scholarly journals Retrieval of snowflake microphysical properties from multifrequency radar observations

2018 ◽  
Vol 11 (10) ◽  
pp. 5471-5488 ◽  
Author(s):  
Jussi Leinonen ◽  
Matthew D. Lebsock ◽  
Simone Tanelli ◽  
Ousmane O. Sy ◽  
Brenda Dolan ◽  
...  
Keyword(s):  
A Priori ◽  
Number Concentration ◽  
Single Frequency ◽  
Parameter Estimates ◽  
Dual Frequency ◽  
Snow Density ◽  
Radar Observations ◽  
Microphysical Properties ◽  
Triple Frequency ◽  
Multifrequency Radar

Abstract. We have developed an algorithm that retrieves the size, number concentration and density of falling snow from multifrequency 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 multifrequency 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 we test the sensitivity of the algorithm to the prior assumptions. The results suggest that multifrequency 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. We also examine the sensitivity of the retrieval results to the fixed a priori assumptions in the algorithm, showing that the multifrequency method can reliably retrieve snowflake size, while the retrieved number concentration and density are affected significantly by the assumptions.

scholarly journals Retrieval of snowflake microphysical properties from multi-frequency radar observations

2018 ◽  
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.

scholarly journals A Robust Dual-Frequency Radar Profiling Algorithm

2011 ◽  
Vol 50 (7) ◽  
pp. 1543-1557 ◽  
Author(s):  
Mircea Grecu ◽  
Lin Tian ◽  
William S. Olson ◽  
Simone Tanelli
Keyword(s):  
Synthetic Data ◽  
Optimization Procedure ◽  
Correction Method ◽  
Single Frequency ◽  
Retrieval Algorithm ◽  
Dual Frequency ◽  
Ka Band ◽  
Radar Observations ◽  
Distribution Shape ◽  
Ku Band

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.

scholarly journals Retrieving microphysical properties of concurrent pristine ice and snow using polarimetric radar observations

2021 ◽  
Author(s):  
Nicholas J. Kedzuf ◽  
J. Christine Chiu ◽  
Venkatachalam Chandrasekar ◽  
Sounak Biswas ◽  
Shashank S. Joshil ◽  
...  
Keyword(s):  
Water Content ◽  
Number Concentration ◽  
Radiation Budget ◽  
Radar Signal ◽  
Polarimetric Radar ◽  
Radar Observations ◽  
Microphysical Properties ◽  
Uncertainty Estimates ◽  
Ice Water Content ◽  
Ice Water

Abstract. Ice and mixed phase clouds play a key role in our climate system, because of their strong controls on global precipitation and radiation budget. Their microphysical properties have been characterized commonly by polarimetric radar measurements. However, there remains a lack of robust estimates of microphysical properties of concurrent pristine ice and aggregates, because larger snow aggregates often dominate the radar signal and mask contributions of smaller pristine ice crystals. This paper presents a new method that separates the scattering signals of pristine ice embedded in snow aggregates in scanning polarimetric radar observations and retrieves their respective abundances and sizes for the first time. This method, dubbed ENCORE-ice, is built on an iterative stochastic ensemble retrieval framework. It provides number concentration, ice water content, and effective mean diameter of pristine ice and snow aggregates with uncertainty estimates. Evaluations against synthetic observations show that the overall retrieval biases in the combined total microphysical properties are within 5 %, and that the errors with respect to the truth are well within the retrieval uncertainty. The partitioning between pristine ice and snow aggregates also agrees well with the truth. Additional evaluations against in-situ cloud probe measurements from a recent campaign for a stratiform cloud system are promising. Our median retrievals have a bias of 98 % in total ice number concentration and 44 % in total ice water content. This performance is generally better than the retrieval from empirical relationships. The ability to separate signals of different ice species and to provide their quantitative microphysical properties will open many research opportunities, such as secondary ice production studies and model evaluations for ice microphysical processes.

Triple-Frequency Radar Reflectivity Signatures of Snow: Observations and Comparisons with Theoretical Ice Particle Scattering Models

2014 ◽  
Vol 53 (4) ◽  
pp. 1080-1098 ◽  
Author(s):  
Mark S. Kulie ◽  
Michael J. Hiley ◽  
Ralf Bennartz ◽  
Stefan Kneifel ◽  
Simone Tanelli
Keyword(s):  
Microwave Remote Sensing ◽  
Valuable Insight ◽  
Particle Scattering ◽  
Dual Frequency ◽  
Modeling Framework ◽  
Microphysical Properties ◽  
Triple Frequency ◽  
W Band ◽  
Retrieval Algorithms ◽  
Wakasa Bay

