prediction of tropical cyclones chapter 9. tropical weather data from traditional sources (surface...

Prediction of Tropical CyclonesChapter 9

Tropical weather data from traditional sources (surface and radiosonde) is scarce, so remote sensing via other methodsincluding satellite soundings is necessary for observations and initializing numerical models.

Many surface stations in tropics provide sporadic data

Global Observing System

Types of Observed Data

Class 1 instruments, which measure in situ at a point; they occupy a small volume of the phenomena being measured (e.g., air temperature measured by ground station thermometer). Class 2 instruments, which measure area-averaged or volume-averaged variables remotely (e.g., temperature derived from satellite radiance or precipitation derived from radar reflectivity). Class 3 instruments, which measure wind velocity from tracking physical targets and their observed displacement with time (e.g., sondes tracked by Global Positioning Satellites or wind velocity derived from tracking cloud elements in satellite images).

Improvement in Forecasts Using Dropsonde Data

Grid Point vs Spectral Models

Grid point models rely on interactions between adjacent grid boxes.

Spectral models are based on a series of sine and cosine waves, using similar physics

Numerical Models

1. Observations and satellite data are used to initialize the model.

2. The model uses dynamics and physics to advance model patterns to next time step

3. The model output can be processed to give various forecasting products, including MOS (model statistics) and ensemble model output

Tropical Cyclone Prediction IMotion

1. Steering Flow Level depends on Strength of Tropical Cyclone

Tropical Cyclone Prediction IMotion

2. Beta Effect 2. Fujiwhara Effect

Tropical Cyclone Prediction IIIModels

Ensemble Model Forecasts use several model runs of same model

Consensus Model Forecasts use several different models

Statistical Model Forecasts are useful for intensity prediction, and also provide an independent assessment compared to dynamical models