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Recent performance statistics for AMPS real-time forecasts

Recent performance statistics for AMPS real-time forecasts. Kevin W. Manning – National Center for Atmospheric Research NCAR Earth System Laboratory Mesoscale and Microscale Meteorology Division Boulder, CO – NCAR is sponsored by the National Science Foundation –

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Recent performance statistics for AMPS real-time forecasts

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  1. Recent performance statistics for AMPS real-time forecasts Kevin W. Manning – National Center for Atmospheric Research NCAR Earth System Laboratory Mesoscale and Microscale Meteorology Division Boulder, CO – NCAR is sponsored by the National Science Foundation – AMPS is sponsored by the NSF Office of Polar Programs and theNSF UCAR and Lower Atmosphere Facilities Oversight Section – Workshop on Polar Simulations with the Weather Research and Forecasting (WRF) Model 02-03 Nov 2011, Columbus, OH –

  2. AMPS real-time forecasts • 6 two-way interactive grids: • 45-km / 15-km grids to 120 forecast hours • 5-km / 1.67-km grids to 36 forecast hours • Initial conditions • GFS 0.5-degree analyes used as first guess for WRFDA 3D variational data assimilation step (domains 1 and 2) • Sea-ice conditions from near real-time SSM/I daily global ice concentration (NSIDC) (25-km grid) • Domain 1 boundary conditions • GFS 0.5-degree forecast updated at 6-hour intervals • WRF options • 44 vertical levels; lowest half level ~ 12 m above surface; 12 layers below ~1 km above surface • Microphysics: WSM 5-class scheme • LW Radiation: RRTMG • SW Radiation: Goddard SW scheme • Surface Layer Physics: Monin-Obukhov (Janjic Eta) scheme • Land Surface: Noah Land-surface model; 4 subsurface layers • PBL Physics: MYJ (Eta) TKE scheme Workshop on Polar Simulations with the Weather Research and Forecasting (WRF) Model

  3. Workshop on Polar Simulations with the Weather Research and Forecasting (WRF) Model

  4. AMPS statistics – Summertime Surface • Nov-Dec-Jan 2010/2011 season • Temperature • Pressure • Wind • Surface station reports • From GTS • From University of Wisconsin – Antarctic Meteorological Research Center (AMRC) • Three regions • Ross Ice Shelf • East Antarctic plateau • East Antarctic coastal • Older WRF version 3.0.1.1 with Polar modifications Workshop on Polar Simulations with the Weather Research and Forecasting (WRF) Model

  5. Summer T – Ross Ice Shelf Mean Statistics -- ~ 15 stations Forecast Hour (0 – 120) Bias RMSE Correlation (-1.0 to 1.0) Forecast Hour (0 – 120) Workshop on Polar Simulations with the Weather Research and Forecasting (WRF) Model

  6. Summer T – East Antarctic Plateau Mean Statistics ~ 6 stations Forecast Hour (0 – 120) Bias RMSE Correlation (-1.0 to 1.0) Forecast Hour (0 – 120) Workshop on Polar Simulations with the Weather Research and Forecasting (WRF) Model

  7. Summer T – East Antarctic Coastal Mean Statistics -- ~10 stations Forecast Hour (0 – 120) Bias RMSE Correlation (-1.0 to 1.0) Forecast Hour (0 – 120) Workshop on Polar Simulations with the Weather Research and Forecasting (WRF) Model

  8. Summer P – Ross Ice Shelf Mean Statistics -- ~ 15 Stations Forecast Hour (0 – 120) Bias RMSE Correlation (-1.0 to 1.0) Forecast Hour (0 – 120) Workshop on Polar Simulations with the Weather Research and Forecasting (WRF) Model

  9. Summer P – East Antarctic Plateau Mean Statistics -- ~ 6 stations Forecast Hour (0 – 120) Bias RMSE Correlation (-1.0 to 1.0) Forecast Hour (0 – 120) Workshop on Polar Simulations with the Weather Research and Forecasting (WRF) Model

