Spaceborne Radar Observations Research Articles

Post-Event Surface Deformation of the 2018 Baige Landslide Revealed by Ground-Based and Spaceborne Radar Observations

On 11 October and 3 November 2018, two large landslides occurred in Baige Village, Tibet, China, forcing the Jinsha River to be cut off and form a dammed lake, resulting in massive economic damages and deaths. This paper uses ground-based radar (GBR) and spaceborne interferometric synthetic aperture radar (InSAR) technologies to perform dynamic monitoring of the Baige landslide. Firstly, the GBR results suggest that the cumulative deformation from 4 to 10 December 2018 was 1.4 m, and the landslide still exhibits a risk of instability. Secondly, with the Sentinel-1A ascending and descending orbit images from December 2018 to February 2022, the InSAR-stacking technology assisted by the generic atmospheric correction online service (GACOS) and the multidimensional small baseline subset (MSBAS) method are utilized to obtain the annual deformation velocity and cumulative deformation in the satellite radar line of sight (LOS) direction of the landslide. Finally, according to the spatial–temporal deformation characteristics of feature points, combined with optical images, field investigation, and geological conditions, the development trend and inducing factors of the Baige landslide are comprehensively analyzed. It is shown that the Baige landslide is in constant motion at present, and the deformation is spreading from the slope to its right side. This research establishes a framework of combining emergency monitoring (i.e., GBR) with long-term monitoring (i.e., spaceborne InSAR). The framework is more conducive to obtaining the deformation and evolution of landslides, providing a greater possibility for studying the development trend and risk assessment of landslides, and assisting in reducing or even avoiding the losses caused by landslides.

Intercomparison between Ground-Based and Spaceborne Radars’ Echo-Top Heights: Application to the Multi-Radar Multi-Sensor and the Global Precipitation Measurement

Abstract The accuracy and uncertainty of radar echo-top heights estimated by ground-based radars remain largely unknown despite their critical importance for applications ranging from aviation weather forecasting to severe weather diagnosis. Because the vantage point of space is more suited than that of ground-based radars for the estimation of echo-top heights, the use of spaceborne radar observations is explored as an external reference for cross comparison. An investigation has been carried out across the conterminous United States by comparing the NOAA/National Severe Storms Laboratory Multi-Radar Multi-Sensor (MRMS) system with the space-based radar on board the NASA–JAXA Global Precipitation Measurement satellite platform. No major bias was assessed between the two products. An annual cycle of differences is found, driven by an underestimation of the stratiform cloud echo-top heights and an overestimation of the convective ones. The investigation of the systematic biases for different radar volume coverage patterns (VCP) shows that scanning strategies with fewer tilts and greater voids as VCP 21/121/221 contribute to overestimations observed for high MRMS tops. For VCP 12/212, the automated volume scan evaluation and termination (AVSET) function increases the radar cone of silence, causing overestimations when the echo top lies above the highest elevation scan. However, it seems that for low echo tops the shorter refresh rates contribute to mitigate underestimations, especially in stratiform cases.

Integration of ground-based and space-borne radar observations for three-dimensional deformations reconstruction: application to Luanchuan mining area, China

Luanchuan mining area, which is located in Henan Province, China and characterized by undulating topography and large precipitation, is vulnerable to the landslide disasters. The observations from the space-borne radar (ascending and descending TerraSAR-X) images and the ground-based radar (GPRI-II) images are integrated to monitor the surface deformations in Luanchuan mining area. Before the integration of the observations from multi-source SAR images, the coordinate systems of multi-source images are required to be unified by geographical projection. However, the accuracy of the commonly used geographical coding will be degraded by external DEM errors and inaccurate satellite orbit, which makes it difficult to achieve the high-precision registration of space-borne and ground-based radar scenes. Therefore, in this paper the iterative closest point (ICP) method is introduced to register the space-borne and ground-based images, yielding sub-pixel registration accuracy. Reasonable weights are then assigned to multi-source observations through Strain Model and Variance Component Estimation (SM-VCE) method, from which the high-precision three-dimensional deformations can be reconstructed. The results show that the maximum deformation rates in the east-west, north-south and vertical directions of Luanchuan mining area from July 20, 2019, to August 1, 2019, are about 0.2, 0.4 and 0.7 mm/day, respectively. The deformations are mainly on the loose slags in the Luanchuan pit rather than the bedrock, as a result of the accumulation of waste slags. Some suggestions on the installation location of ground-based radar are also provided to better combine the space-borne and ground-based observations.

