The European Centre for Medium-Range Weather Forecasts. European co-operation at its best

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The European Centre for

Medium-Range Weather Forecasts

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ECMWF – a European success story

The European Centre for Medium-Range Weather Forecasts (ECMWF) was established in 1975, in recognition of the need to pool the scientific and technical resources of Europe’s meteorological services and

institutions for the production of medium-range weather forecasts and of the economic and social benefits expected from it.

ECMWF leads the world in global medium-range Numerical Weather Prediction (NWP), the advanced computer observation-analysis modelling technique used to predict the weather. Our operational activities and wide-ranging programmes of research and development have played a pioneering role in the remarkable advancement of data assimilation and weather forecast systems.

In three decades ECMWF has dramatically improved the accuracy and reliability of weather forecasting, working in collaboration with Member and Co-operating States, the European Union and partners such as the World Meteorological Organization (WMO), the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) and the European Space Agency (ESA).

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Who benefits from our

weather forecasts and

products?

Directly

 National Hydro-Meteorological

Services

 Meteorological service providers

 Scientists monitoring the

environment and climate change

Indirectly

 Military and civil defence

authorities

 Local authorities and emergency

services

 The general public

 National health services

 Energy providers, and the

oﬀshore oil and gas industry

 Shipping, commercial ﬁshing

and coastal protection agencies

 Weather-sensitive manufacturing

 All forms of transport

 EU, national and regional policy

makers

A principal player in global weather prediction

ECMWF’s original goal, as deﬁned by our Convention, was to enhance Europe’s medium-range forecasting capabilities by using numerical weather prediction methods to produce forecasts twice daily up to ten days ahead.

Since then, our tools, models and supercomputers have evolved to such an extent that we have been able to extend our forecast range and develop techniques which allow us to produce seasonal forecasts up to one year ahead.

Our duties encompass:

 Preparing medium-range forecasts up to 15 days ahead, as well as monthly

and seasonal forecasts, for distribution to the National Meteorological Services of our Member and Co-operating States to thereby complement their national forecasting and climatological activities and to weather service providers.

 Developing numerical methods for medium-range weather forecasting and

conducting research to improve the quality and accuracy of our forecasts.

 Collecting observations obtained globally from land, sea and space, archiving

the output and data generated from numerical weather prediction models over time and making the information available to Member and Co-operating States.

 Making 25% of our computing facilities available to Member States for their

own needs and providing advanced, specialised training to the scientiﬁc and technical staﬀ of Member and Co-operating States.

 Participating in relevant weather, environmental and climate-related

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Monitoring and predicting the weather

Profiler/ RADAR wind PILOT Buoys – Drifting Moored Dropsondes TEMP Polar-orbiting satellites Ozone SCATT Radiances GPS satellites Geo-stationary satellites Atmospheric motion vector Radiances SYNOP – Ship AIRCRAFT SYNOP – Land METAR

Each day more than 300 million observational data elements are collected round the clock by a variety of Earth observing systems, including satellites, automatic and manned stations, aircraft, ships, weather balloons and buoys.

The weather is determined to a considerable degree by the initial state of the atmosphere, the behaviour of which is governed by physical laws expressible as mathematical equations. These equations represent how atmospheric conditions such as temperature, humidity, wind speed and direction, and air pressure change from their current states. Solving them provides meteorologists with a prediction of the atmosphere’s future state which they can interpret in terms of weather – rain, temperature, sunshine and wind, for example.

Data are collected round the clock by a variety of Earth observing systems, including automatic and manned stations, aircraft, ships, weather balloons and buoys. In recent years the accuracy of forecasts has been greatly enhanced by data from geostationary and polar-orbiting satellites.

The data are analysed and assimilated into ECMWF’s advanced NWP models, which produce detailed forecasts up to 15 days ahead and statistical information about monthly and seasonal forecasts.

Our current NWP model has evolved from an atmospheric model to an Earth-system model which takes into account the complex interactions of the planet’s other four spheres: living organisms, land, ocean and ice. ECMWF’s medium-range forecasts utilise a combination of atmospheric, land and ocean observations, including sea surface temperature, soil moisture and snow cover. Monthly and seasonal forecasts also take into account longer-term inﬂuences on the

atmosphere, such as the circulation of ocean currents. Climate monitoring makes use of observations of changing sea-ice and vegetation cover.

