Presentation on theme: "Operational Numerical Forecasting on Tropical Cyclones Yuqing Wang International Pacific Research Center and Department of Meteorology University of Hawaii."— Presentation transcript:
Operational Numerical Forecasting on Tropical Cyclones Yuqing Wang International Pacific Research Center and Department of Meteorology University of Hawaii at Manoa, Honolulu 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, , 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 Radiation Budget Moist Processe s MODEL PHYSICS Land surface process Sub-grid scale Grid resolved Atmospheri c aerosols & Chemistry Ecosystem Carbon 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).
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) analyses T+120 forecast charts for DT 1200 UTC 29 August 1994 The operational (left) and improved (right) T+120 forecasts Verifying 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 Analysis charts for DT 1200 UTC 29 August 1994
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 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 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 gauge Top right: forecast from R-CLIPER Bottom left: forecast from GFDL hurricane model (from Tuleya et al. 2007) Numerical Forecasting of TC Rainfall after Landfall