Ting Lei, Xuguang Wang University of Oklahoma, Norman, OK, USA. Wang and Lei, MWR, Daryl Kleist (NCEP): dual resolution 4DEnsVar
|
|
- Arron Wilkinson
- 5 years ago
- Views:
Transcription
1 GSI-based four dimensional ensemble-variational (4DEnsVar) data assimilation: formulation and single resolution experiments with real data for NCEP GFS Ting Lei, Xuguang Wang University of Oklahoma, Norman, OK, USA Wang and Lei, MWR, 203 Daryl Kleist (NCEP): dual resolution 4DEnsVar Acknowledgement: NCEP DA team (John Derber, Dave Parrish, Russ Treadon, Miodrag Rancic) and Jeff Whitaker (NOAA ESRL) 6 th WMO Symposium on data assimilation College Park, MD, USA Oct. 7-, 203
2 Background Over the past three years, significant efforts were conducted to develop GSI hybrid system and test with US operational Global Forecast System (GFS). The GSI hybrid DA system showed significant improvement compared to GSI 3DVAR and became operational on May 22, 202 for GFS. It has also been extended to a 4DEnsVar hybrid ( Tangent linear and adjoint model free ) and showed further improvements. Efforts are being conducted to further develop and research GSI hybrid DA for operational regional forecast systems, e.g., Xu Lu poster on GSI hybrid for Hurricane-WRF (HWRF) Xuguang Wang Thur. talk on convective scale weather over CONUS 2 2
3 GSI-based hybrid ensemble-variational DA system member forecast member 2 forecast member k forecast EnKF Ensemble covariance EnKF analysis EnKF analysis 2 EnKF analysis k Re-center EnKF analysis ensemble to control analysis member analysis member 2 analysis member k analysis member forecast member 2 forecast member k forecast control forecast GSI control analysis control forecast data assimilation Wang, Parrish, Kleist, Whitaker, MWR, 203 First guess forecast 3
4 Various hybrid schemes Wang and Lei, MWR, 203 4
5 NCEP pre-implementation test of 3DEnsVar hybrid Significant improvement of operational GFS forecasts 5
6 GSI hybrid for GFS: GSI 3DVar vs. 3DEnsVar vs. EnKF Hybrid was better than 3DVar due to use of flow-dependent ensemble covariance Hybrid was better than EnKF due to the use of tangent linear normal mode balance constraint (TLNMC) Wang, Parrish, Kleist and Whitaker, MWR, 203 6
7 GSI-based 4DEnsVar hybrid: motivation Account for temporal evolution of error covariance that was ignored in 3DEnsVar hybrid. Still need to improve computational efficiency of traditional TL/ADJ 4DVAR being developed for GSI (Rancic et al. 202). An alternative to TL/ADJ 4DVar, i.e., 4D-Ensemble-Variational method (4DEnsVar) is therefore developed Conveniently avoid TL/ADJ of forecast model like earlier work and also applied localization inside variational minimization (e.g., Buehner et al. 200) 7
8 GSI-based 4DEnsVar hybrid: formulation Naturally extended from and unified with GSI-based 3DEnsVar hybrid formula (Wang 200, MWR), which uses extended control variables to incorporate ensemble like in Lorenc 2003, Buehner 2005, Wang et al., 2007, Wang et al. 2008) Add time dimension in 4DEnsVar ' J x, α J J J ' t x 2 ' T 2 B x x α (x ' k K k e static e k ) t o x ' 2 2 α T C α 2 T o T - o ( y t t '-H t y tx ) R t ( t '-Ht x t ) B stat 3DVAR static covariance; R observation error covariance; K ensemble size; C correlation matrix for ensemble covariance localization; x kth ensemble perturbation; ' ' o' x 3DVAR increment; x total (hybrid) increment; y innovation vector; H linearized observation operator; weighting coefficient for static covariance; weighting coefficient for ensemble covariance; α extended control variable. 2 e k 8
9 Experiment Design Time period: Aug Sep ; Model: GFS T90L64 Observations: all operational data Verification: global forecast and hurricane track forecasts. Experiment GSI3DVar 4DEnsVar 4DEnsVar-2hr 3DEnsVar 4DEnsVar-nbc Description The GSI 3DVar experiment 4D ensemble-variational DA experiment with hourly ensemble perturbations 4D ensemble-variational DA experiment with 2-hourly ensemble perturbations 3D ensemble-variational DA experiment Same as 4DEnsVar-2hr except without the use of the tangent linear normal mode balance constraint (TLNMC) 9
10 One obs. example for TC 3h increment propagated by model integration 4DEnsVar 3DEnsVar t=0 t=0 t=0 * -3h 0 3h time 0
11 Another example Temp. Height Downstream impact Upstream impact
12 Global forecasts verified against ECMWF analyses Forecasts from 3DEnsVar are more skillful than GSI3DVar. 4DEnsVar further improves the skill of the forecasts compared to 3DEnsVar. The improvement of 4DEnsVar relative to 3DEnsVar is smaller than the improvement of 3DEnsVar relative to GSI3DVar. 2
13 6-hour forecasts verified against conv. obs. 3DEnsVar and 4DEnsVar are more accurate than GSI3DVar at most levels. More appreciable improvement is seen in the wind than the temperature forecasts. Over NH and SH, 4DEnsVar shows consistent improvement relative to 3DEnsVar for wind forecasts and neutral or slightly positive impact for temperature forecast. K m/s Over TR, 4DEnsVar shows mostly neutral impact compared to 3DEnsVar for both wind and temperature forecasts (not 3 show).
