WoF Challenge: “Spin-Up” Problem

1. Impacts of Running-In-Place on LETKF analyses and forecasts of the 24 May 2011 outbreakCorey K. Potvin1,2, Louis J. Wicker2, and Therese E. Thompson1,31Cooperative Institute for Mesoscale Meteorological Studies2NOAA/OAR National Severe Storms Laboratory3School of Meteorology, University of Oklahoma

2. WoF Challenge: “Spin-Up” Problem • During first several analysis cycles of storm, convective-scale state & error covariances often poor • Initial radar DA thus inefficient • Goal: accelerate spin-up  longer forecast lead time • Spin-up not just at start of DA – new storms can form after initial development

3. Running-In-Place (RIP; Kalnay & Yang 2010) • Under ideal conditions, obs should be used once • During spin-up, re-assimilating obs extracts additional information • Step 0: Regular LETKF update • Step 1: Use LETKF weights at current analysis time tntoupdate xa(tn-1)- “no-cost” smoother • Step 2: Covariance inflation at tn-1 • Step 3: Integrate ensemble tn-1 tn • Repeat until RMS difference between obs & forecasts converge (or max # iters reached)

4. Previous Work • Kalnay and Yang (2010) – RIP rapidly spins up idealized QG model • Yang et al. (2012a) – RIP helpful even given strong nonlinearity (Lorenz-63 model) • Yang et al. (2012b) –perfect-model typhoon OSSEs (WRF) • Yang et al. (2013) – RIP applied to real typhoon • Wang et al. (2013) – iterative EnSRF; perfect- and imperfect-model supercell OSSEs (WRF) • No published real-supercell experiments

5. EnKF Configuration • NSSL-LETKF – uses Miyoshi’s LETKF core • WRF v3.4.1 -- Δ=3km, 170 × 170 × 51 points, Thompson microphysics • 36 members; 5-min analysis cycles • 3 WSR-88D’s (Δ=6km); objectively QC’d • No mesonet assimilation • Additive noise (Dowell & Wicker 2009) + adaptive multiplicative inflation (Miyoshi 2011; Hunt et al. 2007) • GEFS-NME-based ensemble initial condition

6. 1845 UTC Experiments • First storm echoes • IC very poor • Best forecasts obtained: • with 3 vs. 1 RIP iterations • stopping RIP after 19 UTC MRMS 1850 UTC Multi-Radar/Multi-Sensor (MRMS) reflectivity mosaic at 2 km AGL

7. 1910 & 1920 UTC Analyses RIP greatly accelerates spin-up of dBZ, w, ζ 2 km AGL dBZ

8. 1905 UTC 1-h Forecasts CNTL RIP Red = neighborhood (3 × 3) ensemble probζ > .005 s-1somewhere over lowest 3 km Pink = tornado damage paths Contours = 2 km AGL 40 dBZobs at 1905 Z and 2005 Z Dots = interpolated 19-20 UTC NSSL rotation tracks > .005 s-1, .010 s-1, .015 s-1

9. 1910 UTC 1-h Forecasts CNTL RIP Red = neighborhood (3 × 3) ensemble probζ > .005 s-1somewhere over lowest 3 km Pink = tornado damage paths Contours = 2 km AGL 40 dBZobs at 1910 Z and 2010 Z Dots = interpolated 19-20 UTC NSSL rotation tracks > .005 s-1, .010 s-1, .015 s-1

10. 1915 UTC 1-h Forecasts CNTL RIP Red = neighborhood (3 × 3) ensemble probζ > .005 s-1somewhere over lowest 3 km Pink = tornado damage paths Contours = 2 km AGL 40 dBZobs at 1915 Z and 2015 Z Dots = interpolated 19-20 UTC NSSL rotations > .005 s-1, .010 s-1, .015 s-1

11. 2000 UTC Experiments • Begin DA once storms already mature • Mesoscale background storms displaced MRMS 2005 NME prior

12. Cold pool problem • Default RIP (1 or 3 iters) generates too-cold cold pools • Likely from repeated assimilation of large-innovation dBZ in same locations – analysis increments not retained during forecast cycle, as in Dowell et al. (2011) • What helped: • Use only 1 RIP iteration • Don’t update θ – but colds pools still too cold, indicating other covariances problematic • Only assimilate Vr, and only update u, v, w – still too cold! • Increase obs error estimates (“gentle” approach)– mitigates cold pool bias as well as no-theta update • Final solution: 1 iter with σVr = 4, σdBZ = 8

13. 2010 UTC Analyses RIP_default RIP_gentle • Spin-up in gentler approach nearly as fast as in default • w,ζ improved faster than dBZ MRMS CNTL

14. 2015 UTC Analyses RIP_default RIP_gentle • Spin-up in gentler approach nearly as fast as in default • w,ζ improved faster than dBZ MRMS CNTL

15. 2005 UTC 1-h Forecasts CNTL RIP Red = neighborhood (3 × 3) ensemble probζ > .005 s-1somewhere over lowest 3 km Pink = tornado damage paths Contours = 2 km AGL 40 dBZobs at 2005 Z and 2105 Z Dots = interpolated 20-21 UTC NSSL rotation tracks > .005 s-1, .010 s-1, .015 s-1

16. 2010 UTC 1-h Forecasts CNTL RIP Red = neighborhood (3 × 3) ensemble probζ > .005 s-1somewhere over lowest 3 km Pink = tornado damage paths Contours = 2 km AGL 40 dBZobs at 2010 Z and 2110 Z Dots = interpolated 20-21 UTC NSSL rotation tracks > .005 s-1, .010 s-1, .015 s-1

17. 2015 UTC 1-h Forecasts CNTL RIP Red = neighborhood (3 × 3) ensemble probζ > .005 s-1somewhere over lowest 3 km Pink = tornado damage paths Contours = 2 km AGL 40 dBZobs at 2015 Z and 2115 Z Dots = interpolated 20-21 UTC NSSL rotation tracks > .005 s-1, .010 s-1, .015 s-1

18. Conclusions • RIP can accelerate spin-up in radar DA • Added forecast value restricted to 2-3 analysis cycles in this case • Inflated error variances may be useful, at least when mean IC very poor

19. RIP - Outstanding Questions • Better to restrict to large-innovation regions? • Substantial improvement over simply re-assimilating obs (“poor-man’s RIP”) or Quasi-Outer Loop? • Better or worse than directly forcing updrafts (e.g., thermal bubbles, dBZ-based wnudging)? • How does impact change given less favorable environment?