Observation impact on data assimilation with dynamic background error formulation

Similar documents
4. DATA ASSIMILATION FUNDAMENTALS

Generating climatological forecast error covariance for Variational DAs with ensemble perturbations: comparison with the NMC method

Interpretation of two error statistics estimation methods: 1 - the Derozier s method 2 the NMC method (lagged forecast)

Enhancing information transfer from observations to unobserved state variables for mesoscale radar data assimilation

R. E. Petrie and R. N. Bannister. Department of Meteorology, Earley Gate, University of Reading, Reading, RG6 6BB, United Kingdom

(Extended) Kalman Filter

Inter-comparison of 4D-Var and EnKF systems for operational deterministic NWP

Coupled atmosphere-ocean data assimilation in the presence of model error. Alison Fowler and Amos Lawless (University of Reading)

Hybrid variational-ensemble data assimilation. Daryl T. Kleist. Kayo Ide, Dave Parrish, John Derber, Jeff Whitaker

Evolution of Forecast Error Covariances in 4D-Var and ETKF methods

Model error and parameter estimation

The ECMWF Hybrid 4D-Var and Ensemble of Data Assimilations

Introduction to Data Assimilation

Model error in coupled atmosphereocean data assimilation. Alison Fowler and Amos Lawless (and Keith Haines)

Comparing Variational, Ensemble-based and Hybrid Data Assimilations at Regional Scales

M.Sc. in Meteorology. Numerical Weather Prediction

Observability, a Problem in Data Assimilation

Ting Lei, Xuguang Wang University of Oklahoma, Norman, OK, USA. Wang and Lei, MWR, Daryl Kleist (NCEP): dual resolution 4DEnsVar

Numerical Weather prediction at the European Centre for Medium-Range Weather Forecasts (2)

Comparisons between 4DEnVar and 4DVar on the Met Office global model

Cross-validation methods for quality control, cloud screening, etc.

EnKF Review. P.L. Houtekamer 7th EnKF workshop Introduction to the EnKF. Challenges. The ultimate global EnKF algorithm

Assimilating only surface pressure observations in 3D and 4DVAR

AMPS Update June 2016

Improved analyses and forecasts with AIRS retrievals using the Local Ensemble Transform Kalman Filter

A new Hierarchical Bayes approach to ensemble-variational data assimilation

Doppler radial wind spatially correlated observation error: operational implementation and initial results

Data assimilation; comparison of 4D-Var and LETKF smoothers

Met Office convective-scale 4DVAR system, tests and improvement

Comparison of of Assimilation Schemes for HYCOM

Relationship between Singular Vectors, Bred Vectors, 4D-Var and EnKF

Fundamentals of Data Assimilation

Demonstration and Comparison of of Sequential Approaches for Altimeter Data Assimilation in in HYCOM

Hierarchical Bayes Ensemble Kalman Filter

Variational Data Assimilation Current Status

Weak constraint 4D-Var at ECMWF

How does 4D-Var handle Nonlinearity and non-gaussianity?

How 4DVAR can benefit from or contribute to EnKF (a 4DVAR perspective)

Wind tracing from SEVIRI clear and overcast radiance assimilation

Loïk Berre Météo-France (CNRM/GAME) Thanks to Gérald Desroziers

Advances and Challenges in Ensemblebased Data Assimilation in Meteorology. Takemasa Miyoshi

Ensemble 4DVAR for the NCEP hybrid GSI EnKF data assimilation system and observation impact study with the hybrid system

Gaussian Process Approximations of Stochastic Differential Equations

Introduction to Data Assimilation. Reima Eresmaa Finnish Meteorological Institute

Prof. Stephen G. Penny University of Maryland NOAA/NCEP, RIKEN AICS, ECMWF US CLIVAR Summit, 9 August 2017

Quantifying observation error correlations in remotely sensed data

What should an Outer Loop for ensemble data assimilation look like?