Wave forecasting

The state of the sea – currents, tides, wave heights, swells and storm surges – is an important component of the marine weather forecast, and critical to shipping, ﬁsheries, oﬀshore operations and coastal protection. ECMWF’s ocean wave forecasting model provides valuable probabilistic information on sea states to 15 days ahead, and now includes invaluable wave height data from satellites.

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Contours at 5%, 35%, 65%, 95%

Friday 25 November 2005 00UTC ECMWF Forecast120 VT:Wednesday 30 November 2005 00UTC ECMWF FORECAST PROBABILITIES

H H H H H L L 5 5 35 35

10m Wind Speed greater than 10 m/s

H H

10m Wind Speed greater than 15 m/s

H H H H H H H H H L L L L L 5 5 5 5 35 35 35 35 35 35 35 35 65 65 65 65 65 65 65 95 95 95 95

24hr Total Precipitation greater than 1 mm

H H H H H H H H L L L L 5 5 5 5 5 5 5 35 35 35 35 35 35 35 65 65 65

H H H H H H 5 5 5 5 5

H

H H 5 35 35 65

850hPa Temperature Anomaly less than -8 K

H H H H L L L L 5 5 35 35 65 65 65 95 95 95

850hPa Temperature Anomaly less than -4 K

H H H H L L L L L 5 5 35 35 65 65 95 95

850hPa Temperature Anomaly greater than 4 K

H H L

535 65

95 850hPa Temperature Anomaly greater than 8 K

FRI SAT SUN MON TUE WED THU FRI SAT SUN

25

NOVEMBER 26 27 28 29 2005 30 1 2 3 DECEMBER 4

Deterministic Forecasts and EPS Distribution Reading 51.6° N 1.0° W EPS Meteogram -6 -4 -2 0 2 4 6 8 10 12 14

16 2m Temperature Reduced to T511 Orography (deg C) 90M (T511) 90M (T255)

0 2 4 6 8 10 12 14 16 18 20 10m Wind Speed (m/s ) 0 2 4 6 8 10 12 14 16 18 Total Precipitation (mm/6hr ) 0 1 2 3 4 5 6 7 8 Total Cloud Cover (okta)

max min 25% 75% median TL255 CTRL TL511 OPS 0 1 2 3 4 5 6 7 8 9 10 Forecast Day 516 528 540 552 564 576 dam

GEOPOTENTIAL 500 hPa - Probability for 2.5 dam intervals, Range: 72dam

0 1 2 3 4 5 6 7 8 9 10 Forecast Day 0 2 4 6 8 10 12 14 mm

Ensemble members of TOTAL PRECIPITATION - Accum. rate mm/12h

0 1 2 3 4 5 6 7 8 9 10 Forecast Day -5 0 5 10 deg

TEMPERATURE 850 hPa - Probability for 1.0 deg intervals, Range: 20deg READING LAT: 51.45 LONG: -0.95

ECMWF ENSEMBLE FORECASTS FOR: ECMWF

0.5 - 10 % 10 - 30 % 30 - 50 % 50 - 100 %

Oper T255 EMem

A medium-range weather forecast predicts the weather up to 15 days ahead and requires four key ingredients: a state-of-the-art data assimilation and analysis system, sophisticated weather prediction models, a constantly updated database of global weather observations and ultra-powerful computers.

 The starting point for all medium-range forecasts is accurate information about

the current state of the weather worldwide, known as initial conditions. Meteorological observations gathered from all around the world – e.g. air pressure, temperature, and wind speed and direction – are transmitted to ECMWF via specialised telecommunication networks.

 ECMWF analyses these observations continuously and feeds them into a

model representing a 3-D virtual image of the atmosphere.

 This analysis is corrected regularly as new observations come in – these are

used to ‘pull’ each previous forecast closer to the observations.

 Every 12 hours ECMWF processes about 150 million observations which are

used to correct the 500 million ‘numbers’ that deﬁne the model’s virtual atmosphere.

 Global forecasts, using analysis as a starting point, are distributed twice daily to

the National Meteorological Services of our Member and Co-operating States.

ECMWF’s forecasting

system

The ECMWF weather forecasting system consists of an atmospheric model coupled with an ocean wave model. The model formulation can be summarised by six basic physical equations, the way the numerical computations are carried out and the resolution in time and space.

