Environment Canada s Regional Ensemble Kalman Filter
|
|
|
- Carmella Fisher
- 5 years ago
- Views:
Transcription
1 Environment Canada s Regional Ensemble Kalman Filter EnKF Workshop 216 Seung-Jong Baek, Luc Fillion, Dominik Jacques, Thomas Milewski, Weiguang Chang and Peter Houtekamer Meteorological Research Division, Environment and Climate Change Canada
2 214 implementation of the NWP suites EnKF ensemble (GEPS) Regional ensemble (REPS) Background error covariances EnVar deterministic (GDPS) Regional EnVar Regional deterministic (RDPS) system Regional system
3 implementation of the NWP suites EnKF ensemble (GEPS) Regional EnKF Regional ensemble (REPS) Background error covariances Background error covariances EnVar deterministic (GDPS) Regional EnVar Regional deterministic (RDPS) system Regional system
4 Autonomous Regional EnKF Driven by the {x b } EnKF (6Z,18Z) {x a } GEM (5km) {x b } Subsample (Z,12Z) EnKF {x a } GEM (5km) 16 day global {x b } obs Model error driving obs Model error driving {x b } EnKF {x a } GEM-LAM (15km) {x b } EnKF {x a } GEM-LAM (15km) {x b } Regional Subsample 72h regional Regional EnKF starts from global ensemble analyses and then runs autonomously. Environmental Multi-scale (GEM) model is used for the.
5 Comparison Between and Regional EnKF Make the regional configuration close to the global. 256 ensemble members. Same localization distance. Same model error perturbation. Same set of observations. Multi-model for the global but single model for the regional. Each member has different configuration for the global. All members have the same configuration for the regional. Regional EnKF performs better than the global EnKF.
6 Downscaling the EnKF Feed GEM-LAM 15km model with the global analyses. Compare with the regional EnKF. Analyses GEM (5km) driving GEM-LAM (15km) 256 member 6H Trials 21 member 72H Forecast
7 Verification Against Radiosonde:.vs. Regional Northern America Northern America West UU VV UU VV GZ TT GZ TT
8 Verification Against Radiosonde: Downscale.vs. Regional Northern America Northern America West UU VV UU VV GZ TT GZ TT
9 Correlation Structure Comparison 2.5km EnKF over Vancouver (MEOPAR). Driven by the regional EnKF. Compare correlations between global 5km, regional 15km and high resolution 2.5km EnKF. 3 points (a,b,c) on the ocean and 3 points (d,e,f) on the land at 7hPa height are selected. a c b d e f altitude [m] Correlations along the zonal and vertical directions are computed.
![HREnKF [hpa]](/docs-images/92/109628582/images/10-5.jpg "[hpa] [hpa] 5")
10 U-Wind Correlation: 2.5km, 15km and 5km a c b d e f altitude [m] 1 Ocean Land GEnKF REnKF HREnKF [hpa] [hpa] [hpa] HRa) HRb) HRc) HRd ) HRe) HRf ) Ra) Rb) Rc) Rd ) Re) Rf ) Ga) Gb) Gc) Gd ) Ge) Gf ) unitless
![c b d e f 3 2 1 altitude [m] 1](/docs-images/92/109628582/images/11-8.jpg "Ocean Land 1.")
11 U-Wind Correlation: 15km Regional EnKF and Downscale a c b d e f altitude [m] 1 Ocean Land 1. REnKF Trials REnKF Downscaled [hpa] [hpa] Ra) Rb) Rc) Rd ) Re) Rf ) Ra) Rb) Rc) Rd ) Re) Rf ) unitless
12 1m Wind Forecast Error (METAR): vs Regional Forecast from Z 2. m/s Difference Run hour + lead time Forecast from 12Z 2. Standard deviation Bias m/s Difference Run hour + lead time
13 1m Wind Forecast Error (METAR): Downscale vs Regional Forecast from Z m/s Difference Run hour + lead time Standard deviation Bias Forecast from 12Z m/s Difference Run hour + lead time
14 2m Temperature Forecast Error (METAR): vs Regional Forecast from Z degree Difference Run hour + lead time Standard deviation Bias Forecast from 12Z Difference degree Run hour + lead time
15 2m Temperature Forecast Error (METAR): Downscale vs Regional Forecast from Z m/s Difference Run hour + lead time Forecast from 12Z Standard deviation Bias m/s Difference Run hour + lead time
16 Downscaling is enough Regional EnKF is almost twice expensive than global EnKF. Limited improvement against downscaled s. 72 hour regional s for upper air and precipitation also neutral. Need high density observation network to fully exploit high resolution model. METAR, Mesonet, Radar, SWOB, etc.
