In recent years, plenty of researches have accomplished to make the relationship between the climatic variables for daily, monthly, and seasonal rainfall occurrence and magnitude around the world. In this study, monthly rainfall modeling was performed using backward generalized estimating equation (GEE). In this regard, monthly average maximum and minimum temperature, sunshine hours, wind speed, and relative humidity data from 1967–2014 for the Fasa Plain at Fars province, Iran were selected as predictors to investigate their effects on response variable of rainfall. Results indicated that in February, March, April, June, August, and October the term of humidity has positive effect ( B > 0 and P < 0.05) and the terms of maximum and minimum temperature, sunshine hours, and wind speed have negative effects ( B < 0 and P < 0.05) on rainfall. Then, to assess the verification and accuracy of the final equation of GEE model, the monthly rainfall was forecasted and compared with the observed rainfall values. The determination coefficients of more than 96.0% between the observed data and the forecasted values illustrates the goodness of this model in prediction. The goodness of fit indices showed that the GEE nicely modeled the rainfall.

中文翻译：

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使用反向广义估计方程的降雨建模：伊朗法萨平原的案例研究

近年来，大量的研究已经完成了对世界各地日、月和季节性降雨发生与大小之间的气候变量之间的关系的研究。在这项研究中，使用反向广义估计方程（GEE）进行月降雨量建模。在这方面，选择伊朗法尔斯省法萨平原 1967-2014 年的月平均最高和最低温度、日照时数、风速和相对湿度数据作为预测变量，研究它们对降雨响应变量的影响。结果表明，2月、3月、4月、6月、8月和10月，湿度项具有正效应（B > 0，P < 0.05），最高和最低温度、日照时数和风速项具有负效应( B < 0 和 P < 0。05) 关于降雨。然后，为了评估GEE模型最终方程的验证和准确性，预测了月降雨量并与实测降雨值进行了比较。观测数据与预测值之间的决定系数大于96.0%，说明了该模型在预测方面的优势。拟合指数的优度表明 GEE 很好地模拟了降雨量。