NWP in the Met Office © Crown copyright 2006.

Name: NWP in the Met Office © Crown copyright 2006.
Uploaded: 2017-07-11T20:06:08+00:00
Duration: PTM30S59
Channel: Chasity Fickling
Description: NWP in the Met Office © Crown copyright 2006.

Topics to be covered... 1. Describing the atmosphere
2. Using observations 3. Model mathematics 4. Operational models purposes 5. Model outputs © Crown copyright 2006

The Weather Prediction Process
OBSERVATIONS NUMERICAL FORECASTS R&D VERIFICATION CUSTOMERS ARCHIVES Observations are made at set times of the day and increasingly in a continuous manner. Collection of observations from around the world + 5 day fc takes < 4hrs Use the CRAYT3E to produce the forecasts Many customers take output without human intervention eg aviation HUMAN FORECASTER © 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 © Crown copyright 2006

Unified Model configurations
© 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 © Crown copyright 2006

Met Office models Other models including
Wave (Global, European, UK Waters) Surge NAME SSFM © 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 © Crown copyright 2006

1. Describing the atmosphere

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 © Crown copyright 2006

Specify the properties in the grid box
Unified Model is a gridpoint model Grid length Grid point Specify the properties in the grid box from observational data (temp, pressure humidity, wind etc.) © Crown copyright 2006

Vertical co-ordinates in the UM
Hybrid height (z) co-ordinates At the model top  =1 so z=H Above the first flat eta level  =z/H z= H I H z H Below the first flat eta level (I) z= h(1- /I)2 where h=orography height h At the surface  = 0 so z=0 © Crown copyright 2006

GM Vertical resolution – 50 levels
65 km In free atmosphere levels are height coordinates In between levels are a combination of the 2 17.5 km Lowest model levels present/new 70L at 10m/2.5m for wind at 20m/5m for temp The 50 level model has extra levels in stratosphere (The same resolution below). The business case for this is to: improve the analysis of satellite radiance channels, which peak in the stratosphere but have a broad weighting function covering including the significant tropopause levels. move the model upper lid further away from the areas of significant meteorological interest. The upper lid is likely to be at 65km rather than 40km. have a single global model covering all customer interests and are able to pension off the separate stratospheric model and reduce maintenance costs. In a 70 level model, lowest model levels are at 5m for temperature and 2.5 metres for wind, : In boundary layer levels are terrain-following © 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 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) 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) Extra resolution in the boundary layer (when go to 70 levels) The new model will have twice as many levels in the lowest kilometre. (14 compared with 7) The lowest model levels will be 5m for temperature (currently 20m) and 2.5m for wind (currently 10m). This option provides much better chance of resolving thin fog and stratus layers. It is comparable to the vertical resolutions used by the site specific forecast model so should provide the same benefits. Extra resolution in mid tropospheric levels Between 800hPa and 400hPa, the new model will have twice as many levels (24 compared with 12) The coarsest resolution in terms of pressure is 22hPa between the levels at 500hPa compared with 45hPa at present The resolution increase at the mid tropospheric levels provides should benefit cloud and precipitation. Extra resolution at jet levels At present there are 3 levels between 300hPa and 200hPa. In the new model we will 5 levels. The spacing between levels reduces from 1km to 500m (or from about 30hPa to 20hPa). This should reduce model errors at this meteorologically significant level and should improve the model accuracy generally. It also of course has a direct benefit to aviation. © Crown copyright 2006

2. Using observations

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 © 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 © Crown copyright 2006

Using observations Models try to make the best possible use of observations Observations are checked for quality and interpolated onto the model grid points Different types of data have different areas of influence airep GP GP GP sonde sonde ship synop synop synop LAND synop GP GP GP ship synop SEA GP GP GP © Crown copyright 2006

3- and 4-Dimensional VAR 3DVAR 4DVAR
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 © 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 © 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 © Crown copyright 2006

3. Model Mathematics

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. © 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 © 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 © Crown copyright 2006

Global model orography

12km / 4km MES Model orography

New UK 4 km Model Broad Leaf Trees Needle Leaf Trees C3 Grass C4 Grass
Shrubs Urban Lakes Bare Soil Land Ice © Crown copyright 2006

Model variables PRIMARY PROGNOSTIC variables are explicitly calculated using the primitive equations SECONDARY PROGNOSTIC variables are calculated by the parameterisation schemes © 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 © 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 © Crown copyright 2006

1. Layer cloud and precipitation
* * * * * * * * * * * * * © Crown copyright 2006

Convective cloud model
2. Convective cloud and precipitation Convective cloud model © Crown copyright 2006

4. Surface and sub-surface processes

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 © Crown copyright 2006

4. Purposes of Operational Models

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 © Crown copyright 2006

Global model Used for:- regional synoptic guidance
medium range guidance civil aviation products mesoscale model boundary conditions © 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 © 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 © 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 © 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) © Crown copyright 2006

12km Mesoscale model Used for:- UK local detail (ppn, cloud,temp,wind)
Input to other systems (SSFM, and Nowcasting systems etc.) © 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 © 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.) © Crown copyright 2006

Description & Performance
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 © Crown copyright 2006

SSFM land use and orography
100 m resolution orography © 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 © Crown copyright 2006

Model Dependencies (simplified!)
NAME GLOBAL GLOBAL WAVE FOAM NAE MODEL EUROPEAN WAVE SHELF SEAS 4KM MES 12KM MES SSFM UK WATERS WAVE SURGE Scheduling must account for all dependencies and timeliness requirements of each model run © Crown copyright 2006

5. Model output

Criteria for allocating symbols on 6-up charts
mm/hr 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 © Crown copyright 2006

Global model precipitation forecast T+36

4km Mesoscale model visibility forecast. 9Z 21 Nov

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 © Crown copyright 2006

6. NWP Verification

7. Future developments

Future developments 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) © Crown copyright 2006

Trial hi-res meso model

Any questions? GM, NAE and MES output.
NWP Gazette NWP technical reports 4km mesoscale runs:

NWP in the Met Office © Crown copyright 2006.

Similar presentations

Presentation on theme: "NWP in the Met Office © Crown copyright 2006."— Presentation transcript:

Similar presentations

About project

Feedback

Log in

Auth with social network:

NWP in the Met Office © Crown copyright 2006.

Similar presentations

Presentation on theme: "NWP in the Met Office © Crown copyright 2006."— Presentation transcript:

Similar presentations

About project

Feedback