Global Precipitation Measurement Dual-Frequency Precipitation Radar Research Articles

Evaluation of GPM DPR Rain Parameters with North Taiwan Disdrometers

Abstract Global precipitation demonstrates a substantial role in the hydrological cycle and offers tremendous implications in hydrometeorological studies. Advanced remote sensing instrumentations, such as the NASA Global Precipitation Measurement (GPM) mission Dual-Frequency Precipitation Radar (DPR), can estimate precipitation and cloud properties and have a unique capability to estimate the raindrop size information globally at snapshots in time. The present study validates the Level-2 data products of the GPM DPR with the long-term measurements of seven north Taiwan Joss–Waldvogel disdrometers from 2014 to 2022. The precipitation and drop size distribution parameters like rainfall rate (R; mm h−1), radar reflectivity factor (dBZ), mass-weighted mean drop diameter (Dm; mm), and normalized intercept parameter (Nw; m−3 mm−1) of the GPM DPR are compared with the disdrometers. Four different comparison approaches (point match, 5-km average, 10-km average, and optimal method) are used for the validation; among these four, the optimal strategy provided reasonable agreement between the GPM DPR and the disdrometers. The GPM DPR revealed superior performance in estimating the rain parameters in stratiform precipitation than the convective precipitation. Irrespective of algorithm type (dual- or single-frequency algorithm), sensitivity analysis revealed superior agreement for the mass-weighted mean diameter and inferior agreement for the normalized intercept parameter.

Ensemble‐Based Data Assimilation of GPM DPR Reflectivity: Cloud Microphysics Parameter Estimation With the Nonhydrostatic Icosahedral Atmospheric Model (NICAM)

AbstractDirect assimilation of Dual‐frequency Precipitation Radar (DPR) data of the Global Precipitation Measurement (GPM) core satellite is challenging mainly due to its long revisiting intervals relative to the time scale of precipitation, and precipitation location errors. This study explores a method for improving precipitation forecasts using GPM DPR through model parameter estimation. We developed a 28 km mesh global atmospheric data assimilation system that integrates the Nonhydrostatic ICosahedral Atmospheric Model (NICAM) and Local Ensemble Transform Kalman Filter (LETKF) coupled with a satellite radar simulator. Using the NICAM‐LETKF and GPM DPR observations, this study estimates a model cloud physics parameter corresponding to snowfall terminal velocity. To overcome the difficulties of long revisiting intervals and precipitation location errors, we propose a parameter estimation method based on a two‐dimensional histogram known as the contoured frequency by temperature diagram (CFTD). Parameter estimation effectively mitigated the gap between simulated and observed CFTD, resulting in improved 6 hr precipitation forecasts.

Toward a Globally-Applicable Uncertainty Quantification Framework for Satellite Multisensor Precipitation Products Based on GPM DPR

The usefulness of satellite multisensor precipitation products such as NASA’s 30-min, 0.1° Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG) is hindered by their associated errors. Reliable estimates of uncertainty would mitigate this limitation, especially in near-real time when gauge observations are not available. However, creating such estimates is challenging, due to both the complicated nature of satellite precipitation errors and the lack of “ground-truth” data precisely in the places—including oceans, complex terrain, and developing countries—that could benefit most from satellite precipitation estimates. In this work, we use the GPM dual-frequency precipitation radar (DPR)-derived swath-based precipitation products as an alternative to ground-based observations to facilitate IMERG uncertainty estimation. We compare the suitability of two DPR-derived precipitation products, 2ADPR and 2BCMB, against higher fidelity ground validation multiradar multisensor (GV-MRMS) ground reference data over the contiguous United States. The 2BCMB is selected to train error models based on censored shifted gamma distribution (CSGD; a mixed discrete-continuous probability distribution). Uncertainty estimates from these models are compared against alternative error models trained on GV-MRMS. Using information from NASA’s Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) reanalysis, we also demonstrate how IMERG uncertainty estimates can be further constrained using additional precipitation-related predictors. Though several critical issues remain unresolved, the proposed method shows promise for yielding robust uncertainty estimates in near-real time for IMERG and other similar precipitation products at their native resolution across the entire globe.