Understanding the Slow Motion of the Wangjiashan Landslide in the Baihetan Reservoir Area (China) from Space-Borne Radar Observations

The analysis of landslide evolution using archived optical remote-sensing images is common, but it is often limited by the acquisition frequency, cloud cover, and resolution. With the development of space-borne radar observation technology, small baseline subset interferometric synthetic aperture radar (SAR) technology provides a new technical approach for detecting landslide deformation before disasters. The Sentinel-1A SAR datasets (20170219–20210330) were used to study the time-series deformation characteristics of the Wangjiashan landslide in the Baihetan Reservoir area before its impoundment. The time-series results show that the Wangjiashan landslide was in an initial deformation state in the prior four years, and the deformation first occurred in the middle part and then expanded to the landslide toe and crown retrogressive movement characteristics. Combined with an analysis of field deformation signs, these findings suggest that the upper landslide mass formed a local sliding surface, which caused serious deformation of the road. An analysis of historical rainfall data revealed that the Wangjiashan landslide is sensitive to rainfall, and the deformation is not only significantly correlated with cumulative rainfall but also influenced by concentrated heavy rainfall. The research in this study provides a basis for the monitoring, early warning, and risk management of the Wangjiashan landslide during the impoundment period. This work also provides a useful reference for investigations using space-borne SAR Earth observations in geological disaster prevention and control.

Extreme Precipitation Produced by Relatively Weak Convective Systems in the Tropics and Subtropics

AbstractThis study investigates warm‐season extreme precipitation events (EPEs) with the focus on the less‐attended weak convection (WeEPEs) using spaceborne radar observations. WeEPEs are compared to EPEs with intense convection (InEPEs), including their frequency/location, convective structures, and storm environments. WeEPEs and InEPEs both account for about 30% of the EPEs, highlighting the importance of WeEPEs. Overland WeEPEs mainly occur over key monsoon regions and maximize over windward sides of coastal mountains likely due to orographic enhancement. WeEPEs develop under conditions with deep moist profiles and strengthened low‐level onshore flows. Radar, microwave, and lightning proxies all suggest a weak updraft and limited ice contents in WeEPEs. However, vertical radar profiles of WeEPEs exceptionally increase (∼15 dBZ) downward below the melting level, indicating substantial raindrop growth and/or concentration increase through very active warm‐rain processes. This study provides useful guidelines for evaluating high‐resolution model simulations and satellite rainfall retrievals on extreme precipitation.

Three-way calibration checks using ground-based, ship-based, and spaceborne radars

Abstract. This study uses ship-based weather radar observations collected from research vessel Investigator to evaluate the Australian weather radar network calibration monitoring technique that uses spaceborne radar observations from the NASA Global Precipitation Mission (GPM). Quantitative operational applications such as rainfall and hail nowcasting require a calibration accuracy of ±1 dB for radars of the Australian network covering capital cities. Seven ground-based radars along the western coast of Australia and the ship-based OceanPOL radar are first calibrated independently using GPM radar overpasses over a 3-month period. The calibration difference between the OceanPOL radar (used as a moving reference for the second step of the study) and each of the seven operational radars is then estimated using collocated, gridded, radar observations to quantify the accuracy of the GPM technique. For all seven radars the calibration difference with the ship radar lies within ±0.5 dB, therefore fulfilling the 1 dB requirement. This result validates the concept of using the GPM spaceborne radar observations to calibrate national weather radar networks (provided that the spaceborne radar maintains a high calibration accuracy). The analysis of the day-to-day and hourly variability of calibration differences between the OceanPOL and Darwin (Berrimah) radars also demonstrates that quantitative comparisons of gridded radar observations can accurately track daily and hourly calibration differences between pairs of operational radars with overlapping coverage (daily and hourly standard deviations of ∼ 0.3 and ∼ 1 dB, respectively).

