...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Geophysical Research. Biogeosciences >Scale issues in verification of precipitation forecasts
【24h】

Scale issues in verification of precipitation forecasts

机译：验证降水预报中的规模问题

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Precipitation forecasts from numerical weather prediction models are often compared to rain gauge observations to make inferences as to model performance and the "best" resolution needed to accurately capture the structure of observed precipitation. A common approach to quantitative precipitation forecast (QPF) verification is to interpolate the model-predicted areal averages (typically assigned to the center point of the model grid boxes) to the observation sites and compare observed and predicted point values using statistical scores such as bias and RMSE. In such an approach, the fact that the interpolated values and their uncertainty depend on the scale (model resolution) of the values from which the interpolation was done is typically ignored. This interpolation error, which comes from scale effects, is referred to here as the "representativeness error." It is a nonzero scale-dependent error even for the case of a perfect model and thus can be seen as independent of model performance. The scale dependency of the representativeness error can have a significant effect on model verification, especially when model performance is judged as a function of grid resolution. An alternative method is to upscale the gauge observations to areal averages and compare at the scale of the model output. Issues of scale arise here too, with a different scale dependency in the representativeness error. This paper examines the merits and limitations of both verification methods (area-to-point and point-to-area) in view of the pronounced spatial variability of precipitation fields and the inherent scale dependency of the representativeness error in each of the verification procedures. A composite method combining the two procedures is introduced and shown to diminish the scale dependency of the representativeness error. [References: 20]
机译：通常将数值天气预报模型的降水预测与雨量计的观测结果进行比较，以推断出模型性能和准确捕获观测到的降水结构所需的“最佳”分辨率。定量降水预报（QPF）验证的一种常用方法是将模型预测的面积平均值（通常分配给模型网格框的中心点）内插到观测点，并使用诸如偏差之类的统计得分比较观测点和预测点的值和RMSE。在这种方法中，通常会忽略插值及其不确定性取决于进行插值的值的比例（模型分辨率）这一事实。这种来自比例效应的插值误差在此称为“代表性误差”。即使对于完美的模型，它也是一个非零比例相关的误差，因此可以看作与模型性能无关。代表性误差的比例依赖性可能会对模型验证产生重大影响，尤其是在将模型性能判断为网格分辨率的函数时尤其如此。另一种方法是将量规观测值放大到面积平均值，并在模型输出的比例下进行比较。规模问题也出现在这里，代表性误差的规模依赖性不同。鉴于降水场的明显空间变异性以及每种验证程序中代表误差的固有尺度依赖性，本文研究了两种验证方法（面对点和点对面）的优缺点。介绍了一种结合了这两个过程的复合方法，并证明了该方法可以减小代表性误差的比例依赖性。 [参考：20]

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号