Climate change is expected to change the intensity and frequency of heavy storms. Thus, understanding different characteristics of this phenomena (i.e., intensity, size, speed, direction, etc.) is vital for the effective climate adaptation. Many extreme storms have small areas and short lifetimes (sub-daily/hourly) and can have destructive impacts, especially over urban areas. Therefore, it is vital to understand the nature of changes in these extremes to reduce the risk of their destructive impacts on cities. The overarching goal of this thesis is to quantify various storm characteristics, including their changes, using radar and satellite observations. Using an object-based technique, I compare the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) and ground radar based Multi-Radar Multi-Sensor Quantitative Precipitation Estimates (MRMS) over the United States and show that the object-based storm properties are not sensitive to the observational platforms. However, there are differences that are statistically significant. Secondly, I investigate the error sources associated with different types of contributing data in the IMERG during the hurricane days occurred in 2016-2018 with MRMS as the reference. The results show that IMERG have better agreement with MRMS during the passive microwave (PMW) observations compared to rainfall estimates come from the combination of the interpolation techniques and infrared observations (morph/IR). Also, the quality of morph/IR estimates deteriorates with the longer absence of PMW observations. Thirdly, I establish an object-based climatology of rain systems using radar data near Sydney, Australia. The results show that rain systems in different seasons have distinct object-based characteristics, and these differences are dependent on their source of origins and also their positions over land and ocean. Using a two-step clustering algorithm, I have found five system types over Sydney peaking in different seasons. While overall rainfall statistics don't show any link to climate modes, links do appear for some system types using a multivariate approach. Finally, I show that there is a robust increasing trend of 20% per decade in sub-hourly extreme rainfall in the Sydney region over 20 years, despite no evidence of trends on hourly or daily scales. I am able to obtain this new result via a novel analysis of long-term radar data, including cross-checking between neighboring radars.