Multi-Model Ensemble Wake Vortex Prediction
|
|
|
- Amelia Turner
- 5 years ago
- Views:
Transcription
1 Multi-Model Ensemble Wake Vortex Prediction Stephan Körner *, Frank Holzäpfel *, Nash'at Ahmad+ * German Aerospace Center (DLR) Institute of Atmospheric Physics + NASA Langley Research Center WakeNet 2015, Amsterdam
2 DLR.de Chart 2
3 DLR.de Chart 3 Revision of P2P - Motivation wake vortex descent 1 88 landings 2 Γ*=Γ/Γ0, z*=z/b0, y*=y/b0, t*=t/t0 b0 = initial vortex spacing w0 = initial vortex descent speed 70 landings
4 DLR.de Chart 4 Revision of P2P wake vortex descent prim. vortices b0 - secondary vortices weakened by 30 % after first orbit (0.28 * Γ0) - tertiary vortices weakened by 30 % from the beginning (0.28 * Γ0) sec. vortices ground 2 image vortices - vortex-ground interaction above b0: not yet further investigated - vortex ground interaction not only distance but also time dependent?
5 DLR.de Chart 5 Revision of P2P bias= model - observation
6 DLR.de Chart 6 Revision of P2P
7 DLR.de Chart 7 Multi Model Ensemble Sugar How to mix several good ingredients? Water Lemon Juice Lemonade
8 DLR.de Chart 8 Why not use the best ensemble member exclusively? Why not use the best ensemble member exclusively? which is the best member? in average best performing member can sometimes be the worst one y y t y t t Can an ensemble outperform its best member? success of ensemble appr.: any model can be the best sometimes consistently low performing models no increase of skill Yes! Hagedorn et al., 2005
9 DLR.de Chart 9 Ensemble Members NASA-DLR cooperation D2P deterministic output of P2P based on decaying potential vortex, adapted to LES results (DLR) TDP 2.1 considers effect of crosswind shear on vortex descent (NASA) APA 3.2 decay and transport model according to Sarpkaya (NASA) APA 3.4 reduced effect of stratification (NASA) Probability that one of the models delivers the best forecast (in ground-effect, on the basis of rmse for 99 example cases)
10 DLR.de Chart 10 Multi-Model Ensemble Reliability Ensemble Averaging (REA) model performance (a-priori) model convergence iteration loop Giorgi and Mearns, 2002
11 DLR.de Chart 11 Multi-Model Ensemble Reliability Ensemble Averaging natural variability RD,i depends on distance to ensemble mean: z z ensemble mean if bias or distance to ensemble mean < nv model reliable (RB,i or RD,i = 1) natural variability t t nv = model resolution limit
12 DLR.de Chart 12 Multi-Model Ensemble Reliability Ensemble Averaging uncertainty bounds: reliable less reliable uncertainty bounds depend on ensemble spread weighted ensemble average fi according to Giorgi and Mearns, 2002
13 DLR.de Chart 13 Application to Wake Vortex Models Reliability Ensemble Averaging Training RB,i and RD,i mixture of landings from WakeFRA, RB,z,i(t), RB,y,i(t), RB,Γ,i(t), RD,z,i(t), RD,y,i(t), RD,Γ,i(t) Δt*=2 t0 separately for luff and lee vortices weights for reliability factors: RB,z,i : m=1.0, RD,z,i : n=0.3 WakeMUC and WakeOP 95 selected cases Uncertainty envelope initial condition uncertainty added (not considered in original approach): variable variable unit unit σ (standard (standard deviation) deviation) variable true airspeed air density weight z0 y0 [m/s] [kg/m³] [kg] [m] [m] if initial conditions derived from lidar: z0 y0 Г0 unit [m] [m] [m²/s] σ (standard deviation)
14 DLR.de Chart 14 Application to Wake Vortex Models REA natural variability, Γ* N* ε* v* = N*t0 = (ε *b0)1/3/w0 = v/w0
15 DLR.de Chart 15 Application to Wake Vortex Models REA natural variability, z*
16 DLR.de Chart 16 Results REA forecast (one single landing) 89.1 % 89.1 % 69.7 % 75.6 % enhancement: rmse z*,tdp=0.158 rmse z*,rea=0.148 rmse Γ*,D2P=0.085 rmse Γ*,REA=0.072 probability levels according to - 99 testcases - WakeFRA & WakeOP 62.9 % 62.6 %
