New earth observation methods separating atmospheric and surface signals in satellite images have improved climate and weather predictions.
The Global Environmental Modelling and Earth Observation (GEMEO) group at Swansea University led research with leading meteorological agencies and satellite data providers, resulting in improved weather prediction models and publicly available satellite datasets.
Researchers from the GEMEO group at Swansea University focused on improving global measurement and modelling of two key quantities for climate, surface albedo and atmospheric aerosol.
The researchers developed new methods that accurately separate atmospheric signals from surface signals in satellite images. The innovation was by modelling how the land surface reflectance changes at multiple angles of view, coupled with precise modelling of atmospheric scattering and absorption.
The key scientific advance was to separate differing albedo contributions from vegetation and soil, and for vegetation types. This allowed accurate modelling of spatial distribution of albedo, as well as seasonal change, and the provision of the first global dataset of soil albedo based on satellite observations.
The aerosol work has been used to create new operational products for the recently launched Sentinel-3 satellite, intended for use by global weather forecast, air quality and climate prediction agencies.
The Swansea aerosol dataset has been used by the European Centre for Medium-Range Weather Forecasts (ECWMF) to produce a continuous spatial and temporal map of aerosol, and understand atmospheric particulate matter (PM2.5, PM10) and air quality.
The aerosol dataset has improved the global climate record available to develop and test climate models for IPCC, providing verification and testing for 14 global models of aerosol optical properties in the IPCC 6th Assessment.
The albedo project resulted in a 15-year dataset of global land surface albedo used by meteorological agencies in Europe, Australia, the US and India. The research has also improved the UK Met Office Unified Model (UM) used for weather forecasts across a range of timescales and daily forecasting for the UK. This resulted in a bias reduction of 20% at the centre of continental land masses in summer.
Read the full impact case study in the REF 21 database
Improving climate and weather prediction - Swansea University
Institution: Swansea University
Researchers: Peter North, Mike Barnsley, Sietse Los, Caroline Houldcroft, William Grey, Will Davies, Andreas Heckel, Kevin Pearson
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Royal Geographical Society (with IBG) (2023) Global satellite data for improved climate and weather predictions. Available at www.rgs.org/Global-satellite-data-for-improved-climate-and-weather-predictions Last accessed on: <date>
Featured image: Zbynek Burival / Unsplash
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