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Operational atmosphere models at met.no Status and future directions

Operational atmosphere models at met.no Status and future directions. Jon Albretsen, Jørn Kristiansen and Morten Køltzow. OPNet meeting, Highland,. STATUS October 2007. The main models at met.no: HIRLAM20/HIRLAM10. What about the future?. Ocean models. UM.

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Operational atmosphere models at met.no Status and future directions

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  1. Operational atmosphere models at met.noStatus and future directions Jon Albretsen, Jørn Kristiansen and Morten Køltzow OPNet meeting, Highland,

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

  3. The HIRLAM domains

  4. 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. 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. 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

  7. 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.

  8. 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)

  9. 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

  10. UM4 (large domain) H20/H10 (H8) D+6000,12UTC OPR and EXPUM1 (small domains) UM1; air quality prediction (AirQUIS) UM1; forecasting airport turbulence (Simra) Værnes=Værnes+Værnes

  11. 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

  12. 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”)

  13. UM:Planspossibilitiespriorities • 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

  14. 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

  15. ECMWF • 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

  16. Common Models & Methods inR&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

  17. 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

  18. 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)

  19. Validation of ocean forecasts Examples of ongoing validation of results from the Arctic-20km model: Examples of ongoing validation of results from the WAM-10km model: • Operational validation of: • Wave height (buoys) • Sea level (deterministic and EPS) • SST (OSISAF) • Sea Ice conc. (OSISAF) • Ice drift (buoys)

  20. Why substitute MIPOM with ROMS Comparison between current measurements and model results: Current speed PDF Current direction PDF

  21. 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

  22. 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)

  23. - 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

  24. What about the future? • Operational use vs R&D“Short time” vs “long time” (cf. Øyeblikkets tyranni, Thomas Hylland Eriksen) 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?

  25. 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.)

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