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QPF verification of the 4 model versions at 7 km res. (COSMO-I7, COSMO-7, COSMO-EU, COSMO-ME) with the 2 model versions at 2.8 km res. (COSMO- I2, COSMO-IT)

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Presentation on theme: "QPF verification of the 4 model versions at 7 km res. (COSMO-I7, COSMO-7, COSMO-EU, COSMO-ME) with the 2 model versions at 2.8 km res. (COSMO- I2, COSMO-IT)"— Presentation transcript:

1 QPF verification of the 4 model versions at 7 km res. (COSMO-I7, COSMO-7, COSMO-EU, COSMO-ME) with the 2 model versions at 2.8 km res. (COSMO- I2, COSMO-IT) Specifications: Dataset: high resolution network of rain gauges coming from COSMO dataset and Civil Protection Department  1300 stations Method: 24h/6h averaged cumulated precipitation value over 90 meteo-hydrological basins Model selection: run 00UTC, D+1 Precipitation verification comparison in 2008/2009 among the several COSMO-Model versions (Elena Oberto, Massimo Milelli - ARPA Piemonte)

2 The aims Long period verification (seasonal trend) (from djf’04 to mam’09)  1.Statistical indices for low thres (0.2mm/24h) 2.Statistical indices for high thres (20mm/24h) Verification ovest last year ( )  1. Cosmo-7/Cosmo-EU comparison 2. Cosmo-I7/Cosmo-ME comparison 3. Driving model comparison: ecmwf/Cosmo-I7/Cosmo-I2 4. Driving model comparison: ecmwf/Cosmo-ME/Cosmo-IT 5. Diurnal cycle for all the model 6. Spatial error distribution (latest season: MAM’09)

3 % correctly forecasted not- events (specificity)

4 Seasonal trend (low thres) Bias reduction trend Seasonal cycle: big peak during summertime Biggest overestimation peak for cosmo-I7 Underestimation for cosmo-7 during latest seasons

5 Seasonal trend (low thres) Stable trend/slightly worsening in time Best performance during spring/summertime Worsening for Cosmo-7 during latest seasons

6 Seasonal trend (low thres) False alarm number reduction esp. in wintertime Worse performance during summertime, esp. for Cosmo-I7

7 Seasonal trend (low thres) Slightly improvement trend Seasonal cycle: better during moist seasons, worse during dry seasons

8 Seasonal trend (high thres) Bias reduction trend, at least during last year Seasonal cycle: big peak during spring-summertime (convective period)  seems to disappear during last summer (… why?) General good performance during last year Pronounced underestimation for Cosmo-7 during last seasons

9 Seasonal trend (high thres) Slightly improvement in time Worse performance during summertime (except 2007) Worsening for Cosmo-7 during last seasons

10 Seasonal trend (high thres) False alarm number reduction Worse performance during summertime

11 Seasonal trend (high thres) Slightly improvement trend Worse performance during summertime (except 2007)

12 Last year verif.: Cosmo-7/Cosmo-EU vs. thres Good performance for Cosmo-EU Underestimation for Cosmo-7 More correctly forecasted non-events for Cosmo-7

13 Last year verif.: Cosmo-7/Cosmo-EU fixed thres, seasonal Good performance for Cosmo-EU Underestimation for Cosmo-7 Positive trend for both the models

14 Last year verif.: Cosmo-I7/Cosmo-ME vs. thres Similar (good) performance: slightly better for Cosmo-ME

15 Last year verif.: Cosmo-I7/Cosmo-ME fixed thres, seasonal Similar (good) performance: slightly better for Cosmo-ME Positive trend for both the models

16 Driving model comp: Ecmwf/Cosmo-I7/Cosmo-I2 vs. thres Big gap between Ecmwf and Cosmo-model Cosmo-I7  slightly overestimation; Cosmo-I2  underestimation; Ecmwf  overest. for low thres/ underest. for high thres. I7 equivalent or slightly better then I2 (even if less false alarm for I2)

17 Driving model comp: ecmwf/Cosmo-I7/Cosmo-I2 fixed thres, seasonal Big gap between Ecmwf and Cosmo-model Positive trend for both I7 and I2 I2  Underestimation tendency I7 is generally better

18 Driving model comp: Ecmwf/Cosmo-ME/Cosmo-IT vs. thres Big gap between Ecmwf and Cosmo-model IT and ME –> quite similar, it is difficult to decide the winner IT  tendency to overestimation

19 Driving model comp: ecmwf/Cosmo-ME/Cosmo-IT fixed thres, seasonal Big gap between Ecmwf and Cosmo-model IT and ME –> quite similar, it is difficult to decide the winner IT  tendency to overestimation

20 Last year verif.: diurnal cycle for all the versions Low thresHigh thres Bias overestimation peak during midday Spin-up problem for all the models especially COSMO-I7 and COSMO-I2 General worsening with forecast time The spin-up seems to disappear, underestimation during the first 6h Bias overestimation peak during midday COSMO-7 underestimates In general: slight improvement with respect to the previous year

21 To sum up Long period verification (seasonal trend) (from djf’04 to mam’09)  1.General improvement trend 2.For low thres (rain/no rain): overestimation during spring/summertime with more probability of detection but also more false alarms 3.For high thres: the worse skills during spring/summertime (convective period) 4.General Cosmo-7 worsening during last year Verification over last year ( )  1.Good performance for Cosmo-EU 2.Similar performance Cosmo-I7/ Cosmo-ME (slightly better Cosmo-ME) 3. Cosmo-I7/ Cosmo-I2 comparison: similar skill, underestimation I2 4.Cosmo-ME/Cosmo-IT comparison: similar skill, overestimation IT 5.Diurnal cycle: bias overestimation peak during midday, in general slight improvement with respect to previous year

22 COSMO-7COSMO-ME COSMO-EU COSMO-I2COSMO-IT COSMO-I7 BIAS mm/24H

23 COSMO-7COSMO-MECOSMO-I7 Rel Error MAM ‘09 COSMO-EUCOSMO-IT COSMO-I2

24 COSMO-7COSMO-ME COSMO-I2 COSMO-I7 POD mm/24H COSMO-ITCOSMO-EU

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