1. Sub-seasonal prediction at ECMWF
Frédéric Vitart
European Centre for Medium-Range Weather Forecasts

2. Forecasting systems at ECMWF
Product
ECMWF:
Weather and Climate
Dynamical Forecasts
Medium-Range
Forecasts
Day 1-10(15)
Monthly
Forecast
Day 10-32
Seasonal
Month 2-7
Slide 2: The monthly forecasting system fills the gap between two currently operational forecasting systems at ECMWF: medium-range weather forecasting and seasonal forecasting. Medium-range weather forecasting produces weather forecasts out to 15 days, whereas seasonal forecasting produces forecasts out to 7 months. The two systems have different physical bases. Medium-range weather forecasting is essentially an atmospheric initial value problem. Since the time scale is too short for variations in the ocean significantly to affect the atmospheric circulation, the ECMWF medium-range weather forecasting system is based on atmospheric-only integrations. SST anomalies are simply persisted. Seasonal forecasting (2-7 months forecasts), on the other hand, is justified by the long predictability of the oceanic circulation (of the order of several months) and by the fact that the variability in tropical SSTs has a significant global impact on the atmospheric circulation. Since the oceanic circulation is a major source of predictability in the seasonal scale, the ECMWF seasonal forecasting system is based on coupled ocean-atmosphere integrations.

3. The ECMWF monthly forecasting system
A 51-member ensemble is integrated for 32 days twice a week (Mondays and Thursdays at 00Z)
Atmospheric component: IFS with the latest operational cycle and with a T639L62 resolution till day 10 and T319L62 after day 10.
Persisted SST anomalies till day 10 and ocean-atmosphere coupling from day 10 till day 32.
Oceanic component: NEMO with a zonal resolution of about 1 degree.
Coupling: OASIS (CERFACS). Coupling every 3 hours.
Slide 5:Description of the VarEPS-monthly forecasting system. Each week, the coupled model is integrated forward to make a 32 day forecast with 51 different initial conditions, in order to create a 51-member ensemble.

4. The ECMWF VarEPS-monthly forecasting system
Current system (twice a week, 51 ensemble members):
EPS Integration at T639
Initial condition
Day 10
Heat flux, Wind stress, P-E
Day 9
Day 32
Coupled forecast at TL319
Slide 36: New monthly forecasting system. Previously the monthly forecasting system consisted of coupled ocean-atmosphere integrations with an atmospheric horizontal resolution at TL159. Now, the monthly forecasting system and the VarEPS system have been merged. This graphic displays the new configuration: atmosphere-only at TL639 forced by persisted SST anomalies till day 10 twice a day. After day 10, the atmospheric model at a TL319 resolution is coupled to an ocean model till day 32.
The coupled model consists of the ECMWF atmospheric model (the same cycle as the deterministic forecast), coupled to an ocean general circulation model, which is a version of the Hamburg Ocean Primitive Equation model (HOPE), developed at the Max Plank Institute for Meteorology, Hamburg. The ocean model has lower resolution in the extratropics but higher resolution in the equatorial region, in order to resolve ocean baroclinic waves and processes, which are tightly trapped at the equator. The ocean model has 29 levels in the vertical. The atmosphere and ocean communicate with each other through a coupling interface, called OASIS, developed at CERFACS, France. The atmospheric fluxes of momentum, heat and fresh water are passed to the ocean every 3 hours and, in exchange, the ocean sea surface temperature (SST) is passed to the atmosphere.
Ocean only integration

5. The ECMWF monthly forecasting system
Atmospheric initial conditions: ECMWF operational analysis
Oceanic initial conditions: “Accelerated” ocean analysis
Perturbations:
Atmosphere: Singular vectors + stochastic physics + EDA
Ocean: Wind stress perturbations during the data assimilation
Slides 7 In order to initiate monthly forecasts, initial conditions for both the ocean and atmosphere are required. Atmospheric and land surface initial conditions are obtained from the ECMWF operational atmospheric analysis/reanalysis system. Oceanic initial conditions originate from the oceanic data assimilation system used to produce the initial conditions of the seasonal forecasting system 2. However, this oceanic data assimilation system lags about 12 days behind real-time. The lag is partially due to the fact that the SST, obtained by interpolating in time the weekly OIv2 SSTs produced by NCEP, can be up to 12 days behind real-time. A first option would be to wait for the oceanic initial condition to be created by the data assimilation system to start the forecast, as in seasonal forecasting. This would mean losing 12 days of forecast and is not therefore suitable for monthly forecasting. A second option would be to persist the SST anomalies of the latest ocean analysis. However, we have some information about the wind stress and heat fluxes during the last 12 days of the ECMWF atmospheric analysis; this information can be used to help determine the present ocean initial state. Therefore, the option that has been chosen for monthly forecasting consists in integrating the ocean model from the last ocean analysis forced by analyzed wind stress, heat fluxes and P-E. During this 'ocean forecast', the sea surface temperature is relaxed towards persisted SST, with a damping rate of 100 W/m2/K.

