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Recent Advances and Future Challenges in Hurricane Prediction

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Recent Advances and Future Challenges in Hurricane Prediction Recent Advances and Future Challenges in Hurricane Prediction Yuh-Lang Lin Professor Departments of Physics Department of Energy & Environ. Systems Senior Scientist NCAT ISET Center Outlines 1. The Need for Skillful Hurricane Prediction 2. Origins of Tropical Cyclones 3. Numerical Weather Prediction 4. Hurricane Track Prediction 5. Hurricane Intensity and Rainfall Predictions 6. Seasonal Hurricane Forecasts 7. Effects of Global Warming on Hurricanes 8. Summary 2 1. The Need for Skillful Tropical Cyclone Prediction More people live along coastal areas – it takes longer time to evacuate. Emergency managements are very costly: (e.g., it costs ~$1M per mile of coastline evacuation). Evacuation decision making is very sensitive to the prediction of hurricane track, intensity and size. More hurricane related fatalities now due to storm surge or inland flooding which depends on accurate TC prediction. More and stronger hurricanes are coming due to global warming?! 3 2. Origin of Tropical Cyclones Tropical cyclones form over tropical oceans with sufficient sea-surface temperature (> 26.5oC), circulation (vorticity), moisture and instability, and weak vertical wind shear. Definitions of tropical cyclones: Tropical Tropical Storm Hurricane/ Depression Typhoon 17 m/s 33 m/s (38 mph) (75 mph) Hurricane Patricia (2015) Major Hurricane [89 m/s (200 mph), 879 mb] 50 m/s (112 mph) Super Typhoon Typhoon Haiyan (2013) 67 m/s [87 m/s (195 mph), 895 mb] (150 mph) 4 About 85% of major hurricanes were initiated by African easterly waves (AEWs) [e.g., pre-Hurricane Alberto (2000) AEW] 5 (From Lin et al. 2005), based on EUMETSAT Some Basic Dynamics of Hurricane Genesis are still not well understood: Why are there so many easterly waves and so few tropical storms? What processes ”choose” a particular Easterly Wave? What are the major formation mechanisms of hurricanes? . Conditional Instability of the Second Kind (CISK) (Charney & Eliassen 1964) [dominant theory for 1964 - 1990’s.] . Cooperative Intensification (Ooyama 1964, 1969) . Wind-Induced Surface Heat Exchange (Emanuel 1986) . Marsupial Paradigm (Montgomery et al. 2008) 6 3. Numerical Weather Prediction (NWP) Observations Preprocessing (Initialization) Analysis Running Data NWP Model Assimilation Postprocessing Numerical Model Output Weather Prediction System Forecasters (Adapted after Users Uccellini 2006) 7 Physically, the Newton’s second law is applied to describe air motion in x, y, and z directions: F du F F ma a a x m x dt m du F dv F dw F x ; y ; z dt m dt m dt m This gives three momentum equations. The conservation of mass is applied to derive the continuity equation. The conservation of energy and ideal gas law are used to derive the thermodynamics equation. 8 Mathematically, a NWP model solves an initial-value and boundary-value problem (IVP & BVP) in a rotating frame of reference: (Primitive Equations) du 1 p fv F x-momem. eq. (1) dt x rx dv 1 p fu F y-momem. eq. (2) dt y ry dw 1 p g F z-momem. eq. (3) dt z rz d u v w Continuity eq. (4) dt x y z dT Q Thermo. energy eq. (5) dt p RT Eq. of state (6) 9 NWP Model Development: A numerical model based on the above primitive equations may be developed step by step. For example, an Advection Model can be constructed based on the inviscid nonlinear Burger equation u' u' (U u') 0 t x Apply a finite difference method at discrete points in x and t ut1 ut1 ut ut i i (U ut ) i1 i1 0 2t i 2x t1 Solve for ui t ut1 ut1 (U ut )ut ut i i x i i1 i1 10 The Advection Model may be used to study some basic wave properties and extend to more complicated models. u' u' (U u') 0 t x 11 Sensitivity test can be performed to understand the nonlinear effects u' u' U 0 t x 12 The advection model can be extended to 2D & 3D shallow-water tank models based on shallow-water systems 2D Tank Model 3D Tank Model 13 The 3D Tank Model can then be further extended to build a simple NWP model for solving the primitive equations (1)–(7). In 1922, Lewis Richardson, did the very first numerical weather prediction based on a simple primitive equation model. He made a 6-h forecast with hand calculators which took more than 6 weeks. The first successful NWP was performed using the ENIAC digital computer in 1950 by Charney, Fjotoft, von Neumann et al. Today’s NWP: http://www.ncdc.noaa.gov/sites/default/files/NAM_20120710_ 0000_refcclm-small.gif (NOAA NCDC) 14 Mathematically, there exist challenges. For example, 1. Lower, upper, and lateral boundary conditions. 2. A need of initialization: initial conditions, i.e., observed data needs to be put on model grid points and consistent with equations. 3. Data assimilation was developed to incorporate new obs into NWP model, such as Nudging, 3DVAR (variational assimilation), 4DVAR, and EnKF (Ensemble Kalman Filter). 4. The need of conservation of mass of global model leads to the development of staggered grids. 