Extreme weather in Taiwan becomes serious issue recently, and the monitoring and prediction of rainfalls is more important than ever. Clouds present the feature of weather changes, and they are also very important for weather prediction. Cloud phase, cloud type, and the changes of cloud top properties (temperature, height and pressure) all provide information for cloud development and rain fall prediction. To analyze the changes of cloud properties, we must track the movement, appearance and disappearance of clouds in pixel level. The geosynchronous weather satellites orbit with the rotation of the Earth. They can provide semi-sphere image with high temporal resolution. The MTSAT-2 provides an image every 30 minutes, and it might delay to an hour if transmission interference occurs. Some small convection clouds may develop and disappear within 30 minutes, so MTSAT-2 cannot track them. If the clouds move fast, it is also difficult to track them. Japan Meteorological Agency launched Himawari-8 to Earth’s orbit. This satellite began to distribute images in July, 2015. It not only increases the number of channels to 16, but also increases the temporal resolution to 10 minutes. It also has the capability to monitor a small area every 2.5 minutes. With Himawari-9, FY-4 and GEO-KOMPSAT-2, it is possible to monitor hemisphere with 3 minutes resolution. In this 2-year proposal, we will first improve the research of cloud tracking with cloud concentration instead of cloud mask, using particle swarm organization (PSO) and artificial neural network (ANN) to track the cloud pixels and estimate the development of cloud system. We will also calculate the accuracy of short term and long term prediction of cloud movement. In the second year, we will combine the parameters from other related project and find the relationship with rainfalls and time delay by linear and nonlinear multivariable regression function as part of weather prediction.
|Effective start/end date||1/08/20 → 31/07/21|
UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):
- Cloud pixel tracking
- Particle Swarm Organization
- Artificial neural network
- Multivariable regression function
- Weather prediction.
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