We present the first-light airborne measurements of a new W-band atmospheric radar, part of the CloudCube instrument. CloudCube is a multifrequency, ultra-compact, low-cost, modular radar system for vertical profiling of clouds, convection, and precipitation structures and dynamics from space. The instrument employs a novel radar architecture that directly upconverts the baseband signal to the RF band, eliminating the need for intermediate frequencies or further multiplication schemes. This architecture, combined with pulse compression techniques, offers a straightforward and effective solution while achieving the necessary performance and robustness for airborne or spaceborne radar instruments. The W-band channel was installed and operated aboard NASA’s DC-8 Airborne Laboratory, completing two test flights over the coast of California and operated as a technology demonstration during science flights as part of the CPEX field deployment. A detailed pulse compression validation and comparison of reflectivity profiles across multiple frequency bands demonstrate the instrument’s performance for atmospheric profiling.

This article presents a novel method to retrieve spectral widths from measurements of spaceborne Doppler profiling radars (SDPRs) operating in low-Earth orbit. The proposedExplicit Sy-Tanelli (ExpliSyT)approach is based on a formulation of the Doppler broadening in terms of the distributions of reflectivity and velocity in the footprint. This formulation makes it possible to estimate the broadening and remove it from the spectral-width measurement. Two implementations of the ExpliSyT correction are described: 1) a numerical implementation that uses Wiener’s deconvolution and 2) an analytical implementation based on Taylor expansions of the radar observations. The ExpliSyT corrections enable retrievals of higher-order Doppler information, such as posterior power spectra. Results are shown for simulations of the radar of the Earth Cloud Aerosol Radiation Explorer (EarthCARE developed by the European and Japanese space agencies) and a Displaced Phase Center Antenna (DPCA) configuration, for which the ExpliSyT corrections perform significantly better. A novelcanonical Doppler resampling diagramis introduced to explain the correlation between nonuniform beamfilling (NUBF) and spectral broadening. This tool shows that: 1) the NUBF of reflectivity modulates the spacecraft-induced spectral broadening and 2) the NUBF of velocity can either broaden orshrinkthe Doppler spectrum. This spectral shrinkage, although important, is not typically thought of when dealing with spaceborne Doppler radars. The ability to estimate spectral widths accurately from space paves the way for future microphysical and dynamical characterizations of hydrometeors in shear or turbulence.

NASA recently chose the Investigation into Convective Updrafts (InCUs) proposal as the next Earth Ventures program mission. INCUS will use a convoy of three identical Ka-band radars measuring radar reflectivity within their common swath to infer the characteristics of any convective updrafts that they observe. We summarize the theoretical basis for this approach, with justification from ground-based zenith profiler data as well as sensitivity analyses of convection-permitting simulations. We then describe and quantify the performance of the approach to detect updrafts from the radar observations. Finally, we illustrate the expected performance of retrievals of vertical transport, and evaluate their ability to meet the objectives of the INCUS mission. How this observation strategy can be adapted to miniaturized passive mm-wave radiometers is also discussed.

This is the third of three papers that quantify the high added value of frequent satellite microwave observations of the atmosphere (with a “refresh” time on the order of one minute) to capture the dynamics of weather systems. Recent advances in small-satellite and microwave miniaturization, such as the “Temporal Experiment for Storms and Tropical systems” (TempEST) millimeter-wave radiometer developed at the Jet Propulsion Laboratory, are paving the way for the design of convoys of spaceborne radars that can directly observe the evolution of severe weather at very fine temporal scales. The analyses presented here are to establish the relation between passive microwave observations and their change in time and the underlying cloud variables and processes, and to evaluate the sensitivity to the different physical and instrument parameters. In this third part, simulations are used to demonstrate and quantify the direct sensitivity of a time sequence of mm-wave radiances to the vertical structure of the vertical updrafts in convective storms. It is demonstrated that the brightness temperatures from a pair of low-Earth-orbit radiometers maintaining a separation of one or two minutes can indeed be used to retrieve the column-maximum magnitude of the vertical wind as well as the height of the maximum. While such passive retrievals do not provide the vertical detail that a pair of radars would produce, the radiometers have a vast swath and therefore enable the observation of an entire storm. The application is therefore quite different from the case of a convoy of radars: rather than compiling radar statistics over multiple years, the radiometer-convoy measurements can be used to analyze every storm that is observed.

