Sanghun Lim

Hydrometeor classification system using dual-polarization radar measurements: model improvements and in situ verification

A hydrometeor classification system based on a fuzzy logic technique using dual-polarization radar measurements of precipitation is presented. In this study, five dual-polarization radar measurements (namely horizontal reflectivity, differential reflectivity, specific differential phase, correlation coefficient, and linear depolarization ratio) and altitude relating to environmental melting layer are used as input variables of the system. The hydrometeor classification system chooses one of nine different hydrometeor categories as output. The system presented in this paper is a further development of an existing hydrometeor classification system model developed at Colorado State University (CSU). The hydrometeor classification system is evaluated by comparing inferred results from the CSU CHILL Facility dual-polarization radar measurements with the in situ sample data collected by the T-28 aircraft during the Severe Thunderstorm Electrification and Precipitation Study.

A New Dual-Polarization Radar Rainfall Algorithm: Application in Colorado Precipitation Events

The efficacy of dual-polarization radar for quantitative precipitation estimation (QPE) has been demonstrated in a number of previous studies. Specifically, rainfall retrievals using combinations of reflectivity (Zh), differential reflectivity (Zdr), and specific differential phase (Kdp) have advantages over traditional Z‐R methods because more information about the drop size distribution (DSD) and hydrometeor type are available. In addition, dual-polarization-based rain-rate estimators can better account for the presence of ice in the sampling volume. An important issue in dual-polarization rainfall estimation is determining which method to employ for a given set of polarimetric observables. For example, under what circumstances does differential phase information provide superior rain estimates relative to methods using reflectivity and differential reflectivity? AtColoradoStateUniversity(CSU),anoptimizationalgorithmhasbeendevelopedandusedforanumberof years to estimate rainfall based on thresholds of Zh, Zdr, and Kdp. Although the algorithm has demonstrated robust performance in both tropical and midlatitude environments, results have shown that the retrieval is sensitive to the selection of the fixed thresholds. In this study, a new rainfall algorithm is developed using hydrometeor identification (HID) to guide the choice of the particular rainfall estimation algorithm. A separate HID algorithm has been developed primarily to guide the rainfall application with the hydrometeor classes, namely, all rain, mixed precipitation, and all ice. Both the data collected from the S-band Colorado State University‐University of Chicago‐Illinois State Water Survey (CSU‐CHILL) radar and a network of rain gauges are used to evaluate the performance of the new algorithm in mixed rain and hail in Colorado. The evaluation is also performed using an algorithm similar to the one developed for the Joint Polarization Experiment (JPOLE). Results show that the new CSU HID-based algorithm provides good performance for the Colorado case studies presented here.

Simulation of X-Band Rainfall Observations from S-Band Radar Data

Abstract To design X-band radar systems as well as evaluate algorithm development, it is useful to have simultaneous X-band observation with and without the impact of path attenuation. One way to develop that dataset is through theoretical models. This paper presents a methodology to generate realistic range profiles of radar variables at attenuating frequencies, such as X band, for rain medium. Fundamental microphysical properties of precipitation, namely, size and shape distribution information, are used to generate realistic profiles of X band starting with S-band observation. Conditioning the simulation from S band maintains the natural distribution of rainfall microphysical parameters. Data from the Colorado State University’s University of Chicago–Illinois State Water Survey (CHILL) radar and the National Center for Atmospheric Research S-band dual-polarization Doppler radar (S-POL) are used to simulate X-band radar variables. Three procedures to simulate the radar variables and sample applications ...

A Robust C-Band Hydrometeor Identification Algorithm and Application to a Long-Term Polarimetric Radar Dataset

AbstractA new 10-category, polarimetric-based hydrometeor identification algorithm (HID) for C band is developed from theoretical scattering simulations including wet snow, hail, and big drops/melting hail. The HID is applied to data from seven wet seasons in Darwin, Australia, using the polarimetric C-band (C-POL) radar, to investigate microphysical differences between monsoon and break periods. Scattering simulations reveal significant Mie effects with large hail (diameter > 1.5 cm), with reduced reflectivity and enhanced differential reflectivity Zdr and specific differential phase Kdp relative to those associated with S band. Wet snow is found to be associated with greatly depreciated correlation coefficient ρhv and moderate values of Zdr. It is noted that large oblate liquid drops can produce the same electromagnetic signatures at C band as melting hail falling quasi stably, resulting in some ambiguity in the HID retrievals. Application of the new HID to seven seasons of C-POL data reveals that hail ...

Constrained iterative technique with embedded neural network for dual-polarization radar correction of rain path attenuation

A new stable backward iterative technique to correct for path attenuation and differential attenuation is presented here. The technique named, neural network iterative polarimetric precipitation estimator by radar (NIPPER), is based on a polarimetric model used to train an embedded neural network, constrained by the measurement of the differential phase along the rain path. Simulations are used to investigate the efficiency, accuracy, and the robustness of the proposed technique. The precipitation is characterized with respect to raindrop size, shape, and orientation distribution. The performance of NIPPER is evaluated by using simulated radar volumes scan generated from S-band radar measurements. A sensitivity analysis is performed in order to evaluate the expected errors of NIPPER. These evaluations show relatively better performance and robustness of the attenuation correction process when compared with currently available techniques.

