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Improvements in Forecasting Intense Rainfall: Results from the FRANC (Forecasting Rainfall Exploiting New Data Assimilation Techniques and Novel Observations of Convection) Project

Sciprofile linkSarah L. Dance, Sciprofile linkSusan P. Ballard, Sciprofile linkRoss N. Bannister, Sciprofile linkPeter Clark, Sciprofile linkHannah L. Cloke, Sciprofile linkTimothy Darlington, Sciprofile linkDavid L. A. Flack, Sciprofile linkSuzanne L. Gray, Sciprofile linkLee Hawkness-Smith, Sciprofile linkNawal Husnoo, Sciprofile linkAnthony J. Illingworth, Sciprofile linkGraeme A. Kelly, Sciprofile linkHumphrey W. Lean, Sciprofile linkDingmin Li, Sciprofile linkNancy K. Nichols, Sciprofile linkJohn C. Nicol, Sciprofile linkAndrew Oxley, Sciprofile linkRobert S. Plant, Sciprofile linkNigel M. Roberts, Sciprofile linkIan Roulstone, Sciprofile linkDavid Simonin, Sciprofile linkRobert J. Thompson, Sciprofile linkJoanne A. Waller
Published: 7 March 2019
 by  MDPI
Atmosphere , Volume 10; doi:10.3390/atmos10030125

Abstract: The FRANC project (Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection) has researched improvements in numerical weather prediction of convective rainfall via the reduction of initial condition uncertainty. This article provides an overview of the project’s achievements. We highlight new radar techniques: correcting for attenuation of the radar return; correction for beams that are over 90% blocked by trees or towers close to the radar; and direct assimilation of radar reflectivity and refractivity. We discuss the treatment of uncertainty in data assimilation: new methods for estimation of observation uncertainties with novel applications to Doppler radar winds, Atmospheric Motion Vectors, and satellite radiances; a new algorithm for implementation of spatially-correlated observation error statistics in operational data assimilation; and innovative treatment of moist processes in the background error covariance model. We present results indicating a link between the spatial predictability of convection and convective regimes, with potential to allow improved forecast interpretation. The research was carried out as a partnership between University researchers and the Met Office (UK). We discuss the benefits of this approach and the impact of our research, which has helped to improve operational forecasts for convective rainfall events.
Keywords: flooding / predictability / Data assimilation / convection / radar reflectivity / Intense Rainfall / observation uncertainty / Initial Condition Uncertainty / Radar Refractivity / Doppler radar winds

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