Analysing spatio-temporal patterns in wintertime rainfall across Iran’s deserts using GPM DPR data

Although known as the warmest deserts of the world, rainfall and integrated water of Lut and Kavir Deserts is still unknown due to insufficient weather stations. The Dual-Frequency Precipitation Radar (DPR) onboard the Global Precipitation Measurement (GPM) mission satellite created an opportunity for use to study the rainfall and vertically integrated liquid water content (LWC) and integrated non-liquid (ice) water content (IWC) using statistical and distance analyses over the two deserts during winter months (December to March) of 2015–2020. The results showed good similarity between the GPM DPR and station data with a Pearson correlation coefficient of 0.81. March has the highest contribution in rain amount, which is about 37% in both deserts. March also has the largest contribution of the rainy days, being about 41.4 and 37.6% in the Lut and Kavir Deserts, respectively. LWC has the highest amount in the Lut Desert, while IWC is the largest in the Kavir Desert. Distance analysis showed that there is a significant increasing trend of rainfall from west to east in the Lut Desert. Elevation does not affect rainfall distribution strongly, but rainfall is highly influenced by the atmospheric-driven large-scale parameters.

Wind Speed Retrieval Using Global Precipitation Measurement Dual-Frequency Precipitation Radar Ka-Band Data at Low Incidence Angles

In this study, sea surface wind speed was retrieved using the Global Precipitation Measurement (GPM) dual-frequency precipitation radar (DPR) Ka-band data. In order to establish the Ka-band model at low incidence angles, the dependence of the DPR Ka-band normalized radar cross section (NRCS) on the wind speed, incidence angle, sea surface temperature (SST), significant wave height (SWH), and sea surface current speed (CSPD) was analyzed first. We confirmed that the normalized radar cross section depends on the wind speed, incidence angle, and SST. Second, an empirical model at low incidence angles was established. This model links the Ka-band NRCS to the incidence angle, wind speed, and SST. Additionally, the wind speed was retrieved by the model and was validated via the GPM Microwave Imager (GMI) wind product. The validation yielded a root mean square error (RMSE) of 1.45 m/s and the RMSE was better at a lower incidence angle and a higher SST. This model may expand the use of GPM DPR data in enriching the sea surface wind speed data set. It is also helpful for other Ka-band spaceborne radars at low incidence angles to measure wind speed in the future.

On the relationship between convective precipitation and aerosol pollution in North China Plain during autumn and winter

Aerosols can modulate the convective cloud-precipitation system in complex ways, which was rarely investigated in North China during the heavily polluted autumn and winter. In this study, the relationships between aerosol pollution and convective precipitation in the North China Plain (112 to 116°E, 36 to 42°N) during autumn and winter were elucidated by combining the data of Global Precipitation Measurement Dual-Frequency Precipitation Radar (GPM-DPR) and the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). A positive correlation was found between the aerosol optical depth (AOD) and convective rain rate (RR). Comparative analyses between polluted and clean conditions were conducted to understand this AOD-RR relationship. Higher droplet concentration with smaller size were noted under the polluted condition, associated with higher latent heat released, implying that the increased aerosols may favor the development of the convective cloud-precipitation system through the “aerosol invigoration effect”. Besides, the impacts of ambient meteorological conditions on the AOD-RR relationship were also analyzed. It was found that correlations between AOD and RR were always positive in the studied region, irrespective of precipitable water vapor, lower tropospheric stability, and wind shear. A stronger AOD-RR correlation can be found when the lower tropospheric stability or wind shear becomes weaker. Our work provided evidence on the “aerosol invigoration effect” during the heavily polluted months in North China, which may improve the understanding of the complex interactions between aerosol and precipitation.

