Operational Numerical

Forecasting on Tropical Cyclones

Yuqing Wang

International Pacific Research Center and Department of Meteorology

University of Hawaii at Manoa, Honolulu 96822 Hawaii, USA

Outline• Introduction• Background

Numerical Weather Prediction

Data Assimilation

Uncertainties and Ensemble Forecast

• Numerical Models for Operational Tropical Cyclone Forecasting

Global Models

Regional Models

• Skills of Numerical Forecasts for Tropical Cyclones

Error and Skill

National Hurricane Center Official Forecast

Numerical Track and Intensity Forecasts

Numerical Forecasting of Tropical Cyclone Rainfall after Landfall

• Concluding Remarks

1. Introduction

• A tropical cyclone prediction model is a computer program that uses meteorological data to predict the motion and intensity of tropical cyclones.

• The development of data assimilation together with the launching of many targeted satellites in the past decade or so has greatly reduced the numerical track prediction errors.

• The mean position errors of the best available numerical models range from 100 to 150, 200-250, and 300 to 350 km after 24, 48, and 72 h prediction time in the Atlantic, respectively

• The intensity prediction is still of rather poor quality

2. Background

2.1. Numerical Weather Prediction• Data Analysis• Model Initialization• Model• Model output

Components of a Numerical Weather Prediction System

Data Analysis

Model output

Model Assimilatio

n

Bogus data and evenly spaced model output

MODEL

Unevenly spaced obs.

Climato. data and

information

Components & Interactions In An Earth System Model

Surface Processes

DYNAMICAL CORE

PBL Vertical Mixing

Ocean coupling

RadiationBudget

MoistProcesse

s

MODEL PHYSICS

Land surface process

Sub-grid scale

Grid resolved

Atmospheric aerosols & Chemistry

EcosystemCarbon cycle

Lateral mixing

• Optimal interpolation (OI)• 3-dimensional Variational Data Assimilation (3DVar)• 4-dimensional Variational Data Assimilation (4DVar)• Kalman Filter (KF)/Ensembel Kalman Filter (EKF)

2.2. Data Assimilation

2.3 Uncertainties and Ensemble Forecast

Uncertainties• Uncertainties in Initial conditions• Uncertainties in model physics• Uncertainties in boundary forcing

Ensemble• Ensemble Mean• Super-ensemble

Fig. 1. An illustration of error growth due to the difference in the initial start of the forecast in Met Office global model in 1994 (From Met Office website).

3. Numerical Models for Operational Tropical Cyclone Forecasting

Global Models• NOGAPS: T239L30, 144h

• GFS: T382L64, 180h

• IFS/EC, T799L62, 168h

• GSM/JMA, T319L40, 216h

• GSM/CMA, T213L31, 120h

• GASP, T239L29, 168h

Fig. 2. An illustration of the continuous improvements in 5-day prediction skill of some operational global models from major centers in the last 22 years (from NCEP website).

The operational (left) and improved (right) analysesT+120 forecast charts for DT 1200 UTC 29 August 1994

The operational (left) and improved (right) T+120 forecastsVerifying analysis chart for DT 1200 UTC 3 September 1994

Fig. 3. An illustration of the improved forecast due to the improvements in model physics parameterization and the initialization scheme in Met Office global model in

1994.

Analysis charts for DT 1200 UTC 29 August 1994

Regional Models• TYM/JMA, 24km/L25, 84h

• GFDL,18km/L42, 120h

• HWRF, 20km/L42, (experimental)

• TXLAPS, 0.375 degree 72h

• LBAR/NHC

4. Skill of Numerical Forecasts for Tropical Cyclones

• Forecast Error (Absolute error)• Forecast Skill (Error relative to CLIPER/SHFOR)

4.2. National Hurricane Center Official Forecast

4.1. Error and Skill

Fig. 4. Recent trends in NHC official track forecast error (top) and skill (bottom) for the Atlantic basin (from Franklin 2006).

Fig. 5. Recent trends in NHC official intensity forecast error (top) and

skill (bottom) for the Atlantic basin (from Franklin 2006).

Fig. 6. Recent trends in NHC official track forecast error (top) and skill (bottom) for the eastern North Pacific basin (from Franklin 2006).

Fig. 7. Trends in CPHC official track forecast error for the eastern North Pacific basin (adopted from CPHC website).

4.2. Numerical Track and Intensity Forecast

Table 1. Comparison of Atlantic basin early track guidance model errors (n mi) for 2005. Errors smaller than the NHC official forecast are shown in bold-face (Franklin 2006)*

Fig. 8. Track forecast skill of Atlantic basin early guidance models for 2005 (Franklin 2006)

Table 2. Comparison of Atlantic basin late track guidance model errors (n mi) for 2005. Errors from CLP5, an early model, are shown for comparison. The smallest

errors at each time period are displayed in bold-face (from Franklin 2006).

Fig. 9. Intensity forecast skill of Atlantic basin early forecast guidance models for 2005 (Franklin 2006)

Fig. 10. Intensity forecast skill of Atlantic basin early forecast guidance models for 2005 pre-landfall cases only (Franklin 2006)

Fig. 11. Track forecast skill of Eastern Pacific basin early forecast guidance models for 2005.

Fig. 12. Intensity forecast skill of Eastern Pacific basin early forecast guidance models for 2005.

Fig.13. HWRF intensity forecasts for Hurricane Ivan (2004) using GFDL hurricane model initial conditions

Fig.14. HWRF track forecasts for Hurricane Ivan (2004) using GFDL hurricane model initial conditions

Fig. 15. Track forecasts for Hurricane Ivan (2004) by 5 different numerical models

Fig. 16. Ensemble forecasts of a South Indian basin tropical cyclone track by NCEP hurricane forecast project.

Fig. 17. As in Fig. 16 but for a different tropical cyclone case in the South Indian basin.

Fig. 18. Storm total rainfall for Hurricane Fran (1996) for period from 1200 UTC 9 Sep to 1200 UTC 12 Sep. Top left: observed from rain gaugeTop right: forecast from R-CLIPERBottom left: forecast from GFDL hurricane model (from Tuleya et al. 2007).

4.4. Numerical Forecasting of TC Rainfall after Landfall