Dual-frequency Precipitation Radar Research Articles

Life Cycle of Precipitating Cloud Systems from Synergistic Satellite Observations: Evolution of Macrophysical Properties and Precipitation Statistics from Geostationary Cloud Tracking and GPM Active and Passive Microwave Measurements

Abstract Observations of clouds and precipitation in the microwave domain from the active dual-frequency precipitation radar (DPR) and the passive Global Precipitation Measurement (GPM) Microwave Imager (GMI) onboard the GPM Core Observatory satellite are used in synergy with cloud tracking information derived from infrared imagery from the GOES-13 and Meteosat-7 geostationary satellites for analysis of the life cycle of precipitating cloud systems, in terms of temporal evolution of their macrophysical characteristics, in several oceanic and continental regions of the tropics. The life cycle of each one of the several hundred thousand cloud systems tracked during the 2-yr (2015–16) analysis period is divided into five equal-duration stages between initiation and dissipation. The average cloud size, precipitation intensity, precipitation top height, and convective and stratiform precipitating fractions are documented at each stage of the life cycle for different cloud categories (based upon lifetime duration). The average life cycle dynamics is found remarkably homogeneous across the different regions and is consistent with previous studies: systems peak in size around midlife; precipitation intensity and convective fraction tend to decrease continuously from the initiation stage to the dissipation. Over the three continental regions, Amazonia (AMZ), central Africa (CAF), and Sahel (SAH), at the early stages of clouds’ life cycle, precipitation estimates from the passive GMI instrument are systematically found to be 15%–40% lower than active radar estimates. By highlighting stage-dependent biases in state-of-the-art passive microwave precipitation estimates over land, we demonstrate the potential usefulness of cloud tracking information for improving retrievals and suggest new directions for the synergistic use of geostationary and low-Earth-orbiting satellite 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.

Reconstruction of 3D DPR Observations Using GMI Radiances

AbstractThree‐dimensional global precipitation observation is crucial for understanding climate and weather dynamics. While spaceborne precipitation radars provide precise but limited observations, passive microwave imagers are available much more frequently. In this study, we propose a deep learning approach to reconstruct active radar observations using passive microwave radiances. We introduce the Hybrid Deep Neural Network (HDNN) model, which utilizes reflectivity profiles from the Dual‐frequency Precipitation Radar (DPR) onboard the Global Precipitation Measurement (GPM) Core Observatory Satellite as the “target” and combines radiances from the GPM Microwave Imager (GMI) with supplementary reanalysis data to serve as the “features.” Results underscore the HDNN's exemplary performance, with a root mean square error below 4 dBZ across all altitude levels, and a consistent accuracy across different precipitation types. Its efficacy is further illustrated when applied to typhoon cases of Haishen and Khanun, emerging as a superior tool for capturing 3D structures of expansive precipitation systems.

Sea Ice Detection Method Using the Dependence of the Radar Cross-Section on the Incidence Angle

The method for sea ice detection using the data from the Dual-frequency Precipitation Radar (DPR) onboard the Global Precipitation Measurement (GPM) satellite data is suggested. The approach is based on the analysis of the shape of normalized radar cross-section dependence on the incidence angle. The coefficient of kurtosis of surface slopes probability density function is introduced as a parameter to distinguish between open water and ice cover. The approach was validated using the data on sea ice concentration from the AMSR-2 radiometer in the Antarctic region.

Detection and characterization of hailstorms over France using DPR data onboard the GPM Core Observatory

Hailstorms cause heavy losses, especially when their hailstones reach a large size. One of the European regions most affected by these severe atmospheric events is southern France, where a valuable and extensive hailpad network has been operational for more than three decades. These direct observations are extremely useful because they allow for the definitive verification of hailfall at the ground. Space-based sensors have seen increasing importance in hail monitoring. Global Precipitation Measurement (GPM) is an international mission designed to advance precipitation measurements from multispectral sensors. The GPM core satellite carries a powerful and unprecedented Dual-Frequency Precipitation Radar (DPR) for studying 3D precipitation characteristics. The objective of the present study is to evaluate the DPR sensor ability to identify hailstorms. We identified eight hailstorms over France where DPR data were coincident with ground-based observations from a hailpad network during 2014–2021. In addition, variables provided by the DPR sensor indicative of hail presence were studied and five detection algorithms were tested. This research serves as background for future work and the development of prediction algorithms based on empirical relationships with GPM data.

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.

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.

