Development of Optimized Radar Data Assimilation Capability within - PowerPoint PPT Presentation

Development of Optimized Radar Data Assimilation Capability within the Fully Coupled EnKF‐EnVar Hybrid System for Convective‐Permitting Ensemble Forecasting and Testing via NOAA Hazardous Weather Testbed Spring Forecasting Experiments Chengsi Liu 1 , Ming Xue 1, 2 , Youngsun Jung 1 , Lianglv Chen 3 , Rong Kong 1 and Jingyao Luo 3 1 Center for Analysis and Prediction of Storms and 2 School of Meteorology University of Oklahoma, USA 3 China Meteorological Administration, China ISDA 2019

Hazardous Weather Testbed (HWT) Ensembles 2007‐2018 • Aims to guide the near‐future operational CAM ensemble • A distinct feature of the CAPS Storm‐ Scale Ensemble Forecasts (SSEFs): assimilation of full‐volume radial velocity and reflectivity data from the WSR‐88D network • Part of the Community Leveraged Unified Ensemble (CLUE) since 2016. Models: ARPS, WRF‐ARW, WRF‐NMM, COAMPS, FV3 2008 4‐km 3DVAR/cloud analysis ensemble with a 2‐km member 2009‐11 1‐km CONUS member 2013‐14 Added 12Z Members 2015 resolution change from 4‐km to 3‐km 2016 Added a cycled EnKF ensemble 2017 convection‐allowing FV3 member (Slide provided by Youngsun J.) 2018 Added ensemble of coupled global‐regional FV3 forecast

(Slide provided by Youngsun J.) 2018 HWT Model and Configuration • Forecast model: WRF‐ARW v3.9.1 and FV3 • DA systems: CAPS 3DVAR/cloud analysis, GSI‐EnKF, CAPS EnKF • 3‐km horizontal grid spacing, 51 vertical levels http://www.caps.ou.edu/~fkong/sub_atm/spring18.html http://www.caps.ou.edu/~fkong/sub_atm/spring18‐enkf.html Forecast domain (1620 X 1120)

(Slide provided by Youngsun J.) Neighborhood ETS of 1‐hour rainfall (R=15km) a) ≥ 1.27 mm b) ≥ 6.35 mm ≥ 1.27 mm Experiment (number of cases): 3DVar/cloud analysis_2016 (18), EnKF_2016 (16), 3DVar/cloud analysis_2017 (22), EnKF_2017 (21)

The capability to directly assimilate reflectivity developed by CAPS in the NCEP’s operational DA system (GSI) • One unique feature of the CAPS ensemble is the assimilation of full‐volume operational WSR‐88D radar radial wind and reflectivity data at the native model grid resolution (4 km initially, 3 km since 2016) with a complex multi‐ moment microphysics scheme. • Using temperature‐dependent background error profiles for static B of hydrometeors (Liu et al. 2018) • some special treatments to deal with issues associated with the non‐linear reflectivity operator has been implemented.(Liu et al. 2018 ISDA) • Three options of hydrometeors control variables • Using mixing ratio as control variable (CVq) • Using logarithmic mixing ratio as control variable (CVlogq) • Using power transforming mixing ratio as control variable(CVpq, new development)

Advantage and disadvantage for CVq and CVlogq （ according to previous studies based on 3DVar: Liu et al. ISDA 2018 ） • CVq:  Have a better analysis for small reflectivity than CVlogq (< 30 dBZ)  Worse radial wind analysis  Slower convergence for minimization • CVlogq:  Have a better analysis for large reflectivity than CVq (> 30 dBZ)  Better radial wind analysis  Faster convergence for minimization

Current progress DA System Model Control Variable in DA Experiment configuration ISDA ARPS‐3DVar ARPS CVq and CVlogq OSSE 2018 ISDA GSI‐EnKF/3DVar/hybrid En3DVar WRF‐ARW CVq, CVlogq and CVpq Real cases of NOAA 2019 operational HWT/WoF

Configuration • A studied case: A Mesoscale convective system(MCS) occurred on 16 th May 2016 in Texas, U.S. GSI EnKF/3DVar/Hybrid En3DVar forecast

The 5 th cycle background and analysis En3DVar_CVlogq En3DVar_CVq background • The analysis and forecast of the leading edges of MCS from Cvlogq is better than Obs. that from CVq. • The analysis of CVq is more balanced than CVlogq and has less spurious echoes in the forecast. Analysis

Comparison between CVq and CVlogq CVq CVlogq • The Vr analysis of CVlogq outperforms CVq in terms of smaller RMSIs. • However, CVlogq has larger RMSIs in the forecasts due to the imbalance in the analysis.

