1. Overview of data impact studies within the HIRLAM community Nils Gustafsson and Harald Schyberg with contributions from Bjarne Amstrup, Carlos Geijo, Xiang-Yu Huang, Magnus Lindskog, Kirsti Salonen, Martin Stengel, Vibeke W. Thyness, Henrik Vedel, John de Vries, Xiaohua Yang

2. The HIRLAM A program Participants: Denmark, Finland, Iceland, Ireland, Netherlands, Norway, Spain, Sweden + France 5 year period 2006 - 2010 3 general targets: (1) Improved synoptic scale forecasting system (10 km); (2) Mesoscale forecasting system (a few km); (3) Probabilistic forecasting system Development of mesoscale forecasting system in collaboration with the ALADIN community

3. The HIRLAM synoptic scale forecasting system Hydrostatic gridpoint model Semi-implicit, semi-Lagrangian, 2-time level scheme Physics: ISBA surface, CBR turbulence, Rasch- Kristjansson & Kain-Fritch condensation and convection, Savijärvi radiation 3D-Var and 4D-Var OI surface and soil assimilation Applied at 5 – 20 km horizontal resolution A “reference” model version being continuously updated and tested

4. HIRLAM 3D-Var and 4D-Var TL and AD models based on the semi- Lagrangian, semi-implicit and spectral version of HIRLAM Statistical balance background constraint based spectral transforms, moisture included in the balance constraints Weak digital filter constraint Variational quality control

5. The HARMONIE mesoscale forecasting system Developed jointly with the ALADIN project Non-hydrostatic model Code is based on IFS 3D-Var; 4D-Var to be developed; Aim to base background error constraint on ensemble information Several physics packages are available Applied at 2 – 10 km horizontal resolution To replace HIRLAM also at synoptic scale resolutions (10 km) - 2011?

6. HIRLAM impact studies in connection with development of observation operators AMSU-A over sea AMSU-A over land and sea ice AMSU-B HIRS Scatterometer winds MODIS wind MODIS water vapour SEVIRI water vapour channel radiances Radar radial winds Radar VAD wind profiles Wind profilers GPS zenith delays GPS slant delays

7. Limitations of HIRLAM observation usage To some extent driven by externally funded research projects and by PhD projects; not by the need from the weather services to improve forecast quality Project have often been finished before operational implementation No instructions prepared for operational NWP groups on how to access the data and do the needed pre-processing and with the consequence Only AMSU-A data over sea are utilized by the reference HIRLAM system in addition to conventional observations (local implementations may have more)

8. Example: MODIS winds (Carlos Geijo)‏ Two one month (January and July 2006) impact studies with MODIS winds and AMSU-A radiances over sea. HIRLAM reference RCR domain Positive impact of MODIS winds in January 2006 and also of AMSU-A in January 2006, but not from the combination – not yet understood!

9. Example: GPS zenith delays From Vedel and Huang (2004)‏: Accumulated precipitation from 0 to 12h forecast time (case study) Several HIRLAM groups have carried out impact studies with ground-based GPS zenith delay data. Difficult to show impact with conventional forecast verification scores. Positive impact demonstated in individual precipitation forecasts The need for bias correction is an open question

10. Example: Radar radial winds Ten-day assimilation experiment: 1-10 December, 1999 Integration area and radar sites Observation fit statistics Verification of time-series of +24 h wind forecasts (against observations)‏

11. Some differences from global NWP setups Higher resolution model (and focus on shorter forecast ranges, other verification measures) Limited area: inflow of information from lateral boundaries - impact of obs. system decreases with forecast range less representative results than global systems for same period length Cutoff time shorter (some types of satellite data and some radiosondes arrive late) Uses less satellite data operationally (limited resources)  relative importance of radiosonde vs satellite larger  may change with development of assimilation scheme

12. HIRLAM impact studies for EUCOS; met.no Extra Studies performed at DMI and met.no, observation scenarios specified from EUCOS Two periods: December 2004-January 05 (storms passing Northern Europe), August 2005 HIRLAM 3D-Var with AMSU+Scatt+Meteosat AMV

