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 ofThe Institute for Mathematics in Geophysics(NCAR/CISL/IMAGe/DAReS)

Outline

Brief Description of the Data Assimilation Research Testbed

Past (recent)PresentFuture

DART Functionality

Model Space Performance

Observation Space Performance

UPGRADES since CCSM workshop

A. adaptive spread correction algorithm

UPGRADES since CCSM workshop

• 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.

B. Observations set(s) incorporated in a more flexible way:

UPGRADES since CCSM workshop

C. Kalman smoother implemented by Shree Khare

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

UPGRADES since CCSM workshop

D. CAM-fv version plugged into DART by Ave Arellano (ACD)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

CURRENT DART-CAM PROJECTS

1) Assimilation of MOPITT CO by Ave Arellano and Peter Hess

Experiments use:CAM3.1, FV core, 4x5 resolution with aSimplified CO chemistry model attached.6 hour forecasts at which timeMOPITT 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).

CURRENT DART-CAM PROJECTS

2) GCSS/WGNE Pacific Cross-section Intercomparison (GPCI) Cecile Hannay and Dave Williamson

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.

CURRENT DART-CAM PROJECTS

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.

3) Ensemble forecast study of model spin-up Jim Boyle & Steve Klein (LLNL)

CURRENT DART-CAM PROJECTS

a) Relationships Between Arctic Tropopause and Surface Circulation; assimilate Arctic tropopause temperatures into DART-CAM and diagnose the resulting changes in near surface variables.

b) A parameterization of cloud droplet effective radius

in marine boundary layer clouds developed through satellite data assimilation.

c) This may lead to an effort to parameterize aerosol indirect effects in CAM.

4) Justin Bagley and Eric DeWeaver (U Wisc)

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, …

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

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.

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.”