RainCube (Radar in a CubeSat) and TEMPEST-D (Temporal Experiment for Storms and Tropical Systems - Demonstration) demonstrated in 2018 that deployment of active and passive microwave sensors to monitor storms and precipitation from space is possible on platforms as small as 6U CubeSats. Despite their implementation as high-risk technology demonstrations, with very low budgets compared to their predecessors, they both survived more than two years in orbit (well beyond their commitments). These demonstrations opened the gates to satisfy several long-standing unmet needs by the scientific and operational weather and climate communities. Among them is the need to observe the evolution of the vertical structure of convective storms in the Tropics at the temporal scales relevant to convective processes (i.e., tens of seconds to few minutes) in order to advance our understanding of convective processes and the environmental conditions behind them via modeling and analysis. The INCUS (Investigation of Convective Updrafts) mission concept aims at addressing this need by deploying 3 small satellites each carrying an augmented version of the RainCube radar. One of the 3 small satellites also includes a millimeter wave radiometer inherited from TEMPEST-D. In this presentation we present the status of the INCUS project at the end of Phase A.
RainCube (Radar in a CubeSat) is a technology demonstration mission to enable Ka-band precipitation radar technologies on a low-cost, quick-turnaround platform. The 6U CubeSat, features a Ka-band nadir pointing precipitation radar with a half-meter parabolic antenna. RainCube first observed rainfall over Mexico in August 2018 and in the following months captured the distinct structures of a variety of storms as well as characteristic signatures of Earth’s surface essential to diagnose pointing and calibration. In this presentation we will focus on the characteristics of the observed scenes, specifically to convey the potential, as well as the limitations, of a radar of this class in addressing the goal of observing weather processes from space.
RainCube (Radar in a CubeSat) is a technology demonstration mission to enable Ka-band precipitation radar technologies on a low-cost, quick-turnaround platform. The 6U CubeSat, currently in orbit, features a radar payload built by the Jet Propulsion Laboratory and a spacecraft bus and operations provided by Tyvak Nano-Satellite Systems. Following the deployment of the half-meter parabolic antenna, the radar first observed rainfall over Mexico. The mission continues to operate and has met all requirements through repeated observations of precipitation in the atmosphere. RainCube is funded through the Science Mission Directorate’s Research Opportunities in Space and Earth Science 2015 In-Space Validation of Earth Science Technologies solicitation. We report on the first radar observations of precipitation.
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 evo- lution of short time-scale weather phenomena and improve numerical weather prediction models. A constellation of cloud- and precipitation-profiling instruments in LEO would provide this essential capability, but the cost and timeframe of typical satellite platforms and instruments constitute a possibly prohibitive challenge. A new radar instrument architecture that is compatible with low-cost satellite platforms, such as CubeSats and SmallSats, has been designed at JPL. Its small size, moderate mass and low power requirement enable constellation missions, which will vastly expand our ability to observe weather systems and their dynamics and thermodynamics at sub-diurnal time scales down to the temporal resolutions required to observe developing convection. In turn, this expanded observational ability can revolutionize weather now-casting and medium-range forecasting, and enable crucial model improvements to improve climate predictions.
In the past few years, we have demonstrated how the surface return measured by the active
instruments onboard CloudSat and CALIPSO could be used to retrieve the optical depth and backscatter
phase function (lidar ratio) of aerosols and ice clouds. This methodology lead to the development of a
data fusion product publicly available at the ICARE archive center using the Synergized Optical Depth of
Aerosols and Ice Clouds (SODA & ICE) algorithm1. This algorithm, also allowing to derive ocean surface
wind speed, has been extended to include dense cloud surface return to analyze aerosol and cloud
properties above such clouds.
