Dual-Frequency Precipitation Radar Observations Research Articles

Sea Surface and Snowflakes as Natural Targets Connecting FY‐3G and GPM‐CO Dual‐Frequency Radars

AbstractThe Precipitation Measurement Radar (PMR) onboard FengYun‐3G consists of a Ku‐/Ka‐band radar, which is characterized by similar configurations with the Dual‐frequency Precipitation Radar (DPR) carried by Global Precipitation Measurement mission Core Observatory. However, directly comparing observations from two radars is challenging due to a scarcity of their coincidences. In this study, sea surface echoes in their track intersections were employed to cross‐calibrate PMR. Then, we show that the dual‐frequency ratio (DFR) in snow stably increases with Ku‐band reflectivity in statistics, allowing for an assessment of the consistency between PMR and DPR observations. Surprisingly, our results reveal a underestimation of DFR in DPR inner swath, while observations from PMR are in good agreement with those from DPR outer swath. This study demonstrates the novel use of natural targets for spaceborne dual‐frequency radar calibration, and presents a unique view into the connection between the two spaceborne precipitation radar missions in operation.

Land-sea contrast of vertical structure of precipitation over Sumatra revealed by GPM DPR observations

Land-sea contrast of vertical structure of precipitation over Sumatra revealed by GPM DPR observations

Enhancing consistency of microphysical properties of precipitation across the melting layer in dual-frequency precipitation radar data

Abstract. Stratiform rain and the overlying ice play crucial roles in Earth's climate system. From a microphysics standpoint, water mass flux primarily depends on two variables: particles' concentration and their mass. The Dual-frequency Precipitation Radar (DPR) on the Global Precipitation Measurement mission core satellite is a spaceborne instrument capable of estimating these two quantities through dual-wavelength measurements. In this study, we evaluate bulk statistics on the ice particle properties derived from dual-wavelength radar data in relation to the properties of rain underneath. Specifically, we focus on DPR observations over stratiform precipitation, characterized by columns exhibiting a prominent bright band, where the melting layer can be easily detected. Our analysis reveals a large increase in the retrieved mass flux as we transition from the ice to the rain phase in the official DPR product. This observation is in disagreement with our expectation that mass flux should remain relatively stable across the bright band in cold-rain conditions. To address these discrepancies, we propose an alternative retrieval algorithm that ensures a gradual transition of Dm (mean mass-weighted particle melted-equivalent diameter) and the precipitation rate across the melting zone. This approach also helps in estimating bulk ice density above the melting level. These findings demonstrate that DPR observations can not only quantify ice particle content and their size above stratiform rain regions but also estimate bulk density, provided uniform conditions that minimize uncertainties related to partial beam filling.

Reconstructing radar reflectivity by integrating GPM and INSAT‐3D observations for near real‐time monitoring of convective storms over India

AbstractThis study focuses on integrating dual‐frequency precipitation radar (DPR) observations on‐board the Global Precipitation Mission with the storm index from INSAT‐3D for the development of reconstructed radar reflectivity. Performance of the derived product was tested against the independent DPR observations. A correlation coefficient of 0.81 is reported between the reconstructed radar reflectivity and the DPR observations. Derived reconstructed reflectivity is shown to be an excellent product for the weather monitoring over the regions without radar coverage.

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.

Adapting the COSP Radar Simulator to Compare GCM Output and GPM Precipitation Radar Observations

AbstractComparisons of precipitation between general circulation models (GCMs) and observations are often confounded by a mismatch between model output and instrument measurements, including variable type and temporal and spatial resolutions. To mitigate these differences, the radar-simulator Quickbeam within the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) simulates reflectivity from model variables at the subgrid scale. This work adapts Quickbeam to the dual-frequency precipitation radar (DPR) on board the Global Precipitation Measurement (GPM) satellite. The longer wavelength of the DPR is used to evaluate moderate to heavy precipitation in GCMs, which is missed when Quickbeam is used as a cloud radar simulator. Latitudinal and land–ocean comparisons are made between COSP output from the Community Atmospheric Model version 5 (CAM5) and DPR data. Additionally, this work improves the COSP subgrid algorithm by applying a more realistic, nondeterministic approach to assigning GCM gridbox convective cloud cover when convective cloud is not provided as a model output. Instead of assuming a static 5% convective cloud coverage, DPR convective precipitation coverage is used as a proxy for convective cloud coverage. For example, DPR observations show that convective rain typically only covers about 1% of a 2° grid box, but that the median convective rain area increases to over 10% in heavy rain cases. In our CAM5 tests, the updated subgrid algorithm improved the comparison between reflectivity distributions when the convective cloud cover is provided versus the default 5% convective cloud-cover assumption.

