MERRAMax Automated feature selection
Description
There is growing interest in using Intergovernmental Panel on Climate Change (IPCC)-class climate model outputs in ecological research. These models provide realistic, global representations of the climate system, projections for hundreds of variables (including Essential Climate Variables), and combine observations from an array of satellite, airborne, and in-situ sensors. Unfortunately, direct use of this important class of data has been limited due to the large size and complexity of model output collections, internal file complexity, and limited means for dynamically creating derived products of interest. To address these limitations, we have developed an AI-based stochastic convergence technology, called MERRA/Max, that combines HPC and Princeton's Maximum Entropy (MaxEnt) software to rapidly subset and identify potential drivers of change among the hundreds of variables in a climate model output collection. MERRA/Max reduces dimensionality by iteratively drawing on MaxEnt's capacity for feature selection to winnow randomly selected climate variables until a stable set of predictors is found. Preliminary work focuses on the MERRA reanalysis, a product of NASA's GEOS-5 modeling framework. At 1 petabyte in size, MERRA comprises over 700 climate variables and spans 1970 to the present at high temporal resolution. We evaluated MERRA/Max by modeling the bioclimatic envelope of Cassin's Sparrow using MERRA and BioClim variables.
Detailed example
Predictions
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
MERRAMax Automated feature selection
Controls / human review
ATO: Not reported; PIA: Not published