Producing a medium-range weather forecast

3 0°N 3 0 °N 40° N 50° N 60° N 70° N 50°W 50°W 30°W 30°W 10°W 10°W 10°E 10°E 30°E 30°E 50°E 50°E m 1

Significant wave height. Forecast t+48 VT: Sunday 27 November

2 3 4 5 6 >6

5.0m/s

Ensemble size = 40, climate size = 75 Mean precipitation anomaly ECMWF Seasonal Forecast

Solid contour at 1% level Shaded areas significant at 10% level

75°S 60°S 45°S 30°S 15°S 15°N 30°N 45°N 60°N 75°N

150°W 120° W 90°W 60°W 30° W 0° 30°E 60° E 90°E 120° E 150°E

<-200 -200..-100 -100..- 50 - 50..0 No Signal 0.. 50 50..100 100..200 > 200m m

0°

10-day forecast model

facts

Resolution T1279 Number of grid points 2,140,704 Distance between grid points 16 km Vertical levels 91 Time step 600 s Number of time steps for a ten-day forecast 1440 Floating point operations needed 6.3 × 1015 30°N 30°N 40°N 50°N 60°N 70°N 60°W 60°W 40°W 40°W 20°W 20°W 0° 0° 20°E 20°E 40°E 40°E 60°E 60°E H H H H H H H H H H H H H H L L L L L L L L L L L L L 990 100 0 1000 1000 1010 1010 102 0 1020 102 0 1020 1020 1020 1020 102 0 1030 1030 1030 1030

Surface: Mean sea level pressure / 12hr Accumulated precipitation (VT-6h/VT+6h) Tuesday 23 October 2007 12UTC ©ECMWF Forecast t+120 VT: Sunday 28 October 2007 12UTC

30°N 60°N 70°N 0.5 2 4 10 25 50 100 300

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Observing Earth’s weather from space

Improvements in the quality and quantity of satellite data over the last decade, and in the ability to process and assimilate information into NWP models, have made space-based data the most important source of observations for NWP.

Satellites deliver regular, highly detailed measurements and images of many of the key variables which inﬂuence the weather, including ozone, carbon dioxide, aerosols and other atmospheric constituents, as well as

temperature, humidity, cloud conditions, wind speed and direction, vegetation cover, ocean circulation, wave height and sea ice.

ECMWF is one of the most advanced users of satellite data for weather prediction and climate monitoring. We collaborate closely with satellite data providers such as the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), the European Space Agency (ESA), the National Aeronautical Space Administration (NASA), and the National Oceanic and Atmospheric Administration (NOAA).

We continually strive to enhance our use of satellite observations to improve the accuracy of our NWP. Recent enhancements in our techniques have led to

improvements in our humidity forecasts in the tropics and our analysis and tracking of tropical cyclones.

Improving weather forecasting techniques

ECMWF has played – and continues to play – a pivotal role in the remarkable advancement of NWP systems. From our earliest days, we have pursued a wide-ranging programme of research and development directed at improving the scope, accuracy and range of forecast products for the medium range and beyond. The fruits of this research are:

 The most comprehensive Earth-system and assimilation models in use by NWP

centres around the world.

 Progressively more accurate medium-range forecasts.

 A constantly expanding range of operational forecasting products for

meteorologists and end users.

As well as engaging in research and development of our own model, ECMWF collaborates closely with National Meteorological Services, scientists and researchers around the world towards the common goal of improving the accuracy of weather forecasting and severe weather prediction.

Observing System

Experiments

We participate in Observing System Experiments (OSEs). These OSEs aid in particular the EUMETNET Composite Observing System (EUCOS)

programme, which aims to deliver terrestrially based operational observations for the improvement of regional NWP in Europe. To ensure the EUCOS programme evolves and meets customer needs for regional NWP, ECMWF was one of the NWP centres commissioned to carry out OSEs to evaluate and compare the contribution of the components of the in-situ observing system and the satellite-based observing system to numerical weather forecasts.

