A Robust Dual-Frequency Radar Profiling Algorithm

AbstractIn this study, an algorithm to retrieve precipitation from spaceborne dual-frequency (13.8 and 35.6 GHz, or Ku/Ka band) radar observations is formulated and investigated. Such algorithms will be of paramount importance in deriving radar-based and combined radar–radiometer precipitation estimates from observations provided by the forthcoming NASA Global Precipitation Measurement (GPM) mission. In GPM, dual-frequency Ku-/Ka-band radar observations will be available only within a narrow swath (approximately one-half of the width of the Ku-band radar swath) over the earth’s surface. Therefore, a particular challenge is to develop a flexible radar retrieval algorithm that can be used to derive physically consistent precipitation profile estimates across the radar swath irrespective of the availability of Ka-band radar observations at any specific location inside that swath, in other words, an algorithm capable of exploiting the information provided by dual-frequency measurements but robust in the absence of Ka-band channel. In the present study, a unified, robust precipitation retrieval algorithm able to interpret either Ku-only or dual-frequency Ku-/Ka-band radar observations in a manner consistent with the information content of the observations is formulated. The formulation is based on 1) a generalized Hitschfeld–Bordan attenuation correction method that yields generic Ku-only precipitation profile estimates and 2) an optimization procedure that adjusts the Ku-band estimates to be physically consistent with coincident Ka-band reflectivity observations and surface reference technique–based path-integrated attenuation estimates at both Ku and Ka bands. The algorithm is investigated using synthetic and actual airborne radar observations collected in the NASA Tropical Composition, Cloud, and Climate Coupling (TC4) campaign. In the synthetic data investigation, the dual-frequency algorithm performed significantly better than a single-frequency algorithm; dual-frequency estimates, however, are still sensitive to various assumptions such as the particle size distribution shape, vertical and cloud water distributions, and scattering properties of the ice-phase precipitation.

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An Initial Assessment of the Surface Reference Technique Applied to Data from the Dual-Frequency Precipitation Radar (DPR) on the GPM Satellite

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-15-0044.1 ◽

2015 ◽

Vol 32 (12) ◽

pp. 2281-2296 ◽

Cited By ~ 35

Author(s):

Robert Meneghini ◽

Hyokyung Kim ◽

Liang Liao ◽

Jeffrey A. Jones ◽

John M. Kwiatkowski

Keyword(s):

Cross Section ◽

Radar Data ◽

Single Frequency ◽

Initial Assessment ◽

Dual Frequency ◽

Precipitation Radar ◽

Frequency Method ◽

Ka Band ◽

Spaceborne Radar ◽

Ku Band

AbstractIt has long been recognized that path-integrated attenuation (PIA) can be used to improve precipitation estimates from high-frequency weather radar data. One approach that provides an estimate of this quantity from airborne or spaceborne radar data is the surface reference technique (SRT), which uses measurements of the surface cross section in the presence and absence of precipitation. Measurements from the dual-frequency precipitation radar (DPR) on the Global Precipitation Measurement (GPM) satellite afford the first opportunity to test the method for spaceborne radar data at Ka band as well as for the Ku-band–Ka-band combination.The study begins by reviewing the basis of the single- and dual-frequency SRT. As the performance of the method is closely tied to the behavior of the normalized radar cross section (NRCS or σ0) of the surface, the statistics of σ0 derived from DPR measurements are given as a function of incidence angle and frequency for ocean and land backgrounds over a 1-month period. Several independent estimates of the PIA, formed by means of different surface reference datasets, can be used to test the consistency of the method since, in the absence of error, the estimates should be identical. Along with theoretical considerations, the comparisons provide an initial assessment of the performance of the single- and dual-frequency SRT for the DPR. The study finds that the dual-frequency SRT can provide improvement in the accuracy of path attenuation estimates relative to the single-frequency method, particularly at Ku band.

