EarthCARE Cloud Profiling Radar Research Articles

An evaluation of microphysics in a numerical model using Doppler velocity measured by ground-based radar for application to the EarthCARE satellite

Abstract. The Cloud Profiling Radar (CPR) of the Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) has a new capability to observe the Doppler velocity related to the vertical air motion of the terminal velocity of hydrometeors. The new observation from space will be used to evaluate and improve the model. Before the launch of EarthCARE, we need to develop a methodology for using the CPR data for model evaluations. In this study, we evaluated simulated data by a stretched version of the global non-hydrostatic model over Japan with a ground-based CPR using an instrument design similar to the EarthCARE CPR. We chose two cases with different precipitation events in September 2019 using two cloud microphysics schemes. We introduced the categorization method for evaluating microphysics using Doppler velocity. The results show that the liquid and solid phases of hydrometeors are divided in Doppler velocity, and the model's terminal velocities of rain, snow, and graupel categories can be evaluated with the observation. The results also show that the choice of microphysics scheme has a more significant impact than the dependence on precipitation cases. We discussed the application of the EarthCARE-like simulation results using a satellite simulator.

An intercomparison of EarthCARE cloud, aerosol, and precipitation retrieval products

Abstract. The objective of the Earth Cloud, Aerosol, and Radiation Explorer (EarthCARE) mission is to infer attributes of cloud, aerosol, precipitation, and radiation from observations made by four complementary instruments. This requires the development of single-instrument and multiple-instrument (i.e. synergistic) retrieval algorithms that employ measurements made by one, or more, of EarthCARE's cloud-profiling radar (CPR), atmospheric lidar (ATLID), and multi-spectral imager (MSI); its broadband radiometer (BBR) places the retrieved quantities in the context of the surface–atmosphere radiation budget. To facilitate the development and evaluation of ESA's EarthCARE production model prior to launch, sophisticated instrument simulators were developed to produce realistic synthetic EarthCARE measurements for simulated conditions provided by cloud-resolving models. While acknowledging that the physical and radiative representations of cloud, aerosol, and precipitation in the test scenes are based on numerical models, the opportunity to perform detailed evaluations wherein the “truth” is known provides insights into the performance of EarthCARE's instruments and retrieval algorithms. This level of omniscience will not be available for the evaluation of in-flight EarthCARE retrieval products, even during validation activities coordinated with ground-based and airborne measurements. In this study, we compare EarthCARE retrieval products both statistically across all simulated scenes and from a specific time series from a single scene. For ice clouds, it is shown that retrieved profiles of ice water content and effective particle size made by the ATLID-CPR-MSI cloud, aerosols, and precipitation (ACM-CAP) synergistic algorithm are consistently more accurate than those from its single-instrument counterparts. While liquid clouds are often difficult to detect from satellite-borne sensors, especially for multi-layered clouds, ACM-CAP benefits from combined constraints from lidar backscatter, solar radiances, and radar-path-integrated attenuation but still exhibits non-trivial random error. For precipitation retrievals, the CPR cloud and precipitation product (C-CLD) and ACM-CAP have a similar performance when well-constrained by CPR measurements. The greatest differences are in coverage, with ACM-CAP reporting retrievals in the melting layer, and in heavy precipitation, where CPR signals are dominated by multiple scattering and attenuation. Aerosol retrievals from ATLID compensate for a high degree of measurement noise in a number of ways, with the ATLID extinction, backscatter, and depolarisation (A-EBD) product and ACM-CAP demonstrating similar performance. The multi-spectral imager (MSI) cloud optical properties (M-COP) product performs very well for unambiguous cloud layers. Similarly, the MSI aerosol optical thickness (M-AOT) product performs well when radiances are unaffected by cloud, but both products provide little information about vertical profiles of properties. Finally, a summary of the performance of all retrieval products and their random errors is provided.

Cloud and precipitation microphysical retrievals from the EarthCARE Cloud Profiling Radar: the C-CLD product

Abstract. The Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) satellite mission is a joint endeavour developed by the European Space Agency (ESA) and the Japan Aerospace Exploration Agency (JAXA) and features a 94 GHz Doppler Cloud Profiling Radar. This paper presents the theoretical basis of the cloud and precipitation microphysics (C-CLD) EarthCARE Level 2 (L2) algorithm. The C-CLD algorithm provides the best estimates of the vertical profiles of water mass content and hydrometeor characteristic size, obtained from radar reflectivity, path-integrated signal attenuation and hydrometeor sedimentation Doppler velocity estimates using optimal estimation (OE) theory. To obtain the forward model relations and the associated uncertainty, an ensemble-based method is used. This ensemble consists of a collection of in situ measured drop size distributions that cover natural microphysical variability. The ensemble mean and standard deviation represent the forward model relations and their microphysics-based uncertainty. The output variables are provided on the joint standard grid horizontal and EarthCARE Level 1b (L1b) vertical grid (1 km along track and 100 m vertically). The OE framework is not applied to liquid-only clouds in drizzle-free and lightly drizzling conditions, where a more statistical approach is preferred.