Relating snowfall observations to Greenland ice sheet mass changes: an atmospheric circulation perspective

Abstract. Snowfall is the major source of mass for the Greenland ice sheet (GrIS) but the spatial and temporal variability of snowfall and the connections between snowfall and mass balance have so far been inadequately quantified. By characterizing local atmospheric circulation and utilizing CloudSat spaceborne radar observations of snowfall, we provide a detailed spatial analysis of snowfall variability and its relationship to Greenland mass balance, presenting first-of-their-kind maps of daily spatial variability in snowfall from observations across Greenland. For identified regional atmospheric circulation patterns, we show that the spatial distribution and net mass input of snowfall vary significantly with the position and strength of surface cyclones. Cyclones west of Greenland driving southerly flow contribute significantly more snowfall than any other circulation regime, with each daily occurrence of the most extreme southerly circulation pattern contributing an average of 1.66 Gt of snow to the Greenland ice sheet. While cyclones east of Greenland, patterns with the least snowfall, contribute as little as 0.58 Gt each day. Above 2 km on the ice sheet where snowfall is inconsistent, extreme southerly patterns are the most significant mass contributors, with up to 1.20 Gt of snowfall above this elevation. This analysis demonstrates that snowfall over the interior of Greenland varies by up to a factor of 5 depending on regional circulation conditions. Using independent observations of mass changes made by the Gravity Recovery and Climate Experiment (GRACE), we verify that the largest mass increases are tied to the southerly regime with cyclones west of Greenland. For occurrences of the strongest southerly pattern, GRACE indicates a net mass increase of 1.29 Gt in the ice sheet accumulation zone (above 2 km elevation) compared to the 1.20 Gt of snowfall observed by CloudSat. This overall agreement suggests that the analytical approach presented here can be used to directly quantify snowfall mass contributions and their most significant drivers spatially across the GrIS. While previous research has implicated this same southerly regime in ablation processes during summer, this paper shows that ablation mass loss in this circulation regime is nearly an order of magnitude larger than the mass gain from associated snowfall. For daily occurrences of the southerly circulation regime, a mass loss of approximately 11 Gt is observed across the ice sheet despite snowfall mass input exceeding 1 Gt. By analyzing the spatial variability of snowfall and mass changes, this research provides new insight into connections between regional atmospheric circulation and GrIS mass balance.

Present-day and future Greenland Ice Sheet precipitation frequency from CloudSat observations and the Community Earth System Model

Abstract. The dominant mass input component of the Greenland Ice Sheet (GrIS) is precipitation, whose amounts and phase are poorly constrained by observations. Here we use spaceborne radar observations from CloudSat to map the precipitation frequency and phase on the GrIS, and we use those observations, in combination with a satellite simulator to enable direct comparison between observations and model, to evaluate present-day precipitation frequency in the Community Earth System Model (CESM). The observations show that substantial variability of snowfall frequency over the GrIS exists, that snowfall occurs throughout the year, and that snowfall frequency peaks in spring and fall. Rainfall is rare over the GrIS and only occurs in regions under 2000 m elevation and in the peak summer season. Although CESM overestimates the rainfall frequency, it reproduces the spatial and seasonal variability of precipitation frequency reasonably well. Driven by the high-emission, worst-case Representative Concentration Pathway (RCP) 8.5 scenario, CESM indicates that rainfall frequency will increase considerably across the GrIS, and will occur at higher elevations, potentially exposing a much larger GrIS area to rain and associated meltwater refreezing, firn warming, and reduced storage capacity. This technique can be applied to evaluate precipitation frequency in other climate models and can aid in planning future satellite campaigns.

Характеристики вихрей в Чукотском море и море Бофорта по данным спутниковых радиолокационных наблюдений

Характеристики вихрей в Чукотском море и море Бофорта по данным спутниковых радиолокационных наблюдений

Rainfall Estimation From Ground Radar and TRMM Precipitation Radar Using Hybrid Deep Neural Networks

AbstractRemote sensing of precipitation is critical for regional, continental, and global water and climate research. This study develops a deep learning mechanism to link between point‐wise rain gauge measurements, ground‐based, and spaceborne radar reflectivity observations. Two neural network models are designed to construct a hybrid rainfall system, where the ground radar is used to bridge the scale gaps between rain gauge and satellite. The first model is trained for ground radar using rain gauge data as target labels, whereas the second model is for spaceborne Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) using ground radar estimates as training labels. Data from 1 year of observations in Florida during 2009 are utilized to illustrate the application of this hybrid rainfall system. Validation using independent data in 2009, as well as 2‐year comparison against the standard PR products, demonstrates the promising performance and generality of this innovative rainfall algorithm.

