Vertical Profile Of Reflectivity Research Articles

Investigation of the meteorological conditions, dynamical, and microphysical characteristics of convective precipitation over the rainfall center of South China in the Pre-summer Rainy Season

This study investigates the meteorological conditions, dynamics, and microphysical characteristics of convective precipitation in Longmen, South China, during the Pre-summer Rainy Season (PRS) from 2016 to 2020, focusing on the influence of the South China Sea summer monsoon (SCSSM) onset. Utilizing the ERA5 reanalysis dataset and observations from the C-band Vertical Pointing Radar (VPRC) and Two-Dimensional Video Disdrometer (2DVD), we analyzed 4560 Convective Precipitation Features (CPFs) and classified them into shallow convection (SC), middle convection (MC), and deep convection (DC) based on the maximum height of 35 dBZ echo-top. Key findings reveal that the onset of the SCSSM significantly enhances convective rainfall. Specifically, it increases the proportion of convective rainfall by 11 % and intensifies rainfall duration and intensity by approximately 2.2 times. Enhanced moisture convergence and stronger convective instability drive these changes. The microphysical processes are distinct across different CPF types. SCs display warm-rain processes, MCs indicate mixed-phase processes, and DCs are associated with ice-phase processes. Each type contributes uniquely to precipitation characteristics, vertical reflectivity profiles, and raindrop size distributions. These insights emphasize the SCSSM's critical role in regional precipitation patterns and provide valuable insights into the underlying processes affecting convective systems in South China, ultimately contributing to improving the capabilities of prediction in atmospheric research.

CloudSense: A model for cloud type identification using machine learning from radar data

The knowledge of type of precipitating cloud is crucial for radar based quantitative estimates of precipitation. We propose a novel model called CloudSense which uses machine learning to accurately identify the type of precipitating clouds over the complex terrain locations in the Western Ghats (WG) of India. CloudSense uses vertical reflectivity profiles collected during July–August 2018 from an X-band radar to classify clouds into four categories namely stratiform, mixed stratiform-convective, convective and shallow clouds. The machine learning (ML) model used in CloudSense was trained using a dataset balanced by Synthetic Minority Oversampling Technique (SMOTE), with features selected based on physical characteristics relevant to different cloud types. Among various ML models evaluated Light Gradient Boosting Machine (LightGBM) demonstrate superior performance in classifying cloud types with a BAC (Balanced Accuracy) of 0.79 and F1-Score of 0.8. CloudSense generated results are also compared against conventional radar algorithms and we find that CloudSense performs better than radar algorithms. For 200 samples tested, the radar algorithm achieved a BAC of 0.69 and F1-Score of 0.68, whereas CloudSense achieved a BAC of 0.8 and F1-Score of 0.79. Our results show that ML based approach can provide more accurate cloud detection and classification which would be useful to improve precipitation estimates over the complex terrain of the WG.

Development of Vertical Radar Reflectivity Profiles Based on Lightning Density Using the Geostationary Lightning Mapper Dataset in the Subtropical Region of Brazil

The study aims to develop vertical radar reflectivity profiles based on lightning density data from the Geostationary Lightning Mapper (GLM) on the GOES-16 satellite in the subtropical region of Brazil. The primary objective is to improve the assimilation of lightning data in numerical weather prediction models. The methodology involves the analysis of polarimetric radar data from Chapecó-SC and Jaraguari-MS, spanning from January 2019 to December 2023, and their correlation with lightning data from the GLM. Radar reflectivity profiles were created for different lightning density classes, categorized into six classes based on geometric progression. Results show a significant relationship between lightning activity and radar reflectivity, with distinct profiles for convective and stratiform events. These findings demonstrate the potential of using GLM data to enhance short-term weather forecasting, particularly for severe weather events. The study concludes that the integration of GLM data into weather models can lead to more accurate predictions of intense precipitation events, contributing to better preparedness and response strategies.

