Improvements in Forecasting Intense Rainfall: Results from the FRANC (Forecasting Rainfall Exploiting New Data Assimilation Techniques and Novel Observations of Convection) Project
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 pr...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-03-01
|
| Series: | Atmosphere |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2073-4433/10/3/125 |
| id |
doaj-878ff57e3c574868a0a223544f98aa7e |
|---|---|
| record_format |
Article |
| spelling |
doaj-878ff57e3c574868a0a223544f98aa7e2020-11-24T23:59:01ZengMDPI AGAtmosphere2073-44332019-03-0110312510.3390/atmos10030125atmos10030125Improvements in Forecasting Intense Rainfall: Results from the FRANC (Forecasting Rainfall Exploiting New Data Assimilation Techniques and Novel Observations of Convection) ProjectSarah L. Dance0Susan P. Ballard1Ross N. Bannister2Peter Clark3Hannah L. Cloke4Timothy Darlington5David L. A. Flack6Suzanne L. Gray7Lee Hawkness-Smith8Nawal Husnoo9Anthony J. Illingworth10Graeme A. Kelly11Humphrey W. Lean12Dingmin Li13Nancy K. Nichols14John C. Nicol15Andrew Oxley16Robert S. Plant17Nigel M. Roberts18Ian Roulstone19David Simonin20Robert J. Thompson21Joanne A. Waller22Department of Meteorology, University of Reading, Reading RG6 6BB, UKMetOffice@Reading, Meteorology Building, University of Reading, Reading RG6 6BB, UKDepartment of Meteorology, University of Reading, Reading RG6 6BB, UKDepartment of Meteorology, University of Reading, Reading RG6 6BB, UKDepartment of Meteorology, University of Reading, Reading RG6 6BB, UKMet Office, Fitzroy Rd, Exeter EX1 3PB, UKDepartment of Meteorology, University of Reading, Reading RG6 6BB, UKDepartment of Meteorology, University of Reading, Reading RG6 6BB, UKMetOffice@Reading, Meteorology Building, University of Reading, Reading RG6 6BB, UKMet Office, Fitzroy Rd, Exeter EX1 3PB, UKDepartment of Meteorology, University of Reading, Reading RG6 6BB, UKMetOffice@Reading, Meteorology Building, University of Reading, Reading RG6 6BB, UKMetOffice@Reading, Meteorology Building, University of Reading, Reading RG6 6BB, UKMetOffice@Reading, Meteorology Building, University of Reading, Reading RG6 6BB, UKDepartment of Meteorology, University of Reading, Reading RG6 6BB, UKDepartment of Meteorology, University of Reading, Reading RG6 6BB, UKDepartment of Mathematics, University of Surrey, Guildford GU2 7XH, UKDepartment of Meteorology, University of Reading, Reading RG6 6BB, UKMetOffice@Reading, Meteorology Building, University of Reading, Reading RG6 6BB, UKDepartment of Mathematics, University of Surrey, Guildford GU2 7XH, UKMetOffice@Reading, Meteorology Building, University of Reading, Reading RG6 6BB, UKDepartment of Meteorology, University of Reading, Reading RG6 6BB, UKDepartment of Meteorology, University of Reading, Reading RG6 6BB, UKThe 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.http://www.mdpi.com/2073-4433/10/3/125floodingconvectionintense rainfallradar reflectivityradar refractivityDoppler radar windsdata assimilationobservation uncertaintyinitial condition uncertaintypredictability |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Sarah L. Dance Susan P. Ballard Ross N. Bannister Peter Clark Hannah L. Cloke Timothy Darlington David L. A. Flack Suzanne L. Gray Lee Hawkness-Smith Nawal Husnoo Anthony J. Illingworth Graeme A. Kelly Humphrey W. Lean Dingmin Li Nancy K. Nichols John C. Nicol Andrew Oxley Robert S. Plant Nigel M. Roberts Ian Roulstone David Simonin Robert J. Thompson Joanne A. Waller |
| spellingShingle |
Sarah L. Dance Susan P. Ballard Ross N. Bannister Peter Clark Hannah L. Cloke Timothy Darlington David L. A. Flack Suzanne L. Gray Lee Hawkness-Smith Nawal Husnoo Anthony J. Illingworth Graeme A. Kelly Humphrey W. Lean Dingmin Li Nancy K. Nichols John C. Nicol Andrew Oxley Robert S. Plant Nigel M. Roberts Ian Roulstone David Simonin Robert J. Thompson Joanne A. Waller Improvements in Forecasting Intense Rainfall: Results from the FRANC (Forecasting Rainfall Exploiting New Data Assimilation Techniques and Novel Observations of Convection) Project Atmosphere flooding convection intense rainfall radar reflectivity radar refractivity Doppler radar winds data assimilation observation uncertainty initial condition uncertainty predictability |
| author_facet |
Sarah L. Dance Susan P. Ballard Ross N. Bannister Peter Clark Hannah L. Cloke Timothy Darlington David L. A. Flack Suzanne L. Gray Lee Hawkness-Smith Nawal Husnoo Anthony J. Illingworth Graeme A. Kelly Humphrey W. Lean Dingmin Li Nancy K. Nichols John C. Nicol Andrew Oxley Robert S. Plant Nigel M. Roberts Ian Roulstone David Simonin Robert J. Thompson Joanne A. Waller |
| author_sort |
Sarah L. Dance |
| title |
Improvements in Forecasting Intense Rainfall: Results from the FRANC (Forecasting Rainfall Exploiting New Data Assimilation Techniques and Novel Observations of Convection) Project |
| title_short |
Improvements in Forecasting Intense Rainfall: Results from the FRANC (Forecasting Rainfall Exploiting New Data Assimilation Techniques and Novel Observations of Convection) Project |
| title_full |
