An analysis of cloud distribution to rainfall occurrence for future forecast improvement affecting urban living
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Abstract
Meteorological Remote Sensing data contain a vast amount of weather information. It is a valuable source of information in weather forecasting and early prediction of different atmospheric disturbances. Cloud conditions play an important role in many types of research such as weather and climate-related which can be utilized from the weather satellite data. In this research, Multi-functional Transport Satellite (MTSAT) meteorological satellite images are used for automatic extraction of cloud-top temperature feature for estimation rainfall occurrence which can be useful for urban living. The weather, especially the rainfall occurrence, has been found to have a significant impact on some phenomena related to human behavior in urban living. The gray level co-occurrence matrix (GLCM) algorithm is applied for measuring of cloud texture. Euclidean distance analysis was used to compare the similarity condition among rainfall occurrences. The relationship between rainfall distribution and the associated cloud properties using satellite image are evaluated for future forecast improvement in this study. The precision experiment yields 87.5% which can imply that the analysis is significantly improving if there is a sufficient spatial and temporal distribution of data exists.
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