KTP Associate – Weather Data Analytics Lead

The aim of this pioneering Knowledge Transfer Partnership (KTP) project is to support the business partner, Voltitude Ltd, in realising the full potential of autonomous stratospheric platforms to transform the global weather data services market. This includes advancing scientific understanding and practical use of novel atmospheric observations, particularly data from microdropsondes dispensed from constellations of long-endurance, high-altitude stratospheric platforms.

The Associate will develop robust methodologies to assess the impact of these observations on numerical weather prediction (NWP) models, with a focus on Ensemble Sensitivity Analysis (ESA) to quantify forecast improvements. This project offers an opportunity to contribute to the emerging field of stratospheric meteorological sensing, addressing key gaps in global observation networks and improving forecasts in data-sparse regions.

The Associate will be lead-supervised by Martin Airey and will become part of the research community within the Meteorology Department at the University of Reading. On a day-to-day basis, the Associate will be embedded in the team at the KTP business partner, Voltitude Ltd. They will work directly with the CEO, Paul Stevens, at Voltitude’s offices in Cody Technology Park, Farnborough. The project will be strongly focused on research and the assessment of observation impact, supporting and informing Voltitude’s commercialisation plan for expanding their weather data services business using stratospheric platforms.

This 30-month project offers the exciting opportunity to contribute to the field of assessing observation impact on weather forecasting to support the development of novel observing systems. The role is at the intersection of meteorological science, data analytics, and informing commercial strategy. The Associate will contribute to and lead the following activities:

develop expertise with both the aerial platform data collection and NWP model usage and methods of assimilating these;

• acquire and manipulate ensemble forecast data to assess model uncertainty and sensitivity;
• perform uncertainty quantification of the observations, and develop the necessary quality control procedures;
• participate in field work to gain a full appreciation of the data acquisition methods and an understanding of how these systems work in practice;
• develop code based on ensemble sensitivity analysis to assess the potential impact of the observations within a numerical weather prediction system;
• present the results to academic and commercial audiences (to colleagues at Reading, at international conferences and workshops, and to business partner colleagues) to promote discussion and gain feedback;
• gain an understanding of the cost-benefit to potential customers to assess the commercial impact of these data retrievals and inform commercial strategy; and
• report major findings via publication at appropriate stages in the project timeline.