Although computer scientists have attempted to use newly emerged social network data to estimate rainfall, as mentioned in the previous section, it is like a ‘dessert’; the main data sources of rainfall measurement are rain gauges, weather radars and satellites, which are the ‘main course’. The ‘dessert’ has some obvious shortages apart from its advantage on data cost and quick response. The use of Twitter data to estimate rainfall or flood situation, as mentioned in the previous section, requires the prevalence of Twitter at a local level, e.g., developed urban area with a large number of users and a wide internet access, which implies the spatial coverage and resolution of the data can be poor in less developed cities and rural areas. The temporal length of the Twitter data is significantly less than the meteorological records, which can be traced back to 1861, while Twitter was launched in 2006. Despite the low cost and quick response of the new data sources foretelling a possible future direction, the existing data sources for rainfall measurement have accumulated a vast quantity of data which can substantially benefit from the big data technology. Table 1 shows information of some widely used datasets containing precipitation data. The features of precipitation data from different sources vary significantly due to the different measuring mechanisms and processing algorithms.