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Page 1© Crown copyright 2005 Numerical space weather prediction: can meteorologists forecast the way ahead? Dr Mike Keil, Dr Richard Swinbank and Dr Andrew Bushell ESWW, November 2005
Page 2© Crown copyright 2005 Introduction What National Met Services (NMS) do and how does this fit in with Space Weather? How did they get there? What can be learned for Numerical Space Weather Prediction? What does the future hold?
Page 3© Crown copyright 2005 National Met Services What do they do?
Page 4© Crown copyright 2005 How’s it done? Numerical Weather Prediction Model Observations Analysis data assimilation Forecast T+1T+2T+3T+4T+5T+6T+12T+24T+48T+…
Page 5© Crown copyright 2005 Development of NWP: Vilhelm Bjerknes (1862-1951) had a vision! L.F. Richardson’s first forecast sometime between 1916 and 1918. 1950 Charney ran the first forecast on a computer It took longer to subjectively quantify the ICs than run the forecasts! So far, no mention of Data Assimilation… Clearly a need for an objective way of specifying the initial conditions and analysis
Page 6© Crown copyright 2005 Development of DA: 1949 Panofski had been creating objective analysis using interpolation techniques 1954 Gilchrist and Cressman had two ideas: numerical forecasts as a source of background info automatic quality control of data 1955 Bergothorsson and Doos – analyse observation increments 1961 Thompson – use DA to propagate info into data voids
Page 7© Crown copyright 2005 NWP in the present day: State Time Corrected forecast Initial forecast T+0T+6 Observations Development of NWP models and increased computer performance has led to more sophisticated assimilation schemes
Page 8© Crown copyright 2005 The virtuous cycle observations assimilation modelling science
Page 9© Crown copyright 2005 1953 Storm
Page 10© Crown copyright 2005 The virtuous cycle observations assimilation modelling science
Page 11© Crown copyright 2005 Lessons from Numerical Weather Prediction Data Assimilation combines information from observations with a background state. The background state could come from a number of sources: subjective analysis, climatological averages, empirical models To exploit the full potential of data assimilation, the background state should be produced using a physically-based numerical model. This should be the approach to follow for SW assimilation
Page 12© Crown copyright 2005 Lessons from Numerical Weather Prediction A physically-based numerical model is not just required for data assimilation. A physically-based model is an essential part in fully establishing the virtuous cycle. Empirical models can serve a useful purpose; however their potential for development is restricted. Physically-based models provide a route for long-term space weather scientific growth
Page 13© Crown copyright 2005 Lessons – data issues Satellite data is the most obvious crossover area Co-ordination is handled by WMO Global Observing System info / education / transition Most NMS assimilate data from around 25 operational satellites What about experimental satellites? WMO set the “rules” GTS infrastructure NMS have experience in handling and processing vast amounts of data
Page 14© Crown copyright 2005 Lessons – common data sources GPS RO observations is a good example Mid-90s humidity and temperature profiles from GPS Realistic assimilation first carried out at the Met Office Operational use next year Techniques can be applied to assimilate TEC COSMIC: Constellation Observing System for Meteorology, Ionosphere and Climate 6 space craft – provide TEC, allow operational monitoring Data available in near real time for scientific research
Page 15© Crown copyright 2005 Lessons – other questions along the way There are issues relevant to SW that have already been tackled by the met community: Bias correction of data Assimilation of derived products or raw values? Pain before the gain – increasing complexity Potential for development Timeliness of data Ensembles
Page 16© Crown copyright 2005 The future: operational met models Most operational met models are pushing beyond the stratosphere Why? Met Office global model will have a lid at 63km Research model with a 86km lid Other centres go higher – eg CMAM 210km Sensible to have a joined-up approach to common issues
Page 17© Crown copyright 2005 The future: scientific collaboration The Met Office are interested in Space Weather science! Potential areas of research: Coupling between weather and space weather models Lower boundary forcings? Upwards/downwards control? Fully coupled models (whole atmosphere approach)? Applying data assimilation expertise to space weather assimilation Radio occultation assimilation experience Funding
Page 18© Crown copyright 2005 The future: numerical space weather prediction Within a decade (?) there will be a requirement for operational numerical space weather prediction Why? Primarily military with commercial applications How? Following the framework used in operational NWP Learning from met experience in key areas Utilising the facilities of NMS eg supercomputers, observation supply, 24/7 capabilities, down-stream dissemination to end users This way of working already exists in operational oceanography at the Met Office
Page 19© Crown copyright 2005 Conclusions The development over many years of NWP presents a framework for Numerical Space Weather Prediction Fully establish the “virtuous cycle” for SW Some pain can be avoided by learning from the met community! Science can be pushed forward through collaboration Operational Space Weather within a decade? National Met Services offer crucial facilities Successful partnerships of this kind already exist Thanks for listening!
