Estimation of rain height and one-minute rain rate based on tropical rainfall measuring mission product

Abstract

Rainfall is a major factor which affects radio communication systems operating at frequencies more than 10 GHz. Thus, measurements of rainfall are extensively carried out using either space-based or ground-based instruments. Now, Tropical Rainfall Measuring Mission (TRMM) satellite is considered one of spacebased instruments that is able to provide reliable rainfall data. However, most studies involving TRMM are meant for hydrological and climatological purposes. Therefore, analyses are made to show the applicability of TRMM in radiowave propagation through the estimation of rain height and one-minute rain rate required in the step by step calculation of long term rain attenuation statistics by ITU-R recommendation P.618-10. First, bright-band height is estimated from TRMM Precipitation Radar observation available in 2A23 and 2A25 dataset. Then, the 0oC isotherm height is calculated by assuming that it lies 500m above the bright-band. Comparison of the estimated bright-band and the 0oC isotherm height with ground-based measurement data shows quite good agreement indicating the reliability of TRMM in estimating rain height. Next, a modified model for the prediction of one-minute integrated Complementary Cumulative Distribution Function (CCDF) of the rain rate (P(R)1) valid for tropical and equatorial regions is proposed. The model inherits its analytical formulation from ITU-R recommendation P.837-6 Annex 1 and relies on TRMM PR 3A25 and TMPA 3B43 data for the extraction of probability of rain (P0) and mean yearly accumulated rainfall (Mt) respectively in place of ERA40 database which suffers poor spatial resolution. Based on the testing, the prediction performance of the modified model with TRMM data shows better performance compared to the same procedure with ERA40 data. This study shows the applicability of TRMM to be employed in radiowave propagation, especially for the purpose of improving rain attenuation prediction model due to its reliability and higher spatial resolution database.