RainCube (Radar In a CubeSat), developed by the Jet Propulsion Laboratory (JPL) and launched in 2018, was a technology demonstration supported by NASA. RainCube’s radar is the first spaceborne profiling radar fitting on a platform as small as a 6U ( $10\times 20\times 30\,\,\mathrm {cm^{3}}$ ) CubeSat. This article shows how, despite its smaller size compared to traditional spaceborne radars, RainCube was able to measure clouds and precipitation in the mid-latitude and intertropical regions. Moreover, since RainCube’s measurements are oversampled in the along-track (AT) direction, the horizontal resolution can be enhanced by a robust Wiener deconvolution algorithm. After more than two and a half years of operation, the RainCube mission came to an end on 24 December 2020. The collected record of Ka-band radar profiles compares favorably to collocated measurements from other ground-based and spaceborne radars both radiometrically and geophysically. The examples of multiradar collocations also provide some insights into the potential of constellations of spaceborne radars to study clouds and storms.

This article presents cross validation of nearly simultaneous observations between the Temporal Experiment for Storms and Tropical Systems-Demonstration (TEMPEST-D) and Radar in a CubeSat (RainCube) satellite microwave sensors over precipitation systems. RainCube senses the atmosphere using a Ka-band nadir-pointing radar, and TEMPEST-D senses using multifrequency millimeter-wave radiometers. Nine precipitation systems were used in this cross-validation study. Occurring over nearly a two-year period, these storms were scattered over many regions of the world from the Tropics to the midlatitudes. A shift correction algorithm was developed to remove the uncertainty between the two sensors’ observations due to the time difference and the storm motion between the two instrument overpasses. Correlation coefficients were calculated between self-normalized, inverted RainCube cumulative reflectivity and self-normalized TEMPEST-D brightness’ temperatures. As expected, these correlation coefficients are consistently higher for the four TEMPEST-D high-frequency channels than for the single low-frequency channel. The shift correction algorithm improved the average correlation coefficient by 19% for the four high-frequency channels and by 58% for the single low-frequency channel. The average correlation coefficient after shift correction is 0.76 for TEMPEST-Ds four high-frequency channels and 0.54 for the single low-frequency channel. The comparisons demonstrated high consistency between the TEMPEST-D and RainCube observations, even though the two microwave sensors’ fundamental physics is different; one is passive, and the other is active.

A statistical analysis of simultaneous observations of more than 800 hailstorms over the continental United States performed by the Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR) and the ground-based Next Generation Weather Radar (NEXRAD) network has been carried out. Several distinctive features of DPR measurements of hail-bearing columns, potentially exploitable by hydrometeor classification algorithms, are identified. In particular, the height and the strength of the Ka-band reflectivity peak show a strong relationship with the hail shaft area within the instrument field of view (FOV). Signatures of multiple scattering (MS) at the Ka band are observed for a range of rimed particles, including but not exclusively for hail. MS amplifies uncertainty in the effective Ka reflectivity estimate and has a negative impact on the accuracy of dual-frequency rainfall retrievals at the ground. The hydrometeor composition of convective cells presents a large inhomogeneity within the DPR FOV. Strong nonuniform beamfilling (NUBF) introduces large ambiguities in the attenuation correction at Ku and Ka bands, which additionally hamper quantitative retrievals. The effective detection of profiles affected by MS is a very challenging task, since the inhomogeneity within the DPR FOV may result in measurements that look remarkably like MS signatures. The shape of the DPR reflectivity profiles is the result of the complex interplay between the scattering properties of the different hydrometeors, NUBF, and MS effects, which significantly reduces the ability of the DPR system to detect hail at the ground.

By comparing the observations of heavy storms performed by the Dual-frequency Precipitation Radar (DPR) and the corresponding hydrometeor classification based on ground-based polarimetric measurements of the Next Generation Weather Radar (NEXRAD) we showed that the DPR measurements are heavily affected by non-uniform beam filling (NUBF). The presence of heavily rimed particles within the instrument field of view generates significant signal enhancements at the Ka band caused by multiple scattering (MS). MS and NUBF introduce large ambiguities in the estimate of effective reflectivity below the freezing level (FL), especially at Ka band, which strongly reduces DPR capabilities for detecting hail at the ground.

PLANT (Polarimetric-interferometric Lab and Analysis Tools) is a new collection of software tools developed at the Jet Propulsion Laboratory to support processing and analysis of Synthetic Aperture Radar (SAR) data for ecosystem and land-cover/land-use change science and applications. PLANT inherits code components from the Interferometric Scientific Computing Environment (ISCE) to generate high-resolution, coregistered polarimetric-interferometric SLC stacks from Level-0/1 data for a variety of airborne and spaceborne sensors. The goal is to provide the ecosystem and land-cover/land-use change communities with rigorous and efficient tools to perform multi-temporal, polarimetric and tomographic analyses in order to generate calibrated, geocoded and mosaicked Level-2 and Level-3 products (e.g., maps of above-ground biomass and forest disturbance). In this paper we introduce the capabilities of PLANT and report first results obtained with the tools developed up to date.