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 Experiments for PyeongChang 2018 Olympic and Paralympic winter games (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 shows 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 (

Estimation of snowfall rates and other properties from weather radar is made difficult by many of the same challenges that exist for rainfall estimation (primarily, the discrepancy between the sixth-moment dependence of radar reflectivity factor

Improvements upon these situational

Polarimetric radar measurements have shown value in inferring ongoing ice growth processes, due to the dependence of these measurements on the distributions of particle shapes, orientations, and sizes. In particular, enhancements in the differential reflectivity (

From the extensive literature on multi-frequency and polarimetric radar studies of snow, it is evident that complementary information is contained in these measurements. However, relatively few studies have been performed to assess the information content of multi-frequency, dual-polarization radar measurements of snow, partially because of a lack of radar platforms with these capabilities deployed in locations subject to frequent and high-accumulation snowfall events. The NASA dual-polarization, dual-frequency Doppler radar (D3R) is a premier radar for making such measurements. The D3R was built to provide ground validation measurements for the NASA Global Precipitation Measurement (GPM) mission's dual-frequency precipitation radar

Recognizing the potential utility of D3R measurements to provide unique information about microphysics, dynamics, and quantitative precipitation estimation (QPE) in snowstorms, the Korean Meteorological Administration (KMA), organizers of the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic winter games (ICE-POP 2018) cooperated with NASA to deploy the D3R radar in Daegwallyeong, South Korea, from November 2017–March 2018. The D3R was part of an extensive network of ground-based remote sensing and in situ instrumentation deployed during ICE-POP 2018 and formed a central observation point for measurements aligned perpendicular to the coastal mountain ranges of eastern South Korea. This measurement strategy was devised to examine the distribution of precipitation from the coast to the mountains in different winter synoptic weather situations and evaluate high-resolution numerical weather prediction in this complex topographic region

The objective of this study is to use the data collected by the D3R during ICE-POP 2018 develop a snow retrieval algorithm using realistic scattering models of pristine, aggregate, and rimed snow particles to further our understanding of the complementary nature of the dual-frequency and polarimetric radar measurements and their utility regarding snow microphysical characterization and QPE. The output of this algorithm is intended to aid in identifying microphysical processes during ICE-POP events and provide snow QPE during the deployment. The extensive network of ground instrumentation is leveraged to validate the algorithm output. The manuscript is organized as follows: the observational datasets are listed in Sect.

The NASA D3R radar is a polarimetric Doppler weather radar operating at Ku (13.91 GHz) and Ka (35.56 GHz) bands, which utilizes novel design features including aligned antennas, solid-state transceivers, and a digital waveform generator to enable deployment in a wide range of environmental conditions on a mobile trailer platform

During ICE-POP 2018, the D3R was located on the roof of the Daegwallyeong (DGW) regional weather office (36.677

We use the D3R data available from the NASA ICE-POP data archive held at the Global Hydrology Resource Center

Selected snowfall events during ICE-POP 2018. Synoptic classification is from

Time–height plots of vertical profiles of

The D3R

We examined time series of the PDFs of these quantities for the events listed in Table

Time-series histograms of near-zenith Ku

The primary source of validation for the microphysical retrievals in this study is PIP

PIP determines the characteristics of the precipitation particles using an algorithm written using the National Instruments IMage AQuisition (IMAQ) software package. This algorithm determines the shape of the precipitation particle (i.e., the long and the short dimensions) by fitting an ellipse to the PIP-imaged particle. The IMAQ software package defines the fitted ellipse as the ellipse having both the same area and the same perimeter as the PIP-imaged particle. During our preliminary analysis of the data, we found that the IMAQ-fitted ellipses tended to overestimate the long dimension of the particle and underestimate the short dimension, resulting in an underestimate of the aspect ratio. As such, we have reprocessed the PIP data using an alternative, custom-built ellipse-fitting strategy

The PIP-determined orientation angle is defined as the counterclockwise angle from the positive horizontal axis, where positive is to the right, to the longest dimension of the particle. This results in orientation angles ranging from 0 to 180

Radiosonde launches were performed every 3 h during precipitation events from the DGW Regional Weather Office, supplementing the normal 12-hourly observations, and used Meteomodem M10 radiosondes

Gaining useful information from polarimetric radar measurements requires scattering properties of hydrometeors with preferred mean orientations. We incorporate such scattering properties into the retrieval algorithm described herein, by way of lookup tables (LUTs) that are derived by integrating these scattering properties over a prescribed PSD. Specifically, we use scattering properties for a pristine plate, an aggregate of plates, and graupel (all at a wide range of sizes) from the Atmospheric Radiative Transfer Simulator

Properties of the ARTS scattering database calculations used herein.

NA: not available.

