A new operational convection-permitting NWP system for tropical cyclone forecasting in the SW Indian Ocean

Transcription

1 A new operational convection-permitting NWP system for tropical cyclone forecasting in the SW Indian Ocean Olivier BOUSQUET, Soline BIELLI, David BARBARY, Marie-Dominique LEROUX, Christelle BARTHE, Dominique MEKIES and Remy LEE Laboratoire de l Atmosphère et des Cyclones (LACy) ; Saint Denis de La Réunion, France Ghislain FAURE, Thibaut MONTMERLE and Pierre BROUSSEAU Centre National de Recherche Météorologiques (CNRM) ; Toulouse, France

2 Current NWP systems used in French overseas territories The French Weather Service provides its tropical overseas territories with forecast products from both global and regional NWP systems Global models : IFS (~16 km) and ARPEGE (~30 km) Regional LAM ALADIN is run with a 8 km horizontal resolution over 4 domains, DA) ALADIN CARIBBEANS ALADIN FRENCH POLYNESIA ALADIN REUNION ALADIN NEW CALEDONIA

3 Future NWP systems used in French overseas territories Within the next few years most major forecasting centers will upgrade their GCMs The horizontal resolution of IFS will be increased to 10 kilometers ALADIN NWP systems (8 km) should no longer be useful In 2016 all ALADIN models will be replaced by 5 high resolution models based on the operational model AROME used in mainland France since AROME overseas models AROME Indian Ocean

4 Main specifications of AROME «Overseas» models Domain sizes range from 1000 km to 3000 km Horizontal resolution of 2.5 km (static grid) 90 vertical levels LBCs provided by IFS model Two (72-H) or four (48-H) daily runs (still discussed depends on computational cost and model performance at medium range ) The initial conditions will either come from ECMWF s deterministic model, or from a limited area 3D-Var data assimilation cycle

5 AROME Indian Ocean : current (research) configuration 3000 km Domain of 3000 km x 1500 km Comoros Mayotte Madagascar Réunion Mauritius Rodrigues AROME IO is the prototype of all Overseas Arome systems 1500 km Mesoscale 3D-VAR assimilation scheme with 3-H assimilation cycles Coupled with a 1D mixed layer ocean model AROME IO is designed to improve short term TC forecasting over inhabited islands of the SWIO basin with particular emphasis on landfalling TCs

6 TEST CASE : BEJISA (2014, 04S) A stable version of AROME IO is available since Oct 2014 D ~80 km «pinhole eye», D ~10 km RI ( 65kt and 40 hpa in 15h) ERC Ascat 30/12/ Z 29/12/13, 1224Z, N18, DT 32 kt (60 km/h) BEJISA (04S) 30/12/13, 1213Z, N18, CTI 95 kt

7 TEST CASE : BEJISA (2014, 04S) Composite radar reflectivity (02 jan 8 UTC) 48h rainfall accumulation (01-03 jan) Maximum wind gusts Rain gauge data ~1000 mm of precipitation in 48h over high terrain ~ mm in coastal areas Max wind gusts (~180 km/h) recorded over high terrain, especially in the «volcano» area

8 TEST CASE : BEJISA (2014, 04S) 72-h model forecast (31/12 at 00 UTC)

9 TEST CASE : BEJISA (2014, 04S) Model analyses Intensity (mslp) VMAX and RMW RMW (km) AROME ALADIN-REUNION 40 hpa IFS BEST TRACK (RSMC) ERC VMAX (m/s) 30 Dec 31 Dec 1 Jan 2 Jan 30 Dec 31 Dec 1 Jan 2 Jan AROME does a better job than other models but is unable to capture the RI phase of the system (Béjisa entered AROME domain at the beginning of RI) Errors on analyzed VMAX are usually close to 0 except during RI (10 m/s) Errors on RMW are comprised between 0 and 10 km.

