1. Use and Interpretation of ECMWF Products

2. Use and Interpretation of ECMWF Products Numerical weather prediction
The behaviour of the atmosphere is governed by a set of physical laws which express how the air moves, the process of heating and cooling, the role of moisture, and so on.
Given a description of the current state of the atmosphere, numerical models can be used to propagate this information forwards to produce a forecast for future weather.
Resolution of the model is determined by available computing resources. It does not correspond to any natural scale separation.
Processes not resolved by the model must be ‘parametrized’.
Effective resolution is not same as model grid spacing.
Numerical algorithms are compromise between accuracy and speed; care needed to ensure numerical stability.

3. Use and Interpretation of ECMWF Products
Observations
Acquisition/Pre-processing/Quality control/Bias correction
Data assimilation
Dynamical fit to observations
Forecasts
Product dissemination and archiving
Verification
operational/ pre-operational validation
Data Monitoring

4. Use and Interpretation of ECMWF Products
Short-range weather forecast (0-2/3 days ahead)
Detailed prediction - regional forecasting system
Produce forecast few hours after observations are made
Medium-range weather forecast (2/3 days - 2 weeks ahead)
Less detailed prediction - global forecasting system
Produce forecast up to several hours after observations are made
Long-range weather forecast (more than 2 weeks ahead)
Predict statistics of weather for coming month or season
Climate prediction
Predicts the climate evolution on the basis of pre-defined scenarios (CO2, O3, …)

5. ECMWF forecasting systems
Use and Interpretation of ECMWF Products
ECMWF forecasting systems
Medium-range
deterministic
forecasts
Medium-range EPS
Monthly
Forecasts
Seasonal
Forecasts
Atmospheric model
Wave model
Ocean model
Atmospheric model
Wave model
Ocean model
Atmospheric model
Wave model
Ocean data assimilation with 10-day window every 10 days. Forcing from ocean to atmosphere through SST+ocean currents (wave model), vice versa through P-E, wind stress, heat flux.

6. D+10 12UTC not coupled (persisted SST anom.)
Assimilation
4DVar
resolution
No of members
Area
Forecast range
Forecast frequency
Ocean coupling
Deterministic model
12h
≈16 Km
L91 1
Global
10 days
Twice a day No
EPS
≈30/60 Km L62 51
15 days
Twice a day
00UTC Coupled after
D+10 12UTC not coupled (persisted SST anom.)
1 month
Once a week
Yes
HOPE -- L29
1°(extratropics) 0.33 (equator)
Seasonal
≈125 Km
L62 41
7 months
Once a month
Boundary condition 6h
3 days
4 times a day
Ocean waves
OI 6h
28 Km
10 Km
European waters
5 days
≈30/60 Km

7. Major assimilated data sets
Use and Interpretation of ECMWF Products
Major assimilated data sets
Polar, infrared
Surface stations
Polar, microwave
Radiosonde balloons
Aircraft
Geostationary, IR
Use and Interpretation of ECMWF Products --- January/February 2011

8. Data assimilation system (4D-Var)
Use and Interpretation of ECMWF Products
Data assimilation system (4D-Var)
The observations are used to correct errors in the short forecast from the previous analysis time.
Every 12 hours we assimilate ~8,000,000 observations to correct the 100,000,000 variables that define the model’s virtual atmosphere.
This is done by a careful 4-dimensional interpolation in space and time of the available observations; this operation takes as much computer power as the 10-day forecast.
Use and Interpretation of ECMWF Products --- January/February 2011

9. Use and Interpretation of ECMWF Products
The ECMWF IFS deterministic Model
Spacing of grid points
~16 km
Time step
10 minutes
Number of grid points in model
2,140,704
Number of computations required for each ten-day forecast
>> 1,630,000,000,000,000
Physical processes with smaller scales than can be resolved by the model grid are represented by so-called “Parametrization Schemes” which represent the effect of the small-scale processes on the large-scale flow.

10. Use and Interpretation of ECMWF Products Operational model grid
22/04/2017
Use and Interpretation of ECMWF Products
Operational model grid
Use and Interpretation of ECMWF Products --- January/February 2011 10

11. Use and Interpretation of ECMWF Products Operational model levels
High Resolution Deterministic forecast
T1279:91 vertical levels
First EPS vertical level
Ensemble Prediction System (including Monthly) T639/T319:
62 vertical levels
Below this arrow EPS levels and High Res Deterministic model vertical levels are identical
Use and Interpretation of ECMWF Products --- January/February 2011

12. Use and Interpretation of ECMWF Products
ECMWF’S IFS web pages for WMO users…
Username: wmo_tsms
Password: v14DqK

13. Use and Interpretation of ECMWF Products
Forecasts are sometimes wrong How do we deal with that? ECMWF’s Ensemble Prediction System

