Air-pollution Innovation in Regional-forecasts utilising operational Satellite Applications and Technologies (AIRSAT)

Deteriorating air quality (AQ) is an ongoing challenge for all major global economies and is now recognised as the largest environmental stress on human health. Key air pollutants include gases like ozone (O3), nitrogen dioxide (NO2) and aerosols (particles of pollution suspended in the air), which can cause health ailments such as respiratory and cardiovascular illness.

Globally, it has been estimated that air pollution is the cause of ~9 million premature deaths/year, while in the UK, it results in ~40,000 premature deaths/year. As a result, the UK Met Office (UKMO) uses its AQ forecast model, to provide the forewarning of hazardous AQ episodes for the general public (e.g. individuals with respiratory illnesses) and government departments/bodies (e.g. the NHS to prepare for increases hospital emissions).

The UKMO uses observations from surface sites (known as the Automated Urban and Rural network, AURN) to evaluate the performance of their AQ forecasts and developed a statistical scheme to correct them (e.g. if the model surface ozone concentrations are too large when compared to the AURN observations, the forecast values will be lowered in that region).

While this is a powerful method to improve the skill of the UKMO AQ forecasts, the surface network only consists of ~100 sites. Therefore, there are large data gaps across the UK where the forecasts cannot be verified. However, in the last decade, there has been rapid development and advancements in observing air pollution from space.

We now have satellite instruments which can detect pollution hotspots (e.g. cities) at a spatial resolution of several kilometres. The satellite platforms can provide daily coverage across the globe (and thus the UK) and provide the exciting opportunity to exploit this data for model forecast evaluation and improvement. As such, these satellite AQ products can be integrated into the same statistical scheme as the surface AURN observations to provide daily forecast corrections and updates. This proposal aims to do this in three steps:

1. Develop the necessary processing and data analysis tools (e.g. programing codes) to compare past model forecasts with readily available satellite data products. This will help identify which satellite AQ products are most useful for model forecast verification and correction.

2. Use well established statistical methods to extract useful surface information from the satellite data. Most satellite products provide information on an air pollutant throughout the atmosphere, so known statistical relationships between atmospheric and surface pollution can be used to extract important surface information.

3. Integrate the satellite surface information into the UKMO's statistical bias correction scheme for historical case studies (e.g. forecasts of previous AQ episodes), which can then be independently assessed against the AURN observations. Once fully functional off-line, the UKMO can then assimilate the satellite component into their operational statistical bias correct scheme to improve the public AQ forecasts.

Ultimately, this project aims to integrate satellite data sets into the UKMO operational AQ forecasts to improve the quality of this important public service. As very few national meteorological agencies (e.g. including the UKMO) include Earth observation (EO) products into their routine evaluation of AQ forecast models, this represents an innovative step to utilise satellite AQ products beyond their most common use with in academia (e.g. used in scientific studies).