Ensemble Forecasting at RSMC La Runion WMO/WWRP Training, Nanjing, dec 2011 Matthieu Plu, Thierry Dupont, Sbastien Langlade, Nicole Girardot METEO-FRANCE RSMC La Runion Laboratoire de lAtmosphre et des Cyclones Outline Presentation of RSMC La Runion Available products from ensemble forecasts Mto-France data ECMWF data TIGGE data Track forecasts Intensity forecasts Wave forecasts Storm surge SWFDP RSMC La Runion website RSMC La Runion Since 1993, Mto-France La Runion: WMO RSMC for tropical cyclones in the South-West Indian Ocean (SWIO) RSMC La Runion Cyclone activity in the SWIO : 12% of the total worldwide cyclone activity Impacts : many human losses every year (particularly in Madagascar), and significant economical impact in the richer countries (example : Dina 2002 95 M losses in La Runion) Severe winds and intense rainfall (La Runion records), waves and storm surge (African coast and atolls) Role of RSMC La Runion for tropical cyclones : Provide appropriate guidance information (analyses, forecasts) in real- time ; forecast term until 5 days Climatology, database of TC Training and R&D activities RSMC La Runion but individual tracks are not so regular RSMC La Runion Equatorial waves (Kelvin waves, Rossby waves, Madden-Julian oscillation, ) modulate cyclone activity. Mid-latitude events also play a significant role on cyclone dynamics (track and intensity) Density of occurrence of a mid-latitude Rossby-wave breakings at 200hPa during the rapid Intensification of a cyclone RSMC La Runion Track error of deterministic models in the SWIO: Direct position error ( season) Available products from ensemble forecasts Mto-France PEARP Available products from ensemble forecasts ECMWF ensemble prediction system (EPS) Available products from ensemble forecasts Other TIGGE products MOGREPS (UKMO) : cxml Available products from ensemble forecasts Other TIGGE products (cxml) GEFS (NCEP) : cxml + wind and precipitation Available products from ensemble forecasts Other TIGGE products (cxml) CENS (CMC) : cxml + wind and precipitation TC track forecasts General method for track prediction Step 1 : Build the official RSMC track forecast This step relies on deterministic models and on consensus of several models Ensemble tracks may help to eliminate from the consensus the outlying deterministic tracks TC track forecasts General method for track prediction Step 2 : Writing the bulletin Ensemble forecasts are used to : Provide some confidence of the forecast Give some possible alternative scenarios TC track forecasts Illustration on the case of Bingiza Tropical cyclone Bingiza 13 February UTC (Terra image source : TC track forecasts Illustration on the case of Bingiza: early prediction of impact zone Bingiza, UTC Moderate Tropical Storm TC track forecasts Illustration on the case of Bingiza: early prediction of impact zone Bingiza, UTC Moderate Tropical Storm TC track forecasts Illustration on the case of Bingiza: early prediction of impact zone Bingiza, UTC Moderate Tropical Storm PEARP EPS TC track forecasts Illustration on the case of Bingiza: early prediction of impact zone Bingiza, UTC Moderate Tropical Storm TC track forecasts Illustration on the case of Bingiza: early prediction of impact zone Most of the models and ensembles indicate movement towards the South The spread of ensemble tracks is large BULLETIN : UTC [] The track forecasts by numerical models are highly spread. There is some agreement that the storm movement may be somehow erratic during the next 48h. Then, some models suggest a track towards the South-West towards Madagascar, some others towards the South. The inhabitants of Mauritius, La Runion and of Madagascar should follow the evolution of this system. Not a precise warning TC track forecasts Illustration on the case of Bingiza: short-term prediction of landfall Bingiza, UTC Tropical cyclone TC track forecasts Illustration on the case of Bingiza: short-term prediction of landfall Bingiza, UTC Tropical cyclone TC track forecasts Illustration on the case of Bingiza: short-term prediction of landfall TC track forecasts Illustration on the case of Bingiza: short-term prediction of landfall The ensembles confirm the RSMC track scenario BULLETIN : 2011/02/12 06UTC [] Models indicate a direction towards the East coast of Madagascar and landfall between Masaola and Sainte Marie Island on Monday. The environmental conditions are favorable to intensification before landfall. The inhabitants of the East coast of Madagascar must follow with great attention the evolution of Bingiza. 