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Conducting and applying atmospheric research and data mining to ease our society's transition to a more sustainable future. Applying machine learning techniques and statistical models to transform short-term and medium-term numerical weather model forecasts into intelligent operational guidance and risk management products. Specialities include design and implementation of operational short-term and medium-term weather forecast guidance products for high-impact weather events and wildfires. Skills:Linux server and high-performance cluster administrationProgramming (Python, NCL, NCO, Fortran, MySQL)StatisticsMachine Learning: Logistic regression, self-organizing maps, artificial neural networks (ANN), etc.Weather Research and Forecasting (WRF) ModelSpecialities/Interests: Model Output Statistics (MOS) design, programming and operational implementationApplications of machine learning methods (Logistic regression, SOM, ANN) to weather forecastingStatistical and dynamical-downscaling of atmospheric data to resolutions useful for guiding and informing the public and utility operationsMeteorology and climatologyNear-surface wind dynamics and boundary layer processesWildfire-centric weather variable research and forecastingWind energy resource assessmentHigh-resolution climatological dataset construction and analysisExtreme weather event analysis and prediction
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Principal ScientistTechnosylva Oct 2023 - Present -
Co-Founder And PrincipalAtmospheric Data Solutions Dec 2015 - Oct 2023Jackson Hole, WyomingI have extensive experience designing, implementing and maintaining operational weather forecast guidance products for companies primarily in the utilities sector. At the core of most of my products are numerical weather models. I have over a decade of experience designing, building and maintaining high performance computing (HPC) platforms required to run operational weather models and have had the opportunity to work with the latest HPC technologies. Our (Atmospheric Data Solutions) recent utility load and outage forecasting products have used innovative machine learning methods to bridge the gap between the desired predictors and high-resolution multi-decadal past weather data. I have assisted in the original and continued development and operational support of the Santa Ana Wildfire Threat Index (SAWTI, http://sawti.fs.fed.us/). Additionally, Atmospheric Data Solutions uses machine learning applied to past climatological data to provide seasonal outlooks for various weather variables including Santa Ana Winds.Atmospheric Data Solutions, LLC was acquired by Technosylva in 2023.
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Senior Science PartnerVertum Partners Jun 2010 - Dec 2015Los AngelesMesoscale meteorological research with applications to renewable energy, operational weather forecasting guidance and climate dataset construction. High performance Linux cluster/server configuration/maintenance/administration, Python and Fortran programming. Managing product development for climate data web-based products and the construction of an atmospheric downscaling framework to be used for the creation of various atmospheric science datasets. Overseeing the deployment of dynamical and statistical weather forecasting guidance products for various client companies.
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Co-Founder/Vice PresidentClean Education Mar 2007 - Sep 2015IrvineAlliance formed with the department of Earth System Science at the University of California, Irvine to design and conduct public outreach regarding climate change and other environmental issues.
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Assistant Researcher/Atmospheric ScientistThe University Of California, Los Angeles Nov 2012 - Sep 2013Http://Www.Atmos.Ucla.Edu/Csrl/Working to assess and predict wind and solar energy using state-of-the-art regional scale atmospheric models including WRF. Conducting research which improves renewable energy resource assessment within these models. Dynamical downscaling of future climate change scenarios from CMIP5 models using WRF. Application of these future climate change scenarios includes studying the dynamics behind offshore winds in southern California and predicting future changes in frequency, duration and intensity. Also, assessing future climate change impacts to renewable energy resources. -
Postdoctoral Scholar Atmospheric ScientistUniversity Of California, Los Angeles Nov 2009 - Nov 2012UclaWorking to assess and predict wind and solar power throughout Southern California using state-of-the-art regional scale atmospheric models including WRF. Conducting research which improves renewable energy resource assessment within these models. Dynamical downscaling of future climate change scenarios from CCSM4 using WRF. Application of these future climate change scenarios includes studying the dynamics behind offshore winds in southern California and predicting future changes in frequency, duration and intensity. Also, assessing future climate change impacts to renewable energy resources.
Scott Capps Skills
Scott Capps Education Details
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4.0 Gpa -
Cal PolyFinance, Real Estate And Law -
Atmospheric Physics And Dynamics
Frequently Asked Questions about Scott Capps
What company does Scott Capps work for?
Scott Capps works for Technosylva
What is Scott Capps's role at the current company?
Scott Capps's current role is Principal Scientist at Technosylva.
What is Scott Capps's email address?
Scott Capps's email address is sc****@****uci.edu
What is Scott Capps's direct phone number?
Scott Capps's direct phone number is +194991*****
What schools did Scott Capps attend?
Scott Capps attended University Of California, Irvine, Cal Poly, Uc Irvine, University Of California, Los Angeles, University Of California, Los Angeles, University Of California, Los Angeles, University Of California, Los Angeles.
What are some of Scott Capps's interests?
Scott Capps has interest in Python, Teaching, Fortran, Skiing, Gardening, Hiking, Camping, Music, High Performance Computing, Tennis.
What skills is Scott Capps known for?
Scott Capps has skills like Renewable Energy, Sustainability, Climate Change, Environmental Impact Assessment, Science, Climate, Wind, Research, Python, Statistics, Meteorology, Fortran.
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