Using Satellite Observations of Lightning and Precipitation to Diagnose the Behavior of Deep Convection in Tropical Cyclones Traversing the Midlatitudes

AbstractThis study uses a unique combination of geostationary and low‐Earth orbiting satellite‐based lightning and precipitation observations, respectively, to examine the evolution of deep convection during the tropical cyclone (TC) lifecycle. The study spans the 2018–2021 Atlantic Basin hurricane seasons and is unique as it provides the first known analysis of total lightning (intra‐cloud and cloud‐to‐ground) observed in TCs through their extratropical transition and post‐tropical cyclone (PTC) phases. We consider the TC lifecycle stage, geographic location (e.g., land, coast, and ocean), shear strength, and quadrant relative to the storm motion and environmental shear vectors. Total lightning maxima are found in the forward right quadrant relative to storm motion and downshear of the TC center, consistent with previous studies using mainly cloud‐to‐ground lightning. Increasing environmental shear focuses the lightning maxima to the downshear right quadrant with respect to the shear vector in tropical storm phases. Vertical profiles of radar reflectivity from the Global Precipitation Measurement mission show that super‐electrically active convective precipitation features (>75 flashes) within the PTC phase of TCs have deeper mixed phase depths and higher reflectivity at −10°C than other phases, indicating the presence of more intense convection. Differences in the net convective behavior observed throughout TC evolution manifest in both the TC‐scale frequency of lightning‐producing cells and the intensity variations amongst individual convective cells. The combination of continuous lightning observations and precipitation snapshots improves our understanding of convective‐scale processes in TCs, especially in PTC phases, as they traverse the tropics and mid‐latitudes.

Forest 3D Radar Reflectivity Reconstruction at X-Band Using a Lidar Derived Polarimetric Coherence Tomography Basis

Tomographic Synthetic Aperture Radar (SAR) allows the reconstruction of the 3D radar reflectivity of forests from a large(r) number of multi-angular acquisitions. However, in most practical implementations it suffers from limited vertical resolution and/or reconstruction artefacts as the result of non-ideal acquisition setups. Polarisation Coherence Tomography (PCT) offers an alternative to traditional tomographic techniques that allow the reconstruction of the low-frequency 3D radar reflectivity components from a small(er) number of multi-angular SAR acquisitions. PCT formulates the tomographic reconstruction problem as a series expansion on a given function basis. The expansion coefficients are estimated from interferometric coherence measurements between acquisitions. In its original form, PCT uses the Legendre polynomial basis for the reconstruction of the 3D radar reflectivity. This paper investigates the use of new basis functions for the reconstruction of X-band 3D radar reflectivity of forests derived from available lidar waveforms. This approach enables an improved 3D radar reflectivity reconstruction with enhanced vertical resolution, tailored to individual forest conditions. It also allows the translation from sparse lidar waveform vertical reflectivity information into continuous vertical reflectivity estimates when combined with interferometric SAR measurements. This is especially relevant for exploring the synergy of actual missions such as GEDI and TanDEM-X. The quality of the reconstructed 3D radar reflectivity is assessed by comparing simulated InSAR coherences derived from the reconstructed 3D radar reflectivity against measured coherences at different spatial baselines. The assessment is performed and discussed for interferometric TanDEM-X acquisitions performed over two tropical Gabonese rainforest sites: Mondah and Lopé. The results demonstrate that the lidar-derived basis provides more physically realistic vertical reflectivity profiles, which also produce a smaller bias in the simulated coherence validation, compared to the conventional Legendre polynomial basis.