  10. Summer P – East Antarctic Coastal Mean Statistics -- ~10 stations Forecast Hour (0 – 120) Bias RMSE Correlation (-1.0 to 1.0) Forecast Hour (0 – 120) Workshop on Polar Simulations with the Weather Research and Forecasting (WRF) Model

  11. Summer Wind Speed – Ross Ice Shelf Mean Statistics -- ~ 15 stations Forecast Hour (0 – 120) Bias RMSE Correlation (-1.0 to 1.0) Forecast Hour (0 – 120) Workshop on Polar Simulations with the Weather Research and Forecasting (WRF) Model

  12. Summer Wind Speed – East Antarctic Plateau Mean Statistics -- ~ 6 stations Forecast Hour (0 – 120) Bias RMSE Correlation (-1.0 to 1.0) Forecast Hour (0 – 120) Workshop on Polar Simulations with the Weather Research and Forecasting (WRF) Model

  13. Summer Wind Speed – East Antarctic Coastal Mean Statistics -- ~10 stations Forecast Hour (0 – 120) Bias RMSE Correlation (-1.0 to 1.0) Forecast Hour (0 – 120) Workshop on Polar Simulations with the Weather Research and Forecasting (WRF) Model

  14. Summertime Summary • Warm bias on plateau. Mixed temperature bias in other regions. • Warming trend in East Antarctic plateau and Ross Ice Shelf regions. Very little temperature trend for coastal stations. • Plateau stations show greatest temperature error growth (RMSE) over 120 hours. Little temperature error growth (RMSE) for Ross Ice Shelf and coastal regions. • Pressure statistics show high correlation in all three regions, but steady error (RMSE) growth. • Low pressure bias increasing in time over Ross Ice Shelf. • Slight high wind-speed bias Workshop on Polar Simulations with the Weather Research and Forecasting (WRF) Model

  15. AMPS Behavior – Wintertime Surface • May-Jun-Jul 2011 • Newer WRF version 3.2.1 with Polar Modifications Workshop on Polar Simulations with the Weather Research and Forecasting (WRF) Model

  16. Winter T -- Ross Ice Shelf region Mean Statistics -- ~21 stations Forecast Hour (0 – 120) Bias RMSE Correlation (-1.0 to 1.0) Forecast Hour (0 – 120) Workshop on Polar Simulations with the Weather Research and Forecasting (WRF) Model

  17. Winter T -- East Antarctic Plateau Mean Statistics – ~13 stations Forecast Hour (0 – 120) Bias RMSE Correlation (-1.0 to 1.0) Forecast Hour (0 – 120) Workshop on Polar Simulations with the Weather Research and Forecasting (WRF) Model

  18. Winter T -- East Antarctic Coastal Mean Statistics -- ~15 stations Forecast Hour (0 – 120) Bias RMSE Correlation (-1.0 to 1.0) Forecast Hour (0 – 120) Workshop on Polar Simulations with the Weather Research and Forecasting (WRF) Model

  19. Winter P -- Ross Ice Shelf region Mean Statistics -- ~21 stations Forecast Hour (0 – 120) Bias RMSE Correlation (-1.0 to 1.0) Forecast Hour (0 – 120) Workshop on Polar Simulations with the Weather Research and Forecasting (WRF) Model

  20. Winter P -- East Antarctic Plateau Mean Statistics -- ~13 stations Forecast Hour (0 – 120) Bias RMSE Correlation (-1.0 to 1.0) Forecast Hour (0 – 120) Workshop on Polar Simulations with the Weather Research and Forecasting (WRF) Model

  21. Winter P -- East Antarctic Coastal Mean Statistics -- ~15 stations Forecast Hour (0 – 120) Bias RMSE Correlation (-1.0 to 1.0) Forecast Hour (0 – 120) Workshop on Polar Simulations with the Weather Research and Forecasting (WRF) Model

  22. Winter Wind Speed -- Ross Ice Shelf region Mean Statistics -- ~21 stations Forecast Hour (0 – 120) Bias RMSE Correlation (-1.0 to 1.0) Workshop on Polar Simulations with the Weather Research and Forecasting (WRF) Model

