EGU22-3256, updated on 27 Mar 2022
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Dual-frequency radar retrievals of snowfall using Random Forest

Tiantian Yu1, Chandra V.Chandrasekar2, Hui Xiao3, Ling Yang1, and Li Luo4
Tiantian Yu et al.
  • 1Chengdu University of Information Technology, Chengdu, China
  • 2Colorado State University, Colorado, USA
  • 3Institute of Atmospheric Physics, Chinese Academy of Sciences, China
  • 4Institute of Urban Meteorology (IUM), Beijing, China

The microphysical parameters of snowfall directly impact the hydrological and atmospheric models. Dual-frequency radar retrievals of particle size distribution (PSD) parameters are developed and evaluated over complex terrain during the International Collaborative Experiment held during the Pyeongchang 2018 Olympics and Paralympic winter games (ICE-POP 2018). The observations used to develop retrievals were included the NASA Dual-frequency Dualpolarized Doppler Radar (D3R) and a collection of second-generation Particle Size and Velocity (PARSIVEL2) disdrometer. Conventional look-up table method (LUT) and random forest method are applied to the disdrometer data to develop retrievals for volume-weighted mean diameter Dm, the shape factor mu, snowfall rate S, and ice water content IWC. Evaluations are performed between D3R radar and disdrometer observations using these two methods. The results show that the random forest method performs better in retrieving microphysical parameters because the mean errors of the retrievals relative to disdrometer observations are small compared with the LUT method.

How to cite: Yu, T., V.Chandrasekar, C., Xiao, H., Yang, L., and Luo, L.: Dual-frequency radar retrievals of snowfall using Random Forest, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3256,, 2022.