Environment Canada s Regional Ensemble Kalman Filter

Assimilation of Wind Power Data to Improve Numerical Weather Prediction and Wind Power Prediction

An Ensemble Kalman Filter for NWP based on Variational Data Assimilation: VarEnKF

Impact of hyperspectral IR radiances on wind analyses

Simulation of error cycling

Kalman Filter and Ensemble Kalman Filter

GSI Tutorial Background and Observation Errors: Estimation and Tuning. Daryl Kleist NCEP/EMC June 2011 GSI Tutorial

Quantifying observation error correlations in remotely sensed data

Validation of Complex Data Assimilation Methods

Assimilation of Wind Power Data to Improve Numerical Weather Prediction and Wind Power Prediction

4DEnVar. Four-Dimensional Ensemble-Variational Data Assimilation. Colloque National sur l'assimilation de données

Variable localization in an Ensemble Kalman Filter: application to the carbon cycle data assimilation

Can the assimilation of atmospheric constituents improve the weather forecast?

4D-Var or Ensemble Kalman Filter? TELLUS A, in press

Localization in the ensemble Kalman Filter

Comparing Local Ensemble Transform Kalman Filter with 4D-Var in a Quasi-geostrophic model

Summary of activities with SURFEX data assimilation at Météo-France. Jean-François MAHFOUF CNRM/GMAP/OBS

Background and observation error covariances Data Assimilation & Inverse Problems from Weather Forecasting to Neuroscience

Some ideas for Ensemble Kalman Filter

Parameter Estimation in EnKF: Surface Fluxes of Carbon, Heat, Moisture and Momentum

Current Limited Area Applications

Radiance Data Assimilation with an EnKF

Revision of the ECMWF humidity analysis: Construction of a gaussian control variable

The hybrid ETKF- Variational data assimilation scheme in HIRLAM

Estimation of Surface Fluxes of Carbon, Heat, Moisture and Momentum from Atmospheric Data Assimilation

Ensemble Data Assimilation and Uncertainty Quantification

Univ. of Maryland-College Park, Dept. of Atmos. & Oceanic Science. NOAA/NCEP/Environmental Modeling Center

Ensemble Kalman Filter potential

Recent experience at Météo-France on the assimilation of observations at high temporal frequency Cliquez pour modifier le style du titre

Representation of inhomogeneous, non-separable covariances by sparse wavelet-transformed matrices

OOPC-GODAE workshop on OSE/OSSEs Paris, IOCUNESCO, November 5-7, 2007

Model Uncertainty Quantification for Data Assimilation in partially observed Lorenz 96

Accelerating the spin-up of Ensemble Kalman Filtering

Can hybrid-4denvar match hybrid-4dvar?

Fundamentals of Data Assimila1on

Dynamic Inference of Background Error Correlation between Surface Skin and Air Temperature

COMPARATIVE EVALUATION OF ENKF AND MLEF FOR ASSIMILATION OF STREAMFLOW DATA INTO NWS OPERATIONAL HYDROLOGIC MODELS

Data Assimilation Working Group

Development, Validation, and Application of OSSEs at NASA/GMAO. Goddard Earth Sciences Technology and Research Center at Morgan State University

Comparison of 3D-Var and LETKF in an Atmospheric GCM: SPEEDY

Ocean data assimilation for reanalysis

A data-driven method for improving the correlation estimation in serial ensemble Kalman filter

T2.3: Use of ensemble information in ocean analysis and development of efficient 4D-Var

Four-Dimensional Ensemble Kalman Filtering

Sensitivity analysis in variational data assimilation and applications

COS Lecture 16 Autonomous Robot Navigation

The Big Leap: Replacing 4D-Var with 4D-EnVar and life ever since

Ensemble forecasting and flow-dependent estimates of initial uncertainty. Martin Leutbecher

Brian J. Etherton University of North Carolina

Computational Challenges in Big Data Assimilation with Extreme-scale Simulations

Report on the Joint SRNWP workshop on DA-EPS Bologna, March. Nils Gustafsson Alex Deckmyn.

