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Understanding Systematic Errors in Weather Models: Sensitivity to Horizontal Resolution

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36 diapositives · 517 Views · Oct 04, 2014

This document discusses the insights gained from experimental setups at various horizontal resolutions in ECMWF weather models, focusing on the cycles from 1990-2005. The analysis covers computational efforts for different resolutions and their effects on accuracy in forecasting and climate predictions, particularly in the context of orographic gravity waves and cyclonic activity. Sensitivity studies highlight the importance of model formulation and adjustments needed for accurate forecast outputs, explaining how increased resolution can impact the representation of tropical and extratropical systems.

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  1. Systematic Errors (Part II) Thomas Jung European Centre for Medium-Range Weather Forecasts

  2. Scope of Part II • Sensitivity to horizontal resolution • some recent results • some more diagnostics • Some methods for understanding the origin of systematic model

  3. Experimental Setup • Cycle 31R1 (oper: 12/09/06-04/06/07) + other cycles for ref • Various horizontal resolutions: • TL95 (200 km = climate prediction) • TL159 (120km = seasonal forecasting) • TL255 (80 km = monthly forecasting) • TL511 (40km = NWP) • 91 levels in the vertical • Period considered: 1990-2005 (-2006) • Two seasons • DJFM with start on 1st November • JJAS with start on 1st May Seasonal integration with the ECMWF model:

  4. Computational Effort • 12% of all CPUs on HPCE cluster • Wall clock time about 20 hours • About 70 times more expensive than TL95! 1 Integration (151 days) @ TL511L91:

  5. Zonal Mean U Error (DJFM) TL95-ERA40 TL159-ERA40 TL255-ERA40 TL511-ERA40

  6. Zonal Mean U Error @ TL95 (DJFM) 31R1 31R1modGWD

  7. TL95L60 TL255L60 TL799L60 Orographic Gravity Waves over Greenland Jung and Rhines, JAS

  8. Weak-Strong Polar Vortex Stratospheric Origin of Mean Z500 Error (DJFM) 31R1modGWD-31R1

  9. Synoptic Z500 Activity (DJFM) TL95-ERA04 TL511-TL95

  10. Extratropical Cyclone Tracking • 6-hourly MSLP data • All data truncated to T40 prior to tracking • Searching for and tracking of MSLP minima • Application of selection criteria (e.g., migratory long-lived systems only) Method of Gulev et al.:

  11. “Observed” Tracks of Long-lived Cyclones >2 days

  12. Number of Extratropical Cyclones (Winter) Lifetime > 1day

  13. Number of Extratropical Cyclones (DJFM) Lifetime > 1day

  14. Sensitivity: Different Model Cycles 29R1 31R1

  15. Cy31R1 (T159L91) Cy30R2-Cy31R1 (T159L91) Sensitivity to Model Formulation • Revised cloud scheme • Implicit calculation of convective transports • Modifications to the treatment of orography

  16. Tropical Cyclone Tracking • Detection: • Find area with MSLP below a certain threshold • Check whether there is a warm core above the MSL minimum • Tracking: • Compute trajectories from “different” low pressure minima • Tuning based on ERA-40 data

  17. 2005 2006 TL511 TL255 TL159 TL95 Tropical Cyclone Tracks (Atlantic)

  18. Resolution and Intensity of Tropical Storms

  19. TL95-ERA40 TL511-ERA40 TL511-TL95 Vertical Wind Shear (JJAS)

  20. ERA40 TL95-ERA40 TL511-ERA40 TL511-TL95 Synoptic Activity: Vrot @ 700hPa (JJAS)

  21. Madden-and-Julian Oscillation (DJFM) ERA40 TL95 TL159 TL255 TL511

  22. NOAA TL95 TL511 Convectively Coupled Tropical Waves: Sym. OLR Anomalies (JJAS)

  23. Convectively Coupled Tropical Waves: Sym. OLR Anomalies (JJA) NOAA 32R2 @ TL159 32R3 @ TL159

  24. Some Methods for Identifying Model Error • Diagnosing your model output (e.g., climate run) • Easy to perform/diagnose • Everything becomes entangled later in the forecast (difficult to understand causes of errors) • Sensitivity experiments • Suitable to study the response to specific changes • “Fishing”-type experiments (changes usually not optimal) • Comes for free @ ECMWF due to the frequent introduction of new model cycles (“E-suites”) • “Entanglement” might be an issue (studying transient response can help) • Initial tendency and analysis increment approach • Powerful approach to understand model error due to “locality” (see Mark Rodwell’s presentation) • Excellent approach for model assessments (data assimilation system required) • Adjoint sensitivity studies • Provides a set of optimal forcing perturbations which reduce random and systematic forecast error • Relaxation experiments

  25. Transient Response Experiments

  26. Stream Function @ 200 hPa and Precip (JJA) 32R3-32R2

  27. Perturbation Growth: E-O, SN, Urot and Precip Precipitation Urot and Stream Function Tropical Kelvin waves c=30 m/s -> 2500 km/d

  28. Relaxation Experiments

  29. Tropical-Extratropical Interactions: Motivation El Nino Multi-model biases: Similar precipitation biases may lead to similar biases in the extratropics La Nina

  30. Question (and solution?) How do forecast errors in the tropics influence the extratropical flow?

  31. Experimental Setup • Seasonal integrations with prescribed SSTs • Relaxation details: • U, V, T and lnps • Tropics only (20S-20N) • Relaxation strength λ=0.1 • Experimental setup: • TL95L60 (32R1) • Start 10th November • Diagnostics for DJFM 1982-2001

  32. Mask used for localization Mask 0.10 0.09 0.08 0.07 0.06 0.05 0.04 0.03 0.02 0.01 0.00

  33. MJO in ERA40, control and relaxed forecasts ERA-40 Control Relaxed

  34. Systematic Z500 Error: Control vs relaxed forecasts Control Relaxed

  35. Conclusions I • Influence of increasing resolution from TL95 to TL511 has been studied • High resolution is beneficial (e.g, tropical and extratropical cyclones) • Care has to be taken, though, when interpreting results from resolution studies (See GWD example) • Simply increasing resolution alone is not necessarily a cure (see MJO) • The impact of resolution seems to be model dependent (cyclones in 30r2 vs 31r1) • Important: Increasing resolution has to go hand in hand with further model development!

  36. Conclusions II • An overview has been given of techniques, which can be used to identify the origin model problems. • Systematic error: • Simple to do • Not necessarily easy to understand + pitfalls • Sensitivity experiments • Good way to study the impact of model changes • Not necessary optimal (adjoint techniques?) • Initial tendency method (see M Rodwell’s lecture) • Looking at short-range forecasts is a powerful approach (locality, coupling between processes) • Requires a data assimilation system • Relaxation technique: • Potentially powerful tool • More to come (particularly on predictability aspects)