The WIVERN (WInd VElocity Radar Nephoscope) mission, currently under the Phase-0 of the ESA Earth Explorer program, promises to provide new insight in the coupling between winds and microphysics by globally observing, for the first time, vertical profiles of horizontal winds in cloudy areas. The objective of this work is to explore the potential of the WIVERN conically scanning Doppler 94GHz radar for filling the wind observation gap inside tropical cyclones. To this aim, realistic WIVERN notional observations of TCs are produced by combining the CloudSat 94GHz radar reflectivity observations from 2007 to 2009 with ECMWF co-located winds. Despite the short wavelength of the radar (3mm), which causes strong attenuation in presence of large amount of liquid hydrometeors, the system can profile most of the tropical cyclones, particularly the cloudy areas above the freezing level and the precipitating stratiform regions. The statistical analysis of the results shows that, (i) because of its lower sensitivity, a nadir pointing WIVERN would detect 80% of the clouds (60% of winds with 3m/s accuracy) observed by CloudSat, (ii) but thanks to its scanning capability, WIVERN would actually provide 52 times more observations of clouds (40~times more observations of horizontal winds) than CloudSat in TCs, (iii) this corresponds to more than 400 (300) million observations of clouds (accurate winds) every year. Such observations could be used in data assimilation models in order to improve numerical weather prediction and by modellers in order to shed light on the physical processes underpinning the evolution of tropical cyclones.

Surface snowfall rate estimates from the Global Precipitation Measurement (GPM) mission’s core satellite sensors and CloudSat radar are compared to those from the Multi-Radar Multi-Sensor (MRMS) radar composite product generated over the continental United States (CONUS). The considered algorithms include: Dual-Frequency Precipitation Radar (DPR) product and its single frequency counterparts (Ka- and Ku-only); the combined DPR and multifrequency microwave imager (CORRA) product; the CloudSat SnowProfile product (2C-SNOW-PROFILE); two passive microwave products i.e. the Goddard PROFiling algorithm (GPROF) and the Snow retrievaL ALgorithm fOr gMi (SLALOM). The spaceborne and ground-based snowfall products are collocated spatially and temporally and compared at the spatial resolution of spaceborne instruments over the period spanning from January 2016 to March 2020 (4 winters). Detection capabilities of the sensors is assessed in terms of the most commonly used forecast metrices (Probability of Detection, False Alarm Ratio, etc.) whereas precision of the products is quantified by the mean error (ME) and root-mean-square-error (RMSE). 2C-SNOW product agrees with MRMS by far better than any other product. Passive microwave algorithms tend to detect more precipitation events than the DPR and CORRA retrievals, but they also trigger more false alarms. Due to limited sensitivity, DPR detects only approx. 30% of the snow events. All the retrievals underestimate snowfall rates, for the detected snowstorms they produce approximately only a half of the precipitation reported by MRMS. Large discrepancies (RMSE from 0.7 to 2.5 mm/h) between spaceborne and ground-based snowfall rate estimates is the result of limitations of both systems and complex ice scattering properties. The MRMS product is based on a power law relation and it has difficulties in detecting precipitation at far ranges; the DPR system is affected by low sensitivity while the GPM Microwave Imager (GMI) measurements are affected by the confounding effect of the background surface emissivity for snow-covered surfaces and of the emission of supercooled liquid droplet layers.