The Performance of a Weather-Adaptive 3DVAR System for Convective-scale RUA and some suggestions for future GSI-based RUA Jidong Gao NOAA/National Severe Storm Laboratory Rapidly Updating Analysis Workshop, Boulder, CO June 3, 2015 June 3, 2015

Research & development goal -- one component of WoF project To perform weather-adaptive 3DVAR RUA at high spatial resolution (1km) & high time frequency (5 min) in real time using 88D radar data with floating domains. To use the analysis product to assist a forecaster’s awareness of the current state of the hazardous weather (Gao et al. 2013; Smith et al. 2014, Calhoun et al. 2014, WaF). To perform 0-2 hour short-term forecasts initialized from high-resolution RUA and use them in the warning decision process.

30 min 3DVAR 5 min 3DVAR 10 min 3DVAR 15 min 3DVAR 20 min 3DVAR 25min 3DVAR (1) RUA cycles forecast Configuration of RUA & forecast experiments 0 min 3DVAR (2) Forecast cycle 120 min forecast 120 forecast Both cycles with weather-adaptive floating domains

Flow Chart of the weather-adaptive HUA System Identify potential convection active domains (Automatic, or On-demand domain) WDSSII 2D composite Z To get bcgrd, B & b. c. by interpolating the product into the analysis domains Select radars that cover analysis domains, QC, and interpolate data into the domains NCEP NAM & SREF NWP product Operational 88D data, Mesonet data 3DVAR analysis with 50 iterations Post processing, calculating Z, w,, D Awipes-2 online product - images from various variables In APRS/WRF grid WDSS2 Plotting package

Floating domains: 4 automated, or user-controlled 3DVAR analysis 200 x 200 km Radar selection 400 x 400 km

Example Analysis

Examples from 2011 Warning Operations: 9 June DVAR 2350 UTC (South KS) KICT 0.9 deg 2353 UTC 1km 3DVAR winds 3DVAR low-level vertical vorticity (s -1 )

Examples from 2011 Warning Operations: 17 May DVAR 0030 UTC, NE Colorado Updraft Composite (top) Vertical Vorticity, max 3-7 km 3DVAR merged reflectivity & winds

Floating domains working together Updraft Track (6-hour)Vorticity Track (6-hour) Floating Domain 14 April 2011: Eastern OK

May 20 th, 2013 Moore Tornadoes 2:00 pm2:20 pm2:45 pm 2:55 pm3:05 pm 3:35 pm

May 20 th, 2013 Moore Tornadoes 2:00 pm 2:20 pm 2:45 pm 2:55 pm 3:05 pm 3:35 pm

Forecaster Feedback in HWT Favorite products: Updraft & Vertical Vorticity Useful when “trying to diagnose a large number of storms” and “sitting on the fence” (about issuing a warning)

Forecaster Feedback Favorite products: Updraft & Vertical Vorticity More “efficient to view than existing algorithms” to diagnosis storm intensity and rotation

Real-time data Issues: Data Latency (approx 5 min) Distance from Radar (lack of low-level input) Occasional unrealistic updraft/downdraft near interacting storms (radar QC & mass continuity constraints) Forecaster Feedback

WoF project 2015 Hazardous Weather Testbed Spring Experiments 1.EnKF: NSSL WoF group focuses on testing DART EnKF system using 4000 cores (not focus in this talk). 2.3DVar: Using NCEP SREF derived background error covariancs in the 3DVar RUA cycles with 5 min model integrations using 128 cores (3DEnVar, Gao and Stensrud 2014, MWR)

April 11, 2015,7-8 pm, SW Corner KS (pre-HWT)

April 12, pm, S. KS

May 06, 2015 OKC metro area

Phase Error Problem Use NAM as backgroundUse 0.5 GFS as background

Several Issues and Ideals for Solving Them  Phase errors is one of the big problems (CAPS, Incremental Analysis Updating; Phase Error Corrections. Restart cycling every 12 hours?)  Radar Quality Control (Xu et. al QJ; 2014, MWR) and also use NSSL MRMS 3D reflectivity product.  Convective-scale NWP models are not mature and cloud microphysics needs improvement. NSSL has some good studies in recent years (Mansell et al, 2010, 2013, JAS;Yussouf 2013, MWR)

Several Issues and Ideals for Solving Them (Con’d)  Radar is the only instrument for in-storm structures currently, GOES-R may provide more info. about in-storm structures in future. (Use retrieved GOES-R LCWP & ICWP, Thomas Jones’ work, and GOES-R lightning data, Don. MacGorman’s group).  Previous research indicates that it is important to put correct info. about liquid water and ice to NWP model. NSSL hydrometer classification algorithm may be used for NWP purpose. More reflectivity DA is needed (Gao and Stensrud, 2012, JAS)  Need ensembles to provide probability products because of butterfly effects. Use GSI-based hybrid En-3DVar system (Wang et al MWR); We may also need to develop a hybrid system with NSSL WoF group’s EnKF system.  Develop balance constraints based on NWP models for 3DVAR ? NSF grant: 03/01/ /28/2017, Assimilation of Doppler Radar Data with an Ensemble-based Variational Method for Storm-Scale NWP. Total budget: $510,367. (PI: Gao, Wang and Stensrud).

The Ref. for system description & verification: Gao J., Smith T. M., D. J. Stensrud, C. Fu, K. Calhoun, K. L. Manross, J. Brogden, V. Lakshmanan, Y. Wang, K. W. Thomas, K. Brewster, and M. Xue, 2013: A realtime weather-adaptive 3DVAR analysis system for severe weather detections and warnings with automatic storm positioning capability. Wea. Forecasting, 28, Smith, T. M., J. Gao, K. M. Calhoun, D. J. Stensrud, K. L. Manross, K. L. Ortega, C. Fu, D. M. Kingfield, K. L. Elmore, V. Lakshmanan, and C. Riedel, 2014: Performance of a real-time 3DVAR analysis system in the Hazardous Weather Testbed. Wea. Forecasting, 29, Calhoun, K., M., T. M. Smith, D. M. Kingfield, J. Gao, and D. J. Stenrud, 2014: Forecaster Use and Evaluation of realtime 3DVAR analyses during Severe Thunderstorm and Tornado Warning Operations in the Hazardous Weather Testbed. Wea. Forecasting, 29, Clark, A. J., J., Gao, P. T. Marsh, T. M. Smith, J. S. Kain, J. Correia Jr., M. Xue, and F. Kong, 2013: Tornado path length forecasts from 2011 using a 3- dimensional object identification algorithm applied to ensemble updraft helicity, Wea. Foreacasting, 28,