I’m an atmospheric and data scientist that believes real innovation happens when we push the boundaries of meteorology instead of working within the boxes that others have drawn. These days I work on ways to modernize meteorological forecasts using neural networks and other advanced statistical techniques at subseasonal lead times. My goal is to make the best forecasts possible. Period.
Long-range forecasting, statistical calibrations and consolidations of dynamical models, model verification, model database creation and management, and neural network forecasts for weeks 3/4.
As a consultant I spearheaded a number of projects to improve predictions using intraseasonal and interannual modes of variability for a variety of private sector clients.
I managed the transition of metadata for >1,000 products to new database and created multiple Python programs to interface with RESTful API for myself and others to use.
Full-time yet temporary position in which I created and taught Introduction to Meteorology, Tropical Meteorology, Physical Meteorology (Thermodynamics), Environmental Issues, and Senior Seminar.