Met Office Forecasting

Met Office Forecasting Dr Teil Howard – Senior Applied Scientist

Who are we?

• A world-leading weather and climate service, working closely with governments, individuals and organisations to share our expert scientific knowledge and advice

• A Trading Fund within the UK government Department for Business Innovation and Skills, operating on a commercial basis with customers across the world

• Over 500 scientists working across weather and climate science

• Scientific papers co-authored with scientists from 144 institutions across 44 countries - peer reviewed and published

• Over 300 qualified forecasters working in our operations centre

Our performance - how we compare to other National Met Services

Verification results plotted as a percentage difference to ECMWF

Wind Production Forecast

Wind Production

Forecast

• WPF: A site specific forecast at customer

defined height for wind farms

• WPF+: An upgraded version of WPF that

uses customer observations to provide a

more accurate forecast using basic

nowcasting and Kalman filtering

• Customer obs can be protected so that

they will not be used to improve another

customer’s forecast

Wind Production

Forecast

Single models:

• Historic data available

• Usually Euro4

• Hourly to T+57 hours 4 times a day

• 3 hourly to T+120 hours 2 times a day

Blended data:

• More accurate

• Hourly data to T+14 days

Surface/near surface:

• 10m wind speed and direction

• Shortwave radiation

• Mean sea level pressure

• Screen temperature

• Precipitation

• Sensible heat flux

• Lightning risk

• ….

Parameters

Hub height:

• Wind speed

• Wind direction

• Wind gust

• Temperature

• Pressure

• Air density

• Relative humidity

Probabilistic Wind

Production Forecast

Percentiles:

• 2.5%, 5%, 10%, 20%, 25%, 30%, 40%, 50%,

60%, 70%, 75%, 80%, 90%, 95%, 97.5%

Other fields:

• Mean

• Mode

• Standard deviation

Time granularity:

• Hourly to T+120 hours the 6 hourly to T+14

How do we produce the forecast?

Observations

Our observations - Surface

Supercomputer upgraded 2012

IBM Power 775 supercomputer

• Peak performance is now 1,200,000,000,000,000 (1,200 trillion) calculations per second

• Equivalent to about 30,000 desktop PCs

• Storage for 40,000,000 gigabytes of data

• Equivalent to 50 million CDs or 750 million 4-drawer filing cabinets

Our deterministic NWP

models

Global

– 25km 70 Levels – 48hr forecast twice/day – 144hr forecast twice/day

– 1.5km 70 Levels – 36hr forecast eight times/day

– 4km 70 Levels – 60hr forecast twice/day – 120hr forecast twice/day

Ensembles

Analysis

Initial condition uncertainty

Climatology

Forecast uncertainty

Deterministic forecast

Our probabilistic NWP models

MOGREPS-15

– 60km 70 Levels – 15 day forecast twice/day – 24 members

MOGREPS-UK

– 2.2-4km 70 Levels – 36hr forecast four times/day – 12 members

MOGREPS-G

– 32km 70 Levels – 72hr forecast four times/day – 12 members

Combining forecast systems

Forecast system 1

Forecast system 2

Consensus forecast system

Reduction

Observation

Models from other centres

– 16km 137 Levels – 15 day forecast twice/day – 51 members

levels

surface

Orographic Adjustment

Site specific

processing

Model sea

Model land

Coastal Adjustment

levels

Convection Diagnosis Procedure

Wind corrections

Hub Height

Example: The Lake District

Height in Metres

4 km model orography Terrain at 100 m resolution

Forecast

Error=A+Bx

Kalman filter

Basic nowcasting

Raw forecast + observations = Basic Nowcast

Questions

Met Office Forecasting

Documents

© Crown copyright Met Office Met Office seasonal forecasting for winter 2010-11 Jeff Knight (with thanks to many colleagues)

Near real time forecasting of biogeochemistry in global GCMs Rosa Barciela, NCOF, Met Office

Met Office - Microclimate

Marine data and observations for storm surge forecasting and other Met Office ocean modelling

GLOSEA4 the New Met Office Monthly/Seasonal Forecasting System · 2015-12-30 · GLOSEA4 –the New Met Office Monthly/Seasonal Forecasting System Tim Hewson Thanks to: Alberto Arribas,

© Crown copyright Met Office Plans for Met Office contribution to SMOS+STORM Evolution James Cotton & Pete Francis, Satellite Applications, Met Office,

Monge Ampere Based Moving Mesh Methods for Weather Forecasting Chris Budd (Bath) Chiara Piccolo, Mike Cullen (Met Office) Emily Walsh (Bath,SFU), Phil

MET Observations Underpinning Space Weather Forecasting

Aerosol forecasting at UK Met Office - Observability Meetingsicap.atmos.und.edu/AERP/MeetingPDFs/Overviews/4.UKMO_JMulcahy… · 2.5 as a new component, alongside ... © Crown copyright

© Crown copyright Met Office Assimilating cloud affected infrared radiances at the Met Office Ed Pavelin and Roger Saunders, Met Office, Exeter

© Crown copyright Met Office Met Office - MONSooN John Whitlock: IT Manager

© Crown copyright 2007 Monthly-Seasonal forecasting Alberto Arribas Monthly to Decadal group, Met Office Hadley Centre Geneva, December 2011

Met Office Intercomparison of Vaisala RS92 and RS41 ... · 2014/12/02 · Met Office Intercomparison of Vaisala RS92 and RS41 ... ... Met Office

Remember - Met Office · Remember The Met Office in World War One and ... Forecasting An important role of ... of wind direction and strength at points along the front that were of

Www.ncof.gov.uk Assimilating SST and Ocean Colour into ocean forecasting models Rosa Barciela, NCOF, Met Office rosa.barciela@metoffice.gov.uk

Assimilating SST and Ocean Colour into ocean forecasting models Rosa Barciela, NCOF, Met Office

© Crown copyright Met Office Communication of Probability Forecasts for PWS Ken Mylne Ensemble Forecasting Manager, Met Office, UK Chair WMO Expert Team

© Crown copyright Met Office Met Office dust forecasting Using the Met Office Unified Model™ David Walters: Manager Global Atmospheric Model Development,

Page 1© Crown copyright 2004 Seasonal forecasting activities at the Met Office Long-range Forecasting Group, Hadley Centre Presenter: Richard Graham ECMWF

Guide - Met Office