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 byChasity Fickling
Modified about 1 year ago
Page 1© Crown copyright 2006 NWP in the Met Office
Page 2© Crown copyright 2006 Topics to be covered... 1. Describing the atmosphere 2. Using observations 3. Model mathematics 4. Operational models purposes 5. Model outputs
Page 3© Crown copyright 2006 OBSERVATIONS HUMAN FORECASTER CUSTOMERS NUMERICAL FORECASTS The Weather Prediction Process R&D ARCHIVES VERIFICATION
Page 4© Crown copyright 2006 Unified Model Met. Office has several requirements: local forecasting global forecasting climate modelling ocean and wave modelling a common model: shares code and operating structure is modular where differences are necessary gives considerable savings in maintenance cost
Page 5© Crown copyright 2006 Unified Model configurations
Page 6© Crown copyright 2006 Met Office models Based on Unified Model Global North Atlantic and European model 4km and 12km Mesoscales Crisis Area Mesoscale Models Stratospheric FOAM ocean forecasting models
Page 7© Crown copyright 2006 Met Office models Other models including Wave (Global, European, UK Waters) Surge NAME SSFM
Page 8© Crown copyright 2006 Fundamentals of NWP 1. Specify atmospheric initial conditions in a numerical form 2. Use equations describing atmospheric physical processes to predict how the initial state will evolve 3. Output the forecast in a useful form for the user
1. Describing the atmosphere
Page 10© Crown copyright 2006 Unified Model information Unified Model is a grid point model Non-hydrostatic Semi-Lagrangian advection Semi-implicit time integration 20 min timestep: Global 5 min timestep: Mes and NAE Horizontal staggering - Arakawa C grid Vertical staggering - Charney – Philips Uses 4-D Var data assimilation
Page 11© Crown copyright 2006 Specify the properties in the grid box from observational data (temp, pressure humidity, wind etc.) Grid length Grid point Unified Model is a gridpoint model
Page 12© Crown copyright 2006 Vertical co-ordinates in the UM Hybrid height (z) co-ordinates At the model top =1 so z=H At the surface = 0 so z=0 Above the first flat eta level =z/H z= H H H z I ) Below the first flat eta level ( I ) z= h(1- / I ) 2 where h=orography height h IIII
Page 13© Crown copyright 2006 GM Vertical resolution – 50 levels In boundary layer levels are terrain- following In free atmosphere levels are height coordinates In between levels are a combination of the 2 Lowest model levels present/new 70L at 10m/2.5m for wind at 20m/5m for temp 65 km 17.5 km
Page 14© Crown copyright 2006 Global, North Atlantic & European, mesoscale models Global Model (GM) Horizontal Resolution: Mid-latitude 40km Timestep: 20mins Vertical levels: 50, then 70 Grid: Standard lat/long type, with filtering near the poles Global Model (GM) Horizontal Resolution: Mid-latitude 40km Timestep: 20mins Vertical levels: 50, then 70 Grid: Standard lat/long type, with filtering near the poles Mesoscale Model (MES) Horizontal Resolution: 12km/4km Timestep: 5/ 1.7 mins Vertical levels: 38, eventually 70 Grid: Rotated lat/long (‘Equatorial Lat-long Fine-mesh’ - ELF) Mesoscale Model (MES) Horizontal Resolution: 12km/4km Timestep: 5/ 1.7 mins Vertical levels: 38, eventually 70 Grid: Rotated lat/long (‘Equatorial Lat-long Fine-mesh’ - ELF) North Atlantic & European Model (NAE) Horizontal Resolution: 12km Timestep: ~5 mins Vertical levels: 38, eventually 70 Grid: Rotated lat/long (‘Equatorial Lat-long Fine-mesh’ - ELF) North Atlantic & European Model (NAE) Horizontal Resolution: 12km Timestep: ~5 mins Vertical levels: 38, eventually 70 Grid: Rotated lat/long (‘Equatorial Lat-long Fine-mesh’ - ELF)
2. Using observations
Page 16© Crown copyright 2006 Data assimilation GM uses 4-D VAR; 12km MES and NAE 3-D VAR 4km MES has no data assimilation yet Model is run for an assimilation period prior to the forecast 6 hrs for GM model 3 hrs for the MES and NAE
Page 17© Crown copyright 2006 Data assimilation Observations firstly quality controlled against climate data model background field nearby obs. Then inserted into the run at or near their validity time to nudge the model towards reality
Page 18© Crown copyright 2006 Using observations Models try to make the best possible use of observations GP ship airep synop sonde Observations are checked for quality and interpolated onto the model grid points Different types of data have different areas of influence SEA LAND
Page 19© Crown copyright 2006 3- and 4-Dimensional VAR 3DVAR minimises the equation for a given time (e.g. 12Z) forms an analysis at a point in time forms an analysis which is consistent with a static state of the atmosphere MES T+12 and T+24 forecasts differences verifying at same time give error characteristics of model (8 months of cases used) 4DVAR minimises the equation by running the model backwards and forwards over an analysis period (e.g. 6 hours) forms an analysis for a period forms an analysis which is consistent with dynamical evolution of the atmosphere