This low level data fusion of CALIPSO and CloudSat ocean surface echoes has been used by several
researchers to explore different research paths. Among them, we can cite:
• A new characterization of the lidar ratio of cirrus clouds2
• The analysis of the precipitable water and development of a new Millimeter-Wave Propagation
Model for the W-Band observations (EMPIRIMA3)
• The analysis of the lidar ratio of sea-spray aerosols4, and of Aerosol multilayer lidar ratio and
extinction5
• A contribution to the retrieval of the subsurface particulate backscatter coefficients of
phytoplankton particles6
In this paper, we present the main features of SODA & ICE, summarizing some of the results obtained.
This low level data fusion of CALIPSO and CloudSat ocean surface echoes has been used by several
researchers to explore different research paths. Among them, we can cite:
A new characterization of the lidar ratio of cirrus clouds2
The analysis of the precipitable water and development of a new Millimeter-Wave Propagation
Model for the W-Band observations (EMPIRIMA3)
The analysis of the lidar ratio of sea-spray aerosols4, and of Aerosol multilayer lidar ratio and
extinction5
A contribution to the retrieval of the subsurface particulate backscatter coefficients of
phytoplankton particles6
In this paper, we present the main features of SODA & ICE, summarizing some of the results obtained.
Toshio Iguchi, Shinta Seto, Robert Meneghini, Naofumi Yoshida, Jun Awaka, Takuji Kubota, Toshiaki Kozu, V. Chandra, Minda Le, Liang Liao, Simone Tanelli, Steve Durden
This paper describes the planned level 2 algorithm that retrieves precipitation profiles from data to be obtained by the
Dual-frequency Precipitation Radar (DPR) on the core satellite of the Global Precipitation Measurement (GPM) mission.
The general idea behind the algorithms is to determine general characteristics of the precipitation, correct for attenuation
and estimate profiles of the precipitation water content, rainfall rate and, when dual-wavelength data are available,
information on the particle size distributions in rain and snow. It is particularly important that dual-wavelength data will
provide better estimates of rainfall and snowfall rates than the TRMM PR data by using the particle size information and
the capability of estimating the height at which the precipitation transitions from solid to liquid.
The NASA Earth Observing System Simulators Suite (NEOS3) is a modular framework of forward simulations tools for remote sensing of Earth’s Atmosphere from space. It was initiated as the Instrument Simulator Suite for Atmospheric
Remote Sensing (ISSARS) under the NASA Advanced Information Systems Technology (AIST) program of the Earth
Science Technology Office (ESTO) to enable science users to perform simulations based on advanced atmospheric and
simple land surface models, and to rapidly integrate in a broad framework any experimental or innovative tools that they
may have developed in this context. The name was changed to NEOS3 when the project was expanded to include more advanced modeling tools for the surface contributions, accounting for scattering and emission properties of layered
surface (e.g., soil moisture, vegetation, snow and ice, subsurface layers). NEOS3 relies on a web-based graphic user
interface, and a three-stage processing strategy to generate simulated measurements. The user has full control over a
wide range of customizations both in terms of a priori assumptions and in terms of specific solvers or models used to
calculate the measured signals.This presentation will demonstrate the general architecture, the configuration procedures
and illustrate some sample products and the fundamental interface requirements for modules candidate for integration.
The Ku-/Ka-band, Doppler, scanning, polarimetric airborne radar, known as the Airborne Dual-Frequency Precipitation
Radar (APR-2) has been collecting data since 2001 in support of many spaceborne instruments and missions aiming at
the observation of clouds and precipitation (e.g., TRMM, AMSR-E, GPM, CloudSat, ACE). The APR-2 suite of
processing and retrieval algorithms (ASPRA) produces Level 1 (L1) products, microphysical classification and retrievals,
and wind intensity estimates. ASPRA was also generalized to operate on an arbitrary set of radar configuration
parameters to study the expected performance of multi-frequency spaceborne cloud and precipitation radars such as the
GPM DPR (Global Precipitation Measurement mission, Dual-Frequency Precipitation Radar) and a notional radar for the
Aerosol/Clouds/Ecosystem (ACE) mission.
