1. Numerical Weather Prediction Models
Prepared by C. Tubbs, P. Davies, Met Office UK
Revised, delivered by P. Chen, WMO Secretariat
SWFDP-Eastern Africa Training Workshop
Bujumbura, Burundi, November 2013

2. NWP Model Formulation Different types of model Model Characteristics
General strengths and weaknesses of NWP models

3. Types of atmospheric model
Climatological
Global Climate Models (GCM’s)
Hindcasts and forecasts
Climate change – global warming
Non-operational weather forecasting models

5. Types of atmospheric model
Long-term and seasonal
Coupled ocean-atmosphere models
Aims to infer climate from indicators such as Sea Surface Temperature (El Nino)
Forecasts issued by ECMWF every month
Typically based on ensemble methods

7. Types of atmospheric model
Global NWP models
Operational forecasting models
Run twice to four times daily
Generally short- to medium-range (typically t+144h)
Global output coverage (datasets)
Hi-res “deterministic” vs EPS

9. Types of atmospheric model
Limited Area Models (mesoscale/LAMs)
Add local detail to broad picture from global model
Take boundary conditions from global
Higher resolution, so better representation of small scale events
Shorter forecast range (typically t+48h)
Careful (especially if approaching cloud-scale!): kinematic effects vs dynamical processes

11. Types of atmospheric model
Very-short-range (<12h) incl. Nowcasting
Nowcasting aims to give best forecast for time period of <4-6h hours lead-time
Blend of model and observational data
e.g. UK Met Office uses the NIMROD system
Specific applications
Atmospheric Dispersion
Air quality
Lee-wave forecasting models

12. ? forecast range / max. lead-time ?
? T574L64 ~27km ?
Models
? EPS 50 members +1 control ?
? forecast range / max. lead-time ?
t+144h ?
? domain ?
« Anticipated advances in NWP, and the
growing technology gap in weather forecasting »
(2013)
See Annex II for NWP systems

13. ECMWF Global High-Res. IFS
Horizontal resolution of T1279 (16km), 91 vertical levels
240h forecast range (10 days ahead)
4-D VAR
Global EPS – Ensemble Prediction System
50 members, T319 (65km), 62 levels
T= spectral truncation

14. NCEP National Centers for Environmental Prediction (Washington, USA)
GFS (Global Forecasting System) model – T574 L64 (27km, 2010), T1148 (18km, planned 2013), and
GEPS ensemble model – 42 members, T190 (70km)

15. Met Office UK Global model “UM”
Horizontal resolution of 25 km and L70 vertical levels
4 times daily
Run out to t+144h
4DVar
MOGREPS-15 (UM)
Global 23 members, 60km, L70

16. UK Met Office Limited Area Models North Atlantic European (NAE)
12 km horizontal resolution, 70 vertical levels
Stretches from Newfoundland to Eastern Mediterranean and Northern Scandinavia to North Africa
Four times daily to t+48h

17. UK Met Office
Africa LAM – retired October 2013

18. UK Met Office Lake Victoria LAM
4 km horizontal resolution, 70 vertical levels
Available via password protected website
Username is afr_nms and password is uk_alam
Intermittent data assimilation
Run to t+48h

20. Strengths & Weaknesses of NWP models

21. Strengths & Weaknesses
There are generic problems common to most NWP
If we know about these we can account for them in our initial verification
Most problems are related to resolution

22. NWP Strengths Convection Extra-tropical latitudes
General area of convection is well captured
Extra-tropical latitudes
Model is much better here
Frontal systems are well represented
Orographically enhanced rainfall better than Global Model

23. Generic Problems Inaccurate Initial Conditions Resolution Lack of data
Imperfect data assimilation
Resolution
Horizontal resolution may cause small scale features to be missed
Vertical profile may not capture full detail e.g. inversions, localised temperature advection

24. Generic Problems Orography
Generally flattened – less steep and less high
Some features completely omitted
Orography in LAMs is better than in global models but still not perfect

26. Generic Problems Lateral Boundary Conditions Only a problem for LAM’s
Spin up problems when transposing low resolution data onto a high resolution grid
Potential problems at edge of domain

27. NWP Weaknesses Tropical Convection
Representation of diurnal cycle is poor
Convection initiated too early and is too widespread
ppn accumulation frames contain much spurious ppn but can indicate areas of activity
Fails to develop large scale, long-lived mesoscale convective systems

28. NWP convection switched on….

29. NWP convection switched off….

31. Questions & Answers