Jeffrey Anderson, NCAR Data Assimilation Research Section

Slides:



Advertisements
Similar presentations
30 th September 2010 Bannister & Migliorini Slide 1 of 9 High-resolution assimilation and weather forecasting Ross Bannister and Stefano Migliorini (NCEO,
Advertisements

Representing Model Error in Ensemble DA Chris Snyder (NCAR) NCAR is supported by the National Science Foundation.
Empirical Localization of Observation Impact in Ensemble Filters Jeff Anderson IMAGe/DAReS Thanks to Lili Lei, Tim Hoar, Kevin Raeder, Nancy Collins, Glen.
Using ensemble data assimilation to investigate the initial condition sensitivity of Western Pacific extratropical transitions Ryan D. Torn University.
ASSIMILATION of RADAR DATA at CONVECTIVE SCALES with the EnKF: PERFECT-MODEL EXPERIMENTS USING WRF / DART Altuğ Aksoy National Center for Atmospheric Research.
Advanced data assimilation methods- EKF and EnKF Hong Li and Eugenia Kalnay University of Maryland July 2006.
Ensemble Data Assimilation and Uncertainty Quantification Jeffrey Anderson, Alicia Karspeck, Tim Hoar, Nancy Collins, Kevin Raeder, Steve Yeager National.
MPO 674 Lecture 9 2/10/15. Hypotheses 1.Singular Vector structures are dependent on the barotropic instability condition in the initial vortex.
Observing Strategy and Observation Targeting for Tropical Cyclones Using Ensemble-Based Sensitivity Analysis and Data Assimilation Chen, Deng-Shun 3 Dec,
Eidgenössisches Departement des Innern EDI Bundesamt für Meteorologie und Klimatologie MeteoSchweiz Statistical Characteristics of High- Resolution COSMO.
The fear of the LORD is the beginning of wisdom 陳登舜 ATM NCU Group Meeting REFERENCE : Liu., H., J. Anderson, and Y.-H. Kuo, 2012: Improved analyses.
Computational Issues: An EnKF Perspective Jeff Whitaker NOAA Earth System Research Lab ENIAC 1948“Roadrunner” 2008.
Alan Robock Department of Environmental Sciences Rutgers University, New Brunswick, New Jersey USA
Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss High-resolution data assimilation in COSMO: Status and.
Vaisala/University of Washington Real-observation Experiments Vaisala/University of Washington Real-observation Experiments Clifford Mass, Gregory Hakim,
“A view of the 2 nd and 3 rd August COPE cases using storm-permitting ensemble MOGREPS-UK” S. Dey Supervisors: R. Plant, N. Roberts and S. Migliorini COPE.
Sophie RICCI CALTECH/JPL Post-doc Advisor : Ichiro Fukumori The diabatic errors in the formulation of the data assimilation Kalman Filter/Smoother system.
Assimilating chemical compound with a regional chemical model Chu-Chun Chang 1, Shu-Chih Yang 1, Mao-Chang Liang 2, ShuWei Hsu 1, Yu-Heng Tseng 3 and Ji-Sung.
11 Background Error Daryl T. Kleist* National Monsoon Mission Scoping Workshop IITM, Pune, India April 2011.
Pg 1 A Loosely Coupled Ocean-Atmosphere Ensemble Assimilation System. Tim Hoar, Nancy Collins, Kevin Raeder, Jeffrey Anderson, NCAR Institute for Math.
Data assimilation, short-term forecast, and forecasting error
Eidgenössisches Departement des Innern EDI Bundesamt für Meteorologie und Klimatologie MeteoSchweiz First Experience with KENDA at MeteoSwiss Daniel Leuenberger,
9. Impact of Time Sale on Ω When all EMs are completely uncorrelated, When all EMs produce the exact same time series, Predictability of Ensemble Weather.
Deutscher Wetterdienst Vertical localization issues in LETKF Breogan Gomez, Andreas Rhodin, Hendrik Reich.
Testing LW fingerprinting with simulated spectra using MERRA Seiji Kato 1, Fred G. Rose 2, Xu Liu 1, Martin Mlynczak 1, and Bruce A. Wielicki 1 1 NASA.
Application of COSMIC refractivity in Improving Tropical Analyses and Forecasts H. Liu, J. Anderson, B. Kuo, C. Snyder, and Y. Chen NCAR IMAGe/COSMIC/MMM.
Local Predictability of the Performance of an Ensemble Forecast System Liz Satterfield and Istvan Szunyogh Texas A&M University, College Station, TX Third.
Use ensemble error covariance information in GRAPES 3DVAR Jiandong GONG, Ruichun WANG NWP center of CMA Oct 27, 2015.