17 DLR.de Chart 17 Results REA reliability factors (one single landing) no correlation between RD and RB can be found!
18 DLR.de Chart 18 Results REA scoring 99 randomly chosen cases skill factor s: median 2nd best best
19 DLR.de Chart 19 Results REA scoring 99 randomly chosen cases skill factor s: 2nd best best median advanced MME approach outperforms Direct Ensemble Average (DEA)
20 DLR.de Chart 20 PDD of models and ensemble overconfident ensemble: ensemble spread too low well-dispersed ensemble: coverage of full spectrum of possible solutions Weigel et al., 2008, Hagedorn et al.,2004 overconfident ensemble small or no rmse improvement well-dispersed model forecasts rmse improvement
21 DLR.de Chart 21 Conclusion ensemble can improve quality of wake vortex forecasts on average however only 1.6 % improvement compared to best model reasons: - ensemble is overconfident for z* and y* - uncertainties from env. and natural variability dominate model uncertainty but: models might behave differently in particular ambient weather conditions and out-of-ground investigation with pdds
22 DLR.de Chart 22 Further Development How does a good training data set look like? Can the results be further improved by distinguishing various weather conditions? How does the Bayesian Model Averaging (BMA) perform? source: Raftery et al., 2005
23 DLR.de Chart 23 Backup
24 DLR.de Chart 24 Results REA forecast reliability (one single landing) low reliability for y - forecast high reliability for z - forecast medium reliability for Γ - forecast
25 DLR.de Chart 25 Wake Vortex Predictions Motivation - optimization of tactical separation at airports 1 - hazard warning system Wake v traject ortex ory - Wake Encounter Avoidance & Advisory System (WEAA) 2 - Free Flight
26 DLR.de Chart 26 Ensemble Methoden Bayesian Model Averaging P(B) = Wahrscheinlichkeit des Eintretens von B P(B A) = Wahrscheinlichkeit für B, unter Vorraussetzung A PDF = Probability Density Function (Wahrscheinlichkeitsdichtefunktion) Law of total probability: Beispiel: Wir befinden uns auf einem Schiff: - wir wollen die Position B bestimmen - 3 Crew-Mitglieder (A1,A2,A3) wissen wie es geht, haben aber unterschiedliche Methoden according to Grimmett and Welsh.,
27 DLR.de Chart 27 Ensemble Methoden Bayesian Model Averaging Law of total probability: A1 A2 A3 Methode s= vn*tn individuelle Wahrscheinlichkeit, dass die Methode Erfolg hat: P(B An) Wahrscheinlichkeit, dass wir A1, A2 or A3 fragen: P(An) P(B)=
28 DLR.de Chart 28 Ensemble Methoden Bayesian Model Averaging Law of total probability: A1 A3 A2 Methode s= vn*tn PDF der Methode (Modell-Unsicherheiten): P(B An) Wahrscheinlichkeit, dass wir A1, A2 or A3 fragen: P(An)
29 DLR.de Chart 29 Ensemble Methoden Bayesian Model Averaging Law of total probability: angewandt auf Vorhersage-Modelle: Annahme: es gibt immer ein bestes Ensemble-Glied An = Modell n B = vorherzusagende Größe BT = Trainings-Daten P(An) = Wahrscheinlichkeit, dass An das beste Modell ist (Gewichtungsfaktor, basierend auf BT) P(B An) = PDF of An alone (Gaussian distribution, given that An is the best forecast) gewichtete Summe von Wahrscheinlichkeitsdichtefunktionen (PDFs) according to Raftery et al.,
30 DLR.de Chart 30 Ensemble Methoden Bayesian Model Averaging BMA applied on 48-h surface temperature forecast (bias corrected) ensemble forecast individual model PDF individual model forecast 90% interval verification source: Raftery et al.,
31 DLR.de Chart 31 Multi-Model Ensemble benefit increase deterministic skill predict forecast skill provide probabilistic forecast
32 DLR.de Chart 32 Multi-Model Ensemble
Probabilistic Wake Vortex Decay Model Predictions Compared with Observations of Four Field Measurement Campaigns
Probabilistic Wake Vortex Decay Model Predictions Compared with Observations of Four Field Measurement Campaigns Frank Holzäpfel Institut für Physik der Atmosphäre,, DLR Oberpfaffenhofen,, Germany P2P
Short-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
In-Flight Wake Encounter Prediction with the Wake Encounter Avoidance and Advisory System (WEAA)
In-Flight Wake Encounter Prediction with the Wake Encounter Avoidance and Advisory System (WEAA) Tobias Bauer, Fethi Abdelmoula Institute of Flight Systems, German Aerospace Center (DLR) WakeNet-Europe
Wake vortex severity assessment a core element of the safety case. German Aerospace Center DLR
Wake vortex severity assessment a core element of the safety case German Aerospace Center DLR Carsten Schwarz, Klaus-Uwe Hahn - Institute of Flight Systems Frank Holzäpfel, Thomas Gerz - Institute of Atmospheric
Aircraft Wake Vortex State-of-the-Art & Research Needs
WakeNet3-Europe EC Grant Agreement No.: ACS7-GA-2008-213462 Compiled by:... F. Holzäpfel (DLR) et al. Date of compilation:... (for a complete list of contributors see page 3) Dissemination level:... Public
Is the assumption of straight vortices valid for encounter hazard assessment?