This article presents a new method to retrieve dynamic information from spaceborne Doppler radar observations. The method is based on a Complex Convolution Doppler Resampling (CConDoR) formulation, which links the spaceborne pulse-pair correlation measurements to high-resolution pulse-pair products that are not affected by the spacecraft motion. The CConDoR formalism allows to easily simulate Doppler products (forward simulations) via convolution products. The CConDoR representation enables also retrievals by complex deconvolution. This approach is illustrated using a Wiener deconvolution algorithm. Results are shown for simulations of EarthCARE’s (Earth Cloud Aerosol Radiation Explorer developed by the European and Japanese space agencies) radar. The proposed corrections improve the accuracy of the measured mean velocities (by correcting for non-uniform beam-filling biases) and spectral widths (by correcting for the spectral broadening, which to the best of the Authors’ knowledge is novel).

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 paper presents some of the first nearly simultaneous observations between TEMPEST-D and RainCube, two CubeSat missions supported by NASA for on-orbit validation of technology for studying the Earth's atmosphere. This paper presents simultaneous observations by a Ka-band radar and multi-frequency millimeter-wave radiometers over precipitation systems, in three widely dispersed locations over the globe. The first storm was near Mexico's Pacific coast, whereas the second storm was over the South Pacific Ocean near the Solomon Islands, and the third storm was near Houston, Texas, USA. The comparisons showed good physical consistency between the TEMPEST-D and RainCube observations.

Pulse compression has enabled a new generation of low-cost and compact spaceborne weather radar systems. To successfully utilize pulse compression techniques for cloud and precipitation applications, the effects of Doppler-range migration must be considered during the design and operation of the radar. Pulse compression for spaceborne weather applications introduces additional interdependence between the radar system and the operations when compared with traditional pulsed radar systems, primarily as a result of the large platform velocities. Pulse compression signals for weather radar can be simulated with high fidelity to predict and optimize the radar's performance. In this article, we evaluate the pulse compression performance of RainCube, a Ka-band precipitation radar in a CubeSat, through analysis and comparison of observations and radar simulations. Through these comparisons, design and operational considerations for pulse compression weather radar are discussed. This work shows that the optimal pointing angle for RainCube to achieve the finest vertical resolution is not at nadir, but when pointing forward approximately 2.25°, in the direction of the spacecraft's orbit.

Previous work has shown that the relatively large resolution volume of spaceborne precipitation radars can affect precipitation measurements in several ways. During the Olympic Mountains Experiment (OLYMPEX), Ku- and Ka-band airborne radar measurements of precipitation were obtained during two Global Precipitation Measurement (GPM) overpasses, one case over land and one case over ocean. The authors compare the GPM-measured radar quantities with the same quantities inferred from the higher spatial resolution airborne radar data. Differences include smaller maximum reflectivity and path attenuation and more significant surface clutter in mountainous areas for GPM.

The recent successful space-borne demonstration of a miniaturized CubeSat precipitation radar is highlighted. The low cost of such a radar, together with the availability of small satellite, platforms to carry it, now make it feasible to consider employing a more distributed approach to observe important atmospheric processes that relate to precipitation. An approach to quantify the transport of water and air by deep convection is described based on a clustering of small radar satellites providing measurements seconds apart. This strategy now adds time as a new dimension for observing such processes. A mission concept, referred to as D-train, comprised of a train of three satellites 30, 90, and 120 s apart is described, and the expected performance of it for providing measures of convective transport is examined based on a large ensemble of simulations of convection with an advanced cloud-resolving model.

This is the first of two papers that quantify the high added value of frequent 3-D radar observations of the atmosphere to capture the dynamics of weather systems. Recent advances in small-satellite and radar technologies, such as the “Radar in Cubesat” developed at the Jet Propulsion Laboratory, are paving the way for the design of convoys of spaceborne radars that can directly observe the evolution of severe weather at very fine temporal scales. The analyses presented here are to establish the relation between such observations to the underlying cloud variables and processes, and to quantify the sensitivity to the different physical and instrument parameters. In this first part, a robust algorithm is proposed to estimate the horizontal advection from successive radar reflectivity measurements, and use it to compute total time derivatives$d_{t} Z$of the observed radar reflectivity factors$Z$. As illustrated using Next-Generation Radar measurements in a blizzard coupled with an atmospheric river in California, the maps of$d_{t} Z$reveal features about locations of sources and sinks of condensed water, which are, otherwise, not visible in the maps of$Z$alone. Using numerical simulations of the blizzard and a radiative-transfer model to forward calculate the corresponding reflectivity factors$Z$in the S-band, we show the robust correlation between$d_{t} Z$and the moistening of the troposphere.

