Arctic Emissivity of Snow for Operational Prediction (AESOP) of Weather

Consequently, vast quantities of potentially usable satellite data are currently rejected by weather forecasting systems. New data and recent theoretical advances in surface emissivity models now provides potential for separation of surface and atmospheric emissivity signals to top of atmosphere microwave brightness temperature, thus allowing previously discarded satellite observations to be used for operational numerical weather prediction. AESOP will provide the host organisation with the justification and knowledge needed to implement changes in the operational system at the appropriate point in its development cycle. This placement uses existing science and expertise, leveraging recent scientific advances funded by NERC and the European Space Agency to meet two Met Office primary needs. Firstly, the Met Office requires ground-based data and evaluation of simulations of airborne brightness temperature measurements taken during Measurements of Arctic Clouds, Snow, and Sea Ice nearby the Marginal Ice ZonE (MACSSIMIZE : Met Office funded Facility for Airborne Atmospheric Measurements campaign). The necessary ground measurements of snow microstructure have recently been collected and quality-controlled by staff at Northumbria University (funded by NERC UK and Canada Arctic Partnership bursary ‘Characterisation of Arctic snow for modelling microwave and thermal properties’) and are available to AESOP. Secondly, the Met Office would like to harness an innovative new snow emission modelling approach (SMRT ) developed by Sandells and colleagues. This is a substantial development over single radiative transfer model approaches previously used due to multiple model representations in SMRT, which allows an estimate of the model error needed for weather forecast systems. Model code and computational expertise to run it will be provided to the Met Office from Northumbria University for collaborative use through and beyond the timescale of this placement. The host organisation will benefit from translation of data and modelling expertise into the RTTOV radiative transfer model used in their operational system, and this placement will provide clear evidence to the Met Office of the benefits of making changes to their operational modelling scheme. This is a critical step to contribute to future developmental changes in the operational weather prediction at the Met Office, as full data assimilation trials within their operational system are hugely expensive.