Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables
Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables
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DOI：
10.1007/s00382-017-3580-6
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发表时间：
2018-01-01
期刊：
影响因子：
4.6
通讯作者：
Cannon, Alex J.
中科院分区：
文献类型：
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作者：
Cannon, Alex J.
气候学中使用的大多数偏差校正算法，例如分位数映射，都适用于单变量时间序列。它们忽略了不同变量之间的相关性。那些多变量的通常只修正有限的联合相关性的度量，如皮尔逊或斯皮尔曼等级相关性。这里，设计用于将颜色信息从一幅图像传递到另一幅图像的图像处理技术-N维概率密度函数变换-被适配为用于多个气候变量的气候模型预测/预测的多变量偏差校正算法(MBCn)。MBCn是分位数映射的多变量推广，它将观测到的连续多变量分布的所有方面转换为气候模型中相应的多变量分布。当应用于气候模型预测时，每个变量在历史和预测期之间的分位数的变化也被保留。MBCn算法在三个案例研究中得到了验证。首先，将该方法应用于具有模拟气候投影问题的特征的图像处理实例。其次，MBCn用于校正来自加拿大气候建模和分析区域气候模式(CanRCM4)的北美区域的一套每小时一次的地面气象变量。然后计算加拿大森林火灾天气指数(FWI)系统的组成部分，并与观测值进行验证。FWI系统是一组复杂的多变量指数，用于表征野火风险。第三，利用MBCn修正了CanRCM4降水场空间相关结构的偏差。将结果与忽略变量之间相关性的单变量分位数映射算法和两种多变量偏差校正算法进行比较，每种算法校正不同形式的变量间相关结构。MBCn的性能优于这些备选方案，往往有很大的差距，特别是在降水场的FWI分布和时空自相关的年最大值方面。
Most bias correction algorithms used in climatology, for example quantile mapping, are applied to univariate time series. They neglect the dependence between different variables. Those that are multivariate often correct only limited measures of joint dependence, such as Pearson or Spearman rank correlation. Here, an image processing technique designed to transfer colour information from one image to another-the N-dimensional probability density function transform-is adapted for use as a multivariate bias correction algorithm (MBCn) for climate model projections/predictions of multiple climate variables. MBCn is a multivariate generalization of quantile mapping that transfers all aspects of an observed continuous multivariate distribution to the corresponding multivariate distribution of variables from a climate model. When applied to climate model projections, changes in quantiles of each variable between the historical and projection period are also preserved. The MBCn algorithm is demonstrated on three case studies. First, the method is applied to an image processing example with characteristics that mimic a climate projection problem. Second, MBCn is used to correct a suite of 3-hourly surface meteorological variables from the Canadian Centre for Climate Modelling and Analysis Regional Climate Model (CanRCM4) across a North American domain. Components of the Canadian Forest Fire Weather Index (FWI) System, a complicated set of multivariate indices that characterizes the risk of wildfire, are then calculated and verified against observed values. Third, MBCn is used to correct biases in the spatial dependence structure of CanRCM4 precipitation fields. Results are compared against a univariate quantile mapping algorithm, which neglects the dependence between variables, and two multivariate bias correction algorithms, each of which corrects a different form of inter-variable correlation structure. MBCn outperforms these alternatives, often by a large margin, particularly for annual maxima of the FWI distribution and spatiotemporal autocorrelation of precipitation fields.