Revealing the Precipitation Characteristics of Tropical Cyclone Ockhi from Polarimetric Doppler Weather Radar in Conjunction with Disdrometer Observations

Abstract This paper describes the rainfall and microphysical structure of precipitation associated with Tropical Cyclone Ockhi using polarimetric Doppler weather radar (PDWR) products. The study reports the statistical analysis of precipitation types of tropical cyclone cloud systems over the north Indian Ocean, by combining the observations of the PDWR and disdrometer for the first time. There are few studies that mention initial DWR observations of TC over this low-latitude region below 23.5°N. We tried to further carry out a statistical analysis of precipitating clouds in a cyclone from its depression stage to severe cyclonic stage. This study tries to classify and quantify the contribution of convective and stratiform rain to the total TC rainfall. Precipitating clouds have been classified into convective and stratiform based on reflectivity measurements. The vertical profiles (VPRs) of the radar reflectivity Z and the differential reflectivity ZDR obtained for the stratiform and the convective events are compared. A study of the VPR of the convective events reveals that the Z and ZDR parameters tend to increase as the raindrops descend toward the ground owing to enhanced collision–coalescence processes. The VPR of stratiform rain shows signatures of the bright band (BB). The drop size distribution (DSD) parameters and rainfall rate pertaining to the two different precipitation regimes are estimated from the radar data and have been compared. The Joss–Waldvogel Disdrometer measurements have been used to derive DSD parameters and polarimetric rain-rate relation.

Cloud Characteristics in South China Using Ka-Band Millimeter Cloud Radar Datasets

In this study, we investigate the seasonal and diurnal variations in cloud occurrence frequency, as well as cloud vertical structure (CVS) characteristics under different seasons and precipitation intensities over the Guangzhou region in South China, based on the analysis of millimeter-wave cloud radar (MMCR) and ground automatic weather station rainfall observations from May 2019 to August 2021. The results showed that the occurrence frequency of clouds exhibits a bimodal distribution throughout the year, with peaks in March to June and October, reaching its highest occurrence in May at approximately 80% and its lowest from December to February at around 40%. Additionally, there are distinct diurnal variations in occurrence frequency, with the lowest rates occurring around 0005 LST, rapidly increasing after 0006 LST, and peaking during the afternoon to evening hours. Cloud top height (CTH) shows bimodal distributions during the pre-flood and post-flood seasons. The most frequently occurring range of CTH during the pre-flood season is below 3 km, accounting for approximately 43%, while during the post-flood season, it ranges from 11 to 14 km, constituting about 37%. For precipitation clouds, CTH can extend beyond 12 km, with the radar reflectivity decreasing gradually with increasing height. The highest frequencies of radar echoes are observed below 2 km and between 4 and 7 km, exhibiting clear diurnal variations, with echoes mainly below 2 km and between 4 to 6 km during the early morning, intensifying and shifting to higher altitudes during the day and reaching their maximum below 4 km during the afternoon to nighttime hours, while both the frequency and intensity increase in the height range of 4 to 12 km. Vertical profiles of radar reflectivity and cloud ice/liquid water content (IWC/LWC) exhibit similar trends under different precipitation intensities. The main differences are observed below 4 km, where both radar reflectivity and IWC/LWC generally increase with increasing precipitation intensity. These findings contribute to a better understanding of cloud characteristics in the South China region, enhance the accuracy of model simulations, and provide a scientific basis for accurate forecasting and warning of meteorological disasters.

Precipitation Microphysics in Tropical Cyclones: A Global Perspective Using the NASA Global Precipitation Measurement Mission Dual‐Frequency Precipitation Radar

AbstractPrecipitation microphysics in tropical cyclones (TCs) are often poorly represented in numerical simulations, which ultimately affects TC structure, evolution, and prediction. This provides a large incentive to better observe and understand the underlying microphysical processes in TCs in order to improve precipitation forecasts and warning operations. 112 TCs from 2014 to 2020 were matched up with overpasses from the NASA Global Precipitation Measurement (GPM) mission Dual‐Frequency Precipitation Radar (DPR) on a global scale to identify cloud properties and associated precipitation processes by quantifying vertical slopes of reflectivity in the liquid and ice phase. Further, vertical profiles of reflectivity were partitioned into different 850–200 hPa shear‐relative quadrants of each storm, different annuli from the storm center, and 5 TC ocean basins globally. Preliminary results showed the highest echo top heights occurred in the Northwest Pacific and Indian Ocean basins, with all basins and quadrants exhibiting median negative slopes of KuPR in the liquid phase. Additionally, all shear‐relative quadrants revealed negative slopes of reflectivity in the ice phase implying ice hydrometeor growth. These findings can potentially be used to improve the representation of cloud properties and the accuracy of the DPR particle size distribution algorithm in TCs.

