1 / 23

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

howie
Download Presentation

Status of DART-CAM

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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.”

More Related