Dual‐polarization radar signatures in snowstorms: Role of snowflake aggregation

In this article a potential role of snowflake growth by aggregation on formation of dual-polarization radar signatures in winter storms is discussed. We advocate that the observed bands of increased values of specific differential phase (Kdp) can be linked to the onset of aggregation. These bands are caused by high number concentrations of oblate relatively dense ice particles and take place in regions where an ice phase “seeder-feeder” is active. On the other hand, the differential reflectivity (Zdr) bands, in absence of detectable Kdp values, are observed in the areas where crystal growth is the dominating snow growth mechanism and ice particle number concentration is lower. This distinction in underlying processes explains why Kdp and Zdr bands are not always observed at the same time. Furthermore, based on surface observations of snowflakes, it is determined that early aggregates, consisting of a small number of ice crystals, are oblate. These oblate particles could contribute to the reported dual-polarization radar signatures in snow, especially to the Kdp. This could help to explain why, where observed at the same type, Kdp and Zdr bands do not match and the altitude of the peak value of Kdp is usually lower than the Zdr one. It also means that dual-polarization radar signatures of snowflakes may depend on a stage of aggregation.

Precipitation Classification and Quantification Using X-Band Dual-Polarization Weather Radar: Application in the Hydrometeorology Testbed

AbstractThis paper presents new methods for rainfall estimation from X-band dual-polarization radar observations along with advanced techniques for quality control, hydrometeor classification, and estimation of specific differential phase. Data collected from the Hydrometeorology Testbed (HMT) in orographic terrain of California are used to demonstrate the methodology. The quality control and hydrometeor classification are specifically developed for X-band applications, which use a “fuzzy logic” technique constructed from the magnitude of the copolar correlation coefficient and the texture of differential propagation phase. In addition, an improved specific differential phase retrieval and rainfall estimation method are also applied. The specific differential phase estimation is done for both the melting region and rain region, where it uses a conventional filtering method for the melting region and a self-consistency-based method that distributes the total differential phase consistent with the reflectiv...

A peer-to-peer collaboration framework for multi-sensor data fusion

A peer-to-peer collaboration framework for multi-sensor data fusion in resource-rich radar networks is presented. In this high data volume real-time application, data from multiple radars are combined to improve the accuracy of radar scans (e.g., correct for attenuation) and to provide a composite view of the area covered by the radars. Data fusion process is subject to two constraints: (1) the accuracy requirement of the final fused results, which may be different at different end nodes, and (2) the real-time requirements of the application. The accuracy requirement is achieved by dynamically selecting the appropriate set of data to exchange among the multiple radar nodes. A mechanism for selecting a dataset based on current application-specific needs is presented. We also present a dynamic peer-selection algorithm, Best Peer Selection (BPS), that chooses a set of peers based on their computation and communication capabilities to minimize the data processing time per integration algorithm. Simulation-based results show that BPS can deliver a significant performance improvement, even when the peers have high variability in available network and computation resources.

Retrieval of reflectivity in a networked radar environment

This paper describes a methodology for reflectivity and attenuation retrieval in a networked radar environment. Electromagnetic waves backscattered from a common volume are attenuated differently along the different paths. Solution of the specific attenuation distribution is proposed by solving the integral equation for reflectivity, in a manner similar to that used with a differential phase constraint. The set of governing integral equations describing the backscatter and propagation of common resolution volume are solved simultaneously with constraints on observed total path attenuation. The algorithms developed are evaluated on simulated X-band radar observations in rain obtained from S-band measurements by CSU-CHILL radar. Retrieved reflectivity and specific attenuation using the iterative method show good agreement with intrinsic reflectivity and specific attenuation

Reflectivity retrieval in a networked radar environment: Demonstration from the CASA IP1 radar network

A network-based reflectivity retrieval technique has been developed within the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA). The concept of a networked- radar system is simultaneous observations of the same precipitation event by multiple radars operating at the attenuating frequency such as X-band and scanning in a low elevation plane. This paper presents the preliminary demonstration of the network-based retrieval using data from the first Integration Project (IP1) radar network in Oklahoma. Electromagnetic waves backscattered from a common volume in a networked radar system are attenuated differently along the different paths. The CASA networked-retrieval method is based on a set of governing integral equations describing the backscatter and propagation of common volume with constraints of total path attenuation. The method has been implemented in a multiprocessor environment, which operate simultaneously and collaboratively to meet the real time requirement of CASA. The performance of the implemented retrieval algorithm such as computation requirement will be presented. Comparison of the CASA networked retrieval is made against the conventional attenuation correction based on the principle of coupling the specific attenuation, differential propagation phase and reflectivity. The preliminary results show good agreement with conventional differential phase base attenuation correction.