We think you have liked this presentation. If you wish to download it, please recommend it to your friends in any social system. Share buttons are a little bit lower. Thank you!
Presentation is loading. Please wait.
Published byCarter Ballard
Modified over 3 years ago
© Crown Copyright 2012. Source: Met Office Dale Barker, Tomas Landelius, Eric Bazile, Christoph Frei, Phil Jones 2 April 2012 EURO4M – WP2: Regional Reanalysis Overview © Crown Copyright 2012 Source: Met Office
© Crown Copyright 2012. Source: Met Office EURO4M WP2 WP2.1 Building capacity for advanced regional data assimilation (MetO) WP2.2 Dynamical downscaling of ERA (SMHI). WP2.3 2D mesoscale downscaling (Météo France). WP2.4 Evaluation (MeteoSwiss). WP2.5 Improvement of input data for reanalyses (UEA) Now
© Crown Copyright 2012. Source: Met Office WP2.1 Building capacity for advanced regional data assimilation Current 12km grid 480 x 384 Horizontal domain unchanged from last year. Decision made in FY2011-12 to align EURO4M vertical levels, physics, etc with global NWP/climate model (previously aligned with regional NWP ensemble).
© Crown Copyright 2012. Source: Met Office UM Running at ECMWF ECMWF interface Met Office
© Crown Copyright 2012. Source: Met Office Regional NWP – Why Bother? Regional NWP+ Regional DA Upper-Air Temperature Upper-Air Wind Speed PMSL 6hrly Acc. Precipitation Surface Wind-SpeedT+6 – T+48T+0 – T+6 Cloud AmountT+0 – T+48T+0 – T+6 VisibilityT+0 – T+48T+0 – T+12 Surface TemperatureT+0 – T+48 (UK only)T+0 – T+12/24 (NAE/UK) Benefit Of European Regional NWP vs 25km global model (UM): Focus for EURO4M: Regional Reanalysis – Why Bother?
© Crown Copyright 2012. Source: Met Office ERA-Interim EURO4M 12km, 70 levels 12-36km 4D-Var 6-hour analysis window assimilate: conventional obs incl visibility satellite radiances Ground-based GPS Cloud Precipitation Initial state and boundary conditions from ERA-Interim/ Clim analyses T255* (80km), 60 levels T159 (125km) 4D-Var 12-hour analysis window Assimilate: conventional obs satellite radiances Global/Regional Reanalysis Configurations * Note ERA-Clim up to T511 (~40km)
© Crown Copyright 2012. Source: Met Office First attempt at reanalysis... May 2010 June 2010 July 2010 Floods in Poland, eastern Europe Severe storms France/Spain Russian heatwave spreading West, forest fires
© Crown Copyright 2012. Source: Met Office Russian heatwave, July 2010 Tmax 10-07-10 ERA-Interim 12km EURO4M e-obs
© Crown Copyright 2012. Source: Met Office Verification vs Radiosonde T+0T+6 European Temperature rms error: May-July 2010 ERA-Interim EURO4M 12km GA3: See Renshaw talk
© Crown Copyright 2012. Source: Met Office Developments for 2012/13 Variational bias correction ODB – obs monitoring. ECMWF collaboration. Extend observations dataset Cloud and Precipitation assimilation Validation – extreme statistics Collaborate on cross-validation Pre-Production Reanalysis: 2010 – 2011 period Impact of 4D-Var assimilation resolution (12-36km)
© Crown Copyright 2012. Source: Met Office 3D-Var Re-analysis at 22 km, 60Levels over Europe (SMHI) 2D analysis at x ~5 km x ~ 5 km over Europe Downscaling Courtesy of T. Landelius (SMHI) More observations Dynamical downscaling and reanalysis over Europe (WP2.2 – 2.3) By adding details with topography and more observations, the quality of the analysis should improve … ~ 4000 obs (1200 over France)
© Crown Copyright 2012. Source: Met Office WP2.3 2D reanalysis (SMHI) HIRLAM 22 kmDownscaled to 5 kmMESAN t2m analysis (observations as black dots)
© Crown Copyright 2012. Source: Met Office WP2.3 observations for 2D reanalysis (SMHI) In total some 10,000 unique stations. This is still far from all observations available in the national archives! GA3: See Unden, Landelius talks
© Crown Copyright 2012. Source: Met Office #Validation of the analyses Against observations (particularly for T2m, Rh2m, 24-h cumulated precipitation) Assessment of the snow depth and the river flow by using a surface scheme and a hydrological module forced with reanalyses surface variables. #Improvement of the downscaling method: Problem of the vertical interpolation over the mountainous areas especially for the temperature ; usage of a lake climatology to improve the lake surface temperature. #Development of precipitation analysis, the usage of Tmin and Tmax for reanalysis #Technical issues: To create a reanalysis domain (~5km scale) over Europe that best fit SMHI domain (at 22 km scale), i.e. changing the geometry from rotated lat-lon to Lambert conformal); Converting GRIB files from SMHI to specific format type Usage of additional observations not available in GTS. #Specific treatment for the wind with a dynamical adaptation or by DFI ? WP2.3 Ongoing work GA3: See Soci talk
© Crown Copyright 2012. Source: Met Office Overall Process WP2.5 Improved Input Data (UEA) GA3: See Jones talk
© Crown Copyright 2012. Source: Met Office WP2.4 Evaluation (MeteoSwiss) Using existing datasets, reanalyses, and datasets developed in WP1 Formally begins April 2012. MeteoSwiss: precipitation variations in Alpine regions. Met Office: Reanalysis sensitivity to resolution, technique. Observation impact. SMHI: compare MetO/HIRLAM reanalyses. MeteoFrance: Evaluate MESAN/SAFRAN. DWD: verify WV, cloud, precip, radiation with CM-SAF Investigate satellite radiance calibration.
