DTC & NUOPC Ensemble Design Workshop, Sep 10-13, Boulder, CO
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1 DTC & NUOPC Ensemble Design Workshop, Sep 10-13, Boulder, CO
2 Key points There is model uncertainty in weather prediction. It is essential to represent model uncertainty. Stochastic parameterizations and multi-models are ways to represent this uncertainty and increase forecast skill
3 Outline Comparison of a stochastic kinetic-energy backscatter scheme (SKEBS) and multi-physics (PHYS) in the WRF mesoscale ensemble. Impact on forecast skill Where is the skill coming from? Just from an increased spread? Structural differences in different model-error scheme Skill of recalibrated ensemble systems
4 Stochastic parameterization schemes Stochastic kinetic-energy backscatter scheme (SKEBBS) Rationale: A fraction of the dissipated kinetic-energy is scattered upscale and available as forcing for the resolved flow (Shutts, 2005) Stochastically perturbed parameterization scheme (SPPT) Rationale: Especially as resolution increases, the equilibrium assumption is no longer valid and fluctuations of the subgrid-scale state should be sampled (Buizza et al. 1999)
5 But Istvan, DTC-NYPC, Sep 11: This doesn t mean they are fundamentally right
6 Potential to reduce model error Stochastic parameterizations can change the mean and variance of a PDF Potential Impacts variability of model (e.g. internal variability of the atmosphere) Impacts systematic error (e.g. blocking precipitation error) PDF Weak noise Strong noise Unimodal Multi-modal
7 Bias of z500 in ECMWF IFS CNTL SKEBS Berner et al. 2012
8 Experimental Setup Weather Research and Forecast Model 15 dates between Nov 2008 and Dec 2009, 00Z and 12Z, 30 cycles or cases 40km horizontal resolution and 41 vertical levels Limited area model: Continuous United States (CONUS) Initial and boundary conditions from GFS (downscaled from NCEPs Global Forecast System) Ensemble CNTL: 10 member ensemble with control physics Ensemble PHYS: 10 member ensemble with multi-physics scheme Ensemble STOCH: 10 member ensemble with backscatter scheme Analysis performed in model space for U,V; analysis error not taken into account
9 Multi-Physics combinations
10 Spread U CNTL SKEBS PHYS Spread thick RMS Error thin Berner et al. 2011
11 Brierscore, U CNTL SKEBS PHYS Spread thick RMS Error thin Small Brier score denotes best forecast. Diamonds denote significance at 95% confidence level. Berner et al. 2011
12 Is it just spread? CNTL SKEBS PHYS Spread thick RMS Error thin Berner et al. 2011
13 Is it just spread? CNTL SKEBS PHYS Spread thick RMS Error thin Inflate variance to have comparable spreads
14 Calibration of ensemble systems Two conditions: 1. The standard deviation of the inflated prediction is the same as that of the reference analysis (Hamill and Colucci, 1998) 2. The potentially predictable signal after inflation, as defined in Kharin and is made equal to the correlation of the ensemble mean with the observations (Kharin and Zwiers, 2003a,b)
15 Impact of calibration on spread
16 Brierscore, U, raw CNTL SKEBS PHYS Spread thick RMS Error thin
17 Brierscore, U, calibrated CNTL SKEBS PHYS Spread thick RMS Error thin
18 Brierscore, U, calibrated CNTL SKEBS PHYS Spread thick RMS Error thin Not debiased
19 Brierscore, U, uncalibrated, biased CNTL SKEBS PHYS Spread thick RMS Error thin
20 Brierscore, U, calibrated, biased CNTL SKEBS PHYS Spread thick RMS Error thin
21 Brierscore, U, calibrated, debiased CNTL SKEBS PHYS Spread thick RMS Error thin
22 Scatterplot of impact on Brierscore of u and v, raw ensemble
23 Summary of impact in U,V
24 Conclusions Analysis error not taken into account!!! Including a model-error representation increases forecast skill SKEBS outperforms PHYS except at the surface (modelspecific) for the raw ensemble system Most of the benefit comes from increasing spread In recalibrated, biased ensemble forecast SKEBS still has slightly more skill than CNTL PHYS ensemble benefits from the different member biases
25 How to design an ensemble system: Multi-models Stochastic parameterizations Resources, resources, resources Each member has different invariant distribution/climatology Pro: Samples bias in different members Pro: Different physical parameterizations will perform differently for a given atmospheric state All members have the same underlying distribution Pro/Cons: All members have same bias Pro: As the core models improve all members improve at the same time Con: Not a distribution, ensemble of opportunity
26 Dependency of Multi-Models CAM3.5 CAM5 If many models agree, how do you know if they are correct or just related? Masson and Knutti, 2008
27 Extras
28 CNTL PHYS STOCH WRF-DART:RMS innovations of T2
29 Taylor diagrams for Spread and RMS error for a leadtime of 60h
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