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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
Univ Reading, Dept Meteorol, Reading RG6 6BB, Berks, England;Univ Reading, Dept Math & Stat, Reading RG6 6AX, Berks, England.
Univ Reading, MetOff Reading, Meteorol Bldg, Reading RG6 6BB, Berks, England.
Univ Reading, Dept Meteorol, Reading RG6 6BB, Berks, England.ORCID iD: 0000-0002-6846-8297
Univ Reading, Dept Meteorol, Reading RG6 6BB, Berks, England.
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2019 (English)In: Atmosphere, ISSN 2073-4433, E-ISSN 2073-4433, Vol. 10, no 3, article id 125Article, review/survey (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
MDPI , 2019. Vol. 10, no 3, article id 125
Keywords [en]
flooding, convection, intense rainfall, radar reflectivity, radar refractivity, Doppler radar winds, data assimilation, observation uncertainty, initial condition uncertainty, predictability
National Category
Meteorology and Atmospheric Sciences
Identifiers
URN: urn:nbn:se:uu:diva-382800DOI: 10.3390/atmos10030125ISI: 000464172800001OAI: oai:DiVA.org:uu-382800DiVA, id: diva2:1313376
Available from: 2019-05-03 Created: 2019-05-03 Last updated: 2019-05-03Bibliographically approved

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