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Jason Welsh (SSAI)

Title: Research Scientist
Technical Focus Area: Chemistry & Dynamics, Air Quality & Weather
Study Topics: Clouds and tropospheric chemistry

About:

Experienced working in the higher education industry and in government. Skilled in Python, Java, MySQL, (have worked with R and Matlab briefly), Microsoft Excel, Microsoft Word, Microsoft PowerPoint, worked with datasets such as AQS (Air Quality System) data and different meteorological datasets, and can give presentations as well. I’ve given a lecture to about 80 people and the Vice President of the Missouri Botanical Garden said I did an excellent job. In addition, I have run the Weather Researching and Forecasting (WRF) model, Motor Vehicle Emissions Model (MOVES2014), and Community Air Quality model with Extensions (CAMx). Strong research and data analysis professional with a PhD degree in atmospheric chemistry (graduated in May 2018).

Publication Bibliography:

Select Publications:

Awards:
  • Sigma Xi Award – Saint Louis University Chapter of Sigma Xi, The Scientific Research Honor Society
  • Presented at the Sigma Xi Research Symposium – “An Analysis of Ozone Data in St. Louis: dirty air is getting cleaner and clean air is getting dirtier” (Thesis, 05/2014)
  • Alpha Sigma Nu Member – The Honorary Society of Jesuit Colleges and Universities, Highest honor in a Jesuit College or University; was inducted Spring 2017

Professional Memberships:

  • American Chemical Society – 2010-present
  • American Meteorological Society –2017-present
  • American Geophysical Union –2017-present

Education/Professional Experience:

  • Saint Louis University St. Louis, MO Doctor of Philosophy – Meteorology May, 2018 GPA: 3.8 Title: The Development of a High-Resolution Chemical-Transport Model for Investigating Urban-Scale Processes: A Tool for Assessing Future Satellite Capabilities and Anomalous Localized Air Pollution Events (Python and MATLAB), Details on PhD research: While completing my PhD work, I used python to post process MOtor Vehicle Emissions Simulation Model (MOVES2014) model. Our group at East West Gateway, helped develop python code to post process the data using SQLite python library package. We developed this unique software package that allows us to visualize the MOVES2014 emission data for the St. Louis, MO metropolitan area. Once the data has been visualized we had hoped to place this data within our WRF model but due to time restraints we didn’t proceed with this process. Instead, we used the standard emissions that came with our air quality model (CAMx). Once, we ran the WRF model, I used python to post process the netCDF files and visualize the vector wind fields and temperature data onto a map of the St. Louis, MO metropolitan area. After completing the atmospheric chemical model runs (with CAMx), I then translated MATLAB code that was written to compute the total column ozone and nitrogen dioxide concentrations into a script written in Python. Something to note about the calculations of total column values is that I had to read in at least 4 to 6 different files and compute the atmospheric ozone concentration at that particular level. The script I used was much shorter in length than in MATLAB and more efficiently calculated the total column values. All my resultant total column concentrations, I converted into netCDF format and visualized those files within a single script.
  • Saint Louis University St. Louis, MO Master of Science – (Research) Meteorology May, 2014 GPA: 3.76 Title: The Paradoxical analysis of St. Louis ozone data between 1980-2012: Dirty Air is Getting Cleaner and Clean Air is Getting Dirtier, Details on Master’s research: used Python to analyze Air Quality Site (AQS) data that was obtained from Environmental Protection Agency’s (EPA) website. Before analyzing the data, I used the Remote Sensing Information Gateway (RSIG) to obtain spatially dependent surface ozone datasets from local (St. Louis, MO) monitoring sites. Then I used Python and libraries within python such as numpy, scipy, and others to post process the downloaded text files. In this process, I had to read and write files that were in text file format in the post processing part of the research. I’ve been able to make linear graphs with trend lines to fit the data within python.
  • Worcester State College Worcester, MA Bachelor of Science – Chemistry May ,2010 GPA: 3.4 Center for Sustainability, Saint Louis University St. Louis, MO Graduate Certificate in Advanced Remote Sensing and Geographic Information Systems (GIS) May, 2017

Hobbies:

I love to hike up various mountains in my free time! Also, bicycle riding is another enjoyable activity! For other forms of enjoyment, I enjoy spending time with my family or friends going bowling or going out to eat!

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The NASA Prediction Of Worldwide Energy Resources (POWER) Project improves the accessibility and usage NASA Earth Observations (EO) supporting community research in three focus areas: 1) renewable energy development, 2) building energy efficiency, and 3) agroclimatology applications. The latest POWER version enhances its distribution systems to provide the latest NASA EO source data, be more resilient, support users more effectively, and provide data more efficiently. The update will include hourly-based source Analysis Ready Data (ARD), in addition to enhanced daily, monthly, annual, and climatology ARD. The daily time-series now spans 40 years for meteorology available from 1981 and solar-based parameters start in 1984. The hourly source data are from Clouds and the Earth's Radiant Energy System (CERES) and Global Modeling and Assimilation Office (GMAO), spanning 20 years from 2001.

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