Is the assumption of straight vortices valid for encounter hazard assessment? Dennis Vechtel DLR Institute of Flight Systems Brétigny-sur-Orge, May 13 th, 2014 WakeNet-Europe 2014 Workshop Technology,
A Comparison of Wake-Vortex Models for Use in Probabilistic Aviation Safety Analysis
In Proceedings of the 25th International System Safety Conference, eds. A. G. Boyer and N. J. Gauthier, Baltimore, A Comparison of Wake-Vortex Models for Use in Probabilistic Aviation Safety Analysis J.
Calibration of extreme temperature forecasts of MOS_EPS model over Romania with the Bayesian Model Averaging
Volume 11 Issues 1-2 2014 Calibration of extreme temperature forecasts of MOS_EPS model over Romania with the Bayesian Model Averaging Mihaela-Silvana NEACSU National Meteorological Administration, Bucharest
Aircraft Wake Vortex State-of-the-Art & Research Needs
WakeNet3-Europe EC Grant Agreement No.: ACS7-GA-2008-213462 Aircraft Wake Vortex Compiled by:... F. Holzäpfel (DLR) et al. Date of compilation:... (for a complete list of contributors see page 3) Dissemination
Turbulence Measurements. Turbulence Measurements In Low Signal-to-Noise. Larry Cornman National Center For Atmospheric Research
Turbulence Measurements In Low Signal-to-Noise Larry Cornman National Center For Atmospheric Research Turbulence Measurements Turbulence is a stochastic process, and hence must be studied via the statistics
Inflow Forecasting for Hydropower Operations: Bayesian Model Averaging for Postprocessing Hydrological Ensembles
Inflow Forecasting for Hydropower Operations: Bayesian Model Averaging for Postprocessing Hydrological Ensembles Andreas Kleiven, Ingelin Steinsland Norwegian University of Science & Technology Dept. of
Probabilistic Analysis of Wake Vortex Hazards for Landing Aircraft Using Multilateration Data
Shortle and Jeddi 1 Probabilistic Analysis of Wake Vortex Hazards for Landing Aircraft Using Multilateration Data * Corresponding author Word Count: 4,876 Number of Figures: 1 Submission Date: 11/15/6
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
Recent advances in Tropical Cyclone prediction using ensembles
Recent advances in Tropical Cyclone prediction using ensembles Richard Swinbank, with thanks to Many colleagues in Met Office, GIFS-TIGGE WG & others HC-35 meeting, Curacao, April 2013 Recent advances
Application and verification of ECMWF products in Austria
Application and verification of ECMWF products in Austria Central Institute for Meteorology and Geodynamics (ZAMG), Vienna Alexander Kann 1. Summary of major highlights Medium range weather forecasts in
Application and verification of ECMWF products in Austria
Application and verification of ECMWF products in Austria Central Institute for Meteorology and Geodynamics (ZAMG), Vienna Alexander Kann, Klaus Stadlbacher 1. Summary of major highlights Medium range
ALREADY-EXISTING and expected capacity limits at major
JOURNAL OF AIRCRAFT Vol. 4, No., March April 8 Skill of an Aircraft Wake-Vortex Model Using Weather Prediction and Observation Michael Frech and Frank Holzäpfel DLR, German Aerospace Center, Oberpfaffenhofen,
Probabilistic wind speed forecasting in Hungary
Probabilistic wind speed forecasting in Hungary arxiv:1202.4442v3 [stat.ap] 17 Mar 2012 Sándor Baran and Dóra Nemoda Faculty of Informatics, University of Debrecen Kassai út 26, H 4028 Debrecen, Hungary
Ensemble Copula Coupling (ECC)
Ensemble Copula Coupling (ECC) Tilmann Gneiting Institut für Angewandte Mathematik Universität Heidelberg BfG Kolloquium, Koblenz 24. September 2013 Statistical Postprocessing of Ensemble Forecasts: EMOS/NR
Feature-specific verification of ensemble forecasts
Feature-specific verification of ensemble forecasts www.cawcr.gov.au Beth Ebert CAWCR Weather & Environmental Prediction Group Uncertainty information in forecasting For high impact events, forecasters
Ensemble Verification Metrics
Ensemble Verification Metrics Debbie Hudson (Bureau of Meteorology, Australia) ECMWF Annual Seminar 207 Acknowledgements: Beth Ebert Overview. Introduction 2. Attributes of forecast quality 3. Metrics:
The DLR Project WETTER & FLIEGEN. Simulated Lidar Signals for Wake-Vortex Detection ahead of the Aircraft
The DLR Project WETTER & FLIEGEN Final Colloquium, 14.03.2012 Simulated Lidar Signals for Wake-Vortex Detection ahead of the Aircraft Markus Hirschberger, Institute PA, Lidar division 1 Aircraft moves
Overview of Achievements October 2001 October 2003 Adrian Raftery, P.I. MURI Overview Presentation, 17 October 2003 c 2003 Adrian E.
