5 th International Conference of Mesoscale Meteor. And Typhoons, Boulder, CO 31 October 2006 National Scale Probabilistic Storm Forecasting for Aviation Planning Talk Focus - Storm Coverage Presenter: Dr James O. Pinto, NCAR/RAL Collaborators: Rasmussen, Steiner, Megenhardt, Rehak, Dixon, Phillips Acknowledgements : FAA AWRP

5 th International Conference of Mesoscale Meteor. And Typhoons, Boulder, CO 31 October 2006 Playbook at 1200 UTC Need for Reliable Storm Nowcasts of 8+ hrs for Strategic Planning Fcst valid : 2040 – 0240 UTC 6 hr Probability Forecast Playbook: Revised at 2000 UTC Verification

5 th International Conference of Mesoscale Meteor. And Typhoons, Boulder, CO 31 October 2006 Storm Characteristics Determine Impact of Air Traffic

5 th International Conference of Mesoscale Meteor. And Typhoons, Boulder, CO 31 October 2006 Outline Motivation –Need for probability of storm orientation, organization and coverage (e.g., storm permeability) Current Probabilistic Storm Forecast Systems Current Research – Storm Coverage Forecasts –Assess explicit model prediction of storm coverage and echo top heights –Use model and obs climatologies to determine relationship between storm coverage and forecast valid time, environmental conditions, location.

5 th International Conference of Mesoscale Meteor. And Typhoons, Boulder, CO 31 October 2006 Outline Motivation –Need for probability of storm orientation, organization and coverage (IOW: storm permeability) Current Probabilistic Storm Forecast Systems Current Research – Storm Coverage Forecasts –Assess explicit model prediction of storm coverage and echo top heights –Use model and obs climatologies to determine relationship between storm coverage and forecast valid time, environmental conditions, location.

Extrapolation Deterministic Forecasts of Precipitation - Extrap UTC 1 hr fcst 3 hr fcst 6 hr fcst Successive fcsts valid at same time. Verification

1 h P 2 h P Probabilistic Forecast of Convective Hazard Probabilities may be interpreted as: -likelihood of storms exceeding 35 dbz at a given time and location -the coverage of storms exceeding 35 dbz (if reliable). Probabilities determined via spatial filter and a VIL threshold. Spatial filter increases with fcst lead time (after Germann and Zawadzki 2004) Probabilities also influenced by observed trends, environmental conditions and climo. NCWF2 Hazard Detection with Probabilistic Fcsts

5 th International Conference of Mesoscale Meteor. And Typhoons, Boulder, CO 31 October 2006 Probabilistic Forecasts Systems Probabilities based on: –Spatial coverage of convective precip predicted by the RUC-20 model –3-member time-lagged ensemble –Square filter of 180 km –Precipitation rate threshold for convection (1-2 mm/hr) –Tuned using 40 km truth data set P. RUC Convective Prob. Forecast UTC 9 hr fcst 7 hr fcst Successive fcsts valid at same time. 8 hr fcst } 3-member ensemble valid at 21 UTC 7 hr fcst Valid: 21 UTC

Verification Comparison with Operational 2 hour TSTM Fcst Products From Seseke et al QA Report NOAA/Earth System Research Lab

5 th International Conference of Mesoscale Meteor. And Typhoons, Boulder, CO 31 October 2006 Outline Motivation –Need for probability of storm orientation, organization and coverage (IOW: storm permeability) Current Probabilistic Storm Forecast Systems Current Research – Storm Coverage Forecasts –Assess explicit model prediction of storm coverage and echo top heights –Use model and obs climatologies to determine relationship between storm coverage and forecast valid time, environmental conditions, location.

