Cloud-Type-Dependent 1DVAR Algorithm for Retrieving Hydrometeors and Precipitation in Tropical Cyclone Nanmadol from GMI Data

Abstract

Understanding the structure of tropical cyclone (TC) hydrometeors is crucial for detecting the changes in the distribution and intensity of precipitation. In this study, the GMI brightness temperature and cloud-dependent 1DVAR algorithm were used to retrieve the hydrometeor profiles and surface rain rate of TC Nanmadol (2022). The Advanced Radiative Transfer Modeling System (ARMS) was used to calculate the Jacobian and degrees of freedom (ΔDOF) of cloud water, rainwater, and graupel for different channels of GMI in convective conditions. The retrieval results were compared with the Dual-frequency Precipitation Radar (DPR), GMI 2A, and IMERG products. It is shown that from all channels of GMI, rain water has the highest ΔDOF, at 1.72. According to the radiance Jacobian to atmospheric state variables, cloud water emission dominates its scattering. For rain water, the emission of channels 1–4 dominates scattering. Compared with the GMI 2A precipitation product, the 1DVAR precipitation rate has a higher correlation coefficient (0.713) with the IMERG product and can better reflect the location of TC precipitation. Near the TC eyewall, the highest radar echo top indicates strong convection. Near the melting layer where Ka-band attenuation is strong, the double frequency difference of DPR data reflects the location of the melting. The DPR drop size distribution (DSD) product shows that there is a significant increase in particle size below the melting layer in the spiral rain band. Thus, the particle size may be one of the main reasons for the smaller rain water below the melting layer retrieved from 1DVAR.