Assessment of cloud microphysics and cumulus convection schemes to model extreme rainfall events over the Paraiba do Sul River Basin

Highly urbanized areas with complex terrains and multiple land uses pose challenges to extreme precipitation forecasts. The present study investigates the performance of several cloud microphysics and cumulus convection parameterizations on a severe rainfall event over the Paraiba do Sul River Basin, an area periodically affected by heavy precipitation. The Weather Research and Forecasting (WRF) model was applied to perform high resolution simulations, and the results were compared against ground observations, weather radar data, and satellite-based precipitation estimates. The simulations tend to underestimate precipitation by an average bias of 55%; however, some experiments showed good fittings in terms of time correlations (over 90%). Overall, the radar vertical profile of reflectivity agrees with observations, where the height of the warm and mixed layers was accurately simulated, despite overpredictions of convective depth (by up to 3 km in regions of intense ascending currents). Typically, WRF was more sensitive to distinct microphysics schemes rather than cloud convection ones. Also, urban areas showed greater precipitation and hydrometeor content in comparison with non-urban ones, even under strong synoptic-scale forcing. The framework was able to reproduce convectively active environments, supporting its application on operational runs over urbanized regions for warning issuing and decision-making.

Intercomparisons on the Vertical Profiles of Cloud Microphysical Properties From CloudSat Retrievals Over the North China Plain

AbstractVertical profiles of cloud microphysical properties importantly determine the lifetime and precipitation rate of clouds. The 94‐GHz cloud profiling radar (CPR) onboard the CloudSat satellite can measure the vertical profile of radar reflectivity, from which the microphysical properties of cloud can be retrieved. The retrievals bear variations due to various assumptions and auxiliary products used. This study targets on the mid‐latitude clouds in the northern hemisphere, and intercompares the CloudSat products describing the vertical profiles of cloud microphysics and evaluate the uncertainties for each retrieval algorithm, with further evaluation by aircraft in‐situ observations over the North China Plain region during 2013–2017. For those retrieval products performing phase apportion, the ambient temperature‐based linear apportioning on mixed‐phase clouds can produce reasonable estimation on ice water content, apart from the heavily precipitating clouds. The retrieved liquid water content constrained by cloud optical depth well matched in‐situ observations, however its effective size is overestimated (hereby underestimating the number concentration of water droplets) because of the influence of larger precipitating hydrometeors on size distribution. The CPR‐only retrieval can well produce the effective diameter and number concentration of ice for deep convection clouds, but using additional lidar constraint underestimates the effective diameter due to the intense attenuation by thick clouds. The analysis here suggests the appropriate parameters from various products for different cloud types, and provides guidance for future development of retrieval algorithms on vertical profiles of cloud microphysical properties.

Calibration Transfer Methodology for Cloud Radars Based on Ice Cloud Observations

