Sub-seasonal to seasonal forecast Verification. Frédéric Vitart and Laura Ferranti. European Centre for Medium-Range Weather Forecasts
|
|
|
- Berniece Curtis
- 5 years ago
- Views:
Transcription
1 Sub-seasonal to seasonal forecast Verification Frédéric Vitart and Laura Ferranti European Centre for Medium-Range Weather Forecasts Slide 1 Verification Workshop Berlin 11 May 2017
2 INDEX 1. Context: S2S prediction 2. Issues with S2S verification Space/Time Averaging Conditional skill Use of re-forecasts for calibration and verification 3. Verification of weather regime transitions 4. Extreme weather verification Slide 2 Verification Workshop Berlin 11 May 2017
3 S2S Prediction Slide 3 Verification Workshop Berlin 11 May 2017
4 Bridging the gap between Climate and weather prediction A particularly difficult time range: Is it an atmospheric initial condition problem as medium-range forecasting or is it a boundary condition problem as seasonal forecasting? Predictability Desert Some sources of predictability : Madden Julian Oscillation ENSO Land surface conditions: snow-soil moisture Stratospheric variability Atmospheric dynamical processes (Rossby wave propagations, weather regimes ) Sea ice cover thickness? Skill depends on windows of opportunity! Slide 4 Verification Workshop Berlin 11 May 2017
5 Madden-Julian Oscillation and its impacts The Madden-Julian Oscillation (MJO) is the major fluctuation in tropical weather on weekly to monthly timescales. The MJO can be characterised as an eastward moving 'pulse' of cloud and rainfall near the equator that typically recurs every 30 to 60 days. Slide 5 Verification Workshop Berlin 11 May 2017
6 Sub-Projects WWRP/WCRP Sub-seasonal to Seasonal (S2S) Prediction Project Teleconnections (C. Stan and H. Lin) Madden-Julian Oscillation (D. Waliser and S. Woolnough) Monsoons (H. Hendon) Africa (A. Robertson and R. Graham) Extremes (F. Vitart) Verification and Products (C. Coelho) Research Issues Predictability Teleconnection O-A Coupling Scale interactions Physical processes Modelling Issues Initialisation Ensemble generation Resolution O-A Coupling Systematic errors Multi-model combination S2S Database Slide 6 Verification Workshop Berlin 11 May 2017 Needs & Applications Liaison with SERA (Working Group on Societal and Economic Research Applications)
7 WWRP/WCRP S2S Database Timerange Resol. Ens. Size Freq. Hcsts Hcst length Hcst Freq Hcst Size ECMWF D 0-46 T639/319L /week On the fly Past 20y 2/weekly 11 UKMO D 0-60 N216L85 4 daily On the fly /month 3 NCEP D 0-44 N126L64 4 4/daily Fix /daily 1 ECCC D x0.45 L40 21 weekly On the fly weekly 4 BoM D 0-60 T47L /weekly Fix /month 33 JMA D 0-34 T319L /weekly Fix /month 5 KMA D 0-60 N216L85 4 daily On the fly /month 3 CMA D 0-45 T106L40 4 daily Fix daily 4 CNRM D 0-32 T255L91 51 weekly Fix /monthly 15 CNR-ISAC D x0.56 L54 40 weekly Fix /month 1 HMCR D x1.4 L28 20 weekly Fix weekly 10 s2s.ecmwf.int Slide 7 Verification Workshop Berlin 11 May 2017 s2s.cma.cn
8 Sub-seasonal verification S2S forecasts are based on ensemble forecasts. Metrics used to verify S2S forecasts include: RMSE/correlations (MJO/ENSO ) Reliability diagrams/bs RPS CRPS ROC area Potential Economic value. Usually applied on weekly means/monthly means Slide 8 Verification Workshop Berlin 11 May 2017
9 Wheeler and Hendon MJO Index Combined EOF1 Combined EOF2 Slide 9 Verification Workshop Berlin 11 May 2017 From Wheeler and Hendon, BMRC
10 MJO FORECAST 4 3 ECMWF MONTHLY FORECASTS FORECAST BASED 15/05/ UTC Day 1 Day 5 Day 10 Day 15 Day 20 Analysis Ens. Mean Verification Western Pacific RMM2 0 West Hem. and Africa Maritime Continent Indian Ocean RMM1 Slide 10 Verification Workshop Berlin 11 May
11 Bivariate Correlation with ERA Interim Ensemble Mean re-forecasts Slide 11 Verification Workshop Berlin 11 May 2017
12 Skill of the ECMWF Extended-range forecasts ROC area: 2-meter temperature in the upper tercile Day 5-11 Day Day Day Slide 12 Verification Workshop Berlin 11 May 2017
13 S2S verification Important challenges with S2S verification: Extended-range forecasts have very little skill to predict the day to day variability of the weather. There is a need to verify S2S forecasts over longer time period and larger domains. What is the optimum space/time filtering? Forecast skill is very flow dependent. Need for conditional verification on MJO, ENSO, NAO, IOD and SAM phases as well as on particular weather regimes Models drift quickly towards there own climatology. Calibration is necessary. Operational centres produce re-forecasts to calibrate realtime S2S forecasts and also for skill assessment. Slide 13 Verification Workshop Berlin 11 May 2017
14 The predictability limit is the time when the forecast error crosses a certain threshold. As threshold, m 2 σ was used, where m is the average climatological error. Depends on variables, regions, spatial filtering (Z500, T850, U950, V850) and three regions (NH, SH, TR). Buizza and Leutbecher, 2015 Slide 14 Verification Workshop Berlin 11 May 2017
15 Spatial Filtering Slide 15 Verification Workshop Berlin 11 May 2017
16 Seamless prediction and verification Wheeler et al, 2016 Slide 16 Verification Workshop Berlin 11 May 2017
17 Example of seamless Verification 1d1d Short range 1w1w Medium range 4w4w Extended range Maps of CORa actual skill for precipitation Slide 17 Verification Workshop Berlin 11 May 2017 Wheeler et al, 2016
18 Impact of MJO on S2S skill scores Reliability Diagram Probability of 2-m temperature in the upper tercile Day N. Extratropics Europe obs frequency obs frequency forecast probability MJO in IC NO MJO in IC Slide 18 Verification Workshop Berlin 11 May forecast probability Vitart and Molteni, 2010
19 Impact of SSWs on forecast skill scores Conditional verification is useful: Better understanding the contribution of climate drivers in the model For users to have more/less confidence in a forecast a priori. From Tripathi et al. (2015) Slide 19 Verification Workshop Berlin 11 May 2017 This type of verification needs adequate samples (including reforecasts) to allow sub-setting of the data to provide meaningful verification.