48h before landfall, the forecast is: Precise: day of landfall given, location (~150km) Right: this forecast has been verified Ensembles provided confidence in this forecast TC track forecasts Subjective evaluation of ensemble systems to predict landfall: Position Timing Black: 0-24h Purple: 24h-48h Red: 48h-72h Orange: 72h-96h Yellow: 96h-120h Existence of the observed track in the ensemble tracks: as a majority as a minority not at all EPS 12/02 12UTC TC track forecasts LANDFALL AT MASAOLA ? LANDFALL AT AT 02UTC ? REALISTIC TRACKS AT MEDIUM RANGE ? REALISTIC TRACKS AT LONG RANGE ? UTC UTC UTC UTC UTC UTC UTC UTC UTC UTC CENSEPSGEFS MOGREPS PEARP Poor Man Ensemble TC track forecasts Conclusion on the case of Bingiza: A difficult case for track forecast At early stage, ensembles do not do better than deterministic models for predicting the impact areas 48h before landfall, ensembles provide precise and good information At every stage, ensembles are useful to quantify the confidence of the forecast: early stage: low confidence 48h before landfall: high confidence Such information is conveyed in the RSMC bulletins. Multi-ensemble All forecasts TC track forecasts Some other exemples : Cherono (Tropical storm 2011) Multi-ensemble TC track forecasts Some other exemples : Yasi (TC, Australia 2011) TC track forecasts Some other exemples : Yasi (TC, Australia 2011) TC intensity forecasts Intensity forecasts: a difficult task! Intensity & Structure change of the cyclone Davidson et al. (2008) Hanley et al. (2001) Gray (1979), Emanuel (1986) Montgomery (2008) Nguyen et al. (2006) Wang (2002) Schubert (1999) Initial structure External forcing - Atmosphere - Ocan Internal dynamics Fujita (1952) Chan and Williams (1987) Ma et al. (2011) Leroux et al. (2012) TC intensity forecasts Intensity forecasts: a difficult task! General forecasting method: Interpretation of the internal structure and the environment (favorable/unfavorable) from model analyses and satellite images; Deterministic models indicate a possible trend for the evolution of intensity; Ensembles have not been used for intensity forecasts yet; Exemples for climatological and rapid intensification. TC intensity forecasts TC Gelane (2010): a rapid intensification . INTENSITY (hPa) Before Rapid intensification TC intensity forecasts TC Gelane (2010) before rapid intensification (02/ UTC): All deterministic models predict no intensification RSMC bulletin: 12h-24h Strong Tropical Storm 36h-60h Moderate Tropical Storm 72h Tropical Depression Verification : 12h-24h Tropical Cyclone 36h Intense Tropical Cyclone !! TC intensity forecasts TC Gelane (2010) before rapid intensification (02/ UTC): Available ECMWF ensemble products : Observation: 930hPa !! 980 TC intensity forecasts TC Gelane (2010) INTENSITY (hPa) Before Rapid filling TC intensity forecasts TC Gelane (2010) before rapid filling (02/ UTC): All the deterministic models predicted Gelane to encounter strong vertical wind shear rapid filling The difference between the models when filling will occur, depending on the track forecast Deterministic models EPS strike probabilities STRONG SHEAR TC intensity forecasts TC Gelane (2010) before rapid filling (02/ UTC): Available ECMWF ensemble products : Observation: 930hPa !! TC intensity forecasts Hurricane Irene (North Atlantic, 2011) : a climatological intensification. Does the ensemble bring additional information with regard to the deterministic forecast? TC intensity forecasts Global models fail to represent correctly intensity evolutions, Lagrangian EPSgrams do not bring useful information yet, Some hope with resolution increase and with new products ? TC intensity forecasts Global models fail to represent