Spaceborne precipitation radar had been strongly desired by the world meteorological community since the launch of the first meteorological satellite TIROS-1 in 1960. Spaceborne precipitation radar was finally realized by the launch of the Tropical Rainfall Measuring Mission (TRMM) observatory in 1997. The 13.8-GHz TRMM precipitation radar (PR) worked for＞17 years in orbit to observe various types of precipitation systems. In 2014, the Global Precipitation Measurement (GPM) core satellite with dual-frequency (13.6- and 35.5-GHz) precipitation radar (DPR) was launched and is currently working well in orbit. The very small satellite RainCube with 35.75-GHz rain radar was launched in 2018 and is working in orbit now. The new (i.e., after the GPM) spaceborne precipitation missions are being studied at NASA, JAXA, and other organizations. These spaceborne precipitation radar systems and future missions are described in detail herein, and the possibilities of other types of spaceborne precipitation radar (including conical scan-type small radar in low earth orbit and bistatic precipitation radar in a geostationary orbit) are also discussed.

The Earth, Clouds, Aerosols and Radiation Explorer (EarthCARE) mission is a European-Japanese joint satellite mission that aims to provide the global observations necessary to advance our understanding of clouds and aerosols and their radiative effect on the Earth’s climate system. Toward this goal, the EarthCARE satellite loads two active instruments, Cloud Profiling Radar (CPR) and Atmospheric Lidar (ATLID), offering vertical profiles of clouds and aerosols, together with light drizzles, whose properties are extended horizontally using complementary measurement by Multispectral Imager (MSI). The properties thus obtained are then used to estimate outgoing shortwave and longwave radiation at the top of the atmosphere, which is evaluated against measurements taken by the fourth sensor, Broadband Radiometer (BBR). Such a “closure assessment” is used to give feedback to the microphysical property profiles and optimize them, if necessary, to offer consistent three-dimensional datasets of cloud-aerosol-precipitation-radiation fields. EarthCARE’s integrative global observation of clouds, aerosols and radiation with the new measurement capabilities, particularly with Doppler velocity, is expected to not only extend the A-Train measurement toward a longer-term climate record, but also to advance our perspective on the fundamental role that global clouds have within the climate system in the context of their relationships to dynamical processes and their interactions with aerosols and radiation. This review paper provides an overview of the mission, the satellite and its payloads, with a particular focus on the algorithm and products developed in Japan, and areas of scientific study expected to progress.

We describe the retrieval algorithms applied to study ice microphysics using the cloud profiling radar onboard the CloudSat satellite and the lidar onboard the CALIPSO satellite. Backscattering properties of ice particles for radar lie in the Rayleigh or Mie scattering regime, and those for lidar lie in the geometrical optics regime due to the difference in their wavelengths. Based on their differences, the radar and lidar observations offer unique opportunities to determine ice microphysics. In this study, we applied an extended version of the ice microphysics retrieval algorithm to the CloudSat and CALIPSO data. Input parameters for the algorithm are the radar reflectivity factor from CloudSat and the attenuated backscattering coefficient and depolarization ratio from CALIPSO. Since the laser tilt angle of the CALIPSO lidar changed from 0.3° to 3° off the nadir direction in November 2007, different treatments for ice clouds were required before and after the change. We prepared two different look-up-tables (LUTs) of a quasi-horizontally oriented ice plate (Q2D-plate) for CALIPSO for the two observation periods (i.e., June 2006-November 2007, November 2007-current) and implemented them in the retrieval algorithm for ice clouds. A mixture of randomly oriented bullet ice particles (3D-ice) and a Q2D-plate was considered in the algorithms. These modifications allow the long-term analysis of ice microphysics in a consistent manner. A sensitivity study indicated that the importance of depolarization ratio information in the retrieval of ice microphysics; i.e., when a depolarization ratio is not used and the LUT of a 3D-ice model is applied for all cloud regions, the effective radius is significantly overestimated. The Japan Aerospace Exploration Agency and European Space Agency joint mission ’EarthCARE’ is scheduled to be launched in 2021. EarthCARE will carry a 94-GHz cloud profiling radar with Doppler function (CPR), high spectral resolution atmospheric lidar (ATLID), a multi-spectral imager (MSI), and a broad-band radiometer (BBR). The information of the lidar ratio from the ATLID and the Doppler velocity from the CPR can further improve the retrieval accuracy of ice microphysics.