Abstract This article presents a calibration transfer methodology that can be used between radars of the same or different frequency bands. This method enables the absolute calibration of a cloud radar by transferring it from another collocated instrument with known calibration, by simultaneously measuring vertical ice cloud reflectivity profiles. The advantage is that the added uncertainty in the newly calibrated instrument can converge to the magnitude of the reference instrument calibration. This is achieved by carefully selecting comparable data, including the identification of the reflectivity range that avoids the disparities introduced by differences in sensitivity or scattering regime. The result is a correction coefficient used to compensate measurement bias in the uncalibrated instrument. Calibration transfer uncertainty can be reduced by increasing the number of sampling periods. The methodology was applied between collocated W-band radars deployed during the ICE-GENESIS campaign (Switzerland 2020/21). A difference of 2.2 dB was found in their reflectivity measurements, with an uncertainty of 0.7 dB. The calibration transfer was also applied to radars of different frequency, an X-band radar with unknown calibration and a W-band radar with manufacturer calibration; the difference found was −16.7 dB with an uncertainty of 1.2 dB. The method was validated through closure, by transferring calibration between three different radars in two different case studies. For the first case, involving three W-band radars, the bias found was of 0.2 dB. In the second case, involving two W-band and one X-band radar, the bias found was of 0.3 dB. These results imply that the biases introduced by performing the calibration transfer with this method are negligible.

Dual-Pol VPR Corrections for Improved Operational Radar QPE in MRMS

Abstract The radar bright band is caused by melting ice crystals, and results in inflated reflectivity observations. If uncorrected, the bright band can result in large errors in radar-derived quantitative precipitation estimation (QPE). In the operational Multi-Radar Multi-Sensor (MRMS) system up to version 12.1, the effects of the bright band are corrected through the use of a reflectivity-only, tilt-based apparent vertical profile of reflectivity (tilt-VPR). This study utilizes dual-polarization (dual-pol) radar observations to improve the tilt-VPR methodology. To accomplish this, a brightband area delineation was developed within the MRMS framework and the brightband top and bottom heights were identified for individual tilts of radar data. This information was used to develop a radially dependent dual-pol VPR (dpVPR) model that can better correct reflectivity in situations of nonisotropic bright bands and low brightband events. This algorithm has been tested on 14 varying brightband events across the CONUS and compared with the tilt-VPR and the National Weather Service Weather Surveillance Radar-1988 Doppler Level-3 Digital Precipitation Rate (DPR) products. The radially dependent dpVPR correction provided a more accurate detection of brightband areas and a more effective reduction in QPE errors within and above the bright band than the tilt-VPR and DPR QPEs, especially for precipitation events with low melting layers or with strong variability of vertical motions. The brightband delineation and dpVPR methodology are also evaluated in the real-time MRMS testbed for their robustness and computational efficiency and has been transitioned into operations in 2022.

How uncertain are precipitation and peak flow estimates for the July 2021 flooding event?

Abstract. The disastrous July 2021 flooding event made us question the ability of current hydrometeorological tools in providing timely and reliable flood forecasts for unprecedented events. This is an urgent concern since extreme events are increasing due to global warming, and existing methods are usually limited to more frequently observed events with the usual flood generation processes. For the July 2021 event, we simulated the hourly streamflows of seven catchments located in western Germany by combining seven partly polarimetric, radar-based quantitative precipitation estimates (QPEs) with two hydrological models: a conceptual lumped model (GR4H) and a physically based, 3D distributed model (ParFlowCLM). GR4H parameters were calibrated with an emphasis on high flows using historical discharge observations, whereas ParFlowCLM parameters were estimated based on landscape and soil properties. The key results are as follows. (1) With no correction of the vertical profiles of radar variables, radar-based QPE products underestimated the total precipitation depth relative to rain gauges due to intense collision–coalescence processes near the surface, i.e., below the height levels monitored by the radars. (2) Correcting the vertical profiles of radar variables led to substantial improvements. (3) The probability of exceeding the highest measured peak flow before July 2021 was highly impacted by the QPE product, and this impact depended on the catchment for both models. (4) The estimation of model parameters had a larger impact than the choice of QPE product, but simulated peak flows of ParFlowCLM agreed with those of GR4H for five of the seven catchments. This study highlights the need for the correction of vertical profiles of reflectivity and other polarimetric variables near the surface to improve radar-based QPEs for extreme flooding events. It also underlines the large uncertainty in peak flow estimates due to model parameter estimation.

