Yazdandoost Farhad, Zakipour Mina, Izadi Ardalan
Department of Civil Engineering, K. N. Toosi University of Technology, Tehran, Iran.
Multidisciplinary International Complex (MIC), K. N. Toosi University of Technology, Tehran, Iran.
Heliyon. 2021 Aug 25;7(9):e07877. doi: 10.1016/j.heliyon.2021.e07877. eCollection 2021 Sep.
Using reliable and timely precipitation forecasts on a monthly or seasonal scale could be useful in many water resources management planning, especially in countries facing drought challenges. Amongst many, the North American Multi-Model Ensemble (NMME) is one of the most well-known models. In this study, a Bayesian method based on Copula functions has been applied to improve NMME precipitation forecasts. This method is based on the existence of a correlation between the raw forecast and observational data. Two main factors affect the results of rainfall improvement based on the selected method. This research has presented innovative methods in these regards namely; 1) the approach of selecting the appropriate statistical distribution for variables and 2) the selection method of improved data according to the conditional probability distribution functions (CPDF). To evaluate the effectiveness of the statistical distribution, firstly the precipitation forecast improvement model has been developed based on the application of parametric (Exponential, Normal, Gamma, LogNormal and General Exreteme Value (GEV)) and non-parametric distributions (Standard Normal Kernel). Then the novel mixed distribution function based on GEV parametric distribution and Standard Normal Kernel (non-parametric distribution) has been suggested. As the second aim, a new method for selecting improved data based on the center of mass of estimated CPDF is presented. The evaluation of the proposed method for estimating the statistical distribution of data and improving the forecast precipitation by the NMME model has been performed in Sistan and Baluchestan province in Iran. In this regard, the data of 1982-2010 for the calibration period and the data of 2012-2016 for the validation of the results have been used. According to the results, the non-parametric distribution best fitted with the data in the time series and selecting the appropriate bandwidth increased the efficiency of this distribution. Besides, due to the weakness of non-parametric distributions in the boundaries, the use of GEV distribution with a high ability to estimate boundary conditions as semi-parametric distribution, led to improved performance of the proposed distribution. Finally, the selection of the improved data based on the center of the mass method has efficiently provided much improvement compared to the maximum likelihood method commonly used.
在许多水资源管理规划中，使用可靠且及时的月度或季节尺度降水预报可能会很有用，尤其是在面临干旱挑战的国家。在众多模型中，北美多模式集合（NMME）是最著名的模型之一。在本研究中，一种基于Copula函数的贝叶斯方法已被应用于改进NMME降水预报。该方法基于原始预报与观测数据之间存在相关性。基于所选方法，有两个主要因素会影响降雨改进结果。本研究在这些方面提出了创新方法，即：1）为变量选择合适统计分布的方法，以及2）根据条件概率分布函数（CPDF）选择改进数据的方法。为了评估统计分布的有效性，首先基于参数分布（指数分布、正态分布、伽马分布、对数正态分布和广义极值分布（GEV））和非参数分布（标准正态核）的应用开发了降水预报改进模型。然后提出了基于GEV参数分布和标准正态核（非参数分布）的新型混合分布函数。作为第二个目标，提出了一种基于估计的CPDF质心选择改进数据的新方法。在伊朗的锡斯坦和俾路支斯坦省对所提出的估计数据统计分布和改进NMME模型降水预报的方法进行了评估。在这方面，使用了1982 - 2010年的数据进行校准期分析，并使用2012 - 2016年的数据对结果进行验证。根据结果，非参数分布与时间序列中的数据拟合最佳，选择合适的带宽提高了该分布的效率。此外，由于非参数分布在边界处的弱点，使用具有高估计边界条件能力的GEV分布作为半参数分布，提高了所提出分布的性能。最后，与常用的最大似然法相比，基于质心方法选择改进数据有效地带来了更大的改进。