The Inland Maintenance and Re-intensification of Tropical Storm Bill (2015) Part 2: Precipitation Microphysics

AbstractTropical Storm Bill produced over 400 mmof rainfall to portions of southern Oklahoma from 16-20 June 2015, adding to the catastrophic urban and river flooding that occurred throughout the region in the month prior to landfall. The unprecedented excessive precipitation event that occurred across Oklahoma and Texas during May and June 2015 resulted in anomalously high soil moisture and latent heat fluxes over the region, acting to increase the available boundary layer moisture. Tropical Storm Bill progressed inland over the region of anomalous soil moisture and latent heat fluxes which helped maintain polarimetric radar signatures associated with tropical, warm rain events. Vertical profiles of polarimetric radar variables such as ZH, ZDR, KDP, and ρhv were analyzed in time and space over Texas and Oklahoma. The profiles suggest that Tropical Storm Bill maintained warm rain signatures and collision-coalescence processes as it tracked hundreds of kilometers inland away from the landfall point consistent with tropical cyclone precipitation characteristics. Dual-frequency precipitation radar observations from the NASA GPM DPR were also analyzed post-landfall and showed similar signatures of collision-coalescence while Bill moved over north Texas, southern Oklahoma, eastern Missouri, and western Kentucky.

Rain-Type Classification From Microwave Satellite Observations Using Deep Neural Network Segmentation

The understanding of different characteristics of the stratiform and convective system is important for meteorological research, including precipitation retrievals from satellite observations, precipitation parameterization in numerical prediction models, and precipitation climatology. In this study, the possibility of discriminating rain types using deep learning techniques is examined using satellite microwave observation data. Herein, we build two types of deep neural networks for rain type classification, i.e., U-net (RTC-U-net) and fully connected neural network (RTC-fcNN). Experiments are performed with the data obtained from two sensors onboard the global precipitation measurement (GPM) satellite. The brightness temperatures observed from the GPM microwave imager (GMI) are used as input features, whereas rain types retrieved by dual-frequency precipitation radar (DPR) are used as labels. We generated the merged 40- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> -40 pixel data for training and testing the data set by cropping the GMI observed fields overlapping the DPR observations. Overall, the proposed RTC-U-net and RTC-fcNN effectively classify four rain types, i.e., no-rain, stratiform, convective, and others. The total accuracy for the four rain types is 0.95, and the weighted mean of precision, recall, and f-score for stratiform and convective are 0.76, 0.68, and 0.71, respectively. Results show that the proposed techniques can extract the characteristics of each rain type from the data itself. To the best of our knowledge, this study is the first to apply a segmentation method with U-net for rain type classification.

Tropical Cyclone Rain Retrievals from FY-3B MWRI Brightness Temperatures Using the Goddard Profiling Algorithm (GPROF)

AbstractThis study focuses on the tropical cyclone rainfall retrieval using FY-3B Microwave Radiation Imager (MWRI) brightness temperatures (Tbs). The GPROF, a fully parametric approach based on the Bayesian scheme, is adapted for use by the MWRI sensor. The MWRI GPROF algorithm is an ocean-only scheme used to estimate rain rates and hydrometeor vertical profiles. An a priori database is constructed from MWRI simulated Tbs, the GPM Microwave Imager (GMI) and Dual-Frequency Precipitation Radar (DPR) combined data, and ancillary data resulting in about 100 000 rainfall profiles. The performance of MWRI retrievals is consistent with DPR observations, even though MWRI retrievals slightly overestimate low rain rates and underestimate high rain rates. The total bias of MWRI retrievals is less than 13% of the mean rain rate of DPR precipitation. Statistical comparisons over GMI GPROF, GMI Hurricane GPROF (HGPROF), and MWRI GPROF retrievals show MWRI GPROF retrievals are consistent in terms of spatial distribution and rain estimates for TCs compared with the other two estimates. In terms of the global precipitation, the mean rain rates at different distances from best track locations for five TC categories are used to identify substantial differences between mean MWRI and GMI GPROF retrievals. After correcting the biases between MWRI and GMI retrievals, the performance of MWRI retrievals shows slight overestimate for light rain rates while underestimating rain rates near the eyewall for category 4 and 5 only.

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.