17 METAR Data Assimilation with 2.5km EnKF Forecast for the Pan Am Games in Toronto up to 25m resolution. Coupled with CaLDAS (Canadian Land Data Assimilation System). CaLDAS produces ensemble surface fields (soil temperature/moisture). Assimilate 2m temperature and humidity with 1 hour cycle. 12 hour for every 6 hours. Precipitation pattern improved. 2m temperature and humidity improved up to 3 hours. 2m Temperature rms error degree UTC 214/6/16 UTC 214/6/17 UTC 214/6/18
18 New Direction of the EnKF Development Put more efforts on improving the global EnKF. Coupled with CaLDAS. Reduce assimilation time window: 6H 3H 1H. Apply IAU (Incremental Analysis Update) instead of digital filter. Adaptive localization Higher resolution (1km) regional EnKF with reduced domain. Cover 2.5km national domain. Adopt the features of the global EnKF. Assimilate high resolution high frequency data: METAR, Radar, SWOB, etc. Provide ensemble trials for 2.5km EnVar analysis.
19 Regional 1km Domain over Canada 5km : 8x4 Regional 15km : 744x711 Regional 1km : 73x43 National 2.5km: 2584x1334
Environment Canada s Regional Ensemble Kalman Filter
Environment Canada s Regional Ensemble Kalman Filter May 19, 2014 Seung-Jong Baek, Luc Fillion, Kao-Shen Chung, and Peter Houtekamer Meteorological Research Division, Environment Canada, Dorval, Quebec
Recent Data Assimilation Activities at Environment Canada
Recent Data Assimilation Activities at Environment Canada Major upgrade to global and regional deterministic prediction systems (now in parallel run) Sea ice data assimilation Mark Buehner Data Assimilation
Impact of Assimilating Radar Radial Wind in the Canadian High Resolution
Impact of Assimilating Radar Radial Wind in the Canadian High Resolution 12B.5 Ensemble Kalman Filter System Kao-Shen Chung 1,2, Weiguang Chang 1, Luc Fillion 1,2, Seung-Jong Baek 1,2 1 Department of Atmospheric
Enhancing information transfer from observations to unobserved state variables for mesoscale radar data assimilation
Enhancing information transfer from observations to unobserved state variables for mesoscale radar data assimilation Weiguang Chang and Isztar Zawadzki Department of Atmospheric and Oceanic Sciences Faculty
The Canadian approach to ensemble prediction
The Canadian approach to ensemble prediction ECMWF 2017 Annual seminar: Ensemble prediction : past, present and future. Pieter Houtekamer Montreal, Canada Overview. The Canadian approach. What are the
The Canadian Land Data Assimilation System (CaLDAS)
The Canadian Land Data Assimilation System (CaLDAS) Marco L. Carrera, Stéphane Bélair, Bernard Bilodeau and Sheena Solomon Meteorological Research Division, Environment Canada Dorval, QC, Canada 2 nd Workshop
The 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
Inter-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
Development of the Canadian Precipitation Analysis (CaPA) and the Canadian Land Data Assimilation System (CaLDAS)
Development of the Canadian Precipitation Analysis (CaPA) and the Canadian Land Data Assimilation System (CaLDAS) Marco L. Carrera, Vincent Fortin and Stéphane Bélair Meteorological Research Division Environment
Toward improved initial conditions for NCAR s real-time convection-allowing ensemble. Ryan Sobash, Glen Romine, Craig Schwartz, and Kate Fossell
Toward improved initial conditions for NCAR s real-time convection-allowing ensemble Ryan Sobash, Glen Romine, Craig Schwartz, and Kate Fossell Storm-scale ensemble design Can an EnKF be used to initialize
Improved 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
The Canadian Regional Ensemble Prediction System (REPS)
The Canadian Regional Ensemble Prediction System (REPS) M. Charron1, R. Frenette2, X. Li3, M. K. (Peter) Yau3 1. Recherche en prévision numérique atmosphérique 2. National Laboratory for Severe Weather
Impact Evaluation of New Radiance data, Reduced Thinning and Higher Analysis Resolution in the GEM Global Deterministic Prediction System
Impact Evaluation of New Radiance data, Reduced Thinning and Higher Analysis Resolution in the GEM Global Deterministic Prediction ITSC-17, Monterey, California Presenter: G. Deblonde*1, Co-authors: A.