Studying impacts of the Saharan Air Layer on hurricane development using WRF-Chem/EnKF Jianyu(Richard) Liang Yongsheng Chen 6th EnKF Workshop York University.
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.
Lan Xia (Yunnan University) cooperate with Prof. Hans von Storch and Dr. Frauke Feser A study of Quasi-millennial Extratropical Cyclone Activity using.
Current modification gridinfo.f90Add new subroutine: getgridinfo_reg --- calculate the pressure in each sigma level and the virture temperature gridio.f90Add.
Sean Healy Presented by Erik Andersson
1 Observations of tides and temperatures in the martian atmosphere by Mars Express SPICAM stellar occultations Paul Withers 1, Jean-Loup Bertaux 2, Franck.
Assessing a Local Ensemble Kalman Filter
A Random Subgrouping Scheme for Ensemble Kalman Filters Yun Liu Dept. of Atmospheric and Oceanic Science, University of Maryland Atmospheric and oceanic.
The operational Meteo-France ensemble 4D-Var (L. Berre, G. Desroziers, and co-authors) Ensemble assimilation (operational with 6 members…) :
Mesoscale Atmospheric Predictability Martin Ehrendorfer Institut für Meteorologie und Geophysik Universität Innsbruck Presentation at 14th ALADIN Workshop.
1 3D-Var assimilation of CHAMP measurements at the Met Office Sean Healy, Adrian Jupp and Christian Marquardt.
Potential for estimation of river discharge through assimilation of wide swath satellite altimetry into a river hydrodynamics model Kostas Andreadis 1,
Future Directions in Ensemble DA for Hurricane Prediction Applications Jeff Anderson: NCAR Ryan Torn: SUNY Albany Thanks to Chris Snyder, Pavel Sakov The.
The application of ensemble Kalman filter in adaptive observation and information content estimation studies Junjie Liu and Eugenia Kalnay July 13th, 2007.
Matthew J. Hoffman CEAFM/Burgers Symposium May 8, 2009 Johns Hopkins University Courtesy NOAA/AVHRR Courtesy NASA Earth Observatory.
Korea Institute of Atmospheric Prediction Systems (KIAPS) ( 재 ) 한국형수치예보모델개발사업단 Identical Twin Experiments for the Representer Method with a Spectral Element.
Korea Institute of Atmospheric Prediction Systems (KIAPS) ( 재 ) 한국형수치예보모델개발사업단 LETKF 앙상블 자료동화 시스템 테스트베드 구축 및 활용방안 Implementation and application of LETKF.
Mesoscale Assimilation of Rain-Affected Observations Clark Amerault National Research Council Postdoctoral Associate - Naval Research Laboratory, Monterey,
ECMWF/EUMETSAT NWP-SAF Satellite data assimilation Training Course Mar 2016.
Jeffrey Anderson, NCAR Data Assimilation Research Section
Kerry Emanuel Lorenz Center Massachusetts Institute of Technology
Microwave Assimilation in Tropical Cyclones
Shu-Chih Yang1,Kuan-Jen Lin1, Takemasa Miyoshi2 and Eugenia Kalnay2
Data Assimilation Research Testbed Tutorial
Predictability: How can we predict the climate decades into the future when we can’t even predict the weather for next week? Predictability of the first.
Predictability of Tropical Cyclone Intensity
Jianyu Liang (York U.) Yongsheng Chen (York U.) Zhiquan Liu (NCAR)
Jeff Anderson, NCAR Data Assimilation Research Section
Parallel Implementations of Ensemble Kalman Filters for Huge Geophysical Models Jeffrey Anderson, Helen Kershaw, Jonathan Hendricks, Nancy Collins, Ye.
Jeff Anderson, NCAR Data Assimilation Research Section
Jeff Anderson, NCAR Data Assimilation Research Section
Hui Liu, Jeff Anderson, and Bill Kuo
Vertical localization issues in LETKF
FSOI adapted for used with 4D-EnVar
Chaos Seeding within Perturbation Experiments
Assimilation of Global Positioning System Radio Occultation Observations Using an Ensemble Filter in Atmospheric Prediction Models Hui Liu, Jefferey Anderson,
Comparison of different combinations of ensemble-based and variational data assimilation approaches for deterministic NWP Mark Buehner Data Assimilation.
Data Assimilation Initiative, NCAR
Effects and magnitudes of some specific errors
Project Team: Mark Buehner Cecilien Charette Bin He Peter Houtekamer
Verification of Tropical Cyclone Forecasts
Presentation transcript:

Jeffrey Anderson, NCAR Data Assimilation Research Section Assimilating Observations with Spatially and Temporally Correlated Errors in a Global Atmospheric Model Jeffrey Anderson, NCAR Data Assimilation Research Section EGU, 20 Apr., 2016

Observation Error Time Series Example: Correlated Error AR1 with Variance 1. Single Step Cov 0.999. Fixed for all cases. EGU, 20 Apr., 2016

Observation Error Time Series Example: Correlated Error AR1 with Variance 1. Single Step Cov 0.999. Fixed for all cases. Vary uncorrelated error variance, 0.01 EGU, 20 Apr., 2016

Observation Error Time Series Example: Correlated Error AR1 with Variance 1. Single Step Cov 0.999. Fixed for all cases. Vary uncorrelated error variance, 0.1 EGU, 20 Apr., 2016

Observation Error Time Series Example: Correlated Error AR1 with Variance 1. Single Step Cov 0.999. Fixed for all cases. Vary uncorrelated error variance, 1.0 EGU, 20 Apr., 2016

Observation Error Time Series Example: Correlated Error AR1 with Variance 1. Single Step Cov 0.999. Fixed for all cases. Vary uncorrelated error variance, 10.0 EGU, 20 Apr., 2016

1D Linear Exponential Growth Model True trajectory is always 0. Evolution is Perturbations grow exponentially in time. EGU, 20 Apr., 2016

Assimilating Correlated Observations EGU, 20 Apr., 2016

1D Exponential Growth Model Results Exact Smoother Result. Can’t do better than this. EGU, 20 Apr., 2016

1D Exponential Growth Model Results EAKF Poor Unless Uncorrelated Error Dominates RMSE Spread EGU, 20 Apr., 2016

Two Types of Difference Observations EGU, 20 Apr., 2016

Unlinked Difference Observations EGU, 20 Apr., 2016

1D Exponential Growth Model Results Exact Unlinked Difference Obs Much worse. Exact Unlinked EGU, 20 Apr., 2016

Uninked Difference Observations Unlinked Diff 1 Unlinked Diff 3 Unlinked Diff 5 Obs1 Obs2 Obs3 Obs4 Obs5 Obs6 EGU, 20 Apr., 2016

Uninked Difference Observations Unlinked Diff 1 Unlinked Diff 3 Unlinked Diff 5 Obs1 Obs2 Obs3 Obs4 Obs5 Obs6 EGU, 20 Apr., 2016

1D Exponential Growth Model Results EAKF is nearly exact for Unlinked Difference Obs. RMSE and Spread EGU, 20 Apr., 2016

Linked Difference Observations EGU, 20 Apr., 2016

1D Exponential Growth Model Results Exact linked Difference Obs Nearly Identical to Analytic. Linked EGU, 20 Apr., 2016

Linked Difference Observations EGU, 20 Apr., 2016

Linked Difference Observations EGU, 20 Apr., 2016

Linked Difference Observations EGU, 20 Apr., 2016

1D Exponential Growth Model Results EAKF Linked Diff. Obs. Good when correlated error dominates. RMSE Spread EGU, 20 Apr., 2016

1D Exponential Growth Model Results Comparison to Just Using Raw Observations Obs EGU, 20 Apr., 2016

Lorenz 63 Model Observing System 1 3 Instruments. Each has own correlated error. EGU, 20 Apr., 2016

Lorenz 63 Model Observing System 2 1 Instrument measures x,y,z each time. EGU, 20 Apr., 2016

L63 Results, Linked Difference Obs 3 Instruments 5 ensemble members. Adaptive inflation. Observations every 6 model timesteps. EGU, 20 Apr., 2016