81 83 85 87 89 91 93 95 97 99 01 03 05 07 09 11 Year 100 90 80 70 60 50 40 30 Accuracy (%) Day 3 Day 5 Day 7 Day 10 Northern hemisphere Southern hemisphere Anomaly correlation percentage of

500 hPa height forecasts

Forecasts improved steadily from 1980 as a result of

improvements in the global observing system, more powerful computers, and advances in the science of the ECMWF’s data assimilation system and forecast model. Seven-day forecasts in the northern hemisphere became more accurate than the five-day forecasts of 1980, and five-day forecast accuracy reached that of the three-day forecasts made 25 years earlier.

In the southern hemisphere, the improvement was even more marked. In the early 1980s, because of the lack of observations, the three- and five-day predictions for the southern hemisphere were not much better than those of the northern hemi sphere for five and seven days respectively. Two decades later, forecasts for both hemispheres were of similar accuracy – a gain of about four days in the accuracy of southern hemisphere predictions, mainly thanks to the use of satellite data. The shaded area shows the differences in forecast accuracy between the hemispheres.

Sat ellit e image cour tesy of EUME T SA T

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Despite the increasing accuracy of weather forecasts, there is still an element of uncertainty in all predictions. The two main reasons for this are uncertainties in the initial state of the atmosphere and model errors. Advances in utilizing all available observations of the earth system have enabled us to describe the state of the atmosphere better than ever before. However, the exact state of the atmosphere is never known precisely, and thus there will always be a remaining uncertainty in the initial conditions that are needed to start a forecast. In addition, numerical models cannot fully replicate the laws of physics that govern the behaviour of the atmosphere, resulting in model errors introduced e.g. through the parametrization of sub-gridscale processes. The atmosphere's inherent chaotic nature ampliﬁes these uncertainties, with the result that initially small errors can be magniﬁed over time and profoundly distort the outcome of the forecast.

In order to represent this uncertainty in the forecast system and predict its impact on the resulting forecasts, meteoro logists increasingly use forecasting methods which express the weather’s likely behaviour as a probability. They use ECMWF’s pioneering Ensemble Prediction System (EPS), which provides the tools for estimating the degree of uncertainty and its likely impact on a forecast. Unlike the standard ‘deterministic’ forecasting method, which runs a single model to produce one possible solution for the weather ahead, EPS runs 51 simulations with a coarser resolution, known as an ensemble, in which the forecast’s initial starting conditions vary subtly to provide a range of possible future scenarios. The proportion of ensemble members forecasting a speciﬁc weather event indicates the probability of it occurring.

End users – such as civil protection agencies, health authorities, energy companies, local authorities, transport managers and insurance companies, for example – combine this probability with their own information about their sensitivity to certain weather events to manage risk. How high the probability must be before action is taken depends very much on the potential

consequences of the event for the individual user. In the case of severe weather, users who would suﬀer high losses will take preventive action at much lower probabilities than others who would be less aﬀected by the same weather event.

Predicting the uncertainty of forecasts

During an intensive period of the Atlantic hurricane season in 2008, three hurricanes (Gustav, Hanna and Ike) were active at the same time. On average in this period, the genesis of tropical cyclones was predicted by the model 5 to 7 days before the storms were reported as tropical cyclones. The top panel shows the 5-day deterministic forecast of mean sea-level pressure verifying at 12 UTC on 31 August. Gustav, Hanna and Ike can each be clearly identified, even though the forecast was issued before Hanna and Ike had been officially observed. The bottom panel shows the day-5 EPS probability forecast for tropical cyclones to be in the region in the 24-hour period between 12 UTC on 31 August and 12 UTC on 1 September.

1008 1016

1016

5-day forecast

5-day EPS forecast

5 10 20 30 40 50 60 70 80

Hanna

Gustav

Ike

Seasonal forecasting

As we attempt to predict further into the future, the initial conditions of the atmosphere itself lose impor -tance, while the lower boundary conditions such as sea surface temperature (SST) or soil moisture and snow cover start to exert a signiﬁcant inﬂuence on weather patterns. Ensemble forecasts are used to separate the part of the variability in seasonal weather which can be predicted, from the part which cannot. The future evolution of SST is an important part of the forecast problem, and models of both the atmosphere and ocean are required, together with analyses of the initial state of the ocean.