Error modeling and hierarchical Bayesian fusion for spaceborne and ground radar rainfall data

The optimal fusion of multisource rainfall data can improve the accuracy and resolution of a posteriori estimates, which is beneficial for hydrological and meteorological applications. In this study, the errors of ground-based dual-polarization radar (GR) and dual-frequency precipitation radar (DPR) on global precipitation measurement (GPM) satellite rainfall estimation were modeled, and a hierarchical Bayesian (HB) framework was developed for GR and DPR rainfall data fusion. According to the matched optimal estimated GR rainfall and rain gauge data, the GR rainfall error was analyzed, and the error variance varying with rain type and intensity was described and modeled. The rain types were classified based on disdrometer and dual-polarization radar data. By comparing the DPR rainfall product with GR rainfall, the DPR rainfall error was divided into systematic and random errors. The systematic error was linearly fitted, and the probability distributions for the random error and the variable error parameters were analyzed and modeled. Because the parameters for GR and DPR rainfall errors varied with rain type and intensity, we developed a HB fusion approach considering the varying parameters in rainfall error modeling. The performance of the fusion algorithm was evaluated using matched GR and DPR cases, which showed that the HB fusion can effectively improve the consistency between the original DPR rainfall product and rain gauge data, reducing the relative bias by an average of 30% and improving the correlation by an average of 20%. Compared with the conventional Bayesian (CB) fusion algorithm, which is based on overall error modeling, the HB fusion results can improve the root mean square error by an average of 24% and relative bias by 14%.

Precipitation, microphysical and environmental characteristics for shallow and deep clouds over Yangtze-Huaihe River Basin

The precipitation, microphysical, and environmental characteristics of shallow and deep precipitation clouds (SPCs and DPCs) are investigated over the Yangtze-Huaihe River during the summers from 2014 to 2021, using the observational data from dual-frequency precipitation radar (DPR) on board Global Precipitation Mission and the reanalysis data. DPCs (SPCs) are mainly associated with convective (stratiform) precipitation, contributing approximately 2% ∼ 15% (20% ∼ 40%) to the total precipitation events, while contributing only around 0.05–0.15% (below 0.05%) to the total rainfall. Compared to SPCs, the rain hydrometeors for DPCs are mainly composed of lower concentrations of larger-sized hydrometeors near the surface, showing larger mass-weighted mean diameter (Dm: 1.0 to 3.0 mm) and lower generalized intercept parameter (Nw: 20 to 40 dBNw) at 2 km in altitude. Below the melting layer, the Dm, reflectivity, and rain rate of DPCs decrease rapidly as the decreasing altitude, reflecting the breakup or the evaporation of liquid hydrometeors, while those of SPCs rapidly increase, indicating vigorous processes of collision and coalescence. DPCs tend to occur in environmental conditions with increased convective available potential energy (CAPE) and higher surface air temperatures, but drier low-level humidity, compared to SPCs. Furthermore, the DPCs tend to occur in the environment with more significant updraft (≥5 m s−1) in comparison with SPCs. Especially at 4 km and 10 km in altitude, the updraft fractions could reach 45% and 38%, respectively. These results may provide an observational reference for the microphysical parameterization in numeric models for the vertical development of storms.

Are there any Differences in the Raindrop Size Distributions between Summer Typhoons and Autumn Typhoons？

ABSTRACT To improve typhoon forecasting in southern China, which is one of the most susceptible regions in China by typhoons, it is essential to investigate the microphysical characteristics of various types of typhoon precipitation. In this study, the dual-frequency precipitation radar (DPR) data from the Global Precipitation Measurement (GPM) satellite is adopted to reveal the raindrop size distribution (RSD) characteristics of 18 typhoons that made landfall in southern China during 2014 – 2022. The RSD characteristics of precipitation in the horizontal and vertical directions of summer typhoons (STs) and autumn typhoons (ATs) are examined. There is no obvious difference between the parameters corresponding to STs andATs without considering the precipitation types. The mass-weighted mean diameter (D m ) both STs and ATs are primarily composed of small-sized (D m <1.4 mm) and medium-sized (1.4 mm ≤ D m <2.4 mm) precipitation particles, while STs contain a higher proportion of large-sized (D m ≥2.4 mm) precipitation particles. Convective precipitation during STs contributes most to the large-sized precipitation particles. The STs and the ATs are both dominated by stratiform precipitation, the proportion of convective precipitation is higher in the STs than that in the ATs. While the strong precipitation centres of ATs are primarily made up of small-sized and medium-to-small-sized (1.4 mm ≤ D m <1.7 mm) precipitation particles, the strong precipitation centres of STs are primarily made up of medium-to-large-sized (1.7 mm ≤ D m <2.4 mm) precipitation particles at various vertical profiles. Compared to ATs, the storm height and freezing level height of STs are typically higher, and these variables may contribute to divert D m to relatively larger droplet diameters. An examination of the RSD characteristics of STs and ATs in southern China may assist in advancing our comprehension of the microphysical characteristics of typhoon precipitation.

