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About Me

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.

personal information

Full Name
Kyle MacRitchie
kyle "at" macritchie "dot" me
Washington, DC

programming languages

MATLAB 10 yrs
Python 4 yrs
Web (HTML/CSS/Javascript) 15 yrs

Technical Skills

Basic Statistics
Regression Modeling
Fourier Analysis
Tropical Meteorology
Neural Networks/Keras

Other Noteworthy Skills

time series analysis, signal processing, clustering, significance testing, etc.
netCDF, HDF, Excel, GRIB, flat binary, OPeNDAP

work experience

2016 - present

Climate Prediction Center

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.

2013 - 2016


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.

2015 - 2016

NASA Goddard Space Flight Center / ADNET Systems

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.

2013 - 2015

SUNY College at Oneonta

Full-time yet temporary position in which I created and taught Introduction to Meteorology, Tropical Meteorology, Physical Meteorology (Thermodynamics), Environmental Issues, and Senior Seminar.


2009 - 2014

PhD Atmospheric Science

University at Albany

Specialization in tropical waves (e.g. MJO, Kelvin, etc.) and statistical big data methods.

2005 - 2009

BS Atmospheric Science & Mathematics

University at Albany