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Estimating Mean Surface Backscatter from GPM Surface Backscatter Observations

Eng ◽

10.3390/eng2040031 ◽

2021 ◽

Vol 2 (4) ◽

pp. 492-500

Author(s):

Stephen L. Durden

Keyword(s):

Adaptive Method ◽

Spatial Inhomogeneity ◽

Sample Variance ◽

Single Frequency ◽

Optimal Interpolation ◽

Dual Frequency ◽

Sample Standard Deviation ◽

Spatial Homogeneity ◽

Ka Band ◽

Ku Band

The radar on the Global Precipitation Measurement (GPM) mission observes precipitation at 13.6 GHz (Ku-band) and 35.6 GHz (Ka-band) and also receives echoes from the earth’s surface. Statistics of surface measurements for non-raining conditions are saved in a database for later use in estimating the precipitation path-integrated attenuation. Previous work by Meneghini and Jones (2011) showed that while averaging over larger latitude/longitude bins increase the number of samples, it can also increase sample variance due to spatial inhomogeneity in the data. As a result, Meneghini and Kim (2017) proposed a new, adaptive method of database construction, in which the number of measurements averaged depends on the spatial homogeneity. The purpose of this work is to re-visit previous, single-frequency results using dual-frequency data and optimal interpolation (kriging). Results include that (1) temporal inhomogeneity can create similar results as spatial, (2) Ka-band behavior is similar to Ku-band, (3) the Ku-/Ka-band difference has less spatial inhomogeneity than either band by itself, and (4) kriging and the adaptive method can reduce the sample variance. The author concludes that finer spatial and temporal resolution is necessary in constructing the database for single frequencies but less so for the Ku-/Ka-band difference. The adaptive approach reduces sample standard deviation with a relatively modest computational increase.

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Cross-Validation of Observations between the GPM Dual-Frequency Precipitation Radar and Ground Based Dual-Polarization Radars

Remote Sensing ◽

10.3390/rs10111773 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1773 ◽

Cited By ~ 14

Author(s):

Sounak Biswas ◽

V. Chandrasekar

Keyword(s):

Large Data ◽

Radar Data ◽

Dual Frequency ◽

Dual Polarization ◽

Precipitation Radar ◽

Data Set ◽

Ka Band ◽

Radar Observations ◽

Radar Calibration ◽

Ku Band

The Global Precipitation Measurement (GPM) mission Core Observatory is equipped with a dual-frequency precipitation radar (DPR) with capability of measuring precipitation simultaneously at frequencies of 13.6 GHz (Ku-band) and 35.5 GHz (Ka-band). Since the GPM-DPR cannot use information from polarization diversity, radar reflectivity factor is the most important parameter used in all retrievals. In this study, GPM’s observations of reflectivity at dual-frequency and instantaneous rainfall products are compared quantitatively against dual-polarization ground-based NEXRAD radars from the GPM Validation Network (VN). The ground radars, chosen for this study, are located in the southeastern plains of the U.S.A. with altitudes varying from 5 to 210 m. It is a challenging task to quantitatively compare measurements from space-based and ground-based platforms due to their difference in resolution volumes and viewing geometry. To perform comparisons on a point-to-point basis, radar observations need to be volume matched by averaging data in common volume or by re-sampling data to a common grid system. In this study, a 3-D volume matching technique first proposed by Bolen and Chandrasekar (2003) and later modified by Schwaller and Morris (2011) is applied to both radar data. DPR and ground radar observations and products are cross validated against each other with a large data set. Over 250 GPM overpass cases at 5 NEXRAD locations, starting from April 2014 to June 2018, have been considered. Analysis shows that DPR Ku- and Ka-Band reflectivities are well matched with ground radar with correlation coefficient as high as 0.9 for Ku-band and 0.85 for Ka-band. Ground radar calibration is also checked by observing variation in mean biases of reflectivity between DPR and GR over time. DPR rainfall products are also evaluated. Though DPR underestimates higher rainfall rates in convective cases, its overall performance is found to be satisfactory.

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

Remote Sensing ◽

10.3390/rs13224565 ◽

2021 ◽

Vol 13 (22) ◽

pp. 4565

Author(s):

Maria Panfilova ◽

Vladimir Karaev

Keyword(s):

Wind Speed ◽

Good Accuracy ◽

Retrieval Algorithm ◽

Dual Frequency ◽

Precipitation Radar ◽

Precipitation Measurement ◽

Global Precipitation Measurement ◽

Global Precipitation ◽

Ku Band ◽

Wide Swath

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.

