Hydrometeor Classification Research Articles

S밴드 이중편파레이더를 이용한 대기수상체 분류 사례분석

S밴드 이중편파레이더를 이용한 대기수상체 분류 사례분석

정량적 강우강도 정확도 향상을 위한 단일편파와 이중편파레이더 강수량 합성

The limits of S-band dual-polarization radars in Korea are not reflected on the recent weather forecasts of Korea Meteorological Administration and furthermore, they are only utilized for rainfall estimations and hydrometeor classification researches. Therefore, this study applied four merging methods [SA (Simple Average), WA (Weighted Average), SSE (Sum of Squared Error), TV (Time-varying mergence)] to the QPE (Quantitative Precipitation Estimation) model [called RAR (Radar-AWS Rainfall) calculation system] using single-polarization radars and S-band dual-polarization radar in order to improve the accuracy of the rainfall estimation of the RAR calculation system. As a result, the merging results of the WA and SSE methods, which are assigned different weights due to the accuracy of the individual model, performed better than the popular merging method, the SA (Simple Average) method. In particular, the results of TVWA (Time-Varying WA) and TVSSE (Time-Varying SSE), which were weighted differently due to the time-varying model error and standard deviation, were superior to the WA and SSE. Among of all the merging methods, the accuracy of the TVWA merging results showed the best performance. Therefore, merging the rainfalls from the RAR calculation system and S-band dual-polarization radar using the merging method proposed by this study enables to improve the accuracy of the quantitative rainfall estimation of the RAR calculation system. Moreover, this study is worthy of the fundamental research on the active utilization of dual-polarization radar for weather forecasts.

Hydrometeor classification from two-dimensional video disdrometer data

Abstract. The first hydrometeor classification technique based on two-dimensional video disdrometer (2DVD) data is presented. The method provides an estimate of the dominant hydrometeor type falling over time intervals of 60 s during precipitation, using the statistical behavior of a set of particle descriptors as input, calculated for each particle image. The employed supervised algorithm is a support vector machine (SVM), trained over 60 s precipitation time steps labeled by visual inspection. In this way, eight dominant hydrometeor classes can be discriminated. The algorithm achieved high classification performances, with median overall accuracies (Cohen's K) of 90% (0.88), and with accuracies higher than 84% for each hydrometeor class.

Fuzzy Logic Classification of S-Band Polarimetric Radar Echoes to Identify Three-Body Scattering and Improve Data Quality

AbstractThe three-body scatter signature (TBSS) is a radar artifact that appears downrange from a high-radar-reflectivity core in a thunderstorm as a result of the presence of hailstones. It is useful to identify the TBSS artifact for quality control of radar data used in numerical weather prediction and quantitative precipitation estimation. Therefore, it is advantageous to develop a method to automatically identify TBSS in radar data for the above applications and to help identify hailstones within thunderstorms. In this study, a fuzzy logic classification algorithm for TBSS identification is developed. Polarimetric radar data collected by the experimental S-band Weather Surveillance Radar-1988 Doppler (WSR-88D) in Norman, Oklahoma (KOUN), are used to develop trapezoidal membership functions for the TBSS class of radar echo within a hydrometeor classification algorithm (HCA). Nearly 3000 radar gates are removed from 50 TBSSs to develop the membership functions from the data statistics. Five variables are investigated for the discrimination of the radar echo: 1) horizontal radar reflectivity factor ZH, 2) differential reflectivity ZDR, 3) copolar cross-correlation coefficient ρhv, 4) along-beam standard deviation of horizontal radar reflectivity factor SD(ZH), and 5) along-beam standard deviation of differential phase SD(ΦDP). These membership functions are added to an HCA to identify TBSSs. Testing is conducted on radar data collected by dual-polarization-upgraded operational WSR-88Ds from multiple severe-weather events, and results show that automatic identification of the TBSS through the enhanced HCA is feasible for operational use.