First Demonstration of Space-Borne Polarization Coherence Tomography for Characterizing Hyrcanian Forest Structural Diversity

Structural diversity is recognized as a complementary aspect of biological diversity and plays a fundamental role in forest management, conservation, and restoration. Hence, the assessment of structural diversity has become a major effort in the primary international processes, dealing with biodiversity and sustainable forest management. Because of prohibitive costs associated with the ground measurements of forest structure, despite their high accuracy, space-borne polarization coherence tomography (PCT) can introduce an alternative approach given its ability to provide a vertical reflectivity profile and spatiotemporal resolutions related to detecting forest structural changes. In this study, for the first time ever, the potential of space-borne PCT was evaluated in a broad-leaved Hyrcanian forest of Iran over 308 circular sample plots with an area of 0.1 ha. Two aspects of horizontal structure diversity, including standard deviation of diameter at breast height (σdbh) and the number of trees (N), were predicted as important characteristics in wood production and biomass estimation. In addition, the performance of prediction algorithms, including multiple linear regression (MLR), k-nearest neighbors (k-NN), random forest (RF), and support vector regression (SVR) were compared. We addressed the issue of temporal decorrelation in space-borne PCT utilizing the single-pass TanDEM-X interferometer. The data were acquired in standard DEM mode with single polarization of HH. Consequently, airborne laser scanning (ALS) was used to estimate initial values of height hv and ground phase φ0. The Fourier–Legendre series was used to approximate the relative reflectivity profile of each pixel. To link the relative reflectivity profile averaged within each plot with corresponding ground measurements of σdbh and N, thirteen geometrical and physical parameters were defined (P1−P13). Leave-one-out cross validation (LOOCV) showed a better performance of k-NN than the other algorithms in predicting σdbh and N. It resulted in a relative root mean square error (rRMSE) of 32.80%, mean absolute error (MAE) of 4.69 cm, and R2* of 0.25 for σdbh, whereas only 22% of the variation in N was explained using the PCT algorithm with an rRMSE of 41.56%. This study revealed promising results utilizing TanDEM-X data even though the accuracy is still limited. Hence, an entire assessment of the used framework in characterizing the reflectivity profile and the possible effect of the scale is necessary for future studies.

Comparison of vertical profile of raindrop size distribution from micro rain radar with global precipitation measurement over Western Java Island

The NASA-JAXA Global Precipitation Measurement (GPM) mission core satellite, launched in February 2014, carries the Dual-Frequency Precipitation Radar (DPR), which retrieves the three-dimensional structure of precipitation from space, including raindrop size distribution (DSD) parameters: mass-weighted mean diameter (Dm, in mm) and normalized DSD scaling parameter for concentration (Nw, in mm−1 m−3). This study compares DSD's vertical structure from the observations of GPM and ground-based radar (24 GHz Micro Rain Radar MRR) for the first time in Indonesia, precisely in Serpong (6.359°S, 106.673°E). The vertical profile of radar reflectivity, Dm, and Nw gradient was used to express the vertical gradient of DSD in rain columns (0.75–3 km). The gradient was calculated using linear regression as a function of parameters and height. Negative values indicated a downward decrease (DD) of parameters toward the surface; while, positive rates indicated a downward increase (DI) pattern. The DI of radar reflectivity from MRR was found more dominant than that of GPM Ku-band and Ka-band, with DI/DD ratios for MRR, Ku-band, and Ka-band at 7.2, 1.1, and 2.2, respectively. The three observations showed that the DI/DD ratio for convective rain was larger than that of stratiform rain. Most of the rain profiles from GPM showed a DI gradient for Nw, with a DI/DD ratio of 1.9, different from the MRR with the ratio of 0.6 only, indicating much more dominantion of DD. Furthermore, the DI/DD ratio for Dm from GPM observation was 0.8, while for MRR it was 4.55. Thus, the vertical structure of DSD from MRR was different from the one obtained by GPM, especially in convective rain. Besides the difference in profile trends, the Nw value of MRR is much larger than GPM; on the other hand, the Dm value of MRR was found much lower. Although there were some differences in DSD's vertical structure between GPM and MRR, both instruments were able to capture seasonal and diurnal variations of the DSD parameters.