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The Life-Cycle of Cloud and Precipitation Microphysics in Radar Observation and Numerical Model

10.5194/egusphere-egu21-15328 ◽

2021 ◽

Author(s):

Gregor Möller ◽

Florian Ewald ◽

Silke Groß ◽

Martin Hagen ◽

Christoph Knote ◽

...

Keyword(s):

Prediction Models ◽

Model Simulation ◽

Weather Prediction ◽

Horizontal Resolution ◽

Polarimetric Radar ◽

Dual Frequency ◽

Microphysics Scheme ◽

Ka Band ◽

Radar Observations ◽

Conversion Rates

The representation of microphysical processes in numerical weather prediction models remains a main source of uncertainty. To tackle this issue, we exploit the synergy of two polarimetric radars to provide novel observations of model microphysics parameterizations. In the framework of the IcePolCKa project (Investigation of the initiation of Convection and the Evolution of Precipitationusing simulatiOns and poLarimetric radar observations at C- and Ka-band) we use these observations to study the initiation of convection as well as the evolution of precipitation. At a distance of 23 km between the C-band PoldiRad radar of the German Aerospace Center (DLR) in Oberpfaffenhofen and the Ka-band Mira35 radar of the Ludwig-Maximilians-University of Munich (LMU), the two radar systems allow targeted observations and coordinated scan patterns. A second C-band radar located in Isen and operated by the German Weather Service (DWD) provides area coverage and larger spatial context. By tracking the precipitation movement, the dual-frequency and polarimetric radar observations allow us to characterize important microphysical parameters, such as predominant hydrometeor class or conversion rates between these classes over a significant fraction of the life time of a convective cell. A WRF (Weather Research and Forecasting Model) simulation setup has been established including a Europe-, a nested Germany- and a nested Munich- domain. The Munich domain covers the overlap area of our two radars Mira35 and Poldirad with a horizontal resolution of 400 m. For each of our measurement days we conduct a WRF hindcast simulation with differing microphysics schemes. To allow for a comparison between model world and observation space, we make use of the radar forward-simulator CR-SIM. The measurements so far include 240 coordinated scans of 36 different convective cells over 10 measurement days between end of April and mid July 2019 as well as 40 days of general dual-frequency volume scans between mid April and early October 2020. The performance of each microphysics scheme is analyzed through a comparison to our radar measurements on a statistical basis over all our measurements.

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Cross-validation of microwave snowfall products over the continental United States

10.5194/egusphere-egu21-9971 ◽

2021 ◽

Author(s):

Kamil Mroz ◽

Mario Montopoli ◽

Giulia Panegrossi ◽

Luca Baldini ◽

Alessandro Battaglia ◽

...

Keyword(s):

United States ◽

Skill Score ◽

Single Frequency ◽

Retrieval Algorithm ◽

Dual Frequency ◽

Precipitation Measurement ◽

Composite Product ◽

Low Sensitivity ◽

Global Precipitation Measurement ◽

Global Precipitation

In this talk, surface snowfall rate estimates from the Global Precipitation Measurement (GPM) mission&#8217;s Core Observatory sensors and the CloudSat radar are compared to those from the Multi-Radar Multi-Sensor (MRMS) radar composite product over the continental United States. The analysis spans a period between Nov. 2014 and Sept. 2020 and covers the following products: the Dual-Frequency Precipitation Radar&#160;product&#160;(2A.GPM.DPR) and its single frequency counterparts (2A.GPM.Ka, 2A.GPM.Ku); GPM Combined Radar Radiometer Algorithm (2B.GPM.DPRGMI.CORRA); the CloudSat Snow Profile product (2C-SNOW-PROFILE) and two passive microwave retrievals i.e. the Goddard PROFiling algorithm (2A.GPM.GMI.GPROF) and the Snow retrievaL ALgorithm fOr gMi (SLALOM).&#160;The 2C-SNOW product has the highest Heidke Skill Score (HSS=75%) for detecting snowfall among all the analysed products. SLALOM ranks the second (60%) while the Ka-band products falls at the end of the spectrum, with the HSS of 10% only. Low detection capabilities of the DPR products are a result of its low sensitivity. All the GPM retrievals underestimate not only the snow occurances but also snowfall volumes. Underestimation by a factor of two is present for all the GPM products compared to MRMS data. Large discrepancies (RMSE of 0.7 to 1.5 mm/h) between space-borne and ground-based snowfall rate estimates can be attributed to the complexity of ice scattering properties and differences in the algorithms' assumptions.