A WATER CYCLE PREDICTION SYSTEM
A WATER CYCLE PREDICTION SYSTEM FOR THE GREAT LAKES AND ST. LAWRENCE RIVER V. Fortin 1, D. Durnford 2, G. Smith 1, P. Matte 1, M. Mackay 1, N. Bernier 1... and many others 1 Meteorological Research Division,
Dynamic Inference of Background Error Correlation between Surface Skin and Air Temperature
Dynamic Inference of Background Error Correlation between Surface Skin and Air Temperature Louis Garand, Mark Buehner, and Nicolas Wagneur Meteorological Service of Canada, Dorval, P. Quebec, Canada Abstract
Improving 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);
Univ. 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
Soil Moisture Prediction and Assimilation
Soil Moisture Prediction and Assimilation Analysis and Prediction in Agricultural Landscapes Saskatoon, June 19-20, 2007 STEPHANE BELAIR Meteorological Research Division Prediction and Assimilation Atmospheric
An Ensemble Kalman Filter for NWP based on Variational Data Assimilation: VarEnKF
An Ensemble Kalman Filter for NWP based on Variational Data Assimilation: VarEnKF Blueprints for Next-Generation Data Assimilation Systems Workshop 8-10 March 2016 Mark Buehner Data Assimilation and Satellite
Regional Deterministic Prediction System (RDPS) Technical Note
Regional Deterministic Prediction System (RDPS) Technical Note Last update: November 18, 2014 from version 3.2.1 to version 4.0.0 1 Version Date Author Variation or revocation 1.0 01/10/2014 J.-F. Caron
Numerical 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
The Canadian Precipitation Analysis (CaPA) and the Canadian Land Data Assimilation System (CaLDAS)
The Canadian Precipitation Analysis (CaPA) and the Canadian Land Data Assimilation System (CaLDAS) Vincent Fortin (and many collaborators) Division de la recherche en météorologie Environnement en Changement
Data 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
Can 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
Recent achievements in the data assimilation systems of ARPEGE and AROME-France
Recent achievements in the data assimilation systems of ARPEGE and AROME-France P. Brousseau and many colleagues from (CNRM/GMAP) 38th EWGLAM and 23 SRNWP Meeting Rome, 04 October 2016 Meteo-France NWP
Nonlinear Balance at Mesoscale: Balanced vertical motion with respect to model convection temperature tendencies
Motivations and Methodology Results Summary Nonlinear Balance at Mesoscale: Balanced vertical motion with respect to model convection temperature tendencies Christian Pagé Peter Zwack page.christian@uqam.ca
4DEnVar. 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
Comparing 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
Scatterometer Wind Assimilation at the Met Office
Scatterometer Wind Assimilation at the Met Office James Cotton International Ocean Vector Winds Science Team (IOVWST) meeting, Brest, June 2014 Outline Assimilation status Global updates: Metop-B and spatial
Pierre Gauthier Department of Earth and Atmospheric Sciences Université du Québec à Montréal (UQAM)
Pierre Gauthier Department of Earth and Atmospheric Sciences Université du Québec à Montréal (UQAM) Contributions: Katja Winger, Kamel Chikhar, René Laprise, Laxmi Sushama Department of Earth and Atmospheric
Impact of IASI assimilation in convective scale model AROME
Impact of IASI assimilation in convective scale model AROME Vincent GUIDARD Pierre BROUSSEAU, Nadia FOURRIE, Florence RABIER Météo-France and CNRS / CNRM-GAME 2 Overview 1. What is AROME? 2. IASI in next
Francis O. 1, David H. Bromwich 1,2
Impact of assimilating COSMIC GPS RO moisture and temperature profiles on Polar WRF simulations of West Antarctic cyclones Francis O. O@eno 1, David H. Bromwich 1,2 1 Polar Meteorology Group BPRC 2 Atmospheric
Motivation & 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
Convective-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
Development 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,
Convective-Scale Data Assimilation