The ARTS database aims to provide scattering properties for a set of particles over a large range of frequencies so that applications using both active and passive remote sensing measurements are consistent. The derivation of polarimetric radar variables from this database is described in Appendix

Dimensions of the lookup tables derived from the particle types selected from the ARTS single-scattering database.

Variables stored within the lookup tables.

The LUTs are constructed for each particle species. The LUT dimensions and ranges of the LUT indices are given in Table

In addition to the radar variables, we simulate several variables (denoted with subscript P) that represent PIP-measured quantities. Because the PIP measures 2-D projections of 3-D particles, assumptions must be made to convert the PIP measurements to physical variables. While several formulas have been proposed to achieve this purpose

The normalized intercept

In order to evaluate the capability of the D3R polarimetric measurements to retrieve a bulk measurement of the aspect ratio, we defined the area-weighted mean ellipse aspect ratio (

While it is not feasible to illustrate every dimension of the LUTs, a few examples are provided to preview the expected sensitivity of the D3R measurements to microphysical parameters. In Fig.

Ku–Ka dual-wavelength ratio for the three ice species listed in Table

Polarimetric variables

Optimal estimation (OE;

The cost function that is minimized by optimal estimation is

The state vector consists of quantities that describe the PSD, relative contribution of each species, as well as other quantities known to affect the Ku- and Ka-band polarimetric radar measurements:

Names, definitions, a priori value, standard deviation, and allowed ranges of quantities in the state vector (Eq.

The observation error covariance matrix

Measurement and forward model components of the error covariance matrix

The optimal estimation process along a ray of radar data is illustrated in Fig.

No significant cloud liquid water is detected or needed to explain the DWR observations. In fact, the near-zero DWR at the far ranges implies that there is little differential attenuation, and the observed near-field DWR can be attributed to particle size effects.

D3R observations of reflectivity

The most significant impact of these parameter changes is to increase

Retrieved

As a consistency check, we show that the retrieval algorithm effectively reproduces the spatial distribution of the radar variables for an RHI (Fig.

Observed and simulated

Some further insight into the adjustments made to the parameters can be gained by examining the Jacobian matrix

While the Jacobian is state dependent, the sign and relative magnitude of each element from the example ray shown in the previous section are illustrative of the general sensitivity of the forward model to the retrieval parameters. The Jacobian matrix is composed of blocks that are either diagonal or triangular, depending on whether the parameter has a significant downrange propagation effect on an observation. The effect of modifying the parameters in

Increasing

Increasing

Increasing

Increasing

Increasing

Increasing cloud liquid results in an increase in the downrange DWR due to increased differential attenuation but has no impact on the polarimetric measurements.

Increasing

It is noteworthy that the sign of the observation response to perturbations varies in different ways for the different parameters in

Jacobian matrix of partial derivatives of the simulated D3R measurements to the retrieval parameters. The partial derivatives have been normalized element wise by the square root corresponding diagonal element of

Values close to 1 indicate strong influence of measurements, and values close to 0 indicate that the retrieval is heavily influenced by the prior. In Fig.

Quantiles of the averaging kernel matrix diagonal elements and off-diagonal elements representing different parameters at the same range gate.

The primary tool used for validation of the D3R retrievals is the PIP located at the MHS location. The D3R retrievals were matched to the PIP by averaging the retrieved quantities in a 600 m wide by 500 m tall box centered above MHS. The lower altitude limit of this box was placed 250 m above the surface to avoid ground clutter contamination along the radials used for averaging. To evaluate the impact of the dual-frequency and dual-polarization measurements, four retrieval experiments were conducted:

Ku-only: a single-frequency retrieval, equivalent to prescribing a temperature-dependent

DWR-only: a dual-frequency retrieval without polarimetric information; similar information content to the GPM dual-frequency precipitation radar (DPR);

Ku-pol: a single-frequency polarimetric retrieval using

All-obs: a dual-frequency polarimetric retrieval using DWR and the

Some different tendencies are noted for each event. The 9 January case had a consistently higher PIP-derived reflectivity than the D3R measurement, especially during the middle hours of the event. This was a low-echo-top cold low case, and D3R-derived time–height profiles (Fig.

The validation statistics presented in this section are derived from matchups of the D3R-derived and PIP-measured quantities listed in Table

Time series of D3R-observed and PIP-simulated Ku-band reflectivity (top row), D3R-observed DWR (second row), D3R-observed

The time series of snow water equivalent rate (

Error statistics for volumetric snowfall rate (

Time series of D3R- and PIP-derived snow water equivalent rate

Mean bias, MAE, and correlation coefficient (

The D3R retrieval provides an estimate of the PIP-measured area-weighted mean particle volume diameter (

Time series of D3R- and PIP-derived mean volume-weighted particle diameter

The statistics of the

The retrieved effective particle density

Mean bias, absolute error (MAE), and correlation coefficient (

The use of polarimetric measurements in the Ku-pol and All-obs retrievals has the most impact on the retrieved pristine fraction, ratio of pristine to aggregate mean particle size, and pristine population orientation distribution. All of these parameters combine to influence the area-weighted ellipse (