14. The atmosphere is ”chaotic”! Atmosphere
Use and Interpretation of ECMWF Products
”The Blame Game” or ”The Passing of The Buck”
The atmosphere is ”chaotic”!
Atmosphere
Erroneous observations
misled the NWP!
Scientists
Computer models
The NWP misled me!
Forecaster
The forecaster misled me!
Customer/Public

15. Use and Interpretation of ECMWF Products
MSLP 66-hour forecasts, VT: 16-Oct-1987, 6 UTC
T399 EPS

16. Use and Interpretation of ECMWF Products
Why are forecasts sometimes wrong?
Initial condition uncertainties
Lack of observations
Observation error
Errors in the data assimilation
Model uncertainties
Limited resolution
Parameterisation of physical processes
The atmosphere is chaotic
small uncertainties grow to large errors (unstable flow)
small scale errors will affect the large scale (non-linear dynamics)
error-growth is flow dependant
Even very good analyses and forecast models are prone to errors

17. Use and Interpretation of ECMWF Products
What is an Ensemble Prediction System?
A set of forecasts run from slightly different initial conditions to account for initial uncertainties
At ECMWF perturbations are generated using singular vectors
The forecast model also contains approximations that can affect the forecast evolution
Model uncertainties are represented using “stochastic physics”
The ensemble of forecasts provides a range of future scenarios consistent with our knowledge of the initial state and model capability
Provides explicit indication of uncertainty in today’s forecast

18. Deterministic Forecasting
Use and Interpretation of ECMWF Products
Deterministic Forecasting
Is this forecast “correct”?
Temperature
Initial
Uncertainty
Forecast time
Initial condition
Forecast
Model Error

19. Use and Interpretation of ECMWF Products
Ensemble Forecasting
Temperature
Forecast time
Initial condition
Forecast
Complete description of weather prediction in terms of a
Probability Density Function (PDF)

20. Flow dependence of forecast errors
Use and Interpretation of ECMWF Products
Flow dependence of forecast errors
26th June 1995
26th June 1994
If the forecasts are coherent (small spread) the atmosphere is in a more predictable state than if the forecasts diverge (large spread)

21. Use and Interpretation of ECMWF Products
EPS grid

22. Use and Interpretation of ECMWF Products
EPS skill (RPSS-Ranked Probability Skill Score, temperature at 850hPA, NH)

23. EPS skill compared to other centres
Use and Interpretation of ECMWF Products
EPS skill compared to other centres
T-2m, DJF 2008/09
NH (20°N - 90°N)
BC vs. ERA-interim

24. Early warnings of severe weather
Use and Interpretation of ECMWF Products
Early warnings of severe weather
ECMWF: early warnings (3-4 days ahead, or more)
Users are generally forecasters in Member States, not public
ECMWF products for severe weather:
Extreme Forecast Index (EFI)
Tropical cyclone tracks, strike probabilities

25. EPS forecasts (field probabilities)
Use and Interpretation of ECMWF Products
EPS forecasts (field probabilities)
Probability of precipitation more than 10mm in 24 hours
Probability of 10m wind gusts more than 15 m/s
Base Sun 12/10/08 12UTC, Valid Thu 16/10/08 12UTC

26. EPS forecasts (Extreme forecast index)
Use and Interpretation of ECMWF Products
EPS forecasts (Extreme forecast index)
EFI for 24-hour 10m wind gusts
EFI for 24 hours precipitation
Base Sun 12/10/08 12UTC, Valid Thu 16/10/08 12UTC

27. EPS forecasts: timeseries (EPSgram)
Highest value of all members
90th centile
75th centile
Median
25th centile
10th centile
Lowest value of all members
EPSgram for Reading
Base Sun 11/10/09 00UTC

28. EPS forecasts: timeseries (EPSgram)
Highest value of all members
90th centile
75th centile
Median
25th centile
10th centile
Lowest value of all members
EPSgram for Addis Abbaba
Base Sun 11/10/09 00UTC

29. Katrina forecasts (days from landfall)
Use and Interpretation of ECMWF Products
Katrina forecasts (days from landfall)
4 days
3 days
1.5 days