ECMWF’s Council approved a programme of involving coupled modelling to address seasonal forecasting in December 1994. Experimental seasonal forecasts were issued from December 1997 onwards, and the system became fully operational in 2002. ECMWF also produces EUROSIP multi-model seasonal forecast products, based on input from four forecasting systems (ECMWF, the United Kingdom’s Met Oﬃce, Météo-France and the US National Centers for Environmental Prediction (NCEP)). Additionally, the European Union is supporting research at ECMWF and a number of other institutes around Europe to establish the predictability of seasonal climate ﬂuctuations using ensemble forecast techniques.

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Delivering consistent and reliable services

As the world’s leading NWP centre, our reputation rests on our ability to deliver a consistent and reliable service 24 hours a day, 7 days a week. At the core of our operational and research activities lies one of the largest computing centres in Europe. Our extremely powerful supercomputers, supported by the rest of our computing infrastructure, enable ECMWF to:

 Collect and check approximately 550 million observations daily and actively

assimilate some 35 million a day into the NWP models.

 Produce global forecasts.

 Perform research experiments to advance NWP techniques and products.

 Provide Member States with invaluable resources for their own research into

NWP.

Our computer and ancillary facilities, such as data handling and networking, are regularly upgraded to accommodate the greater computational power demanded by the assimilation of even more observational data and the ongoing evolution of our forecasting models.

Data Handling System

ECMWF’s Data Handling System is based on IBM’s High Performance Storage System. About 31 petabytes of data are held on about 20,000 tapes, which are stored in

Sun SL8500 automated tape libraries.

High Performance

Computing Facility

ECMWF’s HPCF currently comprises two identical IBM supercomputer 1600 clusters. Each one is based on 272 IBM pSeries p6-575 compute servers interconnected by a low-latency high-speed network. Total number of processors ~17,400

Peak performance 330 TFlops

Sustained performance ~20 TFlops

RMDCN

The RMDCN connects ECMWF, EUMETSAT and the National Meteo -rological Services of many countries in WMO regions I, II, IV, V and VI, providing connectivity based on IPVPN MPLS network technology.

Data handling

The massive amount of data required for and generated by weather forecasting is archived in our Data Handling System. Since 1975, ECMWF has built up a unique archive of meteorological data for use by researchers as well as for educational and commercial purposes. As of July 2012, the archive holds 31 petabytes of data.

More than 1000 researchers from over 300 diﬀerent organisations access the meteorological archive for their own work.

RMDCN

ECMWF’s forecast products are disseminated over the Regional Meteorological Data Communications Network (RMDCN). Its primary purpose is to provide a network infrastructure for both the connections between ECMWF and its Member and Co-Operating States and most of the Global Telecommunications System (GTS) connections for WMO Regional Association VI. Operational since March 2000, the network has expanded to encompass countries in many parts of the world with connections to Japan, China, India and South Korea in the Far East as well as the USA and Australia and South Africa and connects 49 sites in 45 countries The network infrastructure is provided by Orange Business Services (OBS), with ECMWF managing the project and monitoring the network on behalf of the sites

connected under an agreement concluded with the WMO.

High performance computing

infrastructure

ECMWF has operated a High Performance Computing Facility (HPCF) for weather forecasting, since the installation of its ﬁrst CRAY-1 supercomputer in 1978. Over time, various supercomputer architectures have been used including vector shared-memory systems, vector distributed memory systems and clusters of scalar shared-memory processing systems. From the very beginning ECMWF has ensured that its codes are portable and has invested considerable resources in ensuring that they remain for most prevailing high performance computing architectures.

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Analysing climate change

Until recently, scientists monitored changes in the Earth’s climate by analysing temperature, wind and rainfall observations. They have been particularly interested in looking for evidence of warming and increased frequency of severe weather events.

ECMWF was one of the pioneers of an approach known as reanalysis, which involves reprocessing weather observations collected over decades using a modern NWP system to recreate past atmospheric, sea- and land-surface conditions over speciﬁc time periods to obtain a clearer picture of how the climate has changed.

To date, and with support from Europe’s National Meteorological Services and the European Commission, ECMWF has conducted several reanalyses of the global atmosphere. Our work has yielded products of unprecedented scope and quality.