14 96-hour forecasts verified against conv. obs. Temperature forecasts from 4DEnsVar show overall positive impact relative to 3DEnsVar for both NH and SH. 4DEnsVar shows neutral impact on wind forecasts over NH and positive impact over SH. Over TR, 4DEnsVar shows positive impact relative to 3DEnsVar only for wind forecasts at low level (not show) K m/s 4
15 Verification of hurricane track forecasts: cases 6 named storms in Atlantic and Pacific basins during 200 5
16 Verification of hurricane track forecasts: RMSE and percentage of better forecast 3DEnsVar outperforms GSI3DVar. 4DEnsVar are more accurate than 3DEnsVar after the 2- day forecast lead time. 6
17 Impact of number of time levels of ensemble perturbations and TLNMC Negative impact of less time levels of ensemble perturbations. Positive impact of TLNMC for global forecasts. 7
18 Impact of number of time levels of ensemble perturbations and TLNMC Negative impact of less time levels of ensemble perturbations Negative impact of TLNMC on TC track forecasts 8
19 Convergence rate and computational cost Slightly slower convergence for the first outer loop and slightly faster convergence for the second outer loop The cost of 4DEnsVar variational minimization is approximately.5 times of that of 3DEnsVar. 9
20 Summary and Discussion GSI based 4DEnsVar was developed and tested for NCEP GFS. 4DEnsVar further improved upon 3DEnsVar At short lead times, the improvement of 4DEnsVar relative to 3DEnsVar over NH was similar to that over SH. At longer forecast lead times, 4DEnsVar showed more improvement in SH than in NH. The improvement of 4DEnsVar over TR was neutral or slightly positive when forecasts were verified against the in-situ observations. The hurricane track forecasts initialized by 4DEnsVar were more accurate than 3DEnsVar after the 2-day forecast lead time. Temporal localization is being developed within GSI-based 4DEnsVar. Preliminary tests showed positive impact of the temporal localization. Further development of TLNMC. Tests of 4DEnsVar at dual resolution mode (Daryl Kleist) 20
21 Cited references Buehner, M., 2005: Ensemble-derived stationary and flow-dependent background-error covariances: evaluation in a quasi-operational NWP setting.quart. J. Roy. Meteor. Soc.,3, Buehner, M.,P. L. Houtekamer, C. Charette, H. L. Mitchell, and B. He, 200: Intercomparison of Variational Data Assimilation and the Ensemble Kalman Filter for Global Deterministic NWP. Part I: Description and Single-Observation Experiments. Mon. Wea. Rev.,38, Lorenc, A. C. 2003: The potential of the ensemble Kalman filter for NWP a comparison with 4D-VAR. Quart. J. Roy. Meteor. Soc.,29, Wang, X., C. Snyder, and T. M. Hamill, 2007: On the theoretical equivalence of differently proposed ensemble/3d-var hybrid analysis schemes. Mon. Wea. Rev., 35, Wang, X., D. Barker, C. Snyder, T. M. Hamill, 2008: A hybrid ETKF-3DVAR data assimilation scheme for the WRF model. Part I: observing system simulation experiment. Mon. Wea. Rev., 36, Wang, X., 200: Incorporating ensemble covariance in the Gridpoint Statistical Interpolation (GSI) variational minimization: a mathematical framework. Mon. Wea. Rev., 38, Wang, X., D. Parrish, D. Kleist and J. S. Whitaker, 203: GSI 3DVar-based Ensemble-Variational Hybrid Data Assimilation for NCEP Global Forecast System: Single Resolution Experiments. Mon. Wea. Rev., in press. 2
Development and research of GSI based hybrid EnKF Var data assimilation for HWRF to improve hurricane prediction
Development and research of GSI based hybrid EnKF Var data assimilation for HWRF to improve hurricane prediction Xuguang Wang, Xu Lu, Yongzuo Li School of Meteorology University of Oklahoma, Norman, OK,
More informationHybrid Variational Ensemble Data Assimilation for Tropical Cyclone
Hybrid Variational Ensemble Data Assimilation for Tropical Cyclone Forecasts Xuguang Wang School of Meteorology University of Oklahoma, Norman, OK Acknowledgement: OU: Ting Lei, Yongzuo Li, Kefeng Zhu,
More informationEnsemble 4DVAR for the NCEP hybrid GSI EnKF data assimilation system and observation impact study with the hybrid system
Ensemble 4DVAR for the NCEP hybrid GSI EnKF data assimilation system and observation impact study with the hybrid system Xuguang Wang School of Meteorology University of Oklahoma, Norman, OK OU: Ting Lei,
More informationEnsemble 4DVAR and observa3on impact study with the GSIbased hybrid ensemble varia3onal data assimila3on system. for the GFS
Ensemble 4DVAR and observa3on impact study with the GSIbased hybrid ensemble varia3onal data assimila3on system for the GFS Xuguang Wang University of Oklahoma, Norman, OK xuguang.wang@ou.edu Ting Lei,
More informationRetrospective and near real-time tests of GSIbased EnKF-Var hybrid data assimilation system for HWRF with airborne radar data
Retrospective and near real-time tests of GSIbased EnKF-Var hybrid data assimilation system for HWRF with airborne radar data Xuguang Wang, Xu Lu, Yongzuo Li University of Oklahoma, Norman, OK In collaboration
More informationGSI 3DVar-based Ensemble-Variational Hybrid Data Assimilation for NCEP Global Forecast System: Single Resolution Experiments
1 2 GSI 3DVar-based Ensemble-Variational Hybrid Data Assimilation for NCEP Global Forecast System: Single Resolution Experiments 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
More informationXuguang Wang and Ting Lei. School of Meteorology, University of Oklahoma and Center for Analysis and Prediction of Storms, Norman, OK.
