JCSDA Workshop Brock Burghardt August

Model Configuration WRF-ARW v3.5.1 Forecasts integrated 30 hours Cycled every 6 hours (non-continuous boundaries) Hourly state output d01: dx=36 km d02: dx=12 km d03 : dx=4 km 38 vert. levels

Data Assimilation DART EAKF system (Kodiak; Anderson et al. 2009) 50 members Prior adaptive inflation NCEP observation error variances MADIS obs filtered into 6 h forecast background RAOB, ACARS, Cloud-Track winds, marine, metar, mesonet (except d01) Gaspari-Cohn cutoff function for localization covariance Half radiusD01D02D03 Vert. Local.4.5 km5 km2.5 km Horiz. Local.600 km300 km

Rank Histograms Image from: _forec/uos4b/uos4b_ko1.htm

Ensemble Dispersion

Forcing spread  Multiplicative inflation  Prior  Posterior (tendency to be unstable)  Physics parameterizations  Explicit changes  Physics tendencies  Stochastic Kinetic-Energy Backscatter (SKEB) -Easy to implement (R2O; ESA) -Shown to improve mean forecast (Yussouf et al. 2013)

Varied Physics  Choose widely used, similar computational cost parameterizations that are sufficiently different  Vary physics across different domains (allowing ESA)  Applying (initially) to three convective events MicrophysicsCumulus Parameterization Planetary Boundary Layer Surface Layer Thompson*Kain-Fritsch*YSU*MM5 Monin-Obukhov* MorrisonBetts-Miller-JanjicMellor-Yamada- Janjic Monin-Obukhov (Janjic) WDM5Grell-FreitasACM2 (Pleim)Pleim-Xiu WSM5

Fixed vs Varied Physics

Defining Objects Specifically looking at the ‘initiation’ of convective rainfall objects (ideally dBZ). Defined by hourly rainfall rate thresholds Utilizing multi-sensor Stage IV analysis Visually this appears to well represent storm-scale aspects of the forecast. Limitations: Temporal discretization (hourly windows) Potential overproduction bias in Mexico Member 45 Objects 00z Observed Objects 00z

Fixed vs Varied Physics Date is Date is Date is Physics is fixedPhysics is variedPhysics is fixedPhysics is variedPhysics is fixedPhysics is varied Total number of matched objects= 1465 Total number of matched objects= 1447 Total number of matched objects= 1066 Total number of matched objects= 1172 Total number of matched objects= 494 Total number of matched objects= 641 Avg C error per object= Avg C error per object= Avg C error per object= Avg C error per object= Avg C error per object= Avg C error per object= final_FAR= final_FAR= final_FAR= final_FAR= final_FAR= 0 final_POD= final_POD= final_POD= final_POD= final_POD= final_POD= final_BIAS= final_BIAS= final_BIAS= final_BIAS= final_BIAS= final_BIAS= final_TS= final_TS= final_TS= final_TS= final_TS= final_TS= Underproduction (low bias) Slight late timing bias

Initial results and future Little to no improvement in spread when varying physics on parent domains (particularly low levels where underdispersion is most notable) Tendency for too few convective rainfall objects (underdispersion?) Future -Generate more statistics (significance) -Continue running varied physics on inner domains

Spread in terms of convection In theory, a sufficiently large, well calibrated ensemble should encapsulate (time-space) observed convective objects r=200 km t=+/- 2 hr