Page 1© Crown copyright 2005 Ensemble Forecasting: THORPEX and the future of NWP Richard Swinbank, with thanks to Ken Mylne and David Richardson UTLS International School, Cargese, October 2005

Page 2© Crown copyright 2005 Ensembles - Outline  Why Ensemble forecasts?  Ensemble forecasting at the Met Office  THORPEX – improving the prediction of high- impact weather  Multi-model ensembles - TIGGE and NAEFS  The future of forecasting

Ensemble Forecasts

Page 4© Crown copyright 2005 Forecast failures  Today’s NWP systems are one of the great scientific achievements of the 20 th Century, but…  We've all heard of high-profile forecast failures:  Oct '87 – still difficult with today’s systems  Dec '99 French storms  Less severe errors are much more common, especially in medium-range forecasts  So what causes errors in forecasts?  Analysis Errors  Model Errors and Approximations  Unresolved Processes

Page 5© Crown copyright 2005 Ensembles Forecasts  Small errors in initial conditions will always amplify and, together with model errors and approximations, limit the useful forecast range.  By running an ensemble of many model forecasts with small differences in initial conditions (and model formulation) we can:  take account of uncertainty  sample the distribution of forecast states  estimate probabilities

Page 6© Crown copyright 2005 Ensemble forecasting time Forecast uncertainty Climatology Initial Condition Uncertainty X Deterministic Forecast Analysis

Page 7© Crown copyright 2005 Lorenz Model  Variations in predictability can be illustrated using the Lorenz (1963) model: Simple non-linear system. Possible atmospheric analogue: Zonal Flow Blocked Flow

Page 8© Crown copyright 2005 Ensemble Forecasting in the Lorenz Model 1. Predictable - deterministic OK 2. Predictable at first - probability OK 3. Unpredictable climatology OK

Page 9© Crown copyright 2005 Desirable properties of ensembles  By sampling the initial (and forecast model) uncertainties an ensemble forecast system aims to forecast the PDF (probability density function).  To achieve this we need:  All members equally probable  RMS spread of members is similar to RMS error of control forecast  If these criteria are met, the ensemble can be used to estimate probabilities:  If 20% of members predict X, then the probability of X is estimated to be 20%

Page 10© Crown copyright 2005 Rank histograms  For each ensemble forecast rank members by forecast parameter, e.g. temperature at station locations  Identify rank of each verifying observation  Plot histogram of observation ranks  Ideal is flat  Typically get excessive outliers

Two simple ways of showing all ensemble members together Spaghetti Plot Postage stamp plot Visualising Ensemble Forecasts (1)

Visualising Ensemble Forecasts (2)  An EPS meteogram portrays probabilistic information at a particular location  (In this case an ECMWF forecast for Cargèse – how did it work out?)

Ensemble forecasting at the Met Office

Page 14© Crown copyright 2005 Use of ECMWF EPS at Met Office ECMWF ensemble forecasts are used to assess the most probable outcome before creating the medium-range forecast charts

Page 15© Crown copyright 2005 Probability Forecasts from Ensembles  Probability forecast products available to end-users  assess and manage risk  Post-processing of site-specific forecasts  Applied routinely in offshore-oil operations

Page 16© Crown copyright 2005 First Guess Early Warnings Project National Severe Weather Warning Service: Met Office issues Early Warnings up to 5 days ahead - when probability  60% of disruption due to:  Severe Gales  Heavy rain  Heavy Snow  FGEW System provides forecasters with alerts and guidance from EPS  Probs for regions of UK Prob in UK=67%

Page 17© Crown copyright 2005 Short-range Ensembles  ECMWF EPS has transformed the way we do Medium- Range Forecasting  Uncertainty also in short-range:  Rapid cyclogenesis often poorly forecast deterministically (e.g. Dec 1999)  Many customers most interested in short-range  Assess ability to estimate uncertainty in local weather  QPF  Cloud Ceiling, Fog  Winds etc

