1. Meteorologisk Institutt met.no Operational atmosphere models at met.no Status and future directions Jon Albretsen, Jørn Kristiansen and Morten Køltzow OPNet meeting, Highland,

2. Meteorologisk Institutt met.no The main models at met.no: HIRLAM20/HIRLAM10 Ocean models UM Other HIRLAM based systems and set-ups STATUS October 2007 What about the future?

3. Meteorologisk Institutt met.no The HIRLAM domains

4. Meteorologisk Institutt met.no Main model: HIRLAM20 Version: 6.4.2 Horizontal resolution: 0.2dg, 40 vertical levels Forecast start: 00,06,12,18 UTC Forecast length: +60h Generation of Initial field: 3D-var + indep. surface analysis Lateral boundaries: ECMWF forecasts Why HIRLAM20: –Cover areas where met.no has forecasting responsibility –give high quality forecasts –provide forcing data to other met.no models

5. Meteorologisk Institutt met.no Main model: HIRLAM10 Version: 6.4.2 Horizontal resolution: 0.1dg, 40 vertical levels Forecast start: 00,12 UTC Forecast range: +66t Generation of initial field: interpolation from H20 analysis Lateral boundaries: ECMWF forecasts Why HIRLAM10: –Finer horizontal resolution gives better quality on forecasts –Decrease the grid resolution ratio when nesting fine scale models –A smaller domain also allows high quality lateral boundaries (ECMWF) close to Norway

6. Meteorologisk Institutt met.no

8. The new main model set-up: HIRLAM12 (version 7.1.2) –12km resolution, 60 vertical levels –Forecast start: 00,06,12,18 UTC +60h –3D-var analyses, lateral boundaries from ECMWF –Identical domain as HIRLAM20 HIRLAM08 (version 7.1.2) –8km horizontal resolution, 60 vertical levels –Forecast start: 00,06,12,18 UTC +66h –3D-var analyses, lateral boundaries from ECMWF –Identical domain as HIRLAM10

9. Meteorologisk Institutt met.no New main model set-up and quality Summarized results for August and September 2007: MSLP: Day 1: similar in quality Day 2: H10/08 slightly better than H20/H12 After 48h: H08 shows less skill T2m: Less systematic error - with increasing resolution - with version 7.1.2 FF10m: Increased wind strength in new version.

10. Meteorologisk Institutt met.no More HIRLAM HIRLAM4 –Forecasts at 00 and 12 UTC NORLAMEPS –Forecasts at 18 UTC (HIRLAM20) R&D –Assimilation and surface analyses –NORLAMEPS –Coupling HIRLAM to a ocean wave model (WAM) –HARMONIE Hirlam Aladin Regional/Meso-scale Operational Nwp In Europe Non-hydrostatic (1-5km horizontal resolution)

11. Meteorologisk Institutt met.no Future plans within the HIRLAM co-operation HIRLAM –10km and coarser HARMONIE –ARPEGE/IFS (Cycle 32t2) –physical parameterization with ALADIN, ALARO, HIRLAM physics (HIRALD) or AROME physics –Non-hydrostatic – high resolution! –Available for operational use within 2009 –Focus on user friendliness

12. Meteorologisk Institutt met.no UM1; air quality prediction (AirQUIS) UM1; forecasting airport turbulence (Simra) Værnes=Værnes+Værnes UM4 (large domain) H20/H10 (H8) D+60 00,12UTC OPR and EXP UM1 (small domains)

13. Meteorologisk Institutt met.no UM4 operational status Delayed –Surface temperature cold bias in snow covered regions –Convection too active Operational status soon –The initial fields will probably improve with HIRLAM8 –Cold bias; work in progress (UKMO tiger team, met.no), improved snow scheme is introduced –Targeted diffusion of moisture may be a solution

14. Meteorologisk Institutt met.no UM1 “Hardangerbrua” showed good results compared to observations (met.no report 07/2006) “Western” showed realistic fields Slight improvement w.r.t. MM5 (met.no report 8/2007) But noisy and/or unrealistic temperature fields –related to the (~1km resol.) land use data (e.g. “grass cold, urban warm”)

15. Meteorologisk Institutt met.no UM: Plans possibilities priorities UM1: fewer but larger domains UM is easy to use, has a good user interface, several physics options, i.e. well suited for small projects like “Hardangerbrua” and “Western” Australia and South-Africa are, as Norway, part of the UM operational user group (meetings - science workshops) External data sources (ancillaries) can be included, e.g. land-use on 90m UM as a stand alone model system UM data assimilation

16. Meteorologisk Institutt met.no All models and set-ups are important to cover all possible needs in daily production of skillful forecasts at met.no: HIRLAM20/10 shows high skill for MSLP in areas covered by the met.no forecast responsibility UM shows high skill on wind (mountain, coast) HIRLAM shows good quality in forecasting temperature Increased resolution in HIRLAM shows less systematic errors in temperature UM shows realistic patterns for topography steered and convective precipitation HIRLAM is important in forecasting Polar Lows Atmospheric, ocean and wave models covering coastal areas and adjacent seas are in particular important for search-and-rescue and oil-drift forecasting High quality forecasts on wind and MSLP are important for accurate predictions of sea level HIRLAM20/10 data is used as driving data for several model set ups

17. Meteorologisk Institutt met.no ECMWF Deterministic forecast Ensemble Prediction System Monthly forecast Seasonal forecast T799 L91T399 L62T159 L62 Approx. 20kmApprox. 40km1.125 deg 10 days10 days (15)32 days6 months Twice daily Once a weekOnce a month 1 member51 members Analysis in IFS: 4DVar Ocean model coupled in monthly/seasonal forecasts: HOPE (Hamburg Ocean Primitive Equation Model from MPI) Hor. resolution lower in extratropics and higher in equatorial region 29 levels in the vertical

