2.1 Developments in the Assimilation of Satellite Observations in the Met Office Global Model

Tuesday, 12 January 2016: 11:00 AM
Room 335/336 ( New Orleans Ernest N. Morial Convention Center)
William Bell, Met Office, Exeter, United Kingdom; and J. Cameron, A. Booton, P. Weston, A. Lorenc, D. Li, E. Pavelin, S. Newman, K. Lean, A. Doherty, S. Migliorini, F. Carminati, F. Smith, Y. Pradhan, M. Ujiie, R. Saunders, D. Barker, and A. Smith

Global NWP skill continues to improve through a combination of advances in dynamical schemes, forecast model physics, data assimilation systems and continuous enhancements to the global observing system. For example, root-mean-square errors (RMSEs) in extra-tropical 500hPa geopotential heights have been reducing at a rate of ~4% per year at leading NWP centres over the last decade. We illustrate the contribution to this improvement from advances in the use of satellite data at the Met Office. Over the last three years the introduction of new satellite observations (including data from Suomi-NPP and EUMETSAT's MetOp-B satellite), coupled with the improved use of existing satellite data, has delivered significant benefits. As a specific example we show that the most recent upgrade (due to become operational in early 2016) is expected to deliver improvements of 5-10% in extra-tropical 500hPa RMSEs, and significantly improves the Met Office's global humidity analysis. This upgrade includes the introduction of data from seven satellites previously unused (SSMIS on DMSP F-17, -18 and -19, SAPHIR on Megha Tropiques, MWHS on FY-3C, AMSR-2 on GCOM-W and Rapidscat on the ISS) as well as improvements in the treatment of biases and observation errors in the Met Office system. The addition of these new sensors will help to make the assimilation more robust to occasional instrument failures or data outages. Future performance gains are expected from a combination of new satellite data (e.g. doppler wind lidar, geostationary hyperspectral sounders) as well as continued improvements in the treatment of existing data, such as making progress in the notoriously challenging areas of utilising observations in cloudy and precipitating areas, and the more aggressive use of surface sensitive radiance observations
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