# Editorial for Special Issue \"Remote Sensing of Precipitation: Part II\"
## 2 Overview of Contributions
Additionally, using Level 1 data, they investigated how well the (un-)corrected single-frequency data from the dual-frequency receiver can be used for meteorological applications. [PERSON] et al. [9] focused their research on a spatiotemporal object-based bias correction method to reduce several systematic errors in storm events estimated by satellite. The method, called Spatiotemporal Contiguous Object-based Rainfall Analysis for Bias Correction, uses the main storm characteristics of the satellite and observed events detected to remove errors due to displacement in space and time and volume. This method was evaluated over the lower Mekong Basin in Thailand to correct several storm event types in IMERG during the monsoon seasons from 2014 to 2017. [PERSON] et al. [10] proposed to gauge-adjust satellite-based estimates with respect to position by using a morphing method. This approach takes both the position and the intensity of a rain event into account, and its potential was investigated with two case studies. In the first case, the rain events were synthetic, represented by elliptic shapes, while, in the second case, use was made of real data from a rainfall event occurring during the monsoon season in Southern Ghana. In the second case, the satellite-based estimate IMERG was adjusted to gauge data from the Trans-African Hydro-Meteorological Observatory (TAHMO) network. In the framework of JAXA's Global Satellite Mapping of Precipitation (GSMaP) project, the GSMaP Near-Real-Time Precipitation Products were generated (GSMaP_Gauge_NRT), aiming to improve the accuracy of the near-real-time product of GSMaP. [PERSON] and [PERSON] [11] used GSMaP_Gauge_NRT to validate their performance by using gauge observations over Mainland China. [PERSON] et al. [12] proposed a two-step framework to improve the accuracy of satellite precipitation estimates. The first step was data merging based on the optimum interpolation, and the second step was downscaling based on geographically weighted regression. The IMERG product was used to demonstrate the effectiveness of the above two-step procedure in the Tianshan Mountains, China. The primary purpose of the study by [PERSON] et al. [13] was to conduct a comprehensive analysis of an extremely heavy rainfall event that hit Guangdong Province, China, from 27 August to 1 September 2018. Their analysis was based on various in situ and remote sensing observations, including rain gauges, polarimetric radars, disdrometers and reanalysis data, to gain a better understanding of the epic flood events. In particular, the study aimed to explore the potential of polarimetric radars to resolve the microphysics and quantify the precipitation. [PERSON] et al. [14] conducted a thorough validation of the IMERG product over Iran. The study focused on investigating the performance of daily and monthly IMERG products by comparing them with ground-based precipitation data at synoptic stations throughout the country during 2014-2017. The spatial and temporal performance of the IMERG was evaluated using eight statistical criteria. [PERSON] et al. [15] highlighted the performance evaluation of gauge-based and satellite-based gridded precipitation products at the annual, winter and summer monsoon scales by using a multiple statistical approach over Punjab Province, Pakistan. The results revealed that the temporal magnitude of all the gridded precipitation products was different and deviated up to 100-200 mm with the overall spatial pattern of underestimation and overestimation from north to south. [PERSON] et al. [16] assessed the performance of a soil moisture-to-rain algorithm that can be used for direct precipitation estimation from in-situ and/or satellite-based soil moisture observations. The algorithm makes use of the inverted soil water balance equation. Their study covered four different climate regions of Pakistan using rain gauge observations. The assessment was carried out on a daily scale in the period 2000-2015. [PERSON] et al.