18. Meteorologisk Institutt met.no Common Models & Methods in R&D and Operational Use Important aspects –Easy to use documentation, implementation, modification –Well known data formats (I/O) netCDF, GRIB –Associated graphical tools DIANA, GrADS, MetView –Associated verification/analysis procedures –CPU efficient (at least potentially) CPU resources for R&D are limited –International and national collaboration –Results of high scientific quality Synergy –High level of expertise –Enhanced problem solving –Leading role in projects –Attracts high quality staff

19. Meteorologisk Institutt met.no

20. Ocean forecasting The following applications generate forecasts today: WAM-50km: waves: 4#/day WAM-10km: waves: 2#/day SWAN-500m: waves (Trondheim fjord): 2#/day Stormsurge-20km: sea level from surge: 2#/day Arctic-20km: sea level from surge, currents, hydrography, sea ice: 1#/day Nordic-4km: total sea level, currents, hydrography: 2#/day Nordic-4km_ noatm : sea level from tides: 2#/day NseaSkag-1.5km: total sea level, currents, hydrography: 1#/day Oslofjord-300m: total sea level, currents, hydrography: 1#/day Westcoast-200m: total sea level, currents, hydrography: 1#/day Ofotfjord-500m: total sea level, currents, hydrography: 1#/day NorthSea-20km: total sea level, currents, hydrography, biogeoche.: 1#/day NorthSea-4km: total sea level, currents, hydrography, biogeoche.: 1#/day

21. Meteorologisk Institutt met.no Ocean forecasting The list may be replaced by the following applications: WAM-10km: waves SWAN-500m: waves (Trondheim fjord) + several small domains Arctic-20km: total sea level, currents, hydrography, sea ice, biogeoche. Nordic-4km: total sea level, currents, hydrography, sea ice, biogeoche. (Nordic-4km_noatm: sea level from tides) NseaSkag-1.5km: total sea level, currents, hydrography + more 1.5km domains (Barent Sea) Oslofjord-300m: total sea level, currents, hydrography Westcoast-200m: total sea level, currents, hydrography Ofotfjord-500m: total sea level, currents, hydrography + several small domains MIPOM is the ocean model used operationally and is planned to be substituted by ROMS Parallel operational runs with MIPOM and ROMS are necessary The TOPAZ system (HYCOM and EnKF) will be run operationally from 2008 (MERSEA)

22. Meteorologisk Institutt met.no Validation of ocean forecasts Examples of ongoing validation of results from the Arctic-20km model: Operational validation of: Wave height (buoys) Sea level (deterministic and EPS) SST (OSISAF) Sea Ice conc. (OSISAF) Ice drift (buoys) Examples of ongoing validation of results from the WAM-10km model:

23. Meteorologisk Institutt met.no Why substitute MIPOM with ROMS Current speed PDFCurrent direction PDF Comparison between current measurements and model results:

24. Meteorologisk Institutt met.no Why substitute MIPOM with ROMS Why stick to MIPOM: Well known at met.no Well adjusted for Nordic seas (validates well in many aspects) Relatively inexpensive computationally Why switch to ROMS: MIPOM is unsupported by other agencies A community model with developers based at the Rutgers University (and with several contributors) Main ocean model at IMR, already co-operating within several projects More advanced numerics Large potential for coupling to atmospheric, wave and biogeochemical models

25. Meteorologisk Institutt met.no UKMO – met.no, experiences UKMO collaboration group (as of spring 2007) –George Pankiewicz - External collaboration manager –Glenn Greed - External collaboration support scientist –Both in Exeter Improved contact with the UKMO –Exeter based instead of Reading –Glenn is both a problem solver and contact person –Formalized research plan –UKMO eager to solve problems and develop the model –We have identified problems unknown to UMKO –International collaboration Challenges –UKMO not very keen on revealing all the (past and present) problems –met.no uses a different supercomputer (IBM) than UKMO (NEC) –The code management is confusing (a structure change is planed, though)

26. Meteorologisk Institutt met.no - air quality forecasts from UM alone, i.e. not AirQUIS - one way coupling with fine scale ocean models (Vestfjorden, Trondheimsleia, Vestlandet og Oslofjorden) - interest from aviation meteorologists

27. Meteorologisk Institutt met.no What about the future? 1) Can one model/model system cover all our needs? –HIRLAM/HARMONIE, UM, WRF, … –Pros: Easier maintenance and less technical work Resources can be allocated to meteorological improvements The system contains models suited for different scales and with somewhat different qualities –Cons: The system may contain errors present in all models in the system Do we get more vulnerable? –Only knowledge of one model –What if the collaboration fails? Operational use vs R&D “Short time” vs “long time” (cf. Øyeblikkets tyranni, Thomas Hylland Eriksen)

28. Meteorologisk Institutt met.no What about the future? 2) Multi-model approach? –HIRLAM/HARMONIE & UM & WRF & ….. Pros: –More realistic to get high quality forecasts for all parameters –Increased knowledge about model uncertainties –Not dependent on one model, and one international collaboration –Heideman et al. (1993): for an individual forecaster “the relation between information and skill in forecasting weather is complex (…) greater improvement in forecasting might be obtained be devoting resources to improving the use of information over and above those needed to increase the amount of information” –However, predictions are generally improved by utilizing more than one (subjective/forecaster or objective/model) decision-making system! Cons: –More resources are used to maintenance and technical work, less resources available for meteorological improvements. –High dependency on key personnel (#persons/#models, where #persons=const.)