An Improvement to Precipitation Inversion Model Using Oblique Earth–Space Link Based on the Melting Layer Attenuation

Abstract

Based on the power-law relation between rain intensity and corresponding specific attenuation, precipitation inversion by oblique earth–space link (OEL) has become a supplement to conventional method. However, the attenuation caused by melting layer has a significant effect on its performance. According to the microphysics, thermodynamics, and scattering properties of melting hydrometeors, a melting layer model is introduced to improve OEL inversion approach. Meanwhile, the observations by a microrain radar (MRR) located at Nanjing Reference Climatological Station (NRCS) are used and plenty of numerical simulations are also performed, which aim to validate the melting layer model. The choice of raindrop size distribution (DSD) determines the simulated values of radar reflectivity factor (RRF) and path-integrated attenuation (PIA). The gamma distribution gives the results of simulated RRF that are consistent with MRR observations. Considering the nonspherical shape of melting hydrometeors, its effects on attenuation calculation are discussed based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> -matrix, which indicates the error due to hydrometeor shape is negligible below Ka-band. The values of PIA derived from our melting layer model show good agreements with MRR observations. In the simulation experiment, we found the average multiplier of melting layer attenuation is a function of rain intensity and frequency. The relation is established by using the nonlinear least-squares method. According to the multiplier, a novel model is proposed to estimate the melting layer attenuation. Compared with other existing attenuation models, it shows a similar performance in different frequency bands. Moreover, we introduce an equivalent multiplier into OEL rainfall inversion model, which demonstrates the obvious enhancement of effectiveness and robustness of improved model in the validation experiment.