An overview of methods for predicting and estimating precipitation using remote sensing data

Direct influence of precipitation on human life and the role it plays in the development of different countries have resulted in an increase in using methods and algorithms for estimating precipitation. A few decades ago, traditional methods were used for predicting precipitation. Then, the presentation of meteorological satellite revolutionized this field. Considering the high dispersion of weather stations and rain gauges in developing countries like Iran, free access to images taken by sensors like AVHRR and MODIS is an appropriate opportunity to compensate these deficiencies. We can estimate the volume of water vapor ready to be transformed into precipitation using satellite images, water vapor absorption bands and thermal bands in any time, space, and scale. The algorithms used to estimate precipitation in satellite images are classified into three types- infrared, visible, microwave and a combination of the first two types-based on the sensors’ wave length. Methods based on infrared and visible waves have a good spatial and temporal resolution, while microwaves-based methods measure precipitation directly. Yet, these techniques contain many weaknesses, especially in low earth orbits. Combined techniques are used to compensate these weaknesses. Microwave images are not receivable in Iran, thus we cannot take advantage of microwaves and combined methods. As a result, the present article focuses mainly on visible and infrared wave length.