20 Need to calibrate extended-range forecasts 2m-temp forecast day st August start dates Model Bias ( ) Forecast anomalies Biases (eg 2mT as shown here) can have a magnitude larger than the anomalies we want to predict Slide 20 Verification Workshop Berlin 11 May 2017
21 WWRP/WCRP S2S Database Timerange Resol. Ens. Size Freq. Hcsts Hcst length Hcst Freq Hcst Size ECMWF D 0-46 T639/319L /week On the fly Past 20y 2/weekly 11 UKMO D 0-60 N216L85 4 daily On the fly /month 3 NCEP D 0-44 N126L64 4 4/daily Fix /daily 1 ECCC D x0.45 L40 21 weekly On the fly weekly 4 BoM D 0-60 T47L /weekly Fix /month 33 JMA D 0-34 T319L /weekly Fix /month 5 KMA D 0-60 N216L85 4 daily On the fly /month 3 CMA D 0-45 T106L40 4 daily Fix daily 4 CNRM D 0-32 T255L91 51 weekly Fix /monthly 15 CNR-ISAC D x0.56 L54 40 weekly Fix /month 1 HMCR D x1.4 L28 20 weekly Fix weekly 10 Slide 21 Verification Workshop Berlin 11 May 2017
22 The ECMWF ENS re-forecast suite to estimate the M-climate May Tco639 L91 51 Tco319 L y Initial conditions: ERA Interim+ ORAS4 ocean Ics+ Soil reanalysis Perturbations: SVs+EDA(2016)+SPPT+SKEB Slide 22 Verification Workshop Berlin 11 May 2017
23 MJO WH index bivariate correlation ECMWF Real-time forecasts - NDJFM Slide 23 Verification Workshop Berlin 11 May 2017
24 MJO WH index bivariate correlation ECMWF Real-time forecasts - NDJFM Small sample size (~20 cases) MJO skill varies from year to year (e.g. impact of ENSO) Slide 24 Verification Workshop Berlin 11 May 2017
25 MJO WH index bivariate correlation Re-forecasts - common period Slide 25 Verification Workshop Berlin 11 May 2017
26 15 days MJO WH index bivariate correlation CY41r1 CY31r1 CY32r2 CY32r3 CY40r levels Tl159 Tl255 Tl255 Tl levels Coupling day 0 CY31R1: Parameterisation of ice supersaturation CY32R2: McRAD (radiation scheme) CY32R3: Changes in convective scheme (Bechtold at al. 2008) CY40R1: Improved diurnal cycle of precipitation CY41R1: revised organized convective detrainment and the revised convective momentum transport. Improvements in MJO Prediction mostly due to changes in convective parameterization Slide 26 Verification Workshop Berlin 11 May 2017
27 Performance of the extended-range Forecasts 2-metre temperature RPSS over Northern Extratropics Day Day Day Vitart, 2014 Slide 27 Verification Workshop Berlin 11 May 2017
28 Issues with re-forecast verification Re-forecast initialization (re-analyses) is different from the initialization of real-time forecasts (operational analyses) Re-forecast ensemble size is often small (typically 5 members) compared to realtime forecasts. Skill is likely to be underestimated. Number of re-forecast years is generally too small for properly sampling events like ENSO. Slide 28 Verification Workshop Berlin 11 May 2017
29 Impact of re-forecast ensemble Size Brier Skill Score (BSS) with climatology as a reference. Two versions of the BSS are used: - the uncorrected BSS as estimated from the ensemble directly. - an analytical correction of the BSS extrapolating towards a hypothetical infinite ensemble size (Ferro, 2007). Slide 29 Verification Workshop Berlin 11 May 2017 Ferranti, Corti, Weisheimer
30 Verification of Weather regime transition (L. Ferranti) Slide 30 Verification Workshop Berlin 11 May 2017
31 Regimes based on clustering of daily anomalies for 29 cold seasons ( ) NAO+ NAO- 500 hpa geopotential European blocking Atlantic ridge k means clustering applied to EOF pre-filtered data (retaining 80% of variance) m 2 s 2 Slide 31 Verification Workshop Berlin 11 May 2017 October 29,
32 Predicting skill (CRPSS) associated with the Euro-Atlantic Regimes: NAO + Bom NAO - Bom Blocking Atlantic Ridge Slide 32 Verification Workshop Berlin 11 May 2017 October 29,