  23. Winter Wind Speed – East Antarctic Plateau Mean Statistics -- ~13 stations Forecast Hour (0 – 120) Bias RMSE Correlation (-1.0 to 1.0) Forecast Hour (0 – 120) Workshop on Polar Simulations with the Weather Research and Forecasting (WRF) Model

  24. Winter Wind Speed -- East Antarctic Coastal Mean Statistics -- ~15 stations Forecast Hour (0 – 120) Bias RMSE Correlation (-1.0 to 1.0) Forecast Hour (0 – 120) Workshop on Polar Simulations with the Weather Research and Forecasting (WRF) Model

  25. Wintertime summary • In each region, a mix of high and low temperature biases – average bias near zero. • Over plateau, strong signal of initial condition warm bias that the model quickly corrects. • Larger temperature error (RMSE) growth, larger reduction of temperature correlation, than we saw in summer. • As in summer, pressure statistics show significant error growth (RMSE) • High wind speed bias, more notable than in summer. Workshop on Polar Simulations with the Weather Research and Forecasting (WRF) Model

  26. Subsurface Temperature Initialization • BPRC Antarctic results differ from AMPS results • BPRC results show cold bias in summer over East Antarctic Plateau • AMPS results show warm bias in summer over East Antarctica Plateau • One possibly significant difference in the AMPS and BPRC is the initialization of subsurface temperature fields • AMPS cycles the subsurface temperature from one forecast to the next • High resolution details • In balance with WRF physics • Subject to model drift • BPRC initializes subsurface temperature fields using a 40-year annual mean air temperature analysis at deep ice layers, and a 40-year monthly mean air temperature analysis at the shallowest subsurface layer • No spin-up required • Low resolution • Could this account for the different results for forecasts of air temperature? Workshop on Polar Simulations with the Weather Research and Forecasting (WRF) Model

  27. Two experiments • Cycled subsurface temperature fields (CYCLE) • Subsurface temperature fields initialized from monthly mean and annual mean temperatures (MEANT) • Two 72-hour forecasts per day, in the AMPS 45km/15km configuration, from 10 Jan through 06 Feb 2011 (about 4 weeks). • The CYCLE conditions have been spun up for about 6 weeks, starting from AMPS real-time fields (i.e., already using the real-time cycled conditions) from 01 Dec 2010. Workshop on Polar Simulations with the Weather Research and Forecasting (WRF) Model

  28. CYCLE Level 2 (-0.25 m) Ice T MEANT Level 2 (-0.25 m) T Averages at Forecast hour 00 Difference Level 2 T (-0.25 m) CYCLE – MEANT Workshop on Polar Simulations with the Weather Research and Forecasting (WRF) Model

  29. CYCLE Level 4 (-1.5 m) Ice T MEANT Level 4 (-1.5 m) Ice T Averages at Forecast hour 00 Difference Level 4 T (-1.5 m) CYCLE – MEANT Workshop on Polar Simulations with the Weather Research and Forecasting (WRF) Model

  30. CYCLE 2-m air Temperature MEANT 2-m air Temperature Averages at Forecast hour 72 Difference 2-m air T CYCLE – MEANT Workshop on Polar Simulations with the Weather Research and Forecasting (WRF) Model

  31. Workshop on Polar Simulations with the Weather Research and Forecasting (WRF) Model

  32. Short-term plans for sea-ice code in WRF, particularly Noah LSM • Currently, sea-ice code is scattered throughout the Noah LSM code • Difficult to trace the sea-ice processes through the code • Difficult to develop or replace • Plan: Pull sea-ice code out of Noah-LSM, and make the Noah sea-ice treatment its own separate WRF module • Easy to trace the sea-ice processes through the code • Easy to develop or replace • A place to link up with more sophisticated sea-ice schemes or models • Easy to use with other LSM options (e.g., the new Noah-MP LSM) Workshop on Polar Simulations with the Weather Research and Forecasting (WRF) Model

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