This is the second of two papers that quantify the high added value of frequent 3-D radar observations of the atmosphere to capture the dynamics of weather systems. Recent advances in small-satellite and radar technologies, such as the “Radar in Cubesat” developed at the Jet Propulsion Laboratory, are paving the way for the design of convoys of spaceborne radars that can directly observe the evolution of severe weather at very fine temporal scales. The analyses presented here are to establish the relation between such observations to the underlying cloud variables and processes, and to quantify the sensitivity to the different physical and instrument parameters. In this paper, we quantify the uncertainty in the relation between the measured radar reflectivities$Z$and their time derivatives$d_{t} Z$, on one hand, and the underlying rate of change of the condensed-water mass$M$, and fluxes of dry and moist air in convection, on the other hand. The uncertainties are due to the variability of the atmospheric parameters as well as the constraints of an observation strategy that would use pairs of spaceborne instruments. We specifically analyze the sensitivities for pairs of satellites, each carrying a Ka-band profiling radar. Our simulations show that, with a convoy of two spacecraft separated by ~90 s, each with a pointing accuracy of ~0.025° in rms error, a sensitivity of 17 dBZ and a precision of 1 dBZ, the proposed observation strategy would capture more than 70% of the tropical convection between 5 and 10 km of altitude and resolve the air-mass and condensed-water fluxes.

Numerical climate and weather models depend on measurements from space-borne satellites to complete model validation and improvements. Precipitation profiling capabilities are currently limited to a few instruments deployed in Low Earth Orbit (LEO), which cannot provide the temporal resolution necessary to observe the evolution of short time-scale weather phenomena and improve numerical weather prediction models. A constellation of precipitation profiling instruments in LEO would provide this essential capability, but the cost and timeframe of typical satellite platforms and instruments make this solution prohibitive. A new radar instrument architecture that is compatible with low-cost satellite platforms, such as CubeSats and SmallSats, has been designed at JPL that enables constellation missions, which could revolutionize climate science and weather forecasting.

This paper presents a method for filtering the random noise that affects spaceborne Doppler measurements of atmospheric velocities. The proposed method hinges on adaptive low-pass filters that apply to the measured pulse-pair correlation function. The parameters of the filters are found by optimizing the statistics of the velocity residue of the filter. The method is illustrated by simulations of the cloud-profiling radar of the future Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) mission of the European Space Agency and the Japanese Space Exploration Agency. These simulations, which do not include strong convection, show the higher performance of the filters when compared with the traditional increase of the along-track integration length. The results obtained with the filters show that velocity accuracies of 0.48, 0.42, and 0.39 m · s-1are achievable at PRF = {6.1, 7, 7.5} kHz, respectively, while preserving the initial 500-m sampling of the measured EarthCARE data. These results also show the potential benefits of avoiding excessive alongtrack integration, for postprocessing tasks such as dealiasing or the retrieval of the vertical distribution of the atmospheric velocity (e.g., longer than 5 km for cases consistent with the climatologies represented in this data set).

This paper describes the expected performance of the Doppler cloud profiling radar being built for the Earth Cloud Aerosols Radiation Explorer (EarthCARE) mission of the Japanese Aerospace Exploration Agency and the European Space Agency. Spaceborne Doppler radar data are simulated starting from high-resolution Doppler measurements provided by ground-based and airborne Doppler radars, ranging from nonconvective to moderately convective scenarios. The method hinges upon spatial and spectral resampling to consider the specificities of the spaceborne configuration. An error analysis of the resulting Doppler product is conducted to address aliasing and nonuniform beam-filling (NUBF) problems. A perturbation analysis is applied to explore the latter problem and allow for a self-standing systematic correction of NUBF using merely the received reflectivity factor and mean Doppler velocities as measured by the instrument. The results of our simulations show that, at a horizontal integration of 1 km, after proper de-aliasing and NUBF correction, the radar will typically yield a velocity accuracy in the order of 1.3 m·s-1over intertropical regions where the pulse-repetition frequency (PRF)=6.1 kHz, of 0.8 m·s-1where the cloud-profiling radar (CPR) operates at PRF=7 kHz, and, of 0.7 m·s-1over high latitudes where the CPR of EarthCARE will operate at PRF=7.5 kHz.