WWRP working group meeting. on Predictability, Dynamics & Ensemble Forecasting

Transcription

1 WWRP working group meeting on Predictability, Dynamics & Ensemble Forecasting

2 Outline Introduce stochas.c parameteriza.on agenda Include some of your recent research How can the PDEF working group help foster future research in this area

3 Spread and error, T850 over NH, for winter (90 cases) TIGGE Curtosy Buizza

4 Potential to reduce model error Stochas.c parameteriza.ons can change the mean and variance of a PDF Potential Impacts variability of model (e.g. internal variability of the atmosphere) Impacts systema.c error (e.g. blocking precipita.on error) PDF Weak noise Strong noise Unimodal Multi-modal

5 Stochastic parameterization schemes Stochas.c kine.c- energy backscaner scheme (SKEBS) Ra.onale: A frac.on of the dissipated kine.c- energy is scanered upscale and acts as forcing for the resolved flow (ShuNs, 005,Berner et al. 009,11,1,14) Stochas.cally perturbed parameteriza.on scheme (SPPT) Ra.onale: Especially as resolu.on increases, the equilibrium assump.on is no longer valid and fluctua.ons of the subgrid- scale state should be sampled (Buizza et al. 1999, Palmer et al. 009, Berner et al. 014)

6 Mean systematic error of 500 hpa geopotential height fields LOWRES b) SKEBS Reduc.on of z500 bias in all simula.ons with model- refinement d) Z500 Difference f8o8-er40 ( )

7 Mean systematic error of 500 hpa geopotential height fields LOWRES b) SKEBS Degenera.ve response => COMPENSATING MODEL- ERRORS HIGHRES -6 d) Z500 Difference f8o8-er40 ( ) PHYS Berner et al., 01

8 R 0 R 0 R 0 Calibrated Debiased Calibrated & Debiased Change of Model Raw Version ed P1-13 (C) Increasing the skill of probabilistic forecasts: Understanding performance improvements from model-error representations Judith Berner, K. R. Fossell, S.-Y. Ha, Brier J. Skill P. Score Hacker and Reliability C. Snyder 0 The impact of five model- error schemes 10on probabilis.c performance is quan.fied 10in 0 WRF ensemble forecasts. Their benefits 0go beyond increasing spread and reducing bias, 10 in that they can represent certain aspects 0 of structural model error. 0 Brier Skill Score U700 T700 U10 T Lead%me- averaged Bier skill score improvements in % Reliability CNTL PARAM SKEBS PHYS10 PHYS10_SKEBS PHYS3_SKEBS_PARAM Calibrated Debiased Raw Calibrated & Debiased Change of Model Version Calibrated Debiased U700 T700 U10 T U700 U700 T700 T700 U10 U10 T T d & ed 10 Resolution 0 CNTL 0 0 PARAM SKEBS PHYS10 PHYS10_SKEBS PHYS3_SKEBS_PARAM Calibrated Debiased Calibrated & Debiased Raw Change of Model Calibrated Version Verifying observa.on Poten.al 10 without stochas.c 0perturba.ons Poten.al 10 with Brier stochas.c Skill Score perturba.ons U700 T700 U10 U700 T700 T U10 U700 T700 T U10 U7 0 ed Berner et al., 015 Reso CNTL PARAM SKEBS PHYS10 PHYS10 PHYS3_

9 0 Calibra Debia 0 Brier Skill Score Reliability Resolution Brier Skill Score Change of Model Raw Version CNTL PARAM SKEBS PHYS10 PHYS10_SKEBS PHYS3_SKEBS_PARAM U700 T700 U10 T U700 T700 U10 T U700 Mul.ple error schemes produces best models => Debiased

10 0 Calibra Debia 0 Brier Skill Score Reliability Resolution Brier Skill Score Change of Model Raw Version CNTL PARAM SKEBS PHYS10 PHYS10_SKEBS PHYS3_SKEBS_PARAM U700 T700 U10 T U700 T700 U10 T U700 Mul.ple error schemes produces best models => Debiased Radia.on Convec.on Dynamics