Precipitation Microphysics of Tropical Cyclones Over the Western North Pacific Based on GPM DPR Observations: A Preliminary Analysis

AbstractUsing observations from the dual‐frequency precipitation radar (DPR) onboard the Global Precipitation Measurement mission (GPM) satellite, this study analyzes the microphysical structures and processes of tropical cyclone (TC) precipitation over the western North Pacific in terms of different precipitation efficiency indices (PEIs). The statistical results show that the mean mass‐weighted mean diameter of raindrops (Dm) at 2 km is 1.67 mm (1.37 mm) for convective (stratiform) precipitating clouds. Precipitating clouds with high PEI have higher liquid water path than nonliquid water path for both convective and stratiform clouds. The mean Dm of convective and stratiform precipitation increases as the PEI increases. The vertical profiles of Dm and reflectivity (Ze) for convective and stratiform precipitating clouds in TCs differ substantially as the PEI changes. Below the melting level, there is a clear decrease (increase) in Dm and Ze toward the surface for clouds with low (high) PEI. In general, clouds within TCs producing the most efficient precipitation are characterized by strong coalescence, not only for small droplets but also for relatively large raindrops; in contrast, the breakup of hydrometeors is the dominant process in convective and stratiform precipitating clouds with low PEI. These results will help validate and improve the hydrometeor parameterization schemes in cloud and climate models.

A Multiyear Analysis of Global Precipitation Combining CloudSat and GPM Precipitation Retrievals

Abstract Satellite-based instruments are essential to the observation of precipitation at a global scale, especially over remote regions. Each instrument has its own strengths and limitations in accurately determining the rate of precipitation at the surface. By using the complementary strengths of two instruments, a more complete analysis of global precipitation can be performed. The Global Precipitation Measurement (GPM) Core Observatory’s Dual-Frequency Precipitation Radar (DPR) is capable of measuring precipitation at high and medium precipitation rates by using Ku-band (13.6 GHz) radiation. The CloudSat satellite’s Cloud Profiling Radar (CPR) uses higher-frequency W-band (94 GHz) radiation and is therefore capable of measuring precipitation at low rates not detected by the GPM DPR. CloudSat observations from January 2007 to December 2016 and DPR observations from March 2014 to February 2018 are combined and the results examined. Since these datasets are not completely coincident, this study is conducted as a multiyear analysis. Observed precipitation from CloudSat is used starting at the lowest precipitation rates and increasing rates until the occurrence observed by GPM surpasses that of CloudSat, at which point data from GPM are used. The precipitation rate at which this change occurs contains important information on the amount of precipitation missed by each instrument and implications as to the size of the hydrometeors present. Liquid precipitation retrieval from CloudSat is not performed over land; analysis over land is produced here using the information available. By combining the two datasets, a more complete picture of precipitation occurring globally is obtained.

An Algorithm to Identify Surface Snowfall From GPM DPR Observations

The Dual-Frequency Precipitation Radar (DPR) on board the Global Precipitation Measurement (GPM) core satellite has reflectivity measurements at two different frequency bands, namely, Ku- and Ka-bands. The dual-frequency ratio from these measurements has been used to perform rain-type classification and microphysics retrieval in the current DPR level 2 algorithm. In this paper, a surface snowfall identification algorithm is developed using GPM DPR observations. This algorithm provides a new approach to detect snowfall through radar observations, such as measured dual-frequency ratio. This algorithm is developed using GPM DPR data as well as Atmospheric Radiation Measurement (ARM) X/Ka-band radar data during the snowfall experiment. Several snow events observed by both DPR and ground radars are used in the algorithm validation, showing good comparisons.

The GPM Combined Algorithm

AbstractIn this paper, the operational Global Precipitation Measurement (GPM) mission combined radar–radiometer algorithm is thoroughly described. The operational combined algorithm is designed to reduce uncertainties in GPM Core Observatory precipitation estimates by effectively integrating complementary information from the GPM Dual-Frequency Precipitation Radar (DPR) and the GPM Microwave Imager (GMI) into an optimal, physically consistent precipitation product. Although similar in many respects to previously developed combined algorithms, the GPM combined algorithm has several unique features that are specifically designed to meet the GPM objectives of deriving, based on GPM Core Observatory information, accurate and physically consistent precipitation estimates from multiple spaceborne instruments, and ancillary environmental data from reanalyses. The algorithm features an optimal estimation framework based on a statistical formulation of the Gauss–Newton method, a parameterization for the nonuniform distribution of precipitation within the radar fields of view, a methodology to detect and account for multiple scattering in Ka-band DPR observations, and a statistical deconvolution technique that allows for an efficient sequential incorporation of radiometer information into DPR precipitation retrievals.