10 TEST CASE : BEJISA (2014, 04S) Model forecast Verification against best track (RSMC La Reunion) and IFS We consider 15 forecasts of 72H between 31 Dec and 5 Jan Assimilation cycle was started on 29 Dec 2014 Distribution of errors on intensity (mslp) 48H Forecast lead time 48H Forecast lead time AROME IO IFS 20 hpa Lead time (h) Lead time (h) Average error is comprised between 3 and 9 hpa at all lead times (Béjisa is an intense system) Errors are reduced by up to ~ 50% with respect to IFS AROME is more stable

11 TEST CASE : BEJISA (2014, 04S) Model forecast Verification against best track (RSMC La Reunion) and IFS AROME IO We consider 15 forecasts of 72H between 31 Dec and 5 Jan Distribution of errors on V MAX (m/s) 48H Forecast lead time 48H Forecast lead time IFS 10 m/s -10 m/s Lead time (h) Lead time (h) Mean error is comprised between 0 and 5 m/s during the first 24h Significant improvement over IFS in the first 36 to 48H

12 TEST CASE : BEJISA (2014, 04S) Model forecast We consider 15 forecasts of 72H between 31 Dec and 5 Jan Verification against best track (RSMC La Reunion) and IFS Distribution of errors on RMW (km) 48H Forecast lead time 48H Forecast lead time AROME IO IFS 20 km 20 km Lead time (h) Lead time (h) Mean error is comprised between 0 and 20 km at all lead times 50% improvement over IFS

13 TEST CASE : BEJISA (2014, 04S) Model forecast We consider 15 forecasts of 72H between 31 Dec and 5 Jan Verification against best track (RSMC La Reunion) and IFS AROME IO Distribution of track errors (km) 48H Forecast lead time 48H Forecast lead time IFS 40 km Lead time (h) Lead time (h) No improvement over IFS Mean track error of ~ 60 km after 48H The impact of high resolution modeling on trajectory forecast is neutral (trackers?)

14 IMPACT OF HIGH RESOLUTION ON WIND FORECAST Comparison between AROME and ALADIN-R ALADIN-R (8km) AROME IO (2.5 km) Wind forecast valid at 0 UTC, 2 Jan ~ 8 hours before closest point approach 24H forecast lead time Maximum wind gusts 24H forecast lead time Wind speed (m/s) ALADIN-R AROME IO The forecasted wind pattern over la Réunion is consistent with observations

15 QPF QPE (radar and raingauges) 24h rainfall accumulation (01-02 jan 2014 ; T+3 T+27) 500 mm (8km) (2.5 km) (2.5 km) NO DA DA (3DVAR) Analyse (3DVAR) 380 mm (mm) Amount and distribution of forecasted precipitation is very accurate DA helps achieving more intense/realistic rainfall forecast over La Réunion

16 QPF QPE (radar and raingauges) 24h rainfall accumulation (02-03 jan 2014 ; T+3 T+27) ~800 mm (8km) (2.5 km) NO DA 50 km DA (3DVAR) ~750 mm DA allows to mitigate the over-estimation of rainfall over La Réunion

17 Improve initial conditions of the model 2 main research areas NEXT Assimilation of cloudy radiance (done in Toulouse) Assimilation of Z and Vr radar data Colorado S-band Doppler radar ~2017 Piton villers S-band Doppler dual-polar. radar? 2015? Radar data assimilation effective in mainland France since 2010 Should be completed by mid in La Reunion

18 NEXT Improve initial conditions of the model Implement a 3D ocean model (NEMO)

19 NEXT Improve initial conditions of the model Implement a 3D ocean model and a wave model (~2 years) Get additionnal verification data Step 1: Reinforce RS network in SW Indian Ocean for at least 2 cyclonic seasons RS + GPS RS + GPS Oper RS RS + GPS RS+ GPS 2 proposals (French and regional funding agencies) under review

20 NEXT Improve initial conditions of the model Implement a 3D ocean model and a wave model (~2 years) Field experiment in South Indian Ocean and Mascarenes? Step 2? Field experiment (2018 or 2019) RS + GPS RS + GPS RS + GPS RS+ GPS

21 Thank you for your attention