This study evaluates a lightning parameterization that utilizes only large-scale environmental variables (i.e., convective available potential energy (CAPE), column moisture, and lifting condensation level (LCL)) for present-day (2017-19) and end-of-century (2098-2100) RCP8.5 climate scenarios in the Community Atmosphere Model version 5 (CAM5). Using a single equation, the present-day prediction can produce a reasonable land/ocean ratio in lightning occurrence. The end-of-century prediction shows relative increases of about 50% over higher-latitude land, but much more variable increases and decreases across mid-latitude ocean and the tropics such that the overall global lightning occurrence is expected to slightly decrease. Lightning occurrence over land predicted from present-day CAM5 is less than that using MERRA-2 reanalysis because of differences in the basic-state variables used as predictors. In addition, the choice of dilute or undilute CAPE will impact future lightning predictions over land, but the environment-only parameterization results are more consistent than a CAPE x precipitation parameterization.

We explore the use of three advanced statistical and machine learning methods (a generalized linear model, random forest, and neural network) to predict the occurrence and rain rate distribution of three tropical rain types (deep convective, stratiform, and shallow convective) observed by the radar onboard the GPM satellite over the West Pacific at three-hourly, 0.5-degree resolution. Temperature and moisture profiles from MERRA-2 were used as predictors. All three methods perform reasonably well at predicting the occurrence and rain rate distribution of each rain type. However, none of the methods obviously distinguish themselves from one another and each method still has issues with predicting rain too often and not fully capturing the high end of the rain rate distributions, both of which are common problems in climate models.

We explore the use of three advanced statistical and machine learning methods (a generalized linear model, random forest, and neural network) to predict the occurrence and rain rate distribution of three tropical rain types (deep convective, stratiform, and shallow convective) observed by the radar onboard the GPM satellite over the West Pacific. Three-hourly temperature and moisture fields from MERRA-2 were used as predictors. While all three methods perform reasonably well at predicting the occurrence of each rain type, the neural network is the only method able to produce rain rate distributions similar to observations, especially for the top 5-10% of observed values. However, the neural network took the most effort to train and has a relatively high root mean square error, suggesting that it sometimes assigns high rain rates to situations that in reality produce much weaker rain rates.