Page 20© Crown copyright 2005 Questions
Page 21© Crown copyright 2005 The framework of modern DA: Analysis Model Observations data assimilation Forecast T+1T+2T+3T+4T+5T+6 Bjerknes / Richardson / Charney Panofski Gilchrist and Cressman Thompson Bergothorsson and Doos
Page 22© Crown copyright 2005 DA: hierarchy Most assimilation schemes operate sequentially. As long as the evolution of errors is close to linear, an extended Kalman filter is the optimum statistical assimilation method. Hierarchy of different approximations to the Kalman filter: Direct insertion Nudging Statistical interpolation 3D-variational (old Met Office system) 4D-variational (current Met Office global system) Ensemble Kalman Filter Choose appropriate level of complexity / cost.
Page 23© Crown copyright 2005 DA: cost function Analysis is found by minimising a cost function quantifying misfit between model fields x and both obs y o and background x b Where y=H(x) is a prediction of y o In 3D-VAR, the analysis is calculated using observations at one particular time In 4D-VAR, the analysis uses observations at their correct validity time Met Office system written in incremental form
Page 24© Crown copyright 2005 DA: 4D Var 4D-VAR uses observations over a given time window. Allows use of observations at correct time, and exploit information in a time sequence. Requires use of a (simplified, linear) model and its adjoint. (Not clear what level of simplification is appropriate for ionosphere).
Page 25© Crown copyright 2005 DA: summary 3DVAR (with a 6h assimilation cycle for global model) currently used for the stratospheric version of the UM. 4D VAR in the global model was implemented during 2004. Main advantage of 4DVAR is use of observations at correct time / use of time sequence of obs. Requires adjoint of forecast model. (3DVAR only requires adjoint of observation operator.) Kalman Filter updates error covariance as well as state – much more expensive; requires drastic simplification.
Session 5: Methods used in Meteorology Applied to Space Weather Session Introduction Juha-Pekka Luntama Finnish Meteorological Institute Third European.
Page 1 Andrew Lorenc WOAP 2006 © Crown copyright 2006 Andrew Lorenc Head of Data Assimilation & Ensembles Numerical Weather Prediction Met Office, UK Data.
Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss High-resolution data assimilation in COSMO: Status and.
Data assimilation and forecasting the weather (!) Eugenia Kalnay and many friends University of Maryland.
June 20, 2005Workshop on Chemical data assimilation and data needs Data Assimilation Methods Experience from operational meteorological assimilation John.
Page 1© Crown copyright 2004 SRNWP Lead Centre Report on Data Assimilation 2005 for EWGLAM/SRNWP Annual Meeting October 2005, Ljubljana, Slovenia.
A Sequential Hybrid 4DVAR System Implemented Using a Multi-Grid Technique Yuanfu Xie 1, Steven E. Koch 1, and Steve Albers 1,2 1 NOAA Earth System Research.
Page 1© Crown copyright 2006ESWWIII, Royal Library of Belgium, Brussels, Nov 15 th 2006 Forecasting uncertainty: the ensemble solution Mike Keil, Ken Mylne,
Polly Smith, Alison Fowler, Amos Lawless School of Mathematical and Physical Sciences, University of Reading Exploring coupled data assimilation using.
Potential benefits from data assimilation of carbon observations for modellers and observers - prerequisites and current state J. Segschneider, Max-Planck-Institute.
Prepared by Dusanka Zupanski and …… Maximum Likelihood Ensemble Filter: application to carbon problems.
Data Assimilation Andrew Collard. Overview Introduction to Atmospheric Data Assimilation Control Variables Observations Background Error Covariance Summary.
ECMWF reanalysis using GPS RO data Sean Healy Shinya Kobayashi, Saki Uppala, Mark Ringer and Mike Rennie.
© Crown copyright Met Office The EN4 dataset of quality controlled ocean temperature and salinity profiles and monthly objective analyses Simon Good.
Weather forecasting by computer Michael Revell NIWA
Ensemble Data Assimilation and Uncertainty Quantification Jeffrey Anderson, Alicia Karspeck, Tim Hoar, Nancy Collins, Kevin Raeder, Steve Yeager National.
Use of GPS RO in Operations at NCEP Lidia Cucurull NOAA Joint Center for Satellite Data Assimilation.
The Australian Community Climate Earth-System Simulator The Australian Community Climate and Earth System Simulator Kamal Puri (ACCESS Group Leader)
Korea Institute of Atmospheric Prediction Systems (KIAPS) ( 재 ) 한국형수치예보모델개발사업단 LETKF 앙상블 자료동화 시스템 테스트베드 구축 및 활용방안 Implementation and application of LETKF.
