forecasts (ECMWF) (hereafter referred to as FNLexp and ERAexp, respectively). Both of the downscaling simulations generally reproduced the observed spatial patterns of precipitation. However, users should keep in mind that the two downscaled datasets are not exactly the same in terms of observations. In comparison to the remote sensing-based estimation, the FNLexp produced a bias of heavy precipitation centers. In comparison to the ground gauge-based measurements, for the warm season (May to September), the ERAexp produced more precipitation (root-mean-square error (RMSE) = 295.4 mm, across the 43 sites) and more heavy rainfall days, while the FNLexp produced less precipitation (RMSE = 115.6 mm) and less heavy rainfall days. Both the ERAexp and FNLexp produced considerably more precipitation for the cold season (October to April) with RMSE values of 119.5 and 32.2 mm, respectively, and more heavy precipitation days. Along with simulating a higher number of heavy precipitation days, both the FNLexp and ERAexp also simulated stronger extreme precipitation. Sensitivity experiments show that the bias of these simulations is much more sensitive to micro-physical parameterizations than to the spatial resolution of topography data. For the HRB, application of the WSM3 scheme may improve the performance of the WRF model.
机译：气候变化影响评估和适应研究社区需要区域高分辨率（空间）气象数据。这项研究使用天气研究和预报（WRF）模型并结合了最终的分析（FNL），得出了黑河流域（HRB）从2011年到2014年的两个降尺度的降水数据集，其空间分辨率高达3 km x 3 km。欧洲中距离天气预报中心（ECMWF）的国家环境预测中心（NCEP）和ERA-Interim（以下分别称为FNLexp和ERAexp）。两种降尺度模拟通常都再现了观测到的降水空间格局。但是，用户应记住，两个缩小的数据集在观测方面并不完全相同。与基于遥感的估计相比，FNLexp产生了重降水中心的偏差。与基于地面测量仪的测量相比，在暖季（5月至9月），ERAexp产生了更多的降水（43个站点的均方根误差（RMSE）= 295.4 mm）和更多的降雨日，而FNLexp产生更少的降水（RMSE = 115.6 mm），减少了大雨天。 ERAexp和FNLexp在寒冷季节（10月至4月）产生的降水量明显增加，RMSE值分别为119.5和32.2 mm，并且降水日数更大。除了模拟更多的强降水日，FNLexp和ERAexp还模拟了更强的极端降水。敏感性实验表明，这些模拟的偏差对微物理参数设置的敏感性比对地形数据的空间分辨率的敏感性要大得多。对于HRB，WSM3方案的应用可以提高WRF模型的性能。

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  • 来源
    《Theoretical and applied climatology》 |2018年第4期|1249-1259|共11页
  • 作者

    Zhang Xuezhen; Xiong Zhe; Zheng Jingyun; Ge Quansheng;

  • 作者单位

    Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China;

    Chinese Acad Sci, Inst Atmospher Phys, Key Lab Reg Climate Environm East Asia, Beijing 100029, Peoples R China;

    Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China;

    Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China;

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