Development of a methodology for evaluating spaceborne W-band Doppler radar by combined use of Micro Rain Radar and a disdrometer in Antarctica

Ground-based snowfall observations over Antarctica are rare due to the harsh environment and high logistical, equipment maintenance, and operational costs. Satellite measurements are crucial to provide continent-wide precipitation estimates, and this highlights the importance of validating the satellite estimates with measurements collected by ground-based Antarctic stations. The NASA CloudSat satellite, launched in 2006, is equipped with a 94 GHz (W-band) Cloud Profiling Radar (CPR) that provides measurements of reflectivity profiles of clouds and precipitation, whereas the incoming ESA/JAXA EarthCARE mission will add Doppler capability to a 94 GHz radar. This study explores how the synergy between two instruments available at most Antarctic stations, i.e., Micro Rain Radar (24 GHz, K-band) and laser disdrometer, can be used to validate satellite-borne W-band radar measurements, including Doppler estimates. A new validation methodology (K2W) was proposed to combine these instruments for simulating the 94 GHz reflectivity and Doppler measurements from Micro Rain Radar spectra. Assessment of the proposed K2W conversion methodology showed that the CloudSat Ze profiles can be simulated by the method with 0.2 dB mean difference at the lowest satellite radar range bin when time lag within ±12.5 min and the distance within 25 km around the CloudSat overpass were considered. With the K2W method, the 94 GHz Doppler velocity below 1 km altitude that would be observed by EarthCARE was obtained, and the standard deviation of the simulated Doppler velocity was found to be smaller than about 0.2 m s−1. The simulated 94 GHz Doppler radar profile information, which is less affected by attenuation compared to ground-based 94 GHz radar, will significantly improve the quantification of precipitation over Antarctica. This methodology will be applied to further assess the EarthCARE CPR Doppler velocity measurement accuracy as well as the Level 2 standard products for precipitation in Antarctica and at many other ground observation sites.

Processing reflectivity and Doppler velocity from EarthCARE's cloud-profiling radar: the C-FMR, C-CD and C-APC products

Abstract. The Earth Clouds, Aerosols and Radiation (EarthCARE) satellite mission is a joint effort by the European Space Agency (ESA) and the Japanese Aerospace Exploration Agency (JAXA). The EarthCARE mission features the first spaceborne 94 GHz cloud-profiling radar (CPR) with Doppler capability. The raw CPR observations and auxiliary information are used as input to three Level-2 (L2) algorithms: (1) C-APC: Antenna Pointing Characterization; (2) C-FMR: CPR feature mask and reflectivity; (3) C-CD: Corrected CPR Doppler Measurements. These algorithms apply quality control and corrections to the CPR primary measurements and derive important geophysical variables, such as hydrometeor locations, and best estimates of particle sedimentation fall velocities. The C-APC algorithm uses natural targets to introduce any corrections needed to the CPR raw Doppler velocities due to the CPR antenna pointing. The C-FMR product provides the feature mask based on only-reflectivity CPR measurements and quality-controlled radar-reflectivity profiles corrected for gaseous attenuation at 94 GHz. In addition, C-FMR provides best estimates of the path-integrated attenuation (PIA) and flags identifying the presence of multiple scattering in the CPR observations. Finally, the C-CD product provides the quality-controlled, bias-corrected mean Doppler velocity estimates (Doppler measurements corrected for antenna mispointing, non-uniform beam filling and velocity folding). In addition, the best estimate of the particle sedimentation velocity is estimated using a novel technique.

Assessments of Doppler Velocity Errors of EarthCARE Cloud Profiling Radar Using Global Cloud System Resolving Simulations: Effects of Doppler Broadening and Folding

The Earth Clouds, Aerosol, and Radiation Explorer (EarthCARE) is a satellite mission jointly developed by the Japan Aerospace Exploration Agency (JAXA) and the European Space Agency (ESA). One challenging feature of this mission is the observation of Doppler velocity by the Cloud Profiling Radar (EC-CPR). The Doppler measurement accuracy is affected by random errors induced by Doppler broadening due to the finite beamwidth and Doppler folding caused by the finite pulse repetition frequency. We investigated the impact of horizontal (along-track) integration and unfolding methods on the reduction of Doppler errors, in order to improve Doppler data processing in the JAXA standard algorithm. We simulated EC-CPR-observed Doppler velocities from pulse-pair covariances with the latest EC-CPR specifications using the radar reflectivity factor and Doppler velocity fields simulated by a satellite data simulator and a global cloud system resolving simulation. Two representative cases of a cirrus cloud and precipitation were examined. In the cirrus cloud case, the standard deviation of random error was decreased to 0.5 m/s for −10 dB <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${Z} _{\mathbf {e}}$ </tex-math></inline-formula> after 10-km horizontal integration. In the precipitation case, large falling speeds of precipitation caused Doppler folding errors due to larger Doppler velocities than that in the cirrus cloud case. When <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${Z} _{\mathbf {e}}$ </tex-math></inline-formula> is larger than −15 dB <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${Z} _{\mathbf {e}}$ </tex-math></inline-formula> , the standard deviations of random error were less than 1.0 m/s after 10-km horizontal integration and unfolding.

