Echolocation refers to locating the size and distance of objects in the surroundings using echo. Thousands of species use echolocation to navigate the world. Drawing inspiration from this nature-driven technology, humans have designed and built radar systems to detect and track objects remotely.
During the Second World War (when applied sciences flourished), radar technology was used to detect and target enemy's warplanes. A series of false-alarm whenever it rained offered an opportunity for developing radar to observe weather systems.
There has been no turning back since then. Radar is considered an irreplaceable modern technology that can provide accurate rainfall measurement over a large area. Meteorological organizations from around the world soon integrated weather radar into their observation systems. The polar plot (aka radar chart) helped us detect and track organized precipitation systems and provided more insight into the microphysics of rainfall.
Today, more than 4000 radar stations are built worldwide that continuously monitor the weather on a large scale. When ground stations are not sufficient due to their restricted mobility, radars are also carried on specially designed aircraft for conducting field observations such as the Indian summer monsoon.
In 1997, NASA of the U.S and JAXA of Japan jointly launched the Tropical Rainfall Measuring Mission (TRMM) satellite. The satellite was the first to carry a radar onboard to monitor precipitation systems on a global scale within the tropics. This allowed researchers worldwide to tune in to their surface observations whenever the satellite made an overpass.
However, cross-comparison of radar observations from satellite and ground-based poses several challenges. Even though technically, both the instruments work on the same principle, there could be a mismatch between them due to the difference in their viewing geometry, radar frequency, and other issues such as clutter.
In a recent paper co-authored by my research scholar [1], we had compared the ground radar observations maintained by the Indian Meteorological Department (IMD) with TRMM's Precipitation Radar using alignment methodology. The comparison study showed that the ground radar overestimates rainfall during the Indian summer monsoon period of 2013. We demonstrated that the positive bias of the ground radar measurement could be "corrected" to match with TRMM PR observations using an artificial neural network.
[1] Alok Sharma and Srinivasa Ramanujam Kannan, 2021, Intercomparison between IMD ground radar and TRMM PR observations using alignment methodology and artificial neural network, Journal of Earth System Science, Vol. 130, Article ID 0020.