GPM DPR Retrievals: Algorithm, Evaluation, and Validation

The primary goal of the dual-frequency precipitation radar (DPR) aboard the Global Precipitation Measurement (GPM) Core Observatory satellite is to infer precipitation rate and raindrop/particle size distributions (DSD/PSD). The focus of this paper is threefold: (1) to describe the DPR retrieval algorithm that uses an adjustable relationship between rain rate (R) and the mass-weighted diameter (Dm) or an R-Dm relationship in solving for R and Dm simultaneously; (2) to evaluate the DPR algorithm based on the physical simulations that employ measured DSD/PSD to understand the mechanism and error characteristics of the retrieval method; (3) to review ground validation studies for DPR products as well as to analyze the strengths and weaknesses of ground radar and rain gauge/disdrometer validations. Overall, the DPR Version 6 algorithm provides reasonably accurate estimates of R and Dm in rain. Non-uniformity in the rain profile, however, tends to degrade the accuracy of the R and Dm estimates to some extent as the range-independent assumption of the adjustable parameter (ε) of the R-Dm relation is not able to fully account for natural variation of DSD in the vertical profile. The DPR snow rate is underestimated as compared with the independent dual-frequency ratio (DFR) technique. This is possibly the result of the constraint associated with the path integral attenuation (PIA)/differential PIA (δPIA) used in the DPR algorithm to find the best ε and range-independent ε assumption. A range-variable ε model, proposed in the DPR Version 7 algorithm, is expected to improve rain and snow retrieval.

Three-Parameter Rain Drop Size Distributions From GPM Dual-Frequency Precipitation Radar Measurements: Techniques and Validation With Ground-Based Observations

Raindrop size distribution (DSD) parameters need to be estimated to determine rainfall from radar measurements of backscattered signals in the precipitating medium and to study microphysics of precipitation under varying atmospheric conditions. A technique is proposed to estimate the DSD parameters from Global Precipitation Measurement (GPM) dual-frequency products at Ku and Ka bands, which has been validated with ground-based disdrometer measurements obtained during the five-year period 2014-2018 over a tropical location, Kolkata (22°54’N, 88°35’E), India. The present method yields the three parameters of the DSD model in terms of a gamma function namely, intercept ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N₀</i> ), shape (μ) and slope (Λ). Based on a proposed optimization technique, the most appropriate μ-Λ relationship is determined during a GPM satellite pass to obtain the DSD parameters. The derived parameters have yielded mass-weighted mean diameter ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D_m</i> ) data that show much better agreement with ground-based DSD measurements than that provided by GPM DPR product. This method has also been used to obtain the height profiles of DSD that broadly correlate with ground-based micro rain radar (MRR) observations, showing how DSD varies at different altitudes. The present results demonstrate that the proposed technique is effective in obtaining three-parameter DSDs over a tropical location, and offers a useful method to study the microstructure of rain using space-borne radar.

Improvement of the Cloud Microphysics Scheme of the Mesoscale Model at the Japan Meteorological Agency Using Spaceborne Radar and Microwave Imager of the Global Precipitation Measurement as Reference

Abstract In this study, the single-moment 6-class bulk cloud microphysics scheme used in the operational numerical weather prediction system at the Japan Meteorological Agency was improved using the observations of the Global Precipitation Measurement (GPM) core satellite as reference values. The original cloud microphysics scheme has the following biases: underestimation of cloud ice compared to the brightness temperature of the GPM Microwave Imager (GMI) and underestimation of the lower-troposphere rain compared to the reflectivity of GPM Dual-frequency Precipitation Radar (DPR). Furthermore, validation of the dual-frequency rate of reflectivity revealed that the dominant particles in the solid phase of simulation were graupel and deviated from DPR observation. The causes of these issues were investigated using a single-column kinematic model. The underestimation of cloud ice was caused by a high ice-to-snow conversion rate, and the underestimation of precipitation in the lower layers was caused by an excessive number of small-diameter rain particles. The parameterization of microphysics scheme is improved to eliminate the biases in the single-column model. In the forecast obtained using the improved scheme, the underestimation of cloud ice and rain is reduced. Consequently, the prediction errors of hydrometeors were reduced against the GPM satellite observations, and the atmospheric profiles and precipitation forecasts were improved.

