HIRLAM-A Verification Xiaohua Yang

1. HIRLAM-A VerificationXiaohua Yang with contributions from Kees Kok, Sami Niemela, Sander Tijm, Bent Sass, Niels W. Nilsen, Flemming Vejen 29th EWGLAM Meeting

2. Challenges in Meso-scale Verification • Increasing needs of new methods for routine meso-scale verification • Large number of routine HIRLAM runs at 5 km resolution • Almost all HIRLAM services now runs real-time 2.5 km HARMONIE • In lack of mature method, currently routine verification are made with traditional tools, thus mainly of monitoring nature • Traditional (point or event based) verification remains to have value for some parameters, such as mslp, W10m, T2m; but no more applicable for precipitation • Increasingly difficult with verification of high resolution model, especially for precipitation • Model resolution now better than synoptic network • Rich amount of asynoptic data but not in routine verification • Only limited predictability for km scale convective events Meso-scale verification requires use of asynoptic observations and thereby new method

3. Need for use of asynoptic information • Example of a very local, strongly convective case along Danish-German border in the evening of August 20, with a life time of 1-2 hours, with dramatic scenes seldom seen in Scandinavia… …

5. No trace to suggest the dramatic event from the synoptic obs-network!

6. Estimated total precipitation from radar data (Flemming Vejen, DMI)

7. Gauge vs radar retrieval rain(Flemming Vejen, DMI)

8. Radar simulator 3D prognostic: TEMPERATURE HUMIDITY CLOUD CONDENSATE RAIN SNOW (GRAUPEL) Radar reflectivity (dBZ) from the model COMPARISON WITH OBSERVED dBZ IN OBSERAVTION SPACE! Simulated radar data for verification of AROME forecast (Sami Niemela, FMI) Radar simulator (Haase and Crewell 2000)

9. Meso-scale Convective Systems + 13 h AROME OBSERVATIONS

10. Reflectivity Frequency Distribution Areas of strong precipitation overestimated Large hails detected

11. Frontal and convective rain + 9 h AROME OBSERVATIONS

12. Strong convection case Completely missed by AROME... and the host model (HIRLAM - RCR)!

13. Simulated satellite dataIn verification and process studies(Zingerler, FMI) RTTOV 8.5 is used to derive from model data clear/cloud multi-level infrared, to be compared to upscaled satellite data Entity based verification (Ebert & McBride, 2000) • Overcome double penalty dilemma • Error decomposition MSEtot = MSEdispl + MSEvol + MSEpat

14. Assessing Model Predictive Potential with Statistical Post-Processing(Kok, Schereur, Vogelezang) Model1 (LRM) Model2 (HRM) DMO verification potential predictors statistical post-processing Probablistic Forecast equations Probablistic verification

15. Illustration in Comparative Verification • Source: 200602-200707 ECMWF Operational vs EPS Control 12 UTC forecasts for 3 hourly accumulated precipitation (+3, +6, … ,+72) • Oper (HR, N400, 0.225°) vs control (LR, N200, 0.450° ) • Probabilistic information is extracted from these data using MOS, and verified in parallel to DMO, for precipitation data at station De Bilt. This provide an added measure of predictive potential of a model.

16. DMO verification: HR performs worse than LR

17. Potential Predictors in MOS Evaluation * central grid point value * extent of rain area, distance to rain area on different sized neighbourhoods around central station: (25km, 50km, 100km, .., 250km): * mean and maximum precipitation * fraction of grid-points with precip distance-weighted predictors: * maximum precip. weighted with distance

18. Probablistic verification using post-processed model output: No significant difference between HR ad LR

20. Summaries • Fine resolution modelling at 2-5 km scales are now wide-spread in HIRLAM services. Traditional verification methods based on point verification is insufficient to verify convective-scale events. • To verify model results that has higher resolution than synoptic network • To retrieve and compare to asynoptic data • Precipitation verification are focus in several ongoing development for new verification method. In view of limited predictability in short range, use of probablistic approach for deterministic model seems logical • Meso-scale verification has still many development potential. Hirlam has benefited greatly from pioneering efforts in other consortia on meso-scale verification, and look forward to continue collaboration on this area

21. Recent R & D on Verification in HIRLAM-A Synoptic HIRLAM (5-20 km) • Common verification for operational HIRLAM and harmonisation of verification package for HIRLAMD and HARMONIE • Verification using observed and simulated satellite data (Zingerler) • Entity based verification (Zingerler) • Inclusion of variance measure in model evaluation and interpretation of verification scores (Persson) Multi-model synoptic scale EPS (GLAMEPS, HIREPS) • BMA for probablistic forecast (Alkemade, Schreur, Kok) • Adaptation of ECMWF EPS post-processing and verification package for multi-model SREPS (INM) • NORLAMEPS post-processing and verification package (met.no) • Upscaling of model precipitation for comparison with gauge data HARMONIE system (2-10 km, AROME, ALADIN, ALARO, HIRALD) • Joint meso-scale verification working group on sanity check for model physics • Verification using observed and simulated Radar data (Niemela) • Calibrated precipitation retrieval from radar data (SMHI, DMI…) • Statistical post-processing and verification of deterministic forecast with probablistic approach (Kok, Scherur, Vogelezang)