Northeast Yucatan hurricane activity during the Maya Classic and Postclassic periods.

The collapse of the Maya civilization in the late 1st/early 2nd millennium CE has been attributed to multiple internal and external causes including overpopulation, increased warfare, and environmental deterioration. Yet the role hurricanes may have played in the fracturing of Maya socio-political networks, site abandonment, and cultural reconfiguration remains unexplored. Here we present a 2200 yearlong hurricane record developed from sediment recovered from a flooded cenote on the northeastern Yucatan peninsula.

Statistical and Machine Learning Methods Applied to the Prediction of Different Tropical Rainfall Types.

Predicting rain from large-scale environmental variables remains a challenging problem for climate models and it is unclear how well numerical methods can predict the true characteristics of rainfall without smaller (storm) scale information. This study explores the ability of three statistical and machine learning methods to predict 3-hourly rain occurrence and intensity at 0.5° resolution over the tropical Pacific Ocean using rain observations the Global Precipitation Measurement (GPM) satellite radar and large-scale environmental profiles of temperature and moisture from the MERRA-2 reanalysis.

Global multivariate point pattern models for rain type occurrence.

We seek statistical methods to study the occurrence of multiple rain types observed by satellite on a global scale. The main scientific interests are to relate rainfall occurrence with various atmospheric state variables and to study the dependence between the occurrences of multiple types of rainfall (e.g.

Predictive Statistical Representations of Observed and Simulated Rainfall Using Generalized Linear Models.

This study explores the feasibility of predicting subdaily variations and the climatological spatial patterns of rain in the tropical Pacific from atmospheric profiles using a set of generalized linear models: logistic regression for rain occurrence and gamma regression for rain amount. The prediction is separated into different rain types from TRMM satellite radar observations (stratiform, deep convective, and shallow convective) and CAM5 simulations (large-scale and convective). Environmental variables from MERRA-2 and CAM5 are used as predictors for TRMM and CAM5 rainfall, respectively.