AbstractAn observation-based study is presented that utilizes aircraft data from the 2003 Wakasa Bay Advanced Microwave Scanning Radiometer Precipitation Validation Campaign to assess recent advances in the modeling of microwave scattering properties of nonspherical ice particles in the atmosphere. Previous work has suggested that a triple-frequency (Ku–Ka–W band) reflectivity framework appears capable of identifying key microphysical properties of snow, potentially providing much-needed constraints on significant sources of uncertainty in current snowfall retrieval algorithms used for microwave remote sensing instruments. However, these results were based solely on a modeling framework. In contrast, this study considers the triple-frequency approach from an observational perspective using airborne radar observations from the Wakasa Bay field campaign. After accounting for several challenges with the observational dataset, such as beam mismatching and attenuation, observed dual-wavelength ratio results are presented that confirm both the utility of a multifrequency approach to snowfall retrieval and the validity of the unique signatures predicted by complex aggregate ice particle scattering models. This analysis provides valuable insight into the microphysics of frozen precipitation that can in turn be applied to more readily available single- and dual-frequency systems, providing guidance for future precipitation retrieval algorithms.

scholarly journals Assessment of Quad-Frequency Long-Baseline Positioning with BeiDou-3 and Galileo Observations

2021 ◽  
Vol 13 (8) ◽  
pp. 1551
Author(s):  
Liwei Liu ◽  
Shuguo Pan ◽  
Wang Gao ◽  
Chun Ma ◽  
Ju Tao ◽  
...  
Keyword(s):  
A Priori ◽  
Dual Frequency ◽  
Unknown Parameters ◽  
Small Noise ◽  
Long Baseline ◽  
Triple Frequency ◽  
Wide Lane ◽  
Positioning Algorithm ◽  
Function Models ◽  
Long Baselines

Quad-frequency signals have thus far been available for all satellites of BeiDou-3 and Galileo systems. The major benefit of quad-frequency signals is that more extra-wide-lane (EWL) combinations can be formed with quad-frequency than with triple- or dual-frequency, of which the ambiguities can be fixed instantaneously in medium and long baselines. In this paper, the long-baseline positioning algorithm based on optimal triple-frequency EWL/wide-lane (WL) combinations of BeiDou-3 and Galileo is proposed. First, the theoretical precision of multi-frequency combinations of BeiDou-3 and Galileo is studied, and EWL/WL combinations with a small noise amplitude factor and a small ionospheric scalar factor are selected. Then, geometry-free methods are used to estimate the a priori precision of EWL/second EWL/WL signals for different combination schemes. Second, the double-differenced (DD) geometry-based function models of quad-frequency configurations and three different triple-frequency configurations are given, and the DD ionospheric delays are estimated as unknown parameters. In the end, the real BeiDou-3 and Galileo data are used to evaluate the positioning preference. The results show that, when using fixed EWL observations to constrain WL ambiguities, the proposed triple-frequency EWL/WL signals composed of (B1I,B3I,B2a) of BeiDou-3 and (E1,E5a,E6) of Galileo can achieve the same precision as the quad-frequency signals. Therefore, the method proposed in this article can realize long-baseline instantaneous decimeter-level positioning while reducing the dimension of matrix and improving calculation efficiency.

scholarly journals Coincident In-situ and Triple-Frequency Radar Airborne Observations in the Arctic

2021 ◽  
Author(s):  
Cuong M. Nguyen ◽  
Mengistu Wolde ◽  
Alessandro Battaglia ◽  
Leonid Nichman ◽  
Natalia Bliankinshtein ◽  
...  
Keyword(s):  
National Research Council ◽  
Characteristic Size ◽  
The Arctic ◽  
Dual Frequency ◽  
Microphysical Properties ◽  
Triple Frequency ◽  
W Band ◽  
Modelling Studies ◽  
Frequency Ratios

Abstract. This paper describes X-Ka-W-band airborne radar observations and almost perfectly co-located in situ microphysical measurements on board the National Research Council Canada (NRC) Convair-580 aircraft from the Radar Snow Experiment (RadSnowExp). Over 12 hours of flight data with more than 3.4 hours in non-Rayleigh regions for at least one of the radar frequencies provide a unique opportunity for studying the relationship between cloud microphysical properties and radar dual-frequency ratios (DFR). The results from this study are consistent with the main findings of previous modelling studies with specific regions of the DFR plane associated with unique scattering properties of different ice habits, especially in riming conditions. Moreover, the datasets could be used to produce look-up-tables for retrieving cloud bulk density and characteristic size.

scholarly journals Impact of Mass–Size Parameterizations of Frozen Hydrometeors on Microphysical Retrievals: Evaluation by Matching Radar to In Situ Observations from GCPEx and OLYMPEx

