Spaceborne radars offer a unique three‐dimensional view of the atmospheric components of the Earth's hydrological cycle. Existing and planned spaceborne radar missions provide cloud and precipitation information over the oceans and land difficult to access in remote areas. A careful look into their measurement capabilities indicates considerable gaps that hinder our ability to detect and probe key cloud and precipitation processes. The international community is currently debating how the next generation of spaceborne radars shall enhance current capabilities and address remaining gaps. Part of the discussion is focused on how to best take advantage of recent advancements in radar and space platform technologies while addressing outstanding limitations. First, the observing capabilities and measurement highlights of existing and planned spaceborne radar missions including TRMM, CloudSat, GPM, RainCube, and EarthCARE are reviewed. Then, the limitations of current spaceborne observing systems, with respect to observations of low‐level clouds, midlatitude and high‐latitude precipitation, and convective motions, are thoroughly analyzed. Finally, the review proposes potential solutions and future research avenues to be explored. Promising paths forward include collecting observations across a gamut of frequency bands tailored to specific scientific objectives, collecting observations using mixtures of pulse lengths to overcome trade‐offs in sensitivity and resolution, and flying constellations of miniaturized radars to capture rapidly evolving weather phenomena. This work aims to increase the awareness about existing limitations and gaps in spaceborne radar measurements and to increase the level of engagement of the international community in the discussions for the next generation of spaceborne radar systems.

NASA Precipitation Measurement Mission observations are used to evaluate the diurnal cycle of precipitation from three CMIP6 models (NCAR-CESM2, CNRM-CM6.1, CNRM-ESM2.1) and the ERA5 reanalysis. NASA’s global-gridded IMERG product, which combines spaceborne microwave radiometer, infrared sensor, and ground-based gauge measurements, provides high-spatiotemporal-resolution (0.1° and half-hourly) estimates that are suitable for evaluating the diurnal cycle in models, as determined against the ground-based radar network over the conterminous United States. IMERG estimates are coarsened to the spatial and hourly resolution of the state-of-the-art CMIP6 and ERA5 products, and their diurnal cycles are compared across multiple decades of June–August in the 60°N–60°S domain (IMERG and ERA5: 2000–19; NCAR and CNRM: 1979–2008). Low-precipitation regions (and weak-amplitude regions when analyzing the diurnal phase) are excluded from analyses so as to assess only robust diurnal signals. Observations identify greater diurnal amplitudes over land (26%–134% of the precipitation mean; 5th–95th percentile) than over ocean (14%–66%). ERA5, NCAR, and CNRM underestimate amplitudes over ocean, and ERA5 overestimates over land. IMERG observes a distinct diurnal cycle only in certain regions, with precipitation peaking broadly between 1400 and 2100 LST over land (2100–0600 LST over mountainous and varying-terrain regions) and 0000 and 1200 LST over ocean. The simulated diurnal cycle is unrealistically early when compared with observations, particularly over land (NCAR-CESM2 AMIP: −1 h; ERA5: −2 h; CNRM-CM6.1 AMIP: −4 h on average) with nocturnal maxima not well represented over mountainous regions. Furthermore, ERA5’s representation of the diurnal cycle is too simplified, with less interannual variability in the time of maximum relative to observations over many regions.

The intrinsic small spatial scales and low-reflectivity structure of oceanic warm precipitating clouds suggest that millimeter spaceborne radars are best suited to providing quantitative estimates of cloud and rain liquid water paths (LWPs). This assertion is based on their smaller horizontal footprint; high sensitivities; and a wide dynamic range of path-integrated attenuations associated with warm-rain cells across the millimeter wavelength spectrum, with diverse spectral responses to rain and cloud partitioning. State-of-the-art single-frequency radar profiling algorithms of warm rain seem to be inadequate because of their dependence on uncertain assumptions about the rain–cloud partitioning and because of the rain microphysics. Here, high-resolution cloud-resolving model simulations for the Rain in Cumulus over the Ocean field study and a spaceborne forward radar simulator are exploited to assess the potential of existing and future spaceborne radar systems for quantitative warm-rain microphysical retrievals. Specifically, the detrimental effects of nonuniform beam filling on estimates of path-integrated attenuation (PIA), the added value of brightness temperature (T_B) derived adopting radiometric radar modes, and the performances of multifrequency PIA and/or T_B combinations when retrieving liquid water paths partitioned into cloud (c-LWPs) and rain (r-LWPs) are assessed. Results show that (1) Ka- and W-band T_B values add useful constraints and are effective at lower LWPs than the same-frequency PIAs; (2) matched-beam combined T_B values and PIAs from single-frequency or multifrequency radars can significantly narrow down uncertainties in retrieved cloud and rain liquid water paths; and (3) the configuration including PIAs, T_B values and near-surface reflectivities for the Ka-band–W-band pairs in our synthetic retrieval can achieve an RMSE of better than 30 % for c-LWPs and r-LWPs exceeding 100 g m⁻².

The Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation product derived from the Global Precipitation Measurement (GPM) constellation offers a unique opportunity of observing the diurnal cycle of precipitation in the latitudinal band ${60}^{°}$ N–S at unprecedented ${0.1}^{°}\times {0.1}^{°}$ and half-hour resolution. The diurnal cycles of occurrence, intensity and accumulation are determined using four years of data at $2^{°}\times 2^{°}$ resolution; this study focusses on summertime months when the diurnal cycle shows stronger features. Harmonics are fitted to the diurnal cycle using a non-linear least squares method weighted by random errors. Results suggest that mean-to-peak amplitudes for the diurnal cycles of occurrence and accumulation are greater over land (generally larger than 25% of the diurnal mean), where the diurnal harmonic dominates and peaks at ~16–24 LST, than over ocean (generally smaller than 25%), where the diurnal and semi-diurnal harmonics contribute comparably. Over ocean, the diurnal harmonic peaks at ~0–10 LST (~8–15 LST) over open waters (coastal waters). For intensity, amplitudes of the diurnal and semi-diurnal harmonics are generally comparable everywhere (~15–35%) with the diurnal harmonic peaking at ~20–4 LST (~3–12 LST) over land (ocean), and the semi-diurnal harmonic maximises at ~5–8 LST and 17–20 LST. The diurnal cycle of accumulation is dictated by occurrence as opposed to intensity.

NASA’s multisatellite precipitation product from the Global Precipitation Measurement (GPM) mission, the Integrated Multi-satellitE Retrievals for GPM (IMERG) product, is validated over tropical and high-latitude oceans from June 2014 to August 2021. This oceanic study uses the GPM Validation Network’s island-based radars to assess IMERG when the GPM Core Observatory’s Microwave Imager (GMI) observes precipitation at these sites (i.e., IMERG-GMI). Error tracing from the Level 3 (gridded) IMERG V06B product back through to the input Level 2 (satellite footprint) Goddard Profiling Algorithm GMI V05 climate (GPROF-CLIM) product quantifies the errors separately associated with each step in the gridding and calibration of the estimates from GPROF-CLIM to IMERG-GMI. Mean relative bias results indicate that IMERG-GMI V06B overestimates Alaskan high-latitude oceanic precipitation by +147% and tropical oceanic precipitation by +12% with respect to surface radars. GPROF-CLIM V05 overestimates Alaskan oceanic precipitation by +15%, showing that the IMERG algorithm’s calibration adjustments to the input GPROF-CLIM precipitation estimates increase the mean relative bias in this region. In contrast, IMERG adjustments are minimal over tropical waters with GPROF-CLIM overestimating oceanic precipitation by +14%. This study discovered that the IMERG V06B gridding process incorrectly geolocated GPROF-CLIM V05 precipitation estimates by 0.1° eastward in the latitude band 75°N–75°S, which has been rectified in the IMERG V07 algorithm. Correcting for the geolocation error in IMERG-GMI V06B improved oceanic statistics, with improvements greater in tropical waters than Alaskan waters. This error tracing approach enables a high-precision diagnosis of how different IMERG algorithm steps contribute to and mitigate errors, demonstrating the importance of collaboration between evaluation studies and algorithm developers. Significance Statement: Evaluation of IMERG’s oceanic performance is very limited to date. This study uses the GPM Validation Network to conduct the first extensive assessment of IMERG V06B at its native resolution over both high-latitude and tropical oceans, and traces errors in IMERG-GMI back through to the input GPROF-CLIM GMI product. IMERG-GMI overestimates tropical oceanic precipitation (+12%) and strongly overestimates Alaskan oceanic precipitation (+147%) with respect to the island-based radars studied. IMERG’s GMI estimates are assessed as these should be the optimal estimates within the multisatellite product due to the GMI’s status as calibrator of the GPM passive microwave constellation.

This work documents a rain case dominated by evaporation which occurred at the Atmospheric Radiation Measurement site in Oklahoma on 15 September 2011. A recently developed algorithm, applied to radar Doppler spectra measured at K_a and W band, provides the vertical evolution of binned drop size distributions (DSDs) and of the vertical wind. Such retrieved quantities are used in connection with relative humidity (RH) profiles to derive evaporation rates and atmospheric cooling rates. In addition, in regions of stationarity and of light rain, when other microphysical processes are negligible, the presented case study suggests the possibility of retrieving RH profiles from the vertical evolution of the drop size distributions. The key is to characterize the gradient of the rain mass flux between successive levels. Such signal is particularly weak and can be enhanced thanks to a substantial averaging of the retrieved DSD over approximately 5 min and 250 m (eight range gates). The derived profile agrees with the retrieval from coincident Raman lidar observations within a 10% RH difference. These results suggest that other rain microphysical processes could be studied by combining the radar‐based DSD retrieval with ancillary RH observations.