In this paper we illustrate the unique dataset collected during the Global Precipitation Measurement Cold-season
Precipitation Experiment (GCPEx, US/Canada Jan/Feb 2012). We will focus on the significance of these observations
for the development of algorithms for GPM and ACE, with particular attention to classification and retrievals of frozen
and mixed phase hydrometeors.
In this presentation we will discuss the performance of classification and retrieval algorithms for spaceborne cloud and
precipitation radars such as the Global Precipitation Measurement mission [1] Dual-frequency Precipitation Radar
(GPM/DPR) [2], and notional radar for the Aerosol/Clouds/Ecosystem (ACE) [1] mission and related concepts.
Spaceborne radar measurements are simulated either from Airborne Precipitation Radar 2nd Generation (APR-2, [3]) observations, or from atmospheric model outputs via instrument simulators contained in the NASA Earth Observing
Systems Simulators Suite (NEOS3). Both methods account for the three dimensional nature of the scattering field at resolutions smaller than that of the spaceborne radar under consideration. We will focus on the impact of nonhomogeneities of the field of hydrometeors within the beam. We will discuss also the performance of methods to identify
and mitigate such conditions, and the resulting improvements in retrieval accuracy. The classification and retrieval
algorithms analyzed in this study are those derived from APR-2’s Suite of Processing and Retrieval Algorithms
(ASPRA); here generalized to operate on an arbitrary set of radar configuration parameters to study the expected
performance of spaceborne cloud and precipitation radars. The presentation will highlight which findings extend to other
algorithm families and which ones do not.
KEYWORDS: Radar, Doppler effect, Reflectivity, Calibration, Ku band, Ka band, Detection and tracking algorithms, Algorithm development, Antennas, Data processing
Following the successful Precipitation Radar (PR) of the Tropical Rainfall Measuring Mission1, a new airborne, 14/35 GHz rain profiling radar, known as Airborne Precipitation Radar - 2 (APR-2)2, has been developed as a prototype for an advanced, dual-frequency spaceborne radar for a future spaceborne precipitation measurement mission3. This airborne instrument is capable of making simultaneous measurements of rainfall parameters, including co-pol and cross-pol rain reflectivities and vertical Doppler velocities, at 14 and 35 GHz. Furthermore, it also features several advanced technologies for performance improvement, including real-time data processing, low-sidelobe dual-frequency pulse compression, and dual-frequency scanning antenna.
Since August 2001, APR-2 has been deployed on the NASA P3 and DC8 aircrafts in four experiments including CAMEX-4 and the Wakasa Bay Experiment. Raw radar data are first processed to obtain reflectivity, LDR (linear depolarization ratio), and Doppler velocity measurements. The dataset is then processed iteratively to accurately estimate the true aircraft navigation parameters and to classify the surface return. These intermediate products are then used to refine reflectivity and LDR calibrations (by analyzing clear air ocean surface returns), and to correct Doppler measurements for the aircraft motion. Finally, the melting layer of precipitation is detected and its boundaries and characteristics are identified at the APR-2 range resolution of 30m. The resulting 3D dataset will be used for validation of other airborne and spaceborne instruments, development of multiparametric rain/snow retrieval algorithms and melting layer characterization and statistics. In this paper the processing approach is described in detail together with an overview of the resulting data quality and known issues.
Backscattering enhancement from random hydrometeors should increase
as wavelengths of radars reach millimeter regions. For 95 GHz radars,
the reflectivity of backscattering is expected to increase by 2 dB,
due to multiple scattering including backscattering enhancement, for
water droplets of diameter of 1 mm with a density of 5 x 103 m-3. Previous theoretical studies of backscattering enhancement considered infinitely extending plane waves. In this paper, we expand the theory to spherical waves with a Gaussian antenna pattern, including depolarizing effects. While the differences from the plane wave results are not great when the optical thickness is small, as the latter increases the differences become significant, and essentially depend on the ratio of radar footprint radius to the mean free path of hydrometeors. In this regime, for a radar footprint that is smaller than the mean free path, the backscattering-enhancement reflectivity corresponding to spherical waves is significantly less pronounced than in the case of the plane wave theory. Hence this reduction factor must be taken into account when analyzing radar reflectivity factors for use in remote sensing applications.