1 2 3 GSI-based four dimensional ensemble-variational (4DEnsVar) data assimilation: formulation and single resolution experiments with real data for NCEP Global Forecast System 4 5 6 7 8 9 10 11 12 13
More informationGSI 3DVar-Based Ensemble Variational Hybrid Data Assimilation for NCEP Global Forecast System: Single-Resolution Experiments
4098 M O N T H L Y W E A T H E R R E V I E W VOLUME 141 GSI 3DVar-Based Ensemble Variational Hybrid Data Assimilation for NCEP Global Forecast System: Single-Resolution Experiments XUGUANG WANG School
More informationImproving GFS 4DEnVar Hybrid Data Assimilation System Using Time-lagged Ensembles
Improving GFS 4DEnVar Hybrid Data Assimilation System Using Time-lagged Ensembles Bo Huang and Xuguang Wang School of Meteorology University of Oklahoma, Norman, OK, USA Acknowledgement: Junkyung Kay (OU);
More informationHybrid variational-ensemble data assimilation. Daryl T. Kleist. Kayo Ide, Dave Parrish, John Derber, Jeff Whitaker
Hybrid variational-ensemble data assimilation Daryl T. Kleist Kayo Ide, Dave Parrish, John Derber, Jeff Whitaker Weather and Chaos Group Meeting 07 March 20 Variational Data Assimilation J Var J 2 2 T
More informationMultivariate Correlations: Applying a Dynamic Constraint and Variable Localization in an Ensemble Context
Multivariate Correlations: Applying a Dynamic Constraint and Variable Localization in an Ensemble Context Catherine Thomas 1,2,3, Kayo Ide 1 Additional thanks to Daryl Kleist, Eugenia Kalnay, Takemasa
More informationAssimilation of Airborne Doppler Radar Observations Using the Unified GSI based Hybrid Ensemble Variational Data Assimilation System for HWRF
Assimilation of Airborne Doppler Radar Observations Using the Unified GSI based Hybrid Ensemble Variational Data Assimilation System for HWRF Xuguang Wang xuguang.wang@ou.edu University of Oklahoma, Norman,
More informationP 1.86 A COMPARISON OF THE HYBRID ENSEMBLE TRANSFORM KALMAN FILTER (ETKF)- 3DVAR AND THE PURE ENSEMBLE SQUARE ROOT FILTER (EnSRF) ANALYSIS SCHEMES
P 1.86 A COMPARISON OF THE HYBRID ENSEMBLE TRANSFORM KALMAN FILTER (ETKF)- 3DVAR AND THE PURE ENSEMBLE SQUARE ROOT FILTER (EnSRF) ANALYSIS SCHEMES Xuguang Wang*, Thomas M. Hamill, Jeffrey S. Whitaker NOAA/CIRES
More informationComparing Variational, Ensemble-based and Hybrid Data Assimilations at Regional Scales
Comparing Variational, Ensemble-based and Hybrid Data Assimilations at Regional Scales Meng Zhang and Fuqing Zhang Penn State University Xiang-Yu Huang and Xin Zhang NCAR 4 th EnDA Workshop, Albany, NY
More informationHybrid Variational-Ensemble Data Assimilation at NCEP. Daryl Kleist
Hybrid Variational-Ensemble Data Assimilation at NCEP Daryl Kleist NOAA/NWS/NCEP/EMC with acnowledgements to Kayo Ide, Dave Parrish, Jeff Whitaer, John Derber, Russ Treadon, Wan-Shu Wu, Jacob Carley, and
More informationTesting and Evaluation of GSI Hybrid Data Assimilation for Basin-scale HWRF: Lessons We Learned
4th NOAA Testbeds & Proving Ground Workshop, College Park, MD, April 2-4, 2013 Testing and Evaluation of GSI Hybrid Data Assimilation for Basin-scale HWRF: Lessons We Learned Hui Shao1, Chunhua Zhou1,
More informationUniv. of Maryland-College Park, Dept. of Atmos. & Oceanic Science. NOAA/NCEP/Environmental Modeling Center
The Tangent Linear Normal Mode Constraint in GSI: Applications in the NCEP GFS/GDAS Hybrid EnVar system and Future Developments Daryl Kleist 1 David Parrish 2, Catherine Thomas 1,2 1 Univ. of Maryland-College
More informationComparisons between 4DEnVar and 4DVar on the Met Office global model
Comparisons between 4DEnVar and 4DVar on the Met Office global model David Fairbairn University of Surrey/Met Office 26 th June 2013 Joint project by David Fairbairn, Stephen Pring, Andrew Lorenc, Neill
More informationDevelopment and Research of GSI-based EnVar System to Assimilate Radar Observations for Convective Scale Analysis and Forecast
Development and Research of GSI-based EnVar System to Assimilate Radar Observations for Convective Scale Analysis and Forecast Xuguang Wang, Yongming Wang School of Meteorology University of Oklahoma,
More information(Toward) Scale-dependent weighting and localization for the NCEP GFS hybrid 4DEnVar Scheme
(Toward) Scale-dependent weighting and localization for the NCEP GFS hybrid 4DEnVar Scheme Daryl Kleist 1, Kayo Ide 1, Rahul Mahajan 2, Deng-Shun Chen 3 1 University of Maryland - Dept. of Atmospheric
