One of the most difficult weather variables to predict is rain, particularly intense rain. The main limitation is the complexity of the fluid dynamic equations used by predictive models with increasing uncertainties over time, especially in the description of brief, local, and high intensity precipitation events. Although computational, instrumental and theoretical improvements have been developed for models, it is still a challenge to estimate high intensity rainfall events, especially in terms of determining the maximum rainfall rates and the location of the event. Within this context, this research presents a statistical and relationship analysis of rainfall intensity rates, total precipitable water (TPW), and sea surface temperature (SST) over the ocean. An empirical model to estimate the maximum rainfall rates conditioned to TPW values is developed. The performance of the maximum rainfall rate model is spatially evaluated for a case study. High-resolution TRMM 2A12 satellite data with a resolution of 5.1 × 5.1 km and 1.67 s was used from January 2009 to December 2012, over the Eastern Pacific Niño area in the tropical Pacific Ocean (0–5°S; 90–81°W), comprising 326,092 rain pixels. After applying the model selection methodology, i.e., the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), an empirical exponential model between the maximum possible rain rates conditioned to TPW was found with R² = 0.96, indicating that the amount of TPW determines the maximum amount of rain that the atmosphere can precipitate exponentially. Spatially, this model unequivocally locates the rain event; however, the rainfall intensity is underestimated in the convective nucleus of the cloud. Thus, these results provide an additional constraint for maximum rain intensity values that should be adopted in dynamic models, improving the quantification of heavy rainfall event intensities and the correct location of these events.

天气预报是最困难的天气变量之一，尤其是强降雨。主要局限性是预测模型所使用的流体动力学方程的复杂性，其不确定性会随着时间的推移而增加，尤其是在描述短暂，局部和高强度降水事件时。尽管已经对模型进行了计算，工具和理论上的改进，但是估计高强度降雨事件仍然是一个挑战，尤其是在确定最大降雨率和事件位置方面。在此背景下，本研究提供了海洋上的降雨强度速率，总可沉淀水（TPW）和海面温度（SST）的统计和关系分析。建立了一个经验模型来估计以TPW值为条件的最大降雨率。对最大降雨率模型的性能进行空间评估以进行案例研究。从2009年1月至2012年12月，在热带太平洋东太平洋Niño地区（0-5°S； 90-81°W）使用分辨率为5.1×5.1 km和1.67 s的高分辨率TRMM 2A12卫星数据），包括326,092个雨像素。在应用了模型选择方法（即赤池信息准则（AIC）和贝叶斯信息准则（BIC））之后，发现了以TPW为条件的最大可能降雨率之间的经验指数模型。从2009年1月至2012年12月，在热带太平洋的东太平洋Niño地区（0-5°S； 90-81°W）使用了1 km和1.67 s，包括326,092降雨像素。在应用了模型选择方法（即赤池信息准则（AIC）和贝叶斯信息准则（BIC））之后，发现了以TPW为条件的最大可能降雨率之间的经验指数模型。从2009年1月至2012年12月，在热带太平洋的东太平洋Niño地区（0-5°S； 90-81°W）使用了1 km和1.67 s，包括326,092降雨像素。在应用了模型选择方法（即赤池信息准则（AIC）和贝叶斯信息准则（BIC））之后，发现了以TPW为条件的最大可能降雨率之间的经验指数模型。[R² = 0.96，表明TPW的数量决定了大气可以成指数沉淀的最大降雨量。在空间上，该模型明确地确定了降雨事件的位置。然而，云的对流核中的降雨强度被低估了。因此，这些结果为动态模型中应采用的最大降雨强度值提供了额外的约束，从而改善了强降雨事件强度的量化以及这些事件的正确位置。