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Evaluation of GPM-DPR precipitation estimates with WegenerNet gauge data

Atmospheric Measurement Techniques ◽

10.5194/amt-12-5055-2019 ◽

2019 ◽

Vol 12 (9) ◽

pp. 5055-5070 ◽

Cited By ~ 2

Author(s):

Martin Lasser ◽

Sungmin O ◽

Ulrich Foelsche

Keyword(s):

Rainfall Variability ◽

Radar Data ◽

General Level ◽

Dual Frequency ◽

Ka Band ◽

Surface Precipitation ◽

Precipitation Estimates ◽

Radar Rain Rate ◽

Rain Rates ◽

Ku Band

Abstract. The core satellite of the Global Precipitation Measurement (GPM) mission provides precipitation observations measured with the Dual-frequency Precipitation Radar (DPR). The precipitation can only be estimated from the radar data, and therefore independent validations using direct precipitation measurements on the ground as a true reference need to be performed. Moreover, the quality and the accuracy of satellite observational data depend on various influencing factors, such as altitude, topography and rainfall type. In this way, a validation may help to minimise those uncertainties. The DPR Level 2 algorithms provide three different sets of radar rain rate estimates: Ku-band-only rain rates, Ka-band-only rain rates, and a product using both the Ku and Ka band. This study presents an evaluation of the three GPM-DPR surface precipitation estimates based on the gridded precipitation data of the WegenerNet, a local-scale terrestrial network of 153 meteorological stations in southeastern Austria. The validation is based on graphical and statistical approaches, using only data where both Ku- and Ka-band measurements are available. The focus lies on the resemblance of the rainfall variability within the whole network and the over- and underestimation of the precipitation through the GPM-DPR. The analysis rests upon 15 rainfall events observed by the GPM-DPR over the WegenerNet in the last 4 years; the meteorological winter is excluded due to technical challenges of snow measurements. The WegenerNet provides between 8 and 12 gauges within each GPM-DPR footprint. Its biases are well studied and corrected; thus, it can be taken as a robust ground reference. This work also includes considerations on the limits of such comparisons between small terrestrial networks with a high density of stations and precipitation observations from a satellite. Our results show that the GPM-DPR estimates basically match with the WegenerNet measurements, but absolute quantities are biased. The three types of radar estimates deliver similar results, where Ku-band and dual-frequency estimates are very close to each other. On a general level, Ka-band precipitation estimates deliver better results due to their greater sensitivity to low rainfall rates.

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Retrieval of snowflake microphysical properties from multi-frequency radar observations

10.5194/amt-2018-73 ◽

2018 ◽

Cited By ~ 1

Author(s):

Jussi Leinonen ◽

Matthew D. Lebsock ◽

Simone Tanelli ◽

Ousmane O. Sy ◽

Brenda Dolan ◽

...

Keyword(s):

Single Frequency ◽

Parameter Estimates ◽

Dual Frequency ◽

Snow Density ◽

Radar Observations ◽

Microphysical Properties ◽

Triple Frequency ◽

Multiple Frequencies ◽

The Difference

Abstract. We have developed an algorithm that retrieves the microphysical properties of falling snow from multi-frequency radar observations. This work builds on previous studies that have indicated that three-frequency radars can provide information on snow density, potentially improving the accuracy of snow parameter estimates. The algorithm is based on a Bayesian framework, using lookup tables mapping the measurement space to the state space, which allows fast and robust retrieval. In the forward model, we calculate the radar reflectivities using recently published snow scattering databases. We demonstrate the algorithm using multi-frequency airborne radar observations from the OLYMPEX/RADEX field campaign, comparing the retrieval results to hydrometeor identification using ground-based polarimetric radar, and also to collocated in situ observations made using another aircraft. Using these data, we examine how the availability of multiple frequencies affects the retrieval accuracy, and test the sensitivity of the algorithm to the prior assumptions. The results suggest that multi-frequency radars are substantially better than single-frequency radars at retrieving snow microphysical properties. Meanwhile, triple-frequency radars can retrieve wider ranges of snow density than dual-frequency radars, and better locate regions of high-density snow such as graupel, although these benefits are relatively modest compared to the difference in retrieval performance between dual- and single-frequency radars.