More informationAssimilation of Radar Radial Velocity Data with the WRF Hybrid Ensemble-3DVAR System for the Prediction of Hurricane Ike (2008)
Manuscript (non-latex) Click here to download Manuscript (non-latex): Li-Wang-Xue_MWR-D-12-00043.doc 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Assimilation of
More informationDevelopment and Research of GSI- based Var/EnKF/EnVar/ Hybrid System to Assimilate Radar ObservaBons for ConvecBve Scale NWP
Development and Research of GSI- based Var/EnKF/EnVar/ Hybrid System to Assimilate Radar ObservaBons for ConvecBve Scale NWP Xuguang Wang School of Meteorology University of Oklahoma, Norman, OK D Acknowledgement
More informationThe Structure of Background-error Covariance in a Four-dimensional Variational Data Assimilation System: Single-point Experiment
ADVANCES IN ATMOSPHERIC SCIENCES, VOL. 27, NO. 6, 2010, 1303 1310 The Structure of Background-error Covariance in a Four-dimensional Variational Data Assimilation System: Single-point Experiment LIU Juanjuan
More informationMesoscale Ensemble Data Assimilation: Opportunities and Challenges. Fuqing Zhang Penn State University
Mesoscale Ensemble Data Assimilation: Opportunities and Challenges Fuqing Zhang Penn State University Mesoscale EnKF: some incomplete background 1 st proposed by Evensen (1994); Houtekamer and Micthell
More informationKalman Filter and Ensemble Kalman Filter
Kalman Filter and Ensemble Kalman Filter 1 Motivation Ensemble forecasting : Provides flow-dependent estimate of uncertainty of the forecast. Data assimilation : requires information about uncertainty
More informationA Hybrid ETKF 3DVAR Data Assimilation Scheme for the WRF Model. Part I: Observing System Simulation Experiment
5116 M O N T H L Y W E A T H E R R E V I E W VOLUME 136 A Hybrid ETKF 3DVAR Data Assimilation Scheme for the WRF Model. Part I: Observing System Simulation Experiment XUGUANG WANG Cooperative Institute
More informationEstimating Observation Impact in a Hybrid Data Assimilation System: Experiments with a Simple Model
Estimating Observation Impact in a Hybrid Data Assimilation System: Experiments with a Simple Model Rahul Mahajan NOAA / NCEP / EMC - IMSG College Park, MD 20740, USA Acknowledgements: Ron Gelaro, Ricardo
More informationDA/Initialization/Ensemble Development Team Milestones and Priorities
DA/Initialization/Ensemble Development Team Milestones and Priorities Presented by Xuguang Wang HFIP annual review meeting Jan. 11-12, 2017, Miami, FL Fully cycled, self-consistent, dual-resolution, GSI
More informationData Assimilation Development for the FV3GFSv2
Data Assimilation Development for the FV3GFSv2 Catherine Thomas 1, 2, Rahul Mahajan 1, 2, Daryl Kleist 2, Emily Liu 3,2, Yanqiu Zhu 1, 2, John Derber 2, Andrew Collard 1, 2, Russ Treadon 2, Jeff Whitaker
More informationPlans for NOAA s regional ensemble systems: NARRE, HRRRE, and a regional hybrid assimilation
NOAA Earth System Research Laboratory Plans for NOAA s regional ensemble systems: NARRE, HRRRE, and a regional hybrid assimilation Tom Hamill (substituting for Stan Benjamin and team) NOAA Earth System
More informationFour-Dimensional Ensemble Kalman Filtering
Four-Dimensional Ensemble Kalman Filtering B.R. Hunt, E. Kalnay, E.J. Kostelich, E. Ott, D.J. Patil, T. Sauer, I. Szunyogh, J.A. Yorke, A.V. Zimin University of Maryland, College Park, MD 20742, USA Ensemble
More informationUniversity of Miami/RSMAS
Observing System Simulation Experiments to Evaluate the Potential Impact of Proposed Observing Systems on Hurricane Prediction: R. Atlas, T. Vukicevic, L.Bucci, B. Annane, A. Aksoy, NOAA Atlantic Oceanographic
More informationGSI Data Assimilation System Support and Testing Activities: 2013 Annual Update
14Th Annual WRF Users Workshop, Boulder, CO, June 24-28, 2013 GSI Data Assimilation System Support and Testing Activities: 2013 Annual Update Hui Shao1, Ming Hu2, Chunhua Zhou1, Kathryn Newman1, Mrinal
More informationEARLY ONLINE RELEASE
AMERICAN METEOROLOGICAL SOCIETY Monthly Weather Review EARLY ONLINE RELEASE This is a preliminary PDF of the author-produced manuscript that has been peer-reviewed and accepted for publication. Since it
More informationCan hybrid-4denvar match hybrid-4dvar?