L63 Results, Linked Difference Obs 3 Instruments 1 Instrument 5 ensemble members. Adaptive inflation. Observations every 6 model timesteps. EGU, 20 Apr., 2016

L63 Summary Difference obs better unless uncorrelated error variance dominates. Improvement greater for single instrument. Ensembles often under-dispersive (what a surprise!). EGU, 20 Apr., 2016

Lorenz 96 Model, 40-variables Observing System 1 40 Instruments. Each has own correlated error. EGU, 20 Apr., 2016

Lorenz 96 Model, 40-variables Observing System 2 1 instrument measures all 40 variables each time. EGU, 20 Apr., 2016

L96 Results, Linked Difference Obs 40 Instruments 10 ensemble members. Adaptive inflation, 0.2 halfwidth localization. Observations every model timestep. EGU, 20 Apr., 2016

L96 Results, Linked Difference Obs 40 Instruments 1 Instrument 10 ensemble members. Adaptive inflation, 0.2 halfwidth localization. Observations every model timestep. EGU, 20 Apr., 2016

L96 Results, Linked Difference Obs Difference obs better unless uncorrelated error variance dominates. Improvement much greater for single instrument. Ensembles often over-dispersive. Dealing with time correlation harder than space correlation. EGU, 20 Apr., 2016

Low-Order Dry Dynamical Core Slide 7: GPS occultation forward operators Non-local refractivity : (Sokolovskiy et al., MWR 133, 2200-2212) NEED DETAILS OF ALGORITHM step size??? upper limits grid pictures for different latitudes Three pictures exist: ~jla/talks_and_meetings/2009/ams/cam_gps_talk/gps_lat* Expect minor differences with coarse grid, larger near poles Evolution of surface pressure field every 12 hours. Has baroclinic instability: storms move east in midlatitudes. EGU, 20 Apr., 2016

Low-Order Dry Dynamical Core Slide 7: GPS occultation forward operators Non-local refractivity : (Sokolovskiy et al., MWR 133, 2200-2212) NEED DETAILS OF ALGORITHM step size??? upper limits grid pictures for different latitudes Three pictures exist: ~jla/talks_and_meetings/2009/ams/cam_gps_talk/gps_lat* Expect minor differences with coarse grid, larger near poles Evolution of surface pressure field every 12 hours. Has baroclinic instability: storms move east in midlatitudes. EGU, 20 Apr., 2016

Low-Order Dry Dynamical Core Slide 7: GPS occultation forward operators Non-local refractivity : (Sokolovskiy et al., MWR 133, 2200-2212) NEED DETAILS OF ALGORITHM step size??? upper limits grid pictures for different latitudes Three pictures exist: ~jla/talks_and_meetings/2009/ams/cam_gps_talk/gps_lat* Expect minor differences with coarse grid, larger near poles Evolution of surface pressure field every 12 hours. Has baroclinic instability: storms move east in midlatitudes. EGU, 20 Apr., 2016

Low-Order Dry Dynamical Core Slide 7: GPS occultation forward operators Non-local refractivity : (Sokolovskiy et al., MWR 133, 2200-2212) NEED DETAILS OF ALGORITHM step size??? upper limits grid pictures for different latitudes Three pictures exist: ~jla/talks_and_meetings/2009/ams/cam_gps_talk/gps_lat* Expect minor differences with coarse grid, larger near poles Evolution of surface pressure field every 12 hours. Has baroclinic instability: storms move east in midlatitudes. EGU, 20 Apr., 2016

Low-Order Dry Dynamical Core Slide 7: GPS occultation forward operators Non-local refractivity : (Sokolovskiy et al., MWR 133, 2200-2212) NEED DETAILS OF ALGORITHM step size??? upper limits grid pictures for different latitudes Three pictures exist: ~jla/talks_and_meetings/2009/ams/cam_gps_talk/gps_lat* Expect minor differences with coarse grid, larger near poles Evolution of surface pressure field every 12 hours. Has baroclinic instability: storms move east in midlatitudes. EGU, 20 Apr., 2016

Low-Order Dry Dynamical Core Slide 7: GPS occultation forward operators Non-local refractivity : (Sokolovskiy et al., MWR 133, 2200-2212) NEED DETAILS OF ALGORITHM step size??? upper limits grid pictures for different latitudes Three pictures exist: ~jla/talks_and_meetings/2009/ams/cam_gps_talk/gps_lat* Expect minor differences with coarse grid, larger near poles Evolution of surface pressure field every 12 hours. Has baroclinic instability: storms move east in midlatitudes. EGU, 20 Apr., 2016

