Forecasting occurrence and quantity of monthly precipitation simultaneously while accounting for complex serial correlation,International Journal of Climatology

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Forecasting occurrence and quantity of monthly precipitation simultaneously while accounting for complex serial correlation
International Journal of Climatology ( IF 3.5 ) Pub Date : 2022-08-27 , DOI: 10.1002/joc.7839
Xingde Duan ₁ , Renjun Ma ₂ , Xiaolei Zhang ₃

Affiliation

Tweedie's compound Poisson regression models have been introduced in recent years to model and predict daily, monthly and seasonal precipitation data. Tweedie's compound Poisson regression analysis of precipitation data captures the relationship between the mean structure of precipitation and covariates while accounting for its right-skewness and zero-inflation appropriately, and thus extends trend analysis stage of classical time series techniques to handle right-skewed time series data with possible zero-inflation. A distinctive advantage of Tweedie's compound Poisson modelling of precipitation data is that the occurrence and quantity of precipitation can be simultaneously modelled using a single distribution; however, this approach ignores serial correlation between precipitation data observed over time. In this study, we propose to accommodate complex correlation structures of time series precipitation data using the Autoregressive Integrated Moving Average (ARIMA) models at the next stage as done in the classical time series analysis. Our analyses of monthly precipitation data in Australia demonstrated the usefulness of our two-stage approach to prediction of precipitation.

中文翻译：

同时预测每月降水量的发生和数量，同时考虑复杂的序列相关性

近年来引入了 Tweedie 的复合泊松回归模型来模拟和预测日、月和季节降水数据。Tweedie 对降水数据的复合泊松回归分析捕捉了降水的平均结构和协变量之间的关系，同时适当地考虑了它的右偏度和零膨胀，从而扩展了经典时间序列技术的趋势分析阶段来处理右偏时间序列可能零通胀的数据。Tweedie 的降水数据复合泊松模型的一个显着优势是可以使用单一分布同时对降水的发生和数量进行建模；然而，这种方法忽略了随时间观察到的降水数据之间的序列相关性。在这项研究中，我们建议在下一阶段使用自回归积分移动平均 (ARIMA) 模型来适应时间序列降水数据的复杂相关结构，就像在经典时间序列分析中所做的那样。我们对澳大利亚月降水数据的分析证明了我们的两阶段降水预测方法的有用性。

更新日期：2022-08-27

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