Detecting the slope movement after the 2018 Baige Landslides based on ground-based and space-borne radar observations

On Oct. 11 and Nov. 3, 2018, two large-scale landslides occurred in the same location in Baige Village, Tibet, and massive rocks fell and encroached into the Jingsha River. These landslides posed a severe risk to the upstream and downstream areas. The occurrence, development and evolution of landslides are accompanied by a large number of changes in measurable variables. The deformation data are one of most important parameters for characterizing change and development trends of a landslide. This paper is centered on the results derived from ground-based radar and space-borne Synthetic Aperture Radar (SAR) images in the post-event phase to monitor the Baige landslides and to assess their residual risk. Two technologies play important roles in identifying and characterizing impending catastrophic slope failures: ground-based radar reveals the horizontal deformation, and satellite SAR images reveal the azimuth and range offset deformation. By combining satellite and ground-based SAR observations, we obtained high-precision three-dimensional (3D) deformation results and found that the vast majority of the instability regions mainly occur in the source area of the slope failures and that the direction of collapse converges from all sides to the middle. Additional information from UAV orthophoto maps and GNSS measurements also reveal that several cracks are distributed on the trailing edge of the landslide and are still moving. The comprehensive results revealed that the moving rock mass has still been remarkably active after the two landslide events. This study combined ground-based and space-borne SAR data to develop a long-term monitoring and stability evaluation process for implementation after a large landslide disaster. Based on the distribution characteristics of the 3D deformation fields, the present and future stability of the Baige Landslide was analyzed.

How Does Cloud Overlap Affect the Radiative Heating in the Tropical Upper Troposphere/Lower Stratosphere?

AbstractCharacterizing two‐layer cloud systems has historically been difficult. These systems have a strong radiative impact on the composition of and the processes in the upper troposphere‐lower stratosphere (UTLS). Using 4 years of combined spaceborne lidar and radar observations, the radiative impact of two‐layer cloud systems in the tropical UTLS is characterized, and its sensitivity to the properties of top‐ and bottom‐layer clouds is further quantified. Under these overlapping cloud conditions, the bottom‐layer clouds can fully suppress the radiative heating caused by high clouds in the UTLS, by inducing strong longwave cooling. If the vertical separation between the layers is <4 km, the radiative heating of the high cloud changes sign from positive to negative. Furthermore, the radiative effect at the top of the atmosphere is investigated, and it is found that the characteristic net warming by cirrus with ice water path <50 g/m2 is suppressed in the two‐layered system.

Global Virga Precipitation Distribution Derived From Three Spaceborne Radars and Its Contribution to the False Radiometer Precipitation Detection

AbstractThis study first quantifies the virga precipitation occurrence percentage using three spaceborne radar observations, including Tropical Rainfall Measuring Mission Precipitation Radar (PR), Global Precipitation Measurement dual‐frequency PR (Ku‐band PR/Ka‐band PR [KuPR/KaPR]), and CloudSat Cloud Profiling Radar (CPR). PR and KuPR/KaPR show that virga occurrence percentage is over 30% in arid regions (e.g., Sahara desert and deserts of Australia). CPR reveals similar virga geospatial distribution. However, the virga percentage based on CPR is about twice as large as that based on PR and KuPR/KaPR due to better detection sensitivity. Results also show that the majority of the virga clouds are altostratus and cirrus. Second, we investigate the virga precipitation contribution to the passive microwave radiometer false precipitation detection. Virga precipitation accounts for as much as 50% (30%) of Tropical Rainfall Measuring Mission Microwave Imager (Global Precipitation Measurement Microwave Imager) false detection results in arid regions. The underlying reason is because precipitation detection over land primarily relies on the ice scattering signature, while virga and light precipitation (e.g., 1 mm/hr) have similar amounts of ice water path in arid regions.

Multi-sensor InSAR Deformation Monitoring over Urban Area of Bratislava (Slovakia)

The integrated use of multiple Synthetic Aperture Radar (SAR) platforms for the deformation monitoring via satellite radar interferometry offers several perspectives for investigation of the behaviour of new and ageing structures, such as buildings and infrastructures, under varying or hazardous environment. Spanning almost 24 years of space-borne radar observations, this study aims to perform classical PSInSAR (Persistent Scatterer Interferometric Synthetic Aperture Radar) analysis incorporating measurements of ERS, Envisat, TerraSAR-X, Sentinel-1A and Radarsat-2 satellites. The results from the processing of different sensing geometries over Bratislava (Slovakia) urban area are presented, focusing on the description of characteristics associated with the specifics of every satellite platform in use. The discussion over technical feasibility of infrastructure monitoring is accompanied by the outline of possible future needs for the utilisation of the wealth source of information provided by the satellite radar imagery.

