1 / 33

Diagnostics I

Diagnostics I. Mark Rodwell. Meteorological Training Course Predictability, Diagnostics and Extended-Range Forecasting. ECMWF. 9 May 2012. Talk outline. Aspects of diagnostics The need for diagnostics research Seamless diagnostics Equatorial wave diagnostics. Aspects of diagnostics.

decker
Télécharger la présentation

Diagnostics I

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Diagnostics I Mark Rodwell Meteorological Training Course Predictability, Diagnostics and Extended-Range Forecasting ECMWF 9 May 2012

  2. Talk outline • Aspects of diagnostics • The need for diagnostics research • Seamless diagnostics • Equatorial wave diagnostics

  3. Aspects of diagnostics • Understanding the circulation Lorenz Energy Cycle • Understanding casestudies The October Storm • Understanding error • (observation, analysis or forecast error) Radiosonde first-guess departures

  4. The need for diagnostics research Temporal variance of D+1 error of Z500 • Error identification Total wavenumber From Adrian Simmons

  5. The need for diagnostics research • Error identification Number of observations assimilated (106 day-1) • Observation volume From Peter Bauer and Jean-Noel Thepaut

  6. The need for diagnostics research • Error identification Physical processes represented in the IFS model • Observation volume • Model complexity From Peter Bechtold

  7. The need for diagnostics research • Error identification • Observation volume Met Office rainfall radar 2009/11/29 at 10:30 Met Office rainfall radar 2009/11/29 at 10:30 Met Office rainfall radar 2009/11/29 at 10:30 Met Office rainfall radar 2009/11/29 at 10:30 Met Office rainfall radar 2009/11/29 at 10:30 Met Office rainfall radar 2009/11/29 at 10:30 • Model complexity • ‘Gray-zone’ resolution TL96 TL799 TL256 TL2047 TL511 TL1279

  8. The need for diagnostics research • Error identification m2s-1 Shading: spread Ψ250 D+4 • Observation volume • Model complexity mm • ‘Gray-zone’ resolution Shading: RMSE Ψ250 D+4 • Uncertainty estimation Contours: analysed Ψ250. Squares:obs. precip.

  9. The need for diagnostics research • Error identification • Observation volume • Model complexity AMSRE ch7 (~q850) Variational Bias Correction JJA 2010 • ‘Gray-zone’ resolution • Uncertainty estimation • 4D-Var: obsvs model

  10. ‘Seamless’ diagnostics

  11. T500 forecast error as function of lead-time D+1 D+2 x0.01K x0.1K D+5 D+10 x0.1K x0.1K Based on DJF 2007/8 operational analyses and forecasts. Significant values (5% level) in deep colours.

  12. Data assimilation: Perfect model Mean Analysis Increment = 0 (Imperfect, unbiased observations)

  13. Data assimilation: Imperfect model Mean Analysis Increment ≠ 0 −Mean Analysis Increment = Mean Net Initial Tendency (“I.T.” in, e.g., Kcycle-1) = Mean Convective I.T. + Mean Radiative I.T. + … + Mean Dynamical I.T. (summed over all processes in the model)

  14. Confronting models with observations UNIT=0.01K D+1 T500 FORECAST ERROR T500 ANALYSIS INCREMENT • Every 1o square has data every cycle • ~6 Million data values • Independent vertical modes of information: • IASI / AIRS: ~ 15 • HIRS / AMSUA: ~ 5 (~ 2 IN TROP) • Anchors (not bias corrected): • Radiosonde • AMSUA-14 • Radio Occultation AIRS CH 215 OBS-FIRST GUESS Based on DJF 2007/8 operational analyses and forecasts. Significant values (5% level) in deep colours. AIRS CH 215 BRIGHTNESS TEMPERATURE ~T500

  15. Parameter Uncertainties in Climate Sensitivity MANY UNCERTAINTIES ARE ASSOCIATED WITH “FAST PHYSICS”. … WHICH IS ALSO IMPORTANT IN NWP Representative selection of parameters and uncertainties used by Murphy et al., 2004: Nature, 430, 768-772.

  16. Amazon January 2005 initial T tendencies PERFECT MODEL (Almost!) IMPERFECT MODEL (12K Warming) ( Net = Minus the mean analysis increment ) Reduced Entrainment model is out of balance. Reject or down-weight? Amazon = [300oE-320oE, 20oS-0oN]. Mean of 31 days X 4 forecasts per day X 12 timesteps per forecast. 70% confidence intervals are based on daily means. CONTROL model = 29R1,T159,L60,1800S.

  17. Vertically integrated absolute net tendencies CONTROL ENTRAIN/5 T (Kday-1) q (kgm-2day-1) Can be the basis for a “process metric” (as opposed to the more widely used circulationmetrics) Mean of 31 days X 4 forecasts per day X 12 timesteps per forecast. Mass-weighted vertical integrals. CONTROL model = 29R1,T159,L60,1800S.

