The forward simulation of radar reflectivity requires details of clouds and precipitation from general circulation models (GCMs). But such details are represented as sub-grid processes that involve parameterizations and assumptions about the spatial coverage and thus depend on the GCM. In this research, we propose the use of a statistical method to generate sub-grid precipitation for generic use. In addition, the proposed method can be used to provide uncertainty estimates on the signals. The sub-grid variability is obtained from simulation with a global storm-resolving model called NICAM (non-hydrostatic icosahedral atmospheric model). The proposed method first generates precipitation probabilities for the possible scenarios and then sub-grid precipitation rates are generated from the generalized gamma distribution for the given cloud fraction and grid-scale precipitation rates. Compared to the standard method (which neglects the probabilities) that overestimates the precipitation fraction, our method well reproduces the NICAM dataset profiles of both the precipitation fraction and the radar-based cloud fraction. The in-cloud signal frequencies are also reproduced, although less accurately over a tropical region. Inclusion of sub-grid variability in precipitation rates was particularly important for the tropical region to obtain agreement of the precipitation fraction. Application of the two methods to a GCM shows it to have a robust bias for low-level liquid clouds. The proposed method can be used to identify uncertainty in the signals associated with sub-grid variability in the precipitation processes, indicating an effective way to use a global storm-resolving model to evaluate conventional GCMs.

This study aims to create a 21-year, high spatiotemporal resolution Global Satellite Mapping of Precipitation (GSMaP) rainfall product adjusted by rain gauge measurements over the Indian mainland. The targeted resolutions of the GSMaP are hourly and 0.1°× 0.1°. The National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) daily gauge analysis (0.5° × 0.5°) and Indian Meteorological Department (IMD) daily gridded rainfall product (0.25° × 0.25°) were utilized to generate two long-term rainfall products, GSMaP_CPC and GSMaP_IMD rainfall, respectively. After preliminary verification of the GSMaP_CPC and GSMaP_IMD rainfalls with IMD gauges, these rainfall products are evaluated for the Indian Summer Monsoon (ISM) periods of 2000–2020 with comparisons of other merged rainfall products such as the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG). The results suggest GSMaP_IMD has a smaller root-mean-square difference (RMSD) and higher correlation than GSMaP_CPC, evaluated against independent rainfall products. In the three-hour mean analysis with spaceborne precipitation radar data, it is found that the value of RMSD decreases in GSMaP_IMD with respect to GSMaP_CPC throughout the day. The statistics against the hourly dense rain gauge network in Karnataka suggests that the GSMaP_IMD is more effective in capturing large spatiotemporal rainfall variation over India. Thus, validation results with the independent sources suggest that GSMaP_IMD rainfall generally improved over GSMaP_CPC rainfall. These improvements are significant in orographic regions with high rainfall amounts, mainly the western Ghats and northeastern parts of India.

Currently, a future satellite mission of precipitation observations is discussed in Japan. From a low-orbit satellite, it is difficult to directly observe temporal evolution of precipitating clouds. The dynamical structure of precipitation helps better understandings of the lifecycle of precipitating clouds. Thus, the Doppler capability of a spaceborne precipitation radar is expected to provide global information of the motion for various precipitating clouds. However, the Doppler measurements of precipitation from space is challenging because of a fast-moving platform and a radar’s finite field of view (FOV). Since the radar onboard the spacecraft quickly passes above precipitating clouds, the decorrelation of precipitation signals due to the beam broadening effect degrades the Doppler measurement accuracy. Moreover, a spatial variability of precipitation within the FOV causes mixing of the motion between precipitating particles and spacecraft, which is called as an effect of the non-uniform beam filling (NUBF). This study investigates the Doppler capability of the spaceborne precipitation radar based on simulation experiments by using the high-spatial resolution ground radar and numerical model data. Here, we discuss two Ku-band Doppler radar systems: A) a large one antenna system and B) a two-antenna system. Since the contamination of the platform motion is proportional to the platform velocity and the radar’s beamwidth, the large antenna system mitigates the contamination due to the platform motion. On the other hand, the two-antenna system adopts the displaced phase center antenna (DPCA) technique. A signal processing with two antennas cancels out the platform motion so that mitigation of the beam broadening and NUBF effects is expected even if the FOV is coarse than the large antenna system. A quantitative evaluation between the two systems is conducted. For the large antenna system (FOV of 2.5 km), the mean Doppler velocity error of precipitation (> 15 dBZ) is evaluated in the range from 2.3 to 5 .0 m/s. Although the large error is originated from a residual error of the imperfect NUBF correction, the error is mitigated from 0.7 to 1.5 m/s when a 5-km average in the along-track direction is applied. For the two-antenna system (FOV of 5 km), the error is evaluated in the range from 0.6 to 1.1 m/s.