Page 20© Crown copyright 2006 Moisture Observation Pre- processing System (MOPS) Used only in 12km MES/NAE Latent heating and cooling important in driving mesoscale systems MOPS is an analysis of humidity, cloud and precipitation for 12km MES and NAE
Page 21© Crown copyright 2006 Soil moisture in the GM No longer reset weekly to climatology New soil moisture nudging scheme Not as complex as MOPS Produced verifiable improvement, especially surface temperatures
3. Model Mathematics
Page 23© Crown copyright 2006 Model variables PRIMARY PROGNOSTIC variables are explicitly calculated using the primitive equations ANCILLARY FIELDS are fixed lower boundary conditions SECONDARY PROGNOSTIC variables are calculated at each timestep from the prognostic variables.
Page 24© Crown copyright 2006 Primary prognostic variables Horizontal and vertical wind components potential temperature specific humidity cloud water and ice surface pressure surface temperature soil temperature canopy water content snow depth
Page 25© Crown copyright 2006 Ancillary fields land/sea mask soil type vegetation type grid-box mean and variance of orography sea surface temperature proportion of sea-ice cover sea-ice thickness sea surface currents Prognostic variables in coupled atmosphere/ ocean models
Page 26© Crown copyright 2006 Global model orography
Page 27© Crown copyright 2006 NAE Model orography
Page 28© Crown copyright 2006 12km / 4km MES Model orography
Page 29© Crown copyright 2006 New UK 4 km Model Broad Leaf Trees Needle Leaf Trees C3 Grass C4 Grass Shrubs Urban Lakes Bare Soil Land Ice
Page 30© Crown copyright 2006 Model variables PRIMARY PROGNOSTIC variables are explicitly calculated using the primitive equations SECONDARY PROGNOSTIC variables are calculated by the parameterisation schemes
Page 31© Crown copyright 2006 Model variables primary prognostic variables horizontal and vertical wind components potential temperature specific humidity cloud water and ice surface pressure surface temperature soil temperature canopy water content snow depth secondary prognostic variables boundary layer depth sea surface roughness convective cloud amount convective cloud base convective cloud top layer cloud amount ozone mixing ratio
Page 32© Crown copyright 2006 Parametrised processes 1. Layer cloud and precipitation 2. Convective cloud and precipitation 3. Radiative processes 4. Surface and sub-surface processes 5. Gravity wave drag
Page 33© Crown copyright 2006 * 1. Layer cloud and precipitation * * ** **** * * * *
Page 34© Crown copyright 2006 Convective cloud model 2. Convective cloud and precipitation
Page 35© Crown copyright 2006 3. Radiative processes
Page 36© Crown copyright 2006 4. Surface and sub-surface processes
Page 37© Crown copyright 2006 5. Gravity wave drag
Page 38© Crown copyright 2006 Boundary conditions Lower and upper boundaries Lateral boundaries Land & sea: ancillary fields Stratosphere: ‘lid’ to model required in MES and NAE models primary prognostic variables required at each grid point NAE and 12km MES supplied from global model 4km MES supplied from NAE possible source of error
4. Purposes of Operational Models
Page 40© Crown copyright 2006 Global model 4 times daily Run times … 00Z, 06Z,12Z, 18Z Data accepted up to T+1 hour 45 min Out to T+144 (6 days) at 00Z and 12Z, T+48 at 06Z and 18Z Takes 2hr 15 mins to run out to T+144, 1hr 15min for T+48
Page 41© Crown copyright 2006 Global model Used for:- regional synoptic guidance medium range guidance civil aviation products mesoscale model boundary conditions
Page 42© Crown copyright 2006 North Atlantic & European model Run times … 00, 06, 12 and 18Z Takes boundary conditions from Global Model (previous GM run) Run partly overlaps with the GM Out to T+48
Page 43© Crown copyright 2006 North Atlantic & European model Used for:- wider range of products to international customers Improved Synoptic development guidance Better for rapid developments and extremes Boundary conditions for 4km MES
Page 44© Crown copyright 2006 Advantages of NAE Model Large domain Captures developing systems over North Atlantic Covers all of Europe and European Nimrod area includes some other model areas Higher resolution than GM (12km-v-40km) Better for rapid developments and extremes
Page 45© Crown copyright 2006 12km Mesoscale model Run times … 00Z, 06Z, 12Z and 18Z Takes boundary conditions from Global Model Runs in parallel with the GM (starts 10 mins later) Out to T+48 (2 days)
Page 46© Crown copyright 2006 12km Mesoscale model Used for:- UK local detail (ppn, cloud,temp,wind) Input to other systems (SSFM, and Nowcasting systems etc.)
