Assimilation of Wind Power Data to Improve Numerical Weather Prediction and Wind Power Prediction
|
|
- June McKenzie
- 6 years ago
- Views:
Transcription
1 Assimilation of Wind Power Data to Improve Numerical Weather Prediction and Wind Power Prediction Erstellung innovativer Wetter- und Leistungsprognosemodelle für die Netzintegration wetterabhängiger Energieträger - Eine Kooperation von Meteorologie und Energiewirtschaft - Stefan Declair EWEA Technology Workshop Wind Power Forecasting Rotterdam, December 3 rd 2013
2 Energiewende in Gemany: Haves and Wants 2
3 3
4 Who is EWeLiNE? 4
5 Agenda 1. Data Assimilation 2. Data Situation 3. Impact-Study 5
6 Agenda 1. Data Assimilation 2. Data Situation 3. Impact-Study 6
7 Forecast: Can i cross the street without getting hit? Information used: Observations Knowledge about cars, street, etc Experience statistics Forecast errors due to: Observation (estimation) errors Model errors (icy street) Case does not match statistics 7
8 8 LETKF Goal: compute a best-fit initial state for the next model integration step Method: Local Ensemble Transform Kalman Filter Local: localizes spatially around observations Ensemble Transform: works in ensemble space Kalman Filter: tracks means and covariances ) ( ) ( ~ 1 b o b o b a a y y K y y R Y P w T 1 1 1) ( ~ b b T a Y R Y k P ) ( ) ( i a b b i a w X x x b T a b a X P X P ~ )] ( [ )] ( [ ) ( 1 1 x H y R x H y X P X x J o T o b T b b minimize in S transform to observation space ~ Cost function to minimize
9 KENDA Kilometer-scale Ensemble Data Assimilation Priority program within COSMO consortium LETKF for the nonhydrostatic COSMO-DE model of DWD Implementation following Hunt et al., 2007 Basic Idea: perform the analysis in the space of the ensemble pertubations computationally efficient, but also restricted to do corrections to space spanned by the ensemble explicit localization analysis ensemble members are local linear combinations of the first guess ensemble members 9
10 10 KENDA R o-fg o-fg LETKF LETKF LETKF < o-fg o-fg P b K
11 Forward Operator Process chain 11
12 Forward Operator Powercurve Fit 12
13 Forward Operator - Example Input Wind speed/direction from COSMO-DE Analysis Power observations Cost function Depending on orthogonal distance between data points and objective function Results Wind direction sector RMSE=10.1% 13
14 Agenda 1. Data Assimilation 2. Data Situation 3. Impact-Study 14
15 Data - Haves 68 wind farms, node-sharp 15min resolution/10min delay Average hub heights, farm point of mass Installed farm nominal power 15
16 Data - Comparison Wind observations University of Hamburg Height m Available from 2005/01/01 10min temporal resolution Power observations Reference wind farm Average hub height 67m Available from 2012/01/01 15min temporal resolution Reference wind farm Tower Hamburg approx. 47km 16
17 Data - Comparison 17
18 Data - Comparison Jahrespeak (mit viel Phantasie) 6h-Peak 24h-Peak 12h-Peak 18
19 Data - Wants Additional information needed Average hub distance Average blade radius Hub alignment Information content outside of partial power Hub activity Losses between farm (hubs) and nodes account wake effects increase information density because all the uncertainties enter the LETKF via the observation error covariance matrix reduce uncertainties 19
20 Agenda 1. Data Assimilation 2. Data Situation 3. Impact-Study 20
21 OSSE Observation System Simulation Experiment Goal: Test the impact of newly available observations in the data assimilation Method: assimilate artificial observations in slightly perturbed truth Advantages: Truth is known exactly All generated fields can be used as observations Observation system can be altered easily Observation errors Observation densities Temporal resolution/delay 21
22 OSSE Observation System Simulation Experiment Current state artificial wind obs* from model run nature run disturbed run w/ obs* * obs: wind observations at average wind farm hub height control run w/o obs* 25
23 Conclusion Data assimilation KENDA: LETKF data assimilation scheme in COSMO-DE Forward operator: lookup table approach in progress Data situation Available data are of good quality More detailed information necessary to reduce uncertainty for LETKF OSSE Determine the impact of a new observation system Work in progress Thank you for your attention! 26
Assimilation of Wind Power Data to Improve Numerical Weather Prediction and Wind Power Prediction
Assimilation of Wind Power Data to Improve Numerical Weather Prediction and Wind Power Prediction Erstellung innovativer Wetter- und Leistungsprognosemodelle für die Netzintegration wetterabhängiger Energieträger
More informationAssimilating 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.