33 NAO- How can we evaluate the model ability in predicting regimes transitions? Trajectories in phase space (c.f. MJO propagation) EOF1 EOF2 ±EOF1 and +EOF2 represent quite well ±NAO and BL Trajectories in phase space summarise regime evolution Unlike MJO, no preferred direction BL: record-breaking cold temperatures over Europe Based on 5-day running means Winter 2009/10 Winter 2013/14 Blocking NAO+ +NAO: exceptional storminess, but mild temperatures over Europe Slide 33 Verification Workshop Berlin 11 May 2017 October 29,
34 Regime transitions: Slide 34 Verification Workshop Berlin 11 May 2017 October 29,
35 Extreme event verification Slide 35 Verification Workshop Berlin 11 May 2017
36 Slide 36 Verification Workshop Berlin 11 May 2017
37 Extreme Forecast Index (EFI) ensemble predictions for 29 June - 5 July 2015 Climate 15 June June June June 2015 (15-21d) (12-18d) (8-14d) (5-11d) 2m temp Cumulative Distribution Function Slide 37 Verification Workshop Berlin 11 May 2017 Observed anomaly
38 Example of heat wave verification (Excess Heat Factor index > 0) For a heatwave to be present, the temperature averaged over three consecutive days has to be greater than the climatological 95th percentile (T95) of daily mean temperature for a given region. ROC area of the probability of the occurrence of a heatwave (Excess Heat Factor index > 0) [From Hudson and Marshall 2016) Slide 38 Verification Workshop Berlin 11 May 2017
39 S2S prediction/verification of tropical cyclones Prediction of more/less TC activity over a sufficiently large area and time window. Justification: TC genesis is strongly modulated by various models of variability: ENSO, MJO, IOD. Which can be predicted by models weeks in advance. MJO Phase 2-3 MJO Phase 6-7 Leroy and Wheeler 2008 Slide 39 Verification Workshop Berlin 11 May 2017
40 Verification of statistical TC genesis forecast Leroy and Wheeler 2008 Slide 40 Verification Workshop Berlin 11 May 2017
41 Tropical Storm sub-seasonal Verification Verification over the Southern Hemisphere as in Leroy et al (2008): ECMWF STAT CECMWF a) Day Probability of a TC occurrence during a weekly period over 20x15 degree domains Observed Frequency Forecast Probability 1 b) Day Observed Frequency Forecast Probability c) Day Observed Frequency Vitart, Leroy and Wheeler, MWR 2010 Slide 41 Verification Workshop Berlin 11 May Forecast Probability
42 Conclusions S2S prediction and verification is still in an early stage Re-forecasts needed for model calibration and skill assessment A main challenge for S2S verification is computing forecast probabilities under limited (small) ensemble sizes and sometimes relatively small number of re-forecast years in the hindcasts. How can we best address verification in a seamless manner, for comparing forecasts across timescales? The S2S database represents an important resource for intercomparison of S2S forecasting systems and evaluation of the benefits of the multi-model ensemble approach. Slide 42 Verification Workshop Berlin 11 May 2017
43 Opportunity to use information on multiple time scales Red Cross - IRI example Slide 43 Verification Workshop Berlin 11 May 2017
44 Example of heat wave verification (Excess Heat Factor index > 0) ROC area of the probability of the occurrence of a heatwave (Excess Heat Factor index > 0) [From Hudson and Marshall 2016) Slide 44 Verification Workshop Berlin 11 May 2017
MJO prediction Intercomparison using the S2S Database Frédéric Vitart (ECMWF)
MJO prediction Intercomparison using the S2S Database Frédéric Vitart (ECMWF) Slide 1 WGNE Meeting 29 April 2016 1 INDEX The S2S project and S2S Database MJO prediction in S2S models MJO teleconnections
How far in advance can we forecast cold/heat spells?