A Dual-Polarization Radar Hydrometeor Classification Algorithm for Winter Precipitation

Abstract The purpose of this study is to demonstrate the use of polarimetric observations in a radar-based winter hydrometeor classification algorithm. This is accomplished by deriving bulk electromagnetic scattering properties of stratiform, cold-season rain, freezing rain, sleet, dry aggregated snowflakes, dendritic snow crystals, and platelike snow crystals at X-, C-, and S-band wavelengths based on microphysical theory and previous observational studies. These results are then used to define the expected value ranges, or membership beta functions, of a simple fuzzy-logic hydrometeor classification algorithm. To test the algorithm’s validity and robustness, polarimetric radar data and algorithm output from four unique winter storms are investigated alongside surface observations and thermodynamic soundings. This analysis supports that the algorithm is able to realistically discern regions dominated by wet snow, aggregates, plates, dendrites, and other small ice crystals based solely on polarimetric data, with guidance from a melting-level detection algorithm but without external temperature information. Temperature is still used to distinguish rain from freezing rain or sleet below the radar-detected melting level. After appropriate data quality control, little modification of the algorithm was required to produce physically reasonable results on four different radar platforms at X, C, and S bands. However, classification seemed more robust at shorter wavelengths because the specific differential phase is heavily weighted in ice crystal classification decisions. It is suggested that parts, or all, of this algorithm could be applicable in both operational and research settings.

Revisiting haboobs in the southwestern United States: An observational case study of the 5 July 2011 Phoenix dust storm

Convectively-driven dust storms (or haboobs) are common phenomena in the southwestern United States. However, studies about haboobs in this region are limited. Here, we investigate the state and fate of a massive haboob that hit Phoenix, Arizona on 5 July 2011 using satellite, radar, and ground-based observations. This haboob was a result of strong outflow boundaries (with peak wind gusts of 29 m s−1) from storms that were initiated in the southeast of Tucson. In particular, we find three major outflow systems (based on radar data) that were generated by forward propagating storms, ultimately merging near Phoenix. This resulted in peak hourly PM10 and PM2.5 concentrations of 1974 μg m−3 and 907 μg m−3 at US EPA stations near Phoenix. The high PM concentration is consistent in space and time with the dust wall movement based on our analysis of radar data on hydrometeor classification. Enhanced aerosol loadings over metropolitan Phoenix were also observed on 6 July from NASA Terra/Aqua MODIS aerosol optical depth (AOD) retrievals (AOD > 0.8). We infer from CALIOP vertical feature masks and HYSPLIT back trajectories that remnants of the haboob were transported to northwest of Phoenix on 6 July at 2–4 km above ground level. Ratios of PM2.5 to PM10 from IMPROVE stations also imply low-level transport to the east of Phoenix on 8 July. Finally, we find that this haboob, which had local and regional impacts, is atypical of other dust events in this region. We note from this analysis that extreme events such as this haboob require an integrated air quality observing system to provide a more comprehensive assessment of these events.

A New Fuzzy Logic Hydrometeor Classification Scheme Applied to the French X-, C-, and S-Band Polarimetric Radars

AbstractA new fuzzy logic hydrometeor classification algorithm is proposed that takes into account data-based membership functions, measurement conditions, and three-dimensional temperature information provided by a high-resolution nonhydrostatic numerical weather prediction model [the Application of Research to Operations at Mesoscale model (AROME)]. The formulation of the algorithm is unique for X-, C-, and S-band radars and employs wavelength-adapted bivariate membership functions for (ZH, ZDR), (ZH, KDP), and (ZH, ρHV) that were established by using real data collected by the French polarimetric radars and T-matrix simulations. The distortion of membership functions caused by deteriorating measurement conditions (e.g., precipitation-induced attenuation, signal-to-clutter ratio, signal-to-noise ratio, partial beam blocking, and distance) is documented empirically and subsequently parameterized in the algorithm. The result is an increase in the amount of overlapping between the membership functions of the different hydrometeor types. The relative difference between the probability function values of the first and second choice of the hydrometeor classification algorithm is analyzed as a measure of the quality of identification. Semiobjective scores are calculated using an expert-built validation dataset to assess the respective improvements brought by using “richer” temperature information and by using more realistic membership functions. These scores show a significant improvement in the detection of wet snow.

