Evaluation and Development of Ensemble Prediction System for the Operational HWRF Model Zhan Zhang, V. Tallapragada, R. Tuleya, Q. Liu, Y. Kwon, S. Trahan, J. O’Connor, and, W. M. Lapenta 65th Interdepartmental Hurricane Conference Miami, FL. Feb. 28 – Mar. 3

Outline Uncertainties in Hurricane Forecasts; Uncertainties in Hurricane Forecasts; Experiment Design; Experiment Design; Results: Results: - Track Forecasts - Track Forecasts - Intensity Forecasts - Intensity Forecasts Probabilistic Products; Probabilistic Products; Summary & Future Works. Summary & Future Works.

Possible Uncertainties in Hurricane Model Forecasts Initial Large Scale Flows; Initial Large Scale Flows; Lateral Boundary Conditions; Lateral Boundary Conditions; Initial Storm Structure; Initial Storm Structure; Model Physics. Model Physics.

Ensemble Member ID Input Data RMW Perturbation Convection Scheme PBL Scheme M00 – M20 GEFS (T190L28) NoSAS GSF PBL M21 (CTRL) GFS (T574L64) NoSAS GFS PBL M22 GFS (T574L64) NoKain-Fritsch GFS PBL M23-M24 GFS (T574L64) RWM Plus/minus 25%SAS GFS PBL M25-M26 GFS (T574L64) RWM Plus/minus 25%Kain-Fritsch GFS PBL M27-M47 GEFS (T190L28) NoKain-Fritsch GFS PBL M48 GFS (T574L64) NoSAS MYJ PBL M49 GFS (T574L64) NoKain-Fritsch MYJ PBL Experiment Design

List of Experiment: Control: HWRF V3.2 Baseline, M21 Control: HWRF V3.2 Baseline, M21 Large Scale Flow & LBC Perturbations: - M00-M20, 21 members - M27-M47, 21 members Initial Storm Structure Perturbations: - M23-M26, 4 members Physics-Based Perturbations: - M21, M22, M48, M49, 4 members Total: 50 ensemble members Hurricane Earl:

Track Forecasts

More than ~15% improvement in track forecasts Track forecasts are improved by all sub-sets of ensembles; Ensembles have less impacts on the track forecasts before 48h;

GEFS-SASGEFS-KF Perturbed initial structure Physics-based Northeast bias West bias at late stage Relatively narrow track spread

Intensity Forecasts

No clear intensity improvement from all sub-sets of ensembles. GEFS-SAS slightly better

Positive bias for weaker storm Negative bias for stronger storm For Earl, there are overall strong negative sample bias. Init intensity=75kts Init intensity=35kts Init intensity=50kts

Ranked Ensemble members Relative Frequency (%) Ranked Histogram for 10m Max Wind Speed Hurricane Earl, 2010 No sample bias at initial time Strong negative sample bias 00h24h 48h 72h 120hAll time

Equal weights

Intensity forecasts are improved with weighted ensemble mean at all time levels ~ 17% Improvement

Hurricane Probabilistic Products

Ensemble Member-based Storm Strike Probability Forecast for 120h obs. ens. mean Blue: obs Yellow: ens. mean

Ensemble Spread (along/cross) Based Storm Strike Probability Forecast

Ensemble Intensity Forecasts

Probability Forecasts of 10m Wind Speed greater than 30m/s Earl, Double probability max centers Max centers for wind speed and probability are not co-located Contour: Predicted 10m wind speed isotach from CTRL exp. Shading: Probability Forecast of 10m wind speed greater than 30m/s

Danielle Earl 120h Forecast of Strike Probabilities for Wind Speed greater than 20m/s, Earl,

Summary & Future Works Storm track forecasts are improved in all sub-sets of ensembles; Model-based ensemble sample bias can be corrected by applying weights to ranked ensemble members; Storm intensity forecasts are improved by weighted ensemble average; HWRF is not very sensitive to storm initial radius of maximum wind; Physics-based ensemble reduces model-based storm intensity bias; Future works: Sensitivity test for storm initial positions; Optimum combination of ensembles;

Storm Initial Position Uncertainties (All 2010 Storms)

ATL: lat ATL:lon EP: lat EP: lon Storm Initial Position PFD All 2010 Samples Gaussian–like distribution around zero