2020 ◽  
Vol 37 (6) ◽  
pp. 993-1012 ◽  
Author(s):  
Ousmane O. Sy ◽  
Simone Tanelli ◽  
Stephen L. Durden ◽  
Andrew Heymsfield ◽  
Aaron Bansemer ◽  
...  
Keyword(s):  
Cold Season ◽  
Radar Observations ◽  
Microphysical Properties ◽  
Precipitation Experiment ◽  
Mass Size ◽  
Ice Water Content ◽  
Multifrequency Radar ◽  
Ice Water ◽  
In Situ Scattering

AbstractThis article illustrates how multifrequency radar observations can refine the mass–size parameterization of frozen hydrometeors in scattering models and improve the correlation between the radar observations and in situ measurements of microphysical properties of ice and snow. The data presented in this article were collected during the GPM Cold Season Precipitation Experiment (GCPEx) (2012) and Olympic Mountain Experiment (OLYMPEx) (2015) field campaigns, where the true mass–size relationship was not measured. Starting from size and shape distributions of ice particles measured in situ, scattering models are used to simulate an ensemble of reflectivity factors for various assumed mass–size parameterizations (MSP) of the power-law type. This ensemble is then collocated to airborne and ground-based radar observations, and the MSPs are refined by retaining only those that reproduce the radar observations to a prescribed level of accuracy. A versatile “retrieval dashboard” is built to jointly analyze the optimal MSPs and associated retrievals. The analysis shows that the optimality of an MSP depends on the physical assumptions made in the scattering simulators. This work confirms also the existence of a relationship between parameters of the optimal MSPs. Through the MSP optimization, the retrievals of ice water content M and mean diameter Dm seem robust to the change in meteorological regime (between GCPEx and OLYMPEx); whereas the retrieval of the diameter spread Sm seems more campaign dependent.

scholarly journals Snow Microphysical Retrieval from the NASA D3R Radar During ICE-POP 2018

2021 ◽  
Author(s):  
S. Joseph Munchak ◽  
Robert S. Schrom ◽  
Charles N. Helms ◽  
Ali Tokay
Keyword(s):  
Particle Size ◽  
Aspect Ratio ◽  
Size Distribution ◽  
Snow Water Equivalent ◽  
Optimal Estimation ◽  
Orientation Distribution ◽  
Effective Density ◽  
Single Frequency ◽  
Dual Frequency ◽  
Microphysical Properties

Abstract. A method is developed to use both polarimetric and dual-frequency radar measurements to retrieve microphysical properties of falling snow. It is applied to the Ku- and Ka-band measurements of the NASA Dual-polarization, Dual-frequency Doppler Radar (D3R) obtained during the International Collaborative Experiment for PyeongChang Olympic and Paralympics (ICE-POP 2018) field campaign, and incorporates the Atmospheric Radiative Transfer Simulator (ARTS) microwave single scattering property database for oriented particles. The retrieval uses optimal estimation to solve for several parameters that describe the particle size distribution (PSD), relative contribution of pristine, aggregate, and rimed ice species, and the orientation distribution along an entire radial simultaneously. Examination of Jacobian matrices and averaging kernels show that the dual wavelength ratio (DWR) measurements provide information regarding the characteristic particle size, and to a lesser extent, the rime fraction and shape parameter of the size distribution, whereas the polarimetric measurements provide information regarding the mass fraction of pristine particles and their characteristic size and orientation distribution. Thus, by combining the dual-frequency and polarimetric measurements, some ambiguities can be resolved that should allow a better determination of the PSD and bulk microphysical properties (e.g., snowfall rate) than can be retrieved from single-frequency polarimetric measurements or dual-frequency, single-polarization measurements. The D3R ICE-POP retrievals were validated using Precipitation Imaging Package (PIP) and Pluvio weighing gauge measurements taken nearby at the May Hills ground site. The PIP measures the snow PSD directly, and its measurements can be used to derived the snowfall rate (volumetric and water equivalent), mean volume-weighted particle size, and effective density, as well as particle aspect ratio and orientation. Four retrieval experiments were performed to evaluate the utility of different measurement combinations: Ku-only, DWR-only, Ku-pol, and All-obs. In terms of correlation, the volumetric snowfall rate (r = 0.95) and snow water equivalent rate (r = 0.92) were best retrieved by the Ku-pol method, while the DWR-only method had the lowest magnitude bias for these parameters (−31 % and −8 %, respectively). The methods that incorporated DWR also had the best correlation to particle size (r = 0.74 and r = 0.71 for DWR-only and All-obs, respectively), although none of the methods retrieved density particularly well (r = 0.43 for All-obs). The ability of the measurements to retrieve mean aspect ratio was also inconclusive, although the polarimetric methods (Ku-pol and All-obs) had reduced biases and MAE relative to the Ku-only and DWR-only methods. The significant biases in particle size and snowfall rate appeared to be related to biases in the measured DWR, emphasizing the need for accurate DWR measurements and frequent calibration in future D3R deployments.