Climatologies of Mesoscale Convective Systems over China Observed by Spaceborne Radars

Abstract This study investigates the characteristics of mesoscale convective systems (MCSs) as a function of MCS organizational mode over China, using long-term precipitation radar observations from the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement Mission (GPM). The spaceborne radar-based MCS climatology shows maximum population over low-elevation regions, marked decrease over the foothills, and minimum frequency over the Tibetan Plateau. Linear and nonlinear MCSs account for 17% and 83% of the MCSs over China, respectively. Linear MCSs have much stronger convective intensity and heavier precipitation than nonlinear MCSs, as indicated by TRMM convective proxies. Interestingly, though broad-stratiform MCSs have the weakest convection, they produce the heaviest (maximum) rain rate and the largest amount of heavy rainfall among nonlinear MCSs. Among various types of linear MCSs, bow echoes (BEs) and no-stratiform (NS) systems exhibit the strongest convective intensity, embedded lines exhibit the weakest, and convective lines with trailing/leading stratiform in between. BEs and NSs share the most vertically extended structure, strongest microwave ice scattering, and highest lightning flash rates, but NS systems have a much lower surface rain rate likely due to a drier environment. Vertical radar reflectivity profiles suggest that both ice-based and warm-rain processes play an important role in the precipitation processes of linear MCSs over China, including the most intense BE storms. In short, this study helps to better understand the convective organization, precipitation structure, and ensemble microphysical properties of MCSs over China, and potentially provides guidelines for evaluating high-resolution model simulations and satellite rainfall retrievals for monsoonal MCSs.

Multicriteria Accuracy Assessment of Digital Elevation Models (DEMs) Produced by Airborne P-Band Polarimetric SAR Tomography in Tropical Rainforests

The penetration capability of P-band radar waves through dense vegetation, along with the ability of tomography to separate the contributions of different layers in a vertical reflectivity profile, make P-band radar tomography a promising tool for digital terrain modeling in forested areas, specifically in dense tropical forests under which terrain topography remains poorly known. This paper aims to assess the overall quality of a digital terrain model (DTM) produced using tomographic processing of airborne P-band SAR imagery acquired during the TropiSAR campaign in French Guiana. Many quality descriptors are used to evaluate the quality of this DTM. Position and slope accuracies are computed based on a lidar DTM considered as the reference, and the impact of several parameters on these accuracies is studied, namely, slope, slope orientation, off-nadir angle and local incidence angle. The realism of the landforms is also studied according to geomorphological criteria. The results of this multicriteria accuracy assessment show the high potential of P-band SAR tomography in depicting the topography under forests, despite the intrinsic limitations related to the slant range geometry: the absolute elevation error is around 2 m; the slope is overestimated with an error of about 16°, mainly due to a processing artifact for which easy and direct solutions exist. Indeed, this error is equal to about 3° in flat artifact-free areas. These errors vary depending on the acquisition parameters and the local topography. The shapes are globally well preserved. These results are also discussed in the frame of the upcoming BIOMASS mission developed by the European Space Agency (ESA) and expected to be launched in 2024.

