Research Fellow HEPPI

Contract Type: Fixed Term contract up to March 2027 

The Research Fellow will develop ML-based postprocessing methods for precipitation forecasts with the global NEPS-G model of the National Centre for Medium Range Weather Forecasting (NCMRWF, India). The position is within the project ‘HEavy Precipitation forecast Post-processing over India with Machine Learning’ (HEPPI-ML), which is funded through the ‘Weather and Climate Science for Service Partnership’ (WCSSP) programme (<https://www.metoffice.gov.uk/research/approach/collaboration/wcssp/cssp-india/weather-and-climate-science-for-service-partnership-india-wcssp-india>)

The researcher will work with Dr. Martin Widmann, Dr. Ruth Geen and Prof. Gregor Leckebusch at the University of Birmingham, and with Dr. Andrew Orr at the British Antarctic Survey. The project will be undertaken in close collaboration with NCMRWF and the UK Met Office, and include project meetings in India and work visits to the UK Met Office.  HEPPI-ML is one out of several WCSSP-India projects and joint meetings will provide an opportunity for further networking.

Main Duties

• Implement and test different Artificial Neural Network (ANN) architectures, such as multilayer perceptrons and convolutional ANNs, for postprocessing ensemble precipitation forecasts over India from the National Centre for Medium Range Weather Forecasting (NCMRWF) global ensemble prediction system (NEPS-G).
• Develop innovative specifications of input and output of postprocessing that account for the stochastic nature of precipitation and for systematic location errors in the original forecasts.
• Develop research objectives for future own or joint research, with mentorship from the project team.
• Contribute to developing new models, techniques and methods

Person Specification

• PhD (or one near to completion) in a relevant, quantitative field, e.g. meteorology, machine learning, climate science, physics, mathematics, statistics or related fields.
• Evidence of good understanding (or capacity to develop understanding) of statistics and ML. 
• Evidence of a good understanding (or capacity to develop understanding) of meteorological processes and numerical weather prediction, and preferably specific knowledge related to monsoon precipitation.
• Experience working with large meteorological datasets.
• Good programming skills in languages such as Python, MATLAB or R.
• Familiarity with UNIX/LINUX.

Informal enquiries to Martin Widmann email: m.widmann@bham.ac.uk 

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