30. Use and Interpretation of ECMWF Products
EPS appearing on TV
Netherlands
Germany

31. Use and Interpretation of ECMWF Products
ECMWF’S EPS web pages for WMO users…

32. Use and Interpretation of ECMWF Products
Seasonal predictions at ECMWF Can the weather be predicted months in advance?
Predictions may be possible a few months in advance based on the fact that irregular weather variations have been associated with El Niño - a warming of the Pacific Ocean near the equator- and La Niña, a similar event caused by the cooling of equatorial Pacific waters.
The slow changes in the surface temperatures of the oceans are thought to impart a degree of predictability.
Seasonal forecasting is the attempt to predict the probability distribution for weather several months or more into the future. The emphasis is on averages over a month or season, and how the probability distribution differs from "climatology". Seasonal forecasting is possible, because although the details of individual weather systems are not predictable on these time scales, the statistics of them are determined by various factors, some of which can be predicted. The most important factor is sea surface temperature, especially in the tropics. Other factors include soil moisture and snow cover. 32

33. THE EL NIÑO/SOUTHERN OSCILLATION (ENSO) CYCLE
Use and Interpretation of ECMWF Products
THE EL NIÑO/SOUTHERN OSCILLATION (ENSO) CYCLE 33

34. Chaotic nature of the atmosphere
Use and Interpretation of ECMWF Products
Chaotic nature of the atmosphere
To deal with the chaotic processes in the atmosphere we use an ensemble of simulations: on the 1st of the month 40 forecasts are run for 7 months. They have initial conditions from 5-member ensemble of ocean analyses (wind and SST pert.)
In many parts of the tropics, where changes such as those associated with El Nino can have a large impact on global weather patterns, a substantial part of the year-to-year variation in seasonal mean rainfall and temperature is predictable. In mid-latitudes, the level of predictability is lower, and Europe in particular is a difficult area to predict. It is essential to understand that the forecasts are necessarily probabilistic, and that the range of possible values predicted may often differ from climatology by only a modest amount.
Seasonal forecasting does not give exact predictions, but it may allow us to describe the probability that a certain weather event can happen. 34 34

35. Use and Interpretation of ECMWF Products
Ocean Analysis
Seasonal outlook: (up to 7 months ahead)
- Forecasts for Nino3, Nino3.4 and Nino4
- Spatial plots (ens.mean anomaly, terciles ..)
- Climagrams ( similar to Epsgrams)
- Tropical storms
Annual outlook: (up to 10 months ahead) 35

36. Use and Interpretation of ECMWF Products
Sources of seasonal predictability
Atmospheric predictability arises from slow variations in lower-boundary forcing
KNOWN TO BE IMPORTANT:
El Nino variability  biggest single signal
Other tropical ocean SST - important, but multifarious
Climate change - trends in mid-latitudes
Local land surface conditions - e.g. soil moisture in 2003
OTHER FACTORS:
Mid-latitude ocean temperatures - always controversial
Remote soil moisture/snow cover - not yet well established
Sea ice anomalies - local effects, but remote??
Dynamic memory of atmosphere - most likely on 1-2 months
Stratospheric influences - downward propagation of anomalies
Volcanic eruptions - potentially predictable if contained in initial conditions

37. Use and Interpretation of ECMWF Products
- 2m Temperature
Mean sea level pressure
Precipitation
Sea surface temperature
850 hPa temperature
500 hPa geopotential
Forecast is made available on the 15th of each month. 37

38. Use and Interpretation of ECMWF Products
2m Temperature
Mean sea level pressure
Precipitation
Sea surface temperature
850 hPa temperature
500 hPa geopotential
Forecast is made available on the 15th of each month. 38

39. EUROSIP multi-model system
Use and Interpretation of ECMWF Products
EUROSIP multi-model system
3 Coupled Systems: ECMWF, Météo France, Met Office
Ensemble generation for the 3 systems is different
Met Office and Meteo-France systems are both running at ECMWF
Development of multi-model products is ongoing
EUROSIP products are available to WMO users
Three models running at ECMWF:
ECMWF – (as described)
Met Office – HADCM3 model, Met Office ocean analyses
Météo-France – Météo-France model, Mercator ocean analyses
Unified system:
All data in ECMWF operational archive
Common operational schedule (products released at 12Z on 15th)
Coordinated development strategy (e.g. 41 member ensemble)
Products available soon:
ECMWF release of web products is expected soon
Met Office already have a 2-member combination on a website

40. Use and Interpretation of ECMWF Products
EUROSIP

41. Use and Interpretation of ECMWF Products
Summary (1)
Every month a wide range of products from the current seasonal forecast system is made available to the users.
The ECMWF seasonal forecast is a good system for El Niño predictions.
The skill of the past forecast should be used to assess the forecast uncertainties. Various skill estimates are available to the users.
Checking the consistency of large-scale circulation anomalies is important to detect seasonal forecast signals, especially for regions with little skill
Regional skill can be enhanced by defining a larger domain with a coherent climate.
Predictive skill for seasonal averages is better than for monthly data
In the multi-model EUROSIP system the uncertainties due to model errors are better represented so that the EUROSIP forecast is more reliable.
ECMWF Newsletter N.110, N.111 and N.112 available at contain articles describing the upgraded seasonal forecast system and its products 41

