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Status of DART-CAM

Status of DART-CAM. Kevin Raeder; CCSM interface to DART Jeff Anderson; DART development and organization Hui Liu; Observations Tim Hoar; Software, hardware, grayware Nancy Collins; Software Engineering. are the Data Assimilation Research Section of

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Status of DART-CAM

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  1. Status of DART-CAM Kevin Raeder; CCSM interface to DART Jeff Anderson; DART development and organization Hui Liu; Observations Tim Hoar; Software, hardware, grayware Nancy Collins; Software Engineering are the Data Assimilation Research Section of The Institute for Mathematics in Geophysics (NCAR/CISL/IMAGe/DAReS)

  2. Outline • Brief Description of the Data Assimilation Research Testbed • Past (recent) • Present • Future

  3. DART Functionality

  4. Model Space Performance

  5. Observation Space Performance

  6. UPGRADES since CCSM workshop • adaptive spread correction algorithm

  7. UPGRADES since CCSM workshop Observations set(s) incorporated in a more flexible way: • Multiple observation sets can be included at compile time. • A subset of the observed variables can be chosen at run time. • Enables more rapid comparison of assimilations with varying sets of observations.

  8. UPGRADES since CCSM workshop • another method of assimilation, better suited for some problems • Generates even better analyses; uses data from future as well as past. • applied to observation network design • Kalman smoother implemented by Shree Khare

  9. UPGRADES since CCSM workshop • required modification of CAM to put CO onto the initial files, redefine the (DART) state vector with CO, and with U,V on the staggered grid • CAM-fv version plugged into DART by Ave Arellano (ACD)

  10. CURRENT DART-CAM PROJECTS Experiments use: • CAM3.1, FV core, 4x5 resolution with a • Simplified CO chemistry model attached. • 6 hour forecasts at which time • MOPITT CO retrievals are optionally assimilated, with T, U, and V from radiosondes, aircraft, and satellites. • 20 member ensemble with CO added into the “usual” state vector (PS, T, U, V, Q, CLDLIQ, CLDICE). • Assimilation of MOPITT CO by Ave Arellano and Peter Hess

  11. CURRENT DART-CAM PROJECTS • DART-CAM provided T42 analyses every 6 hours for a 5 day forecast study • Analyses (from NCEP BUFR obs and CAM3.1) are used as ICs and as "truth" • The experiments are under way, but have not been evaluated yet. Hannay will present results at the CCSM workshop. • Analyses using the GCSS-DIME observations could be used as well. Would expect improvement in analyses over a region that's relatively data sparse in the NCEP BUFR data. • GCSS/WGNE Pacific Cross-section Intercomparison (GPCI) Cecile Hannay and Dave Williamson

  12. CURRENT DART-CAM PROJECTS Ensemble forecast study of model spin-up Jim Boyle & Steve Klein (LLNL) • Ensemble of analyzed CAM ICs used to start forecasts and evaluate initial rainout and “ringing” of dynamical fields. • Even with ICs which are optimally close to a model's balanced state and the observations, there is still significant rain-out of excess moisture in the first 6 hours.

  13. CURRENT DART-CAM PROJECTS Justin Bagley and Eric DeWeaver (U Wisc) • Relationships Between Arctic Tropopause and Surface Circulation; assimilate Arctic tropopause temperatures into DART-CAM and diagnose the resulting changes in near surface variables. • A parameterization of cloud droplet effective radius in marine boundary layer cloudsdeveloped through satellite data assimilation. • This may lead to an effort to parameterize aerosol indirect effects in CAM.

  14. IN A NUTSHELL DART-CAM provides: • Analyses on the CAM native grid • Objective analysis error estimate of every state variable • So analysis error can be removed from forecast error • Covariance of state variables • Bias and rms error of model vs. observations (not analyses) • Control over which observations are used in the analysis, timing of analyses, quality control, …

  15. PLANS • Anticipate expansion of assimilation of chemical species. • Plugging the single executable CCSM into DART, when it comes on line. • Continued improvements in assimilation algorithms and usability of the software. • Investigating the issues surrounding model parameter estimation. • Increase the breadth and depth of projects to benefit CCSM and DART

  16. OTHER IDEAS • A “metric of bias” is an observation of the system; have DART evaluate that metric as part of the assimilation process or subsequent diagnostics (for time average metrics). • Many biases can show up quickly; assimilate with varying model formulations or parameter values to choose the best. Separate timespans/seasons can be done simultaneously without integrating through the intervening time. • Plug process models (convection, radiation, …) and relevant observations in to DART to attempt parameter estimation in a more controlled environment than CAM is.

  17. COLLABORATIONS • Good momentum in a variety of projects • Good results with very reasonable effort • What can we do for you? raeder@ucar.edu http://www.image.ucar.edu/DAReS/DART “Resistance is futile. You will be assimilated.”

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