6. The ECMWF monthly forecasting system
Background statistics:
5-member ensemble integrated at the same day and same month as the real-time time forecast over the past 18 years (a total of 90 member ensemble)
Initial conditions: ERA Interim
It runs once a week
Slide 39: Because of model errors, a drift occurs in the coupled system. In order to evaluate this model drift, the coupled model is integrated with 5 different initial conditions (5-member ensemble) at the same day and month as the real time forecast, but over the past 18 years, creating a 90-member climate ensemble.

7. The ECMWF monthly forecasting system
Anomalies (temperature, precipitation..) -
Slide 10: Anomaly maps are similar to seasonal forecasting charts, but with weekly means instead of monthly means. Over each point of the map, atmospheric variables such as 2-metre temperature, total precipitation, mean sea-level pressure or surface temperature, have been averaged over a weekly period (week 1: day 5 to 11, week 2: day 12 to 18, week 3: day 19 to 25, and week 4: day 26 to 32) and also over the 51 members of the real-time forecast and the 60 members of the back statistics. The plots display the difference between the ensemble mean of the real-time forecast and the ensemble mean of the back-statistics. The product therefore displays the shift of the forecast ensemble mean from the estimated "climatological" mean (created from ensemble runs over the past 18 years). In addition, a Wilcoxon-Mann-Whitney test (WMW-test, see for instance Wonacott and Wonacott 1977) has been applied to estimate whether the ensemble distribution of the real-time forecast is significantly different from the ensemble distribution of the back-statistics. Regions where the WMW-test displays a significance less than 90% are blank. Regions where the WMW-test displays a significance exceeding 95% are delimited by a solid contour (blue or red depending on whether the anomaly is positive or negative respectively). The blanking of "non-significant" shifts does not mean that there is no signal in the blanked regions, but only that, with the particular sampling we have, we cannot be sure that there is a signal. For this reason, there are likely to be many areas where a signal is real but remains undetected.

8. The ECMWF monthly forecasting system
Probabilities (temperature, precipitation..) -
Slide 11: Probability and tercile maps are also produced. An example of tercile map for the period day is displayed on this slide.

9. The ECMWF monthly forecasting system
Slide 12: Probability and tercile maps are also produced. An example of tercile map for the period day is displayed on this slide.

10. The ECMWF monthly forecasting system

11. Experimental product: Tropical cyclone activity
The ECMWF monthly forecasting system
Experimental product: Tropical cyclone activity
Slide 13: Forecast of tropical cyclone activity. This plot shows the probability of tropical storm strike within 300 km predicted by the monthly forecast starting on 8 April 2010 and for the period day

12. ROC score: 2-meter temperature in the upper tercile
Skill of the ECMWF Monthly Forecasting System
ROC score: 2-meter temperature in the upper tercile
Day 5-11
Day 12-18
Day 19-25
Day 26-32
Slide 16 Map of ROC scores of the probability that 2-meter temperature averaged over the period day is in the upper tercile. Only the scores over land points are shown. The terciles have been defined from the model climatology. The verification period is Oct 2004-May Red areas indicate areas where the ROC score exceeds 0.5 (better than climatology). This plot shows that the coupled model performs better than climatology for the period days
For the period days 19-26, the skill is much lower than for days 12-18, as expected. The red is largely dominating overall, suggesting that the model generally performs better than climatology at this time scale. Europe seems to be a difficult region, with very low skill at this time range. Tropical regions display the strongest skill after 30 days, suggesting that the coupled model at this time range starts to behave more like seasonal forecasting.

13. RPSS Scores Hindcasts (1995-2001) - NH
DAY 5-11

14. RPSS Scores Hindcasts (1995-2001) - NH
DAY 5-11
DAY 12-18
DAY 19-25
DAY 26-32

15. ROC Scores - Tropics
Day 5-11
Day 12-18
Day 19-25
Day 26-32

16. MJO skill scores and amplitude

17. Future Plans Use of new soil initial condition and SSTs for hindcasts.
Extend hindcast length from 18 to 20 years
Increase vertical resolution from 62 levels to ~92 vertical levels
Sea-ice model
Ocean/atmosphere Coupling from day 0
Extend forecast range to days
Future Plans

18. Performance of the monthly Forecasts
Day 12-18
Day 19-25
Day 26-32

19. Precip anomalies : 26 July 2010 – 01 August 2010

20. Days 12-18 Days 19-25 Verifying weeks: 3-9 May to 16-22 Aug 2010.
Precipitation over Pakistan Averaged over (34-25N 60-73E) :
Days 12-18
Days 19-25
Verifying weeks: 3-9 May to Aug 2010.

21. Pakistan Floods – Sept 2011