Narrowing the Blind Zone of the GPM Dual-Frequency Precipitation Radar to Improve Shallow Precipitation Detection in Mountainous Areas

Abstract The Dual-Frequency Precipitation Radar (DPR), which consists of a Ku-band precipitation radar (KuPR) and a Ka-band precipitation radar (KaPR) on board the GPM Core Observatory, cannot observe precipitation at low altitudes near the ground contaminated by surface clutter. This near-surface region is called the blind zone. DPR estimates the clutter-free bottom (CFB), which is the lowest altitude not included in the blind zone, and estimates precipitation at altitudes higher than the CFB. High CFBs, which are common over mountainous areas, represent obstacles to detection of shallow precipitation and estimation of low-level enhanced precipitation. We compared KuPR data with rain gauge data from Da-Tun Mountain of northern Taiwan acquired from March 2014 to February 2020. A total of 12 cases were identified in which the KuPR missed some rainfall with intensity of &gt;10 mm h−1 that was observed by rain gauges. Comparison of KuPR profile and ground-based radar profile revealed that shallow precipitation in the KuPR blind zone was missed because the CFB was estimated to be higher than the lower bound of the range free from surface echoes. In the original operational algorithm, CFB was estimated using only the received power data of the KuPR. In this study, the CFB was identified by the sharp increase in the difference between the received powers of the KuPR and the KaPR at altitude affected by surface clutter. By lowering the CFB, the KuPR succeeded in detection and estimation of shallow precipitation. Significance Statement The Dual-Frequency Precipitation Radar (DPR) on board the GPM Core Observatory cannot capture precipitation in the low-altitude region near the ground contaminated by surface clutter. This region is called the blind zone. The DPR estimates the clutter-free bottom (CFB), which is the lower bound of the range free from surface echoes, and uses data higher than CFB. DPR consists of a Ku-band precipitation radar (KuPR) and a Ka-band precipitation radar (KaPR). KuPR missed some shallow precipitation more than 10 mm h−1 in the blind zone over Da-Tun Mountain of northern Taiwan because of misjudged CFB estimation. Using both the KuPR and the KaPR, we improved the CFB estimation algorithm, which lowered the CFB, narrowed the blind zone, and improved the capability to detect shallow precipitation.

Seasonal Differences of Precipitation and Microphysical Characteristics over the Asian Monsoon Region Using Spaceborne Dual-Frequency Precipitation Radar

Abstract This study aimed to reveal the seasonal climatic variations in the microphysical properties of precipitation over the Asian monsoon region. We used the Dual-Frequency Precipitation Radar satellite product aboard the Global Precipitation Measurement Mission Core Observatory for 8 years from 2014 to 2021 to statistically analyze the mass-weighted mean diameter (Dm) and frequency of heavy ice precipitation (graupel and hail). The results showed statistically significant seasonal changes. The microphysical characteristics of large Dm and frequent heavy ice precipitation were observed over the Indian subcontinent and Indochina Peninsula in the premonsoon season and over the western Himalayan region in the mature-monsoon season, which can be related to the intense and deeply developed precipitation systems. The relationship between precipitation rate and Dm was also examined. The results indicated that changes in Dm were not caused only by changes in precipitation rate but were probably induced by changes in precipitation characteristics. In terms of the relationship between the microphysical properties, heavy ice precipitation particles in the upper atmosphere above the melting layer were observed more frequently as Dm near the surface increased. We also studied lower-atmospheric instability by investigating the vertical gradients of the dry and moist static energies. The results indicated that instability properties were different; dry and wet instabilities were dominant in the premonsoon and monsoon seasons, respectively, consistent with the results of the precipitation characteristics. Significance Statement The purpose of this study was to reveal seasonal variations in precipitation microphysical characteristics, such as precipitation particle size and the existence of graupel and hail in the upper atmosphere, by climatological analysis over the Asian monsoon region. In previous studies, microphysical characteristics have mainly been addressed using ground-based observations. However, more sampling is needed to expand our understanding of climatological perspectives; therefore, we used recently available satellite observations. As a result, we found that precipitation particles at the surface were larger, and more graupel and hail existed in clouds in the premonsoon season when less precipitation was observed, compared to the mature-monsoon season when precipitation amount and frequency were abundant.