Ku- and Ka-Band Ocean Surface Radar Backscatter Model Functions at Low-Incidence Angles Using Full-Swath GPM DPR Data

This paper presents the results of the first characterization of coincident Ku- and Ka-band ocean surface normalized radar cross section measurements at earth incidence angles 0°–18° using one year of wide swath Global Precipitation Measurement (GPM) mission dual frequency precipitation radar (DPR) data. Empirical geophysical model functions were derived for both bands, isotropic and directorial sensitivity were assessed, and finally, sea surface temperature (SST) dependence of radar backscatter, at both bands, were investigated. The Ka-band exhibited higher vector wind sensitivity for a low-to-moderate wind speeds regime, and the SST effects were also observed to be substantially larger at Ka-band than at Ku-band.

A Dual-Frequency Radar Retrieval of Two Parameters of the Snowfall Particle Size Distribution Using a Neural Network

AbstractWith the launch of the Global Precipitation Measurement Dual-Frequency Precipitation Radar (GPM-DPR) in 2014, renewed interest in retrievals of snowfall in the atmospheric column has occurred. The current operational GPM-DPR retrieval largely underestimates surface snowfall accumulation. Here, a neural network (NN) trained on data that are synthetically derived from state-of-the-art ice particle scattering models and measured in situ particle size distributions (PSDs) is used to retrieve two parameters of the PSD: liquid equivalent mass-weighted mean diameter and the liquid equivalent normalized intercept parameter . Evaluations against a test dataset showed statistically significantly improved ice water content (IWC) retrievals relative to a standard power-law approach and an estimate of the current GPM-DPR algorithm. Furthermore, estimated median percent errors (MPE) on the test dataset were −0.7%, +2.6%, and +1% for , , and IWC, respectively. An evaluation on three case studies with collocated radar observations and in situ microphysical data shows that the NN retrieval has MPE of −13%, +120%, and +10% for , , and IWC, respectively. The NN retrieval applied directly to GPM-DPR data provides improved snowfall retrievals relative to the default algorithm, removing the default algorithm’s ray-to-ray instabilities and recreating the high-resolution radar retrieval results to within 15% MPE. Future work should aim to improve the retrieval by including PSD data collected in more diverse conditions and rimed particles. Furthermore, different desired outputs such as the PSD shape parameter and snowfall rate could be included in future iterations.

Evaluation of GPM Dual-Frequency Precipitation Radar (DPR) Rainfall Products Using the Rain Gauge Network over China

AbstractThe Dual-Frequency Precipitation Radar (DPR) on board the Global Precipitation Measurement (GPM) mission core satellite provides the new-generation global observation of rain since 2014. The main objective of this paper is to evaluate the suitability and limitation of GPM-DPR level-2 products over China. The DPR rain rate products are compared with rain gauge data during the summers of 5 years (2014–18). The ground observation network is composed of more than 50 000 rain gauges. The DPR precipitation products for all scans (DPR_NS, DPR_MS, and DPR_HS) generally underestimate rain rates. However, DPR_MS agrees better with gauge estimates than DPR_NS and DPR_HS, yielding the lowest mean error, systematic deviation, and highest Pearson correlation coefficient. In addition, all three swath types show obvious overestimation over gauge estimates between 0.5 and 1 mm h−1 and underestimation when gauge estimates are larger than 1 mm h−1. The DPR_HS and DPR_MS agree better with gauge estimates below and above 2.5 mm h−1, respectively. A deeper investigation was carried out to analyze the variation of DPR_MS’s performance with respect to terrains over China. An obvious underestimation, relative to gauge estimates, occurs in Tibetan Plateau while a slight overestimation occurs in the North China Plain. Furthermore, our comprehensive analysis suggests that in Sichuan Basin, the DPR_MS exhibit the best agreement with gauge estimates.