Sub-seasonal time scales: a user-oriented verification approach How far in advance can we forecast cold/heat spells? Laura Ferranti, L. Magnusson, F. Vitart, D. Richardson, M. Rodwell Danube, Feb 2012
The ECMWF Extended range forecasts
The ECMWF Extended range forecasts Laura.Ferranti@ecmwf.int ECMWF, Reading, U.K. Slide 1 TC January 2014 Slide 1 The operational forecasting system l High resolution forecast: twice per day 16 km 91-level,
Sub-seasonal predictions at ECMWF and links with international programmes
Sub-seasonal predictions at ECMWF and links with international programmes Frederic Vitart and Franco Molteni ECMWF, Reading, U.K. 1 Outline 30 years ago: the start of ensemble, extended-range predictions
Sub-seasonal predictions at ECMWF and links with international programmes
Sub-seasonal predictions at ECMWF and links with international programmes Frederic Vitart and Franco Molteni ECMWF, Reading, U.K. Using ECMWF forecasts, 4-6 June 2014 1 Outline Recent progress and plans
Development of a monthly prediction system for aerosol/ozone
Development of a monthly prediction system for aerosol/ozone Frédéric Vitart and Angela Benedetti With contributions from: Alessio Bozzo, Samuel Remy, Francesca Di Giuseppe, Rossana Dragani, J. Flemming
EMC Probabilistic Forecast Verification for Sub-season Scales
EMC Probabilistic Forecast Verification for Sub-season Scales Yuejian Zhu Environmental Modeling Center NCEP/NWS/NOAA Acknowledgement: Wei Li, Hong Guan and Eric Sinsky Present for the DTC Test Plan and
The Sub-seasonal to Seasonal (S2S) Prediction Project
The Sub-seasonal to Seasonal (S2S) Prediction Project Bridging the gap between weather and climate Slide 1 WGNE Meeting 29 April 2016 Co-chairs: Frédéric Vitart (ECMWF) Andrew Robertson 1 (IRI) Mission
The Sub-seasonal to Seasonal (S2S) Prediction Project
The Sub-seasonal to Seasonal (S2S) Prediction Project Bridging the gap between weather and climate 1 Co-chairs: Frédéric Vitart (ECMWF) Andrew Robertson 1 (IRI) SUB-SEASONAL TO SEASONAL PREDICTION RESEARCH
Evolution of ECMWF sub-seasonal forecast skill scores
Quarterly Journalof the RoyalMeteorologicalSociety Q. J. R. Meteorol. Soc. 140: 1889 1899, July 2014 B DOI:10.1002/qj.2256 Evolution of ECMWF sub-seasonal forecast skill scores Frédéric Vitart* European
Prognostic aerosols in the Ensemble Prediction System and impacts at the monthly/sub-seasonal scales
Prognostic aerosols in the Ensemble Prediction System and impacts at the monthly/sub-seasonal scales Angela Benedetti and Frédéric Vitart With contributions from: Alessio Bozzo, Samuel Rémy, Francesca
Subseasonal to Seasonal (S2S) Prediction Project
Subseasonal to Seasonal (S2S) Prediction Project Bridging the gap between weather and climate Co-Chairs: Frederic Vitart (ECMWF) Andrew Robertson (IRI) Objectives To improve forecast skill and understanding
Sub-seasonal predictions
738 Sub-seasonal predictions F. Vitart, G. Balsamo, R. Buizza, L. Ferranti, S. Keeley, L. Magnusson, F. Molteni and A. Weisheimer Research Department October 204 Special Topic paper on sub-seasonal predictions
(Towards) using subseasonal-to-seasonal (S2S) extreme rainfall forecasts for extendedrange flood prediction in Australia
(Towards) using subseasonal-to-seasonal (S2S) extreme rainfall forecasts for extendedrange flood prediction in Australia Christopher J. White 1, Stewart W. Franks 1 and Darryn McEvoy 2 1 School of Engineering
BCC climate prediction model system: developments and applications
EASCOF-5 BCC climate prediction model system: developments and applications Li Qiaoping Beijing Climate Center (BCC), China Meteorological Administration(CMA) Contributed by: Wu Tongwen, Liu Xiangwen,
The forecast skill horizon
The forecast skill horizon Roberto Buizza, Martin Leutbecher, Franco Molteni, Alan Thorpe and Frederic Vitart European Centre for Medium-Range Weather Forecasts WWOSC 2014 (Montreal, Aug 2014) Roberto
TerraMaris: Proposed UK contribution to YMC
TerraMaris: Proposed UK contribution to YMC Steven Woolnough 1,2 Adrian Matthews 3 (PI), Cathryn Birch 4, John Marsham 4, Nick Klingaman 1,2 Paul Barret 5, Prince Xavier 5 1 National Centre for Atmospheric
MJO Prediction Skill of the S2S Prediction Models
6th International Workshop on Monsoons, ovember 15, 017 MJO Prediction Skill of the SS Prediction Models Yuna Lim 1, Seok-Woo Son 1, Daehyun Kim 1 Seoul ational University, Republic of Korea University
SPECIAL 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
Reanalyses use in operational weather forecasting
Reanalyses use in operational weather forecasting Roberto Buizza ECMWF, Shinfield Park, RG2 9AX, Reading, UK 1 2017: the ECMWF IFS includes many components Model components Initial conditions Forecasts
4. WGSIP Projects: Teleconnection
4. WGSIP Projects: Teleconnection The aim of this initiative is to evaluate the ability of the current state-of -the-art dynamical forecasting systems in representing the tropical-extratropical teleconnections.