MURI Project: Integration and Visualization of Multisource Information for Mesoscale Meteorology: Statistical and Cognitive Approaches to Visualizing Uncertainty, 2001 2006 Overview of Achievements October
Report on EN6 DTC Ensemble Task 2014: Preliminary Configuration of North American Rapid Refresh Ensemble (NARRE)
Report on EN6 DTC Ensemble Task 2014: Preliminary Configuration of North American Rapid Refresh Ensemble (NARRE) Motivation As an expansion of computing resources for operations at EMC is becoming available
Characterization of Forecast Uncertainty by Means of Ensemble Techniques
Characterization of Forecast Uncertainty by Means of Ensemble Techniques Matthias Steiner National Center for Atmospheric Research Boulder, Colorado, USA Email: msteiner@ucar.edu Short-Term Weather Forecasting
Deutscher Wetterdienst
WakeNet3-Greenwake Workshop Wake Vortex & Wind Monitoring Sensors in all weather conditions DWD s new Remote Wind Sensing Equipment for an Integrated Terminal Weather System (ITWS) Frank Lehrnickel Project
Do Vortices Behave Differently Under Non-Lidar-Friendly Weather?
Do Vortices Behave Differently Under Non-Lidar-Friendly Weather? David C. Burnham and Frank Y. Wang WakeNet-Europe 2014, May 13-14, 2014 EUROCONTROL Experimental Centre (EEC) Brétigny-sur-Orge, France
Numerical Weather Prediction. Medium-range multi-model ensemble combination and calibration
Numerical Weather Prediction Medium-range multi-model ensemble combination and calibration Forecasting Research Technical Report No. 517 Christine Johnson and Richard Swinbank c Crown Copyright email:nwp
Wind-Based Robust Trajectory Optimization using Meteorological Ensemble Probabilistic Forecasts
Wind-Based Robust Trajectory Optimization using Meteorological Ensemble Probabilistic Forecasts Daniel González Arribas, Manuel Soler, Manuel Sanjurjo Rivo Area of Aerospace Engineering Universidad Carlos
Research Grant Scheme Braunschweig
Research Grant Scheme Braunschweig Creating wae vortex awareness for pilots and controllers 1 st year progress report INTRODUCTION Most major airports are operating near their capacity limits today and
En-route aircraft wake vortex encounter analysis in a high density air traffic region
En-route aircraft wake vortex encounter analysis in a high density air traffic region Ulrich Schumann 1) and Robert Sharman 2) 1) Institut für Physik der Atmosphäre, DLR, Oberpfaffenhofen 2) Research Applications
Ensemble Copula Coupling: Towards Physically Consistent, Calibrated Probabilistic Forecasts of Spatio-Temporal Weather Trajectories
Ensemble Copula Coupling: Towards Physically Consistent, Calibrated Probabilistic Forecasts of Spatio-Temporal Weather Trajectories Tilmann Gneiting and Roman Schefzik Institut für Angewandte Mathematik
Verifying the Relationship between Ensemble Forecast Spread and Skill
Verifying the Relationship between Ensemble Forecast Spread and Skill Tom Hopson ASP-RAL, NCAR Jeffrey Weiss, U. Colorado Peter Webster, Georgia Instit. Tech. Motivation for generating ensemble forecasts:
3.6 NCEP s Global Icing Ensemble Prediction and Evaluation
1 3.6 NCEP s Global Icing Ensemble Prediction and Evaluation Binbin Zhou 1,2, Yali Mao 1,2, Hui-ya Chuang 2 and Yuejian Zhu 2 1. I.M. System Group, Inc. 2. EMC/NCEP AMS 18th Conference on Aviation, Range,
Developing Operational MME Forecasts for Subseasonal Timescales
Developing Operational MME Forecasts for Subseasonal Timescales Dan C. Collins NOAA Climate Prediction Center (CPC) Acknowledgements: Stephen Baxter and Augustin Vintzileos (CPC and UMD) 1 Outline I. Operational
Model 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
Ensemble forecast and verification of low level wind shear by the NCEP SREF system
