Dusadee Sukawat

Predictability of the Northeast Monsoon Forecasts by the Educational Global Climate Model

The predictability by an atmospheric prediction model is determined by the uncertainties in the initial condition and the imperfection of the model. It is difficult to provide accurate weather prediction and determines the predictability of a model. Atmospheric prediction model efficiency is obtained from the analysis of predictability measurement. Five existing predictability measurements; Lyapunov exponent, finite size Lyapunov exponent, finite time Lyapunov exponent, local Lyapunov exponent and largest Lyapunov exponent are used to measure predictability of the northeast monsoon (winter monsoon) by the Educational Global Climate Model (EdGCM) and to test sensitivity of the model to small initial perturbations. The EdGCM is run for 142-year predictions from the year 1958 to 2100. However, only the outputs of geopotential height at 500hPa of December from 2012 to 2100 are used for predictability measurement. The results show that the EdGCM predictability for the northeast monsoon forecast is about 120 years.

Application of the Ensemble Kalman Filter to a One-Dimensional Linear Advection Model

Data assimilation is used in numerical weather prediction to improve weather forecasts by incorporating observation data into the model forecast. The Ensemble Kalman Filter (EnKF) is a method of data assimilation which updates an ensemble of states to provide a state estimate and associated error at each step. The atmospheric model that is used in this research is a one-dimensional linear advection model. This model describes the motion of a scalar field as it is advected by a known speed field. The result shows that by selecting appropriate initial ensemble, model noise and measurement perturbations, it is possible to achieve a significant improvement in the EnKF results. The accuracy of the EnKF increases when the number of ensemble member grows. That is, the larger ensemble sizes perform better than those of smaller sizes.