Peter Bechtold. 1 European Centre for Medium Range Weather Forecast. COST Summer School Brac 2013: Global Convection

Transcription

1 Global Numerical Weather prediction: the role of convection Peter Bechtold 1 European Centre for Medium Range Weather Forecast Slide 1

2 Content Global picture of convection and methods to analyse waves (Hoevmoeller, Eliassen-Palm fluxes, wavenumber-frequency diagrams) Balances and generation of potential energy and its conversion into kinetic energy Madden-Julian Oscillation Quasi-equilibrium closures for CAPE Diurnal cycle of convection Slide 2

3 How to evaluate model (convection), how to trust the Analysis? Analysis increments SON UKMO Slide 3

4 Day+5 Forecast errors: EPS ensemble mean vs. high-resolution Slide 4

5 Precipitation: SEEPS against other Centres 2010 & /2011 & 2011/12 Slide 5

6 Time-series: Precipitation Slide 6

7 Precipitation climatology mean=2.67 mm/day mean=2.85 mm/day Slide 7

8 Occurrence of deep and shallow convection Slide 8

9 JJA Precip and SWnet errors uncoupled Slide 9

10 Convective Tendencies: total & shallow Slide 10

11 Tropical T tendency budgets rad cloud Slide 11

12 The global Lorenz Energy cycle da dt Generation Conversion NQ NQ Lorenz efficiency factor Net heating R [1 ( 1 1)] T ( 1 1) q P Slide 12

13 Generation rates Total Generation rate (W/kg) Generation rates maximum in upper tropical troposphere Generation rate - radiation Grid-scale conversion rate Radiation does not contribute to the conversion rates but to the generation rate, but even there has only at poles a positive contribution (cooling at cold places) but globally a negative contribution (as in Tropics it is cooling where it is warm) Steinheimer et al. 2008, Tellus Slide 13

14 Conversion rates and convection Grid-scale conversion rate (W/kg) Subgrid conversion rate Grid-scale has positive and negative contributions to kinetic energy conversion rate, maximum in upper-tropical troposphere Subgrid conversion rate - convection Convection so important because contribution always positive! Slide 14

15 Composite of the time-height sections of wavenumber 10 phase for q and Q. Glenn Shutts, 2006, Dyn. Atmos. Ocean 30 km At z~10 km, q and Q in phase time (hours) Think of red/orange as warm regions in m=10 wave and dark shading represents convective warming Slide 15

16 Shallow water system and linear waves V U U e e G z m 2 y /2 ik ( x ct ) 0 0 (, ) 1 2y 2 V V y y e G z m : Hn( y) 2 2 c 2 y /2 0 ( ) 4 1 (, ) k (2n 1), ; 0,1,2,... c 2 k c gh n Kelvin wave, geostrophic c k gh General, Hermite Polynomials Modes alternate asymm./symmetric Dispersion relation G z m e e z/(2 Hs ) imz (, ) Re( ) see T. Matsuno. Quasi-geostrophic motions in the equatorial area. J. Met. Soc. Japan, 44:25-42, Slide 16

17 Wave number Frequency Spectra OLR Cy38r1 (2012) NOAA Cy31r1 (ERAI) Slide 17

18 2D wave propagation with Eliassen-Palm fluxes Slide 18

19 General circulation and equilibrium in the Tropics Horizontal temperature fluctuations in the Tropics are small <1K/1000 km; and in the absence of precipitation the vertical motions(subsidence) tend to balance the cooling through IR radiation loss: w dθ/dz = dθ/dt_rad = -1-2 K/day => w ~ -.5 cm/s When precipitation takes place, heating rates are strong; e.g. 100 mm/day precip ~ energy flux of 2900 W/m2 or an average 30 K/day heating of the atmospheric column => w ~ 8.6 cm/s. However, this positive mean motion is composed of strong ascent of order w ~ 1 m/s in the Cumulus updrafts and slow descending motion around ( compensating subsidence ) Ro=NH/f with N the Brunt-Väisälä frequency and H the tropopause height, is the Rossby radius over which a perturbation spreads. In Tropics it is infinit as f->0, in the midlatitudes it is of O(1000 km). Therefore, daily weather forecasting is much more difficult in Tropics.. But contrary to middle-latitudes where predictability does not go beyond 14-days or so, Tropics have longterm predictability through intraseasonal variability (MJO) and SST coupling (ENSO) Slide 19

20 The MJO U850 U November 2011: Meteosat 7 + IFS Analysis Slide 20

21 YOTC: Hovmoeller of the OLR anomaly Slide 21

22 Progress in MJO prediction Slide 22

23 Correlations with T at 500 hpa for Phase 2/3 and forecast steps dt/dt_conv 60W 0 60E W 60 Precip 60W 0 60E W 60 For energy transformations in MJO see also Yanai, Chen, Tung (2000), and Matthews et a. (1999) Slide YOTC Asian Monsoon Symposium Beijing May

24 P (hpa) P (hpa) Difference in T-tendency: Convection over West Pacific - convection over Indian Ocean P (hpa) Dynamics (K/day) Conv (K/day) 50E W 50E W Cloud (K/day) Radiation (K/day) 50E W 50E W Slide 24

25 Correlations with T at 250 hpa for Phase 2/3 and forecast steps dt/dt_conv 60W 0 60E W 60 Precip 60W 0 60E W 60 Slide YOTC Asian Monsoon Symposium Beijing May

