Ensemble Kalman filter data assimilation for the MPAS system
So-Young Ha National Center for Atmospheric Research
The 6th EnKF Workshop, May 18-22, 2014
Collaborators Chris Snyder, Bill Skamarock, Michael Duda, Laura Fowler in MMM/NCAR Jeffrey Anderson, Nancy Collins, Tim Hoar in IMAGe/UCAR
Overview
1. System overview: MPAS, DART and the interface2. Wind data assimilation strategy thru OSSE3. Real data assimilation in MPAS/DART cycling
1. Sensitivity test on the quasi-uniform mesh2. Comparison to CAM/DART on the uniform mesh3. Comparison to WRF/DART on the variable mesh
4. Extended forecast verification5. Summary and future plans
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
Based on unstructured centroidal Voronoi (hexagonal) meshes using C-grid staggering and
selective grid refinement.
DART development team: Jeff Anderson, Nancy Collins, Tim Hoar (IMAGe/UCAR)
MPAS-DART interface:So-Young Ha and Chris Snyder (MMM/NCAR)
A community facility for ensemble data assimilation developed and maintained by the Data Assimilation
Research Section (DAReS) at NCAR
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 staggeringSolve for normal velocities on cell edges.
SolversFully 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
Motivation: Global Mesh and Integration Options
Global Uniform Mesh Global Variable Resolution Mesh Regional Mesh - driven by(1) previous global MPAS run
(no spatial interpolation needed!)(2) other global model run(3) 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.
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
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)
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
MPAS/DART-Atmosphere: Observation operators
Radial Basis Function
:
Cell_wind
Edge_wind
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
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.
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
Sensitivity test
Filter 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 Variable240km
X1.10242 240-60km
X4.40962
120km
X1.40962
60km
X1.163842
60-15km x4.535554
15km
X1.2621442
Sensitivity test: Adaptive inflation (on and off)
CommonOBS
RMSE
SPRD
- 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.
Adaptive inflation (cont’d)
Sensitivity test: Horizontal localization
Sensitivity test: Vertical localization
Larger localization, larger spread and larger error.
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.
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
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.
GPSRO_REFRACTIVITY verification: 6-h forecast
RMSE
BIAS
MPAS showed slightly larger errors in the lower atmosphere but greatly improved the bias in the entire atmosphere.
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.
Current status
MPAS Version 2.0 was released on 15 Nov 2013 (for both MPAS-Atmosphere and MPAS-Ocean core) http://mpas-dev.github.io/
The latest official release (e.g., the “Lanai” version) of DART includes the MPAS-A and MPAS-O interfaces.http://www.image.ucar.edu/DAReS/DART/Lanai_release.htmlor contact [email protected] or [email protected].
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