More informationUpdate 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
More informationSPECIAL PROJECT PROGRESS REPORT
SPECIAL PROJECT PROGRESS REPORT Progress Reports should be 2 to 10 pages in length, depending on importance of the project. All the following mandatory information needs to be provided. Reporting year
More informationAssimilation of radar reflectivity
Assimilation of radar reflectivity Axel Seifert Deutscher Wetterdienst, Offenbach, Germany Convective-scale NWP at DWD: Plans for 2020 Storm-scale ICON-RUC-EPS: hourly 12h ensemble forecasts based on short
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 informationAssimilation of cloud information into the COSMO model with an Ensemble Kalman Filter. Annika Schomburg, Christoph Schraff
Assimilation of cloud information into the COSMO model with an Ensemble Kalman Filter Annika Schomburg, Christoph Schraff SRNWP-Workshop, Offenbach Mai 2013 annika.schomburg@dwd.de 1 Introduction LETKF
More informationOverview on Data Assimilation Activities in COSMO
Overview on Data Assimilation Activities in COSMO Deutscher Wetterdienst, D-63067 Offenbach, Germany current DA method: nudging PP KENDA for (1 3) km-scale EPS: LETKF radar-derived precip: latent heat
More informationState of the art of wind forecasting and planned improvements for NWP Helmut Frank (DWD), Malte Mülller (met.no), Clive Wilson (UKMO)
State of the art of wind forecasting and planned improvements for NWP Helmut Frank (DWD), Malte Mülller (met.no), Clive Wilson (UKMO) thanks to S. Bauernschubert, U. Blahak, S. Declair, A. Röpnack, C.
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 informationCOSMO Activity Assimilation of 2m humidity in KENDA
COSMO Activity Assimilation of 2m humidity in KENDA Tobias Necker (1), Daniel Leuenberger (2) (1) Hans-Ertel-Centre for Weather Research, Germany (1) Meteorological Institute Munich, LMU Munich, Germany
More informationCross-validation methods for quality control, cloud screening, etc.
Cross-validation methods for quality control, cloud screening, etc. Olaf Stiller, Deutscher Wetterdienst Are observations consistent Sensitivity functions with the other observations? given the background
More informationLocal Ensemble Transform Kalman Filter
Local Ensemble Transform Kalman Filter Brian Hunt 11 June 2013 Review of Notation Forecast model: a known function M on a vector space of model states. Truth: an unknown sequence {x n } of model states
More informationImproving the use of satellite winds at the Deutscher Wetterdienst (DWD)
Improving the use of satellite winds at the Deutscher Wetterdienst (DWD) Alexander Cress Deutscher Wetterdienst, Frankfurter Strasse 135, 63067 Offenbach am Main, Germany alexander.cress@dwd.de Ø Introduction
More informationThe Local Ensemble Transform Kalman Filter (LETKF) Eric Kostelich. Main topics
The Local Ensemble Transform Kalman Filter (LETKF) Eric Kostelich Arizona State University Co-workers: Istvan Szunyogh, Brian Hunt, Ed Ott, Eugenia Kalnay, Jim Yorke, and many others http://www.weatherchaos.umd.edu
More informationThe Use of a Self-Evolving Additive Inflation in the CNMCA Ensemble Data Assimilation System
The Use of a Self-Evolving Additive Inflation in the CNMCA Ensemble Data Assimilation System Lucio Torrisi and Francesca Marcucci CNMCA, Italian National Met Center Outline Implementation of the LETKF
More informationRelationship between Singular Vectors, Bred Vectors, 4D-Var and EnKF
Relationship between Singular Vectors, Bred Vectors, 4D-Var and EnKF Eugenia Kalnay and Shu-Chih Yang with Alberto Carrasi, Matteo Corazza and Takemasa Miyoshi 4th EnKF Workshop, April 2010 Relationship
More informationObservation impact on data assimilation with dynamic background error formulation
Observation impact on data assimilation with dynamic background error formulation ALEXANDER BECK alexander.beck@univie.ac.at Department of, Univ. Vienna, Austria Thanks to: Martin Ehrendorfer, Patrick
More informationOn Ensemble and Particle Filters for Numerical Weather Prediction
On Ensemble and Particle Filters for Numerical Weather Prediction Roland Potthast Hendrik Reich, Christoph Schraff Andreas Rhodin, Ana Fernandez Alexander Cress, Dora Foring Annika Schomburg, Africa Perianez