Radar Vertical Profile of Reflectivity Correction with TRMM Observations Using a Neural Network Approach

Abstract Complex terrain poses challenges to the ground-based radar quantitative precipitation estimation (QPE) because of partial or total blockages of radar beams in the lower tilts. Reflectivities from higher tilts are often used in the QPE under these circumstances and biases are then introduced due to vertical variations of reflectivity. The spaceborne Precipitation Radar (PR) on board the Tropical Rainfall Measuring Mission (TRMM) satellite can provide good measurements of the vertical structure of reflectivity even in complex terrain, but the poor temporal resolution of TRMM PR data limits their usefulness in real-time QPE. This study proposes a novel vertical profile of reflectivity (VPR) correction approach to enhance ground radar–based QPEs in complex terrain by integrating the spaceborne radar observations. In the current study, climatological relationships between VPRs from an S-band Doppler weather radar located on the east coast of Taiwan and the TRMM PR are developed using an artificial neural network (ANN). When a lower tilt of the ground radar is blocked, higher-tilt reflectivity data are corrected with the trained ANN and then applied in the rainfall estimation. The proposed algorithm was evaluated with three typhoon precipitation events, and its preliminary performance was evaluated and analyzed.

Intercomparison of Vertical Structure of Storms Revealed by Ground‐Based (NMQ) and Spaceborne Radars (CloudSat‐CPR and TRMM‐PR)

Spaceborne radars provide great opportunities to investigate the vertical structure of clouds and precipitation. Two typical spaceborne radars for such a study are the W-band Cloud Profiling Radar (CPR) and Ku-band Precipitation Radar (PR), which are onboard NASA's CloudSat and TRMM satellites, respectively. Compared to S-band ground-based radars, they have distinct scattering characteristics for different hydrometeors in clouds and precipitation. The combination of spaceborne and ground-based radar observations can help in the identification of hydrometeors and improve the radar-based quantitative precipitation estimation (QPE). This study analyzes the vertical structure of the 18 January, 2009 storm using data from the CloudSat CPR, TRMM PR, and a NEXRAD-based National Mosaic and Multisensor QPE (NMQ) system. Microphysics above, within, and below the melting layer are studied through an intercomparison of multifrequency measurements. Hydrometeors' type and their radar scattering characteristics are analyzed. Additionally, the study of the vertical profile of reflectivity (VPR) reveals the brightband properties in the cold-season precipitation and its effect on the radar-based QPE. In all, the joint analysis of spaceborne and ground-based radar data increases the understanding of the vertical structure of storm systems and provides a good insight into the microphysical modeling for weather forecasts.

Reduction of Nonuniform Beam Filling Effects by Vertical Decorrelation: Theory and Simulations

Algorithms for estimating precipitation rates from spaceborne radar observations of apparent radar reflectivity depend on attenuation correction procedures. The algorithm suite for the Ku-band precipitation radar aboard the Tropical Rainfall Measuring Mission satellite is one such example. The well-known problem of nonuniform beam filling is a source of error in the estimates, especially in regions where intense deep convection occurs. The error is caused by unresolved horizontal variability in precipitation characteristics such as specific attenuation, rain rate, and effective reflectivity factor. This paper proposes the use of vertical decorrelation for correcting the nonuniform beam filling error developed under the assumption of a perfect vertical correlation. Empirical tests conducted using ground-based radar observations in the current simulation study show that decorrelation effects are evident in tilted convective cells. However, the problem of obtaining reasonable estimates of a governing parameter from the satellite data remains unresolved.