11 0 Calibra Debia 0 Brier Skill Score Reliability Resolution Brier Skill Score Change of Model Raw Version CNTL PARAM SKEBS PHYS10 PHYS10_SKEBS PHYS3_SKEBS_PARAM U700 T700 U10 T U700 T700 U10 T U700 Mul.ple error schemes produces best models => Debiased Radia.on Radia.on uncertainty Convec.on Convec.on uncertainty Dynamics Trunca.on error

12 } WGNE needs help with it. } Physical basis Need good understanding of model errors, particularly related to physical parameterizations. } Critical for predictability Defines limit of predictability with perfect filtered state initial conditions. } Ensemble forecasting Only way to make ensemble members statistically indistinguishable from truth particularly at long lead times. } Data assimilation If done correctly, will accurately inform DA scheme of trustworthiness of first guess in error directions associated with stochastic effect of sub-filter scales.

13 Perturbations added a posteriori Model Forecast Uncertain.es

14 A priori vs a posteriori - process- based uncertainty estimates If you develop a parameteriza.on, I urge you to develop an uncertainty scheme alongside OTHERWISE I WILL

15 and you don t want that!

16 and you don t want that!

17 A posteriori or a priori? Uncertainty assessment is not an aherthought

18 A priori vs a posteriori Model Forecast Uncertain.es Process Uncertain.es Model e.g. Plant-Craig parameterization

19 Roles of Large and Small Scales (Groenemeijer and Craig, 01) Sochastic convective parameterisation l COSMO-LEPS 7km l Forecasts driven by ECMWF ensemble members l 10x10 = 100 member ensemble Radar Tiedtke Plant-Craig Compare variance in subensembles with same driving forecast to total l Variance from stochastic scheme scales with convective precipitation (Poisson scaling) l Contributes up to half of total variance in weak forcing Courtesy G. Craig

20 Pre-PS36 boundary spin-up th Courtesy R. Swinbank 10 Dec 014 Better initiation and organisation of showers in NW and over Ireland And cloud cover over SW Approaches [Thanks to Adrian Lock] Op PS35 Pre-PS36

21 Courtesy G. Craig Stochastic parameterisation issues I l l l Kinds Physically-based (subgrid model) Parameter uncertainty Pragmatic (e.g. SPPT) Scaling properties Resolution Amplitude Relevant processes depends on resolution Convection (e.g. dx = 0 km) Boundary layer (e.g. dx = km) } the same?

22 Courtesy G. Craig Stochastic parameterisation issues II l l Interaction with dynamical core Numerical noise Damping Upscale growth Interaction with other parameterisations Correlations Double-counting

23 Consistent uncertainty representation Structural uncertainty What s needed to get good spread/forecast uncertainty Process- based uncertainty Subgrid- uncertainty

24 Consistent and unified uncertainty representation

25 Unified treatment of model- error in DA and forecast t 0 t1 t 0 Process- based uncertainty t t1 Uncertainty from inverse analysis: how much spread is needed t

26 From George Craig s talk EDA Ensemble data assimila.on (EDA) Analysis uncertainty Analysis uncertainty SPPT Stochas.cally perturbed parameteriza.on scheme SKEBS Stochas.c kine.c- energy backscaner scheme Model- error Model- error Equilibrium assump.on no longer holds - > sample subgrid- state Projec.on of unrepresented subgrid- scale state on resolved state SV Singular Vectors All the rest Op.mal growth in reduced phase space, mimics growth along missing degrees of freedoms not represented + boundary uncertainty

27 Another take EDA Ensemble data assimila.on (EDA) Analysis uncertainty Analysis uncertainty SPPT Stochas.cally perturbed parameteriza.on scheme SKEBS Stochas.c kine.c- energy backscaner scheme Model- error Model- error Equilibrium assump.on no longer holds - > sample subgrid- state Projec.on of unrepresented subgrid- scale state on resolved state SV Singular Vectors All the rest Op.mal growth in reduced phase space, mimics growth along missing degrees of freedoms not represented

