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Understanding Tropical Cyclone Movement Dynamics at Nanjing, China

Learn the basic concept of TC movement, explore the role of steering flow, and analyze a case study on Typhoon Sinlaku's path using NWP models and visualizations.

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Understanding Tropical Cyclone Movement Dynamics at Nanjing, China

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  1. Topic No. 1 Tropical Cyclone Movement Tropical Cyclone Ensemble Forecast Nanjing, China 9:00 – 12:00 2011.12.15 (Thr) Munehiko YamaguchiTyphoon Research Department, Meteorological Research Institute of the Japan Meteorological Agency

  2. Basic concept of TC movement The basic idea of TC movement is that the TC vortex is “steered” by its surrounding flow (Chan 2010, Global Perspectives on Tropical Cyclones). Dynamically, steering is the advection of the relative vorticity (ζ) of the TC by the surrounding horizontal flow (V) Advection of the relative vorticity by the surrounding horizontal flow Time evolution of the relative vorticity of the TC

  3. Steering flow The advection effect causes the TC to move downstream along the direction of V, which is referred to as the “steering flow”. Although this concept of steering is very simple, it has been used extensively to explain and predict TC movement with relatively good success especially in short-tem forecasts. (Chan 2010, Global Perspectives on Tropical Cyclones). Let’s take a look at how much the steering concept is valid to explain the TC movement in NWP models.

  4. Case study to visualize the steering flow Let’s have a look at the steering flow at the before-, during-, and after-recurvature stages of Typhoon Sinlaku in 2008. Observed Track of Typhoon Sinlaku (2008) In order to visualize the steering flow, spatial low-pass filter is applied to a total wind field to separate the TC circulation and the surrounding, steering flow.

  5. Total wind (streamfunction) field before recurvature First of all, let’s see the Sinlaku’s movement in an NWP model. Here the ECMWF’s NWP model is considered as an example. Streamfunction field at 500 hPa at T+0 Typhoon Sinlaku

  6. Total wind (streamfunction) field before recurvature First of all, let’s see the Sinlaku’s movement in an NWP model. Here the ECMWF’s NWP model is considered as an example. Streamfunction field at 500 hPa at T+24 (forecasted field) Sinlaku moves north in the model at this time Typhoon Sinlaku

  7. Separation of the total field Total field spatial low-pass filter Steering flow TC circulation +

  8. Layer image Steering flow from the south to north can be seen. The direction of the steering flow matches with that of the movement of Typhoon Sinlaku. Typhoon Sinlaku

  9. Total wind (streamfunction) field during recurvature Let’s see the Sinlaku’s movement in an NWP model. Here the ECMWF’s NWP model is considered as an example. Streamfunction field at 500 hPa at T+0 Typhoon Sinlaku

  10. Total wind (streamfunction) field during recurvature Let’s see the Sinlaku’s movement in an NWP model. Here the ECMWF’s NWP model is considered as an example. Streamfunction field at 500 hPa at T+24 (forecasted field) Sinlaku moves northeast in the model at this time Typhoon Sinlaku

  11. Separation of the total field Total field spatial low-pass filter Steering flow TC circulation +

  12. Layer image Steering flow from the southwest to northeast can be seen. The direction of the steering flow matches with that of the movement of Typhoon Sinlaku. Typhoon Sinlaku

  13. Total wind (streamfunction) field after recurvature Let’s see the Sinlaku’s movement in an NWP model. Here the ECMWF’s NWP model is considered as an example. Streamfunction field at 500 hPa at T+0 Typhoon Sinlaku

  14. Total wind (streamfunction) field after recurvature Let’s see the Sinlaku’s movement in an NWP model. Here the ECMWF’s NWP model is considered as an example. Streamfunction field at 500 hPa at T+24 (forecasted field) Sinlaku moves east-northeast in the model at this time Typhoon Sinlaku

  15. Separation of the total field Total field spatial low-pass filter Steering flow TC circulation +

  16. Layer image Steering flow from the west-southwest to east-northeast can be seen. The direction of the steering flow matches with that of the movement of Typhoon Sinlaku. Typhoon Sinlaku

  17. Practice Let’s calculate the steering vector and compare it with the TC motion vector in the model!!! Sketch the steering vector on the distributed answer sheet on which the TC motion vector is already plotted. Use to calculate the amplitude of the vector. For the direction of the vector, visually determine it. Note that the TC motion vector is calculated from the minimum sea level pressure positions at T+0h and T+24h (forecasted field of ECMWF). Later, we will compare the steering vector and the TC motion vector with the observed track.

  18. Steering vector before recurvature Streamfunction (ψ) field Contour interval: 2 x 10^5 Unit: m^2/s Steering flow Use 321 km for Central position of Typhoon Sinlaku

  19. Steering vector before recurvature Estimate from the figure, calculate the amplitude of the steering vector and plot it. 1m/s 2m/s 3m/s 4m/s 5m/s 6m/s Note that the TC motion vector (arrow in green) is plotted based on the minimum sea level pressure positions at T+0h and T+24h (forecasted field of ECMWF). Later, we will compare the TC motion vector with the observed track.