Empirical Test of Theoretically Based Correction for Path Integrated Attenuation in Simulated Spaceborne Precipitation Radar Observations

Data from the TRMM precipitation radar is providing unprecedented information on the 3D structure of precipitation systems and estimates of precipitation rates over the oceans and land of the global tropics and subtropics. Algorithms for estimating precipitation rates from observations of apparent radar reflectivity depend on procedures for correcting for attenuation, especially in regions where intense deep convection occurs. The well-known problem of non-uniform beam filling is a source of error in the estimates, caused by unresolved horizontal variability in highly correlated characteristics of precipitation, such as specific attenuation, rain rate, and the effective radar reflectivity factor, that are fundamentally related to the size distribution of hydrometeors. This paper presents an empirical test of a theoretically based procedure for correcting for attenuation by means of a simulation study. Data for simulating spaceborne radar observations were obtained from a ground based scanning radar in Okinawa during a field experiment in June 2004. The correction procedure, reviewed briefly here, has been developed by formulating and analytically solving a statistically based model of non-uniform beam filling. The empirical test shows that the correction has the potential to improve retrievals of rain rate in intense convection, provided that reasonable estimates of a governing parameter can be obtained from the satellite data.

Multiple scattering identification in spaceborne W-band radar measurements of deep convective cores

[1] CloudSat observations have indicated that multiple scattering affects 94 GHz spaceborne radar observations. The ESA EarthCARE explorer mission scheduled to launch in 2015 features also a spaceborne 94-GHz radar with Doppler capability for providing a global data set of convective motions and particle sedimentation rates. Vertical velocity measurements will be collected in all cloud conditions, including deep convection where multiple-scattering is expected to contaminate the signal. Thus, before the spaceborne Doppler radars are used for science application, it is imperative to develop a method to identify radar range gates contaminated by multiple scattering contributions. Based on simulations, a criterion to identify the onset of multiple scattering is presented in this paper; the cumulative integrated reflectivity from the top of the atmosphere is a proxy of the multiple scattering enhancement and can be confidently used to detect the onset of multiple scattering. Analysis of a limited (two months) CloudSat data set reveals that, for deep tropical convective cores, the onset of significant multiple scattering typically occurs in the region between 9–10 km and more than 35% of the range bins above the freezing level height and with reflectivity above −20 dBZ are not affected by multiple scattering. This assessment offers a conservative upper limit for EarthCARE 94-GHz radar multiple scattering effects due to the narrower field of view of the Doppler radar compared to CloudSat's radar. Identification of multiple scattering contamination in the CloudSat and EarthCARE radar observations facilitates the following objectives: (1) to constrain the region of validity of currently developed CloudSat products based on single scattering theory (e.g. 2B-CWC-RO, 2B-CWC-RVOD) and (2) to filter out multiple scattering affected range bins in any analysis aimed at the assessment of the feasibility and of the accuracy of the EarthCARE Doppler estimates within deep convective cores.

Cross Validation of Spaceborne Radar and Ground Polarimetric Radar Aided by Polarimetric Echo Classification of Hydrometeor Types

AbstractGround-based polarimetric weather radar is arguably the most powerful validation tool that provides physical insight into the development and interpretation of spaceborne weather radar algorithms and observations. This study aims to compare and resolve discrepancies in hydrometeor retrievals and reflectivity observations between the NOAA/National Severe Storm Laboratory “proof of concept” KOUN polarimetric Weather Surveillance Radar-1988 Doppler (WSR-88D) and the spaceborne precipitation radar (PR) on board NASA’s Tropical Rainfall Measuring Mission (TRMM) platform. An intercomparison of PR and KOUN melting-layer heights retrieved from 2 to 5 km MSL shows a high correlation coefficient of 0.88 with relative bias of 5.9%. A resolution volume–matching technique is used to compare simultaneous TRMM PR and KOUN reflectivity observations. The comparisons reveal an overall bias of <0.2% between PR and KOUN. The bias is hypothesized to be from non-Rayleigh scattering effects and/or errors in attenuation correction procedures applied to Ku-band PR measurements. By comparing reflectivity with respect to different hydrometeor types (as determined by KOUN’s hydrometeor classification algorithm), it is found that the bias is from echoes that are classified as rain–hail mixture, wet snow, graupel, and heavy rain. These results agree with expectations from backscattering calculations at Ku and S bands, but with the notable exception of dry snow. Comparison of vertical reflectivity profiles shows that PR suffers significant attenuation at lower altitudes, especially in convective rain and in the melting layer. The attenuation correction performs very well for both stratiform and convective rain, however. In light of the imminent upgrade of the U.S. national weather radar network to include polarimetric capabilities, the findings in this study will potentially serve as the basis for nationwide validation of space-based precipitation products and also invite synergistic development of coordinated space–ground multisensor precipitation products.