Page 47© Crown copyright 2006 4km MES model Run times … 03Z, 09Z, 15Z, 21Z Takes boundary conditions from NAE Model Out to T+36 No data assimilation
Page 48© Crown copyright 2006 The Site Specific Forecast Model Philosophy is a fast but comprehensive 1D physical model based on the UM is coupled to NWP output makes timely use of local observations more rapidly than is feasible with NWP adds “local” detail of surface and (limited) orography provides (semi-) automated forecasts of near- surface temperatures, radiation fog, very low cloud (etc.)
Page 49© Crown copyright 2006 The SSFM 1D model based on UM “physics” - full column! Greatly increased resolution in BL & soil “Dynamics”=“Forcing data”: grad p, advection, etc. Simple forcing correction for orography MOSES with tile surface exchange for separate treatment of land use types Radiative canopy coupled to surface exchange Upwind satellite derived land-use determines drag & surface fluxes of heat, moisture Surface landuse weighting via a stability dependent Source Area Model Description & Performance
Page 50© Crown copyright 2006 Source Areas Site Wind Source Area Weighting
Page 51© Crown copyright 2006 SSFM land use and orography 100 m resolution orography
Page 52© Crown copyright 2006 SSFM Applications First guess in Open Road Semi-automatic TAF system First guess for utilities forecasts (METGAS, NGC) Input for air quality forecasts Products for media database
Page 53© Crown copyright 2006 Model Dependencies (simplified!) GLOBAL 12KM MES GLOBAL WAVE FOAM UK WATERS WAVE EUROPEAN WAVE SURGE SHELF SEAS NAME Scheduling must account for all dependencies and timeliness requirements of each model run SSFM NAE MODEL 4KM MES
5. Model output
Page 55© Crown copyright 2006 If convective rate reaches 0.5 mm/hr heavy shower symbol is plotted (same for snow) For dynamic rainfall rate of 4 mm/hr heavy rain symbol is plotted If dynamic rainfall rate <0.1 mm/hr type with highest rate is plotted If dynamic rate is >0.1 mm/hr dynamic symbols are plotted 0.03 0.1 0.5 4.0 mm/hr Criteria for allocating symbols on 6-up charts
Page 56© Crown copyright 2006 6-up Frames-problems Grid point averaging Thinned grid Snow prob lines Symbolic representation
Page 57© Crown copyright 2006 Global model precipitation forecast T+36 http://sp0100/~meso/current_mesglob_charts.html
Page 58© Crown copyright 2006 T+36 forecast rainfall/MSLP Valid 6Z, 1 st Dec 2003. Global ModelNAE Model
Page 59© Crown copyright 2006 T+12 forecast rainfall/MSLP Valid 12Z, 1st Jan 2005. GM NAE MES
Page 60© Crown copyright 2006 T+18 forecast rainfall/MSLP Valid 18Z, 4th September 2005. GM NAE 4km MES 12km MES
Page 61© Crown copyright 2006 4km Mesoscale model visibility forecast. 9Z 21 Nov
Page 62© Crown copyright 2006 Site Specific Forecast Model meteogram Cloud cross-section Temperature, precipitation, road condition plot Wind plot
Page 63© Crown copyright 2006 Boscastle, Cornwall. Flooding 16 th August 2004 RADAR
Page 64© Crown copyright 2006 Nowcasting Systems in the Met Office Nimrod- dynamic rain, and other weather elements 0-6 hrs, 5km res Gandolf- convective rain, 0-6 hrs, 2km res Convection Diagnosis Project – Probabilistic convective product. 0-36 hrs, 5km res
Page 65© Crown copyright 2006 Gandolf Nowcasts from 13UTC 16/8/04 T+1: 14UTCT+3: 16 UTC
6. NWP Verification
Page 67© Crown copyright 2006 Global NWP Index Weightings applied Weightings add to 100 Weighted by relative importance to customers 36-month running means
Page 68© Crown copyright 2006 Global NWP Index
Page 69© Crown copyright 2006
