New research suggests that the rate of rainfall within a storm system is linked to the structure and form of the precipitation area as seen on radar. This discovery relies heavily on the "morphology" of radar signatures, including shape (big, small), and size (high, short or plump, thin).

Compared to buying diamonds, morphological characteristics are an important reference factor for pricing. Fascinated by "popcorn-shaped" clouds over the Tibetan Plateau, atmospheric scientists have been inspired to study the relationship between cloud shape, precipitation intensity, and the morphology of radar signatures.

"It is not easy to find the information we need from the abundant data," says Dr. CHEN Yilun from the University of Science and Technology of China (USTC) of CAS, the lead author of a precipitation area study recently published in Advances in Atmospheric Sciences. "It is necessary to develop an objective method to identify precipitation areas and definite its morphological characteristics."

A precipitation area, or precipitation object, is a system composed of spatially continuous precipitation pixels. Essentially, this is how a storm appears on radar. Areas sometimes look organized, such as the spiral belts, or rain bands, of tropical cyclones. More often, they show chaotic forms that are difficult to describe. That said, plentiful radar data in the Tibetan Plateau has led to important discoveries.

"Linear precipitation areas have the lowest rain rate, whereas square-shaped precipitation areas have the highest rain rate over the Tibetan Plateau," says Dr. CHEN. "This phenomenon is most significant over the eastern Tibetan Plateau."

Modern dual-polarization radar allows for raindrop size analysis and a vertical (3D) cross section of a storm. While traditional echoes were considered in this study, the vertical structure of the precipitation area is notably sensitive to both size and 3D morphology.

"The morphological characteristics of precipitation areas are closely related to the precipitation intensity," says Prof. FU Yunfei, a corresponding author in this study and professor of USTC. "It could potentially be used to forecast precipitation and verify numerical models."

Research paper

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