The capability of reanalysis is illustrated well by one of Europe’s severest storms of the 20th_{century, which occurred on 31 January/1 February 1953 and caused the greatest} surge on record for the North Sea area as a whole. Flood levels reached 2.74 m and 2.97 m in height at Southend and King’s Lynn in England and 3.36 m in the Netherlands. Almost 100,000 hectares of Eastern England were flooded and 307 people died. The Netherlands fared worse still, as 50 dykes burst and 1,800 people drowned. The figure shows the reanalysed near-surface wind (maximum speed about 30 ms-1_{) and} ocean-wave height (metres) for 00 UTC, 1 February 1953. The strong winds from the north have a long fetch (the distance over which wind blows without changing direction) to develop waves with heights of over nine metres over a large area. The combination of both high tide and waves had a devastating effect. In order to better understand the effects of the storm, the global reanalysed fields have also been used as boundary conditions for high-resolution, limited-area, atmospheric models, which in turn drive detailed storm surge models.

15.0 m/s 5 4 6 7 3 9 8 7

Applications of reanalysis

The success of reanalysis as a tool for global climate monitoring can be measured by the number, variety and quality of applications of its products. There are few spheres of life that are not touched by weather and climate. Reanalyses have produced applications for sectors such as agriculture, water, air quality, health, ecosystems and biodiversity. Direct applications in the ﬁelds of weather and climate include studies of predictability from days to seasons ahead, estimation of the long-range transport of pollutants, investigation of recent climate change and assessment of the capability of climate-prediction models to simulate such change. As reanalysis systems are further reﬁned, their products will increasingly form the backbone of the quantitative information essential for climate related policy and decision making in a changing global environment.

Reanalysis data are used extensively by the worldwide scientific community: about 10,000 registered users from more than 130 countries have accessed the reanalysis archive.

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Monitoring the environment

ECMWF contributes actively to the development of the European GMES (Global Monitoring for Environment and Security) initiative, which aims to make environmental information more readily available to scientists, policy makers and industry, and to create a shared European system for exchanging a range of information.

As a contribution to GMES, the EU-funded project MACC (Monitoring Atmospheric Composition and Climate) has been established to continue development and pilot operation of an atmospheric monitoring service which is generating valuable new global analysis and forecasts of atmospheric composition, and producing more detailed short- and medium-range

forecasts indicating air quality and pollution patterns across Europe. We are co-ordinating the project and are responsible for developing and operating the global system. MACC continues work originally undertaken in a project called GEMS, which was also coordinated by ECMWF.

The project is developing state-of-the-art variational estimates of sources and sinks, and estimates of inter-continental transports of many trace gases and aerosols. These estimates, based on both retrospective and near-real-time analyses, will support the science underlying policy makers’ key information requirements relevant to the Kyoto and Montreal protocols and the UN Convention on Long-Range Transboundary Air Pollution (CLRTAP).

Global warming

Monitoring the state of the environment and addressing the challenges of climate change are growing concerns for governments worldwide – and all the more pressing as global warming increases the risk and frequency of dangerous weather events. ECMWF, as the leading provider of NWP, is ideally positioned to play an ongoing and increasingly signiﬁcant role in the timely protection of the Earth and its peoples.

New information requirements

 Policy

– Kyoto Protocol – Montreal Protocol – CLRTAP

 Environment agencies

 Air quality users

 Health agencies

 Science

MACC system

 Global pre-operational

assimilation and forecast capability for greenhouse gases, reactive gases and aerosols.

 Improved forecasts for regional

air quality from an ensemble of European air quality models.

MACC products  Information on atmospheric composition – Greenhouse gases – Reactive gases – Aerosols – Regional air quality

 Monitoring, assessment and

prediction – Current status

– Sources, sinks and transport – Impact of global change

Improved Earth observations

 Instruments – Satellite – Ground based – Airborne  Atmosphere – Composition – Dynamics  Land surface – Biomass burning – Vegetation  Ocean Installation

Responds to Data flow

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Looking to the future

In June 2011, the ECMWF Member States unanimously adopted the ECMWF’s Strategy for the period 2011 to 2020. The vision of this Strategy is for ECMWF to be the

acknowledged world leader in global, medium-range numerical weather prediction. This is to provide the best possible forecast products to our members, particularly to national weather services, for the beneﬁt of society. The aims are that by 2020

 reliable early warnings for severe weather will be routinely

received and acted upon by European citizens, and

Severe weather

Over the last two decades the majority of the victims of natural disasters lost their lives as a result of windstorms, tropical cyclones, ﬂoods, cold and heat waves. Our ten-year strategy commits us to improving our capabilities in severe weather prediction. It has already yielded a number of innovative products for mapping life-threatening severe weather events. Such products include the Extreme Forecast Index, tropical cyclone track and strike probability maps, seasonal tropical cyclone frequency, extreme rainfall and freak waves.