Comparing ensemble-variational assimilation methods for NWP: Can hybrid-4denvar match hybrid-4dvar? WWOSC, Montreal, August 2014. Andrew Lorenc, Neill Bowler, Adam Clayton, David Fairbairn and Stephen
More informationAccelerating the spin-up of Ensemble Kalman Filtering
Accelerating the spin-up of Ensemble Kalman Filtering Eugenia Kalnay * and Shu-Chih Yang University of Maryland Abstract A scheme is proposed to improve the performance of the ensemble-based Kalman Filters
More informationInter-comparison of 4D-Var and EnKF systems for operational deterministic NWP
Inter-comparison of 4D-Var and EnKF systems for operational deterministic NWP Project eam: Mark Buehner Cecilien Charette Bin He Peter Houtekamer Herschel Mitchell WWRP/HORPEX Workshop on 4D-VAR and Ensemble
More informationH. LIU AND X. ZOU AUGUST 2001 LIU AND ZOU. The Florida State University, Tallahassee, Florida
AUGUST 2001 LIU AND ZOU 1987 The Impact of NORPEX Targeted Dropsondes on the Analysis and 2 3-Day Forecasts of a Landfalling Pacific Winter Storm Using NCEP 3DVAR and 4DVAR Systems H. LIU AND X. ZOU The
More informationImproved analyses and forecasts with AIRS retrievals using the Local Ensemble Transform Kalman Filter
Improved analyses and forecasts with AIRS retrievals using the Local Ensemble Transform Kalman Filter Hong Li, Junjie Liu, and Elana Fertig E. Kalnay I. Szunyogh, E. J. Kostelich Weather and Chaos Group
More informationGSI Tutorial Background and Observation Errors: Estimation and Tuning. Daryl Kleist NCEP/EMC June 2011 GSI Tutorial
GSI Tutorial 2011 Background and Observation Errors: Estimation and Tuning Daryl Kleist NCEP/EMC 29-30 June 2011 GSI Tutorial 1 Background Errors 1. Background error covariance 2. Multivariate relationships
More informationRelative Merits of 4D-Var and Ensemble Kalman Filter
Relative Merits of 4D-Var and Ensemble Kalman Filter Andrew Lorenc Met Office, Exeter International summer school on Atmospheric and Oceanic Sciences (ISSAOS) "Atmospheric Data Assimilation". August 29
More informationMonthly Weather Review The Hybrid Local Ensemble Transform Kalman Filter
Monthly Weather Review The Hybrid Local Ensemble Transform Kalman Filter --Manuscript Draft-- Manuscript Number: Full Title: Article Type: Corresponding Author: Corresponding Author's Institution: First
More informationAssimilation of Radar Radial Velocity Data with the WRF Hybrid Ensemble 3DVAR System for the Prediction of Hurricane Ike (2008)
NOVEMBER 2012 L I E T A L. 3507 Assimilation of Radar Radial Velocity Data with the WRF Hybrid Ensemble 3DVAR System for the Prediction of Hurricane Ike (2008) YONGZUO LI, XUGUANG WANG, AND MING XUE School
More informationEstimating Observation Impact in a Hybrid Data Assimilation System: Experiments with a Simple Model
Estimating Observation Impact in a Hybrid Data Assimilation System: Experiments with a Simple Model NOAA / NCEP / EMC College Park, MD 20740, USA 10 February 2014 Overview Goal Sensitivity Theory Adjoint
More information4DEnVar. Four-Dimensional Ensemble-Variational Data Assimilation. Colloque National sur l'assimilation de données
Four-Dimensional Ensemble-Variational Data Assimilation 4DEnVar Colloque National sur l'assimilation de données Andrew Lorenc, Toulouse France. 1-3 décembre 2014 Crown copyright Met Office 4DEnVar: Topics
More informationThe Developmental Testbed Center: Update on Data Assimilation System Testing and Community Support
93rd AMS Annual Meeting/17th IOAS-AOLS/3rd Conference on Transition of Research to Operations, Austin, TX, Jan 6-10, 2013 The Developmental Testbed Center: Update on Data Assimilation System Testing and
More informationData assimilation; comparison of 4D-Var and LETKF smoothers
Data assimilation; comparison of 4D-Var and LETKF smoothers Eugenia Kalnay and many friends University of Maryland CSCAMM DAS13 June 2013 Contents First part: Forecasting the weather - we are really getting
More informationMotivation & Goal. We investigate a way to generate PDFs from a single deterministic run
Motivation & Goal Numerical weather prediction is limited by errors in initial conditions, model imperfections, and nonlinearity. Ensembles of an NWP model provide forecast probability density functions
More informationGenerating climatological forecast error covariance for Variational DAs with ensemble perturbations: comparison with the NMC method
Generating climatological forecast error covariance for Variational DAs with ensemble perturbations: comparison with the NMC method Matthew Wespetal Advisor: Dr. Eugenia Kalnay UMD, AOSC Department March
More informationA pseudo-ensemble hybrid data assimilation system for HWRF
A pseudo-ensemble hybrid data assimilation system for HWRF Xuyang Ge UCAR visiting postdoctoral scientist at PSU/NCEP Contributors: Fuqing Zhang and Yonghui Weng (PSU) Mingjing Tong and Vijay Tallapragada
More informationNumerical Weather Prediction: Data assimilation. Steven Cavallo
Numerical Weather Prediction: Data assimilation Steven Cavallo Data assimilation (DA) is the process estimating the true state of a system given observations of the system and a background estimate. Observations
