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 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 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 km / 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 Radiative processes
Page 36© Crown copyright Surface and sub-surface processes
Page 37© Crown copyright 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 km 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 km Mesoscale model Used for:- UK local detail (ppn, cloud,temp,wind) Input to other systems (SSFM, and Nowcasting systems etc.)
Page 47© Crown copyright km 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 mm/hr Criteria for allocating symbols on 6-up charts
Page 56© Crown copyright up Frames-problems Grid point averaging Thinned grid Snow prob lines Symbolic representation
Page 57© Crown copyright 2006 Global model precipitation forecast T+36
Page 58© Crown copyright 2006 T+36 forecast rainfall/MSLP Valid 6Z, 1 st Dec Global ModelNAE Model
Page 59© Crown copyright 2006 T+12 forecast rainfall/MSLP Valid 12Z, 1st Jan GM NAE MES
Page 60© Crown copyright 2006 T+18 forecast rainfall/MSLP Valid 18Z, 4th September GM NAE 4km MES 12km MES
Page 61© Crown copyright km 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 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 km 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 from 00 UTC 12km from 00 UTC 4km 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 Z, 31 Jan Hi res MES models. T+7 precip & radar 12km4km1km
Any questions? GM, NAE and MES output. NWP Gazette wp_gazette/index.html NWP technical reports s/papers/technical_reports/index.html 4km mesoscale runs: nwp/~meso/current_uk4mesglob_charts.html
© Crown copyright Met Office Regional climate model formulation PRECIS Workshop, Reading University, 23 rd – 27 th April 2012.
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.
An Overview of Numerical Weather Prediction Models.
Regional Modelling Prepared by C. Tubbs, P. Davies, Met Office UK Revised, delivered by P. Chen, WMO Secretariat SWFDP-Eastern Africa Training Workshop.
Page 1© Crown copyright 2005 Use of EPS at the Met Office Ken Mylne and Tim Legg.
© Crown copyright Met Office Scientific background and content of new gridded products Bob Lunnon, Aviation Outcomes Manager, Met Office WAFS Workshop.
Training Course 2009 – NWP-PR: The Seasonal Forecast System at ECMWF 1 The Seasonal Forecast System at ECMWF Tim Stockdale European Centre for Medium-Range.
Page 1© Crown copyright Operational Use of ECMWF products at the Met Office: Current practice, Verification and Ideas for the future Tim Hewson 17 th June.
Recent developments on the NWP suite of Environment Canada Pierre Gauthier Department of Earth and Atmospheric Sciences Université du Québec à Montréal.
ECMWF DA/SAT Training Course, May The Operational Data Assimilation System Lars Isaksen, Data Assimilation, ECMWF Overview of the operational data.
Page 1© Crown copyright Some Strengths and Weaknesses of ECMWF Forecasts for the UK Tim Hewson 15 th June 2006 Contributors include: Eleanor Crompton,
Numerical Weather Prediction (Met DA) The Analysis of Satellite Data (lecture 1:Basic Concepts) Tony McNally ECMWF.
Parametrizations in Data Assimilation ECMWF Training Course May 2012 Philippe Lopez Physical Aspects Section, Research Department, ECMWF (Room 113)
Arctic observing system for regional NWP Harald Schyberg (met.no), Frank Thomas Tveter (met.no) Roger Randriamampianina (met.no), Trygve Aspelien (met.no)
13 th SRNWP / 28 th EWGLAM Meeting Zürich, 9 – 12 Oct current status long-term strategy mid-term strategy some ongoing.
Data Assimilation Strategies for Operational NWP at Meso-scale and Implication for Nowcasting Thibaut Montmerle CNRM-GAME/GMAP WMO/WWRP Workshop on Use.
© Crown copyright Met Office Met Office progress report Andy Brown WGNE, Tokyo, October 2010.
Data Assimilation Training Course, Reading, 5-14 May 2010 Observation Operators in Variational Data Assimilation David Tan, Room 1001
© Crown copyright Met Office UM 4D-Var Regional Reanalysis Progress Richard Renshaw, Stephen Oxley, Adam Maycock, Peter Jermey, Dale Barker, DingMin Li.
Global Earth-system Monitoring using Space & in-situ data, A.Hollingsworth ECMWF Seminar Sept 2003 Slide 1 GEMS Global Earth-system Monitoring using Space.
Parameterizations in Data Assimilation Philippe Lopez Physical Aspects Section, Research Department, ECMWF (Room 113) ECMWF Training Course May 2010.
The Global Observing System A composite space and ground based observing system PowerPoint Summarization IMPLEMENTATION PLAN FOR EVOLUTION OF SPACE AND.
Development of Data Assimilation Systems for Short-Term Numerical Weather Prediction at JMA Tadashi Fujita (NPD JMA) Y. Honda, Y. Ikuta, J. Fukuda, Y.
Cloud Resolving Models: Their development and their use in parametrization development Richard Forbes, Adrian Tompkins.
© Crown copyright Met Office Met Office Experiences with Convection Permitting Models Humphrey Lean Reading, UK Nowcasting Workshop,
© Crown copyright Met Office UM 4D-Var Regional Reanalysis Progress Richard Renshaw, Stephen Oxley, Adam Maycock, Peter Jermey, Dale Barker, Tom Green,
Training Course 2009 – NWP-NM: Operational and Research Activities at ECMWF 1/46 Operational and Research Activities at ECMWF Renate Hagedorn European.
Numerical Weather Prediction (Met DA) The Analysis of Satellite Data lecture 2 Tony McNally ECMWF.
Chapter 13 – Weather Analysis and Forecasting. The National Weather Service The National Weather Service (NWS) is responsible for forecasts several times.
© 2016 SlidePlayer.com Inc. All rights reserved.