KEYWORDS: Doppler effect, Radar, Antennas, Signal to noise ratio, Velocity measurements, Satellites, Turbulence, Monte Carlo methods, Spectral resolution, Atmospheric particles
Knowledge of the global distribution of the vertical velocity of precipitation is important in the study of energy transportation in the atmosphere, the climate and weather. Such knowledge can only be directly acquired with the use of spaceborne Doppler precipitation radars (DPR). Although the high relative speed of the radar with respect to the rainfall particles introduces significant broadening in the Doppler spectrum, recent studies have shown that the average vertical velocity can be measured to acceptable accuracy levels by appropriate selection of radar parameters. Furthermore, methods to correct for specific errors arising from non-uniform beam filling (NUBF) effects and pointing uncertainties have recently been developed. In this paper we will present the results of the trade studies on the performances of a spaceborne Doppler radar with different system parameters configurations. Particular emphases will be placed on the choices of: 1) the PRF vs. antenna size ratio, 2) the observational strategy, 3) the operating frequency; and 4) processing strategy. The results show that accuracies of 1 m/s or better can be achieved with the currently available technology.
Based on the evidence of the correlation between certain differential spectral parameters that can be estimated through attenuation measurements in the 18-22 GHz spectral range and the columnar content of atmospheric water vapor (IWV: Integrated Water Vapor), recently we pointed out that such correlation can be profitably exploited to provide direct estimates of the IWV along vertical Earth-satellite links, showing in particular that at 19 GHz a practically deterministic relationships holds between the IWV and such differential spectral parameters. In this paper we present some new simulation results to show that the parameters can be estimated by means of a 19 GHz CW-FM nadir pointing radar, providing in this way a continuous monitoring of the IWV along vertical atmospheric sections. Differential attenuation measurements are made by exploiting the backscatter from the Earth surface. Simulations, that are based on real vertical profiles of temperature, pressure and water vapor concentration as provided by a large radiosonde dataset, refer to a LEO satellite and to an airborne configuration, indicate the possibility to retrieve the IWV in both cases.
Knowledge of the global distribution of the vertical velocity of precipitation is important for estimating latent heat fluxes, and therefore in the general study of energy transportation in the atmosphere. Such knowledge can only be acquired with the use of spaceborne Doppler precipitation radars. Recent studies have shown that the average vertical velocity can be measured to acceptable accuracy levels by appropriate selection of radar parameters. Furthermore, methods to correct for specific errors arising from Non-Uniform Beam Filling effects and pointing uncertainties have recently been developed. As detailed in the Global Precipitation Mission (GPM) preparatory studies, the use of a dual-frequency precipitation radar allows improved estimation of the main parameters of the hydrometeor size distribution (bulk quantity and one shape parameter). Such parameters, in turn, lead to improved estimates of latent heat fluxes. In this paper we address the performance of a dual- frequency Doppler Precipitation Radar (DDPR) in estimating the latent heat fluxe from the measured rainfall vertical velocity and DSD parameters.
In this paper an in-depth analysis on the performance of the Fourier analysis in estimating the first moment of Doppler spectra of rain signals from a spaceborne radar is presented. Spectral moments estimators based on Fourier analysis (DFT-SME) have been widely used by Doppler weather radars in measuring rainfall velocity and they have been found to be almost optimal for narrow normalized spectral widths (wN). They are also more computationally efficient than the Maximum Likelihood estimators. However, the existing analytical approaches for evaluating the DFT-SME performance have mostly been focused on a limited range of small wN (e.g., wN< 0.1) that are typical of ground based and airborne Doppler weather radars. With the rapid advances in spaceborne radar technologies, the flying of a Doppler precipitation radar in space to acquire global data sets of vertical rainfall velocity has become a real possibility. The objective of this work is to develop a generalized analytical approach by extending it to cover larger values of wN (e.g., wN ~ 0.2) in spaceborne radar applications. In particular, a method has been developed to properly treat the aliasing effects, which have become a significant error source in spaceborne applications. Several DFT-SME algorithms (differing in the adopted strategy for noise handling and the initial estimate of the mean Doppler velocity) have been analyzed with this generalized approach. The analytical results are in excellent agreement with those obtained through simulation. Such encouraging results suggest that the proposed approach is a reliable technique for fast and accurate prediction of DFT-SME performance for spaceborne Doppler weather radars.