More informationEvolution of Forecast Error Covariances in 4D-Var and ETKF methods
Evolution of Forecast Error Covariances in 4D-Var and ETKF methods Chiara Piccolo Met Office Exeter, United Kingdom chiara.piccolo@metoffice.gov.uk Introduction Estimates of forecast error covariances
More informationImplementation and Evaluation of WSR-88D Radial Velocity Data Assimilation for WRF-NMM via GSI
Implementation and Evaluation of WSR-88D Radial Velocity Data Assimilation for WRF-NMM via GSI Shun Liu 1, Ming Xue 1,2 1 Center for Analysis and Prediction of Storms and 2 School of Meteorology University
More informationCurrent Issues and Challenges in Ensemble Forecasting
Current Issues and Challenges in Ensemble Forecasting Junichi Ishida (JMA) and Carolyn Reynolds (NRL) With contributions from WGNE members 31 th WGNE Pretoria, South Africa, 26 29 April 2016 Recent trends
More informationThe ECMWF Hybrid 4D-Var and Ensemble of Data Assimilations
The Hybrid 4D-Var and Ensemble of Data Assimilations Lars Isaksen, Massimo Bonavita and Elias Holm Data Assimilation Section lars.isaksen@ecmwf.int Acknowledgements to: Mike Fisher and Marta Janiskova
More informationEnsemble Kalman Filter potential
Ensemble Kalman Filter potential Former students (Shu-Chih( Yang, Takemasa Miyoshi, Hong Li, Junjie Liu, Chris Danforth, Ji-Sun Kang, Matt Hoffman), and Eugenia Kalnay University of Maryland Acknowledgements:
More informationFundamentals of Data Assimilation
National Center for Atmospheric Research, Boulder, CO USA GSI Data Assimilation Tutorial - June 28-30, 2010 Acknowledgments and References WRFDA Overview (WRF Tutorial Lectures, H. Huang and D. Barker)
More informationHow 4DVAR can benefit from or contribute to EnKF (a 4DVAR perspective)
How 4DVAR can benefit from or contribute to EnKF (a 4DVAR perspective) Dale Barker WWRP/THORPEX Workshop on 4D-Var and Ensemble Kalman Filter Intercomparisons Sociedad Cientifica Argentina, Buenos Aires,
More informationPSU HFIP 2010 Summary: Performance of the ARW-EnKF Real-time Cloud-resolving TC Ensemble Analysis and Forecasting System.
PSU HFIP 2010 Summary: Performance of the ARW-EnKF Real-time Cloud-resolving TC Ensemble Analysis and Forecasting System Fuqing Zhang Penn State University Contributors: Yonghui Weng, John Gamache and
More informationConfiguration of All-sky Microwave Radiance Assimilation in the NCEP's GFS Data Assimilation System
Configuration of All-sky Microwave Radiance Assimilation in the NCEP's GFS Data Assimilation System Yanqiu Zhu 1, Emily Liu 1, Rahul Mahajan 1, Catherine Thomas 1, David Groff 1, Paul Van Delst 1, Andrew
More informationConvective-scale NWP for Singapore
Convective-scale NWP for Singapore Hans Huang and the weather modelling and prediction section MSS, Singapore Dale Barker and the SINGV team Met Office, Exeter, UK ECMWF Symposium on Dynamical Meteorology
More informationImpact of Assimilating Aircraft Reconnaissance Observations in Operational HWRF
Impact of Assimilating Aircraft Reconnaissance Observations in Operational HWRF Mingjing Tong, Vijay Tallapragada, Emily Liu, Weiguo Wang, Chanh Kieu, Qingfu Liu and Banglin Zhan Environmental Modeling
More informationChengsi Liu 1, Ming Xue 1, 2, Youngsun Jung 1, Lianglv Chen 3, Rong Kong 1 and Jingyao Luo 3 ISDA 2019
Development of Optimized Radar Data Assimilation Capability within the Fully Coupled EnKF EnVar Hybrid System for Convective Permitting Ensemble Forecasting and Testing via NOAA Hazardous Weather Testbed
More informationThe Big Leap: Replacing 4D-Var with 4D-EnVar and life ever since
The Big Leap: Replacing 4D-Var with 4D-EnVar and life ever since Symposium: 20 years of 4D-Var at ECMWF 26 January 2018 Mark Buehner 1, Jean-Francois Caron 1 and Ping Du 2 1 Data Assimilation and Satellite
More informationAssimilate W88D Doppler Vr for Humberto 05
Assimilate W88D Doppler Vr for Humberto 05 WRF/EnKF Forecast vs. Observations vs. 3DVAR Min SLP Max wind The WRF/3DVAR (as a surrogate of operational algorithm) assimilates the same radar data but without
More informationJ1.3 GENERATING INITIAL CONDITIONS FOR ENSEMBLE FORECASTS: MONTE-CARLO VS. DYNAMIC METHODS
J1.3 GENERATING INITIAL CONDITIONS FOR ENSEMBLE FORECASTS: MONTE-CARLO VS. DYNAMIC METHODS Thomas M. Hamill 1, Jeffrey S. Whitaker 1, and Chris Snyder 2 1 NOAA-CIRES Climate Diagnostics Center, Boulder,
More informationCurrent and Future Experiments to Improve Assimilation of Surface Winds from Satellites in Global Models
Current and Future Experiments to Improve Assimilation of Surface Winds from Satellites in Global Models Sharan Majumdar, RSMAS/UMiami Bob Atlas, NOAA/AOML Current and Future Collaborators: Ryan Torn (SUNY
More informationHFIP ENSEMBLE PLAN. Jun Du (EMC/NCEP), presenting on behalf of the HFIP Ensemble Team:
HFIP ENSEMBLE PLAN Jun Du (EMC/NCEP), presenting on behalf of the HFIP Ensemble Team: Sim Aberson (HRD) Sim.Aberson@noaa.gov Tom Hamill (ESRL) tom.hamill@noaa.gov Carolyn Reynolds (NRL) carolyn.reynolds@nrlmry.navy.mil
More informationIn the derivation of Optimal Interpolation, we found the optimal weight matrix W that minimizes the total analysis error variance.