Ensemble forecast and verification of low level wind shear by the NCEP SREF system Binbin Zhou*, Jeff McQueen, Jun Du, Geoff DiMego, Zoltan Toth, Yuejian Zhu NOAA/NWS/NCEP/Environment Model Center 1. Introduction
Wind prediction to support reduced aircraft wake vortex separation standards
Wind prediction to support reduced aircraft wake vortex separation standards Rodney Cole Weather Sensing Group WakeNet-2 Europe Dec 1, 2004 Wakenet-2 Europe-1 Outline Overview of closely spaced parallel
FLYSAFE meteorological hazard nowcasting driven by the needs of the pilot
FLYSAFE meteorological hazard nowcasting driven by the needs of the pilot R. W. Lunnon, Met Office, Exeter, EX1 3PB, United Kingdom., Thomas Hauf, Thomas Gerz, and Patrick Josse. 1. Introduction The FLYSAFE
Power. Wintersemester 2016/17. Jerome Olsen
Power Wintersemester 2016/17 Jerome Olsen Power True result H0 ist wahr H0 ist falsch Test result H0 Annahme (kein sig. Ergebnis) H0 Verwerfung (sig. Ergebnis) 1-α β 95% 20% α 1-β 5% 80% Type-II error
Current 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
Using Bayesian Model Averaging to Calibrate Forecast Ensembles
MAY 2005 R A F T E R Y E T A L. 1155 Using Bayesian Model Averaging to Calibrate Forecast Ensembles ADRIAN E. RAFTERY, TILMANN GNEITING, FADOUA BALABDAOUI, AND MICHAEL POLAKOWSKI Department of Statistics,
Evaluating Forecast Quality
Evaluating Forecast Quality Simon J. Mason International Research Institute for Climate Prediction Questions How do we decide whether a forecast was correct? How do we decide whether a set of forecasts
A comparison of ensemble post-processing methods for extreme events
QuarterlyJournalof theroyalmeteorologicalsociety Q. J. R. Meteorol. Soc. 140: 1112 1120, April 2014 DOI:10.1002/qj.2198 A comparison of ensemble post-processing methods for extreme events R. M. Williams,*
Advances in weather and climate science
Advances in weather and climate science Second ICAO Global Air Navigation Industry Symposium (GANIS/2) 11 to 13 December 2017, Montreal, Canada GREG BROCK Scientific Officer Aeronautical Meteorology Division
GL Garrad Hassan Short term power forecasts for large offshore wind turbine arrays
GL Garrad Hassan Short term power forecasts for large offshore wind turbine arrays Require accurate wind (and hence power) forecasts for 4, 24 and 48 hours in the future for trading purposes. Receive 4
Regional Production, Quarterly report on the daily analyses and forecasts activities, and verification of the EURAD-IM performances
ECMWF COPERNICUS REPORT Copernicus Atmosphere Monitoring Service Regional Production, Quarterly report on the daily analyses and forecasts activities, and verification of the EURAD-IM performances March
WMO Aeronautical Meteorology Scientific Conference 2017
Session 1 Science underpinning meteorological observations, forecasts, advisories and warnings 1.6 Observation, nowcast and forecast of future needs 1.6.1 Advances in observing methods and use of observations
Practical Applications of Probability in Aviation Decision Making
Practical Applications of Probability in Aviation Decision Making Haig 22 October 2014 Portfolio of TFM Decisions Playbook Reroutes Ground Stops Ground Delay Programs Airspace Flow Programs Arrival & Departure
AMPS Update June 2016
AMPS Update June 2016 Kevin W. Manning Jordan G. Powers Mesoscale and Microscale Meteorology Laboratory National Center for Atmospheric Research Boulder, CO 11 th Antarctic Meteorological Observation,
Regional Production Quarterly report on the daily analyses and forecasts activities, and verification of the ENSEMBLE performances
Regional Production Quarterly report on the daily analyses and forecasts activities, and verification of the ENSEMBLE performances December 2015 January 2016 February 2016 Issued by: METEO-FRANCE Date:
The Role of Meteorological Forecast Verification in Aviation. Günter Mahringer, November 2012