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 informationApplication of Mean Recentering Scheme to Improve the Typhoon Track Forecast: A Case Study of Typhoon Nanmadol (2011) Chih-Chien Chang, Shu-Chih Yang
6 th EnKF workshop Application of Mean Recentering Scheme to Improve the Typhoon Track Forecast: A Case Study of Typhoon Nanmadol (2011) Chih-Chien Chang, Shu-Chih Yang National Central University, Taiwan
More informationC. 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
More informationIncreased wind power forecast skill due to improved NWP in the last decade
Increased wind power forecast skill due to improved NWP in the last decade Lueder von Bremen ForWind Center for Wind Energy Research of the Universities Oldenburg, Hannover und Bremen EWEA Workshop on
More informationAssimilation of MSG visible and near-infrared reflectivity in KENDA/COSMO
Assimilation of MSG visible and near-infrared reflectivity in KENDA/COSMO Leonhard Scheck1,2, Tobias Necker1,2, Pascal Frerebeau2, Bernhard Mayer2, Martin Weissmann1,2 1) Hans-Ertl-Center for Weather Research,
More informationThe convection-permitting COSMO-DE-EPS and PEPS at DWD
Deutscher Wetterdienst The convection-permitting COSMO-DE-EPS and PEPS at DWD Detlev Majewski based on Chr. Gebhardt, S. Theis, M. Paulat, M. Buchhold and M. Denhard Deutscher Wetterdienst The model COSMO-DE
More informationStatus Overview on PP KENDA Km-scale ENsemble-based Data Assimilation
Status Overview on PP KENDA Km-scale ENsemble-based Data Assimilation Lucio Torrisi - CNMCA-Italian Met. Centre on behalf of Christoph Schraff DWD (P.L.) Contributions / input by: Hendrik Reich, Andreas
More informationData Assimilation: Finding the Initial Conditions in Large Dynamical Systems. Eric Kostelich Data Mining Seminar, Feb. 6, 2006
Data Assimilation: Finding the Initial Conditions in Large Dynamical Systems Eric Kostelich Data Mining Seminar, Feb. 6, 2006 kostelich@asu.edu Co-Workers Istvan Szunyogh, Gyorgyi Gyarmati, Ed Ott, Brian
More informationRecent developments for CNMCA LETKF
Recent developments for CNMCA LETKF Lucio Torrisi and Francesca Marcucci CNMCA, Italian National Met Center Outline Implementation of the LETKF at CNMCA Treatment of model error in the CNMCA-LETKF The
More information(Extended) Kalman Filter
(Extended) Kalman Filter Brian Hunt 7 June 2013 Goals of Data Assimilation (DA) Estimate the state of a system based on both current and all past observations of the system, using a model for the system
More informationVerification of wind forecasts of ramping events
Verification of wind forecasts of ramping events Matt Pocernich Research Application Laboratory - NCAR pocernic@ucar.edu Thanks to Brice Lambi, Seth Linden and Gregory Roux Key Points A single verification
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 informationErgodicity in data assimilation methods
Ergodicity in data assimilation methods David Kelly Andy Majda Xin Tong Courant Institute New York University New York NY www.dtbkelly.com April 15, 2016 ETH Zurich David Kelly (CIMS) Data assimilation
More informationSatellite Retrieval Assimilation (a modified observation operator)
Satellite Retrieval Assimilation (a modified observation operator) David D. Kuhl With Istvan Szunyogh and Brad Pierce Sept. 21 2009 Weather Chaos Group Meeting Overview Introduction Derivation of retrieval
More informationWRF-LETKF The Present and Beyond
November 12, 2012, Weather-Chaos meeting WRF-LETKF The Present and Beyond Takemasa Miyoshi and Masaru Kunii University of Maryland, College Park miyoshi@atmos.umd.edu Co-investigators and Collaborators:
More informationAssimilation in the PBL
Assimilation in the PBL Joshua Hacker hacker@ucar.edu National Center for Atmospheric Research, Research Applications Program Data Assimilation Initiative review, Sept 2004 p.1/17 Outline DAI in my world
More informationError (E) Covariance Diagnostics. in Ensemble Data Assimilation. Kayo Ide, Daryl Kleist, Adam Kubaryk University of Maryland
Error (E) Covariance Diagnostics in Ensemble Data Assimilation Kayo Ide, Daryl Kleist, Adam Kubaryk University of Maryland 7 th EnKF Data Assimilation Workshop May 2016 Outline Part I. Background: E-diagnostics
More informationReport on the Joint SRNWP workshop on DA-EPS Bologna, March. Nils Gustafsson Alex Deckmyn.
