Experience in numerical forecast verification in the Hydrometeorological Centre of Russia

1. COSMO 7th General Meeting Experience in numerical forecast verification in the Hydrometeorological Centre of Russia N. P. Shakina, E. N. Skriptunova, A. R. Ivanova Zürich 2005

2. The models: • SM HMC, global spectral model (T85L31) of Hydrometeorological Centre of Russia • SLM HMC, global semi-Lagrangian model, resolution 0.9x0.720, 28 σ-levels to 6 hPa • UKMO global model • The objective analyses: • OA UKMO, 2.5x2.5o • OA HMC, operative version, 2.5x2.5o and 1.25x1.25o • OA DAS (Data Assimilation System) of SLM HMC

3. WMO/ICAO standards for high levels: Wind speed, error above 250 hPa  10 m/s in 90 % of points Wind speed, error below 250 hPa  7 m/s in 90 % of points Wind direction error  30 deg in 90 % of points Max wind level height, error  600 m in 70 % of points Tropopause height error  600 m in 70 % of points Convective cloud top error  600 m in 70 % of points

4. Standard estimates of 250 hPa wind speed forecasts (Laboratory for Forecast Testing), August 2005, 00 UTC, Northern Hemisphere

5. Maximum wind and jet streams(standard approach) From the cubic spline approximation of wind component profiles, maximum wind speed, MW, and level, H(MW), are determined in the gridpoints

6. Percentage of ICAO standards fulfilled for maximum wind speed by the SLM, SM and UKMO models, with different OA taken as “fact”:1-10 Oct 2004, 24-h projection

7. Distribution of maximum wind speed 24-h forecast biases for SLM, SM HMC and UKMO models along the maximum wind speed spectrum, 1-10 Oct 2004

8. Typical profiles of temperature and PV in the troposphere and lower stratosphere temperature potential vorticity

9. Comparison of thermal and dynamic tropopause 24-h forecasting bias, hPa (UKMO model), with respect to the corresponding UKMO objective analysis, 1-10 Oct 2004

10. Distribution of the tropopause height bias as dependent on the tropopause height; 1-10 July and 1-10 October 2004

11. Percentage of ICAO standard fulfilled for the tropopause height predicted by SM HMC and UKMO model Note: the UKMO thermal tropopause 24-h forecasts meet the ICAO standard in 77% of the gridpoints (100W-0-1800E-1600W, 35-750N)

12. Characteristics of convective instability • Level of neutral buoyancy, LNB, km • Convective available potential energy, CAPE, J • Convective inhibition energy, CIN, J • Free convection level, FC, km • Downdraft origination level, DOL, km

13. Comparison of LNB>2km 24-h forecasts against different objective analyses taken as ‘facts”, 1-10 October 2004

14. Comparison of LNB>2km 24-h forecasts against different objective analysis taken as ‘facts”, 1-10 October 2004 (cont.)

15. Comparison of LNB>2km 24-h forecasts against different objective analyses taken as ‘facts”, 1-10 October 2004 (cont.)

16. CONCLUSIONS: • Apart from standard criteria of numerical forecast accuracy, we estimate regularly (10 days in central month of seasons) accuracy of predicted quantities obtained from the model output data by means of post-processing. • The quantities under consideration represent cumulative characteristics of • wind profile (maximum wind), • wind and temperature profiles and horizontal distributions (potential vorticity), • temperature and humidity profiles (convective instability).

17. CONCLUSIONS (cont.): • 3. Accuracies of maximum wind, tropopause, convection (and others) are estimated for the operatively used global models (SM HMC, SLM HMC, UKMO model) and for the corresponding objective analyses. • 4. The results allow us revealing and quantitatively estimating features of models and their DAS which are not clearly seen from the standard accuracy criteria, for example: • spectral dependence of wind errors; • smoothing of tropopause funnels and domes by the models; • “diurnal cycle” of convective instability

18. CONCLUSIONS (cont.): • 5. Analysis of these effects, in a close co-operation with the model developers and with numerical forecast users (aviation forecasters) leads to better interpretation of the numerical forecasts and to improving of the models.

19. Thank you!