Numerical Weather Prediction Process Prepared by C. Tubbs, P. Davies, Met Office UK Revised,...
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Transcript of 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
© 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
Questions & Answers