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Daniela Rezacova, Zbynek Sokol IAP ASCR, Prague, Czech Republic

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Presentation on theme: "Daniela Rezacova, Zbynek Sokol IAP ASCR, Prague, Czech Republic"— Presentation transcript:

1 Daniela Rezacova, Zbynek Sokol IAP ASCR, Prague, Czech Republic
THE USE OF RADAR DATA IN THE VERIFICATION OF A HIGH RESOLUTION QUANTITATIVE FORECAST OF CONVECTIVE PRECIPITATION 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/or QPF 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 Convective event 150702 15/7/2002 multicellular convection
15-17UTC: convective storm in nearly steady position,  daily max. ground rainfall 171 mm local flash flood, local damage 14 – 19 UTC

5 Rainfall UTC R+G  LLM, SLM 

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

7 LLM  SLM rainfall Comparison of LLM and SLM produced QPF
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 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. f.A f.B „True“ (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} to the 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
LLM std [mm] R+G : 5.19 LLM : 6.75 <4.93, 10.05> SLM : 5.70 <4.98, 6.63> SLM


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