A Comparative Study of Rain/No-Rain Classification Results Using PCT From GPM/GMI Data by Precipitation Type

Satellite-based microwave sensors that respond to the vertical distribution of hydrometeors have been continuously employed in the investigation of precipitation systems characteristics. Rain/no-rain classification (RNC) methods often are either applied before retrieving precipitation information from a number of algorithms based on passive microwave measurements or adopted to build the precipitation event-based databases. As a simple rain indicator, the polarized corrected temperature (PCT) at 89-GHz (PCT89) method using the global precipitation measurement (GPM) microwave imager (GMI) has been employed by many researchers, because it can estimate the scattering intensity while minimizing the effects of the surface emissivity at high resolution. This article presents a new consideration using the PCT89-based RNC method through statistical verification. Precipitating clouds were subdivided into 11 types (three stratiform types and four convective types) by the GPM dual frequency precipitation radar (DPR) precipitation classification algorithm. Quantitative comparison of verification results was performed in the tropics from January to December 2015 and major sources of uncertainty were analyzed from the perspective of the precipitation mechanism. Results showed a tendency of false identification for stratiform types except for those located near the convective core, and thus the method was susceptible to failure in the identification of convective types. Consequently, this method leads to an increase of 70% and 54% in the number of two significant stratiform types compared to DPR, while the convective types decreased by up to 53%. This article suggests that the precipitations identified by the PCT89 have features that enhance the bias toward the stratiform type.

Linkage Between the Vertical Evolution of Clouds and Droplet Growth Modes as Seen From FY‐4A AGRI and GPM DPR

AbstractBased on matched scans from the FY‐4A satellite's Advanced Geostationary Radiation Imager (AGRI) and the NASA/JAXA Global Precipitation Measurement (GPM) Core Observatory's Dual‐frequency Precipitation Radar (DPR), the microphysical mechanisms of the derived vertical evolution of cloud effective radius (Re) and its correspondence with precipitation droplet growth modes are revealed in this study. The results show that there were two turning points in the Re vertical evolution, which divided the Re profile into solid‐phase, mixed‐phase, and liquid‐phase zones from top to bottom. There were also two turning points in the upper layer of DPR reflectivity, which showed good correspondence with Re turning points in both height and physical sense. The main droplet growth modes in the three zones are nucleation/glaciation, deposition, and riming, respectively. The linkage between Re vertical evolution and droplet growth modes would be useful in real‐time monitoring of cloud microphysical processes.

Evaluation of the Microphysical Assumptions within GPM-DPR Using Ground-Based Observations of Rain and Snow

The Global Precipitation Measurement Dual-Frequency Precipitation Radar (GPM-DPR) provides an opportunity to investigate hydrometeor properties. Here, an evaluation of the microphysical framework used within the GPM-DPR retrieval was undertaken using ground-based disdrometer measurements in both rain and snow with an emphasis on the evaluation of snowfall retrieval. Disdrometer measurements of rain show support for the two separate prescribed relations within the GPM-DPR algorithm between the precipitation rate (R) and the mass weighted mean diameter (&#x0D; &#x0D; &#x0D; &#x0D; D&#x0D; m&#x0D; &#x0D; &#x0D; &#x0D; ) with a mean absolute percent error (&#x0D; &#x0D; &#x0D; &#x0D; M&#x0D; A&#x0D; P&#x0D; E&#x0D; &#x0D; &#x0D; &#x0D; ) on R of &#x0D; &#x0D; &#x0D; 29%&#x0D; &#x0D; &#x0D; and &#x0D; &#x0D; &#x0D; 47%&#x0D; &#x0D; &#x0D; and a mean bias percentage (&#x0D; &#x0D; &#x0D; &#x0D; M&#x0D; B&#x0D; P&#x0D; &#x0D; &#x0D; &#x0D; ) of &#x0D; &#x0D; &#x0D; &#x0D; −&#x0D; 6%&#x0D; &#x0D; &#x0D; &#x0D; and &#x0D; &#x0D; &#x0D; &#x0D; −&#x0D; 20%&#x0D; &#x0D; &#x0D; &#x0D; for the stratiform and convective relation, respectively. Ground-based disdrometer measurements of snow show higher MAPE and MBP values in the retrieval of R, at &#x0D; &#x0D; &#x0D; 77%&#x0D; &#x0D; &#x0D; and &#x0D; &#x0D; &#x0D; &#x0D; −&#x0D; 52%&#x0D; &#x0D; &#x0D; &#x0D; , respectively, compared to the stratiform rain relation. An investigation using the disdrometer-measured fall velocity and mass in the calculation of R and &#x0D; &#x0D; &#x0D; &#x0D; D&#x0D; m&#x0D; &#x0D; &#x0D; &#x0D; illustrates that the variability found in hydrometeor mass causes a poor correlation between R and &#x0D; &#x0D; &#x0D; &#x0D; D&#x0D; m&#x0D; &#x0D; &#x0D; &#x0D; in snowfall. The results presented here suggest that &#x0D; &#x0D; &#x0D; &#x0D; R&#x0D; −&#x0D; &#x0D; D&#x0D; m&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; retrieval is likely not optimal in snowfall, and other retrieval techniques for R should be explored.

