Numerical Weather Prediction Process Prepared by C. Tubbs, P. Davies, Met Office UK Revised,...

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 – fvdt ∂xdv = ∂p + fu dt ∂yp = RTρ

Risk Analysis & Communication

Knowledge

70 levels25km

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

Observing the world

© Crown copyright Met Office

Sharing the observationsThe WIS (GTS): Global system for data and info exchange

Exeter

Quality control- buddy checks- climatology- temporal consistency- background field

Interpolated ontothe model grid points

GP

GPGP

GP GP

GPGP

GP

GP

ship

ship

airep

synopsynop

synop

synop

synop

sonde 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

Data Assimilation

• The challenge:– To compute the model state from which the

resulting forecast best matches the available observations

T-3 T-2 T-1 T+0

T+1

T+2

T+3

T+144

First guess

Observations

Temporal Resolution

Hail shaft

Thunderstorm

Front

Tropical Cyclone

Space Scale

Lifetime

Predictability?

10mins 1 hr 12hrs 3 days

30mins 3 hrs 36hrs 9 days

1000km

100km

10km

1km

MCS

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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

Questions & Answers