Standard algorithms for the Japan Aerospace Exploration Agency (JAXA) EarthCARE Multi-Spectral Imager (MSI) Project are described. The CLAUDIA-2 algorithm and the CAPCOM algorithm have been implemented in the JAXA data analysis system for cloud discrimination and for cloud property retrieval, respectively. CLAUDIA-2 is inspired by the MODIS standard algorithm MOD35, but they have different characteristics, and thus MOD35 has been designed under the “clear conservative” concept. CLAUDIA-2 has less bias for both cloudy and clear skies. A synergistic use of cloud profiling radar and imager is also described herein. Indeed, the contoured frequency by optical depth diagram (CFODD) viewgraph obtained by radar reflectivities, cloud optical thickness, and cloud effective particle radii clearly depicted the cloud evolution processes statistically.

Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) is a joint Japanese-European satellite observation mission to understand interaction between cloud, aerosol, and radiation processes in the earth climate. Four sensors of 95GHz cloud profiling radar, broadband radiometer, multi-spectral imager (MSI) and 355nm high spectral resolution lidar (ATLID) are installed on the satellite. We develop an algorithm to produce ATLID level 2 data distributed by JAXA, using the ATLID level 1 data of Mie copol, Mie crosspol, and Rayleigh attenuated backscatter coefficients at 355nm (ATLID algorithm). The algorithm retrieve extinction coefficients (α_p ), backscatter coefficients (β_p ) and depolarization ratios (δp) of particles (aerosol and cloud) without assuming a particle lidar ratio (S_p), using an optimization method. This algorithm identifies molecule-rich, aerosol-rich, or cloud-rich slab layers using the derived β_p ; it also classifies aerosol type (e.g., dust and marine) and cloud type (e.g., water-droplet and ice-crystal) using the derived S_p, δ_p, and α_p . The CALIPSO satellite-borne lidar CALIOP have provided the products as similar as those abovementioned, though the wavelength and retrieval methods are different. This indicates that continuity of the products will be kept from CALIPSO mission to EarthCARE mission. As new products provided from the ATLID measurements, planetary boundary layer height is retrieved. Furthermore, extinction coefficients for several main aerosol components in the atmosphere (water-soluble, black carbon, dust, and sea-salt particles) are retrieved using the retrieved α_p, β_p, and δp. In addition, mode radii of fine-mode and coarse-mode aerosols are retrieved using MSI level 1 data as well as ATLID data (ATLID-MSI synergy algorithm). In this paper, we describe the products and algorithms; we also report the current status of the algorithm development and its future work.

To increase the feed self-sufficiency of livestock and management efficiency of dairy farming on a grassland, it is necessary to improve the quality and production of feed grass through grassland renovation. Remote sensing analysis can be used to monitor renovated grassland over a broad area. A few studies have investigated renovated grasslands; however, these contain a misjudgment between renovated grassland and other land use/land cover. Therefore, in this study, we developed a method to integrate pixel- and object-based image analysis to conduct plot based estimation and applied it to grasslands on the Konsen plateau in Hokkaido. First, we created a farmland segment. Second, we overlaid the supervised classification results and decided the final land use/land cover classification. Performing farmland segmentation using SPOT 6 enhanced the kappa coefficient significantly compared with the traditional supervised classification results obtained using both Landsat 8 OLI and SPOT 6. The classification accuracy is also higher compared with that achieved in previous studies.