Convective-Scale Data Assimilation Dale Barker, with contributions from Met Office colleagues, and: Jelena Bolarova (HIRLAM), Yann Michel (Meteo France), Luc Fillion (Environment Canada), Kazuo Saito (JMA/MRI)
The Nowcasting Demonstration Project for London 2012
The Nowcasting Demonstration Project for London 2012 Susan Ballard, Zhihong Li, David Simonin, Jean-Francois Caron, Brian Golding, Met Office, UK Introduction The success of convective-scale NWP is largely
Variational ensemble DA at Météo-France Cliquez pour modifier le style des sous-titres du masque
Cliquez pour modifier le style du titre Variational ensemble DA at Météo-France Cliquez pour modifier le style des sous-titres du masque L. Berre, G. Desroziers, H. Varella, L. Raynaud, C. Labadie and
Assimilation of Doppler radar observations for high-resolution numerical weather prediction
Assimilation of Doppler radar observations for high-resolution numerical weather prediction Susan Rennie, Peter Steinle, Mark Curtis, Yi Xiao, Alan Seed Introduction Numerical Weather Prediction (NWP)
Ensemble 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,
EnKF Localization Techniques and Balance
EnKF Localization Techniques and Balance Steven Greybush Eugenia Kalnay, Kayo Ide, Takemasa Miyoshi, and Brian Hunt Weather Chaos Meeting September 21, 2009 Data Assimilation Equation Scalar form: x a
Canadian contribution to the Year of Polar Prediction: deterministic and ensemble coupled atmosphere-ice-ocean forecasts
Canadian contribution to the Year of Polar Prediction: deterministic and ensemble coupled atmosphere-ice-ocean forecasts G.C. Smith, F. Roy, J.-F. Lemieux, F. Dupont, J-M Belanger and the CONCEPTS team
ICON. Limited-area mode (ICON-LAM) and updated verification results. Günther Zängl, on behalf of the ICON development team
ICON Limited-area mode (ICON-LAM) and updated verification results Günther Zängl, on behalf of the ICON development team COSMO General Meeting, Offenbach, 07.09.2016 Outline Status of limited-area-mode
THORPEX Data Assimilation and Observing Strategies Working Group: scientific objectives
THORPEX Data Assimilation and Observing Strategies Working Group: scientific objectives Pierre Gauthier Co-chair of the DAOS-WG Atmospheric Science and Technology Branch Environment Canada Data assimilation
Radiance Data Assimilation with an EnKF
Radiance Data Assimilation with an EnKF Zhiquan Liu, Craig Schwartz, Xiangyu Huang (NCAR/MMM) Yongsheng Chen (York University) 4/7/2010 4th EnKF Workshop 1 Outline Radiance Assimilation Methodology Apply
The CMC Monthly Forecasting System
The CMC Monthly Forecasting System Hai Lin Meteorological Research Division RPN seminar May 20, 2011 Acknowledgements Support and help from many people Gilbert Brunet, Bernard Dugas, Juan-Sebastian Fontecilla,
Current 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
Ensemble-based Data Assimilation of TRMM/GPM Precipitation Measurements
January 16, 2014, JAXA Joint PI Workshop, Tokyo Ensemble-based Data Assimilation of TRMM/GPM Precipitation Measurements PI: Takemasa Miyoshi RIKEN Advanced Institute for Computational Science Takemasa.Miyoshi@riken.jp
The hybrid ETKF- Variational data assimilation scheme in HIRLAM
The hybrid ETKF- Variational data assimilation scheme in HIRLAM (current status, problems and further developments) The Hungarian Meteorological Service, Budapest, 24.01.2011 Nils Gustafsson, Jelena Bojarova
Comparisons 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
OSSE to infer the impact of Arctic AMVs extracted from highly elliptical orbit imagery
OSSE to infer the impact of Arctic AMVs extracted from highly elliptical orbit imagery L. Garand 1 Y. Rochon 1, S. Heilliette 1, J. Feng 1, A.P. Trishchenko 2 1 Environment Canada, 2 Canada Center for
Hybrid 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,
Assimilation of the IASI data in the HARMONIE data assimilation system
