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

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

Numerical Weather Prediction Process Prepared by C. Tubbs, P. Davies, Met Office UK Revised, delivered by P. Chen, WMO Secretariat SWFDP-Eastern Africa Training Workshop Bujumbura, Burundi, 11-22 November 2013

Changing requirements Urban Climate change Renewable Energy Computing Communication Sustainability Mobility Focus on impacts

A short history of NWP 1904: Weather prediction approached from the standpoint of mechanics & physics 1922: Weather Prediction by Numerical Process 1950: The ENIAC experiment 1967: Predicting frontal precipitation with a 10 level model 1974 – I saw my first NWP model !

du = ∂p – fv dt ∂x dv = ∂p + fu dt ∂y p = RT ρ Risk Analysis & Communication Knowledge 70 levels 25km 80km high Today’s Numerical Modelling System Forecast Model Observations Application of classical laboratory physics to the thin shell of turbulent gases that is our atmosphere

Sharing the observations The WIS (GTS): Global system for data and info exchange Exeter

Quality control - buddy checks - climatology - temporal consistency - background field Interpolated onto the model grid points GP ship airep synop sonde SEA LAND Different types of data have different areas of influence Using Observations

NWP cannot rely solely on observations to produce its initial conditions –Why? –There are too few –Point observations may not be representative of a grid box A short period forecast from a previous run of the model fills the gaps –Model background field Using Observations

Method used to blend real and model data Model is run for an assimilation period prior to the forecast Data is inserted into the run at or near their validity time to nudge the model towards reality Data Assimilation

The challenge: –To compute the model state from which the resulting forecast best matches the available observations T-3T-2T-1T+0T+1T+2T+3 T+144 First guess Observations

Temporal Resolution Hail shaft Thunderstor m Front Tropical Cyclone Space Scale Lifetime Predictability? 10mins1 hr12hrs3 days 30mins3 hrs36hrs9 days 1000km 100km 10km 1km MCS

© Crown copyright Met Office High resolution weather forecast models Satellite Observations Storm-permitting forecast:

Chaos in the atmosphere The atmosphere is a chaotic system: “… one flap of a seagull’s wing may forever change the future course of the weather”, (Lorenz, 1963) When potential energy is available for conversion to kinetic energy & a trigger is present, small disturbances may grow rapidly into weather systems Small errors may rapidly lead to large forecast errors

Quantifying uncertainty with ensembles time Forecast uncertainty Climatology Initial Condition Uncertainty X Deterministic Forecast Analysis CHAOS