Analyses based on precipitation data may be limited by the quality of the data, the size of the available historical series and the efficiency of the adopted methodologies; these factors are especially limiting when conducting analyses at the daily scale. Thus, methodologies are sought to overcome these barriers. The objective of this work is to develop a hybrid model through the maximum overlap discrete wavelet transform (MODWT) to estimate daily rainfall in homogeneous regions of the Tocantins-Araguaia Hydrographic Region (TAHR) in the Amazon (Brazil). Data series from the Climate Prediction Center morphing (CMORPH) satellite products and rainfall data from the National Water Agency (ANA) were divided into seasonal periods (dry and rainy), which were adopted to train the model and for model forecasting. The results show that the hybrid model had a good performance when forecasting daily rainfall using both databases, indicated by the Nash–Sutcliffe efficiency coefficients (0.81–0.95), thus, the hybrid model is considered to be potentially useful for modelling daily rainfall.

中文翻译：

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考虑MODWT-ANN混合模型的季节性因素的每日降雨量估算

基于降水量数据的分析可能会受到数据质量，可用历史序列的大小以及所采用方法的效率的限制；这些因素在按日进行分析时尤其受到限制。因此，寻求方法来克服这些障碍。这项工作的目的是通过最大重叠离散小波变换（MODWT）开发一个混合模型，以估计亚马逊（巴西）托坎丁斯-阿瓜瓜水文区（TAHR）的同质区域的日降水量。来自气候预测中心变质（CMORPH）卫星产品的数据系列和来自美国国家水利局（ANA）的降雨数据被分为季节（干旱和多雨），用于训练模型和进行模型预测。