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Jason Welsh (SSAI)

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


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:

  • 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


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!

Need to get in touch with Jason Welsh? Fill out the contact form below. 

SD Profiles Contact

  • CAPABLE/CRAVE Full Site Photo from left to right site enclosures: 1196A NASA LaRC, MPLnet, Virginia DEQ
    CAPABLE/CRAVE Full Site Photo from left to right site enclosures: 1196A NASA LaRC, MPLnet, Virginia DEQ

  • NASA LaRC NAST-I and HU ASSIST side-by-side for intercomparison
    NASA LaRC NAST-I and HU ASSIST side-by-side for intercomparison

  • Virginia DEQ, NASA and Penn State-NATIVE Enclosures (from right to left)
    Virginia DEQ, NASA and Penn State-NATIVE Enclosures (from right to left)

  • Ozone-sonde away.
    Ozone-sonde away.
  • About to lift.
    About to lift.
PurpleAir PA-II-SD Air Quality Sensor
Laser Particle Counters
Type (2) PMS5003
Range of measurement 0.3, 0.5, 1.0, 2.5, 5.0, & 10 μm
Counting efficiency 50% at 0.3μm & 98% at ≥0.5μm
Effective range
(PM2.5 standard)*
0 to 500 μg/m³
Maximum range (PM2.5 standard)* ≥1000 μg/m³
Maximum consistency error (PM2.5 standard) ±10% at 100 to 500μg/m³ & ±10μg/m³ at 0 to 100μg/m³
Standard Volume 0.1 Litre
Single response time ≤1 second
Total response time ≤10 seconds
Pressure, Temperature, & Humidity Sensor
Type BME280
Temperature range -40°F to 185°F (-40°C to 85°C)
Pressure range 300 to 1100 hPa
Humidity Response time (τ63%): 1 s
Accuracy tolerance: ±3% RH
Hysteresis: ≤2% RH

Pandora capabilities










Total Column O3, NO2, HCHO, SO2, H2O, BrO

0.01 DU

0.1 DU



Virginia Department of Environment Quality in-situ instrumentation






Thermo Scientific 42C (Molybdenum converter)

60 s

NO and NOx

50 pptv


Teledyne API 200EU w/ photolytic converter
(EPA) PI-Szykman

20 s


50 pptv


Thermo Scientific 49C (VADEQ)

20 s


1 ppbv


Thermo Scientific 48i (VADEQ)

60 s


40 ppbv


Thermo Scientific 43i (VADEQ)

80 s


0.2 ppbv


Thermo Scientific 1400AB TEOM (VADEQ)

600 s

PM2.5 (continuous)


1 3%

Thermo Scientific Partisol Plus 2025 (VADEQ)

24 hr

PM2.5 (filter-based FRM)- 1/3 days



Large area view.
Latitude: 37.1038
Longitude: -76.3872
Elevation: 3 m Above sea level
Scenes: urban, marsh, bay, river and farm.


  • The inner red circle is a 20km CERES foot print centered on the BSRN-LRC site.
  • The pink circle represents a possible tangential 20km foot print.
  • The middle red circle represents the area in which a 20km foot print could fall and still see the site.
  • Yellow is a sample 40 deg off nadir foot print.
  • The outer red circle is the region which would be seen by a possible 40 deg off nadir foot print.
The BSRN-LRC sun tracker at the NASA Langley Research Center on a snowy day (02/20/2015) The BSRN-LRC sun tracker at the NASA Langley Research Center on a snowy day (02/20/2015)
CAPABLE-BSRN Google Site Location Image

Team Satellite Sensor G/L Dates Number of obs Phase angle range (°)
CMA FY-3C MERSI LEO 2013-2014 9 [43 57]
CMA FY-2D VISSR GEO 2007-2014
CMA FY-2E VISSR GEO 2010-2014
CMA FY-2F VISSR GEO 2012-2014
JMA MTSAT-2 IMAGER GEO 2010-2013 62 [-138,147]
JMA GMS5 VISSR GEO 1995-2003 50 [-94,96]
JMA Himawari-8 AHI GEO 2014- -
EUMETSAT MSG1 SEVIRI GEO 2003-2014 380/43 [-150,152]
EUMETSAT MSG2 SEVIRI GEO 2006-2014 312/54 [-147,150]
EUMETSAT MSG3 SEVIRI GEO 2013-2014 45/7 [-144,143]
EUMETSAT MET7 MVIRI GEO 1998-2014 128 [-147,144]
CNES Pleiades-1A PHR LEO 2012 10 [+/-40]
CNES Pleiades-1B PHR LEO 2013-2014 10 [+/-40]
NASA-MODIS Terra MODIS LEO 2000-2014 136 [54,56]
NASA-MODIS Aqua MODIS LEO 2002-2014 117 [-54,-56]
NASA-VIIRS NPP VIIRS LEO 2012-2014 20 [50,52]
NASA-OBPG SeaStar SeaWiFS LEO 1997-2010 204 (<10, [27-66])
NASA/USGS Landsat-8 OLI LEO 2013-2014 3 [-7]
NOAA-STAR NPP VIIRS LEO 2011-2014 19 [-52,-50]
NOAA GOES-10 IMAGER GEO 1998-2006 33 [-66, 81]
NOAA GOES-11 IMAGER GEO 2006-2007 10 [-62, 57]
NOAA GOES-12 IMAGER GEO 2003-2010 49 [-83, 66]
NOAA GOES-15 IMAGER GEO 2012-2013 28 [-52, 69]
VITO Proba-V VGT-P LEO 2013-2014 25 [-7]
KMA COMS MI GEO 2010-2014 60
AIST Terra ASTER LEO 1999-2014 1 -27.7
ISRO OceanSat2 OCM-2 LEO 2009-2014 2

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.

The newly available hourly data will provide users the ARD needed to model the energy performance of building systems, providing information directly amenable to decision support tools introducing the industry standard EPW (EnergyPlus Weather file). One of POWER’s partners, Natural Resource Canada’s RETScreen™, will be simultaneously releasing a new version of its software, which will have integrated POWER hourly and daily ARD products. For our agroclimatology users, the ICASA (International Consortium for Agricultural Systems Applications standards) format for the crop modelers has been modernized.

POWER is releasing new user-defined analytic capabilities, including custom climatologies and climatological-based reports for parameter anomalies, ASHRAE® compatible climate design condition statistics, and building climate zones. The ARD and climate analytics will be readily accessible through POWER's integrated services suite, including the Data Access Viewer (DAV). The DAV has been improved to incorporate updated parameter groupings, new analytical capabilities, and the new data formats. Updated methodology documentation and usage tutorials, as well as application developer specific pages, allow users to access to POWER Data efficiently.

+Visit the POWER Program Site to Learn More.