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INTERCOMPARISON – HIRLAM vs. ARPA-SIM CARPE DIEM AREA 1

INTERCOMPARISON – HIRLAM vs. ARPA-SIM CARPE DIEM AREA 1. Per Kållberg Magnus Lindskog. what I want to show here. the HIRLAM system and experiments comparison of arpa and hirlam analyses verification of forecasts against own analyses verification of forecasts against observations

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INTERCOMPARISON – HIRLAM vs. ARPA-SIM CARPE DIEM AREA 1

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  1. INTERCOMPARISON – HIRLAM vs. ARPA-SIM CARPE DIEM AREA 1 Per Kållberg Magnus Lindskog

  2. what I want to show here the HIRLAM system and experiments comparison of arpa and hirlam analyses verification of forecasts against own analyses verification of forecasts against observations precipitation forecasts conclusions

  3. HIRLAM (HIgh Resolution Limited AreaModel) • 0.1º to 0.4° rotated lat/long grid • Hydrostatic, hybrid coordinates • Spectral (double Fourier with extension zone) • Or gridpoints on the C-grid • Eulerian or semi-Lagrangean time-stepping • Lateral boundary relaxation - usually ECMWF LBC • ISBA soil model • TKE (Turbulent Kinetic Energy) turbulence closure • Kain-Fritsch convection or ’STRACO’ condensation

  4. HIRVDA (HIRlam Variational Data Assimilation) • 3D-Var or 4D-Var • Multivariate statistical balance: vorticity - divergence - mass - moisture • Scale and latitude dependent geostrophy • Boundary layer friction • ’NMC’-method background error statistics • Ensemble assimilations to replace ’NMC’-method • Moisture effects with a revised moisture control variable • Initialization: normal modes or a weak digital filter • Observation operators include: • Conventional (TEMP PILOT AIREP DRIBU SYNOP SHIP SATOB) • Raw radiances (TOVS, ATOVS) • Integrated humidity from GPS • Radial winds from Doppler radars

  5. hirlam experiments – nov.3 to nov.8 cdc HIRLAM 6.1. 0.1°/ 0.1° 40 levels 3D-Var data assimilation digital filter initialization (DFI) ECMWF operational analyses on the boundaries ECMWF operational conventional observations ’straco’ condensation & ’cbr’ turbulence cdd no data assimilation at all, just a +144 forecast from 3 november cde as cdc but with revised horizontal structure functions (slightly smaller scales)

  6. comparisons between the arpa and the hirlam data assimilations

  7. analysis differences arp – cdc 850 hPa geopotential6 Nov. 00Zwe have used different orographies this affects the post-processing to pressure levelsand mean sea level

  8. 2 metre temperature analysis differences 00UTC (left) and 12UTC (right)

  9. arpa (left) and hirlam (right) 10 metre wind analyses

  10. mean sea level pressure analysis and SYNOP observations5 November 1999 12ÙTC arp cdc

  11. mean sea level pressure analysis and SYNOP observations6 November 1999 00ÙTC arp cdc

  12. comparisons between the arpa and the hirlam forecasts (verified against ’own’ analyses)

  13. the analyses and the +24h forecast errors at the analysis timemean sea level pressure on November 7th 1999 cdc cdc 12Z 00Z arp arp

  14. verification against observations

  15. sea level pressure (arp & cdc) fit to SYNOP/SHIP

  16. screen level temperature (arp & cdc) fit to SYNOP/SHIP

  17. screen level dewpoint (top) and total clouds (bottom) (arp & cdc) fit to SYNOP/SHIP

  18. 10 metre windspeed (arp & cdc) fit to SYNOP/SHIP (top) and SHIP only (bottom)

  19. 850hPa geopotential (arp & cdc) fit to TEMP 850hPa windspeed (arp & cdc) fit to TEMP/PILOT

  20. conclusions from the comparisons with observations • P_msl: cdc analysis has smaller standard deviation • (arp postprocessing is noisier)

  21. more conclusions from the comparisons with observations • P_msl: cdc analysis has smaller standard deviation • (arp postprocessing is noisier) • P_msl 24h forecasts have comparable qualities