Page 18© Crown copyright 2005  Ensemble for short-range forecasting  Regional ensemble over N. Atlantic and Europe (NAE)  Nested within global ensemble for LBCs  ETKF perturbations  Stochastic physics  T+72 global, T+36 regional Met Office Global and Regional EPS, MOGREPS NAE

Page 19© Crown copyright 2005 ETKF Generation of Perturbations Observations Analysis (Var) ETKF Xf1Xf2Xf3…Xf1Xf2Xf3… T+12 ETKF similar to Error Breeding but with matrix transformation of all perturbations to provide next set Perturbations scaled according to analysis uncertainty using observation errors

Page 20© Crown copyright 2005 ETKF in global UM  ETKF set up with global UM  Processing all observations used in data assimilation  12-hour cycle (f/c twice per day)  Running in conjunction with stochastic physics to propagate effect  Encouraging growth rate in case studies (ECMWF use singular vectors of linear model to identify rapidly growing modes)

Page 21© Crown copyright 2005 Stochastic Physics Schemes  Three components to current stochastic physics:  Installed in current version:  Stochastic Convective Vorticity (SCV)  Random Parameters (RP)  Under test:  Stochastic Kinetic Energy Backscatter (SKEB)

Page 22© Crown copyright 2005  Current scheme (SCV+RP) has  Substantial impact on surface variables in the short-range (72-h):  PMSL (up to 5 hPa)  T2M (up to 9ºC)  PREC (up to 40% of control values)  Neutral impact on model climate Stochastic Physics Summary New SKEB scheme has: Larger impact Realistic growth rate Increase in spread for an IC- only ensemble 500 hPa geopotential height SKEB RP+SCV

Page 23© Crown copyright 2005 THORPEX

Accelerating improvements in the accuracy of one-day to two weeks high-impact weather forecasts for the benefit of society, economy and environment A photographic collage depicting the societal, economic and ecological impacts of severe weather associated with four Rossby wave-trains that encircled the globe during November …

Page 25© Crown copyright 2005 What is THORPEX?  THORPEX: a World Weather Research Programme Where THORPEX means “THe Observing System Research and Predictability EXperiment”  THORPEX was established in May 2003 by the Fourteenth World Meteorological Congress as a ten- year international global atmospheric research and development programme under the auspices of the WMO Commission for Atmospheric Sciences (CAS).  THORPEX is a part of the WMO World Weather Research Programme (WWRP)

Page 26© Crown copyright 2005 THORPEX Objectives  To reduce and mitigate natural disasters;  To fully realise the societal and economic benefits of improved weather forecasts, especially in developing and least developed countries. This is achievable by: 1.Extending the range of skilful weather forecasts to time scales of value in decision-making (up to 14 days) using probabilistic ensemble forecast techniques; 2.Developing accurate and timely weather warnings in a form that can be readily used in decision-making support tools; 3.Assessing the impact of weather forecasts and associated outcomes on the development of mitigation strategies to minimise the impact of natural hazards.

Page 27© Crown copyright 2005 High-impact weather events  The objective is to improve the forecasting of high-impact weather at short- and medium- range, for instance:  Local scale (UK)  Boscastle – intense rain and flooding August 2004  Regional scale (Europe)  Heatwave in France, August 2003  Global phenomena, such as tropical cyclones  Hurricane Katrina, New Orleans, August 2005

Multi-model Ensembles

Page 29© Crown copyright 2005 Multi-model ensembles  Multi-model ensembles combine ensemble forecasts produced from different models (usually different NWP centres).  This gives access to a bigger ensemble size at relatively little extra cost.  In addition, results from DEMETER (seasonal forecasting project) indicate that there is also a benefit from using different forecast models.

