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Ensemble Kalman filter data assimilation for the MPAS system

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Presentation on theme: "Ensemble Kalman filter data assimilation for the MPAS system"— Presentation transcript:

1 Ensemble Kalman filter data assimilation for the MPAS system
The 6th EnKF Workshop, May 18-22, 2014 Ensemble Kalman filter data assimilation for the MPAS system So-Young Ha National Center for Atmospheric Research Collaborators Chris Snyder, Bill Skamarock, Michael Duda, Laura Fowler in MMM/NCAR Jeffrey Anderson, Nancy Collins, Tim Hoar in IMAGe/UCAR

2 Overview System overview: MPAS, DART and the interface
Wind data assimilation strategy thru OSSE Real data assimilation in MPAS/DART cycling Sensitivity test on the quasi-uniform mesh Comparison to CAM/DART on the uniform mesh Comparison to WRF/DART on the variable mesh Extended forecast verification Summary and future plans

3 Based on unstructured centroidal Voronoi (hexagonal) meshes using C-grid staggering and selective grid refinement. Jointly developed, primarily by NCAR and LANL/DOE MPAS infrastructure - NCAR, LANL, others. MPAS - Atmosphere (NCAR) MPAS - Ocean (LANL) MPAS - Ice, etc. (LANL and others) MPAS-A development team: Bill Skamarock, Joe Klemp, Michael Duda, Laura Fowler, Sang-Hun Park

4 A community facility for ensemble data assimilation developed and maintained by the Data Assimilation Research Section (DAReS) at NCAR DART development team: Jeff Anderson, Nancy Collins, Tim Hoar (IMAGe/UCAR) MPAS-DART interface: So-Young Ha and Chris Snyder (MMM/NCAR)

5 MPAS-Atmosphere Unstructured spherical Centroidal Voronoi meshes
Mostly hexagons, some pentagons and 7-sided cells. Cell centers are at cell center-of-mass. Lines connecting cell centers intersect cell edges at right angles. Lines connecting cell centers are bisected by cell edge. Mesh generation uses a density function. Uniform resolution – traditional icosahedral mesh. C-grid staggering Solve for normal velocities on cell edges. Solvers Fully compressible nonhydrostatic equations Current Physics Noah LSM, Monin-Obukhov surface layer YSU PBL WSM6 microphysics Kain-Fritsch and Tiedtke cumulus parameterization RRTMG and CAM longwave and shortwave radiation Cell centers are at the cell center of mass w.r.t a user-defined density function. A finite-volume approach based on a C-grid staggering; This approach has prognostic eqns for mass at the center of finite-volume cells and the normal component of velocity on the cell edges

6 MPAS Nonhydrostatic Atmospheric Solver
formulation Variables: Vertical coordinate: Equations Prognostic equations for coupled variables. Generalized height coordinate. Horizontally vector invariant eqn set. Continuity equation for dry air mass. Thermodynamic equation for coupled potential temperature. Time integration scheme As in Advanced Research WRF - Split-explicit Runge-Kutta (3rd order) Prognostic equations: Diagnostics and definitions:

7 Motivation: Global Mesh and Integration Options
Global Uniform Mesh Global Variable Resolution Mesh Regional Mesh - driven by previous global MPAS run (no spatial interpolation needed!) other global model run analyses Voronoi meshes allows us to cleanly incorporate both downscaling and upscaling effects (avoiding the problems in traditional grid nesting) and to assess the accuracy of the traditional downscaling approaches used in regional climate and NWP applications.

8 MPAS/DART: Overview Ensemble Kalman filter for MPAS
Implemented through the Data Assimilation Research Testbed (DART) Broadly similar to WRF/DART Interfaces with model, control scripting Forward operators for conventional observations, AMVs, GPS RO data Vertical localization in various vertical coordinates Rejecting observations or reducing the weight of obs in the upper-level due to the strong damping near the top in the model Largely independent of model physics; facilitates testing with different schemes during ongoing model development New features in MPAS/DART Use of unstructured grid meshes in the forward operator Multiple options for updating winds Interfaces to both MPAS-A and MPAS-O are available

9 MPAS/DART: Grid and Variables
Dual mesh of a Voronoi tessellation All scalar fields and reconstructed winds are defined at “cell” center locations (red circles) Normal wind speed (u) is defined at “edge” locations (blue squares) “Vertex” locations (green triangles) are used in the searching algorithm for an arbitrary observation point in the observation operator State vector in DART: Scalar variables, plus horizontal velocity - either reconstructed winds at cell centers ( ) or normal component on the edges (u)

10 MPAS/DART-Atmosphere: Observation operators
Assimilation of scalar variables (x) finds a triangle with the closest cell center ( ) to a given observation point ( ) barycentric interpolation in the triangle observation A1 A2 A3 x1 x2 x3

