Uncertainty analysis of statistical downscaling methods
Uncertainty analysis of statistical downscaling methods
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DOI：
10.1016/j.jhydrol.2005.06.035
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发表时间：
2006-03-15
影响因子：
6.4
通讯作者：
Dibike, Y
中科院分区：
文献类型：
--
作者：
Khan, MS;Coulibaly, P;Dibike, Y
三个降尺度模型，即统计降尺度模型（SDSM），长阿什顿研究站天气发生器（LARS-WG）模型和人工神经网络（ANN）模型进行了比较的各种不确定性评估，在他们的降尺度结果的日降水量，日最高和最低温度。对于日最高和最低温度，通过在95%的置信水平下比较一年中每个月的缩小尺度和观测日最高和最低温度的月平均值和方差来评估不确定性。此外，月平均和方差的不确定度的降尺度的日温度已被计算使用95%的置信区间，这与观测到的平均值和方差的不确定度进行了比较。在日降水量降尺度中，除了比较均值和方差外，还通过比较月平均干湿期长度及其置信区间、月平均日降水量的累积频率分布（cdfs）以及观测日降水量和降尺度日降水量的月干湿日分布来评估不确定性。该研究已经进行了使用40年的观测和缩小尺度的日降水量，日最高和最低温度数据使用NCEP（国家环境预报中心）再分析预测从1961年到2000年。不确定度评估结果表明，SDSM是最有能力再现观测数据的各种统计特征，在其降尺度的结果与95%的置信水平，人工神经网络在这方面的能力是最差的，和LARS-WG之间的SDSM和人工神经网络。（C）2005 Elsevier有限公司版权所有。
Three downscaling models namely Statistical Down-Scaling Model (SDSM), Long Ashton Research Station Weather Generator (LARS-WG) model and Artificial Neural Network (ANN) model have been compared in terms various uncertainty assessments exhibited in their downscaled results of daily precipitation, daily maximum and minimum temperatures. In case of daily maximum and minimum temperature, uncertainty is assessed by comparing monthly mean and variance of downscaled and observed daily maximum and minimum temperature at each month of the year at 95% confidence level. In addition, uncertainties of the monthly means and variances of downscaled daily temperature have been calculated using 95% confidence intervals, which are compared with the observed uncertainties of means and variances. In daily precipitation downscaling, ill addition to comparing means and variances, uncertainties have been assessed by comparing monthly mean dry and wet spell lengths and their confidence intervals, cumulative frequency distributions (cdfs) of monthly mean of daily precipitation, and the distributions of monthly wet and dry days for observed and downscaled daily precipitation. The study has been carried out using 40 years of observed and downscaled daily precipitation, daily maximum and minimum temperature data using NCEP (National Center for Environmental Prediction) reanalysis predictors starting from 1961 to 2000. The uncertainty assessment results indicate that the SDSM is the most capable of reproducing various statistical characteristics of observed data in its downscaled results with 95% confidence level, the ANN is the least capable in this respect, and the LARS-WG is in between SDSM and ANN. (C) 2005 Elsevier Ltd All rights reserved.