scholarly journals Calibration of BeiDou Triple-Frequency Receiver-Related Pseudorange Biases and Their Application in BDS Precise Positioning and Ambiguity Resolution

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3500 ◽  
Author(s):  
Fu Zheng ◽  
Xiaopeng Gong ◽  
Yidong Lou ◽  
Shengfeng Gu ◽  
Guifei Jing ◽  
...  
Keyword(s):  
Ambiguity Resolution ◽  
Single Point ◽  
Satellite System ◽  
Single Frequency ◽  
Navigation Satellite ◽  
Dual Frequency ◽  
Precise Positioning ◽  
Triple Frequency ◽  
Wide Lane ◽  
Point Positioning

Global Navigation Satellite System pseudorange biases are of great importance for precise positioning, timing and ionospheric modeling. The existence of BeiDou Navigation Satellite System (BDS) receiver-related pseudorange biases will lead to the loss of precision in the BDS satellite clock, differential code bias estimation, and other precise applications, especially when inhomogeneous receivers are used. In order to improve the performance of BDS precise applications, two ionosphere-free and geometry-free combinations and ionosphere-free pseudorange residuals are proposed to calibrate the raw receiver-related pseudorange biases of BDS on each frequency. Then, the BDS triple-frequency receiver-related pseudorange biases of seven different manufacturers and twelve receiver models are calibrated. Finally, the effects of receiver-related pseudorange bias are analyzed by BDS single-frequency single point positioning (SPP), single- and dual-frequency precise point positioning (PPP), wide-lane uncalibrated phase delay (UPD) estimation, and ambiguity resolution, respectively. The results show that the BDS SPP performance can be significantly improved by correcting the receiver-related pseudorange biases and the accuracy improvement is about 20% on average. Moreover, the accuracy of single- and dual-frequency PPP is improved mainly due to a faster convergence when the receiver-related pseudorange biases are corrected. On the other hand, the consistency of wide-lane UPD among different stations is improved significantly and the standard deviation of wide-lane UPD residuals is decreased from 0.195 to 0.061 cycles. The average success rate of wide-lane ambiguity resolution is improved about 42.10%.

scholarly journals Retrieving microphysical properties of concurrent pristine ice and snow using polarimetric radar observations

2021 ◽  
Vol 14 (10) ◽  
pp. 6885-6904
Author(s):  
Nicholas J. Kedzuf ◽  
J. Christine Chiu ◽  
V. Chandrasekar ◽  
Sounak Biswas ◽  
Shashank S. Joshil ◽  
...  
Keyword(s):  
Water Content ◽  
Number Concentration ◽  
Radiation Budget ◽  
Radar Signal ◽  
Polarimetric Radar ◽  
Radar Observations ◽  
Microphysical Properties ◽  
Uncertainty Estimates ◽  
Ice Water Content ◽  
Ice Water

Abstract. Ice and mixed-phase clouds play a key role in our climate system because of their strong controls on global precipitation and radiation budget. Their microphysical properties have been characterized commonly by polarimetric radar measurements. However, there remains a lack of robust estimates of microphysical properties of concurrent pristine ice and aggregates because larger snow aggregates often dominate the radar signal and mask contributions of smaller pristine ice crystals. This paper presents a new method that separates the scattering signals of pristine ice embedded in snow aggregates in scanning polarimetric radar observations and retrieves their respective abundances and sizes for the first time. This method, dubbed ENCORE-ice, is built on an iterative stochastic ensemble retrieval framework. It provides the number concentration, ice water content, and effective mean diameter of pristine ice and snow aggregates with uncertainty estimates. Evaluations against synthetic observations show that the overall retrieval biases in the combined total microphysical properties are within 5 % and that the errors with respect to the truth are well within the retrieval uncertainty. The partitioning between pristine ice and snow aggregates also agrees well with the truth. Additional evaluations against in situ cloud probe measurements from a recent campaign for a stratiform cloud system are promising. Our median retrievals have a bias of 98 % in the total ice number concentration and 44 % in the total ice water content. This performance is generally better than the retrieval from empirical relationships. The ability to separate signals of different ice species and to provide their quantitative microphysical properties will open up many research opportunities, such as secondary ice production studies and model evaluations for ice microphysical processes.

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