1. The use of the NWCSAF High Resolution Wind product in the mesoscale AROME model at the Hungarian Meteorological Service Máté Mile, Mária Putsay and Márta Diószeghy (mile.m@met.hu) Hungarian Meteorological Service NWC SAF 2015 Users’ Workshop, 24-26 February 2015, Madrid NWC SAF 2015 Users’ Workshop, 24-26 February 2015, Madrid

2. Outline AROME forecasts AROME data assimilation (DA) system Assimilation of HRW AMV Observations from NWCSAF package Observations through AROME DA system Results Conclusion and Future plans 2. NWC SAF user's workshop

3. AROME at Hungary Non-hydrostatical mesoscale model It runs at the Hungarian Meteorological Service 2.5 km horizontal resolution (500*320 points) 60 vertical model levels Four production runs a day: – 00 UTC (48h); – 06 UTC (39h); – 12 UTC (48h); – 18 UTC (39h) Initial conditions: from AROME/DA 3h Rapid Update Cycle Own data assimilation system data assimilation in every 3 hours Lateral Boundary conditions: from ECMWF/IFS with 1h coupling frequency (in every hours) 3. NWC SAF user's workshop

4. AROME DA system 4. NWC SAF user's workshop SYNOPZT2RH2U RadiosondeZTQU AIREP (AMDAR)TU - 3h Rapid Update Cycle Data assimilation 8 times a day 3DVAR analyses - No surface assimilation for soil parameters (surface updated from 8 km ALADIN soil analysis and previous AROME runs) - Background error statistics based on ensemble DA approach - In the operational version only conventional observations are assimilated Altitude, temperature, moisture, wind

5. 4. NWC SAF user's workshop SYNOP AMDAR Example of ‘data – density’ to assimilate

6. 6. NWC SAF user's workshop HRW vectors were calculated with the SAFNWC/MSGv2013 program package Some modifications in the (safnwc_pge09.cfm) configuration file: BUFR format for assimilation EUM Channels: HRV, VIS0.8, IR10.8, WV6.2, WV7.3 Not modified: WIND_GUESS = 0 Output filtering QI_THRESHOLD = 70% All HRW vectors, 7 July 2014 07:25 UTC HRW vectors from IR10.8, 03 August 2014 12 UTC HRW vectors product for NWCSAF, observations for data assimilation

7. HRW AMV observations (through AROME DA system) 7. NWC SAF user's workshop WV6.2 IR10.8 Number of AMV vectors as a function of the Quality Index in the AROME area in the period of 01-10 August 2014 (Counting only 20 analyses, at 00 and 12 UTC) HRW provides much more observations than MPEF AMV ------ with HRW AMV ------ with MPEF AMV

8. HRW AMV observations (through AROME DA system) 8. NWC SAF user's workshop Number of AMV vectors at pressure levels (Counting 80 analyses) Standard deviation (STDV) of the satellite retrieved AMV minus AROME forecasted wind (Counting 80 analyses) ----- GEOqi70 – using MPEF AMV above 70% ----- HRWqi70 – using HRW AMV above 70% ----- HRWqi80 - using HRW AMV above 80% ----- HRWqi90 - using HRW AMV above 90%

9. HRW AMV observations (through AROME DA system) 9. NWC SAF user's workshop Distribution of HRW AMV on various levels in the AROME analysis 4 August 2014, 12 UTC

10. 10. NWC SAF user's workshop RESULTS Two experiments were performed: AROME with HRW and AROME with MPEF AMV. Their analyses and the forecasts were compared with the operational AROME results.

11. Impact on analysis DFS computation: (Chapnik et al., 2006) Girard's method DFS = Trace(HK) Trace(HK) ≈ (y’ - y) T R -1 (Hx’ a - Hx a ) 10. NWC SAF user's workshop ZTD: zenith total delay, DOW: doppler wind DFS (Degree of Freedom for Signal) was computed to evaluate the impact of the observations on analysis, Just for one analysis (14 August 2014 12 UTC). Pre-preliminary results DFS characterises the impact of the observation type on the analyses. The impact depends on the number of the observations and on their accuracy. Absolute contribution of the HRW AMV is moderate. However, its relative importance is comparable to Radiosonde and AMDAR measurements!