In this paper we present a sea surface radar echo spectral analysis technique to correct for the rainfall velocity error caused by radar pointing uncertainty. The correction procedure is quite straightforward when the radar is observing a homogeneous rainfall field. On the other hand, when NUBF occurs and attenuating frequencies are used, additional steps are necessary in order to correctly estimate the antenna pointing direction. This new technique relies on the application of Combined Frequency-Time (CFT) algorithm to correct for uneven attenuation effects on the observed sea surface Doppler spectrum. The performance of this correction technique was evaluated by Monte Carlo simulation of the Doppler precipitation radar backscatter model, and the high-resolution 3D rain fields generated by a cloud resolving numerical model. Our preliminary results show that the antenna pointing induced error can indeed be successfully removed by the proposed technique.
A sampling strategy and a signal processing technique are proposed to overcome Non Uniform Beam Filling (NUBF) errors on mean Doppler velocity measurements made by spaceborne weather radars. Effects of non uniformity of rainfall within the main antenna lobe in terms on the accuracy of standard estimators are first briefly shown, so as to point out that the bias introduced by NUBF on mean Doppler velocity estimates can be greater than the standard deviation of the estimated velocity, and that it depends on the along-track distribution of reflectivity. Then the sampling strategy is described, based on an oversampling of the integrated data in the along-track direction in order to retrieve information about the reflectivity pattern at the sub-beam scale. The proposed processing technique, named Combined Frequency-Time (CFT) technique, exploits the time series of spectra at fixed range to resolve the NUBF induced bias. The results and the evaluation of performances achievable by means of CFT, were obtained by applying a 3D spaceborne Doppler radar simulator to a 3D dataset of reflectivity and mean Doppler velocity measured through the NASA/JPL airborne Doppler radar ARMAR. The radar system considered here is a nadir-looking, Ku band radar with a sufficiently wide antenna. It is shown how the error on mean Doppler velocity estimates can be reduced by means of CFT to the level predicted for such a radar system in the case of uniformly filled resolution volume (UBF).
KEYWORDS: Radar, Doppler effect, Reflectivity, Antennas, Satellites, Particles, Statistical analysis, Meteorology, Signal processing, Signal to noise ratio
This paper studies the performance of a spaceborne precipitation radar in measuring vertical Doppler velocity of rainfall. As far as a downward pointing precipitation radar is concerned, one of the major problems affecting Doppler measurement at the nadir direction arises from the Non-Uniform Beam-Filling effect (NUBF). That is, when significant variation in rain rate is present within the radar IFOV in the along track direction. The Doppler shift caused by the radial component of the horizontal speed of the satellite is weighted differently among the portions of IFOV. The effects of this non-uniform weighting may dominate any other contribution. Under this condition, shape, average value and width of the Doppler spectrum may not be directly correlated with the vertical velocity of the precipitating particles. However, by using an inversion technique which over-samples the radar measurements in the along track direction, we show that the shift due to NUBF can be evaluated, and that the NUBF induced errors on average fall speed can be reduced.
Recent studies pointed out the correlation existing between the differential attenuation measurements, made using frequencies falling around the 22.235 GHz absorption line of water vapor, and the shape of the water vapor profiles. Such evidence induced us to develop a deterministically based profile retrieval procedure that exploits several differential attenuation measurements made at several frequencies around 22.235 GHz. In this paper, after having described the aforementioned deterministic procedure, a transmission system is proposed to obtain the differential attenuation measurements for a quasi-vertical satellite- Earth multifrequency link. Such transmission system is based on a sinusoidal amplitude modulation and a feasibility study about the minimum exploitable signal-to-noise ratio was considered as well. Both the retrieval procedure and the results of the feasibility study on the transmission system are then tested through simulations of multifrequency attenuation measurements. Such simulations are based on an atmospheric propagation model (MPM: Millimeter-wave Propagation Model) and on real radiosonde data providing profiles of temperature, pressure, and water vapor concentration.
