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.

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.

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 2007. Another E-RTFDDA has been built for Xcel Energy to support the wind power forecast.

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

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.

Case description and experiment design 2012033118 – 2012040318 Western US The domain of this research locates at western US, where US army Dugway proving ground locates. The time period is from 2012033118 to 2012040318. 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.

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.

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

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

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.

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.

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.

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

(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.

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.

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.