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

Slides:

Advertisements

Similar presentations

Filtering for High Dimension Spatial Systems Jonathan Briggs Department of Statistics University of Auckland.

Advertisements

On the importance of meteorological downscaling for short, medium and long-range hydrological ensemble prediction over France G. Thirel (1), F. Regimbeau.

Sharing Experiences in Operational Consensus Track Forecasting Rapporteur: Andrew Burton Team members: Philippe Caroff, James Franklin, Ed Fukada, T.C.

A unified linear Model Output Statistics scheme for both deterministic and ensemble forecasts S. Vannitsem Institut Royal Météorologique de Belgique Monterey,

Report of the Q2 Short Range QPF Discussion Group Jon Ahlquist Curtis Marshall John McGinley - lead Dan Petersen D. J. Seo Jean Vieux.

A Brief Guide to MDL's SREF Winter Guidance (SWinG) Version 1.0 January 2013.

Comparing and Contrasting Post-processing Approaches to Calibrating Ensemble Wind and Temperature Forecasts Tom Hopson Luca Delle Monache, Yubao Liu, Gregory.

Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss Statistical adaptation of COSMO predictions with the Kalman.

Bayesian methods for combining climate forecasts (*): Department of Meteorology, The University of Reading 1.Introduction 2.Conditioning and Bayes’ theorem.

Gridded OCF Probabilistic Forecasting For Australia For more information please contact © Commonwealth of Australia 2011 Shaun Cooper.

Statistical Postprocessing of Weather Parameters for a High-Resolution Limited-Area Model Ulrich Damrath Volker Renner Susanne Theis Andreas Hense.

Similar Day Ensemble Post-Processing as Applied to Wildfire Threat and Ozone Days Michael Erickson 1, Brian A. Colle 1 and Joseph J. Charney 2 1 School.

1 Kalman filter, analog and wavelet postprocessing in the NCAR-Xcel operational wind-energy forecasting system Luca Delle Monache Research.

Convection-permitting forecasts initialized with continuously-cycling limited-area 3DVAR, EnKF and “hybrid” data assimilation systems Craig Schwartz and.

Performance Characteristics of a Pseudo-operational Ensemble Kalman Filter April 2006, EnKF Wildflower Meeting Greg Hakim & Ryan Torn University of Washington.

Seasonal Volume Forecasts Using Ensemble Streamflow Prediction for the 2008 Water Year Ray Fukunaga, Senior Hydrologist Northwest River Forecast Center.

Seasonal Volume Forecasts Using Ensemble Streamflow Prediction for the 2008 Water Year Steve King, Hydrologist Northwest River Forecast Center.

Data assimilation Derek Karssenberg, Faculty of Geosciences, Utrecht University.

Department of Meteorology and Geophysics University of Vienna since 1851 since 1365 TOWARDS AN ANALYSIS ENSEMBLE FOR NWP-MODEL VERIFICATION Manfred Dorninger,

Introduction to Numerical Weather Prediction and Ensemble Weather Forecasting Tom Hamill NOAA-CIRES Climate Diagnostics Center Boulder, Colorado USA.

Ensemble Post-Processing and it’s Potential Benefits for the Operational Forecaster Michael Erickson and Brian A. Colle School of Marine and Atmospheric.

A comparison of hybrid ensemble transform Kalman filter(ETKF)-3DVAR and ensemble square root filter (EnSRF) analysis schemes Xuguang Wang NOAA/ESRL/PSD,

NCAR Efficient Production of High Quality, Probabilistic Weather Forecasts F. Anthony Eckel National Weather Service Office of Science and Technology,

Chapter 13 – Weather Analysis and Forecasting. The National Weather Service The National Weather Service (NWS) is responsible for forecasts several times.

Forecasting wind for the renewable energy market Matt Pocernich Research Applications Laboratory National Center for Atmospheric Research

Rank Histograms – measuring the reliability of an ensemble forecast You cannot verify an ensemble forecast with a single.

Tutorial. Other post-processing approaches … 1) Bayesian Model Averaging (BMA) – Raftery et al (1997) 2) Analogue approaches – Hopson and Webster, J.

STEPS: An empirical treatment of forecast uncertainty Alan Seed BMRC Weather Forecasting Group.

Use of sea level observations in DMIs storm surge model Jacob L. Høyer, Weiwei Fu, Kristine S. Madsen & Lars Jonasson Center for Ocean and Ice, Danish.

Celeste Saulo and Juan Ruiz CIMA (CONICET/UBA) – DCAO (FCEN –UBA)

11 th JCSDA Science Workshop on Satellite Data Assimilation Latest Developments on the Assimilation of Cloud-Affected Satellite Observations Tom Auligné.

Accounting for Uncertainties in NWPs using the Ensemble Approach for Inputs to ATD Models Dave Stauffer The Pennsylvania State University Office of the.