To improve our severe weather warning capabilities at extended ranges, we have developed a highly-skilled seasonal forecasting capability and introduced our monthly forecasting system in 2005. We have also developed a multi-model seasonal forecasting system based on our forecasts and those of our project partners, the United Kingdom’s Met Oﬃce and Météo-France.

The potential humanitarian and economic beneﬁts of our work are considerable. We can forecast, for instance, heat waves and cold spells in European countries several weeks in advance; predict the probability of occurrence and likely intensity of El Niño-related eﬀects in the tropics several months before they happen; and help to alleviate human misery in Africa and other vulnerable developing regions by estimating the probability of severe droughts, excessive rainfall and outbreaks of devastating diseases such as malaria. H L L -24 -16 -8 -8 0 8 16 50°N 20°W 0°

Windy Extreme wind Heavy precipitation Extreme precipitation

An intense winter storm (named Klaus) caused significant destruction across southern France and northern Spain on 24 January, 2009. The storm developed in the Atlantic on 22 and 23 January and moved very rapidly in the strong westerly flow, crossing France and continuing to also affect the western Mediterranean. The chart shows areas where there is a high risk of strong winds on 24 January based on the EPS forecast from 00 UTC on 21 January. The shading shows the Extreme Forecast Index (EFI) for wind gusts, which compares the EPS forecast to the distribution of the model climate; higher values indicate a greater risk of extreme weather. The contours show the analysed 1000 hPa height field for 06 UTC on 24 January, when the storm struck the French coast.

 many national weather services will deliver tailored

medium-range forecasts using direct ECMWF model output.

 we will deliver operationally global analyses and forecasts

of atmospheric composition.

 we will continue to support climate monitoring with

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Member States Co-operating States

About ECMWF

ECMWF is an independent inter govern -mental organisation supported by more than 30 States. Its headquarters is located in Reading, 60 km west of London, where about 160 staﬀ and 60 consultants are employed.

ECMWF’s budget is funded almost entirely from annual contributions from Member and Co-operating States according to a scale based on their gross national income.

ECMWF’s governing body, the Council, is formed of two repre sent -atives from each Member State. Twelve members of the Scientiﬁc Advisory Committee are appointed in their personal capacity. Four Committees comprising experts from the Member States advise the Council: the Technical Advisory Committee, the Finance Committee, the Policy Advisory Com -mit tee and the Advisory Com-mittee for Data Policy. In addition, the Advisory Committee of Co-operating States comprises representatives of States with which the organisa tion has co-operation agreements. Operations Department Computer Division Meteorological Division Data Division Model Division Predictability Division Director-General Research Department Administration Department

ECMWF’s Director-General, appointed by the Council, is responsible for implementing the organisa tion’s objectives and oversees three departments: Operations, Research and Administration.

Atmospheric Composition Division

ECMWF is an intergovernmental organisation supported by more than 30 States. It provides weather services with medium-range forecasts of global weather to 15 days ahead as well as with monthly and seasonal forecasts. ECMWF’s computer system at its headquarters in Reading, United Kingdom, is one of the largest for meteorology worldwide and contains the world’s largest archive of numerical weather prediction data. It runs a sophisticated medium-range prediction model of the global atmosphere and oceans. The National Meteorological Services of Member States and Co-operating States use ECMWF’s products for their own national duties, in particular to give early warning of potentially damaging severe weather.

European Centre for Medium-Range Weather Forecasts (ECMWF) Shinﬁeld Park, Reading RG2 9AX, United Kingdom

Tel: +44 (0) 118 949 9000 Fax: +44 (0) 118 986 9450 Website: www.ecmwf.int

The Convention establishing ECMWF and its remit came into force on 1 November 1975. Amendments made to the Convention were adopted by the Council in April 2005. They came into force on 6 June 2010 permitting additional European States to become Member States and formally expanding ECMWF’s mission to include environmental and climate monitoring.