An algorithm for storm tracking through weather radar data is presented. It relies on the crosscorrelation principle as in TREC (Tracking Radar Echoes by Correlation) and derived algorithms. The basic idea is to subdivide the radar maps in Cartesian format in a grid of square boxes and to exploit the so called local translation hypothesis. The motion vector is estimated as the space shift such that corresponding boxes at different times exhibit the maximum correlation coefficient. The discussed technique adopts a multiscale, multiresolution, and partially overlapped box grid which adapts to the radar reflectivity pattern. Multiresolution decomposition is performed through 2D wavelet based filtering. Correlation coefficients are calculated taking into account unreliable data (e.g. due to ground clutter or beam shielding) in order to avoid strong undesired motion estimation biases due to the presence of such stationary features. Data are gathered through a C-band multipolarimetric doppler weather radar. Results show that the technique overcomes some problems highlighted by researchers in previous related studies. Comparison with radial velocity maps shows good correlation values; although they may vary depending on the specific event and on the orographic complexity of the considered area, estimated motion fields are consistent with the shift of the pattern determined through simple visual inspection.
In this paper we address the problem of estimating vertical profiles of atmospheric water vapor by means of attenuation measurements simultaneously made at different frequencies along a vertical satellite-ground link. The operating frequencies are those around the spectral absorption lines of water vapor at 22.235 GHz, the number of frequencies depending on the required vertical detail. A simulation is presented of such a system, based on a atmospheric propagation model and on radiosonde data providing true profiles of temperature, pressure and water vapor.
Most satellite measurements of atmosphere related quantities and parameters come from passive instrumentation, that provides huge amounts of data for global scale atmospheric analysis with quite limited spatial resolution. When higher resolution is desired, ground based systems are opportunely exploited. The increasing use of satellites pushes the research towards the realization of systems, based both on spaceborne and ground instrumentation, designed to exploit attenuation measurements at the infrared. The distribution of atmospheric molecular components can be retrieved from such measurements through ad-hoc tomographic processing. In this paper we describe a methodology that allows attenuation measurements at infrared to estimate mean concentrations of atmospheric molecular components along quasi-vertical rectilinear paths. A number of ground passive infrared stations is needed, distributed along a baseline in the area of interest, and spaceborne monochromatic infrared sources. Measurements made along all rectilinear paths defined by each satellite pass above the site, are processed following an ad hoc tomographic inversion technique to provide the 2D vertical distribution of the atmospheric molecular components of interest. Some simulation results are presented to demonstrate the applicability of the cited tomographic technique. Carbon Monoxide has been considered as the molecular test species in the simulations, based on standard atmospheric models.
The general problem of 2D image reconstruction based on a tomographic approach is here explored in a particular case. Exploiting the relationship between microwave attenuation and rainfall intensity in a microwave tomography approach was demonstrated to be a valid possibility for the reconstruction of rainfall fields in limited areas. At each time step, path-integrated attenuation measurements are provided by a set of microwave transmitter-receiver pairs, and two-dimensional Gaussian basis functions are utilized to reconstruct the space-time distribution of the rainfall intensity field. The inversion problem to be solved is highly ill-conditioned, due to the practical (and economical) impossibility to set up an adequate network for performing classical tomography, therefore a global optimization stochastic technique has been developed to obtain valid results in quasi real time, the implemented multiresolution algorithm is explored. Some significant examples are briefly shown in order to demonstrate the speed, precision and flexibility of the technique. Therefore a significant analysis of the typical algorithm output is presented to show how the problem of recognizing the best solution could be faced.
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