1. Conditional verification of all COSMO countries: first results
COSMO General Meeting, September 2012, Lugano
by the members of WG Verification

2. Objectives of conditional verification
Contribute to COSMO model development
Improve the understanding of forecast errors
Identify possible sources of errors in COSMO
Contribute to guidelines on how to use COSMO forecasts

3. Feedback loop within COSMO
operational application and development
Conditional verification
Physical aspects (atmosphere and soil)
Data assimilation
Predictability and EPS

4. Common conditional verification results
Cloud cover clearly stratifies the COSMO forecast error of T2M (no matter which diagnostic)
Under observed clear sky conditions, the mean error has a pronounced daily cycle:
All models underestimate daytime T2M and overestimate nighttime T2M
Under observed overcast conditions, this behaviour is not observed
T2M for overcast conditions
T2M for clear sky conditions
RMSE ME
Autumn 2011, ME and RMSE for many COSMO models
Spring 2012, ME and RMSE for many COSMO models

5. Searching for clues…
Additional stratification to look at cases with stable boundary layer → distinguish between dynamical and radiation dominated processes

6. COSMO-ME Conditional Verification T2m when observed TCC ≤ 25%6

7. COSMO-ME Conditional Verification T2m when observed TCC ≤ 25% & wind speed ≤ 2 m/s7

8. COSMO-7 Conditional Verification T2m when forecast TCC ≤ 25%8

9. COSMO-7 Conditional Verification T2m when forecast TCC ≤ 25% & wind speed ≤ 2.5 m/s9

10. Searching for clues…
Additional stratification to look at cases with stable boundary layer → distinguish between dynamical and radiation dominated processes
→ in calm wind conditions, the underestimation of the daily temperature amplitude is even more pronounced→ overestimated thermal mixing (minimal diffusion coefficient?)
Nighttime overestimation from insufficient radiative cooling? Thermal conductivity of the soil?
Daytime underestimation from underestimated sensible heat flux? Impact of soil moisture?

11. T2M with fcst soil moisture condition
ME, RMSE dry conditions
ME, RMSE wet conditions
ME, RMSE no conditions
Similar systematic error properties as for cloud cover conditions, but phase slightly shifted and different data sample → new box opened…
Similar systematic error properties but different data sample

12. WG5 COSMO General Meeting, Lugano 2012
Precipitation with CAPE conditions
ETS
Unstable (CAPE>=50J/kg)
Stable (CAPE<50J/kg)
FBI
Overestimation of preci amount for lower thresholds in high CAPE cases and ETS a bit reduced
WG5 COSMO General Meeting, Lugano 2012

13. Precipitation with CAPE conditions
FBI
Unstable (CAPE>=50J/kg)
Stable (CAPE<50J/kg)
ETS

14. Precipitation with CAPE
FBI
Higher performance (in terms of ETS, also POD) in stable conditions, but CAPE condition displaced
→ find appropriate time period, other condition
Unstable (CAPE>=50J/kg)
Stable (CAPE<50J/kg)
ETS

15. Conclusions
Conditional verification provides us with tools for analysing rather complex COSMO model errors
Use of intensive measurement sites (e.g. sensible and latent fluxes for clear sky temperature error, soil moisture for temperature and dewpoint error) and radiosoundings
Identify suitable stratifications for precipitation (e.g. appropriate time integrations for CAPE or convective time scale, so far no success with weather classes)
Tight interaction with WG3 and others

17. CAPE>50
CAPE<50
Very high POD values for unstable conditions, FAR not so different
WG5 COSMO General Meeting, Lugano 2012

18. WG5 COSMO General Meeting, Lugano 2012
2mT, Td with dry or wet soil conditions
Fall
Winter
Spring
W_SO Water content of first soil layer(kg/m2) 1cm.
Td: Higher error in dry soil and larger underestimation
2mT: Higher error in wet soil and larger understimation
Fall
Winter
Spring
WG5 COSMO General Meeting, Lugano 2012

19. TD2M with soil moisture condition
ME, RMSE dry conditions
ME, RMSE wet conditions
ME, RMSE no conditions

20. FF10M with grid point height
ME, RMSE all stations
ME, RMSE gp height > 800m
ME, RMSE gp height < 800m

21. FF10M scatter plot
Winter 2012