Report on the Joint SRNWP workshop on DA-EPS Bologna, 22-24 March Nils Gustafsson Alex Deckmyn http://www.smr.arpa.emr.it/srnwp/ Purpose of the workshop On the one hand, data assimilation techniques require
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 informationAssimilation of Satellite Infrared Brightness Temperatures and Doppler Radar Observations in a High-Resolution OSSE
Assimilation of Satellite Infrared Brightness Temperatures and Doppler Radar Observations in a High-Resolution OSSE Jason Otkin and Becky Cintineo University of Wisconsin-Madison, Cooperative Institute
More informationPractical Aspects of Ensemble-based Kalman Filters
Practical Aspects of Ensemble-based Kalman Filters Lars Nerger Alfred Wegener Institute for Polar and Marine Research Bremerhaven, Germany and Bremen Supercomputing Competence Center BremHLR Bremen, Germany
More informationRecent tests with the operational CNMCA-LETKF system
Recent tests with the operational CNMCA-LETKF system Francesca Marcucci and Lucio Torrisi IAF Operational Center for Meteorology (COMET), Pratica di Mare Italy COSMO-GM 2015 07-10 September 2015, Wroclaw,
More informationEnKF 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
More informationData assimilation in high dimensions
Data assimilation in high dimensions David Kelly Kody Law Andy Majda Andrew Stuart Xin Tong Courant Institute New York University New York NY www.dtbkelly.com February 3, 2016 DPMMS, University of Cambridge
More informationDynamic 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
More informationEnhancing the Barcelona Supercomputing Centre chemical transport model with aerosol assimilation
www.bsc.es Enhancing the Barcelona Supercomputing Centre chemical transport model with aerosol assimilation Enza Di Tomaso 1, Nick Schutgens 2, Oriol Jorba 1, George Markomanolis 1 1 Earth Sciences Department,
More informationNew developments in data assimilation in MIKE 21/3 FM Assimilation of along-track altimetry data with correlated measurement errors
New developments in data assimilation in MIKE 21/3 FM Assimilation of along-track altimetry data with correlated measurement errors EnKF Workshop 2016-06-20 Jesper Sandvig Mariegaard Henrik Andersson DHI
More informationJi-Sun Kang. Pr. Eugenia Kalnay (Chair/Advisor) Pr. Ning Zeng (Co-Chair) Pr. Brian Hunt (Dean s representative) Pr. Kayo Ide Pr.