Low-Order Dry Dynamical Core Slide 7: GPS occultation forward operators Non-local refractivity : (Sokolovskiy et al., MWR 133, 2200-2212) NEED DETAILS OF ALGORITHM step size??? upper limits grid pictures for different latitudes Three pictures exist: ~jla/talks_and_meetings/2009/ams/cam_gps_talk/gps_lat* Expect minor differences with coarse grid, larger near poles Evolution of surface pressure field every 12 hours. Has baroclinic instability: storms move east in midlatitudes. EGU, 20 Apr., 2016

Low-Order Dry Dynamical Core Slide 7: GPS occultation forward operators Non-local refractivity : (Sokolovskiy et al., MWR 133, 2200-2212) NEED DETAILS OF ALGORITHM step size??? upper limits grid pictures for different latitudes Three pictures exist: ~jla/talks_and_meetings/2009/ams/cam_gps_talk/gps_lat* Expect minor differences with coarse grid, larger near poles Evolution of surface pressure field every 12 hours. Has baroclinic instability: storms move east in midlatitudes. EGU, 20 Apr., 2016

Low-Order Dry Dynamical Core Slide 7: GPS occultation forward operators Non-local refractivity : (Sokolovskiy et al., MWR 133, 2200-2212) NEED DETAILS OF ALGORITHM step size??? upper limits grid pictures for different latitudes Three pictures exist: ~jla/talks_and_meetings/2009/ams/cam_gps_talk/gps_lat* Expect minor differences with coarse grid, larger near poles Evolution of surface pressure field every 12 hours. Has baroclinic instability: storms move east in midlatitudes. EGU, 20 Apr., 2016

Low-Order Dry Dynamical Core Slide 7: GPS occultation forward operators Non-local refractivity : (Sokolovskiy et al., MWR 133, 2200-2212) NEED DETAILS OF ALGORITHM step size??? upper limits grid pictures for different latitudes Three pictures exist: ~jla/talks_and_meetings/2009/ams/cam_gps_talk/gps_lat* Expect minor differences with coarse grid, larger near poles Evolution of surface pressure field every 12 hours. Has baroclinic instability: storms move east in midlatitudes. EGU, 20 Apr., 2016

Low-Order Dry Dynamical Core: Grid Slide 7: GPS occultation forward operators Non-local refractivity : (Sokolovskiy et al., MWR 133, 2200-2212) NEED DETAILS OF ALGORITHM step size??? upper limits grid pictures for different latitudes Three pictures exist: ~jla/talks_and_meetings/2009/ams/cam_gps_talk/gps_lat* Expect minor differences with coarse grid, larger near poles 30x60 horizontal grid, 5 levels. Surface pressure, temperature, wind components. 28,800 variables. EGU, 20 Apr., 2016

Low-Order Dry Dynamical Core: Observations Slide 7: GPS occultation forward operators Non-local refractivity : (Sokolovskiy et al., MWR 133, 2200-2212) NEED DETAILS OF ALGORITHM step size??? upper limits grid pictures for different latitudes Three pictures exist: ~jla/talks_and_meetings/2009/ams/cam_gps_talk/gps_lat* Expect minor differences with coarse grid, larger near poles Assimilate once per day. 0.2 radian localization. Observe each surface pressure grid point. Uncorrelated obs error variance 100 Pa. EGU, 20 Apr., 2016

Low-Order Dry Dynamical Core: Observations Slide 7: GPS occultation forward operators Non-local refractivity : (Sokolovskiy et al., MWR 133, 2200-2212) NEED DETAILS OF ALGORITHM step size??? upper limits grid pictures for different latitudes Three pictures exist: ~jla/talks_and_meetings/2009/ams/cam_gps_talk/gps_lat* Expect minor differences with coarse grid, larger near poles Uncorrelated obs error variance 100 Pa. Correlated obs error along ‘simulated polar orbiter track’. Vary ratio of correlated to uncorrelated obs error variance. EGU, 20 Apr., 2016