  20. Steering vector during recurvature Streamfunction (ψ) field Contour interval: 5 x 10^5 Unit: m^2/s Steering flow Use 266 km for Central position of Typhoon Sinlaku

  21. Steering vector during recurvature Estimate from the figure, calculate the amplitude of the steering vector and plot it. 1m/s 2m/s 3m/s 4m/s 5m/s 6m/s Note that the TC motion vector (arrow in green) is plotted based on the minimum sea level pressure positions at T+0h and T+24h (forecasted field of ECMWF). Later, we will compare the TC motion vector with the observed track.

  22. Steering vector after recurvature Streamfunction (ψ) field Contour interval: 5 x 10^5 Unit: m^2/s Steering flow Use 281 km for Central position of Typhoon Sinlaku

  23. Steering vector after recurvature Estimate from the figure, calculate the amplitude of the steering vector and plot it. 1m/s 2m/s 3m/s 4m/s 5m/s 6m/s Note that the TC motion vector (arrow in green) is plotted based on the minimum sea level pressure positions at T+0h and T+24h (forecasted field of ECMWF). Later, we will compare the TC motion vector with the observed track.

  24. Let’s check the answers.

  25. Steering vector before recurvature 1m/s 2m/s 3m/s 4m/s 5m/s 6m/s

  26. Steering vector during recurvature 1m/s 2m/s 3m/s 4m/s 5m/s 6m/s

  27. Steering vector after recurvature 1m/s 2m/s 3m/s 4m/s 5m/s 6m/s

  28. Let’s discuss reasons of the difference between the steering vector and the TC motion vector.

  29. Discussion We learned that the steering concept is largely valid. However, there are some differences between the steering vector and the TC motion vector (in the model). Let’s discuss the reasons! Before recurvature During recurvature After recurvature

  30. Reason 1 -Practical problem- Total field It is technically impossible to exactly separate the steering flow and the TC circulation from the total wind field. spatial low-pass filter Technically impossible Steering flow TC circulation +

  31. Reason 2 -Practical problem- The TC motion vector (arrow in green) is computed assuming the motion vector is constant over the first 24 hours while the steering vector is an instantaneous vector at T+0h. Before recurvature Before recurvature TC motion vector (arrow in green) is calculated based on the minimum sea level pressure positions at T+0h and T+24h (forecasted field of ECMWF). TC motion vector (arrow in green) is calculated based on the minimum sea level pressure positions at T+0h and T+6h (forecasted field of ECMWF).

  32. Reason 3 –Scientific issue- The TC is not steered by the “steering flow” at single level layer (500 hPa in this presentation). Concept of deep layer mean • Mass weighted deep-layer mean wind in several layers such as 850 hPa, 500 hPa, 250 hPa is widely used as the steering flow (e.g. George and Gray 1976). • However, there are still controversial arguments among researchers about • the depth of the “deep-layer”, • the width of the radial band to average the winds, and • the dependency of the depth on the TC intensity.

  33. Reason 4 –Scientific issue- The asymmetric component of the TC circulation also advects the TC vortex. Asymmetric forcing • -External forcing • Vertical wind shear • PV anomaly in the mid- and upper-troposphere • Surface inhomogeneities including the effect of geography • -Internal forcing • Dynamical forcing • 1) Beta effect • Thermodynamic forcing • 2) Convection

  34. Decomposition of flows in the vicinity of TCs Steering vector Background flows associated with synoptic features Total flow Spatial low-pass filter Axisymmetric circulation TC circulation itself Asymmetric propagation vector Total flow minus Background flow Asymmetric circulation L H

  35. Distinctive feature of azimuthal wavenumber 1 perturbation Only azimuthal wavenumber 1 perturbation can create (advection) flows over the maximum vortex area. Azimuthal wavenumber 1 perturbation Azimuthal wavenumber 2 perturbation Azimuthal wavenumber 3 perturbation H H L L L H L L H H H L Advection flow canceled

  36. Beta effect Meridional gradient of the Coriolis parameter creates a wavenumber 1 asymmetry, which advent the TC vortex toward the northwest. Beta gyres Contour: Stremfunction 1200km Advection flow L H Fiorino and Elsberry (1989) 2400km

  37. Move of TC movement by beta effect -Experiment using a nondivergent barotropic model- Initially symmetric TC-like vortex moves toward the northwest

  38. Let’s see the difference between the TC motion vector in the model and the actual TC motion vector based on the best track data.

  39. Prediction error TC motion vector in the model (arrow in green) is calculated based on the minimum sea level pressure positions at T+0h and T+24h (forecasted field of ECMWF) while the actual TC motion vector (arrow in blue) is based on the best track positions at T+0h and T+24h. Before recurvature During recurvature After recurvature The difference of the TC motion vectors in green and blue is the prediction error of TC track prediction.

  40. Discussion What causes the prediction error? • Analysis errors: Analysis errors in initial conditions for NWP evolve into large forecast errors. Note that NWP models affect the accuracy of the initial conditions because they are created by blending observations and the best- estimate of the atmosphere, which is a short-range, say six-hour, forecast by NWP models. • Model errors: Our NWP models are not perfect (discretization, computational errors, approximations in the physics schemes, etc.) • Others: There might be some physics that we have not known yet

  41. What we have learned so far is that the steering concept is largely valid. Move on to the next topic on the initial condition sensitivity of TC track prediction What we will learn from now is that the representation of the steering flow in NWP models is critical for accurate TC track predictions.

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