Carbon Cycle Data Assimilation Using a Coupled Atmosphere-Vegetation Model and the LETKF Ji-Sun Kang Committee in charge: Pr. Eugenia Kalnay (Chair/Advisor) Pr. Ning Zeng (Co-Chair) Pr. Brian Hunt (Dean
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 informationEnKF 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
More informationBSC Data Assimilation Updates
www.bsc.es BSC Data Assimilation Updates Enza Di Tomaso*, Nick Schutgens, Oriol Jorba *Severo Ochoa fellow Earth Sciences Department Barcelona Supercomputing Center Special thanks to Francesco Benincasa
More informationParameter Estimation in EnKF: Surface Fluxes of Carbon, Heat, Moisture and Momentum
Parameter Estimation in EnKF: Surface Fluxes of Carbon, Heat, Moisture and Momentum *Ji-Sun Kang, *Eugenia Kalnay, *Takemasa Miyoshi, + Junjie Liu, # Inez Fung, *Kayo Ide *University of Maryland, College
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 informationGefördert auf Grund eines Beschlusses des Deutschen Bundestages
Gefördert auf Grund eines Beschlusses des Deutschen Bundestages Projektträger Koordination Table of Contents 2 Introduction to the Offshore Forecasting Problem Forecast challenges and requirements The
More informationEnsemble-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
More informationEnhancing 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
More informationEnvironment 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
More informationDeutscher Wetterdienst
Deutscher Wetterdienst NUMEX Numerical Experiments and NWP-development at DWD 14th Workshop on Meteorological Operational Systems ECMWF 18-20 November 2013 Thomas Hanisch GB Forschung und Entwicklung (FE)
More informationAn Integrated Approach to the Prediction of Weather, Renewable Energy Generation and Energy Demand in Vermont
1 An Integrated Approach to the Prediction of Weather, Renewable Energy Generation and Energy Demand in Vermont James P. Cipriani IBM Thomas J. Watson Research Center Yorktown Heights, NY Other contributors
More informationTask 36 Forecasting for Wind Power
Task 36 Forecasting for Wind Power Gregor Giebel, DTU Wind Energy 24 Jan 2017 AMS General Meeting, Seattle, US Short-Term Prediction Overview Orography Roughness Wind farm layout Online data GRID End user
More informationAeronautica Militare
Aeronautica Militare Centro Operativo per la Meteorologia C.O.Met. Italian Air Force Meteorological Centre ECMWF visit, 7 th April 2016 The Italian Air Force operational Numerical Weather Prediction System
More informationComparing Local Ensemble Transform Kalman Filter with 4D-Var in a Quasi-geostrophic model
Comparing Local Ensemble Transform Kalman Filter with 4D-Var in a Quasi-geostrophic model Shu-Chih Yang 1,2, Eugenia Kalnay 1, Matteo Corazza 3, Alberto Carrassi 4 and Takemasa Miyoshi 5 1 University of
More informationA State Space Model for Wind Forecast Correction
A State Space Model for Wind Forecast Correction Valrie Monbe, Pierre Ailliot 2, and Anne Cuzol 1 1 Lab-STICC, Université Européenne de Bretagne, France (e-mail: valerie.monbet@univ-ubs.fr, anne.cuzol@univ-ubs.fr)
More informationData assimilation in high dimensions
Data assimilation in high dimensions David Kelly Courant Institute New York University New York NY www.dtbkelly.com February 12, 2015 Graduate seminar, CIMS David Kelly (CIMS) Data assimilation February
More informationoperational status and developments
COSMO-DE DE-EPSEPS operational status and developments Christoph Gebhardt, Susanne Theis, Zied Ben Bouallègue, Michael Buchhold, Andreas Röpnack, Nina Schuhen Deutscher Wetterdienst, DWD COSMO-DE DE-EPSEPS
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 informationAddressing the nonlinear problem of low order clustering in deterministic filters by using mean-preserving non-symmetric solutions of the ETKF
Addressing the nonlinear problem of low order clustering in deterministic filters by using mean-preserving non-symmetric solutions of the ETKF Javier Amezcua, Dr. Kayo Ide, Dr. Eugenia Kalnay 1 Outline
More informationEnsemble activitiesin COSMO
Ensemble activitiesin COSMO Chiara Marsigli Arpae SIMC, Bologna, Italy COSMO WG on Predictability and EPS Ensemble systems COSMO-DE-EPS COSMO-E TLE-MVE COSMO-IT-EPS COSMO-Ru2-EPS COSMO-LEPS COSMO-ME-EPS