Coupled Ocean-Atmosphere Assimilation

Ensemble-based Data Assimilation of TRMM/GPM Precipitation Measurements

Transcription:

Observation impact on data assimilation with dynamic background error formulation ALEXANDER BECK alexander.beck@univie.ac.at Department of, Univ. Vienna, Austria Thanks to: Martin Ehrendorfer, Patrick Haas, and Mike Fisher

Motivation! Poor-man s OSSEs within a conceptual data assimilation system.! Focus on fundamental issues.! Extract as much as possible information from given set of observations.! Quantify impact of background-error formulation for different observational configurations. Third WMO Workshop on the Impact of Various Observing Systems on NWP; Alpbach, Austria, 9-12 March 2004 2

Background error covariance modeling! Specification of B is crucial for DA-system! Several approaches (NMC method, ensemble of analyses, )! Operational implementations use static B! Does a flow-dependent background-error formulation improve the analyses and subsequent forecasts? Third WMO Workshop on the Impact of Various Observing Systems on NWP; Alpbach, Austria, 9-12 March 2004 3

Static & dynamic background errors Z500 variance field implied through B and (QG3L model)! Static B: Flat background specification! Errors of the day are not taken into account! available from Kalman filter Third WMO Workshop on the Impact of Various Observing Systems on NWP; Alpbach, Austria, 9-12 March 2004 4

The quasigeostrophic assimilation system! quasigeostrophic T21/L3 model (n=1449; Marshall & Molteni 1993)! Artificial observations sampled from truth-run with specified observation errors! Static background-error covariance matrix B obtained from NMC approach! Tuning of static B matrix for each observational configuration Third WMO Workshop on the Impact of Various Observing Systems on NWP; Alpbach, Austria, 9-12 March 2004 5

Tuning of static B matrix! B matrix needs to be adapted given model dynamics, length of assimilation interval and observational configuration.! Avoid systematic trend in analysis quality from cycle-to-cycle.! Within QG-framework: Estimate B from statistics. Third WMO Workshop on the Impact of Various Observing Systems on NWP; Alpbach, Austria, 9-12 March 2004 6

Data assimilation in cycling environment static dynamic Analysis error covariance matrix obtained from Hessian of costfunction. Forecast error covariance matrix available from Kalman filter theory. Third WMO Workshop on the Impact of Various Observing Systems on NWP; Alpbach, Austria, 9-12 March 2004 7

Role of initial B specification in KF Evolution of trace for dynamic background-error formulation (KF) started with different initial covariance matrices. Third WMO Workshop on the Impact of Various Observing Systems on NWP; Alpbach, Austria, 9-12 March 2004 8

Results: Assimilation window length Third WMO Workshop on the Impact of Various Observing Systems on NWP; Alpbach, Austria, 9-12 March 2004 9

Z200 Increments: 12h vs. 48h window static dynamic 12h 48h Analysis increments more similar for 48h exp s compared to 12h exp s. Third WMO Workshop on the Impact of Various Observing Systems on NWP; Alpbach, Austria, 9-12 March 2004 10

Results: Spatial observation density Third WMO Workshop on the Impact of Various Observing Systems on NWP; Alpbach, Austria, 9-12 March 2004 11

Temporal distribution & observation errors Impact of observation quality Third WMO Workshop on the Impact of Various Observing Systems on NWP; Alpbach, Austria, 9-12 March 2004 12

Summary! Cycling experiments in QG/4D-Var system! Comparison of static and dynamic background-error formulation! Pre-specified artificial observations! Analysis & forecast errors computed with respect to truth-run Third WMO Workshop on the Impact of Various Observing Systems on NWP; Alpbach, Austria, 9-12 March 2004 13

Conclusions! Mean analysis error decreases with increasing length of assimilation window.! Impact of dynamic background-error formulation is reduced for long ass. windows.! Dynamic background formulation is beneficial for non-uniformly distributed observations.! Importance of model error needs to be addressed ( weak-constraint 4D-Var ). Thank you for your attention! Third WMO Workshop on the Impact of Various Observing Systems on NWP; Alpbach, Austria, 9-12 March 2004 14