1. Integration of NCAR DART-EnKF to NCAR-ATEC multi-model, multi-physics and mutli- perturbantion ensemble-RTFDDA (E-4DWX) forecasting system
Linlin Pan 1, Yubao Liu 1, Gregory Roux 1, Wanli Wu 1, Yonghui Wu 1, Jason Knievel 1, John Pace 2, Scott Halvorson 2, Frank Gallagher 2
1NCAR/Research Application Laboratory
2Dugway Proving Ground, US Army
Hi, I am Linlin Pan from National Center for Atmospheric Research (NCAR), this presentation will focus on the new developments of NCAR E-RTFDDA system. Here are the coauthers from NCAR and US Army.

2. Outline Introduction: E-RTFDDA New enhancement to E-RTFDDA
Preliminary verification
Summary and future work
Here is the outline of the presentation, a brief introduction is given in the beginning, followed by New enhancement to E-RTFDDA, and preliminary verification. Summary and future work are given in the end.

3. RTFDDA  E-RTFDDA
RTFDDA stands for real-time four dimensional data assimilation (RTFDDA) system. RTFDDA based ensemble system is E-RTFDDA, which is a multi-model multi-approach mesoscale ensemble 4-dimensional data assimilation and forecasting system. E-RTFDDA was first built for US Army and have been operated at DPG since Another E-RTFDDA has been built for Xcel Energy to support the wind power forecast.

4. E-RTFDDA Perturbation Approaches
Perturbations
in components
may be
combined
Radiation
Precipitation
Upper-air
weather
forcing
Vegetation …
ECMWF
GFS
NAM
LSM LDAS
Stat. perturb.
Other. Perturb.
Parameters
Physics
Schemes and
Parameter
perturbations
MM5
WRF
Here are the schematics of detailed perturbations, which include, GFS/NAM boundary conditions, MM5/WRF models and model physics perturbations, observation and data assimilation weight perturbations, and land surface forcing perturbations. Model errors and initial conditions errors for mesoscale modeling are inherent more complicated than global model. We have been continuously working on improving and optimizing E-RTFDDA perturbation approaches.
Obs
Cycling
Perturbations
And scaling
Data
Assimilation
ETKF
Weights
 200+
members

5. E-RTFDDA Design and New Features
Multimodal models
WRF-ARW, MM5, WRF-NMM
Multi-boundary conditions
GFS, NAM, GEM
Multi-physics
Major WRF physics parameterization schemes; Stochastic Kinetic Energy Backscattering
Multiple data assimilation
RTFDDA, 3DVAR, DART-EnKF
In this work, we enhance E-RTFDDA perturbation approaches to include WRF-NMM, Canadian GEM model output as boundary conditions, stochastic kinetic energy backscattering, and DART-EnKF. This presentation will mainly focus on the effects of including GEM and DART-ENKF through a case study.

6. Case description and experiment design–
Western US
The domain of this research locates at western US, where US army Dugway proving ground locates. The time period is from to Model is run every 6 hours, 4 cycles daily. The left panel gives the domain terrain, and the right panel gives geopotential height and wind at 500 hpa. The weather during the case study period is controlled by a upper-level trough along the western US coast area with medium rainfall.

7. 06 Z, April 1, 2012 (6h Forecasts) GFS GEM DART NAM
Here are surface 10m wind, 2m temperature and sea level pressure from GFS, NAM, GEM and DART. In general they are very similar. They all show north-west wind at the ocean and a trough along the coast area.

8. Surface Observations DART-ENKF
Here are comparisons between DART and surface observations. The model results are reasonable consistent with observations.
DART-ENKF
Surface Observations

9. Wind Speed NAM GEM GFS DART Wind Direction NAM GEM GFS DART
This slide shows the verification of the members from GFS, NAM, GEM and DART for one cycle with observations for the whole domain. The upper panels is for wind speed, and the lower is for wind direction. The bias and root mean square errors of GEM and DART members, for this forecast cycles, are comparable, and slightly better, than GFS and NAM members. Similar verifications results can be seen for Relative humidity and surface temperature, which are not shown here.
GFS
DART

10. Spread of U, valid at 20120406, 6h fcsts
GFS
GEM
NAM
DART
Now let’s examine how adding GEM and DART affect the E-RTFDDA spread. The spreads of zonal wind U from GFS, NAM, GEM and DART are given here. NAM has larger spread than GFS, and GEM has larger spread than DART.

11. Spread of V, valid at 20120406, 6h fcsts
GFS
GEM
NAM
DART
The spreads of meridional wind V from GFS, NAM, GEM and DART are given here. NAM has larger spread than GFS, and GEM has larger spread than DART.

12. Spread of T, valid at 20120406, 6h fcsts
GFS
GEM
NAM
DART
The spreads of T from GFS, NAM, GEM and DART are given here. NAM has larger spread than GFS, and GEM has larger spread than DART.

13. All gfs+nam Combined spread U U V V
The spread from all the member, we can see when more member is included, the spread is increased, which is able to mitigate the excessive underestimation of E-RFDDA.
All
gfs+nam

14. (need a meaningful title here!)
Now let’s take a look at a sample spaghetti plots for 2m temperature, wind 10-m wind speed, NAM, GEM, Dart, and GFS are red, blue, green, and yellow, Large separation between model implies more difficult to forecast. Left panel is for Boulder station and right panel is for salt lake station.

15. Summary
NCAR E-RTFDDA is a multi-model, multi-physics, multi- boundary conditions, multi-data assimilation scheme, mesoscale ensemble weather analysis and forecast system for real-time operational forecasting.
In the present work, we introduces several enhancements to the E-RTFDDA perturbation schemes， using WRF- NMM, GEM, DART-ENKF and SKEB model and/or technologies.
Initial tests show that these additional perturbation schemes work properly， and help mitigate the known underestimation of the E-RTFDDA spread.
Currently we are implemented these technologies into the operational E-RTFDDA systems. Validation of the real-time forecasts will be conducted to further quantify the effect.
Just learn-by-heart (clearly 背诵 (or if can not, read) these conclusion remarks! Do not try to use your own words.

16. Combined spread T
The spread from all the member, we can see when more member is included, the spread is increased, which is able to mitigate the excessive underestimation of E-RFDDA.