A simple method for seamless verification applied to precipitation hindcasts from two global models
A simple method for seamless verification applied to precipitation hindcasts from two global models Matthew Wheeler 1, Hongyan Zhu 1, Adam Sobel 2, Debra Hudson 1 and Frederic Vitart 3 1 Bureau of Meteorology,
The Madden Julian Oscillation in the ECMWF monthly forecasting system
The Madden Julian Oscillation in the ECMWF monthly forecasting system Frédéric Vitart ECMWF, Shinfield Park, Reading RG2 9AX, United Kingdom F.Vitart@ecmwf.int ABSTRACT A monthly forecasting system has
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
GPC Exeter forecast for winter Crown copyright Met Office
GPC Exeter forecast for winter 2015-2016 Global Seasonal Forecast System version 5 (GloSea5) ensemble prediction system the source for Met Office monthly and seasonal forecasts uses a coupled model (atmosphere
MJO modeling and Prediction
MJO modeling and Prediction In-Sik Kang Seoul National University, Korea Madden & Julian Oscillation (MJO) index Composite: OLR & U850 RMM index based on Leading PCs of Combined EOF (OLR, U850, U200) P-1
Seasonal prediction of extreme events
Seasonal prediction of extreme events C. Prodhomme, F. Doblas-Reyes MedCOF training, 29 October 2015, Madrid Climate Forecasting Unit Outline: Why focusing on extreme events? Extremeness metric Soil influence
Wassila Mamadou Thiaw Climate Prediction Center
Sub-Seasonal to Seasonal Forecasting for Africa Wassila Mamadou Thiaw Climate Prediction Center NOAA Forecast Con/nuum e.g. Disaster management planning and response e.g. Crop Selec6on, Water management
Prospects for subseasonal forecast of Tropical Cyclone statistics with the CFS
Prospects for subseasonal forecast of Tropical Cyclone statistics with the CFS Augustin Vintzileos (1)(3), Tim Marchok (2), Hua-Lu Pan (3) and Stephen J. Lord (1) SAIC (2) GFDL (3) EMC/NCEP/NOAA During
World Weather Research Program: a ten years vision. PM Ruti, F Vitart, S Majumdar IWTC VIII Dec Jeju
World Weather Research Program: a ten years vision PM Ruti, F Vitart, S Majumdar IWTC VIII Dec 014 - Jeju An overarching question Disaster risk reduction Climate services How should we move from weather
Living with the butterfly effect: a seamless view of predictability
from Newsletter Number 45 Autumn 25 VIEWPOINT Living with the butterfly effect: a seamless view of predictability Νicholashan/iStock/Thinkstock doi:957/x4h3e8w3 This article appeared in the Viewpoint section
Monthly forecast and the Summer 2003 heat wave over Europe: a case study
ATMOSPHERIC SCIENCE LETTERS Atmos. Sci. Let. 6: 112 117 (2005) Published online 21 April 2005 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/asl.99 Monthly forecast and the Summer 2003
Forecasting Extreme Events
Forecasting Extreme Events Ivan Tsonevsky, ivan.tsonevsky@ecmwf.int Slide 1 Outline Introduction How can we define what is extreme? - Model climate (M-climate); The Extreme Forecast Index (EFI) Use and
Clustering Techniques and their applications at ECMWF
Clustering Techniques and their applications at ECMWF Laura Ferranti European Centre for Medium-Range Weather Forecasts Training Course NWP-PR: Clustering techniques and their applications at ECMWF 1/32
Behind the Climate Prediction Center s Extended and Long Range Outlooks Mike Halpert, Deputy Director Climate Prediction Center / NCEP
Behind the Climate Prediction Center s Extended and Long Range Outlooks Mike Halpert, Deputy Director Climate Prediction Center / NCEP September 2012 Outline Mission Extended Range Outlooks (6-10/8-14)
Probabilistic predictions of monsoon rainfall with the ECMWF Monthly and Seasonal Forecast Systems
Probabilistic predictions of monsoon rainfall with the ECMWF Monthly and Seasonal Forecast Systems Franco Molteni, Frederic Vitart, Tim Stockdale, Laura Ferranti, Magdalena Balmaseda European Centre for
Vertical Moist Thermodynamic Structure of the MJO in AIRS Observations: An Update and A Comparison to ECMWF Interim Reanalysis
Vertical Moist Thermodynamic Structure of the MJO in AIRS Observations: An Update and A Comparison to ECMWF Interim Reanalysis Baijun Tian 1 Duane Waliser 1, Eric Fetzer 1, and Yuk Yung 2 1.Jet Propulsion
ECMWF: Weather and Climate Dynamical Forecasts
ECMWF: Weather and Climate Dynamical Forecasts Medium-Range (0-day) Partial coupling Extended + Monthly Fully coupled Seasonal Forecasts Fully coupled Atmospheric model Atmospheric model Wave model Wave
Application and verification of the ECMWF products Report 2007
Application and verification of the ECMWF products Report 2007 National Meteorological Administration Romania 1. Summary of major highlights The medium range forecast activity within the National Meteorological
Verification statistics and evaluations of ECMWF forecasts in
635 Verification statistics and evaluations of ECMWF forecasts in 29-21 D.S. Richardson, J. Bidlot, L. Ferranti, A. Ghelli, T. Hewson, M. Janousek, F. Prates and F. Vitart Operations Department October
TIGGE at ECMWF. David Richardson, Head, Meteorological Operations Section Slide 1. Slide 1
TIGGE at ECMWF David Richardson, Head, Meteorological Operations Section david.richardson@ecmwf.int Slide 1 Slide 1 ECMWF TIGGE archive The TIGGE database now contains five years of global EPS data Holds
The Maritime Continent as a Prediction Barrier
The Maritime Continent as a Prediction Barrier for the MJO Augustin Vintzileos EMC/NCEP SAIC Points to take back home. Forecast of the MJO is at, average, skillful for lead times of up to circa 2 weeks.