Multiple-Scattering-Induced “Ghost Echoes” in GPM DPR Observations of a Tornadic Supercell

AbstractEvidence of multiple-scattering-induced pulse stretching for the signal of both frequencies of the Dual-Frequency Precipitation Radar (DPR) on the Global Precipitation Measurement (GPM) mission Core Observatory satellite is presented on the basis of collocated ground-based WSR-88D S-band observations of an extreme case: a tornadic supercell. The ground-based observations clearly show a tilted convective core with a so-called bounded weak-echo region—that is, locations where precipitation is absent or extremely light at the ground while large amounts of liquid or frozen precipitation are present aloft. The satellite observations in this region show reflectivity profiles that extend all the way to the surface despite the absence of near-surface precipitation: these are here referred to as “ghost echoes.” Furthermore, the Ku- and Ka-band profiles exhibit similar slopes, which is a typical sign that the observed power is almost entirely due to multiple scattering. A novel microphysical retrieval that is based on triple-frequency (S–Ku–Ka) observations shows that a dense ice core located between 4 and 14 km with particle sizes exceeding 2.5 cm and integrated ice contents exceeding 7.0 kg m−2 is the source of the ghost echoes of the signal in the lower layers. The level of confidence of this assessment is strengthened by the availability of the S-band data, which provide the necessary additional constraints to the radar retrieval that is based on DPR data. This study shows not only that multiple-scattering contributions may become predominant at Ka already very high up in the atmosphere but also that they play a key role at Ku band within the layers close to the surface. As a result, extreme caution must be paid even in the interpretation of Ku-based retrievals (e.g., the TRMM PR dataset or any DPR retrievals that are based on the assumption that Ku band is not affected by multiple scattering) when examining extreme surface rain rates that occur in the presence of deep dense ice layers.

A Statistical Method for Reducing Sidelobe Clutter for the Ku-Band Precipitation Radar on board the GPM Core Observatory

AbstractA statistical method to reduce the sidelobe clutter of the Ku-band precipitation radar (KuPR) of the Dual-Frequency Precipitation Radar (DPR) on board the Global Precipitation Measurement (GPM) Core Observatory is described and evaluated using DPR observations. The KuPR sidelobe clutter was much more severe than that of the Precipitation Radar on board the Tropical Rainfall Measuring Mission (TRMM), and it has caused the misidentification of precipitation. The statistical method to reduce sidelobe clutter was constructed by subtracting the estimated sidelobe power, based upon a multiple regression model with explanatory variables of the normalized radar cross section (NRCS) of surface, from the received power of the echo. The saturation of the NRCS at near-nadir angles, resulting from strong surface scattering, was considered in the calculation of the regression coefficients.The method was implemented in the KuPR algorithm and applied to KuPR-observed data. It was found that the received power from sidelobe clutter over the ocean was largely reduced by using the developed method, although some of the received power from the sidelobe clutter still remained. From the statistical results of the evaluations, it was shown that the number of KuPR precipitation events in the clutter region, after the method was applied, was comparable to that in the clutter-free region. This confirms the reasonable performance of the method in removing sidelobe clutter. For further improving the effectiveness of the method, it is necessary to improve the consideration of the NRCS saturation, which will be explored in future work.

Estimating Nonraining Surface Parameters to Assist GPM Constellation Radiometer Precipitation Algorithms

AbstractThe joint National Aeronautics and Space Administration (NASA) and Japanese Aerospace Exploration Agency (JAXA) Global Precipitation Measurement (GPM) is a constellation mission, centered upon observations from the core satellite dual-frequency precipitation radar (DPR) and its companion passive microwave (MW) GPM Microwave Imager (GMI). One of the key challenges for GPM is how to link the information from the single DPR across all passive MW sensors in the constellation, to produce a globally consistent precipitation product. Commonly, the associated surface emissivity and environmental conditions at the satellite observation time are interpolated from ancillary data, such as global forecast models and emissivity climatology, and are used for radiative transfer simulations and cataloging/indexing the brightness temperature (TB) observations and simulations within a common MW precipitation retrieval framework.In this manuscript, the feasibility of an update to the surface emissivity state at or near the satellite observation time, regardless of surface type, is examined for purposes of assisting these algorithms with specification of the surface and environmental conditions. Since the constellation MW radiometers routinely observe many more nonprecipitating conditions than precipitating conditions, a principal component analysis is developed from the noncloud GMI–DPR observations as a means to characterize the emissivity state vector and to consistently track the surface and environmental conditions. The method is demonstrated and applied over known complex surface conditions to probabilistically separate cloud and cloud-free scenes. The ability of the method to globally identify “self-similar” surface locations from the TB observations without requiring any ancillary knowledge of geographical location or time is demonstrated.