Can We Estimate the Uncertainty Level of Satellite Long-Term Precipitation Records?

Abstract Several decades of continuous improvements in satellite precipitation algorithms have resulted in fairly accurate level-2 precipitation products for local-scale applications. Numerous studies have been carried out to quantify random and systematic errors at individual validation sites and regional networks. Understanding uncertainties at larger scales, however, has remained a challenge. Temporal changes in precipitation regional biases, regime morphology, sampling, and observation-vector information content, all play important roles in defining the accuracy of satellite rainfall retrievals. This study considers these contributors to offer a quantitative estimate of uncertainty in recently produced global precipitation climate data record. Generated from intercalibrated observations collected by a constellation of passive microwave (PMW) radiometers over the course of 30 years, this data record relies on Global Precipitation Measurement (GPM) mission enterprise PMW precipitation retrieval to offer a long-term global monthly precipitation estimates with corresponding uncertainty at 5° scales. To address changes in the information content across different constellation members the study develops synthetic datasets from GPM Microwave Imager (GMI) sensor, while sampling- and morphology-related uncertainties are quantified using GPM’s dual-frequency precipitation radar (DPR). Special attention is given to separating precipitation into self-similar states that appear to be consistent across environmental conditions. Results show that the variability of bias patterns can be explained by the relative occurrence of different precipitation states across the regions and used to calculate product’s uncertainty. It is found that at 5° spatial scale monthly mean precipitation uncertainties in tropics can exceed 10%.

Removing Interfering Signals in Spaceborne Radar Data for Precipitation Detection at Very High Altitudes

Abstract This study investigated the effects of interfering signals on high-altitude precipitation extraction from spaceborne precipitation radar data. Data analyses were performed on the products of the Tropical Rainfall Measuring Mission Precipitation Radar (TRMM PR) and the Global Precipitation Measurement Core Observatory Dual-Frequency Precipitation Radar (GPM DPR) to clarify the effects of removing radio interferences and mirror images, particularly focusing on deep precipitation detection. The TRMM PR acquired precipitation data up to an altitude of approximately 20 km and occasionally captured interferences from artificial radio transmissions in specific areas. Artifacts could be distinguished as isolated profiles exhibiting almost constant radar reflectivity. The number of interferences affecting the TRMM PR gradually increased during the operation period of 1998–2013. A filter was introduced to separate the observed profiles into deep storms that reach the upper observation altitude and contamination caused by radio interference. The former frequently appeared over the Sahel area, where the observation upper limits are lowest. The removal of the latter, radio interference, improved the detection accuracy of the mean precipitation at high altitudes and considerably influenced specific low-precipitation areas such as the Middle East. This spatial feature–based filter allowed us to evaluate the results of screening based on noise limits that are implemented in standard algorithms. The GPM DPR Ku-band radar product contained other unwanted echoes due to the mirror images appearing as second-trip echoes contaminating the high-altitude statistics. Such second-trip echoes constitute a major portion of the echoes observed near the highest altitudes of deep storms. Significance Statement Understanding the current state of separation of naturally occurring precipitation signals from artificial interference signals in spaceborne radar data at altitudes of approximately 20 km is critical for gaining a comprehensive picture of the intensity and structure of precipitation systems. In the case of the TRMM PR data, artifacts could be distinguished as isolated profiles with an almost constant radar reflectivity, and interferences gradually increased during the operation period. The removal of radio interference considerably affects the statistics of extremely deep storms. Improved algorithms and observation techniques have expanded the observation coverage associated with the GPM DPR KuPR data, but there are interferences (mirror images) that should be removed for a thorough discussion of very high-altitude precipitation.

Joint Collaboration on Comparing NOAA’s Ground-Based Weather Radar and NASA–JAXA’s Spaceborne Radar

