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Daniela Rezacova, Zbynek Sokol IAP A S CR , Prague, Czech Republic

THE USE OF RADAR DATA IN THE VERIFICATION OF A HIGH RESOLUTION Q UANTITATIVE F ORECAST OF CONVECTIVE P RECIPITATION. Daniela Rezacova, Zbynek Sokol IAP A S CR , Prague, Czech Republic. MOTIVATION. convective storms  local heavy rainfalls  rapid hydro response  flash floods

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Daniela Rezacova, Zbynek Sokol IAP A S CR , Prague, Czech Republic

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  1. THE USE OF RADAR DATAIN THE VERIFICATIONOF A HIGH RESOLUTION QUANTITATIVEFORECASTOF CONVECTIVEPRECIPITATION Daniela Rezacova, Zbynek Sokol IAP ASCR, Prague, Czech Republic

  2. MOTIVATION • convective storms  local heavy rainfalls  rapid hydro response  flash floods • QPF/warning  difficult • operational NWP models x ~ the order of 1km~ radar resolution • assessment of the QPF accuracy and/orQPF uncertainty • model improvement • interpretation of the forecast

  3. Outline • LM COSMO • LLM : 231x175 g.p., 11km 00UTC+24h, init.con. ECMWF • SLM: 251x191 g.p., 2.8 km 06UTC+18h, init.con. LLM • A cluster of 9 forecasts – shifting the LLM init. fields • CZRAD-CHMI, 2 radars • Gauge adjustment • A verification of QPF{SLM} • local flash flood storm SLM: 703x535 km Verification domain 165x95 g.p. (462x266 km)

  4. 14 – 19 UTC Convective event 150702 • 15/7/2002multicellular convection • 15-17UTC: convective storm in nearly steady position, •  daily max. ground rainfall171mm • local flash flood, local damage

  5. R+G  LLM, SLM  Rainfall 1507 06-18UTC

  6. Modification of LLM initial conditions • Shift of LLM initial conditions • 4 cardial directions • 0.5°, 1.0° ( 50, 100 km) • 9 LLM + SLM forecasts AT500 at 12UTC NS shifts – upper fig. WE shifts – lower fig. AT500 + AT850 in the extend.abstract.

  7. Basic structures similar • SLM - finer area structure of convective rainfall • linear shift implies a nonlinear QPF change LLM  SLM rainfall • Comparison of LLM and SLM produced QPF • Shifts in NS and WE directions • Accumulated Precipitation 00UTC-18UTC • Domain with AP>15 mm

  8. f.B f.A „True“ QPF verification by R+G • R+G prec.  SLM g.p. forecast prec. • Contingency table (Rez. et al. 2004, 2005) • Area related RMSE (N, [x.y]) • Precipitation over a square of N*N g.p. centered in each g.p. (A) =  (B) =  (True) preference to f.A over f.B RMSE (N,[x,y]) = RMSE (F, T) F  {fi},, f1  f2 .. fN*N , T  {ti},, t1  t2 .. tN*N

  9. RMSE - modified inputs area element: 11x11 g.p. 30.8x30.8 km (+) R+G > F (-) R+G < F • forecast underestimation in the storm position • overestimation east from storm position • the “best” forecast produced after 0.5S shift

  10. RMSE - modified inputs

  11. mean RMSE relative to mean R+G • horizontal axis: length of the square side [g.p.] / [km] • mean prec. [mm] • R+G : 1.98 • LLM : 2.68 • <2.18, 4.14> • SLM : 2.70 • <2.10, 3.42> LLM SLM

  12. CONCLUSION-OUTLOOK • Preference of the QPF{SLM} tothe QPF{LLM} • Proposed verification technique (i) is simple, (ii) agrees with the verification “by eye”, and (iii) takes into account the area extent (scale). • The creation of QPF cluster (“ensemble”) by shifting the init.cond. proved to be a useful technique to study the QPF uncertainty. • Outlook: probabilistic forecast (using 2,3) Acknowledgement: DWD (LM COSMO), CHMI (radar data), ECMWF (analyses), COST717

  13. Thank you

  14. 130702: mean 6.12, std 7.69 LLM; mean LLM; std SLM; std SLM; mean

  15. mean RMSE relative to std R+G • std [mm] • R+G : 5.19 • LLM : 6.75 • <4.93, 10.05> • SLM :5.70 • <4.98, 6.63> LLM SLM

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