26 YOTC: OLR anomalies Slide 26

27 Effect of moisture sensitivity in convection scheme to MJO prediction q day+1 conv in 2007 day+5 dq/dt conv 2007-today day+1 day+5 see also Hirons et al. QJ RMS 2013 Slide 27

28 MJO initiation over Indian Ocean Take a long time series of filtered TRMM data and ERA-Interim reanalysis Identify MJO events, distinguish between primary, and successive, and separate from non-mjo convective events see also Ling et al. JAS 2013 to appear Slide 28

29 Anomalies in precipitation and 850 hpa wind Non-MJO day+3 day 0 day-3 day-6 day-9 day-12 day-15 Primary MJO significant easterly wind anomaly to the East Slide 29

30 day 0 day-6 day-12 day-18 day-24 P (hpa) Temperature anomalies Primary MJO Non-MJO significant cold anomaly in mid-troposph days ahead Slide 30

31 day 0 day-6 day-12 day-18 day-24 P (hpa) Specific humidity anomalies Primary MJO Non-MJO significant mid-tropospheric dry anomaly to the East Slide 31

32 Closure in Numerical Weather prediction Define (adiabatic) convective available potential energy and entraining density weighted CAPE=PCAPE compute its temporal derivative Slide 32

33 Closure in Numerical Weather prediction write prognostic equation formally as Define large-scale and convective contribution Slide 33

34 Closure in Numerical Weather prediction Need mass flux, can also estimate convective contribution from compensating subsidence term, M* first-guess mass flux (kg/m2 s) In diagnostic scheme formulate closure as or as quasi-equilibrium definition Slide 34

35 Closure Deep in Numerical Weather prediction Requires however specification of adjustment time-scale Diurnal cycle depending on cloud depth H, mean updraft velocity w and resolution n Slide 35

36 Closure Deep in Numerical Weather prediction Need to take into account the imbalance between deep convective motions and surface forcing, define: Adv+Rad+surf buoyancy flux Slide 36

37 Closure shallow in Numerical Weather prediction Define shallow as cloud depth<200 hpa, used twice as large entrainment as for deep. PCAPE integral singular for very shallow cloud, go back and use boundary-layer equilibrium only Use moist static energy h=cpt+gz+lq Could have also used q instead of h, or surface buoyance flux (as in previous slide) Slide 37

38 Diurnal cycle of Precipitation JJA: Amplitude (mm/d) TRMM CTL NEW Slide 38

39 Diurnal cycle of Precipitation JJA: Phase (LST) TRMM CTL NEW Slide 39

40 Diurnal cycle: Surface Energy Budgets TP=total precipitation SW=shortwave radiation SF&LF=sensible&latent heat flux Note: (i) shift in TP between CTL and NEW, (ii) TP in CTL in phase with SF+LF=wrong! (iii) for Europe LF>SF, Africa SF>LF Slide 40

41 How does diurnal Precip scale? TP=total precipitation HF=surface enthalpy flux BF=surface buoyancy flux NOTE: in NEW = revised diurnal cycle surface daytime precipitation scales as the surface buoyancy flux Slide 41

42 Composite diurnal cycle: Model vs Obs Slide 42

43 Closure and diurnal cycle over Sahel June 2012 Slide 43

44 Diurnal evolution of total heating profile -radiation congestus Deep convection Turbulent heat flux Shallow convection Slide 44

45 Diurnal cycle Sahel June 2012: IFS & CRM Q1-Qrad -Q1 Slide 45

46 Diurnal cycle & change in circulation: analyse soil moisture change in seasonal forecasts Slide 46 46

47 West-African Monsoon during AMMA campaign Daily mean precipitation [mm/day] August 2006: day+1 forecast 12 o N OBS (FEWS RFEv2) Forecast Precip in model is shifted too far South, and too much precip over Ocean Slide 47 47

48 Diurnal cycle: Impact on weather forecasts Slide 48

49 Wintry showers: radar & forecasts Slide 49

50 Conclusions Global picture of convection and methods to analyse waves (Hoevmoeller, Eliassen-Palm fluxes, wavenumber-frequency diagrams) Madden-Julian Oscillation: Big improvement obtained through convection (sensitivity to moisture, charge/discharge cycle, energy conversion, propagation?) quasi-equilibrium closures for CAPE: several possibilities from scaling arguments, but practice tells which is/are optimal Diurnal cycle of convection: Large impact not only one phase of precip but also on circulation (notably African summer monsoon0 and weather forecast Slide 50

51 Towards higher resolution Scalability in Computing Scaling the Globe: the small planet testbed (not shown) Slide 51

52 IFS grid point space: EQ_REGIONS partitioning for 1024 MPI tasks Each MPI task has an equal number of grid points ICON DWD G. Modzynski G. Zängl Slide 52

53 IFS model: current and future model resolutions IFS model resolution Envisaged Operational Implementation Grid point spacing (km) Time-step (seconds) Estimated number of cores 1 T1279 H (L137) K T2047 H K T3999 NH K T7999 NH M 1 a gross estimate for the number of IBM Power7 equivalent cores needed to achieve a 10 day model forecast in under 1 hour (~240 FD/D), system size would normally be ~10 times this number. 2 Hydrostatic Dynamics 3 Non-Hydrostatic Dynamics Slide 53

54 Sustained Exaflop in 2033? Slide 54

55 Exascale problem projections To run a T7999 L137 forecast (~2.5km) may require approximately 1-4 million processors (of current technology) to run in one hour At the same time 1-4 Million processors could run a 50 member ensemble of T3999 L137 in the same hour But first we have to be able to run a T3999 L137 forecast efficiently in one hour! Slide 55