diff --git a/docs/images/MPAS_grid_components.png b/docs/images/MPAS_grid_components.png new file mode 100644 index 0000000000..b2e34585b6 Binary files /dev/null and b/docs/images/MPAS_grid_components.png differ diff --git a/docs/images/MPAS_spaghetti_Z500mb_anal.2008090512.png b/docs/images/MPAS_spaghetti_Z500mb_anal.2008090512.png new file mode 100644 index 0000000000..88372c150e Binary files /dev/null and b/docs/images/MPAS_spaghetti_Z500mb_anal.2008090512.png differ diff --git a/docs/pages/About_Us.md b/docs/pages/About_Us.md index 6229aa6bcf..5752ea8225 100644 --- a/docs/pages/About_Us.md +++ b/docs/pages/About_Us.md @@ -79,7 +79,7 @@ DAReS Support (alphabetically) | postal address | "overnight" deliveries | electronic methods | | :----------------------- | :----------------------- | :------------------- | | Lisa Larson | Lisa Larson | larsonl @ ucar . edu | -| NCAR | NCAR | 303 497 185 | +| NCAR | NCAR | 303 497 1858 | | P.O. Box 3000 | 1850 Table Mesa Dr. | 303 497 2483 (FAX) | | Boulder, CO 80307-3000 | Boulder, CO 80305 | | diff --git a/docs/pages/Research.md b/docs/pages/Research.md index bcce5b5c42..218aa50cb5 100644 --- a/docs/pages/Research.md +++ b/docs/pages/Research.md @@ -23,6 +23,7 @@ looking at systematic features of the increments. - [Model Performance](#modelperformance) - [WACCM](#waccm) + - [MPAS ATM](#mpasatm) - [OpenGGCM](#space) - [Chemical Transport](#chemistry) - [\[novel\] observations like GPS RO](#gpsro) @@ -58,7 +59,91 @@ Kevin Raeder, raeder@ucar.edu - + +----- + +### Model for Prediction Across Scales (MPAS) + + ++++ + + + + + + +

+ Spaghetti diagram of the ensemble analysis in terms of geopotential height at 500 mb valid at 12Z 5 Dec 2008. +		 +Data assimilation for MPAS is available as an ensemble Kalman filter (EnKF) +implemented through (Data Assimilation Research Testbed)[http://dart.ucar.edu] + +This work is done in a collaborative effort between the +[Mesoscale and Microscale Meteorology (MMM)](http://www.mmm.ucar.edu) +Division and the DART development team. DART support for both MPAS-Atmosphere +and MPAS-Ocean are available as part of the standard DART package. + +In collaboration with NOAA's Earth System Research Laboratory, +related efforts are also underway to explore ensemble data assimilation for MPAS-Atmosphere with the +Gridpoint Statistical Interpolation (GSI) scheme that is operational at +the National Centers for Environmental Prediction. +
+ + ++++ + + + + + +
+ +Grid structure used in the MPAS/DART interface +The MPAS/DART interface is +built on MPAS's unstructured centroidal Voronoi mesh, +and does not regrid to latitude-longitude grids. In MPAS, the finite-volume +approach based on a C-grid staggering retains prognostic equations for mass +at the center of finite-volume cells and for the normal component of velocity +(u) at the faces (or edges in 2D) of the cells. +The normal component of velocity (u) at cell edges is then used to +reconstruct zonal and meridional winds at cell centers (V) + using radial basis functions (RBFs). +To avoid the singularity issue on the poles, the cartesian coordinate is employed. + +The forward operators on the unstructured grid mesh are constructed as follows. +The dual of the Voronoi mesh, or the triangular mesh (shown as dashed lines in the +figure), is used to search the closest cell center to an arbitrary (or observation) +point, then find three cell centers of the triangle enclosing the desired point. +Mass fields are then horizontally interpolated from the cell centers to the observation +location using a barycentric (e.g., area-weighted) interpolation. +
+ +While the observed wind quantities are zonal and meridional winds, the normal +component of velocity (u) is the only prognostic wind variable in MPAS, +we thus implement a couple of different ways of assimilating wind observations. +The options determine which wind variables are used in the forward operator to +compute expected observation values; how the horizontal interpolation is computed +in that forward operator; and how the assimilation increments are applied to update +the wind quantities in the state vector of the analysis. + +Preliminary results based on real data assimilation experiments indicate that +performance is better when the zonal and meridional winds are used as input to the +forward operator that uses Barycentric interpolation and the prognostic u +wind is incrementally updated. However, there remain scientific questions about +how best to handle the wind fields under different situations. For the details +on the wind data strategy, refer to the +documentation in DART. + +For any questions or future collaboration, please contact either +Soyoung Ha (syha@ucar.edu) or the DART team (dart@ucar.edu) + + ----- @@ -191,7 +276,7 @@ Alexis Zubrow, azubrow@unc.edu - + ----- @@ -233,7 +318,7 @@ Alexis Zubrow, azubrow@unc.edu - + -----