Assimilation of the IASI data in the HARMONIE data assimilation system Roger Randriamampianina Acknowledgement: Andrea Storto (met.no), Andrew Collard (ECMWF), Fiona Hilton (MetOffice) and Vincent Guidard
NCEP Global Ensemble Forecast System (GEFS) Yuejian Zhu Ensemble Team Leader Environmental Modeling Center NCEP/NWS/NOAA February
NCEP Global Ensemble Forecast System (GEFS) Yuejian Zhu Ensemble Team Leader Environmental Modeling Center NCEP/NWS/NOAA February 20 2014 Current Status (since Feb 2012) Model GFS V9.01 (Spectrum, Euler
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,
Ninth Workshop on Meteorological Operational Systems. Timeliness and Impact of Observations in the CMC Global NWP system
Ninth Workshop on Meteorological Operational Systems ECMWF, Reading, United Kingdom 10 14 November 2003 Timeliness and Impact of Observations in the CMC Global NWP system Réal Sarrazin, Yulia Zaitseva
USE OF SURFACE MESONET DATA IN THE NCEP REGIONAL GSI SYSTEM
6A.7 USE OF SURFACE MESONET DATA IN THE NCEP REGIONAL GSI SYSTEM Seung-Jae Lee *, David F. Parrish, Wan-Shu Wu, Manuel Pondeca, Dennis Keyser, and Geoffery J. DiMego NCEP/Environmental Meteorological Center,
Met 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
Updates to Land DA at the Met Office
Updates to Land DA at the Met Office Brett Candy & Keir Bovis Satellite Soil Moisture Workshop, Amsterdam, 10th July 2014 Land DA - The Story So Far - Kalman Filter 18 months in operations - Analyses of
EnKF Review. P.L. Houtekamer 7th EnKF workshop Introduction to the EnKF. Challenges. The ultimate global EnKF algorithm
Overview 1 2 3 Review of the Ensemble Kalman Filter for Atmospheric Data Assimilation 6th EnKF Purpose EnKF equations localization After the 6th EnKF (2014), I decided with Prof. Zhang to summarize progress
A SATELLITE LAND DATA ASSIMILATION SYTEM COUPLED WITH A MESOSCALE MODEL: TOWARDS IMPROVING NUMERICAL WEATHER PREDICTION
A SATELLITE LAND DATA ASSIMILATION SYTEM COUPLED WITH A MESOSCALE MODEL: TOWARDS IMPROVING NUMERICAL WEATHER PREDICTION Mohamed Rasmy*, Toshio Koike*, Souhail Bousseta**, Xin Li*** Dept. of Civil Engineering,
DAOS plans. Presentation to the THORPEX Executive Committee 25 September 2008
DAOS plans Presentation to the THORPEX Executive Committee 25 September 2008 Merge with OS Work already covered through OPAG-IOS, rest of work would not constitute a WG. Best to combine, go to DAOS (Obs
PSU 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
Uncertainty in Operational Atmospheric Analyses. Rolf Langland Naval Research Laboratory Monterey, CA
Uncertainty in Operational Atmospheric Analyses 1 Rolf Langland Naval Research Laboratory Monterey, CA Objectives 2 1. Quantify the uncertainty (differences) in current operational analyses of the atmosphere
Ensemble aerosol forecasts and assimila1on at ECMWF
Ensemble aerosol forecasts and assimila1on at ECMWF Angela Benede*, Miha Razinger, Luke Jones & Jean- Jacques Morcre
Land Data Assimilation for operational weather forecasting
Land Data Assimilation for operational weather forecasting Brett Candy Richard Renshaw, JuHyoung Lee & Imtiaz Dharssi * *Centre Australian Weather and Climate Research Contents An overview of the Current
Loïk Berre Météo-France (CNRM/GAME) Thanks to Gérald Desroziers
Estimation and diagnosis of analysis/background errors using ensemble assimilation Loïk Berre Météo-France (CNRM/GAME) Thanks to Gérald Desroziers Outline 1. Simulation of the error evolution 2. The operational
Tangent-linear and adjoint models in data assimilation
Tangent-linear and adjoint models in data assimilation Marta Janisková and Philippe Lopez ECMWF Thanks to: F. Váňa, M.Fielding 2018 Annual Seminar: Earth system assimilation 10-13 September 2018 Tangent-linear
Recent experience at Météo-France on the assimilation of observations at high temporal frequency Cliquez pour modifier le style du titre
Recent experience at Météo-France on the assimilation of observations at high temporal frequency Cliquez pour modifier le style du titre J.-F. Mahfouf, P. Brousseau, P. Chambon and G. Desroziers Météo-France/CNRS