The performance of the EarthCARE Cloud Profiling Radar in marine stratiform clouds

AbstractMarine stratiform clouds are a challenging target for spaceborne radars due to their proximity to Earth's surface, limited vertical extent, and low radar reflectivity. The joint European‐Japanese Earth Clouds, Aerosol and Radiation Explorer (EarthCARE) mission is scheduled for launch in 2019 and features the first atmospheric Cloud Profiling Radar (CPR) with Doppler capability in space. Here the performance of the CPR in (i) detecting these clouds and their boundaries and (ii) measuring the Doppler velocities of drizzle particles is evaluated. Extensive observations from the Atmospheric Radiation Measurement Mobile Facility in marine stratus regimes are used as input to an EarthCARE CPR simulator and to compare the resulting reflectivity factors, Doppler velocities, and cloud detections. Cloud detection of the CPR is 70–80% that of the ground‐based radars, depending upon integration length and feature mask configuration. For clouds entirely contained within the surface clutter, detection is limited but is predicted to be an order of magnitude greater for the EarthCARE CPR than for CloudSat due to the improved range sampling rate of the former. The EarthCARE‐CPR range resolution is found to introduce cloud top height and reflectivity biases of +100 m (equal to the range sampling rate) and +1.3 dB; by applying a constrained linear inversion to the range resolution, these are reduced to 30 m and 0.1 dB, respectively. The analysis indicates that a velocity uncertainty of 0.5 ms−1 is achievable through either a 5 km along‐track integration or a combination of matched spatial filters and 1 km along‐track integration.

Impact of Receiver Saturation on Surface Doppler Velocity Measurements From the EarthCARE Cloud Profiling Radar

Antenna-pointing techniques that rely on Earth's surface Doppler velocity measurements are expected to play a crucial role in enabling antenna mispointing corrections in spaceborne radar systems. Here, the impact of the EarthCARE cloud profiling radar (CPR) receiver saturation on the quality of the surface Doppler velocity measurements is discussed. The CPR linear receiver is expected to saturate always from surface echoes. Our results based on an I/Q simulation framework show that for the EarthCARE radar configuration: 1) biases introduced by saturation will be negligible; 2) the standard deviation of the velocity estimates will increase by 30-50% when moving from unsaturated to completely saturated surface return for a corresponding pulse repetition frequency in the range between 6.1 and 7.5 kHz. As a consequence longer integration times will be necessary to achieve the same accuracy in presence of complete saturation.

Evaluation of EarthCARE Cloud Profiling Radar Doppler Velocity Measurements in Particle Sedimentation Regimes

Abstract The joint European Space Agency–Japan Aerospace Exploration Agency (ESA–JAXA) Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) mission is scheduled for launch in 2016 and features the first atmospheric Cloud Profiling Radar (CPR) with Doppler capability in space. Here, the uncertainty of the CPR Doppler velocity measurements in cirrus clouds and large-scale precipitation areas is discussed. These regimes are characterized by weak vertical motion and relatively horizontally homogeneous conditions and thus represent optimum conditions for acquiring high-quality CPR Doppler measurements. A large dataset of radar reflectivity observations from ground-based radars is used to examine the homogeneity of the cloud fields at the horizontal scales of interest. In addition, a CPR instrument model that uses as input ground-based radar observations and outputs simulations of CPR Doppler measurements is described. The simulator accurately accounts for the beam geometry, nonuniform beam-filling, and signal integration effects, and it is applied to representative cases of cirrus cloud and stratiform precipitation. The simulated CPR Doppler velocities are compared against those derived from the ground-based radars. The unfolding of the CPR Doppler velocity is achieved using simple conditional rules and a smoothness requirement for the CPR Doppler measurements. The application of nonuniform beam-filling Doppler velocity bias-correction algorithms is found necessary even under these optimum conditions to reduce the CPR Doppler biases. Finally, the analysis indicates that a minimum along-track integration of 5000 m is needed to reduce the uncertainty in the CPR Doppler measurements to below 0.5 m s−1 and thus enable the detection of the melting layer and the characterization of the rain- and ice-layer Doppler velocities.