12. Impact on analysis 11. NWC SAF user's workshop Merged classes

13. Verification results Impact on forecast ------ without AMVs ------ with MPEF AMV ------ with HRW 12. NWC SAF user's workshop Pre-preliminary results Impact study has been run on summer period from 01-18 August 2014 HRW used with QI > 90 %, pressure < 700 hPa Comparison of Operational AROME, Experimental AROME with MPEF AMV, Experimental AROME with HRW AMV. Traditional scores show rather neutral impact on T2m and wind10m. T2m RMSE-BIAS Wind10m RMSE-BIAS

14. Verification results / impact on the forecast T2m Wind10m 13. NWC SAF user's workshop RMSE of the forecast without AMV assimilation Minus RMSE of the forecast with HRW assimilation Positive difference indicates that the HRW assimilation had positive impact, that it improved the forecast.

15. Verification results/ impact on forecast Wind speed profile STDV-BIAS 14. NWC SAF user's workshop Mainly positive impact can be seen on the scores of wind speed profile The impact is not as neutral as for T2m or Wind10m ------ without AMVs ------ with MPEF AMV ------ with HRW

16. Verification results/ impact on forecast 14. NWC SAF user's workshop EDI SEDI mi a klbseg? Verified with rain gauge data Skill scores for precipitation are more promising EDI, SEDI: special skill scores for verification deterministic forecasts of rare binary events. Precipitation forecast is better with AMV assimilation than without. For light/moderate precipitation MPEF AMV seems to be more useful, for intense precipitation NWCSAF HRW was more useful. ------ without AMVs ------ with MPEF AMV ------ with HRW Prec12h EDI 12 hour accumulated precipitation Prec12h SEDI

17. Cases from the experiments (focusing on precipitation) - OPERHRW AMVRADARMPEF AMV 15. NWC SAF user's workshop 3h accumulated precipitation forecasts compared with the (corrected) radar data The experiment using HRW AMV gave the best forecast 6h AROME forecasts started from 6 August 2014

18. OPERHRW AMVRADARMPEF AMV RADAR at analysis 16. NWC SAF user's workshop Cases from the experiments (focusing on precipitation) 3h accumulated precipitation forecasts compared with the (corrected) radar data The experiment using HRW AMV gave the best forecast 6h AROME forecasts started from 7 August 2014 The storm was in mature phase at the time of the analyses (00 UTC) then it dissipated. The forecasted precipitation field was the closest to the radar when HRW was assimilated.

19. 17. NWC SAF user's workshop Conclusions: The AROME 3 hourly DA system is able to use many HRW vectors even over a relatively small domain HRW provides big amount of data with good quality The impact of HRW on AROME analyses, forecasts is beneficial especially for precipitation Assimilation of HRW has moderate impact on the analyses. The traditional scores were neutral for the 2m T and 10 m wind speed forecast The traditional scores showed small but mainly positive impact on the wind profile forecast The scores and the precipitation case studies show positive impact on the precipitation forecast Plans: To study of the impact of the HRW vectors for each SEVIRI channels separately (this might help the backlisting) To modify HRW configuration file to get HRW vectors also with lower quality indexes and retrieved from more SEVIRI channels To verifiy the impact on different seasons (winter etc.) To perform the study for a longer time period and to evaluate more case studies.

20. Thank you for your attention! Thanks for AEMET (esp. Javier) and the Hungarian Satellite Group for helps Questions? (mile.m@met.hu)

21. Christopher A. T. Ferro and David B. Stephenson: Extremal Dependence Indices: improved verification measures for deterministic forecasts of rare binary events. National Centre for Atmospheric Science, University of Exeter, Exeter, United Kingdom Extremal Dependence Index Symmetric Extremal Dependence Index Where H hit rate, F false alarm rate