GPM Satellite Radar Observations of Precipitation Mechanisms in Atmospheric Rivers

Abstract Despite numerous studies documenting the importance of atmospheric rivers (AR) to the global water cycle and regional precipitation, the evolution of their water vapor fluxes has been difficult to investigate given the challenges of observing and modeling precipitation processes within ARs over the ocean. This study uses satellite-based radar reflectivity profiles from the Global Precipitation Measurement Dual-Frequency Precipitation Radar (GPM-DPR), combined with kinematic and thermodynamic conditions in the vicinity of the precipitation diagnosed from the Climate Forecast System Reanalysis, to evaluate the characteristics and dynamical origins of precipitation in ARs over the northeast Pacific Ocean. Transects of 192 ARs between 2014 and 2018 are examined. Both stratiform and convective precipitation were abundant in these GPM transects and the precipitation was most often generated by forced ascent in the vicinity of a cold front in frontogenetic environments. Conditioning composite vertical profiles of reflectivity and latent heating from GPM-DPR on frontogenesis near the moist-neutral low-level jet demonstrated the importance of frontally forced precipitation on atmospheric heating tendencies. A case study of a high-impact landfalling AR is analyzed using the Weather Research and Forecasting Model, which showed how the precipitation processes and subsequent latent heat release offshore strongly influenced AR evolution. Although these precipitation mechanisms are present in global-scale models, the difficulty that coarse-resolution models have in accurately representing resultant precipitation likely translates to uncertainty in forecasting heating tendencies, their feedbacks on AR evolution, and ultimately the impacts of ARs upon landfall in the western United States.

Comparison of the GPM DPR Single- and Double-Frequency Products Over the Mediterranean Area

The NASA/JAXA Global Precipitation Measurement (GPM) Core Observatory (CO) carries, for the first time, a dual-frequency precipitation radar (DPR) designed to provide insights into the 3-D structure of precipitating clouds and rain intensity by using its Ka- and Ku-band frequencies. Single-frequency (SF) (both Ku- and Ka-only) and double-frequency (DF) based products provide particle-size distribution (PSD) parameters, as well as precipitation rates. Background surface type, precipitation type and phase, and vertical extension of the storm are also provided. In this paper, an intercomparison between the SF and DF DPR outputs over the Mediterranean area during rainfall events in the first four years of GPM-CO mission is carried out. The goal is to investigate the reliability of SF-based products by assessing their quality compared to the DF-based ones, treated as a reference. The vertical profiles and the near-surface values of the corrected reflectivity of the PSD parameters (mean mass-weighted diameter and normalized intercept) and of the rainfall rate have been analyzed. The data have been categorized for surface type (land and sea) and precipitation type (stratiform and convective). The results show a more marked difference between the DF and SF Ka-only based products than between DF and SF Ku-only based products. The feature is confirmed by the analysis of vertical profiles of the SF- and DF-based retrieved parameters. The statistical scores do not differ significantly between land and sea while they differ noticeably between stratiform and convective precipitation.

Physical Evaluation of GPM DPR Single- and Dual-Wavelength Algorithms.