A Centimeter-Wavelength Snowfall Retrieval Algorithm Using Machine Learning

Abstract Remote sensing snowfall retrievals are powerful tools for advancing our understanding of global snow accumulation patterns. However, current satellite-based snowfall retrievals rely on assumptions about snowfall particle shape, size, and distribution that contribute to uncertainty and biases in their estimates. Vertical radar reflectivity profiles provided by the vertically pointing X-band radar (VertiX) instrument in Egbert, Ontario, Canada, are compared with in situ surface snow accumulation measurements from January to March 2012 as a part of the Global Precipitation Measurement (GPM) Cold Season Precipitation Experiment (GCPEx). In this work, we train a random forest (RF) machine learning model on VertiX radar profiles and ERA5 atmospheric temperature estimates to derive a surface snow accumulation regression model. Using event-based training–testing sets, the RF model demonstrates high predictive skill in estimating surface snow accumulation at 5-min intervals with a low mean-square error of approximately 1.8 × 10−3 mm2 when compared with collocated in situ measurements. The machine learning model outperformed other common radar-based snowfall retrievals (Ze–S relationships) that were unable to accurately capture the magnitudes of peaks and troughs in observed snow accumulation. The RF model also displayed consistent skill when applied to unseen data at a separate experimental site in South Korea. An estimate of predictor importance from the RF model reveals that combinations of multiple reflectivity measurement bins in the boundary layer below 2 km were the most significant features in predicting snow accumulation. Nonlinear machine learning–based retrievals like those explored in this work can offer new, important insights into global snow accumulation patterns and overcome traditional challenges resulting from sparse in situ observational networks.

Vertical structures of abrupt heavy rainfall events over southwest China with complex topography detected by dual‐frequency precipitation radar of global precipitation measurement satellite

AbstractSouthwest China is with complex topography including basins, mountains, hills and plains, where abrupt heavy rainfall events (AHREs) occur frequently and are difficult to quantitatively estimate due to limited ground‐based observations. Using the data of ground rain gauges and GPM dual‐frequency precipitation radar during April to September in 2014–2020, this study investigates vertical structures of AHREs over southwest China. Both mass‐weighted mean diameter (Dm) and reflectivity (Ze) of AHREs increase rapidly in ice‐phase process but slowly in liquid‐phase process. For convective rainfall of AHREs, abundant water vapour and strong atmospheric convective motion cause higher Dm and Ze but lower generalized intercept parameter (dBNw) than those for stratiform rainfall. Moreover, ice‐phase process is active while liquid‐phase process is weak in the mountains, but the situation is opposite in the plains, which results in large‐size and low‐concentration raindrops in the mountains while small‐size and high‐concentration raindrops in the plains. Furthermore, statistical models of vertical profile of reflectivity (VPR) indicate that VPR patterns are affected by surface rain intensity and present different trends around the 0°C level between stratiform and convective rainfalls. In addition, higher rain top height in the mountains is conducive to ice‐phase process while lower terrain height in the plains is favourable for liquid‐phase process. Therefore, the VPR pattern depends on rain type, terrain and rainfall intensity, and its fine model is beneficial for understanding microphysical process of AHREs and improving quantitative precipitation estimation by ground‐based radars.

Consistency of Vertical Reflectivity Profiles and Echo-Top Heights between Spaceborne Radars Onboard TRMM and GPM

Globally consistent long-term radar measurements are imperative for understanding the global climatology and potential trends of convection. This study investigates the consistency of vertical profiles of reflectivity (VPR) and 20-dBZ echo-top height (Topht20) between the two precipitation radars onboard the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) satellites. Results show that VPR coincidently observed by the TRMM’s and GPM’s Ku-band radar agree well for both convective and stratiform precipitation, although certain discrepancies exist in the VPR of weak convection. Topht20s of the TRMM and GPM are consistent either for coincident events, or latitudinal mean during the 7-month common period, all with biases within the radar range resolution (0.1–0.2 km). The largest difference in the Topht20 between the TRMM’s and GPM’s Ku-band radar occurs in shallow precipitation. Possible reasons for this discrepancy are discussed, including sidelobe clutter, beam-mismatch, non-uniform beam filling, and insufficient sampling. Finally, a 23-year (1998–2020) climatology of Topht20 has been constructed from the two spaceborne radars, and the global mean Topht20 time series shows no significant trend in convective depth during the last two decades.