The Role of Meteorological Forecast Verification in Aviation Günter Mahringer, November 2012 Introduction Aviation Meteorology is internationally regulated. Services are standardized and harmonized by
Probabilistic Weather Forecasting and the EPS at ECMWF
Probabilistic Weather Forecasting and the EPS at ECMWF Renate Hagedorn European Centre for Medium-Range Weather Forecasts 30 January 2009: Ensemble Prediction at ECMWF 1/ 30 Questions What is an Ensemble
Probabilities for climate projections
Probabilities for climate projections Claudia Tebaldi, Reinhard Furrer Linda Mearns, Doug Nychka National Center for Atmospheric Research Richard Smith - UNC-Chapel Hill Steve Sain - CU-Denver Statistical
Wind Forecasting using HARMONIE with Bayes Model Averaging for Fine-Tuning
Available online at www.sciencedirect.com ScienceDirect Energy Procedia 40 (2013 ) 95 101 European Geosciences Union General Assembly 2013, EGU Division Energy, Resources & the Environment, ERE Abstract
THE WAKE VORTEX PREDICTION & MONITORING SYSTEM WSVBS
THE WAKE VORTEX PREDICTION & MONITORING SYSTEM WSVBS PART II: PERFORMANCE AND ATC INTEGRATION AT FRANKFURT AIRPORT T. Gerz 1, F. Holzäpfel 1, W. Gerling 2, A. Scharnweber 2, M. Frech 1, A. Wiegele 1, K.
Statistical post-processing of probabilistic wind speed forecasting in Hungary
Meteorologische Zeitschrift, Vol. 22, No. 3, 1 (August 13) Ó by Gebrüder Borntraeger 13 Article Statistical post-processing of probabilistic wind speed forecasting in Hungary Sándor Baran 1,*, András Horányi
Recommendations on trajectory selection in flight planning based on weather uncertainty
Recommendations on trajectory selection in flight planning based on weather uncertainty Philippe Arbogast, Alan Hally, Jacob Cheung, Jaap Heijstek, Adri Marsman, Jean-Louis Brenguier Toulouse 6-10 Nov
Atmospheric Hazards to Flight! Robert Stengel,! Aircraft Flight Dynamics, MAE 331, Frames of Reference
Atmospheric Hazards to Flight! Robert Stengel,! Aircraft Flight Dynamics, MAE 331, 2016!! Microbursts!! Wind Rotors!! Wake Vortices!! Clear Air Turbulence Copyright 2016 by Robert Stengel. All rights reserved.
Model error and seasonal forecasting
Model error and seasonal forecasting Antje Weisheimer European Centre for Medium-Range Weather Forecasts ECMWF, Reading, UK with thanks to Paco Doblas-Reyes and Tim Palmer Model error and model uncertainty
FORECASTING poor air quality events associated with
A Comparison of Bayesian and Conditional Density Models in Probabilistic Ozone Forecasting Song Cai, William W. Hsieh, and Alex J. Cannon Member, INNS Abstract Probabilistic models were developed to provide
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
RECAT-EU proposal, validation and consultation
RECAT-EU proposal, validation and consultation WakeNet-EU 2014 Vincent TREVE Frederic ROOSELEER ATM Airport Unit 13 May 2014 RECAT-EU Proposal RECAT-EU RECAT-EU was developed on the basis of the joint
ENSEMBLE FLOOD INUNDATION FORECASTING: A CASE STUDY IN THE TIDAL DELAWARE RIVER
ENSEMBLE FLOOD INUNDATION FORECASTING: A CASE STUDY IN THE TIDAL DELAWARE RIVER Michael Gomez & Alfonso Mejia Civil and Environmental Engineering Pennsylvania State University 10/12/2017 Mid-Atlantic Water
S-Wake Assessment of Wake Vortex Safety Publishable Summary Report
Nationaal Lucht- en Ruimtevaartlaboratorium National Aerospace Laboratory NLR NLR-TP-2003-243 S-Wake Assessment of Wake Vortex Safety Publishable Summary Report A.C. de Bruin (with input from partners)
A new Hierarchical Bayes approach to ensemble-variational data assimilation
A new Hierarchical Bayes approach to ensemble-variational data assimilation Michael Tsyrulnikov and Alexander Rakitko HydroMetCenter of Russia College Park, 20 Oct 2014 Michael Tsyrulnikov and Alexander
Probabilistic Forecast Verification. Yuejian Zhu EMC/NCEP/NOAA