Monthly forecasting system
424 Monthly forecasting system Frédéric Vitart Research Department SAC report October 23 Series: ECMWF Technical Memoranda A full list of ECMWF Publications can be found on our web site under: http://www.ecmwf.int/publications/
Forecasting the MJO with the CFS: Factors affecting forecast skill of the MJO. Jon Gottschalck. Augustin Vintzileos
Forecasting the MJO with the CFS: Factors affecting forecast skill of the MJO Augustin Vintzileos CPC/NCEP CICS/ESSIC, University of Maryland College Park Jon Gottschalck CPC/NCEP Problem statement: In
The benefits and developments in ensemble wind forecasting
The benefits and developments in ensemble wind forecasting Erik Andersson Slide 1 ECMWF European Centre for Medium-Range Weather Forecasts Slide 1 ECMWF s global forecasting system High resolution forecast
Long range predictability of winter circulation
Long range predictability of winter circulation Tim Stockdale, Franco Molteni and Laura Ferranti ECMWF Outline ECMWF System 4 Predicting the Arctic Oscillation and other modes Atmospheric initial conditions
Initiative on sub-seasonal to seasonal (S2S) forecast in the agricultural sector
Initiative on sub-seasonal to seasonal (S2S) forecast in the agricultural sector POKAM MBA Wilfried 1 YONTCHANG G.Didier 2, KABENGELA Hubert 3, SONWA Denis 4 1 University of Yaoundé 1 2 National Meteorological
An extended re-forecast set for ECMWF system 4. in the context of EUROSIP
An extended re-forecast set for ECMWF system 4 in the context of EUROSIP Tim Stockdale Acknowledgements: Magdalena Balmaseda, Susanna Corti, Laura Ferranti, Kristian Mogensen, Franco Molteni, Frederic
A Framework for Assessing Operational Model MJO Forecasts
A Framework for Assessing Operational Model MJO Forecasts US CLIVAR MJO Working Group Forecast Team** Climate Diagnostics and Prediction Workshop October 26-30, 2009 Monterey, CA ** Jon Gottschalck: NOAA
4.3.2 Configuration. 4.3 Ensemble Prediction System Introduction
4.3 Ensemble Prediction System 4.3.1 Introduction JMA launched its operational ensemble prediction systems (EPSs) for one-month forecasting, one-week forecasting, and seasonal forecasting in March of 1996,
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:
Predictability and prediction of the North Atlantic Oscillation
Predictability and prediction of the North Atlantic Oscillation Hai Lin Meteorological Research Division, Environment Canada Acknowledgements: Gilbert Brunet, Jacques Derome ECMWF Seminar 2010 September
Sub-seasonal to seasonal Prediction Project
Sub-seasonal to seasonal Prediction Project An element of the WWRP Index Background Implementation Plan : Applications Scientific and modelling issues Database Demonstration projects Linkages Recommendations
Experimental Subseasonal Forecasting Of Atmospheric River Variations For The Western U.S. Winters and
International Conferences on Subseasonal to Decadal Prediction 17-21 September, 2018 NCAR, Boulder, CO Experimental Subseasonal Forecasting Of Atmospheric River Variations For The Western U.S. Winters
How ECMWF has addressed requests from the data users
How ECMWF has addressed requests from the data users David Richardson Head of Evaluation Section, Forecast Department, ECMWF David.richardson@ecmwf.int ECMWF June 14, 2017 Overview Review the efforts made
August Description of an MJO forecast metric.
1956-30 Targeted Training Activity: Seasonal Predictability in Tropical Regions to be followed by Workshop on Multi-scale Predictions of the Asian and African Summer Monsoon 4-15 August 2008 Description
Diagnostics of the prediction and maintenance of Euro-Atlantic blocking
Diagnostics of the prediction and maintenance of Euro-Atlantic blocking Mark Rodwell, Laura Ferranti, Linus Magnusson Workshop on Atmospheric Blocking 6-8 April 2016, University of Reading European Centre
Connecting tropics and extra-tropics: interaction of physical and dynamical processes in atmospheric teleconnections
Connecting tropics and extra-tropics: interaction of physical and dynamical processes in atmospheric teleconnections Franco Molteni, Tim Stockdale, Laura Ferranti European Centre for Medium-Range Weather
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
The new ECMWF seasonal forecast system (system 4)
The new ECMWF seasonal forecast system (system 4) Franco Molteni, Tim Stockdale, Magdalena Balmaseda, Roberto Buizza, Laura Ferranti, Linus Magnusson, Kristian Mogensen, Tim Palmer, Frederic Vitart Met.