7. Future developments
Page 71© Crown copyright 2006 Future developments 2005-2006 12km N. Atlantic European Model (Mar 2005) Regional Ensemble capability (Aug 2005) Prognostic cloud/condensate scheme (Nov 2005) 45km Global Model (Dec 2005) Regional 4D-Var (Dec 2005) 4km UK Model (Apr 2006) Multi-model global ensembles out to 14-days (Apr 2006) Use of new data from METOP instruments (Sep 2006) Air quality predictions from NWP (Dec 2006)
Page 72© Crown copyright 2006 Trial hi-res meso model
Page 73© Crown copyright 2006 Comparison of 00 UTC: 12, 4 and 1 km forecasts. 16 Aug 2004 12-18 from 00 UTC 12km 12-18 from 00 UTC 4km 12-18 from 00 UTC 1km resolution
Page 74© Crown copyright 2006 Mechanism for 16 th August 2004 Storm Triggering 10 m wind convergence at 11 UTC (as convection triggered) 12 km 4 km Persistent Convergence Due to coast and orography
Page 75© Crown copyright 2006 19Z, 31 Jan 2003- Hi res MES models. T+7 precip & radar 12km4km1km
Any questions? GM, NAE and MES output. http://www-nwp/~meso/current_mesglob_charts.html NWP Gazette http://www.metoffice.gov.uk/research/nwp/publications/n wp_gazette/index.html NWP technical reports http://www.metoffice.gov.uk/research/nwp/publication s/papers/technical_reports/index.html 4km mesoscale runs: http://www- nwp/~meso/current_uk4mesglob_charts.html
1 Rachel Capon 04/2004 © Crown copyright Met Office Unified Model NIMROD Nowcasting Rachel Capon Met Office JCMM.
Regional Modelling Prepared by C. Tubbs, P. Davies, Met Office UK Revised, delivered by P. Chen, WMO Secretariat SWFDP-Eastern Africa Training Workshop.
Urban Modelling 1 03/2003 © Crown copyright Urban Scale NWP with the Met Office's Unified Model Peter Clark Mesoscale Modelling Group Met Office Joint.
Page 1 Developments in regional DA Oct 2007 © Crown copyright 2007 Mark Naylor, Bruce Macpherson, Richard Renshaw, Gareth Dow Data Assimilation and Ensembles,
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 NAE 4DVAR Mar 2006 © Crown copyright 2006 Bruce Macpherson, Marek Wlasak, Mark Naylor, Richard Renshaw Data Assimilation, NWP Assimilation developments.
Page 1© Crown copyright 2005 DEVELOPMENT OF 1- 4KM RESOLUTION DATA ASSIMILATION FOR NOWCASTING AT THE MET OFFICE Sue Ballard, September 2005 Z. Li, M.
Weather forecasting by computer Michael Revell NIWA
Introducing the Lokal-Modell LME at the German Weather Service Jan-Peter Schulz Deutscher Wetterdienst 27 th EWGLAM and 12 th SRNWP Meeting 2005.
Geophysical Modelling: Climate Modelling How advection, diffusion, choice of grids, timesteps etc are defined in state of the art models.
KMD Consortium for Small-Scale Modeling (COSMO) Strengths and Weaknesses Vincent N. Sakwa RSMC, Nairobi.
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.
Page 1© Crown copyright 2004 SRNWP Lead Centre Report on Data Assimilation 2005 for EWGLAM/SRNWP Annual Meeting October 2005, Ljubljana, Slovenia.
Soil moisture generation at ECMWF Gisela Seuffert and Pedro Viterbo European Centre for Medium Range Weather Forecasts ELDAS Interim Data Co-ordination.
EWGLAM Oct Some recent developments in the ECMWF model Mariano Hortal ECMWF Thanks to: A. Beljars (physics), E. Holm (humidity analysis)
Improved road weather forecasting by using high resolution satellite data Claus Petersen and Bent H. Sass Danish Meteorological Institute.
Laura Davies, University of Reading, UK. Supervisors: Bob Plant, Steve Derbyshire (Met Office)
Chapter 13 – Weather Analysis and Forecasting. The National Weather Service The National Weather Service (NWS) is responsible for forecasts several times.