More informationKENDA at MeteoSwiss. Operational implementation and results. Daniel Leuenberger and the COSMO-NExT team MeteoSwiss, Zürich, Switzerland
Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss KENDA at MeteoSwiss Operational implementation and results Daniel Leuenberger and the COSMO-NExT team MeteoSwiss,
More informationUse of satellite soil moisture information for NowcastingShort Range NWP forecasts
Use of satellite soil moisture information for NowcastingShort Range NWP forecasts Francesca Marcucci1, Valerio Cardinali/Paride Ferrante1,2, Lucio Torrisi1 1 COMET, Italian AirForce Operational Center
More informationShort-Term Weather Forecasting for Probabilistic Wake-Vortex Prediction
Short-Term Weather Forecasting for Probabilistic Wake-Vortex Prediction Frank Holzäpfel Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany Summary
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 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 information1. Current atmospheric DA systems 2. Coupling surface/atmospheric DA 3. Trends & ideas
1 Current issues in atmospheric data assimilation and its relationship with surfaces François Bouttier GAME/CNRM Météo-France 2nd workshop on remote sensing and modeling of surface properties, Toulouse,
More informationData assimilation for the coupled ocean-atmosphere
GODAE Ocean View/WGNE Workshop 2013 19 March 2013 Data assimilation for the coupled ocean-atmosphere Eugenia Kalnay, Tamara Singleton, Steve Penny, Takemasa Miyoshi, Jim Carton Thanks to the UMD Weather-Chaos
More informationSTRONGLY COUPLED ENKF DATA ASSIMILATION
STRONGLY COUPLED ENKF DATA ASSIMILATION WITH THE CFSV2 Travis Sluka Acknowledgements: Eugenia Kalnay, Steve Penny, Takemasa Miyoshi CDAW Toulouse Oct 19, 2016 Outline 1. Overview of strongly coupled DA
More informationTing 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,
More informationImplementation and evaluation of a regional data assimilation system based on WRF-LETKF
Implementation and evaluation of a regional data assimilation system based on WRF-LETKF Juan José Ruiz Centro de Investigaciones del Mar y la Atmosfera (CONICET University of Buenos Aires) With many thanks
More informationDeutscher Wetterdienst
Deutscher Wetterdienst The Enhanced DWD-RAPS Suite Testing Computers, Compilers and More? Ulrich Schättler, Florian Prill, Harald Anlauf Deutscher Wetterdienst Research and Development Deutscher Wetterdienst
More informationAn Efficient Ensemble Data Assimilation Approach To Deal With Range Limited Observation
An Efficient Ensemble Data Assimilation Approach To Deal With Range Limited Observation A. Shah 1,2, M. E. Gharamti 1, L. Bertino 1 1 Nansen Environmental and Remote Sensing Center 2 University of Bergen
More informationEnsemble Kalman Filter Algorithmic Questions
Ensemble Kalman Filter Algorithmic Questions KENDA Mini Workshop Roland Potthast Deutscher Wetterdienst, Offenbach, Germany 28.02.2014, LMU Munich EnKF Angorithmic Questions Roland.potthast@dwd.de 1 Contents
More informationRelationship between Singular Vectors, Bred Vectors, 4D-Var and EnKF
Relationship between Singular Vectors, Bred Vectors, 4D-Var and EnKF Eugenia Kalnay and Shu-Chih Yang with Alberto Carrasi, Matteo Corazza and Takemasa Miyoshi ECODYC10, Dresden 28 January 2010 Relationship
More information4. DATA ASSIMILATION FUNDAMENTALS
4. DATA ASSIMILATION FUNDAMENTALS... [the atmosphere] "is a chaotic system in which errors introduced into the system can grow with time... As a consequence, data assimilation is a struggle between chaotic
More informationCurrent best practice of uncertainty forecast for wind energy
Current best practice of uncertainty forecast for wind energy Dr. Matthias Lange Stochastic Methods for Management and Valuation of Energy Storage in the Future German Energy System 17 March 2016 Overview
More informationEfficient Data Assimilation for Spatiotemporal Chaos: a Local Ensemble Transform Kalman Filter
Efficient Data Assimilation for Spatiotemporal Chaos: a Local Ensemble Transform Kalman Filter arxiv:physics/0511236 v1 28 Nov 2005 Brian R. Hunt Institute for Physical Science and Technology and Department
More informationUniversity of Athens School of Physics Atmospheric Modeling and Weather Forecasting Group
