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Numerical Weather Prediction Models Prepared by C. Tubbs, P. Davies, Met Office UK Revised, delivered by P. Chen, WMO Secretariat SWFDP-Eastern Africa.

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Presentation on theme: "Numerical Weather Prediction Models Prepared by C. Tubbs, P. Davies, Met Office UK Revised, delivered by P. Chen, WMO Secretariat SWFDP-Eastern Africa."— Presentation transcript:

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 1.Different types of model 2.Model Characteristics 3.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

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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

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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

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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

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

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

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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 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 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

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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….

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31 Questions & Answers


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