Improvements in Forecasting Intense Rainfall: Results from the FRANC (Forecasting Rainfall Exploiting New Data Assimilation Techniques and Novel Observations of Convection) Project |
| title_fullStr |
Improvements in Forecasting Intense Rainfall: Results from the FRANC (Forecasting Rainfall Exploiting New Data Assimilation Techniques and Novel Observations of Convection) Project |
| title_full_unstemmed |
Improvements in Forecasting Intense Rainfall: Results from the FRANC (Forecasting Rainfall Exploiting New Data Assimilation Techniques and Novel Observations of Convection) Project |
| title_sort |
improvements in forecasting intense rainfall: results from the franc (forecasting rainfall exploiting new data assimilation techniques and novel observations of convection) project |
| publisher |
MDPI AG |
| series |
Atmosphere |
| issn |
2073-4433 |
| publishDate |
2019-03-01 |
| description |
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. |
| topic |
flooding convection intense rainfall radar reflectivity radar refractivity Doppler radar winds data assimilation observation uncertainty initial condition uncertainty predictability |
| url |
http://www.mdpi.com/2073-4433/10/3/125 |
| work_keys_str_mv |
AT sarahldance improvementsinforecastingintenserainfallresultsfromthefrancforecastingrainfallexploitingnewdataassimilationtechniquesandnovelobservationsofconvectionproject AT susanpballard improvementsinforecastingintenserainfallresultsfromthefrancforecastingrainfallexploitingnewdataassimilationtechniquesandnovelobservationsofconvectionproject AT rossnbannister improvementsinforecastingintenserainfallresultsfromthefrancforecastingrainfallexploitingnewdataassimilationtechniquesandnovelobservationsofconvectionproject AT peterclark improvementsinforecastingintenserainfallresultsfromthefrancforecastingrainfallexploitingnewdataassimilationtechniquesandnovelobservationsofconvectionproject AT hannahlcloke improvementsinforecastingintenserainfallresultsfromthefrancforecastingrainfallexploitingnewdataassimilationtechniquesandnovelobservationsofconvectionproject AT timothydarlington improvementsinforecastingintenserainfallresultsfromthefrancforecastingrainfallexploitingnewdataassimilationtechniquesandnovelobservationsofconvectionproject AT davidlaflack improvementsinforecastingintenserainfallresultsfromthefrancforecastingrainfallexploitingnewdataassimilationtechniquesandnovelobservationsofconvectionproject AT suzannelgray improvementsinforecastingintenserainfallresultsfromthefrancforecastingrainfallexploitingnewdataassimilationtechniquesandnovelobservationsofconvectionproject AT leehawknesssmith improvementsinforecastingintenserainfallresultsfromthefrancforecastingrainfallexploitingnewdataassimilationtechniquesandnovelobservationsofconvectionproject AT nawalhusnoo improvementsinforecastingintenserainfallresultsfromthefrancforecastingrainfallexploitingnewdataassimilationtechniquesandnovelobservationsofconvectionproject AT anthonyjillingworth improvementsinforecastingintenserainfallresultsfromthefrancforecastingrainfallexploitingnewdataassimilationtechniquesandnovelobservationsofconvectionproject AT graemeakelly improvementsinforecastingintenserainfallresultsfromthefrancforecastingrainfallexploitingnewdataassimilationtechniquesandnovelobservationsofconvectionproject AT humphreywlean improvementsinforecastingintenserainfallresultsfromthefrancforecastingrainfallexploitingnewdataassimilationtechniquesandnovelobservationsofconvectionproject AT dingminli improvementsinforecastingintenserainfallresultsfromthefrancforecastingrainfallexploitingnewdataassimilationtechniquesandnovelobservationsofconvectionproject AT nancyknichols improvementsinforecastingintenserainfallresultsfromthefrancforecastingrainfallexploitingnewdataassimilationtechniquesandnovelobservationsofconvectionproject AT johncnicol improvementsinforecastingintenserainfallresultsfromthefrancforecastingrainfallexploitingnewdataassimilationtechniquesandnovelobservationsofconvectionproject AT andrewoxley improvementsinforecastingintenserainfallresultsfromthefrancforecastingrainfallexploitingnewdataassimilationtechniquesandnovelobservationsofconvectionproject AT robertsplant improvementsinforecastingintenserainfallresultsfromthefrancforecastingrainfallexploitingnewdataassimilationtechniquesandnovelobservationsofconvectionproject AT nigelmroberts improvementsinforecastingintenserainfallresultsfromthefrancforecastingrainfallexploitingnewdataassimilationtechniquesandnovelobservationsofconvectionproject AT ianroulstone improvementsinforecastingintenserainfallresultsfromthefrancforecastingrainfallexploitingnewdataassimilationtechniquesandnovelobservationsofconvectionproject AT davidsimonin improvementsinforecastingintenserainfallresultsfromthefrancforecastingrainfallexploitingnewdataassimilationtechniquesandnovelobservationsofconvectionproject AT robertjthompson improvementsinforecastingintenserainfallresultsfromthefrancforecastingrainfallexploitingnewdataassimilationtechniquesandnovelobservationsofconvectionproject AT joanneawaller improvementsinforecastingintenserainfallresultsfromthefrancforecastingrainfallexploitingnewdataassimilationtechniquesandnovelobservationsofconvectionproject |
| _version_ |
1725448672378880000 |