  22. more conclusions from the comparisons with observations • P_msl: cdc analysis has smaller standard deviation • (arp postprocessing is noisier) • P_msl 24h forecasts have comparable qualities • 10-metre windspeeds. • cdc biased high, both in anlyses and – worse – in forecasts • arp strong diurnal variations in the analysis biases – good at night, to weak at day

  23. more conclusions from the comparisons with observations • P_msl: cdc analysis has smaller standard deviation • (arp postprocessing is noisier) • P_msl 24h forecasts have comparable qualities • 10-metre windspeeds. • cdc biased high, both in anlyses and – worse – in forecasts • arp strong diurnal variations in the analysis biases – good at night, to weak at day • screen level temperature - analyses • cdc biased warm at daytime, cool at night • arp biased cool at daytime, warm at night

  24. more conclusions from the comparisons with observations • P_msl: cdc analysis has smaller standard deviation • (arp postprocessing is noisier) • P_msl: 24h forecasts have comparable qualities • 10-metre windspeeds • cdc biased high, both in anlyses and – worse – in forecasts • arp strong diurnal variations in the analysis biases – good at night, to weak at day • screen level temperature - analyses • cdc biased warm at daytime, cool at night • arp biased cool at daytime, warm at night • screen level temperature – forecasts • cdc has a cooling drift (well known in SMHI operations) • arp quite biasfree forecasts

  25. more conclusions from the comparisons with observations • P_msl: cdc analysis has smaller standard deviation • (arp postprocessing is noisier) • P_msl: 24h forecasts have comparable qualities • 10-metre windspeeds • cdc biased high, both in anlyses and – worse – in forecasts • arp strong diurnal variations in the analysis biases – good at night, to weak at day • screen level temperature - analyses • cdc biased warm at daytime, cool at night • arp biased cool at daytime, warm at night • screen level temperature – forecasts • cdc has a cooling drift (well known in SMHI operations) • arp quite biasfree forecasts • total clouds: arp has more clouds than cdc. cdc has a diurnal cycle

  26. more conclusions from the comparisons with observations • 850hPa geopotential: analyses and forecast essentially similar fits • 850hPa windspeed: cdc somewhat smaller bias and standard deviation

  27. accumulated precipitation • cdc 6 Nov. 06Z + 24h • cde 6 Nov. 06Z + 24h • arp 6 Nov. 00Z + 24h • arp 6 Nov. 12Z + 24h • Rubel 6 Nov. 06Z - 7 Nov. 06Z • cdd 6 Nov. 06Z – 7 Nov. 06Z

  28. 24-hour accumulated precipitation 6 Nov 06Z to 7 Nov 06Z exp:cdc

  29. 24-hour accumulated precipitation 6 Nov 00Z to 7 Nov 00Z exp:arp

  30. 24-hour accumulated precipitation 6 Nov 12Z to 7 Nov 12Z exp:arp

  31. 24-hour accumulated precipitation 6 Nov 06Z to 7 Nov 06Z(Rubel & Rudolf, Wien )

  32. 24-hour accumulated precipitation 6 Nov 06Z to 7 Nov 06Z exp:cdd

  33. the somewhat tighter structure functions used in the hirlam cde experiment experiment yields somewhat more intense precipitation than the cdc control experiment

  34. 24-hour accumulated precipitation 6 Nov 06Z to 7 Nov 06Z exp:cdc

  35. 24-hour accumulated precipitation 6 Nov 06Z to 7 Nov 06Z exp:cde

  36. general conclusions from the comparisons • pressure and mean sea level differences due to different orographies and different post-processing algorithms • arp noisier, especially Pmsl and geopotential at 850 • too large scale of the hirlam background errors (0.4°/ 0.4° grid) • new, smaller scale background errors yield slightly more intense precipitation • analysis increments on model levels problematic in steep orography • dfi initialization not ideally tuned for this resolution and such a small area • long integration (cdd) without D.A. still skillful, but D.A. improves the quality • cdc Pmsl forecasts have generally smaller errors against own analysis • precipitation forecasts qualitatively good, • arp has some very intense spots, cdc is somewhat smoother • and not bad quantitatively either

  37. what we still want to do • one more hirlam assimilation with a revised turbulent momentum flux • run some forecasts from each other’s analyses

  38. Grazie mille per la vostra attenzione!

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