S e a s o n a l F o r e c a s t i n g f o r t h e E u r o p e a n e n e r g y s e c t o r
S e a s o n a l F o r e c a s t i n g f o r t h e E u r o p e a n e n e r g y s e c t o r C3S European Climatic Energy Mixes (ECEM) Webinar 18 th Oct 2017 Philip Bett, Met Office Hadley Centre S e a s
ECMWF products to represent, quantify and communicate forecast uncertainty
ECMWF products to represent, quantify and communicate forecast uncertainty Using ECMWF s Forecasts, 2015 David Richardson Head of Evaluation, Forecast Department David.Richardson@ecmwf.int ECMWF June 12,
A review on recent progresses of THORPEX activities in JMA
4th THORPEX workshop 31 Oct. 2012, Kunming, China A review on recent progresses of THORPEX activities in JMA Masaomi NAKAMURA Typhoon Research Department Meteorological Research Institute / JMA Contents
S2S Monsoon Subseasonal Prediction Overview of sub-project and Research Report on Prediction of Active/Break Episodes of Australian Summer Monsoon
S2S Monsoon Subseasonal Prediction Overview of sub-project and Research Report on Prediction of Active/Break Episodes of Australian Summer Monsoon www.cawcr.gov.au Harry Hendon Andrew Marshall Monsoons
Weather and climate: Operational Forecasting Systems and Climate Reanalyses
Weather and climate: Operational Forecasting Systems and Climate Reanalyses M.A. Balmaseda Tim Stockdale, Frederic Vitart, Hao Zuo, Kristian Mogensen, Dominique Salisbury, Steffen Tietsche, Saleh Abdalla,
ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 5 August 2013
ENSO Cycle: Recent Evolution, Current Status and Predictions Update prepared by Climate Prediction Center / NCEP 5 August 2013 Outline Overview Recent Evolution and Current Conditions Oceanic Niño Index
Seasonal forecast from System 4
Seasonal forecast from System 4 European Centre for Medium-Range Weather Forecasts Outline Overview of System 4 System 4 forecasts for DJF 2015/2016 Plans for System 5 System 4 - Overview System 4 seasonal
Techniques and experiences in real-time prediction of the MJO: The BMRC perspective
Techniques and experiences in real-time prediction of the MJO: The BMRC perspective Matthew Wheeler, Harry Hendon, and Oscar Alves Bureau of Meteorology Research Centre P.O. Box 1289k, Melbourne, Vic,
The pilot real-time sub-seasonal MME prediction in WMO LC-LRFMME
The pilot real-time sub-seasonal MME prediction in WMO LC-LRFMME Suhee Park, Jin Ho Yoo, Soonjo Yoon and Miah Kim WMO LC-LRFMME May 2016 1. Introduction WMO and many operational centers realized the necessity
Introduction of products for Climate System Monitoring
Introduction of products for Climate System Monitoring 1 Typical flow of making one month forecast Textbook P.66 Observed data Atmospheric and Oceanic conditions Analysis Numerical model Ensemble forecast
COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 4-17, 2015
COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM AUGUST 4-17, 2015 We expect that the next two weeks will be characterized by below-average amounts (
Challenges in forecasting the MJO
Challenges in forecasting the MJO Augustin Vintzileos University of Maryland ESSIC/CICS-MD Jon Gottschalck NOAA/NCEP/CPC Outline Forecasting at the interface between weather and climate Multi-scale impacts
Forecasting at the interface between weather and climate: beyond the RMM-index
Forecasting at the interface between weather and climate: beyond the RMM-index Augustin Vintzileos University of Maryland ESSIC/CICS-MD Jon Gottschalck NOAA/NCEP/CPC Outline The Global Tropics Hazards
The Australian Summer Monsoon
The Australian Summer Monsoon Aurel Moise, Josephine Brown, Huqiang Zhang, Matt Wheeler and Rob Colman Australian Bureau of Meteorology Presentation to WMO IWM-IV, Singapore, November 2017 Outline Australian
Seasonal forecasting activities at ECMWF
Seasonal forecasting activities at ECMWF An upgraded ECMWF seasonal forecast system: Tim Stockdale, Stephanie Johnson, Magdalena Balmaseda, and Laura Ferranti Progress with C3S: Anca Brookshaw ECMWF June
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,
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
Seasonal Climate Outlook for South Asia (June to September) Issued in May 2014
Ministry of Earth Sciences Earth System Science Organization India Meteorological Department WMO Regional Climate Centre (Demonstration Phase) Pune, India Seasonal Climate Outlook for South Asia (June
Introduction of climate monitoring and analysis products for one-month forecast
Introduction of climate monitoring and analysis products for one-month forecast TCC Training Seminar on One-month Forecast on 13 November 2018 10:30 11:00 1 Typical flow of making one-month forecast Observed
ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 15 July 2013
ENSO Cycle: Recent Evolution, Current Status and Predictions Update prepared by Climate Prediction Center / NCEP 15 July 2013 Outline Overview Recent Evolution and Current Conditions Oceanic Niño Index
YMC Science Issues: Prediction
YMC Science Issues: Prediction Matthew Wheeler Bureau of Meteorology, Melbourne, Australia With input from Harry Hendon (Australia), John McBride (Singapore), and Raizan Rahmat (Singapore). Prediction