28 Another take EDA Ensemble data assimila.on (EDA) Analysis uncertainty Analysis uncertainty SPPT Stochas.cally perturbed parameteriza.on scheme SKEBS Stochas.c kine.c- energy backscaner scheme Model- error Model- error Equilibrium assump.on no longer holds - > sample subgrid- state All the rest including error growth along missing degrees of freedoms not represented SV Singular Vectors All the rest Op.mal growth in reduced phase space, mimics growth along missing degrees of freedoms not represented

29 } WGNE needs help with it. } Physical basis Need good understanding of model errors, particularly related to physical parameterizations. } Critical for predictability Defines limit of predictability with perfect filtered state initial conditions. } Ensemble forecasting Only way to make ensemble members statistically indistinguishable from truth particularly at long lead times. } Data assimilation If done correctly, will accurately inform DA scheme of trustworthiness of first guess in error directions associated with stochastic effect of sub-filter scales.

30 Reliable ensemble system Make ensemble members sta.s.cally indis.nguishable from truth Defines limit of predictability with perfect filtered state ini.al condi.ons. RMS error t 0 t1 ensemble mean analysis t

31 Stochastic parameterization in DA t 0 t1 t Data assimila.on

32 Verification of surface analysis against independent observations V- 10m T- m CNTL SKEBS PHYS Ø Including a model- error representa.on reduces the RMS error of the surface analysis Ø SKEBS has smallest error for 10m- wind; PHYS for m temperature Ha et al 015, in review

33 Stochastic parameterization can replace inflation factor in DA Experiments shown so far employ adap.ve infla.on (Anderson, 009). Increases spread to remedy total effect of all causes of under dispersion (including but not limited to model error) Assimila.on with SKEBS + adap.ve infla.on is slightly bener than SKEBS alone. When used with SKEBS, infla.on has smaller magnitude.

34 Unified treatment of model- error in DA and forecast t 0 t1 Data assimila.on t t 0 Forecast

35 Reliability budget (Mark Rodwell)

36 Some early results 500 hpa height verification Use of analysis increments gives better spread Ensemble mean RMSE & spread Courtesy R. Swinbank

37 Impact of SPPT on sea surface temperature (SST) variability Coupled simula.ons with CAM4, Too much variability in SSTs in Tropical Pacific SPPT reduces bias in SST variability in Tropical Pacific How can a stochas.c parameteriza.on reduce variability?

38 Impact of SPPT on sea surface temperature (SST) variability Coupled simula.ons with CAM4, Too much variability in SSTs in Tropical Pacific SPPT reduces bias in SST variability in Tropical Pacific How can a stochas.c parameteriza.on reduce variability?

39 Other issues Should stochas.c parameteriza.ons be developed alongside physical parameteriza.ons, or added a posteriori As the resolu.on of numerical models - and thus the number of degrees of freedom increases, do we need less more more stochas.c parameteriza.ons? Disentangling ini.al condi.on and model error. Do we need less amplitude in stochas.c parameteriza.ons as our ini.al condi.ons get bener? Scale- aware (stochas.c) parameteriza.ons/concept from Sta.s.cal Mechanics: Can George s ideas be applied to other physical parameteriza.ons? Which ones? Next- next- genera.on NWP: Which processes should be targeted for stochas.c parameteriza.ons once models resolve convec.on

40 How can the PDEF working group help foster future research in this area Joint conference with WGNE on stochas.c parameteriza.on Conference on systema.c model- error STOCHMIP; TIGGE- STOCH; ERROR- MIP?? High- resolu.on and coarse- grained model output Recommenda.on from ECMWF Workshop on model- error (010) Recommenda.on from 009 ECSA Early Career science forum: Connec.ng Weather and Cimate Grants for internship/exchange visits for young scien.sts RO/OR; climate/weather

41 Collaboration with identified scientific challenges Overlap with DA: unified uncertainty representa.on for DA and forecasts (also with DAOS) Can mul.- model performance benefits be used to inform stochas.c physics (inverse approach)

42 Stochastic physics: a couple of issues for PDEF Richard Swinbank May 015

43 A couple of issues What are the most appropriate ways to represent model errors for convective- scale models? Use of data assimilation techniques to learn about model errors. How does that relate to use of stochastic parameterization?