Probabilistic Forecast Verification Yuejian Zhu EMC/NCEP/NOAA Review NAEFS Products (FY07) (December 4 th 2007) Bias corrected NCEP/GFS forecast 4 times daily, every 6 hours, out to 180 hours Bias corrected
IDŐJÁRÁS Quarterly Journal of the Hungarian Meteorological Service Vol. 118, No. 3, July September, 2014, pp
IDŐJÁRÁS Quarterly Journal of the Hungarian Meteorological Service Vol. 118, No. 3, July September, 2014, pp. 217 241 Comparison of the BMA and EMOS statistical methods in calibrating temperature and wind
WMO Aeronautical Meteorology Scientific Conference 2017
Session 2 Integration, use cases, fitness for purpose and service delivery 2.4 Trajectory-based operations (TBO), flight planning and user-preferred routing Recommendations on trajectory selection in flight
Calibrated Surface Temperature Forecasts from the Canadian Ensemble Prediction System Using Bayesian Model Averaging
1364 M O N T H L Y W E A T H E R R E V I E W VOLUME 135 Calibrated Surface Temperature Forecasts from the Canadian Ensemble Prediction System Using Bayesian Model Averaging LAURENCE J. WILSON Meteorological
Basic Verification Concepts
Basic Verification Concepts Barbara Brown National Center for Atmospheric Research Boulder Colorado USA bgb@ucar.edu May 2017 Berlin, Germany Basic concepts - outline What is verification? Why verify?
International Desks: African Training Desk and Projects
The Climate Prediction Center International Desks: African Training Desk and Projects Wassila M. Thiaw Team Leader Climate Prediction Center National Centers for Environmental Predictions 1 African Desk
Application and verification of ECMWF products in Austria
Application and verification of ECMWF products in Austria Central Institute for Meteorology and Geodynamics (ZAMG), Vienna Alexander Kann 1. Summary of major highlights Medium range weather forecasts in
Model verification / validation A distributions-oriented approach
Model verification / validation A distributions-oriented approach Dr. Christian Ohlwein Hans-Ertel-Centre for Weather Research Meteorological Institute, University of Bonn, Germany Ringvorlesung: Quantitative
Contrail cirrus and their climate impact
Contrail cirrus and their climate impact Ulrike Burkhardt DLR Institute for Atmospheric Physics, Oberpfaffenhofen, Germany Wakenet Workshop, 28 June 10 Contrail formation Contrail formation Aircraft engines
operational 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
7.1 The Schneider Electric Numerical Turbulence Forecast Verification using In-situ EDR observations from Operational Commercial Aircraft
7.1 The Schneider Electric Numerical Turbulence Forecast Verification using In-situ EDR observations from Operational Commercial Aircraft Daniel W. Lennartson Schneider Electric Minneapolis, MN John Thivierge
Ensemble forecasting and flow-dependent estimates of initial uncertainty. Martin Leutbecher
Ensemble forecasting and flow-dependent estimates of initial uncertainty Martin Leutbecher acknowledgements: Roberto Buizza, Lars Isaksen Flow-dependent aspects of data assimilation, ECMWF 11 13 June 2007
The 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
The Impact of Horizontal Resolution and Ensemble Size on Probabilistic Forecasts of Precipitation by the ECMWF EPS
The Impact of Horizontal Resolution and Ensemble Size on Probabilistic Forecasts of Precipitation by the ECMWF EPS S. L. Mullen Univ. of Arizona R. Buizza ECMWF University of Wisconsin Predictability Workshop,
OBJECTIVE CALIBRATED WIND SPEED AND CROSSWIND PROBABILISTIC FORECASTS FOR THE HONG KONG INTERNATIONAL AIRPORT
P 333 OBJECTIVE CALIBRATED WIND SPEED AND CROSSWIND PROBABILISTIC FORECASTS FOR THE HONG KONG INTERNATIONAL AIRPORT P. Cheung, C. C. Lam* Hong Kong Observatory, Hong Kong, China 1. INTRODUCTION Wind is
Standardized Anomaly Model Output Statistics Over Complex Terrain.
