Scientist -Data Assimilation and Numerical Weather Prediction

  • Tomorrow.io
  • 5541 Central Avenue West, Suite 270, Boulder, CO
  • Apr 13, 2021

Job Description

Tomorrow.io Space division is responsible for the development of unique spaceborne sensing systems and for the overall space mission, from architecture through implementation. The data being collected will be integrated seamlessly into Tomorrow.io existing forecasting and modelling systems and will revolutionize global weather forecasting and insights.

As an Atmospheric Scientist focused on Data Assimilation, you will work across the Numerical Weather Prediction (NWP) team and Tomorrow.io space division to evaluate various satellite products as candidates for assimilation into Tomorrow.io NWP system. In this role, you will perform research and development involving the evaluation and eventual operational assimilation of remotely sensed data into global NWP models. Research will include performing thorough literature reviews, evaluating potential assimilation methodologies, helping to define observation system requirements, performing Observing System Simulation Experiment (OSSE) and/or Forecast Sensitivity Observation Impact (FSOI) studies to help inform Tomorrow.io strategy of assimilation of novel data sources.

If you want to become part of a team that leverages a wealth of cutting edge cloud computing technologies that remains state-of-the-art to support the scientific goals, and to work with experts in geophysical fluid dynamics, operational NWP, atmospheric data assimilation, atmospheric data science, ensemble forecast, global modeling, applied mathematics, etc, - This is your opportunity to become part of a winning team that enables Tomorrow.io to build modeling systems that are based on rigorous scientific studies and practices while leveraging the growing fields of data analysis and analytics.

What it takes

  • An MS or Ph.D in Meteorology, Atmospheric Science, or similar
  • A research focus in the field of NWP with emphasis on DA methods for spaced-based remote sensing
  • 2+ years experience developing and supporting operational NWP systems (or components)
  • Deep understand and experience with NWP models such as the Weather Research and Forecasting (WRF), the Model for Prediction Across Scales (MPAS) and the Finite­-Volume Cubed-Sphere Dynamical Core (FV3) models
  • Deep understanding and experience with advanced DA methods such as 4DVAR and ensemble-based assimilation methods and hybrid VAR/Ensemble methods
  • Deep understanding of data assimilation methodologies and systems such as the Gridpoint Statistical Interpolation (GSI) data assimilation (DA) system and/or the WRF-DA (WRFDA), Data Assimilation Research Testbed (DART), and the Joint Effort for Data assimilation Integration (JEDI) systems
  • Understanding of the modeling systems such as WRF, GSI, MAPS and/or FV3 functionality at the Fortran code level, not just the namelist level
  • Experience in programming languages such as Python scientific libraries for data analysis and experimentation and Fortran for core NWP system component development
  • Background in developing and supporting NWP systems for operational applications
  • Strong background in a Linux development environment including experience with high-performance computing (HPC)
  • Demonstrated track record of producing written, technical documentation — papers, reports, articles, anything is fine, but written communication and documentation is vital to our job here

Bonus points

  • Experience working with less than Level 2 remotely sensed data with emphasis on data error quantification, observation system requirements
  • Experience performing radar reflectivity DA and/or DA of other precipitation indicators such as from satellites
  • Experience compiling the WRF, GSI, MPAS, and/or FV3 codes including debugging and run-time optimization settings
  • Experience establishing V&V metrics and performing V&V analyses of NWP systems
  • Experience with meteorological observation data QA/QC such as METAR, ROAB, PREPBUFER etc.
  • Experience performing satellite radiance DA in all-sky conditions
  • Experience working on cloud computing systems, especially Amazon AWS or Google Cloud
  • Experience in programming languages such as Python scientific libraries for data analysis and experimentation and Fortran for core NWP system component development
  • Proficient in writing code to visualize, analyze, and assess weather forecasts and data.
    Proof of eligibility to work in the United States lawfully must be provided
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About Tomorrow.io:
Tomorrow.io is the world’s leading Weather Intelligence PlatformTM. Fully customizable to any industry impacted by the weather, customers around the world including Uber, Delta, Ford, National Grid and more use Tomorrow.io to dramatically improve operational efficiency. Tomorrow.io was built from the ground up to help teams predict the business impact of weather, streamline team communication and action plans, improve productivity, and optimize profit margins. Space: In case you have not heard, we are also going to space with our Operation Tomorrow Space initiative. We are building the first-of-its-kind proprietary satellites equipped with radar, and launching them into space to improve weather forecasting technology for everyone on Earth. How we roll: We work in an “one office” environment. We believe that magic happens when people work together. Together also includes Zoom meetings, flexible hours and unlimited vacation days. Your success is achieved by your impact and deliveries and not by the hours you put in. We believe in transparency and directness, putting work before ego and empathy. We grow fast and move faster but we always see people first. Each person has their own career growth path for we believe that the only way for the company to grow is if you grow.

Organization Type

Company

Organization Size

1-10

Sectors

Climate Risk