1 / 16

A community statistical post-processing system

A community statistical post-processing system. Thomas Nipen and Roland Stull University of British Columbia. Motivation. Data assimilation. NWP model. Post processing. NWP products. Component approach. NWP model (e.g. WRF). Land-surface. Microphysics. Boundary layer. Radiation. .

trent
Télécharger la présentation

A community statistical post-processing system

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

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

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

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

  5. Component approach Goal Schemes

  6. Ensemble member selection Goal Select ensemble members Schemes NWP ensemble Climatology Analogs1 • Ensemble • Input data ... 1Delle Monache et al. (2011)

  7. Downscaling Goal Downscale to output locations Schemes Nearest neighbour Linear interpolation Spline interpolation ...

  8. Correction Goal Bias-correct the ensemble Schemes Multivariate regression1 Kalman Filtering2 ... 1Glahn&Lowry (1972) 2Homleid (1995)

  9. Deterministic Goal Convert to deterministic form Schemes Ensemble mean Ensemble median Ensemble mean Weighted average ...

  10. Uncertainty Goal Convert to probabilistic form Schemes Ensemble MOS1 Bayesian model averaging2 ... 1Gneiting et al. (2005) 2Raftery et al. (2005)

  11. Calibration Goal Remove distributional bias Schemes Quantile regression1 PIT-based2 ... 1Bremnes (2004) 2Nipen&Stull (2011)

  12. Updating Goal Incorporate recent observations Schemes PIT-based1 ... Observations 1Nipen,West&Stull (2011)

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

  14. Potential uses Operational • Different combinations yield different results Research • Simplifies development of new methods • Offers facilities for comparing to existing methods Combination 1 Combination 2 SelectionAnalogs SelectionNWP ens. DownscalingNearest N. DownscalingNearest N. CorrectionKalman Filter CorrectionRegression ... ...

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

More Related