Standardized Anomaly Model Output Statistics Over Complex Terrain Reto.Stauffer@uibk.ac.at Outline statistical ensemble postprocessing introduction to SAMOS new snow amount forecasts in Tyrol sub-seasonal
Focus on parameter variation results
Accounting for Model Uncertainty in the Navy s Global Ensemble Forecasting System C. Reynolds, M. Flatau, D. Hodyss, J. McLay, J. Moskaitis, J. Ridout, C. Sampson, J. Cummings Naval Research Lab, Monterey,
Probabilistic Weather Prediction
Probabilistic Weather Prediction George C. Craig Meteorological Institute Ludwig-Maximilians-Universität, Munich and DLR Institute for Atmospheric Physics Oberpfaffenhofen Summary (Hagedorn 2009) Nothing
Ensemble 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
Performance and Verification of HWRF/HMON Ensemble Prediction System in 2017 Real time Parallel Experiment. Zhan Zhang, Weiguo Wang
1 Performance and Verification of HWRF/HMON Ensemble Prediction System in 2017 Real time Parallel Experiment Zhan Zhang, Weiguo Wang and the EMC Hurricane Team Environmental Modeling Center, NOAA/NWS/NCEP,
Gefördert 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
Verification of the linguistic uncertainty of warning uncertainty
Verification of the linguistic uncertainty of warning uncertainty Martin Göber 1,3, Tobias Pardowitz 2,3, Thomas Kox 2,3 1 Deutscher Wetterdienst, Offenbach, Germany 2 Institut für Meteorologie Freie Universität,
GROUND-BASED AND AIR-BORNE LIDAR FOR WAKE VORTEX DETECTION AND CHARACTERISATION
GROUND-BASED AND AIR-BORNE LIDAR FOR WAKE VORTEX DETECTION AND CHARACTERISATION A. Wiegele, S. Rahm, I. Smalikho Institut für Physik der Atmosphäre Deutsches Zentrum für Luft- und Raumfahrt (DLR), Oberpfaffenhofen,
2. Wake and Weather Information Systems for Aerodromes
2. Wake and Weather Information Systems for Aerodromes 2.1 Prediction of Dynamic Pairwise Wake Vortex Separations for Approach and Landing Frank Holzäpfel 1, Klaus Dengler 1, Thomas Gerz 1, Carsten Schwarz
Application and verification of ECMWF products 2017
Application and verification of ECMWF products 2017 Finnish Meteorological Institute compiled by Weather and Safety Centre with help of several experts 1. Summary of major highlights FMI s forecasts are
Application and verification of ECMWF products 2010
Application and verification of ECMWF products Hydrological and meteorological service of Croatia (DHMZ) Lovro Kalin. Summary of major highlights At DHMZ, ECMWF products are regarded as the major source
Introduction to the HWRF-based Ensemble Prediction System
Introduction to the HWRF-based Ensemble Prediction System Zhan Zhang Environmental Modeling Center, NCEP/NOAA/NWS, NCWCP, College Park, MD 20740 USA 2018 Hurricane WRF Tutorial, NCWCP, MD. January 23-25.
Regional Production Quarterly report on the daily analyses and forecasts activities, and verification of the CHIMERE performances
Regional Production Quarterly report on the daily analyses and forecasts activities, and verification of the CHIMERE performances December 2015 January 2016 February 2016 This document has been produced
Wake Vortex Encounter Gust Size and Magnitude Flight Data
Wake Vortex Encounter Gust Size and Magnitude Flight Data A P Brown Flight Research Laboratory, NRC Aerospace presented to WakeNet3-Europe 2 nd Major Workshop Developments in wake Turbulence Safety Toulouse,
Products of the JMA Ensemble Prediction System for One-month Forecast
Products of the JMA Ensemble Prediction System for One-month Forecast Shuhei MAEDA, Akira ITO, and Hitoshi SATO Climate Prediction Division Japan Meteorological Agency smaeda@met.kishou.go.jp Contents
Stochastic methods for representing atmospheric model uncertainties in ECMWF's IFS model
Stochastic methods for representing atmospheric model uncertainties in ECMWF's IFS model Sarah-Jane Lock Model Uncertainty, Research Department, ECMWF With thanks to Martin Leutbecher, Simon Lang, Pirkka
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
An 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
Radar sensing of Wake Vortices in clear air
1 Radar sensing of Wake Vortices in clear air a feasibility study V. Brion*, N. Jeannin** Wakenet workshop, 15-16 may 2013, DGAC STAC, Bonneuil-Sur-Marne *Onera Paris **Onera Toulouse 2 Introduction In-house