Impacts of Running-In-Place on LETKF analyses and forecasts of the 24 May 2011 outbreak Impacts of Running-In-Place on LETKF analyses and forecasts of the 24 May 2011 outbreak Corey K. Potvin 1,2, Louis J. Wicker 2, and Therese E. Thompson 1,3 1 Cooperative Institute for Mesoscale Meteorological Studies 2 NOAA/OAR National Severe Storms Laboratory 3 School of Meteorology, University of Oklahoma

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

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 t n to update x a (t n-1 ) - “no-cost” smoother Step 2: Covariance inflation at t n-1 Step 3: Integrate ensemble t n-1  t n Repeat until RMS difference between obs & forecasts converge (or max # iters reached)

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

EnKF Configuration NSSL-LETKF – uses Miyoshi’s LETKF core WRF v 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 ) GEFS-NME-based ensemble initial condition

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

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

1905 UTC 1-h Forecasts Red = neighborhood (3 × 3) ensemble prob ζ >.005 s -1 somewhere over lowest 3 km Pink = tornado damage paths Contours = 2 km AGL 40 dBZ obs at 1905 Z and 2005 Z Dots = interpolated UTC NSSL rotation tracks >.005 s -1,.010 s -1,.015 s -1 CNTLRIP

1910 UTC 1-h Forecasts Red = neighborhood (3 × 3) ensemble prob ζ >.005 s -1 somewhere over lowest 3 km Pink = tornado damage paths Contours = 2 km AGL 40 dBZ obs at 1910 Z and 2010 Z Dots = interpolated UTC NSSL rotation tracks >.005 s -1,.010 s -1,.015 s -1 RIPCNTL

1915 UTC 1-h Forecasts Red = neighborhood (3 × 3) ensemble prob ζ >.005 s -1 somewhere over lowest 3 km Pink = tornado damage paths Contours = 2 km AGL 40 dBZ obs at 1915 Z and 2015 Z Dots = interpolated UTC NSSL rotations >.005 s -1,.010 s -1,.015 s -1 CNTLRIP

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

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 V r, 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 σ Vr = 4, σ dBZ = 8

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

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

2005 UTC 1-h Forecasts Red = neighborhood (3 × 3) ensemble prob ζ >.005 s -1 somewhere over lowest 3 km Pink = tornado damage paths Contours = 2 km AGL 40 dBZ obs at 2005 Z and 2105 Z Dots = interpolated UTC NSSL rotation tracks >.005 s -1,.010 s -1,.015 s -1 CNTLRIP

2010 UTC 1-h Forecasts Red = neighborhood (3 × 3) ensemble prob ζ >.005 s -1 somewhere over lowest 3 km Pink = tornado damage paths Contours = 2 km AGL 40 dBZ obs at 2010 Z and 2110 Z Dots = interpolated UTC NSSL rotation tracks >.005 s -1,.010 s -1,.015 s -1 CNTLRIP

2015 UTC 1-h Forecasts Red = neighborhood (3 × 3) ensemble prob ζ >.005 s -1 somewhere over lowest 3 km Pink = tornado damage paths Contours = 2 km AGL 40 dBZ obs at 2015 Z and 2115 Z Dots = interpolated UTC NSSL rotation tracks >.005 s -1,.010 s -1,.015 s -1 CNTLRIP

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

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 w nudging)? How does impact change given less favorable environment?