42. Use and Interpretation of ECMWF Products
Summary (2)
A new oper. seasonal forecast system has been implemented in March 2007:
Improved predictions of tropical/summer variability than the previous system.
The range of products has been expanded and new products are made available to WMO users
Products from the EUROsip Multi Model system are available on the web to the members states and will be soon made available to WMO users:
we are building up experience in the use of appropriated calibration/combination methods and verification thanks to the work done within the special projects.
Various skill estimates conformed to the SVS are available to the users.
Statistics from the new system will be soon available as well as EUROsip verification

43. Use and Interpretation of ECMWF Products
ECMWF’S Seasonal Forecats web pages for WMO users…

44. The Extreme Forecast Index (EFI)

45. How can we forecast ‘extreme weather’ ?
Use and Interpretation of ECMWF Products
How can we forecast ‘extreme weather’ ?
What can be used to define ‘extreme’, right across the globe ?
Particular Thresholds ? NO
Return Periods ? Effectively YES
Therefore need reference threshold levels, for each weather parameter of interest, everywhere across the globe
Return Periods from observational data are not available everywhere
However global model forecasts provide output everywhere
Therefore global model forecasts can be used to provide the climatology from which to extract Return-Period-type information
Hindcasts are performed each time a model is upgraded, to provide the relevant climatology – some resolution-related imperfections, but overall a very useful dataset
This forms the basis for the EFI, or ‘Extreme Forecast Index’, whereby, for each gridpoint, the range of solutions (pdf) from the current ensemble, is compared with that gridpoint’s climatology from the hindcast data, to see how extreme these solutions are

46. Use and Interpretation of ECMWF Products
Why is it good to have an EFI ?
The EPS system provides a HUGE amount of information; it is hard to synthesize. EFI provides a summary measure towards potentially extreme situations.
Definition of extreme weather is highly climate dependent, it varies from location to location. EFI delivers model-climate-related information, therefore it can be used as a generic “alarm bell” for extreme weather situations over any area.
Why is the probability product for example (or something similar) not enough ?
It is advantageous to take into consideration the whole of the EPS distribution, not just a section of it. EFI provides a picture with broader basis.
Consider the following two quite extreme 4-value EPS distributions:
Simple probabilities (eg > 35C) will not highlight the differences in the above. EFI will, by accounting for the distribution of all the ensemble members.
20 C° (20 members)
30 C° (10 members)
35 C° (15 members)
40 C° (5 members)
20 C° (5 members)
30 C° (25 members)
35 C° (15 members)
40 C° (5 members)

47. Extreme Forecast Index (EFI)
Use and Interpretation of ECMWF Products
Extreme Forecast Index (EFI)
The EFI is defined on the basis of the Cumulative Distribution Functions (CDF). The abnormality level in the EPS is determined based on the position and shape of the distributions.
model climate
eps forecast
CDF
PDF 1 1
Probability
of not
exceeding
threshold
Density
per
threshold
Interval
(normalised)
-10 -5 5 10 15 20 25
-10 -5 5 10 15 20 25

48. The reference EPS Model Climate
Use and Interpretation of ECMWF Products
The reference EPS Model Climate
For climate related products like the EFI a reliable model climate is essential
Ideally the model climate is a large set of EPS re-forecasts with the latest model configuration (used operationally) for a long enough period (e.g. 30 years)
The current EPS model climate in use:
Running a full EPS re-forecast suite with 4 EPS members and the Control
Always for the most recent 18 years with initial conditions taken from ERA-Interim (the higher resolution successor to ERA-40).
Currently runs every Thursday (therefore climate files are available only for Thursdays. For days in between Thursdays the closest preceding Thursday’s files should be taken)
Integration up 32 days, post-processed fields as for EPS (data every 6 hours)
Use latest model cycle (resolution/ physics / etc.)
Allow an immediate adaptation of EFI and other model climate related products to any EPS model upgrade
For more information about the model climate please refer to:

49. Use and Interpretation of ECMWF Products
Model Climate
We now call this the ‘M-Climate’, which has a specific meaning…
M-Climate means that it is a function of 3 factors:
1. Location, clearly
2. Time of year, to take account of seasonal variations
3. Forecast lead time

50. Use and Interpretation of ECMWF Products
ECMWF’S EFI web pages for WMO users…

51. Use and Interpretation of ECMWF Products
Acknowledgement: Thanks to various ECMWF presentations…