Low-Order Dry Dynamical Core: PS Results Slide 7: GPS occultation forward operators Non-local refractivity : (Sokolovskiy et al., MWR 133, 2200-2212) NEED DETAILS OF ALGORITHM step size??? upper limits grid pictures for different latitudes Three pictures exist: ~jla/talks_and_meetings/2009/ams/cam_gps_talk/gps_lat* Expect minor differences with coarse grid, larger near poles Linked difference better for large correlated error. Standard better for small correlated error. EGU, 20 Apr., 2016

Low-Order Dry Dynamical Core: T Results Slide 7: GPS occultation forward operators Non-local refractivity : (Sokolovskiy et al., MWR 133, 2200-2212) NEED DETAILS OF ALGORITHM step size??? upper limits grid pictures for different latitudes Three pictures exist: ~jla/talks_and_meetings/2009/ams/cam_gps_talk/gps_lat* Expect minor differences with coarse grid, larger near poles Linked difference better for large correlated error. Standard better for small correlated error. EGU, 20 Apr., 2016

PS RMSE Structure: Large Uncorrelated Error, Ratio 4 Base errors largest in storm tracks. Slide 7: GPS occultation forward operators Non-local refractivity : (Sokolovskiy et al., MWR 133, 2200-2212) NEED DETAILS OF ALGORITHM step size??? upper limits grid pictures for different latitudes Three pictures exist: ~jla/talks_and_meetings/2009/ams/cam_gps_talk/gps_lat* Expect minor differences with coarse grid, larger near poles Linked difference errors largest in broad tropical band. EGU, 20 Apr., 2016

PS RMSE Structure: Moderate Uncorrelated Error, Ratio 1 Base errors largest in storm tracks. Slide 7: GPS occultation forward operators Non-local refractivity : (Sokolovskiy et al., MWR 133, 2200-2212) NEED DETAILS OF ALGORITHM step size??? upper limits grid pictures for different latitudes Three pictures exist: ~jla/talks_and_meetings/2009/ams/cam_gps_talk/gps_lat* Expect minor differences with coarse grid, larger near poles Linked difference errors largest in broad tropical band. EGU, 20 Apr., 2016

PS RMSE Structure: Small Uncorrelated Error, Ratio 1/4 Base errors largest in storm tracks. Slide 7: GPS occultation forward operators Non-local refractivity : (Sokolovskiy et al., MWR 133, 2200-2212) NEED DETAILS OF ALGORITHM step size??? upper limits grid pictures for different latitudes Three pictures exist: ~jla/talks_and_meetings/2009/ams/cam_gps_talk/gps_lat* Expect minor differences with coarse grid, larger near poles Linked difference errors largest in broad tropical band. EGU, 20 Apr., 2016

T RMSE Structure: Small Uncorrelated Error, Ratio 1/4 Base errors largest in tropics. Slide 7: GPS occultation forward operators Non-local refractivity : (Sokolovskiy et al., MWR 133, 2200-2212) NEED DETAILS OF ALGORITHM step size??? upper limits grid pictures for different latitudes Three pictures exist: ~jla/talks_and_meetings/2009/ams/cam_gps_talk/gps_lat* Expect minor differences with coarse grid, larger near poles Linked difference errors have similar pattern. EGU, 20 Apr., 2016

Low-Order Dry Dynamical Core Summary Linked difference obs better for large correlated error. Linked difference not sensitive to correlated error size. Adaptive inflation struggles with large correlated error. Could use base approach for uncorrelated obs, difference for correlated error obs. For example, base for sondes, difference for radiances. Difference obs allows assimilating before knowing correlated error characteristics. Slide 7: GPS occultation forward operators Non-local refractivity : (Sokolovskiy et al., MWR 133, 2200-2212) NEED DETAILS OF ALGORITHM step size??? upper limits grid pictures for different latitudes Three pictures exist: ~jla/talks_and_meetings/2009/ams/cam_gps_talk/gps_lat* Expect minor differences with coarse grid, larger near poles EGU, 20 Apr., 2016

Learn more about DART at: www.image.ucar.edu/DAReS/DART Anderson, J., Hoar, T., Raeder, K., Liu, H., Collins, N., Torn, R., Arellano, A., 2009: The Data Assimilation Research Testbed: A community facility. BAMS, 90, 1283—1296, doi: 10.1175/2009BAMS2618.1 EGU, 20 Apr., 2016