5 th International Conference of Mesoscale Meteor. And Typhoons, Boulder, CO 31 October 2006 Methodology for Improving Storm Coverage Fcsts Storm-resolving forecasts using WRF Compare model and obs distribution of storm coverages (regionally dependent) –Focus on Southeastern US where scattered storms common –Assess predictability of storm coverage for each region as a function of environmental conditions, time of day, etc. Verification –standard skill scores to assess reliability –Need for more descriptive skill scores such as those available in MODE (object-based verification). E.g. storm spacing

WRF Model Reflectivity Deterministic Forecasts of Convection Verification – WSI Mosaic Successive fcsts valid at same time. –Storm-resolving realtime fcsts run in collab. with Wang and Weismann –WSM Microphysics, MYJ PBL, Noah LSM –BCs: NAM – 40 km grid 212 –No Data Assimilation –Run Jun/Jul 2005 (0Z) & 2006 (00,12 Z) REPLACE – July 19th 00 …… UTC 8 hr fcst 20 hr fcst Case Study July 19 WRF-ARW (4km ) – 20hr fcst WRF-ARW (4km ) – 08hr fcst

WRF-ARW (4km ) – 20hr fcstWRF-ARW (4km ) – 08hr fcst WRF vs WSI Coverages Deterministic Forecasts of Storm Coverage Verification – WSI Mosaic 00 …… UTC 8 hr fcst 20 hr fcst Successive fcsts valid at same time. –Threshold = 35 dBZ –Impressive accuracy of timing and location of max coverage areas –Coverage forecast improves as fcst length decreases Case Study July 19 model obs WSI Refl UTC

Titan Storm Detections – WSI Reflectivity –WSI reflectivity mosaic from WSR-88D radar –Degraded to 4 km using spatial filter –35 dBZ and 75 km 2 thresholds r =100 km

Storm Spacing – WRF Reflectivity (20 hr fcst) –WRF reflectivity – max in column from 00 UTC run –35 dBZ and 75 km 2 thresholds 100 km Update with WRF image r =100 km

5 th International Conference of Mesoscale Meteor. And Typhoons, Boulder, CO 31 October 2006 Conclusions User needs (e.g., aviation planning) drive the system requirements Likelihood of storm at a given location not enough info for users in decision making Need for PDF expressing likelihood of coverages and joint PDFs of coverage / echo top likelihood. Current technology in predicting storm coverages have limited reliability. Convection resolving simulations may offer hope in predicting storm coverages and spacing.

5 th International Conference of Mesoscale Meteor. And Typhoons, Boulder, CO 31 October END-

NRC Report on Weather Forecasting Accuracy for FAA Traffic Flow Management “Because accurate deterministic 2- to 6-hour forecasts are not available, it is necessary to develop probabilistic forecasts that can readily be used by both humans and automated air traffic management decision support tools.” NRC Report, 2003

NCWF2 Convective Hazard Detection (NCWD) Unisys VILVaisala CG Ltg Unisys Echo Tops Data Feeds

Unisys VIL VIL (Stratiform areas blue) VIL (Stratiform Removed) VIL (Echo Tops > 15 kft contoured)VIL (Echo > 15 kft removed) Hazard Detection (filtered VIL + Ltg) Convective Hazard Detection (NCWD) Inputs: Unisys VIL & Echo Tops, NLDN C-to-G Ltg Steps to produce NCWD 1) Stratiform Filter (Steiner et al. 1995) 2) Echo tops Filter (remove echo < 15 Kft) 3) Combine with Lightning

5 th International Conference of Mesoscale Meteor. And Typhoons, Boulder, CO 31 October Forecast Length, hours Accuracy of Rainfall Nowcasts >2 mm/h Extrapolation Goal - idealized NWP (CSI) Current Skill of Nowcasting Technologies Forecast Skill

5 th International Conference of Mesoscale Meteor. And Typhoons, Boulder, CO 31 October 2006 Algorithm for Blending Probabilistic Forecasts (e.g., 4 hr forecast) Probabilistic Extrapolation Fcsts RUC Probabilistic Convection Forecasts Frontal Interest Climato- logical Interest Thermo- dynamic Mask Merged Forecast * * Amenable to Forecaster Modification Statistical Performance Weights Preprocessing Blending