Long-range Tropical Cyclone Forecasting at ECMWF Frédéric Vitart European Centre for Medium-Range Weather Forecasts
INDEX • ECMWF forecasting systems • ECMWF tropical cyclone tracker • Medium-range TC prediction • TC Predictability at the sub-seasonal time-scale • Sub-seasonal prediction of TCs • Seasonal prediction of TCs
ECMWF: Weather and Climate Dynamical Forecasts Seasonal Forecasts Month 2-7 Medium-Range Forecasts Day 1-10(15) Monthly Forecast Day 10-32 Product Atmospheric model Atmospheric model Wave model Wave model Ocean model Forecasting systems at ECMWF
The operational forecasting systems • High resolution deterministic forecast: twice per day16 km 91-level, to 10 days ahead • Ensemble Prediction System (EPS): twice daily51 members, 30/60 km 62-level, to 15 days ahead • Monthly forecast /EPS extension: twice a week (Mon/Thursdays)51 members, 30/60 km 62 levels, to 1 month ahead • Seasonal forecast: once a month (coupled to ocean model) 41 members, 125 km 62 levels, to 7 months ahead
Procedure for tracking model tropical storms (1) Detection part: A local maximum of 850 hPa vorticity larger than 3.5 10-5 s-1 is located The closest local minimum sea level pressures is located. Finds if there is a warm core above the low pressure (maximum temperature anomaly between 200 and 500 hPa) The distance between the center of the warm core and the minimum sea level pressure should not exceed 200 km. The closest maximum thickness between 1000 and 200 hPa is located. The distance between this maximum and the minimum sea level pressure should not exceed 200 km.
Procedure for tracking model tropical storms (2) • Tracking part • The tropical storm trajectories are built from the low pressure systems that have been detected (fulfil criteria 1 and 2) using the steering wind at t=n as a predictor for the position of the storm at t=n+1 (first guess). • Find the tropical cyclone position at t=n+1 that is the closest to the first guess. The distance should not exceed a certain distance. • A tropical storm is allowed to “disappear” for a period of 24 hours. • To qualify as a tropical storm track, all the criteria (1 to 5) need to be fulfilled at least once and the maximum wind velocity must exceed 17 m/s at least once.
TC strike probability maps • The maps show the "strike probability" based on the number of EPS members that predict a tropical cyclone, each member having equal weight. • This includes tropical cyclones that already exist in the initial conditions and tropical cyclones that the model creates. • The strike probability is the probability that a tropical cyclone will pass within a 300 km radius from a given location and within a time window of 48 hours.
Prediction of TC genesis • Produced twice a day from 00Z and 12Z 51-member ensemble integrations • Three products: - Hurricane/typhoon strike probability (>32m/s) - Tropical storm strike probability (>17m/s) - Tropical depression strike probability (>8 m/s) • Issued over 10 48-hour time windows: 24h-72h, 48h-96h,…240h-288h
TC probability strike map – Harvey and Irene Strike probability (%) map for tropical cyclone activity (systems with maximum wind speed > 8m/s) based on the EPS forecast from 00 UTC on 17 August 2011 for 20-22 August
Monthly forecasting system: • Products from the monthly forecasting system are provided to the ECMWF Member and Co-operating States and to commercial users. • The monthly forecasts are an integral part of the Ensemble Prediction System (EPS) and provide outlooks up to 32 days ahead. • The monthly system runs twice a week (Monday-Thursday)
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.
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 32 Day 9 Coupled forecast at TL319 Ocean only integration
The ECMWF monthly forecasting system • Atmospheric initial conditions: ECMWF operationalanalysis • Oceanic initial conditions: “Accelerated” ocean analysis • Perturbations: • Atmosphere: Singular vectors + stochastic physics + EDA • Ocean: Wind stress perturbations during the data assimilation
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
Main sources of TC predictability on sub-seasonal time scale Madden Julian Oscillation Sea Surface Temperatures Easterly waves Rossby waves Oceanic Kelvin waves
Observational studies • Western North Pacific: Nakazawa (1988); Liebmann et al (1994) • Eastern North Pacific: Molinari et al, (1997); Maloney and Hartmann (2000) • Gulf of Mexico: Maloney and Hartmann (2000); Mo (2002) • South Indian Ocean: Bessafi and Wheeler (2006); Ho et al (2006) • Australian region: Hall et al (2001) • Impact on tropical cyclone genesis index: Camargo et al (2009)
The Madden-Julian Oscillation (MJO) From Madden and Julian (1972)
Impact of the MJO on TC activity GPI and OLR Anom. Composites - JFM First Position Anomaly Composites - JFM Main contributions: • Mid-level relative humidity • Low-level absolute vorticity • 3) Vertical wind shear and potential intensity Camargo, Wheeler, and Sobel, J. Atmos. Sci. (2009)
Impact of the MJO on TC tracks Cluster Analysis of Western North Pacific Tropical cyclone tracks Camargo, Robertson, Gaffney, Smyth and Ghil, Part I & Part II, J. Climate (2007)
Impact of the MJO on the rapid intensification of tropical cyclones in the western North Pacific Wang and Zhou , 2008
Impact of Ocean Kelvin waves on TC genesis Ribble-Verhagen, and Roundy 2010
Example of statistical forecast of TC activity Leroy and Wheeler, MWR 2008 Brier Skill Scores • Predictors: • -Climatological seasonal • cycle of TCs • MJO Index • SST predictors: • NINO 3.4 • Trans-Nino Index • Indian Ocean Dipole http://www.meteo.nc/espro/previcycl/cyclA.php
MJO Prediction Combined EOF1 Combined EOF2 From Wheeler and Hendon, BMRC
MJO Skill and amplitude Correlation with analysis (ERA Interim) Winter All year
Tropical Cyclone Density climatology 1989-2008 – FCst range: day 15-45
Impact of the MJO on Tropical Cyclone Density (JJA) Vitart, GRL 2009
Impact of the MJO on tropical Storm Activity NDJFMA Vitart, 2009
Impact of the MJO on tropical Storm Activity - ASO Vitart, 2009
Sub-seasonal Prediction of TCs at ECMWF • The TC sub-seasonal products are issued twice a week from Thursday 00Z and Monday 00Z integrations. The forecasts are issued at 10:00 PM • Forecasts available to ECMWF member states, cooperative states and commercial users. Not available to WMO. • All the sub-seasonal TC products are based on weekly means. The weeks correspond to calendar weeks (Monday to Sunday): • Thursday forecasts: day 5-11, day 12-18 day 19-25 and day 26-32 • Monday forecasts: day 1-7, day 8-14, day 15-21 and day 22-28
List of Sub-seasonal TC products at ECMWF Grid-point maps: • Tropical cyclone strike probabilities(Hurr/TS/TD) • same as for medium-range forecasts but for 7-day time windows. • 2) Tropical cyclone strike probability anomalies (Hurr/ts/td): • the anomalies are relative to the model climatology • TC statistics: • Number of tropical storm geneses over an ocean basin during a weekly period. • Accumulated cyclone energy computed from all the TCs present over an ocean basin during a weekly period.
ECMWF Sub-seasonal TC Prediction Example: Cyclones ULUI & TOMAS 11-13/03/10
Example: Cyclones ULUI & TOMAS 11-13/03/10 Role of the MJO?
Probability of a tropical storm strike within 300 km Week: 25/05-31/05/09 30/04 day 26-32 07/05 day 19-25 Cyclone Aila 24-26 May 2009 21/05 day 5-11 14/05 day12-18
MJO : Velocity Potential07/05/2009 forecastl Ensemble mean Analysis