Climate Prediction Center Research Interests/Needs
Climate Prediction Center Research Interests/Needs 1 Outline Operational Prediction Branch research needs Operational Monitoring Branch research needs New experimental products at CPC Background on CPC
UPDATE OF REGIONAL WEATHER AND SMOKE HAZE (September 2017)
UPDATE OF REGIONAL WEATHER AND SMOKE HAZE (September 2017) 1. Review of Regional Weather Conditions in August 2017 1.1 Southwest Monsoon conditions continued to prevail in the region in August 2017. The
Evaluation of ECMWF forecasts, including upgrades
817 Evaluation of ECMWF forecasts, including 2016-2017 upgrades T. Haiden, M. Janousek, J. Bidlot, L. Ferranti, F. Prates, F. Vitart, P. Bauer and D.S. Richardson Forecast Department December 2017 Series:
ENSO-DRIVEN PREDICTABILITY OF TROPICAL DRY AUTUMNS USING THE SEASONAL ENSEMBLES MULTIMODEL
1 ENSO-DRIVEN PREDICTABILITY OF TROPICAL DRY AUTUMNS USING THE SEASONAL ENSEMBLES MULTIMODEL Based on the manuscript ENSO-Driven Skill for precipitation from the ENSEMBLES Seasonal Multimodel Forecasts,
Toward Seamless Weather-Climate Prediction with a Global Cloud Resolving Model
DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Toward Seamless Weather-Climate Prediction with a Global Cloud Resolving Model PI: Tim Li IPRC/SOEST, University of Hawaii
Multiple Ocean Analysis Initialization for Ensemble ENSO Prediction using NCEP CFSv2
Multiple Ocean Analysis Initialization for Ensemble ENSO Prediction using NCEP CFSv2 B. Huang 1,2, J. Zhu 1, L. Marx 1, J. L. Kinter 1,2 1 Center for Ocean-Land-Atmosphere Studies 2 Department of Atmospheric,
ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 23 April 2012
ENSO Cycle: Recent Evolution, Current Status and Predictions Update prepared by Climate Prediction Center / NCEP 23 April 2012 Outline Overview Recent Evolution and Current Conditions Oceanic Niño Index
Tropical drivers of the Antarctic atmosphere
Tropical drivers of the Antarctic atmosphere Bradford S. Barrett Gina R. Henderson Oceanography Department U. S. Naval Academy Acknowledge support of: NSF awards ARC-1203843 and AGS-1240143 ONR award N1416WX01752
COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM OCTOBER 13 OCTOBER 26, 2017
COLORADO STATE UNIVERSITY FORECAST OF ATLANTIC HURRICANE ACTIVITY FROM OCTOBER 13 OCTOBER 26, 2017 We expect that the next two weeks will be characterized by above-normal hurricane activity. (as of 13
The ECMWF Diagnostics Explorer : A web tool to aid forecast system assessment and development
from Newsletter Number 117 Autumn 008 METEOROLOGY The ECMWF Diagnostics Explorer : A web tool to aid forecast system assessment and development doi:10.1957/is14d3143 This article appeared in the Meteorology
JMA s Seasonal Prediction of South Asian Climate for Summer 2018
JMA s Seasonal Prediction of South Asian Climate for Summer 2018 Atsushi Minami Tokyo Climate Center (TCC) Japan Meteorological Agency (JMA) Contents Outline of JMA s Seasonal Ensemble Prediction System
Activities of NOAA s NWS Climate Prediction Center (CPC)
Activities of NOAA s NWS Climate Prediction Center (CPC) Jon Gottschalck and Dave DeWitt Improving Sub-Seasonal and Seasonal Precipitation Forecasting for Drought Preparedness May 27-29, 2015 San Diego,
Shaping future approaches to evaluating highimpact weather forecasts
Shaping future approaches to evaluating highimpact weather forecasts David Richardson, and colleagues Head of Evaluation, Forecast Department European Centre for Medium-Range Weather Forecasts (ECMWF)
Exploring and extending the limits of weather predictability? Antje Weisheimer
Exploring and extending the limits of weather predictability? Antje Weisheimer Arnt Eliassen s legacy for NWP ECMWF is an independent intergovernmental organisation supported by 34 states. ECMWF produces
ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 11 November 2013
ENSO Cycle: Recent Evolution, Current Status and Predictions Update prepared by Climate Prediction Center / NCEP 11 November 2013 Outline Overview Recent Evolution and Current Conditions Oceanic Niño Index
Recent Developments in Climate Information Services at JMA. Koichi Kurihara Climate Prediction Division, Japan Meteorological Agency
Recent Developments in Climate Information Services at JMA Koichi Kurihara Climate Prediction Division, Japan Meteorological Agency 1 Topics 1. Diagnosis of the Northern Hemispheric circulation in December
NOTES AND CORRESPONDENCE. Improving Week-2 Forecasts with Multimodel Reforecast Ensembles
AUGUST 2006 N O T E S A N D C O R R E S P O N D E N C E 2279 NOTES AND CORRESPONDENCE Improving Week-2 Forecasts with Multimodel Reforecast Ensembles JEFFREY S. WHITAKER AND XUE WEI NOAA CIRES Climate
Systematic Errors in the ECMWF Forecasting System
Systematic Errors in the ECMWF Forecasting System Thomas Jung ECMWF Introduction Two principal sources of forecast error: Uncertainties in the initial conditions Model error How to identify model errors?
Medium-range Ensemble Forecasts at the Met Office
Medium-range Ensemble Forecasts at the Met Office Christine Johnson, Richard Swinbank, Helen Titley and Simon Thompson ECMWF workshop on Ensembles Crown copyright 2007 Page 1 Medium-range ensembles at