Soil moisture generation at ECMWF Gisela Seuffert and Pedro Viterbo European Centre for Medium Range Weather Forecasts ELDAS Interim Data Co-ordination.

Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss Accounting for Change: Local wind forecasts from the high-

MODEL ERROR ESTIMATION EMPLOYING DATA ASSIMILATION METHODOLOGIES Dusanka Zupanski Cooperative Institute for Research in the Atmosphere Colorado State University.

Probabilistic Forecasting. pdfs and Histograms Probability density functions (pdfs) are unobservable. They can only be estimated. They tell us the density,

ENSEMBLES RT4/RT5 Joint Meeting Paris, February 2005 Overview of the WP5.3 Activities Partners: ECMWF, METO/HC, MeteoSchweiz, KNMI, IfM, CNRM, UREAD/CGAM,

Model Post Processing. Model Output Can Usually Be Improved with Post Processing Can remove systematic bias Can produce probabilistic information from.

. Outline  Evaluation of different model-error schemes in the WRF mesoscale ensemble: stochastic, multi-physics and combinations thereof  Where is.

Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss Local Probabilistic Weather Predictions for Switzerland.

Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss WG 4 activities.

© Crown copyright Met Office The EN4 dataset of quality controlled ocean temperature and salinity profiles and monthly objective analyses Simon Good.

A Multi-Model Hydrologic Ensemble for Seasonal Streamflow Forecasting in the Western U.S. Theodore J. Bohn, Andrew W. Wood, Ali Akanda, and Dennis P. Lettenmaier.

WWRP_WSN05, Toulouse, 5-9 September 2005 / 1 Pertti Nurmi Juha Kilpinen Sigbritt Näsman Annakaisa Sarkanen ( Finnish Meteorological.

Potential for medium range global flood prediction Nathalie Voisin 1, Andrew W. Wood 1, Dennis P. Lettenmaier 1 1 Department of Civil and Environmental.

COSMO-DE-EPS Susanne Theis, Christoph Gebhardt, Michael Buchhold,

Statistical Post Processing - Using Reforecast to Improve GEFS Forecast Yuejian Zhu Hong Guan and Bo Cui ECM/NCEP/NWS Dec. 3 rd 2013 Acknowledgements:

Eawag: Swiss Federal Institute of Aquatic Science and Technology Mechanism-Based Emulation of Dynamic Simulation Models – Concept and Application in Hydrology.

Introduction to the WRF Modeling System

Judith Curry James Belanger Mark Jelinek Violeta Toma Peter Webster 1

VERIFICATION OF A DOWNSCALING SEQUENCE APPLIED TO MEDIUM RANGE METEOROLOGICAL PREDICTIONS FOR GLOBAL FLOOD PREDICTION Nathalie Voisin, Andy W. Wood and.

EVALUATION OF A GLOBAL PREDICTION SYSTEM: THE MISSISSIPPI RIVER BASIN AS A TEST CASE Nathalie Voisin, Andy W. Wood and Dennis P. Lettenmaier Civil and.

Demonstration and Comparison of Sequential Approaches for Altimeter Data Assimilation in HYCOM A. Srinivasan, E. P. Chassignet, O. M. Smedstad, C. Thacker,

NOAA Northeast Regional Climate Center Dr. Lee Tryhorn NOAA Climate Literacy Workshop April 2010 NOAA Northeast Regional Climate.

QJ Wang and Andrew Schepen LAND AND WATER CBaM for post-processing seasonal climate forecasts.

Improving Numerical Weather Prediction Using Analog Ensemble Presentation by: Mehdi Shahriari Advisor: Guido Cervone.

Nathalie Voisin1 , Andrew W. Wood1 , Dennis P. Lettenmaier1 and Eric F

A Guide to Tropical Cyclone Guidance

Tom Hopson, Jason Knievel, Yubao Liu, Gregory Roux, Wanli Wu

Precipitation Products Statistical Techniques

Nathalie Voisin, Andy W. Wood and Dennis P. Lettenmaier

Improving forecasts through rapid updating of temperature trajectories and statistical post-processing Nina Schuhen, Thordis L. Thorarinsdottir and Alex.

Post Processing.

Post Processing.

COSMO-DE-EPS Susanne Theis, Christoph Gebhardt, Michael Buchhold,

Verifying Ensemble Forecasts

MOGREPS developments and TIGGE

Defining the Products: ‘GSICS Correction’

Rapid Adjustment of Forecast Trajectories: Improving short-term forecast skill through statistical post-processing Nina Schuhen, Thordis L. Thorarinsdottir.

Jie He, Fuqing Zhang, Yinghui Lu, Yunji Zhang

Presentation transcript:

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

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

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

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

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

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...

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...

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)...

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...

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)...

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)...

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...

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

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

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  Roland Stull

16