11 MPAS/DART-Atmosphere: Observation operators
Radial Basis Function : MPAS/DART-Atmosphere: Observation operators Edge_wind Cell_wind

12 Wind DA strategy: OSSE in MPAS/DART
Truth: 15-km quasi-uniform global mesh Observation network:1,200 evenly distributed locations on the globe Simulated observation types: sounding temperature, u- and v-wind, geopotential height at 11 mandatory pressure levels and surface pressure Observation errors: 1 K for T, 2 m/s for wind, 10 mb for Psfc, 25 – 450 m for Z Ensemble filter data assimilation design: localization (H/V), adaptive inflation in prior states, 6-hrly cycling for one month of August 2008. Model configuration: 80-member ensemble at ~1-degree quasi-uniform mesh, 41 vertical levels w/ the model top at 30-km WRF-Physics: WSM6 microphysics, YSU PBL, NOAH LSM, Tiedtke cumulus, RRTMG SW/LW radiation schemes Two different wind DA options: Cell_wind vs. Edge_wind

13 Assimilation of horizontal winds: Sounding verification of 6-hr forecast (RAOB_U)
RMSE Spread Cell_wind shows slightly better fits to the sounding observations everywhere. => Cell_wind is default.

14 Assimilation of real observations in MPAS/DART
Model configuration: 80-member ensemble at ~2-degree uniform mesh, 41 vertical levels w/ the model top at 30-km Conventional observations (NCEP PrepBUFR) + GPS RO Ensemble filter data assimilation design: localization (1200H/4V), adaptive inflation in prior state, 6-hrly cycling for one month of August 2008. WRF-Physics: WSM6 microphysics, YSU PBL, NOAH LSM, Tiedtke cumulus parameterization, CAM SW/LW radiation schemes

15 Sensitivity test Filter design Model design
Adaptive inflation: on and off Localization radius: horizontal and vertical Ensemble size Model design Grid resolutions: {1- vs. 2-degree} and {uniform vs. variable} mesh Different physics parameterizations Uniform Variable 240km X 240-60km X 120km X 60km X 60-15km x 15km X

16 Sensitivity test: Adaptive inflation (on and off)
Common OBS RMSE Adaptive covariance inflation (Anderson 2009) improved the 6-h forecast skills by up to 15% throughout the period. Without inflation, ensemble spread was quickly reduced and remained small rejecting more observations, which led to a bad performance. SPRD

17 Adaptive inflation (cont’d)

18 Sensitivity test: Horizontal localization

19 Sensitivity test: Vertical localization
Larger localization, larger spread and larger error.

20 Sensitivity test: Grid resolutions
1-deg vs. 2-deg Uniform vs. variable mesh In quasi-uniform meshes, double the resolution increased the 6-h forecast skill by ~5% (in a verification against common observations). A variable mesh with a 1:4 ratio reduces the grid resolution from 180-km in the globe down to 45-km resolution over the CONUS. In the variable mesh, the fine-mesh area showed the better fits to the observations.

21 Comparison w/ CAM/DART
CAM/DART run by Kevin Reader (IMAGe/NCAR) CCSM4.0 on ~2-degree resolution w/ the model top at 3 mb Assimilating same observations Very similar filter configuration Verification for the same month of August 2008 in observation space CAM/DART cycling continuously for 10 yrs starting in 2000; MPAS/DART begins 1August 2008

22 Sounding verification: Comparison w/ CAM/DART
MPAS/DART poor initially (by construction), then improves over 2-3 d. CAM/DART and MPAS/DART are broadly comparable and reliable.

23 GPSRO_REFRACTIVITY verification: 6-h forecast
RMSE MPAS showed slightly larger errors in the lower atmosphere but greatly improved the bias in the entire atmosphere. BIAS

24 Summary and future plans for MPAS/DART
The MPAS/DART interface is available with the full capability now, released in the latest version of DART. The analysis/forecast cycling was successfully tested assimilating real observations for one month of August 2008. MPAS/DART on the quasi-uniform mesh seems to be reliable and broadly comparable to CAM/DART. MPAS/DART on the variable mesh is promising compared to the quasi- uniform mesh, showing a positive impact of higher resolution grids. The performance skill of MPAS can be further improved by more physics options such as GFS or CAM physics. MPAS/DART will be available in CESM for coupled models soon. More tests will be done for a longer period on the higher resolution meshes focusing on the direct comparison of quasi-uniform and variable meshes on the simulation of regional-scale features, eventually compared to WRF/DART on the mesoscales.

25 Current status MPAS Version 2.0 was released on 15 Nov 2013 (for both MPAS-Atmosphere and MPAS-Ocean core) The latest official release (e.g., the “Lanai” version) of DART includes the MPAS-A and MPAS-O interfaces. or contact or


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