5. Ensemble forecasting is developed to generate a representative sample of possible future states of the atmosphere, as a dynamical system. 6. The number of primitive equations grows when more physical processes are involved, such as moist processes. 7. Then, came the big question of the predictability of the atmosphere, as proposed by Lorentz. 15 Physically, there exist many challenges, too. For example, 1. For a fully-compressible system with sound waves included, CFL criterion will require extremely small time interval. Time-splitting scheme has been developed to resolve this problem. 2. Parameterizations of subgrid-scale processes remain challenging, such as planetary boundary layer, cumulus and cloud microphysics, radiation, air-sea interaction, etc. 3. Inclusion of moisture adds 6 – 7 additional equations and faces challenges in how to parameterize the subgrid processes. 4. Need more accurate, frequent and evenly-distributed data for model initialization. 5. Verification of forecasting results require field experiment (campaign) which are very expensive. 6. NWP models rely on global models to provide i.c. and b.c., thus inherit errors from global model simulations. 7. Need more powerful supercomputers for real-time forecasting. 16 Examples of Special Techniques used in NWP Models: Using a moving, nested grid domain with higher resolution to follow a hurricane: Note that there is not much data over the ocean, which is one major source of forecast errors! 17 A grid mesh moving with hurricanes Gustav (2008) Ike (2008) Hanna (2008) Kyle (2008) 18 Roop, Lin, Tang (2008) Unstructured Adaptive Grid OMEGA Model (SAIC) 19 Numerical Weather Prediction using Global Models Lat/Lon Model Icosahedral Model • Near constant resolution over the globe • Efficient high resolution simulations lk ,i NOAA Earth System Research Laboratory - Boulder, Colorado nk,iPage 20 4. Hurricane Track Prediction A hurricane may move as far as several thousand kilometers away from its origin. Hurricane track prediction has been improved significantly in the last few decades Hurricane tracks are mainly influenced by: • Environmental flow (e.g., Bermuda high) • Synoptic systems (e.g., a cold front) • Variation of Earth’s rotational rates (b effect) • Topography (e.g., Appalachians, Hispaniola, etc.) • Vertical wind shear • Convective heating • Sea surface temperature distribution 21 The tropical cyclone tracks we are dealing with! westerly H H b effect easterly (Neumann 1993, Lin 2007) Genesis locations and tracks of tropical cyclones with wind speeds of at least 17 ms-1 for the period of 1995-2004. 22 Hurricane Dennis’ (1999) track Track Deflection by Appalachians was influenced by frontal system (Liu, Lin & Chen, 2014) Typhoon Haitang (2005) Obs. Wu-Fen- 19/00Z (TY) ★ 25N Shan 18/12Z (TY) 24N Hua- ★ Lien 18/00Z (STY) 23N 17/12Z (STY) 22N (Wang 1980 NSC; Lin 2007) Jian and Wu (2006) 23 Numerical models used for hurricane prediction CLIPER (CLImatology and PERsistence) - a statistical-climatological model - being exceeded by numerical models after the 1980s. NHC98 - a mixed statistical-numerical model Simplified numerical models: BAMS, VICBAR GFDL Model – A triply-nested movable mesh numerical model solving partial differential equations Hurricane WRF (HWRF) Global numerical models: NCEP GFS, MRF, ECMWF, NOGAPS, UKMET *Yellow-highlighted are currently used by the National Hurricane Center (NHC) 24 NATIONALNATIONAL HURRICANEHURRICANE CENTERCENTER ATLANTICATLANTIC TRACKTRACK FORECASTFORECAST ERRORSERRORS 500 Major upgrade in global & 1964-1973 hurricane models 400 1974-1983 300 1984-1993 2003-2005 200 1994-2003 • Higher quality observations • Advances in data input into models 100 • Better numerics and physics in models 0 Error (nauticalmiles) Error 12 24 36 48 72 96 120 (Uccellini Forecast Period (hours) 2006) 23 May 2006 25 Major Improvements • Major upgrade in global & hurricane models • Higher quality observations • Advances in data input into models • Better numerics and physics in models 26 Example: Track Prediction of Hurricane Katrina (2005) Near Landfall Earlier forecast observed 8/29 forecast observed 8/24 Katrina Prediction hr 27 Simulation of Hurricane Katrina (2005) by NASA Global Model (Courtesy of Dr. Bo-Wen Shen NASA/GSFC) 28 Many models had missed forecasting the unusual inland track deflection 5 days before Sandy’s (2012) landfall Forecasts of Sandy (2012) began at 00Z Oct. 23, 24, 25, and 26 for every 12 h by GFDL, HWRF, ECMWF, and GFS (Blake et al. 2013). The NHC best track is denoted by the hurricane symbol. 29 The offshore forecast error may be due to the Omega Block Gall et al. (2013) 30 Major Hurricane Joaquin forecasts by models Observed track We still have plenty room for improvement on longer-term track forecast! 31 Track Forecast Skill Comparison (From NHC 2014 verification report) 32 GFS Forecast versus Reanalysis (Forecast at 1025.00Z) 33 Before landing about 2330 UTC OCT 29 near Brigantine, New Jersey, in addition to the steering of synoptic systems, Sandy seems experiencing a Fujiwhara effect with the inland trough.
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