University of Athens School of Physics Atmospheric Modeling and Weather Forecasting Group http://forecast.uoa.gr Data Assimilation in WAM System operations and validation G. Kallos, G. Galanis and G. Emmanouil
More informationEWEA 2016 Methods for Detection of Icing Losses in Scada Data. Staffan Asplund, Christian Granlund Etha Wind Oy Teppo Hilakivi, Puhuri Oy
EWEA 2016 Methods for Detection of Icing Losses in Scada Data Staffan Asplund, Christian Granlund Etha Wind Oy Teppo Hilakivi, Puhuri Oy Understanding Icing Extreme difference between WTG suppliers operational
More informationApplication of Radio Occultation Data in Analyses and Forecasts of Tropical Cyclones Using an Ensemble Assimilation System
Application of Radio Occultation Data in Analyses and Forecasts of Tropical Cyclones Using an Assimilation System Hui Liu, Jeff Anderson, and Bill Kuo NCAR Acknowledgment: C. Snyder, Y. Chen, T. Hoar,
More informationMesoscale Predictability of Terrain Induced Flows
Mesoscale Predictability of Terrain Induced Flows Dale R. Durran University of Washington Dept. of Atmospheric Sciences Box 3516 Seattle, WA 98195 phone: (206) 543-74 fax: (206) 543-0308 email: durrand@atmos.washington.edu
More informationSmart ideas for a smart energy sector: user-optimized weather and climate information
Smart ideas for a smart energy sector: user-optimized weather and climate information Andrea Steiner, German Weather Service (DWD), Research and Development 29 June 2017 Outline German Energiewende Status
More informationNew Fast Kalman filter method
New Fast Kalman filter method Hojat Ghorbanidehno, Hee Sun Lee 1. Introduction Data assimilation methods combine dynamical models of a system with typically noisy observations to obtain estimates of the
More informationTemporal Wind Variability and Uncertainty
Temporal Wind Variability and Uncertainty Nicholas A. Brown Iowa State University, Department of Electrical and Computer Engineering May 1, 2014 1 An Experiment at Home One Cup of Coffee We Can All Do
More informationDATA ASSIMILATION FOR FLOOD FORECASTING
DATA ASSIMILATION FOR FLOOD FORECASTING Arnold Heemin Delft University of Technology 09/16/14 1 Data assimilation is the incorporation of measurement into a numerical model to improve the model results
More informationState Space Models for Wind Forecast Correction
for Wind Forecast Correction Valérie 1 Pierre Ailliot 2 Anne Cuzol 1 1 Université de Bretagne Sud 2 Université de Brest MAS - 2008/28/08 Outline 1 2 Linear Model : an adaptive bias correction Non Linear
More informationEnsemble-variational assimilation with NEMOVAR Part 2: experiments with the ECMWF system
Ensemble-variational assimilation with NEMOVAR Part 2: experiments with the ECMWF system Toulouse, 20/06/2017 Marcin Chrust 1, Hao Zuo 1 and Anthony Weaver 2 1 ECMWF, UK 2 CERFACS, FR Marcin.chrust@ecmwf.int
More informationProspects for river discharge and depth estimation through assimilation of swath altimetry into a raster-based hydraulics model
Prospects for river discharge and depth estimation through assimilation of swath altimetry into a raster-based hydraulics model Kostas Andreadis 1, Elizabeth Clark 2, Dennis Lettenmaier 1, and Doug Alsdorf
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 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 informationA strategy to optimize the use of retrievals in data assimilation
A strategy to optimize the use of retrievals in data assimilation R. Hoffman, K. Cady-Pereira, J. Eluszkiewicz, D. Gombos, J.-L. Moncet, T. Nehrkorn, S. Greybush 2, K. Ide 2, E. Kalnay 2, M. J. Hoffman
More informationOptimal Localization for Ensemble Kalman Filter Systems
Journal December of the 2014 Meteorological Society of Japan, Vol. Á. 92, PERIÁÑEZ No. 6, pp. et 585 597, al. 2014 585 DOI:10.2151/jmsj.2014-605 Optimal Localization for Ensemble Kalman Filter Systems
More informationMCP and long term wind speed predictions Frank Klintø, Wiebke Langreder Wind&Site Global Competence Centre
MCP and long term wind speed predictions Frank Klintø, Wiebke Langreder Wind&Site Global Competence Centre EWEA Technology Workshop Resource Assessment 2013 Dublin, 25-26 June 2013 1 Agenda Long term data
More information