Dual-frequency Precipitation Radar Data Research Articles

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.

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.

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.

Wind Speed Retrieval Algorithm Using Ku-Band Radar Onboard GPM Satellite

The algorithm to retrieve wind speed in a wide swath from the normalized radar cross section (NRCS) was developed for the data of Dual Frequency Precipitation Radar (DPR) operating in scanning mode installed onboard a Global Precipitation Measurement (GPM) satellite. The data for Ku-band radar were used. Equivalent NRCS values at nadir were estimated in a wide swath under the geometrical optics approximation from off-nadir observations. Using these equivalent NRCS nadir values and the sea buoys data, the new parameterization of dependence between NRCS at nadir and the wind speed was obtained. The algorithm was validated using ASCAT (Advanced Scatterometer) data and revealed good accuracy. DPR data are promising for determining wind speed in coastal areas.

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.

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.

Monitoring of Inland Waters by Dual-Frequency Precipitation Radar: First Results

Permanent monitoring of inland waters in cold regions with long-time snow and ice covers is very important for the population safety. Modern spaceborne radars can significantly extend the capabilities of the terrestrial hydrological network, since they enable one to perform measurements over large areas. In 2014, the Japan Aerospace Exploration Agency (JAXA) launched the spaceborne dual-frequency precipitation radar (DPR). The main objective of the mission is the precipitation measurement. However, the radar cross section (RCS) measured at the maximal range contains information about the scattering surface. For the first time, DPR measurements over the Russian territory were processed. The sources of in situ data were hydrological stations. The Gorky reservoir and the Volga River were selected for sensing small-area inland waters. The data processing confirmed the possibility of detecting inland waters whose area is significantly smaller than the radar footprint. A ground-to-water ratio was introduced to estimate the open water area inside the footprint. Processing of the DPR data obtained for the Lake Baikal enabled us to calculate the dependence of the RCS on the incidence angle and propose a novel algorithm for separating open water and ice cover. The Moksha River was selected as a wetland with many small rivers. The data processing has shown that the spring change of inland waters and soil can be observed in the DPR image.

Documentation of multifactorial relationships between precipitation and topography of the Tibetan Plateau using spaceborne precipitation radars

Precipitation is one of the most important hydrometeorological components of the Tibetan Plateau (TP). The relationships between precipitation and topography in the TP were investigated using 17 years of Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) data and 2 years of Global Precipitation Measurement (GPM) dual-frequency precipitation radar (DPR) data. First, the retrieval and total errors of the two radars were quantified in the TP using ground-based precipitation datasets. The TRMM PR and GPM DPR both significantly underestimate the precipitation, with total errors up to 40% and 53%, respectively. Then, precipitation of the two radars was corrected prior to effective application. The correction was separately performed for each river basin in the TP by adding total errors. Finally, precipitation–topography relationships were documented using corrected radar data. The main findings on precipitation–topography relationships are: (1) precipitation generally decreases with increasing elevation from 2 to 6 km in the TP. However, the trend is inverse in the Qaidam Basin which is characterized by extremely low precipitation in the interior basin and high precipitation along the windward slopes of surrounding mountains; (2) quantitative relationships between precipitation and elevation can be depicted for most exorheic basins in the TP using exponential fitting. The coefficients of determination for the fitting are higher than 0.9 in six basins; (3) the relationships based on GPM DPR data are less robust due to its fewer samples, whereas the GPM DPR probably resolves the problem of overestimating precipitation in some grid pixels with high elevation, compared to the TRMM PR; (4) three typical mountains (i.e., Himalaya, Hengduan, and Tianshan mountains) exhibit significantly negative correlation between precipitation and elevation. Particularly, there are two bands of precipitation maxima along the southern slopes of the central Himalayas (78°E–88°E), corresponding to the two-step topography, which affects the vegetation distribution. This study provides insights into the precipitation–topography relationships in the TP and its surrounding regions based on data from spaceborne precipitation radars.

The First Results of Monitoring the Formation and Destruction of the Ice Cover in Winter 2014–2015 on Ilmen Lake according to the Measurements of Dual-Frequency Precipitation Radar

The launch of the Dual-frequency Precipitation Radar (DPR) opens up new opportunities for studying and monitoring the land and inland waters. It is the first time radar with a swath (±65°) covering regions with cold climate where waters are covered with ice and land with snow for prolonged periods of time has been used. It is also the first time that the remote sensing is carried out at small incidence angles (less than 19°) at two frequencies (13.6 and 35.5 GHz). The high spatial resolution (4–5 km) significantly increases the number of objects that can be studied using the new radar. Ilmen Lake is chosen as the first test object for the development of complex programs for processing and analyzing data obtained by the DPR. The problem of diagnostics of ice-cover formation and destruction according to DPR data has been considered. It is shown that the dependence of the radar backscatter cross section on the incidence angle for autumn ice is different from that of spring ice, and can be used for classification. A comparison with scattering on the water surface has shown that, at incidence angles exceeding 10°, it is possible to discern all three types of reflecting surfaces: open water, autumn ice, and spring ice, under the condition of making repeated measurements to avoid possible ambiguity caused by wind.

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.

Observed differences of triple-frequency radar signatures between snowflakes in stratiform and convective clouds

Observed triple-frequency signatures of snowflakes are investigated using a coincident dataset of the CloudSat Cloud Profiling Radar (CPR) and the Global Precipitation Measurement Dual-frequency Precipitation Radar (DPR) over ocean. A clear difference is noticed in the triple-frequency signatures between stratiform and convective clouds. Analyzing modeled backscattering properties of various snow particles, it is shown that this difference is likely to be a result of prevalent particle modes occurring in stratiform and convective clouds. Namely, snowflakes in stratiform clouds are mainly large unrimed particles (type B), while convective clouds contain abundant considerably aggregated and/or rimed particles(type C). Those very small particles with similarly weak triple-frequency signatures (type A) are less observed in both cloud types. Based on 2-year combined CPR and DPR data, the relation between snowflake types as defined above and air temperature is investigated. Results show that in stratiform clouds, type B particles dominate in warm temperature regions, while type C particles are frequently populated in cold temperature regions. In convective clouds, type B particles take up a large portion at warmer temperature regions, and type C particles are dominant across regions of a broad temperature range. This work demonstrates the potential of triple-frequency radar measurements in improving the understanding of snowflake microphysics and the modeling of snowflake single scattering properties.

Развитие гидрологической обстановки на реках по данным двухчастотного дождевого радиолокатора: первые результаты

Развитие гидрологической обстановки на реках по данным двухчастотного дождевого радиолокатора: первые результаты

Оценка граничного волнового числа для двухмасштабной модели морской поверхности в Ka- и Ku-диапазонах по данным двухчастотного дождевого радиолокатора

Оценка граничного волнового числа для двухмасштабной модели морской поверхности в Ka- и Ku-диапазонах по данным двухчастотного дождевого радиолокатора

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.