© Crown Copyright 2012. Source: Met Office WP2.4 Evaluation (MeteoSwiss) 20 km grid5 km grid Precipitation at meso-scale in complex topography (Alpine region) Consistency between obs. datasets (spatial pattern, annual cycle) for precip extremes? High-resolution regional reanalyses vs. global reanalysis? Representation of interannual to decadal variations by regional reanalyses? GA3: See Frei discussion
© Crown Copyright 2012. Source: Met Office EURO4M WP2 Deliverables/Milestones Year 1 Year 2 Year 3Year 4 Now Deliverables on track. Year 1-2, largely preparation (much technical work). Year 3-4 pre-production runs plus evaluation (enhanced collaboration).
© Crown Copyright 2012. Source: Met Office Questions/Discussion?
© Crown copyright Met Office EURO4M Work Package 2 (chiefly WP2.1) Richard Renshaw EURO4M GA1, De Bilt, April 14 th 2010.
© Crown Copyright Source: Met Office Dale Barker, Tomas Landelius, Eric Bazile, Francesco Isotta, Phil Jones 17 April 2013 EURO4M – WP2: Regional.
© Crown copyright Met Office UM 4D-Var Regional Reanalysis Progress Richard Renshaw, Stephen Oxley, Adam Maycock, Peter Jermey, Dale Barker, DingMin Li.
European Reanalysis and Observations for Monitoring EURO4M: European Reanalysis and Observations for Monitoring EU-project, April 2010 – March 2014, 9.
Demands and expectations at SMHI on the European Reanalysis for observations and climate Per Und én Tomas Landelius SMHI.
Using satellite data to understand uncertainties in reanalyses: UERRA Richard Renshaw, Peter Jermey with thanks to Jörg Trentmann, Jennifer Lenhardt, Andrea.
EURO4M: European Reanalysis and Observations for Monitoring
Regional Re-analyses of Observations, Ensembles and Uncertainties of Climate information Per Undén Coordinator UERRA SMHI.
Page 1© Crown copyright 2004 SRNWP Lead Centre Report on Data Assimilation 2005 for EWGLAM/SRNWP Annual Meeting October 2005, Ljubljana, Slovenia.
EURO4M Météo-France motivations and contributions.
EEA Report – The Alps Water-tower of Europe Vital ecosystem services
Page 1 NAE 4DVAR Oct 2006 © Crown copyright 2006 Mark Naylor Data Assimilation, NWP NAE 4D-Var – Testing and Issues EWGLAM/SRNWP meeting Zurich 9 th -12.
EURO4M 4th GA, Norrköping, April 2013 Overview of the new MESCAN precipitation analysis system C. Soci, E. Bazile, J-F. Mahfouf with contributions.
Introduction to data assimilation in meteorology Pierre Brousseau, Ludovic Auger ATMO 08,Alghero, september 2008.
April Jörg Trentmann, Uwe Pfeifroth, Jennifer Lenhardt, Richard Müller Deutscher Wetterdienst (DWD) Evaluation of EURO4M Reanalysis data using Satellite.
Global vs mesoscale ATOVS assimilation at the Met Office Global Large obs error (4 K) NESDIS 1B radiances NOAA-15 & 16 HIRS and AMSU thinned to 154 km.
European Reanalysis and Observations for Monitoring MACC Conference, Utrecht, May 2011 GEOLAND2 MyOcean MACC SAFER G-Mosaic ??? Global Monitoring.
