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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

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Department of Meteorology, University of Reading, Reading RG6 6BB, UK
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Department of Mathematics and Statistics, University of Reading, Reading RG6 6AX, UK
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[email protected], Meteorology Building, University of Reading, Reading RG6 6BB, UK
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Department of Geography and Environmental Science, University of Reading, Reading RG6 6AB, UK
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Department of Earth Sciences, Uppsala University, 752 36 Uppsala, Sweden
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Met Office, Fitzroy Rd, Exeter EX1 3PB, UK
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Department of Mathematics, University of Surrey, Guildford GU2 7XH, UK
*
Author to whom correspondence should be addressed.
Current affiliation: LMD/IPSL, Département de Géosciences, ENS, PSL Research University, Ecole Polytechnique, Université Paris Saclay, Sorbonne Universités, UPMC Univ Paris 06, CNRS, Paris, France.
Atmosphere 2019, 10(3), 125; https://doi.org/10.3390/atmos10030125
Received: 11 January 2019 / Revised: 8 February 2019 / Accepted: 13 February 2019 / Published: 7 March 2019
(This article belongs to the Special Issue Advances in Applications of Weather Radar Data)
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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. View Full-Text
Keywords: flooding; convection; intense rainfall; radar reflectivity; radar refractivity; Doppler radar winds; data assimilation; observation uncertainty; initial condition uncertainty; predictability flooding; convection; intense rainfall; radar reflectivity; radar refractivity; Doppler radar winds; data assimilation; observation uncertainty; initial condition uncertainty; predictability
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Dance, S.L.; Ballard, S.P.; Bannister, R.N.; Clark, P.; Cloke, H.L.; Darlington, T.; Flack, D.L.A.; Gray, S.L.; Hawkness-Smith, L.; Husnoo, N.; Illingworth, A.J.; Kelly, G.A.; Lean, H.W.; Li, D.; Nichols, N.K.; Nicol, J.C.; Oxley, A.; Plant, R.S.; Roberts, N.M.; Roulstone, I.; Simonin, D.; Thompson, R.J.; Waller, J.A. Improvements in Forecasting Intense Rainfall: Results from the FRANC (Forecasting Rainfall Exploiting New Data Assimilation Techniques and Novel Observations of Convection) Project. Atmosphere 2019, 10, 125.

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