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Classification of Sea Ice Types in the Arctic by Radar Echoes from SARAL/AltiKa

Remote Sensing ◽

10.3390/rs13163183 ◽

2021 ◽

Vol 13 (16) ◽

pp. 3183

Author(s):

Renée Mie Fredensborg Hansen ◽

Eero Rinne ◽

Henriette Skourup

Keyword(s):

Sea Ice ◽

Leading Edge ◽

Open Water ◽

Classification Performance ◽

The Arctic ◽

Classification Algorithms ◽

Dual Frequency ◽

Ka Band ◽

Ku Band

An important step in the sea ice freeboard to thickness conversion is the classification of sea ice types, since the ice type affects the snow depth and ice density. Studies using Ku-band CryoSat-2 have shown promise in distinguishing FYI and MYI based on the parametrisation of the radar echo. Here, we investigate applying the same classification algorithms that have shown success for Ku-band measurements to measurements acquired by SARAL/AltiKa at the Ka-band. Four different classifiers are investigated, i.e., the threshold-based, Bayesian, Random Forest (RF) and k-nearest neighbour (KNN), by using data from five 35 day cycles during Arctic mid-winter in 2014–2018. The overall classification performance shows the highest accuracy of 93% for FYI (Bayesian classifier) and 39% for MYI (threshold-based classifier). For all classification algorithms, more than half of the MYI cover falsely classifies as FYI, showing the difference in the surface characteristics attainable by Ka-band compared to Ku-band due to different scattering mechanisms. However, high overall classification performance (above 90%) is estimated for FYI for three supervised classifiers (KNN, RF and Bayesian). Furthermore, the leading-edge width parameter shows potential in discriminating open water (ocean) and sea ice when visually compared with reference data. Our results encourage the use of waveform parameters in the further validation of sea ice/open water edges and discrimination of sea ice types combining Ka- and Ku-band, especially with the planned launch of the dual-frequency altimeter mission Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL) in 2027.

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Intercomparison of Attenuation Correction Methods for the GPM Dual-Frequency Precipitation Radar

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-14-00065.1 ◽

2015 ◽

Vol 32 (5) ◽

pp. 915-926 ◽

Cited By ~ 33

Author(s):

Shinta Seto ◽

Toshio Iguchi

Keyword(s):

Attenuation Correction ◽

Heavy Precipitation ◽

Frequency Measurement ◽

Correction Method ◽

New Method ◽

Single Frequency ◽

Dual Frequency ◽

Precipitation Radar ◽

Global Precipitation ◽

Better Than

AbstractA new attenuation correction method has been developed for the dual-frequency precipitation radar (DPR) on the core satellite of the Global Precipitation Measurement (GPM) mission. The new method is based on Hitschfeld and Bordan’s attenuation correction method (HB method), but the relationship between the specific attenuation k and the effective radar reflectivity factor Ze (k–Ze relationship) is modified by using the dual-frequency ratio (DFR) of Ze and the surface reference technique (SRT). Therefore, the new method is called the HB-DFR-SRT method (H-D-S method). The previous attenuation correction method, called the HB-DFR method (H-D method), results in an underestimation of precipitation rates for heavy precipitation, but the H-D-S method mitigates the negative bias by means of the SRT. When only a single-frequency measurement is available, the H-D-S method is identical to the HB-SRT method (H-S method).The attenuation correction methods were tested with a simple synthetic DPR dataset. As long as the SRT gives perfect estimates of path-integrated attenuation and the adjustment factor of the k–Ze relationship (denoted by ε) is vertically constant, the H-S method is much better than the dual-frequency methods. Tests with SRT error and vertical variation in ε showed that the H-D method was better than the H-S method for weak precipitation, whereas the H-S method was better than the H-D method for heavy precipitation. The H-D-S method did not produce the best results for both weak and heavy precipitation, but the results are stable. Quantitative evaluation should be done with real DPR measurement datasets.

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