A physical evaluation of the rain profiling retrieval algorithms for the Dual-Frequency Precipitation Radar (DPR) on board the Global Precipitation Measurement (GPM) Core Observatory satellite is carried out by applying them to the hydrometeor profiles generated from measured raindrop size distributions (DSD). The DSD-simulated radar profiles are used as input to the algorithms, and their estimates of hydrometeors' parameters are compared with the same quantities derived directly from the DSD data (or truth). The retrieval accuracy is assessed by the degree to which the estimates agree with the truth. To check the validity and robustness of the retrievals, the profiles are constructed for cases ranging from fully correlated (or uniform) to totally uncorrelated DSDs along the columns. Investigation into the sensitivity of the retrieval results to the model assumptions is made to characterize retrieval uncertainties and identify error sources. Comparisons between the single- and dual-wavelength algorithm performance are carried out with either a single- or dual-wavelength constraint of the path integral or differential path integral attenuation. The results suggest that the DPR dual-wavelength algorithm generally provides accurate range-profiled estimates of rainfall rate and mass-weighted diameter with the dual-wavelength estimates superior in accuracy to those from the single-wavelength retrievals.

Inter-comparison of reflectivity measurements between GPM DPR and NEXRAD radars

This study demonstrates the potential use of the NASA's Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR) to examine ground radar (GR) miscalibration and other uncertainty sources (e.g., partial beam blockage). We acquired the GPM Ground Validation System Validation Network reflectivity matchups between the DPR and three GRs (two in Iowa and one in South Dakota) for 2014–2017. We then refined the matching parameters (e.g., time separation) to reduce uncertainty in the matchup samples by analyzing the sensitivity of the matchup statistical properties to changes in these parameters. To reconcile the same observables (i.e., reflectivity) with different observational properties among the space- and ground-based radars, we developed a statistically integrated framework using inter-comparisons of them all with a Monte Carlo simulation. This method verifies the absolute calibration bias estimated from the refined DPR–GR matchups using relative calibration biases between GRs. We found that taking samples with a narrow temporal gap, estimated by actual measurement time of the DPR and GRs, can significantly reduce sample variability. Through inter-comparisons among the DPR and GRs, we observed that reflectivity differences among GRs in a similar environment (e.g., climatology and geography) are likely to be affected primarily by the calibration mismatch. In this case, the inter-comparison results demonstrated good agreement, and we inferred that the differences can be mitigated by calibration bias correction against the DPR. On the other hand, when the disagreement level of the inter-comparison results is significant, the authors found that other factors, such as partial beam blockage even in relatively plain regions, are more dominant than the calibration bias. In fact, the partial beam blockage effects can manifest themselves as a seasonal pattern in the GR inter-comparison results.

Evaluating Simulated Microphysics during OLYMPEX Using GPM Satellite Observations

Abstract This study evaluates moist physics in the Weather Research and Forecasting (WRF) Model using observations collected during the Olympic Mountains Experiment (OLYMPEX) field campaign by the Global Precipitation Measurement (GPM) satellite, including data from the GPM Microwave Imager (GMI) and Dual-Frequency Precipitation Radar (DPR) instruments. Even though WRF using Thompson et al. microphysics was able to realistically simulate water vapor concentrations approaching the barrier, there was underprediction of cloud water content and rain rates offshore and over western slopes of terrain. We showed that underprediction of rain rate occurred when cloud water was underpredicted, establishing a connection between cloud water and rain-rate deficits. Evaluations of vertical hydrometeor mixing ratio profiles indicated that WRF produced too little cloud water and rainwater content, particularly below 2.5 km, with excessive snow above this altitude. Simulated mixing ratio profiles were less influenced by coastal proximity or midlatitude storm sector than were GMI profiles. Evaluations of different synoptic storm sectors suggested that postfrontal storm sectors were simulated most realistically, while warm sectors had the largest errors. DPR observations confirm the underprediction of rain rates noted by GMI, with no dependence on whether rain occurs over land or water. Finally, WRF underpredicted radar reflectivity below 2 km and overpredicted above 2 km, consistent with GMI vertical mixing ratio profiles.