AN OBSERVING SYSTEM EXPERIMENT OF MTSAT RAPID SCAN AMV USING JMA MESO-SCALE OPERATIONAL NWP SYSTEM
AN OBSERVING SYSTEM EXPERIMENT OF MTSAT RAPID SCAN AMV USING JMA MESO-SCALE OPERATIONAL NWP SYSTEM Koji Yamashita Japan Meteorological Agency / Numerical Prediction Division 1-3-4, Otemachi, Chiyoda-ku,
GSI 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
Direct assimilation of all-sky microwave radiances at ECMWF
Direct assimilation of all-sky microwave radiances at ECMWF Peter Bauer, Alan Geer, Philippe Lopez, Deborah Salmond European Centre for Medium-Range Weather Forecasts Reading, Berkshire, UK Slide 1 17
Workshop on Numerical Weather Models for Space Geodesy Positioning
Workshop on Numerical Weather Models for Space Geodesy Positioning Marcelo C. Santos University of New Brunswick, Department of Geodesy and Geomatics Engineering, Fredericton, NB Room C25 (ADI Room), Head
Introduction to Data Assimilation
Introduction to Data Assimilation Alan O Neill Data Assimilation Research Centre University of Reading What is data assimilation? Data assimilation is the technique whereby observational data are combined
Update on the KENDA project
Christoph Schraff Deutscher Wetterdienst, Offenbach, Germany and many colleagues from CH, D, I, ROM, RU Km-scale ENsemble-based Data Assimilation : COSMO priority project Local Ensemble Transform Kalman
Growth of forecast uncertainties in global prediction systems and IG wave dynamics
Growth of forecast uncertainties in global prediction systems and IG wave dynamics Nedjeljka Žagar and Žiga Zaplotnik Department of physics, Faculty of mathematics and physics, University of Ljubljana,
The impact of assimilation of microwave radiance in HWRF on the forecast over the western Pacific Ocean
The impact of assimilation of microwave radiance in HWRF on the forecast over the western Pacific Ocean Chun-Chieh Chao, 1 Chien-Ben Chou 2 and Huei-Ping Huang 3 1Meteorological Informatics Business Division,
(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
Upgraded usage of MODIS-derived polar winds in the JMA operational global 4D-Var assimilation system
1 Upgraded usage of MODIS-derived polar winds in the JMA operational global 4D-Var assimilation system Koji Yamashita Japan Meteorological Agency Kobo.yamashita@met.kishou.go.jp Background Objectives 2
ERA-CLIM: Developing reanalyses of the coupled climate system
ERA-CLIM: Developing reanalyses of the coupled climate system Dick Dee Acknowledgements: Reanalysis team and many others at ECMWF, ERA-CLIM project partners at Met Office, Météo France, EUMETSAT, Un. Bern,
Juli I. Rubin. NRC Postdoctoral Research Associate Naval Research Laboratory, Monterey, CA
Development of the Ensemble Navy Aerosol Analysis Prediction System and its application of the Data Assimilation Research Testbed in Support of Aerosol Forecasting Juli I. Rubin NRC Postdoctoral Research
Prospects for radar and lidar cloud assimilation
Prospects for radar and lidar cloud assimilation Marta Janisková, ECMWF Thanks to: S. Di Michele, E. Martins, A. Beljaars, S. English, P. Lopez, P. Bauer ECMWF Seminar on the Use of Satellite Observations
MAP Re-Analysis with IFS/ARPEGE/ALADIN
MAP Re-Analysis with IFS/ARPEGE/ALADIN Stjepan Ivatek-Šahdan RC LACE Data Manager Croatian Meteorological and Hydrological Service ivateks@cirus.dhz.hr Contents - Why to care about MAP-SOP Period? - Modification
Near-surface weather prediction and surface data assimilation: challenges, development, and potential data needs
Near-surface weather prediction and surface data assimilation: challenges, development, and potential data needs Zhaoxia Pu Department of Atmospheric Sciences University of Utah, Salt Lake City, Utah,
Ensemble of Data Assimilations methods for the initialization of EPS
Ensemble of Data Assimilations methods for the initialization of EPS Laure RAYNAUD Météo-France ECMWF Annual Seminar Reading, 12 September 2017 Introduction Estimating the uncertainty in the initial conditions