Assimilation of radar data - research plan Marián Jurašek Slovak Hydrometeorological Institute.
Page 1 The Construction and Use of Linear Models in Large-scale Data Assimilation Tim Payne Large-Scale Inverse Problems and Applications in the Earth.
CGMS-40, November 2012, Lugano, Switzerland Coordination Group for Meteorological Satellites - CGMS IROWG - Overview of and Plans for the Newest CGMS Working.
Preliminary results from assimilation of GPS radio occultation data in WRF using an ensemble filter H. Liu, J. Anderson, B. Kuo, C. Snyder, A. Caya IMAGe.
Verification of Numerical Weather Prediction systems employed by the Australian Bureau of Meteorology over East Antarctica during the summer season.
CGMS-43 EUM-WP-12 Presentation1 STATUS OF EUMETSAT STUDY ON RADIO OCCULTATION SATURATION WITH REALISTIC ORBITS.
Exploring strategies for coupled 4D-Var data assimilation using an idealised atmosphere-ocean model Polly Smith, Alison Fowler & Amos Lawless School of.
ROSA – ROSSA Validation results R. Notarpietro, G. Perona, M. Cucca
© Crown copyright Met Office Plans for Met Office contribution to SMOS+STORM Evolution James Cotton & Pete Francis, Satellite Applications, Met Office,
Global vs mesoscale ATOVS assimilation at the Met Office Global Large obs error (4 K) NESDIS 1B radiances NOAA-15 & 16 HIRS and AMSU thinned to 154 km.
MPO 674 Lecture 15 3/3/15. Bayesian update Jeff Anderson’s Tutorial A | C = Prior based on previous information C A | BC = Posterior based on previous.
Introduction to Data Assimilation NCEO Data-assimilation training days 5-7 July 2010 Peter Jan van Leeuwen Data Assimilation Research Center (DARC) University.
The vertical resolution of the IASI assimilation system – how sensitive is the analysis to the misspecification of background errors? Fiona Hilton and.
Introduction to data assimilation in meteorology Pierre Brousseau, Ludovic Auger ATMO 08,Alghero, september 2008.
© Copyright QinetiQ limited 2006 On the application of meteorological data assimilation techniques to radio occultation measurements of.
© The Aerospace Corporation 2014 Observation Impact on WRF Model Forecast Accuracy over Southwest Asia Michael D. McAtee Environmental Satellite Systems.
© Crown copyright Met Office Space Weather Mr John Hirst, Permanent Representative of the UK to WMO 17 th May 2011.
Outline Background Highlights of NCAR’s R&D efforts A proposed 5-year plan for CWB Final remarks.
2 nd GRAS-SAF USER WORKSHOP Assimilation of GPS radio occultation measurements at DAO (soon GMAO) P. Poli 1,2 and J. Joiner 3 Data Assimilation Office.
1 3D-Var assimilation of CHAMP measurements at the Met Office Sean Healy, Adrian Jupp and Christian Marquardt.
© Crown copyright Met Office Data Assimilation Developments at the Met Office Recent operational changes, and plans Andrew Lorenc, DAOS, Montreal, August.
Space and Time Multiscale Analysis System A sequential variational approach Yuanfu Xie, Steven Koch Steve Albers and Huiling Yuan Global Systems Division.
Ocean Data Variational Assimilation with OPA: Ongoing developments with OPAVAR and implementation plan for NEMOVAR Sophie RICCI, Anthony Weaver, Nicolas.
Numerical Weather Prediction Process Prepared by C. Tubbs, P. Davies, Met Office UK Revised, delivered by P. Chen, WMO Secretariat SWFDP-Eastern Africa.
Sol-Terra: A Roadmap to Operational Sun-to- Earth Space Weather Forecasting Mike Marsh 1, David Jackson 1, Alastair Pidgeon 2, Gareth Lawrence 2, Simon.
Maximum Liklihood Ensemble Filter (MLEF) Dusanka Zupanski, Kevin Robert Gurney, Scott Denning, Milia Zupanski, Ravi Lokupitiya June, 2005 TransCom Meeting,
© Crown copyright Met Office Met Office activities related to needs of humanitarian agencies Anca Brookshaw.
June, 2003EUMETSAT GRAS SAF 2nd User Workshop. 2 The EPS/METOP Satellite.
Hydrogeodesy Training Session Approximately 15 attendees, Three seminars 1. Introduction to Hydrogeodesy 2. Introduction to Data Assimilation 3. Introduction.
© Crown copyright Met Office Mismatching Perturbations at the Lateral Boundaries in Limited-Area Ensemble Forecasting Jean-François Caron … or why limited-area.
Status of the assimilation of GPS RO observations: the COSMIC Mission L. Cucurull JCSDA/UCAR J.C. Derber, R. Treadon, and R.J. Purser.
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