44 Stochastic physics for the convective scale Most of the work on stochastic physics has focused on global NWP or climate models. Convective-scale models are becoming more widely used, and have different issues. How best to represent: a) Effect of model error on ensemble spread? b) Upscaling of sub-grid fluctuations (grey zone)? a) is what we re used to thinking about for ensemble forecasting. In MOGREPS-UK we are using random parameters (PANDOWAE talk), others use SPPT. Are these appropriate? How to represent surface uncertainties? What else is needed? b) is part of the model error, but is also important for deterministic forecasts.

45 Use of DA techniques to evaluate model error Chiara Piccolo & Mike Cullen have developed a technique which uses assimilation increments to estimate the model error. (Paper submitted to MWR). Random forcing terms are derived by sampling a dataset of historic analysis increments (same resolution and preferably for correct time of year). Assumes that model error statistics are stationary (i.e., no dependence on current model state). An alternative approach to stochastic physics a challenge that we need to address.

46 More results: 850 hpa wind speed Ensemble RMSE & spread (dashed) Ensemble RMSE & Control RMSE (dashed) Some Questions: Should this approach be used instead of or in combination with stochastic physics? How can we learn from this approach to improve our more physically-based representations of model error?

47 Discussion

48 extras

49 Stochastically perturbed tendency scheme (SPPT) Ra.onale: Especially as resolu.on increases, the equilibrium assump.on is no longer valid and fluctua.ons of the subgrid- scale state should be sampled (Buizza et al. 1999, Palmer et al. 009, Berner et al. 014) X t = DX + (r+1)px Local tendency for variable X Dynamical tendencies => Resolved scales Physical tendencies => Unresolved scales

50 Stochas.c Forcing PaNern Stochas.c- kine.c energy backscaner scheme (SKEBS) Ra.onale: A frac.on of the subgrid- scale energy is scanered upscale and acts as random streamfunc.on and temperature forcing for the resolved- scale flow. Here: simply considered as addi.ve noise with spa.al and temporal correla.ons Similar to ECMWF global ensemble system (ShuNs 005, Berner et. al 08,09) but with constant dissipa.on rate and poten.al temperature perturba.ons (Berner et al. 011). a)

51 Stochastic kinetic- energy backscatter a) scheme Rationale: A fraction of the dissipated energy is scattered upscale and acts as streamfunction forcing for the resolved-scale flow Δψ * Dψ ψ Total Dissipation rate from numerical dissipation, convection, gravity/mountain wave drag. Spectral Markov chain: temporal and spatial correlations prescribed

52 Notes Past adanvcces Not easy to point to a single process that improved NWP, already close collabora.on between RD and OD Translate process understanding to skill score But: Quantum leap e.g. in convec.ve processes if you think MJO is effec.ing extra- topics, you need to get tropical convec.on right Metrics releavnt for end- users might not be for dynamicists A single forecast can destroy seasaonl skill scores not necessarily a bad system Compensa.ng model- errors, good analysis or reforecasts are necessary to assess Forecast skill vs dynamical undertsanding ; numerical models as tool for undertsandin rathe rtan forecast Coupled porcesses / air seas interca.on etc. ; more complexity; more consistentcy between physical parametriza.ons TIGGE/YOTC/refroecasts dataset have been very valubel from THORPRXa and numcerial models ; reasrch/operabon ovelap Future challenges and demands model- error representa.on in DA/uncertaimty ; process- based uncertanty how to do this is s.ll open ; enganee academic community High- freqeuncy, high- resoltuipn limited area models => what can they provide?

53 Stochastic parameterizations increase skill a) Zonal Wind U at 700hPa b) Temperature T at 700hPa Spread;Error c) 0.1 d) 0.06 Brier Score CNTL SPPT SKEBS PHYS10 PHYS10_SKEBS PHYS3_SKEBS f) x Berner et al., 015