Dale Barker, Richard Renshaw, Peter Jermey WWOSC, Montreal, Canada, 20 August 2014 Regional Reanalysis At The Met Office © Crown Copyright 2012 Source:
© Crown copyright Met Office UM 4D-Var Regional Reanalysis Progress Richard Renshaw, Stephen Oxley, Adam Maycock, Peter Jermey, Dale Barker, Tom Green,
E. Bazile, C. Soci, F. Besson & ?? UERRA meeting Exeter, March 2014 Surface Re-Analysis and Uncertainties for.
26 th EWGLAM & 11 th SRNWP meetings, Oslo, Norway, 4 th - 7 th October 2004 Stjepan Ivatek-Šahdan RC LACE Data Manager Croatian Meteorological and Hydrological.
European Reanalysis and Observations for Monitoring EUMETGRID meeting, De Bilt, The Netherlands, 31 August 2011 EURO4M: European Reanalysis and Observations.
© Crown copyright Met Office Climate Projections for West Africa Andrew Hartley, Met Office: PARCC national workshop on climate information and species.
Recent & planned developments to the Met Office Global and Regional Ensemble Prediction System (MOGREPS) Richard Swinbank, Warren Tennant, Sarah Beare,
Activity of SMHI (Swedish Meteorological and Hydrological Institute) Presentation for CARPE DIEM kick-off meeting, DLR-GERMANY, January Contact.
1 00/XXXX © Crown copyright Use of radar data in modelling at the Met Office (UK) Bruce Macpherson Mesoscale Assimilation, NWP Met Office EWGLAM / COST-717.
Page 1 Developments in regional DA Oct 2007 © Crown copyright 2007 Mark Naylor, Bruce Macpherson, Richard Renshaw, Gareth Dow Data Assimilation and Ensembles,
1 Climate Ensemble Simulations and Projections for Vietnam using PRECIS Model Presented by Hiep Van Nguyen Main contributors: Mai Van Khiem, Tran Thuc,
Climate reanalysis at ECMWF Reanalysis is based on analysis methods developed to provide initial states for numerical weather prediction It applies a fixed,
Severe Weather Forecasts
Production of a multi-model, convective- scale superensemble over western Europe as part of the SESAR project PHY-EPS Workshop, June 19 th, 2013 Jeffrey.
Matthew Hendrickson, and Pascal Storck
ENSEMBLES General Assembly, Prague, Czech Republic, November 2007 Potential WP Participants (known absentees underlined): DJF, DISAT, FMI, FUB, LUND,
1 The GEMS production systems and retrospective reanalysis Adrian Simmons.
June 20, 2005Workshop on Chemical data assimilation and data needs Data Assimilation Methods Experience from operational meteorological assimilation John.
The ENSEMBLES high- resolution gridded daily observed dataset Malcolm Haylock, Phil Jones, Climatic Research Unit, UK WP5.1 team: KNMI, MeteoSwiss, Oxford.
Norwegian Meteorological Institute met.no LAMEPS – Limited area ensemble forecasting in Norway, using targeted EPS Marit Helene Jensen, Inger-Lise Frogner,
Chapter 13 – Weather Analysis and Forecasting
Status and difficulties for running 2D re-analyses at 5.5km over Europe E. Bazile & C. Soci special thanks to SMHI: T. Landelius & P. Dahlgren EURO4M 4rd.
Overview of the present HIRLAM surface assimilation Mainly taken from: HIRLAM Technical Report No. 58.
The COSMO-LEPS system at ECMWF
Station lists and bias corrections Jemma Davie, Colin Parrett, Richard Renshaw, Peter Jermey © Crown Copyright 2012 Source: Met Office© Crown copyright.
Assimilation of radar data - research plan
European Reanalysis and Observations for Monitoring EURO4M: European Reanalysis and Observations for Monitoring EU-FP7-SPACE, April 2010.
Verification of Numerical Weather Prediction systems employed by the Australian Bureau of Meteorology over East Antarctica during the summer season.
OSEs with HIRLAM and HARMONIE for EUCOS Nils Gustafsson, SMHI Sigurdur Thorsteinsson, IMO John de Vries, KNMI Roger Randriamampianina, met.no.
WP1.2: Developing gridded datasets based on long-term remote sensing data (DWD) MOHC (results to be used in WP3): max and min surface air temperature products.
Progress in Downscaling Climate Change Scenarios in Idaho Brandon C. Moore.
Use of radar data in ALADIN Marián Jurašek Slovak Hydrometeorological Institute.
Preliminary results from assimilation of GPS radio occultation data in WRF using an ensemble filter H. Liu, J. Anderson, B. Kuo, C. Snyder, A. Caya IMAGe.
© 2017 SlidePlayer.com Inc. All rights reserved.