  18. Old and New Aerosol Optical Thickness OLD (NO ANNUAL CYCLE) OPTICAL DEPTH d AT 550nm NEW (JULY) ATTENUATION FACTOR = e-d SINGLE SCATTERING ALBEDO FOR DESERT AEROSOL ≈ 0.9 • SOIL DUST IS LARGE COMPONENT • SOIL DUST ABSORBS AS WELL AS SCATTERS Old: C26R1 (Tanre et al. 1984), New: C26R3 (Tegen et al. 1997).

  19. JJA Precipitation, v925 and Z500. New-Old mm day-1. 10% Sig.

  20. North Africa Jul 2004 T Tendencies (New-Old) RADIATIATION CHANGES DESTABILISE PROFILE AND LEAD TO MORE CONVECTION … … BUT ULTIMATELY LEAD TO MORE DESCENT AND LESS OVERALL PRECIPITATION North Africa = [5oN-15oN, 20oW-40oE]. Mean of 31 days X 4 forecasts per day X 12 timesteps per forecast. 70% confidence intervals are based on daily means. CONTROL model = 29R1,T159,L60,1800S.

  21. CONVECTION (FAST RESPONSE) DYNAMICS (SLOW RESPONSE) DRYING Local Response to Aerosol Reduction RADIATION IMPORTANCE OF SEMI-DIRECT EFFECT(?) LESS ABSORPTION

  22. Wind and humidity errors: 850 hPa, JJA 2009

  23. Diagnostics Explorer: Seamless & Accessible

  24. Equatorial wave diagnostics

  25. Tropical Waves: Outgoing Long-wave Radiation AUG AUG WESTWARD PROPAGATING WAVES MADDEN-JULIAN OSCILLATION: EASTWARD PROPAGATING, 30-60 DAY (≈10ms-1) JUL JUL MJO JUN JUN 100oW 0o 100oE 7.5oS-17.5oN 5oS-5oN 2006 Wm-2 TIME 100oW 0o 100oE LONGITUDE Data from NOAA

  26. Equatorial Waves (Use of the shallow water equations on the β-plane (f=βy) for understanding tropical atmospheric waves) Momentum: (1) Continuity: v1 u1 u1 (2) v1 Solving for v: (3) Note: No coupling with convection in this model

  27. Limiting solutions η & v η tendency Phase speed Kelvin waves: v≡0, u in geostrophic balance with meridional pressure gradient Equator Gravity waves: Fast, pressure gradient force dominates Rossby waves: Slow, Coriolis affect important, closer to geostrophic balance, less convergence. Take curl of (1)

  28. Free Equatorial Waves V=0: V≠0: Structures (Meridional structures are solutions to Schrodinger’s simple harmonic oscillator) Dispersion (How phase speed is related to spatial scale) East propagating Kelvin Wave • Non-dispersive • In geostrophic balance Substitute into equation for v Hermite Polynomials: Hn(y) • Each successive polynomial has one more node • Modes alternate asymmetric / symmetric about equator For n ≠0: 3 values of ω for each k • West propagating Rossby Wave • E & W propagating Gravity Wave For n=0: 2 values of ω for each k • E & W prop. Mixed Rossby-Gravity (Gravity: associated with first two terms on lhs, Rossby: with last two terms on lhs, Mixed: all three terms)

  29. Interpretation of Free Equatorial Waves Dispersion Diagram SUGGESTS METHOD OF COMPARISON BETWEEN OBSERVATIONS AND MODEL

  30. Wave Power OLR DJF 1990-05 NOAA & 32R3 MJO CONVECTIVELY COUPLED(?) ALIASING OF 12H OBSERVATIONS TOO MUCH LOW FREQUENCY POWER (a) Observed Symmetric (b) Observed Asymmetric KELVIN c≈20ms-1 MIXED ROSSBY ROSSBY (c) Simulated Symmetric (d) Simulated Asymmetric n=-1 n=0 n=1 n=2 n=3 AGREEMENT WITH SHALLOW WATER THEORY IF OLR IS A “SLAVE” TO THE FREE WAVES, LINEARITY ETC.

  31. Gill’s steady solution to monsoon heating • GOOD AGREEMENT WITH THE AEROSOL • CHANGE RESULTS (OPPOSITE SIGN): • NORTH ATLANTIC SUBTROPICAL ANTICYCLONE • CONVECTIVE COUPLING IN KELVIN WAVE REGIME DAMPING/HEATING TERMS TAKE THE PLACE OF THE TIME DERIVATIVES EXPLICITLY SOLVE FOR THE X-DEPENDENCE Colours show perturbation pressure, vectors show velocity field for lower level, contours show vertical motion (blue = -0.1, red = 0.0,0.3,0.6,…) Following Gill (1980). See also Matsuno (1966)

  32. JJA Precipitation, v925 and Z500. New-Old The Extratropical Response will be explained in the next lecture! mm day-1. 10% Sig.

  33. Summary • Aspects of diagnostics • Understanding: the circulation, case-studies, error • The need for diagnostics research • Smaller errors, more observations, model complexity, gray-zone, uncertainty, model vs observation • Seamless diagnostics • Errors that grow are likely to be model problems • Initial tendencies highlight root causes before process interactions • Process-oriented metrics for climate models • Equatorial wave diagnostics • Natural modes of tropical variability • Good way to understand error and uncertainty

More Related