Page 30© Crown copyright 2005 Benefits of multi-model ensembles By better representing the uncertainties within the different modelling systems, a multi-model ensemble gives a much better representation of the probability (risk) of given events occurring Figures show how well the forecast probability of an event match the actual probability that the situation will occur. For a perfect forecast system the line will lie on the diagonal Combined multi-model ECMWFMeteo-France Met Office Reliability: 2m temperature above normal, DEMETER seasonal forecasts

Page 31© Crown copyright 2005 Why should multi-model ensembles be better?  Can a poor model add skill?  If all aspects of a model are poor, perhaps not, unless its errors cancel with another.  How can the multi-model be better than the average single model performance?  Error cancellation and non-linearity of probabilistic diagnostics tend to make multi-model results better in practice.  Why not use the best single model instead?  Models tend to have different strengths and weaknesses, so there is no single best model. (Hagedorn et al, 2005)

Page 32© Crown copyright 2005 Met Office medium-range ensemble  Develop from short range ensemble system (MOGREPS)  Multi-model ensemble, in collaboration with TIGGE partners, including ECMWF and NAEFS.  To be run using UK allocation of resources on ECMWF supercomputer

Page 33© Crown copyright 2005 Medium Range Ensemble Forecast Process Initial Analysis Perturbations Initial Analysis Perturbations Create Initial Conditions Run Ensemble forecast TIGGE archive Multi-model Ensemble Perturbed Initial conditions Single-model ensemble Met Office ECMWF Combine Ensemble forecasts Ensemble forecasts from other models Product generation Products

Page 34© Crown copyright 2005 TIGGE THORPEX Interactive Grand Global Ensemble  Framework for international collaboration in development and testing of ensemble prediction systems  Resource for many THORPEX research projects  Prediction component of THORPEX Forecast Demonstration Projects (FDPs)  A prototype future Global Interactive Forecast System  Global and regional components

Page 35© Crown copyright 2005 TIGGE  Initially develop database of available ensembles, collected in near-real time  Co-ordinate research using this multi-model ensemble data  Compare initial condition methods  Compare multi-model and perturbed physics  Develop ways to combine ensembles  Boundary conditions for regional ensembles  Regime-dependence of ensemble configuration (size, resolution, composition)  Observation targeting (case selection, ETKF sensitive area prediction)  Societal and economic impacts assessment  Close interaction with other THORPEX sub-programmes

Page 36© Crown copyright 2005 TIGGE infrastructure Phase 1  Data collected in near-real time (via internet ftp) at central TIGGE data archives  Can be implemented now at little cost  Can handle current data volumes within available network and storage capabilities TIGGE Centre A EPS 1EPS 2EPS n NHMSacademicEnd user TIGGE Centre B Predictability science Real-world applications

Page 37© Crown copyright 2005 North American Ensemble Forecast System  USA, Canada, and Mexico have set up NAEFS  This is an operational multi- model ensemble forecast system  There are strong links with the TIGGE research programme  Met Office will join on an experimental basis while we evaluate our medium-range ensemble system and the benefit of multi-model ensembles

Forecasting – the future?

Page 39© Crown copyright 2005 Traditional forecast system observationsAssimilationForecastusers

Page 40© Crown copyright 2005 A new interactive NWP process The traditional NWP process is characterized by separate steps with one-way flow of information. In a future NWP process there will be strong feedback among the components, with two-way interaction. Errors and uncertainty will be accounted for. Observing System Data Assimilation Forecast System Applications Data Analysis Single-value forecast Observation targeting Forecast error covariance Targeted forecast requirements Probabilistic forecast Initial state + errors Data + error estimate

Page 41© Crown copyright 2005 A possible Global Interactive Forecast System Forecaster requests high resolution regional ensemble Initial risk from medium-range global ensemble Initiate and maintain links with civil protection agencies Forecaster requests observations in sensitive area Forecaster runs ‘sensitive area’ prediction

Page 42© Crown copyright 2005 Observation targeting Prediction of sensitive areas where extra observations will provide most benefit to forecasts Adaptive control of observing network Targeted use of satellite data (adaptive, intelligent thinning)

Page 43© Crown copyright 2005 Summary  Ensemble forecasting enables us to get a probabilistic perspective on weather forecasts.  This is particularly important to highlight the possibility of high-impact weather events.  A key part of the THORPEX programme is the TIGGE project, intended to lead to the development of a global interactive forecast system.  The Met Office has developed an ensemble forecasting system including ETKF perturbations and stochastic physics that will contribute to the international TIGGE project.

Page 44© Crown copyright 2005 The End