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A community statistical post-processing system Thomas Nipen and Roland Stull University of British Columbia.

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Presentation on theme: "A community statistical post-processing system Thomas Nipen and Roland Stull University of British Columbia."— Presentation transcript:

1 A community statistical post-processing system Thomas Nipen and Roland Stull University of British Columbia

2 Motivation 2 Post processing NWP model Data assimilation NWP products NWP products

3 Component approach 3 Post processing Data assimilation NWP model (e.g. WRF) NWP products NWP products Microphysics Radiation Surface Land-surface Boundary layer... NWP model

4 Component approach 4 Post processing NWP model Data assimilation Statistical model Ensemble Probabilistic Deterministic Uncertainty Deterministic Calibration Updating Downscaling Selection Correction NWP products NWP products

5 Statistical model Ensemble Probabilistic Deterministic Uncertainty Deterministic Calibration Updating Downscaling Selection Correction Component approach GoalSchemes 5

6 Statistical model Ensemble Probabilistic Deterministic Uncertainty Deterministic Calibration Updating Downscaling Selection Correction Ensemble member selection Goal Select ensemble members Schemes 6 NWP ensemble Climatology Analogs 1 1 Delle Monache et al. (2011) Ensemble Input data...

7 Statistical model Ensemble Probabilistic Deterministic Uncertainty Deterministic Calibration Updating Downscaling Selection Correction Downscaling Goal Downscale to output locations Schemes 7 Nearest neighbour Linear interpolation Spline interpolation...

8 Statistical model Ensemble Probabilistic Deterministic Uncertainty Deterministic Calibration Updating Downscaling Selection Correction Goal Bias-correct the ensemble Schemes 8 Multivariate regression 1 Kalman Filtering 2 1 Glahn&Lowry (1972) 2 Homleid (1995)...

9 Statistical model Ensemble Probabilistic Deterministic Uncertainty Deterministic Calibration Updating Downscaling Selection Correction Deterministic Goal Convert to deterministic form Schemes 9 Ensemble mean Ensemble median Weighted average Ensemble mean...

10 Statistical model Ensemble Probabilistic Deterministic Uncertainty Deterministic Calibration Updating Downscaling Selection Correction Uncertainty Goal Convert to probabilistic form Schemes 10 Ensemble MOS 1 Bayesian model averaging 2 1 Gneiting et al. (2005) 2 Raftery et al. (2005)...

11 Statistical model Ensemble Probabilistic Deterministic Uncertainty Deterministic Calibration Updating Downscaling Selection Correction Calibration Goal Remove distributional bias Schemes 11 Quantile regression 1 PIT-based 2 1 Bremnes (2004) 2 Nipen&Stull (2011)...

12 Statistical model Ensemble Probabilistic Deterministic Uncertainty Deterministic Calibration Updating Downscaling Selection Correction Updating Goal Incorporate recent observations Schemes 12 PIT-based 1 1 Nipen,West&Stull (2011) Observations...

13 Potential uses 13 Research Simplifies development of new methods Offers facilities for comparing to existing methods Operational

14 Potential uses 14 Selection Analogs Downscaling Nearest N. Correction Kalman Filter... Selection NWP ens. Downscaling Nearest N. Correction Regression... Combination 1Combination 2 Operational Different combinations yield different results Research Simplifies development of new methods Offers facilities for comparing to existing methods

15 Version 1.0 Available fall 2012 Implement all schemes presented here Ability to contribute new schemes Input/output formats: Flat files NetCDF GRIB 15 For more information Thomas Nipen (tnipen@eos.ubc.ca)  Roland Stull (rstull@eos.ubc.ca) http://weather.eos.ubc.ca

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