Time series of D3R- and PIP-derived area-weighted ellipse

The comparison of the retrieved aspect ratios to those derived from the PIP is inconsistent from event to event. There appears to be little variability in the PIP time series on 9 January for either measure of aspect ratio. There is a steady increase in

Mean bias, absolute error (MAE), and correlation coefficient (

This study describes an algorithm that makes use of both polarimetric and dual-frequency radar measurements to retrieve microphysical properties of falling snow, including snowfall rate (volumetric and water equivalent); ice water content; particle size distribution; the relative contribution of pristine, aggregate, and rimed species; and particle orientation distribution. The algorithm is flexible in that it can use as many or as few measurements as available. In this study, it is applied to the Ku- and Ka-band measurements of the NASA D3R radar obtained during the ICE-POP 2018 field campaign but can be applied to additional or different frequencies. This is possible because it makes use of the ARTS microwave single-scattering property database for oriented particles

The retrieval uses optimal estimation to solve for several parameters that describe the PSD, relative contribution of each species, and the orientation distribution along an entire radial simultaneously. This is necessary (versus a gate-by-gate approach) to account for the measurements sensitive to propagation effects (e.g., DWR and

The D3R ICE-POP retrievals were validated using PIP and Pluvio measurements taken nearby at the May Hills ground site. The PIP measures the snow PSD directly

The D3R is scheduled to be deployed in Storrs, Connecticut, USA, during the 2021–2022 and 2022–2023 winters as part of the NASA-sponsored Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field experiment. A similar deployment setup is planned with nearby PIP measurements. Additionally, the airborne Ku- and Ka-band HIWRAP radar will be available to evaluate the D3R calibration, and other lessons learned (e.g., video monitoring to observe snow accumulation on the radome, siting to avoid ground clutter) will be applied. Ongoing improvements to the PIP processing algorithms, particularly regarding the estimation of particle aspect ratio, will also be advantageous to further refine the algorithm described in this work. Availability of scattering databases for oriented particles with different geometries will facilitate running these retrievals as an ensemble to provide more robust posterior distributions of the retrieved parameters. Finally, the methodology can be expanded to accommodate liquid and melting particles, although scattering databases for the latter, particularly with the polarimetric parameters from oriented melting particles, are not yet mature enough for this application.

Faithfully simulating the observables from polarimetric radar requires considering the incident and scattered Stokes vectors; these vectors are related via

To generate tables of the polarimetric, single-scattering properties, we transform the Stokes matrix elements to single-scattering properties more commonly used in radar meteorology, such as backscatter cross section at horizontal and vertical polarizations (

Expressions are given below for the radar reflectivity factor

The propagation variables,

The D3R measurements that comprise the observation vector

Reflectivity is defined in linear units in this appendix unless otherwise noted.

and the state vectorDWR,

The process of simulating the radar measurements from combined LUTs can be described by considering a LUT for a variable

Now there exists a one-dimensional LUT for each parameter (and frequency for the radar variables) that is indexed by

The D3R data used in this study

SJM led this research by developing the lookup tables and optimal estimation methodology, processing the radar data, and calculating validation statistics. RSS assisted with the generation of lookup tables, polarimetric components of the radar forward model, and assessment of forward model error. AT processed the PIP and Pluvio measurements that were used for validation. CNH performed additional processing of the PIP data to derive the bulk aspect ratio and orientation parameters.

At least one of the (co-)authors is a member of the editorial board of

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This article is part of the special issue “Winter weather research in complex terrain during ICE-POP 2018 (International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic winter games) (ACP/AMT/GMD inter-journal SI)”. It is not associated with a conference.

Robert S. Schrom's and Charles N. Helms' work was supported by an appointment to the NASA Postdoctoral Program at NASA Goddard Space Flight Center, administered by Universities Space Research Association under contract with NASA. S. Joseph Munchak and Ali Tokay were supported by the Global Precipitation Measurement (GPM) Ground Validation program. The authors would also like to acknowledge many insightful discussions about the D3R data quality and calibration procedures with Venkatachalam Chandrasekhar at Colorado State University. The authors greatly appreciate the participants in the World Weather Research Programme Research Development Project and Forecast Demonstration Project, International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic winter games (ICE-POP 2018), hosted by Korea Meteorological Administration (KMA), and would like to acknowledge GyuWon Lee of Kyungpook National University, Daegu, South Korea, for stimulating ongoing scientific research from datasets collected during ICE-POP 2018.

This research has been supported by the National Aeronautics and Space Administration (Internal Scientist Funding Model Work Package: GPM Ground Validation).

This paper was edited by GyuWon Lee and reviewed by two anonymous referees.