Climate model simulations of rainfall in the tropics suffer from pervasive biases, and that can lead to degraded climate simulations in other regions as well. Over the past two decades, high-resolution satellite measurements of tropical rainfall have become available. These data are most commonly used to constrain physics-based climate models by validating statistical properties of rainfall such as means and variances. However, the satellite data contain a wealth of spatiotemporal information on sub-diurnal timescales that can be used to construct predictive models. This study explores the feasibility of predicting rainfall from atmospheric state using a hierarchy of empirical models. Our empirical approach is similar to the physics-based approach in that vertical profiles of atmospheric state at a particular instant of time serve as the predictors, and rainfall over a subsequent time period is the predictand. However, we allow the empirical model to “learn” from data to determine the model parameters. Empirical Orthogonal Function (EOF) decomposition is applied to vertical profiles from NASA MERRA-2 reanalysis to select the dominant predictor modes at analysis time 00 UTC. Rain predictions for the subsequent 6-hour period (00-06 UTC) are separated into different types from TRMM satellite data: stratiform, deep convective, and shallow convective. For each rain type, two generalized linear statistical models (logistic regression for rain occurrence and gamma regression for rain amount) are trained on 2003 data and used to predict during 2004. The results show that the statistical approach can predict spatial patterns and amplitudes of tropical rainfall in the time-averaged sense. The first EOF of humidity and the second EOF of temperature contribute most to prediction. In addition to generalized linear models, other common machine learning techniques (support vector machine and random forest) are compared. Furthermore, marginal nonlinear relationships between predictand and individual predictor are explored via a nonparametric regression technique. Interestingly, incorporating the identified marginal nonlinear relationship into the generalized linear model does not improve the prediction, suggesting that these marginal nonlinear effects are explained by other predictors in the model.

The ability of an observationally-constrained cloud-system resolving model (Weather Research and Forecasting; WRF, 4-km grid spacing) and a global climate model (Energy Exascale Earth System Model; E3SM, 1-degree grid spacing) to represent the precipitation diurnal cycle over the Amazon basin during the 2014 wet season is assessed. The month-long period is divided into days with and without the presence of observed propagating mesoscale convective systems (MCSs) over the central Amazon. The MCSs are strongly associated with rain amounts over the basin and also control the observed spatial variability of the diurnal rain rate. WRF model coupled with a 3-D variational data assimilation scheme reproduces the spatial variability of the precipitation diurnal cycle over the basin and the lifecycle of westward propagating MCSs initiated by the coastal sea-breeze front. In contrast, a single morning peak in rainfall is produced by E3SM for simulations with and without nudging the large-scale winds towards global reanalysis, indicating precipitation in E3SM is largely controlled by local convection associated with diurnal heating. Both models produce contrast in easterly wind profiles between days with and without MCS that are similar to data collected by U.S. DOE Atmospheric Radiation Measurement (ARM) facility during the Green Ocean Amazon (GoAmazon2014/5) campaign and other operational radiosondes. A multivariate perturbation analysis indicates the dryness of low-level air transported from ocean to inland has higher impact on the formation and maintenance of MCS in the Amazon than other processes.