hree-dimensional variational assimilation (3D-Var) In the derivation of Optimal Interpolation, we found the optimal weight matrix W that minimizes the total analysis error variance. Lorenc (1986) showed
More information1. Introduction. Yujie PAN 1,3, Ming XUE 1,2,3, Kefeng ZHU 2,3, and Mingjun WANG 2,3
ADVANCES IN ATMOSPHERIC SCIENCES, VOL. 35, MAY 28, 58 53 Original Paper A Prototype Regional GSIbased EnKFVariational Hybrid Data Assimilation System for the Rapid Refresh Forecasting System: DualResolution
More informationImpact of Targeted Dropsonde Data on Mid-latitude Numerical Weather Forecasts during the 2011 Winter Storms Reconnaissance Program
ESRL Impact of Targeted Dropsonde Data on Mid-latitude Numerical Weather Forecasts during the 2011 Winter Storms Reconnaissance Program Presented by Tom Hamill Forecasts and assimilations : Carla Cardinali,
More informationVariational data assimilation of lightning with WRFDA system using nonlinear observation operators
Variational data assimilation of lightning with WRFDA system using nonlinear observation operators Virginia Tech, Blacksburg, Virginia Florida State University, Tallahassee, Florida rstefane@vt.edu, inavon@fsu.edu
More informationConvective-scale data assimilation in the Weather Research and Forecasting model using a nonlinear ensemble filter
Convective-scale data assimilation in the Weather Research and Forecasting model using a nonlinear ensemble filter Jon Poterjoy, Ryan Sobash, and Jeffrey Anderson National Center for Atmospheric Research
More information13A. 4 Analysis and Impact of Super-obbed Doppler Radial Velocity in the NCEP Grid-point Statistical Interpolation (GSI) Analysis System
13A. 4 Analysis and Impact of Super-obbed Doppler Radial Velocity in the NCEP Grid-point Statistical Interpolation (GSI) Analysis System Shun Liu 1, Ming Xue 1,2, Jidong Gao 1,2 and David Parrish 3 1 Center
More informationDiscussion on HFIP RDITT Experiments. Proposal for extending the life of RDITT for one more year: Future Plans from Individual Groups
Discussion on HFIP RDITT Experiments Proposal for extending the life of RDITT for one more year: Future Plans from Individual Groups 1 EMC: Modifications to one-way hybrid ensemble-variational data assimilation
More informationExploring ensemble forecast calibration issues using reforecast data sets
NOAA Earth System Research Laboratory Exploring ensemble forecast calibration issues using reforecast data sets Tom Hamill and Jeff Whitaker NOAA Earth System Research Lab, Boulder, CO tom.hamill@noaa.gov
More informationDevelopment of the Local Ensemble Transform Kalman Filter
Development of the Local Ensemble Transform Kalman Filter Istvan Szunyogh Institute for Physical Science and Technology & Department of Atmospheric and Oceanic Science AOSC Special Seminar September 27,
More informationFour-dimensional ensemble Kalman filtering
Tellus (24), 56A, 273 277 Copyright C Blackwell Munksgaard, 24 Printed in UK. All rights reserved TELLUS Four-dimensional ensemble Kalman filtering By B. R. HUNT 1, E. KALNAY 1, E. J. KOSTELICH 2,E.OTT
More informationMiddle Atmosphere Operational Data Assimilation with the Use of Ensembles
Middle Atmosphere Operational Data Assimilation with the Use of Ensembles Presenter: David Kuhl (NRL-DC) Karl Hoppel (NRL-DC) Douglas R. Allen (NRL-DC) John McCormack (NRL-DC) Jun Ma (Computational Physics
More informationEARLY ONLINE RELEASE
EARLY ONLINE RELEASE This is a PDF of a manuscript that has been peer-reviewed and accepted for publication. As the article has not yet been formatted, copy edited or proofread, the final published version
More informationA Comparison of Hybrid Ensemble Transform Kalman Filter Optimum Interpolation and Ensemble Square Root Filter Analysis Schemes
MARCH 2007 W A N G E T A L. 1055 A Comparison of Hybrid Ensemble Transform Kalman Filter Optimum Interpolation and Ensemble Square Root Filter Analysis Schemes XUGUANG WANG CIRES Climate Diagnostics Center,
More informationTests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. Part III: Comparison with 3DVAR in a Real-Data Case Study
522 M O N T H L Y W E A T H E R R E V I E W VOLUME 136 Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. Part III: Comparison with 3DVAR in a Real-Data Case Study ZHIYONG
More informationTHE IMPACT OF DIFFERENT DATA FIELDS ON STORM-SSCALE DATA ASSIMILATION
JP1J.3 THE IMPACT OF DIFFERENT DATA FIELDS ON STORM-SSCALE DATA ASSIMILATION Guoqing Ge 1,2,*, Jidong Gao 1, Kelvin Droegemeier 1,2 Center for Analysis and Prediction of Storms, University of Oklahoma,
More informationResearch Article The Development of a Hybrid EnKF-3DVAR Algorithm for Storm-Scale Data Assimilation
Advances in Meteorology Volume, Article ID 6, pages http://dx.doi.org/0.//6 Research Article The Development of a Hybrid EnKF-DVAR Algorithm for Storm-Scale Data Assimilation Jidong Gao,, Ming Xue,, and