Peak Performance Technical Environment FMI NWP Activities.
© Crown copyright Met Office Downscaling ability of the HadRM3P model over North America Wilfran Moufouma-Okia and Richard Jones.
Earth Science Division National Aeronautics and Space Administration 18 January 2007 Paper 5A.4: Slide 1 American Meteorological Society 21 st Conference.
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.
Forecasting and Numerical Weather Prediction (NWP) NOWcasting Description of atmospheric models Specific Models Types of variables and how to determine.
Page 1© Crown copyright Sarah Beare (nee John) Neill Bowler, Marie Dando, Anette Van der Wal, Rob Darvell Performance of the MOGREPS Regional Ensemble.
Modern Era Retrospective-analysis for Research and Applications: Introduction to NASA’s Modern Era Retrospective-analysis for Research and Applications:
Rapid Update Cycle Model William Sachman and Steven Earle ESC452 - Spring 2006.
1 INM’s contribution to ELDAS project E. Rodríguez and B. Navascués INM.
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.
Trials of a 1km Version of the Unified Model for Short Range Forecasting of Convective Events Humphrey Lean, Susan Ballard, Peter Clark, Mark Dixon, Zhihong.
© Crown copyright Met Office SRNWP Interoperability Workshop, ECMWF, January 2008 SRNWP Interoperability Terry Davies Met Office.
1 10/2003 © Crown copyright Unified Model Developments 2003 Clive Wilson NWP Met Office.
An air quality information system for cities with complex terrain based on high resolution NWP Viel Ødegaard, r&d department.
Non-hydrostatic Numerical Model Study on Tropical Mesoscale System During SCOUT DARWIN Campaign Wuhu Feng 1 and M.P. Chipperfield 1 IAS, School of Earth.
Forecasting ATS 113. Forecasts made by PEOPLE Folklore: –Groundhog Day –Fuzzy caterpillars –Walnut shells –Farmers Almanac.
GRAPES-Based Nowcasting: System design and Progress Jishan Xue, Hongya Liu and Hu Zhijing Chinese Academy of Meteorological Sciences Toulouse Sept 2005.
Météo-France / CNRM – T. Bergot 1) Introduction 2) The methodology of the inter-comparison 3) Phase 1 : cases study Inter-comparison of numerical models.
© Crown copyright Met Office Review topic – Impact of High-Resolution Data Assimilation Bruce Macpherson, Christoph Schraff, Claude Fischer EWGLAM, 2009.
The NCEP operational Climate Forecast System : configuration, products, and plan for the future Hua-Lu Pan Environmental Modeling Center NCEP.
Mesoscale Modeling Review the tutorial at: –In class.
1 On the use of radar data to verify mesoscale model precipitation forecasts Martin Goeber and Sean Milton Model Diagnostics and Validation group Numerical.
Land-Surface evolution forced by predicted precipitation corrected by high-frequency radar/satellite assimilation – the RUC Coupled Data Assimilation System.
Report of the Q2 Short Range QPF Discussion Group Jon Ahlquist Curtis Marshall John McGinley - lead Dan Petersen D. J. Seo Jean Vieux.
Page 1© Crown copyright 2004 WP5.3 Assessment of Forecast Quality ENSEMBLES RT4/RT5 Kick Off Meeting, Paris, Feb 2005 Richard Graham.
Regional GEM 15 km OPERATIONAL 48-h RUN (00 or 12 UTC) EVENT GEM-LAM 2.5 km GEM-LAM 1 km MC2-LAM 250 m T+5 T+12 T-1 T-3 36-h run 15-h run 6-h run Microscale.
© Crown copyright Met Office Plans for Met Office contribution to SMOS+STORM Evolution James Cotton & Pete Francis, Satellite Applications, Met Office,
3. Modelling module 3.1 Basics of numerical atmospheric modelling M. Déqué – CNRM – Météo-France J.P. Céron – DClim – Météo-France.
The Problem of Parameterization in Numerical Models METEO 6030 Xuanli Li University of Utah Department of Meteorology Spring 2005.
Weather Research & Forecasting: A General Overview HPC Symposium 2014, IIT Madras Tabish U Ansari MS – Environmental Engineering Department of Civil Engineering,
© Crown copyright Met Office Met Office dust forecasting Using the Met Office Unified Model™ David Walters: Manager Global Atmospheric Model Development,
© 2017 SlidePlayer.com Inc. All rights reserved.