correctly intensity evolutions, Lagrangian EPSgrams do not bring useful information yet, Some hope with resolution increase and with new products: Products in test (not operational) High wave forecasts Two ECMWF products are used: EPSgrams Probability maps High wave forecasts Two ECMWF products are used: EPSgrams High wave forecasts Two ECMWF products are used: Probability maps Ech 24 h Ech 48 hEch 72 h Storm surge forecasts Storm surge highly depends on : Track Intensity Surface wind structure R&D activities have begun to forecast storm surge: Prediction of wind extensions (radius of maximum wind, other thresholds), Generate a climatology of storm surge in the SWIO from a surge model and a climatology of cyclones SWFDP Website of RSMC La Runion Purpose : Provide guidance in real-time regarding tropical cyclones, and extreme wind and rainfall A limited-access to the SWFDP and RA I national meteorological services Available in two languages: French & English Input data : Aladin-Reunion forecast fields For each cyclone, the official RSMC track forecast Deterministic models (Mto-France, ECMWF, UKMO) Forecast tracks from ensembles (PEArp, ECMWF EPS, NCEP, CMC, MOGREPS) and multi-ensemble Fields (mslp, wind, rainfall) from ensembles (PEArp, ECMWF EPS, NCEP, CMC) and multi-ensemble SWFDP Website of RSMC La Runion Permanent products: Aladin-Runion forecasts (00UTC and 12UTC, until 84h) Probabilities and quantiles of strong winds and intense rainfall for 4 ensembles + multi-ensemble mslp spaghettis forecasts (for cyclogenesis) for 4 ensembles + multi- ensemble In case of a cyclone in the basin: Official RSMC track forecast + uncertainty cone Track forecasts from deterministic models Tracks, live time, Strike probabilities, track for the 5 ensembles + multi- ensemble SWFDP Website 2. Website of RSMC La Runion Early detection of the cyclogenesis of Bingiza Illustrations sur le cas de Bingiza (fvrier 2011) Early detection of the cyclogenesis of Bingiza Illustrations sur le cas de Bingiza (fvrier 2011) Early detection of the cyclogenesis of Bingiza Illustrations sur le cas de Bingiza (fvrier 2011) Early detection of the cyclogenesis of Bingiza Illustrations sur le cas de Bingiza (fvrier 2011) Early detection of the cyclogenesis of Bingiza Illustrations sur le cas de Bingiza (fvrier 2011) Early detection of the cyclogenesis of Bingiza Illustrations sur le cas de Bingiza (fvrier 2011) Early detection of the cyclogenesis of Bingiza Illustrations sur le cas de Bingiza (fvrier 2011) Early detection of the cyclogenesis of Bingiza Website of RSMC La Runion Official RSMC forecast Uncertainty cone (derived from EPS) Track forecasts from deterministic models SWFDP website of RSMC La Runion Regions where strong winds and heavy rainfall may occur are indicated in RSMC bulletins before landfall More diagnoses are available in the SWFDP/RSMC La Runion website Probabilities of attaining a threshold : Wind: 28kts, 48kts, 64kts, 90kts Rainfall: 5mm/6h, 10mm/6h, 20mm/6h, 50mm/6h, 5mm/24h, 20mm/24h, 50mm/24h, 100mm/24h Website of RSMC La Runion Official RSMC forecast Uncertainty cone (derived from EPS) Track forecasts from deterministic models Bingiza, before landfall Website of RSMC La Runion Bingiza, before landfall Website of RSMC La Runion Bingiza, before landfall Website of RSMC La Runion Bingiza, before landfall SWFDP website of RSMC La Runion Quantiles 25%, 50%, 75%, 90%: ~ a realistic extreme value the median value 90% 50% 75% 25% SWFDP website of RSMC La Runion the median value a realistic extreme value Conclusion Ensemble prediction products are now a major input at RSMC La Runion, especially for: Track prediction Impacts: waves, rainfall, winds Two main paths for using ensembles: Confirm the RSMC scenario, or propose alternative scenarios Convey some information about uncertainty in the bulletins Future R&D activities: Comparison of the ensemble track forecasts from different ensembles, and from multi-ensemble Calibration of the ensemble forecasts for wind and precipitation A major difficulty: verification and calibration of ensemble forecasts where the observations are sparse