HEn3DVar20 0000 UTC 3DVar En3DVar EnKF （ 20% static B ） 5 th Cycle Obs. BG (15min‐ forecast) ANL • The lead‐edge of MCS in 3DVar forecast is less organized than the others, due to lacking of balance in the static background error covariance B . • Including static B to En3DVar can improve the analysis in the area where ensemble spread is not enough. • Both the convective and stratiform precipitation are underestimated in EnKF.

EnKF 3DVar 30min‐forecast Obs. En3DVar HEn3DVar20

Using power transforming mixing ratio as control variable(CVpq)   p ˆ q ( q 1) / p • When p=1.0, CVpq = CVq • When p ‐> 0, CVpq ‐> Cvlogq p=0.2 p=0.4 p=0.6 • Xue et al. (2010) used a similar power p=0.8 transform in updating total number concentration by radar DA. p=1.0 p=10e‐6 • Yang et al. (2018) also used this power transform in the analyses of surface visibility and cloud ceiling height. 0 0.2 0.4 0.6 0.8 1 1.2 1.4

another test case • Discrete tornadic supercells that occurred on 16 th May 2017 in Elk City .

RMSI of 30min and 60min forecast 7 8 Z Vr 30min forecast 30mim forecast 60min forecast 60min forecast 7 6 dbz dbz 6 5 5 4 4 p10‐6 p0.2 p0.4 p0.6 p0.8 p1.0 p10‐6 p0.2 p0.4 p0.6 p0.8 p1.0 CVlogq CVq CVlogq CVq • Optimal power parameter is obtained based on RMSIs of the forecasts.

outer‐loop iteration Inner‐loop iteration 5.0 Z CVlogq CVpq_p=0.4 CVq 4.0 32.0 x 100000 3.0 RMSI(dBZ) 28.0 24.0 2.0 20.0 cost function 1.0 16.0 12.0 0.0 0 1 2 3 8.0 iteration steps 7.0 Vr CVlogq CVpq_p=0.4 CVq 4.0 Cvloq CVpq_p=0.4 CVq 6.0 0.0 5.0 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 iteration steps RMSI(dbz) 4.0 3.0 • CVq has the slowest convergence speed 2.0 and worst Vr analysis 1.0 0.0 0 1 2 3 iteration steps

5.0 Z(<=25.0) Cvlogq CVpq_p=0.4 CVq 2.5 4.0 2 rain mixing ratio (k kg -1 ) 3.0 RMSI(dBZ) 1.5 2.0 1.0 1 0.0 0 1 2 3 0.5 iteration steps 30.0 Z(>=40.0) Cvlogq CVpq_p=0.4 CVq 0 0 10 20 30 40 50 reflectivity (dBZ) 25.0 RMSI(dBZ) • For large reflectivity(e.g. >40 dBZ), 20.0 more outer‐loop steps are needed due to the high non‐linearity of the 15.0 reflectivity operator. 10.0 0 1 2 3 iteration steps

3DVar EnKF HEn3DVar Obs Background (15 min forecast) No DA Analysis

RMSI of 30min and 60min forecast (4 convective storm cases) 20170518 20170527 20170516 20170509 6 7 10 11 Z 30mim forecast Z 30mim forecast Z 30mim forecast 30mim forecast Z 60min forecast 60min forecast 60min forecast 60min forecast 10 9 5 6 9 dbz dbz dbz dbz 8 8 7 4 5 7 6 5 3 4 6 p10‐6 p0.2 p0.4 p0.6 p0.8 p1.0 p10‐6 p0.2 p0.4 p0.6 p0.8 p1.0 p10‐6 p0.2 p0.4 p0.6 p0.8 p1.0 p10‐6 p0.2 p0.4 p0.6 p0.8 p1.0 8 0 8 10 30min forecast 30min forecast Vr Vr 30min forecast Vr Vr 30min forecast 60min forecast 60min forecast 60min forecast 60min forecast 7 9 9 7 m/s m/s m/s dbz 8 8 6 6 7 7 5 5 6 6 4 4 p10‐6 p0.2 p0.4 p0.6 p0.8 p1.0 p10‐6 p0.2 p0.4 p0.6 p0.8 p1.0 p10‐6 p0.2 p0.4 p0.6 p0.8 p1.0 p10‐6 p0.2 p0.4 p0.6 p0.8 p1.0

Summary • An EnKF coupled with hybrid En3DVar DA system that can directly assimilate full volume radar data has been developed within the GSI framework. • Three sets of control variables (CVq, CVloq and CVpq) were tested in reflectivity DA. CVpq with 0.4‐0.6 power parameter has the best performance in terms of smallest RMSI in the forecasts. • Hybrid En3DVar performs best in better capturing the intensity and structure of the storms.

Future plan • Run the GSI‐based coupled EnKF‐EnVar Hybrid System in real‐time for 2019 NOAA Hazardous Weather Testbed Spring Forecasting Experiments (forecast model will be switched to FV3 in 2020). • Develop the adjoint for Thompson microphysics(two‐moment) reflectivity operator within the GSI variational framework.