Data Impact Studies in the CMC Global NWP system
Third WMO Workshop on the Impact of Various Observing Systems on WP Alpbach, Austria 9 12 March 2004 Data Impact Studies in the CMC Global WP system Gilles Verner, Réal Sarrazin and Yulia Zaitseva Canadian
Ting Lei, Xuguang Wang University of Oklahoma, Norman, OK, USA. Wang and Lei, MWR, Daryl Kleist (NCEP): dual resolution 4DEnsVar
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,
Numerical Weather prediction at the European Centre for Medium-Range Weather Forecasts
Numerical Weather prediction at the European Centre for Medium-Range Weather Forecasts Time series curves 500hPa geopotential Correlation coefficent of forecast anomaly N Hemisphere Lat 20.0 to 90.0 Lon
EXPERIMENTAL ASSIMILATION OF SPACE-BORNE CLOUD RADAR AND LIDAR OBSERVATIONS AT ECMWF
EXPERIMENTAL ASSIMILATION OF SPACE-BORNE CLOUD RADAR AND LIDAR OBSERVATIONS AT ECMWF Marta Janisková, Sabatino Di Michele, Edouard Martins ECMWF, Shinfield Park, Reading, U.K. Abstract Space-borne active
Meteo-France operational land surface analysis for NWP: current status and perspectives
Meteo-France operational land surface analysis for NWP: current status and perspectives Camille Birman, Jean-François Mahfouf, Yann Seity, Eric Bazile, Françoise Taillefer, Zied Sassi, Nadia Fourrié, Vincent
The ECMWF coupled data assimilation system
The ECMWF coupled data assimilation system Patrick Laloyaux Acknowledgments: Magdalena Balmaseda, Kristian Mogensen, Peter Janssen, Dick Dee August 21, 214 Patrick Laloyaux (ECMWF) CERA August 21, 214
A data-driven method for improving the correlation estimation in serial ensemble Kalman filter
A data-driven method for improving the correlation estimation in serial ensemble Kalman filter Michèle De La Chevrotière, 1 John Harlim 2 1 Department of Mathematics, Penn State University, 2 Department
ABSTRACT 3 RADIAL VELOCITY ASSIMILATION IN BJRUC 3.1 ASSIMILATION STRATEGY OF RADIAL
REAL-TIME RADAR RADIAL VELOCITY ASSIMILATION EXPERIMENTS IN A PRE-OPERATIONAL FRAMEWORK IN NORTH CHINA Min Chen 1 Ming-xuan Chen 1 Shui-yong Fan 1 Hong-li Wang 2 Jenny Sun 2 1 Institute of Urban Meteorology,
C. Gebhardt, S. Theis, R. Kohlhepp, E. Machulskaya, M. Buchhold. developers of KENDA, ICON-EPS, ICON-EDA, COSMO-D2, verification
Convective-scale EPS at DWD status, developments & plans C. Gebhardt, S. Theis, R. Kohlhepp, E. Machulskaya, M. Buchhold developers of KENDA, ICON-EPS, ICON-EDA, COSMO-D2, verification 1 Outline new operational
New applications using real-time observations and ECMWF model data
New applications using real-time observations and ECMWF model data 12 th Workshop on Meteorological Operational Systems Wim van den Berg [senior meteorological researcher, project coordinator] Overview
Relative 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
Assimilating cloud information from satellite cloud products with an Ensemble Kalman Filter at the convective scale
Assimilating cloud information from satellite cloud products with an Ensemble Kalman Filter at the convective scale Annika Schomburg, Christoph Schraff This work was funded by the EUMETSAT fellowship programme.
Atmospheric Data Assimilation with an Ensemble Kalman Filter: Results with Real Observations
604 M O N T H L Y W E A T H E R R E V I E W VOLUME 133 Atmospheric Data Assimilation with an Ensemble Kalman Filter: Results with Real Observations P. L. HOUTEKAMER, HERSCHEL L. MITCHELL, GÉRARD PELLERIN,
The Ensemble Prediction Systems at NMC/CMA
The Ensemble Prediction Systems at NMC/CMA Xiaoli Li, Jing Chen, Guo Deng, Hua Tian Numerical Prediction Center/ National Meteorological Center, CMA May 11 2011 Maryland, U.S.A Overview Operational EPSs
Assimilation of Geostationary WV Radiances within the 4DVAR at ECMWF
Assimilation of Geostationary WV Radiances within the 4DVAR at ECMWF Christina Köpken Graeme Kelly, Jean-Noël Thépaut ECMWF EUMETSAT Fellowship ITSC-XII Lorne, Australia, 27 February - 5 March 2002 Assimilation