13. Temps available (a typical analysis time)‏ Baseline CTR Bas + E-ASAPs (Rejections are due to scenario selection, but also due to thinning, QC or arrival after analysis cutoff)‏ Red=rejected

14. Results – all scenarios, winter period Control Scenario (all available in-situ observations)‏ Baseline scenario Add E-ASAPs Add AIREPs

15. Radiosondes: Add E-ASAP to baseline (incl Mike, Ekofisk, winter)‏ “Scandinavian Storms”

16. EUCOS studies met.no; conclusions Conventional observations have large positive impact in our system TEMPs dominating factor for analysis quality in precense of satellite data, wind more than temperature (but developments ongoing towards more use of satellite: AMSU over land, advanced sounders, …)‏ No significant effect of adding moisture information (could also be seen as an assimilation algorithm problem)‏ Aicraft data complement TEMPs (positive impact of adding aircraft in the presence of sondes), but to much larger degree in winter (by chance?)‏ Negative impact from EWP: revising QC and data selection did not help (more work needed?)‏ Significant positive impact from E-ASAP network (also excluding Mike+Ekofisk)‏

17. HIRLAM Comprehensive Impact studies (CIS) – basic ideas Try to advance the use of remote sensing data in HIRLAM through a few coordinated “Great Leaps” with participation from several HIRLAM groups Prepare the operational utilization of all types of new data in parallel with the impact studies; Data transmission and collection, data pre- processing, bias corrections etc. Provide instructions for the other national NWP groups

18. HIRLAM Atlantic scale CIS – model setup HIRLAM RCR domain HIRLAM reference 7.2 RCR domain 16 km hor. resolution 60 levels 4D-Var, 6 h assimilation window 48 km assimilation increrments

19. HIRLAM Atlantic scale CIS - Experiments BASELINE = HIRLAM reference = Radiosonde data + SYNOP + SHIP + DRIBU + AIREP + AMDAR + AMSU-A over sea ALLINCLUSIVE = BASELINE + AMSU-A over ice and land + AMSU-B over sea + QUICKSCATT winds + AMV GEO + AMV MODIS DENIAL 1 (exclude AMSU-A over ice and land from ALLINCLUSIVE)‏ DENIAL 2 (exclude AMSU-B over sea from ALLINCLUSIVE)‏ etc. CONVENTIONAL = BASELINE – AMSU-A over sea To be finished by Summer 2008!

20. HIRLAM Atlantic scale CIS - Forecast verification 1 BASELINE versus ALLINCLUSIVE Note: Impact at +48h is much stronger, but experiments where this was seen were not completely clean (slightly differing forecast models). Will be re-run in a clean way! Verification area: Europe

21. HIRLAM Atlantic scale CIS - Forecast verification 2 BASELINE versus ALLINCLUSIVE Area Europe

22. HIRLAM Atlantic scale CIS - Forecast verification 3 BASELINE versus ALLINCLUSIVE Verification area: UK and Ireland

23. Surface pressure forecast differences for one case of strong impact – 6 February 2007 12 UTC. Needs to be further analyzed, in particular with data denial experiments. +18 h+12 h +06 h+00 h

24. HIRLAM Summer time convection CIS Select a summer month, based on data availability (radar radial wind data)‏ European area with 10 km hor. resolution (5 km later)‏ Observations as in the Atlantic scale CIS + Radar radial winds + Groundbased GPS zenith delays + SEVIRI cloud-free water vapor radiance data (To be prepared during summer 2008 and to be run during autumn 2008)‏

25. Concluding remarks HIRLAM efforts have been quite advanced in development of observation operators for new types of remote sensing data and in impact studies with these data. Operational HIRLAM applications has not had sufficient benefit from these research and development efforts. A series of Comprehensive Impact Studies (CIS) has the ambition to change this – first results are promising! The HIRLAM community is on the move to the ECMWF IFS world – one main motivation is the advanced use of remote sensing data at ECMWF