Abstract The National Aeronautics and Space Administration (NASA) and National Oceanic and Atmospheric Administration (NOAA) have a long and successful history of weather radar research. The NOAA ground-based radars—WSR-88D network—provide nationwide precipitation observations and estimates with advanced polarimetric capability. As a counterpart, the NASA–JAXA spaceborne radar—the Global Precipitation Measurement Dual-Frequency Precipitation Radar (GPM DPR)—has global coverage and higher vertical resolution than ground-based radars. While significant advances from both NOAA’s WSR-88D network and NASA–JAXA’s spaceborne radar DPR have been made, no systematic comparisons between the WSR-88D network and the DPR have been done. This study for the first time generates nationwide comprehensive comparisons at 136 WSR-88D radar sites from 2014 to 2020. Systematic differences in reflectivity are found, with ground radar reflectivity on average 2.4 dB smaller than that of the DPR (DPR version 6). This research found the discrepancies between WSR-88D and DPR arise from different calibration standards, signal attenuation correction, and differences in the ground and spaceborne scattering volumes. The recently updated DPR version 7 product improves rain detection and attenuation corrections, effectively reducing the overall average WSR-88D and DPR reflectivity differences to 1.0 dB. The goal of this study is to examine the systematic differences of radar reflectivity between the NOAA WSR-88D network and the NASA–JAXA DPR and to draw attention to radar-application users in recognizing their differences. Further investigation into understanding and alleviating the systematic bias between the two platforms is needed.

A Preliminary Analysis of Typical Structures and Microphysical Characteristics of Precipitation in Northeastern China Cold Vortexes

The northeastern China cold vortex (NCCV) is the main weather system affecting Northeast China. Based on the precipitation products from the dual-frequency precipitation radar (DPR) onboard the Global Precipitation Measurement core observatory (GPM) satellite, the precipitation structures and microphysical properties for different rain types in 6432 NCCVs from 2014 to 2019 were studied using dynamic composite analysis. Our results show that the precipitation in NCCVs is dominated by stratiform precipitation. Regions with high stratiform and convective precipitation frequency have a comma shape. The growth mechanism of precipitation particles changes at ~4 km in altitude, the lower particles grow through collision (more pronounced in convective precipitation), and the upper hydrometeors grow through the Bergeron process. Additionally, the precipitation structures and microphysical properties exhibit great regional variations in NCCVs. The rainfall for all rain types is the strongest in the southeast region within an NCCV, mainly characterized by higher near-surface droplet concentration, while precipitation events occur more frequently in the southeast region for all rain types. There are active rimming growth processes above the melting layer for convective precipitation in the western region of an NCCV. In the southeast region of an NCCV, the collision growth of droplets in both types of precipitation is the most obvious.

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.

A Deep-Learning Scheme for Hydrometeor Type Classification Using Passive Microwave Observations

This paper proposes a novel approach for hydrometeor classification using passive microwave observations. The use of passive measurements for this purpose has not been extensively explored, despite being available for over four decades. We utilize the Micro-Wave Humidity Sounder-2 (MWHS-2) to relate microwave brightness temperatures to hydrometeor types derived from the global precipitation measurement’s (GPM) dual-frequency precipitation radar (DPR), which are classified into liquid, mixed, and ice phases. To achieve this, we utilize a convolutional neural network model with an attention mechanism that learns feature representations of MWHS-2 observations from spatial and temporal dimensions. The proposed algorithm classified hydrometeors with 84.7% accuracy using testing data and captured the geographical characteristics of hydrometeor types well in most areas, especially for frozen precipitation. We then evaluated our results by comparing predictions from a different year against DPR retrievals seasonally and globally. Our global annual cycles of precipitation occurrences largely agreed with DPR retrievals with biases being 8.4%, −11.8%, and 3.4%, respectively. Our approach provides a promising direction for utilizing passive microwave observations and deep-learning techniques in hydrometeor classification, with potential applications in the time-resolved observations of precipitation structure and storm intensity with a constellation of smallsats (TROPICS) algorithm development.

Impacts of Shape Assumptions on Z–R Relationship and Satellite Remote Sensing Clouds Based on Model Simulations and GPM Observations

In this study, the spherical particle model and ten nonspherical particle models describing the scattering properties of snow are evaluated for potential use in precipitation estimation from spaceborne dual-frequency precipitation radar. The single scattering properties of nonspherical snow particles are computed using discrete dipole approximation (DDA), while those of spherical particles are determined using Mie theory. The precipitation profiles from WRF output are then input to a forward radiative transfer model to simulate the radar reflectivity at Ka-band and Ku-band. The results are validated with Global Precipitation Mission Dual-Frequency Precipitation Radar measurements. Greater consistency between the simulated and observed reflectivity is obtained when using the sector- and dendrite-shape assumptions. For the case in this study, when using the spherical-shape assumption, radar underestimates the error of the cloud’s top by about 300 m and underestimates the error of the cloud’s area by about 15%. As snowflake shapes change with temperature, we use the range between −40 °C and −5 °C to define three temperature layers. The relationships between reflectivity (Z) and precipitation rate (R) are fitted separately for the three layers, resulting in Z=134.59·R1.184 (sector) and Z=127.35·R1.221 (dendrite) below −40 °C.

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.