More information3.23 IMPROVING VERY-SHORT-TERM STORM PREDICTIONS BY ASSIMILATING RADAR AND SATELLITE DATA INTO A MESOSCALE NWP MODEL
3.23 IMPROVING VERY-SHORT-TERM STORM PREDICTIONS BY ASSIMILATING RADAR AND SATELLITE DATA INTO A MESOSCALE NWP MODEL Q. Zhao 1*, J. Cook 1, Q. Xu 2, and P. Harasti 3 1 Naval Research Laboratory, Monterey,
More information2012 and changes to the Rapid Refresh and HRRR weather forecast models
2012 and 2013-15 changes to the Rapid Refresh and HRRR weather forecast models 31 October 2012 Stan Benjamin Steve Weygandt Curtis Alexander NOAA Earth System Research Laboratory Boulder, CO FPAW - 2012
More informationModel error and parameter estimation
Model error and parameter estimation Chiara Piccolo and Mike Cullen ECMWF Annual Seminar, 11 September 2018 Summary The application of interest is atmospheric data assimilation focus on EDA; A good ensemble
More informationThe Use of GPS Radio Occultation Data for Tropical Cyclone Prediction. Bill Kuo and Hui Liu UCAR
The Use of GPS Radio Occultation Data for Tropical Cyclone Prediction Bill Kuo and Hui Liu UCAR Current capability of the National Hurricane Center Good track forecast improvements. Errors cut in half
More informationExperiments of Hurricane Initialization with Airborne Doppler Radar Data for the Advancedresearch Hurricane WRF (AHW) Model
Experiments of Hurricane Initialization with Airborne Doppler Radar Data for the Advancedresearch Hurricane WRF (AHW) Model Qingnong Xiao 1, Xiaoyan Zhang 1, Christopher Davis 1, John Tuttle 1, Greg Holland
More informationRepresentation of model error for data assimilation on convective scale
Representation of model error for data assimilation on convective scale Yuefei Zenga,b, Tijana Janjicb, Alberto de Lozarc, Ulrich Blahakc, Hendrik Reichc, Axel Seifertc, Stephan Raspa, George Craiga a)
More informationAbstract 2. ENSEMBLE KALMAN FILTERS 1. INTRODUCTION
J5.4 4D ENSEMBLE KALMAN FILTERING FOR ASSIMILATION OF ASYNCHRONOUS OBSERVATIONS T. Sauer George Mason University, Fairfax, VA 22030 B.R. Hunt, J.A. Yorke, A.V. Zimin, E. Ott, E.J. Kostelich, I. Szunyogh,
More informationEnsemble Prediction Systems
Ensemble Prediction Systems Eric Blake National Hurricane Center 7 March 2017 Acknowledgements to Michael Brennan 1 Question 1 What are some current advantages of using single-model ensembles? A. Estimates
More informationAdvances and Challenges in Ensemblebased Data Assimilation in Meteorology. Takemasa Miyoshi
January 18, 2013, DA Workshop, Tachikawa, Japan Advances and Challenges in Ensemblebased Data Assimilation in Meteorology Takemasa Miyoshi RIKEN Advanced Institute for Computational Science Takemasa.Miyoshi@riken.jp
More informationAssimilation of cloud/precipitation data at regional scales
Assimilation of cloud/precipitation data at regional scales Thomas Auligné National Center for Atmospheric Research auligne@ucar.edu Acknowledgments to: Steven Cavallo, David Dowell, Aimé Fournier, Hans
More informationNational Center for Atmospheric Research,* Boulder, Colorado. (Manuscript received 26 March 2013, in final form 28 August 2013) ABSTRACT
716 M O N T H L Y W E A T H E R R E V I E W VOLUME 142 Convection-Permitting Forecasts Initialized with Continuously Cycling Limited-Area 3DVAR, Ensemble Kalman Filter, and Hybrid Variational Ensemble
More informationUniversity of Oklahoma, Norman, Oklahoma. June 6, 2012 Submitted to Weather and Forecasting Revised November 3, 2012
Track and Intensity Forecasting of Hurricanes: Impact of Convection-Permitting Resolution and Global Ensemble Kalman Filter Analysis on 2010 Atlantic Season Forecasts Ming Xue 1,2 *, Jordan Schleif 1,2,
More informationP3.11 A COMPARISON OF AN ENSEMBLE OF POSITIVE/NEGATIVE PAIRS AND A CENTERED SPHERICAL SIMPLEX ENSEMBLE
P3.11 A COMPARISON OF AN ENSEMBLE OF POSITIVE/NEGATIVE PAIRS AND A CENTERED SPHERICAL SIMPLEX ENSEMBLE 1 INTRODUCTION Xuguang Wang* The Pennsylvania State University, University Park, PA Craig H. Bishop
More informationMet Office convective-scale 4DVAR system, tests and improvement
Met Office convective-scale 4DVAR system, tests and improvement Marco Milan*, Marek Wlasak, Stefano Migliorini, Bruce Macpherson Acknowledgment: Inverarity Gordon, Gareth Dow, Mike Thurlow, Mike Cullen
More informationApplications of an ensemble Kalman Filter to regional ocean modeling associated with the western boundary currents variations
Applications of an ensemble Kalman Filter to regional ocean modeling associated with the western boundary currents variations Miyazawa, Yasumasa (JAMSTEC) Collaboration with Princeton University AICS Data
More informationSome Applications of WRF/DART
Some Applications of WRF/DART Chris Snyder, National Center for Atmospheric Research Mesoscale and Microscale Meteorology Division (MMM), and Institue for Mathematics Applied to Geoscience (IMAGe) WRF/DART
More information