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

Abstract This 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 reflectivity factor for the rain region. Based on the specific differential phase, rainfall estimations were computed using data obtained from the NOAA polarimetric X-band radar for hydrometeorology (HYDROX) and evaluated using HMT rain gauge observations. The results show that the methodology works well at capturing the high-frequency rainfall variations for the events analyzed herein and can be useful for mountainous terrain applications.

Coupling X-band dual-polarized mini-radars and hydro-meteorological forecast models: the HYDRORAD project

Abstract. Hydro-meteorological hazards like convective outbreaks leading to torrential rain and floods are among the most critical environmental issues world-wide. In that context weather radar observations have proven to be very useful in providing information on the spatial distribution of rainfall that can support early warning of floods. However, quantitative precipitation estimation by radar is subjected to many limitations and uncertainties. The use of dual-polarization at high frequency (i.e. X-band) has proven particularly useful for mitigating some of the limitation of operational systems, by exploiting the benefit of easiness to transport and deploy and the high spatial and temporal resolution achievable at small antenna sizes. New developments on X-band dual-polarization technology in recent years have received the interest of scientific and operational communities in these systems. New enterprises are focusing on the advancement of cost-efficient mini-radar network technology, based on high-frequency (mainly X-band) and low-power weather radar systems for weather monitoring and hydro-meteorological forecasting. Within the above context, the main objective of the HYDRORAD project was the development of an innovative \\mbox{integrated} decision support tool for weather monitoring and hydro-meteorological applications. The integrated system tool is based on a polarimetric X-band mini-radar network which is the core of the decision support tool, a novel radar products generator and a hydro-meteorological forecast modelling system that ingests mini-radar rainfall products to forecast precipitation and floods. The radar products generator includes algorithms for attenuation correction, hydrometeor classification, a vertical profile reflectivity correction, a new polarimetric rainfall estimators developed for mini-radar observations, and short-term nowcasting of convective cells. The hydro-meteorological modelling system includes the Mesoscale Model 5 (MM5) and the Army Corps of Engineers Hydrologic Engineering Center hydrologic and hydraulic modelling chain. The characteristics of this tool make it ideal to support flood monitoring and forecasting within urban environment and small-scale basins. Preliminary results, carried out during a field campaign in Moldova, showed that the mini-radar based hydro-meteorological forecasting system can constitute a suitable solution for local flood warning and civil flood protection applications.

Using WSR-88D Polarimetric Data to Identify Bird-Contaminated Doppler Velocities

As an important part of Doppler velocity data quality control for radar data assimilation and other quantitative applications, an automated technique is developed to identify and remove contaminated velocities by birds, especially migrating birds. This technique builds upon the existing hydrometeor classification algorithm (HCA) for dual-polarimetric WSR-88D radars developed at the National Severe Storms Laboratory, and it performs two steps. In the first step, the fuzzy-logic method in the HCA is simplified and used to identify biological echoes (mainly from birds and insects). In the second step, another simple fuzzy logic method is developed to detect bird echoes among the biological echoes identified in the first step and thus remove bird-contaminated velocities. The membership functions used by the fuzzy logic method in the second step are extracted from normalized histograms of differential reflectivity and differential phase for birds and insects, respectively, while the normalized histograms are constructed by polarimetric data collected during the 2012 fall migrating season and sorted for bird and insects, respectively. The performance and effectiveness of the technique are demonstrated by real-data examples.

Low‐cost laser disdrometer with the capability of hydrometeor imaging

AbstractPrecision of microwave precipitation remote sensing is ensured by studying different hydrometeor classes (raindrops, snowflakes, graupel, and so on), shapes, and drop size distribution (DSD). Recently, DSD estimates and hydrometeor classification algorithms using polarimetric radars were proposed. These algorithm developments require a validation tool providing in situ hydrometeor measurement. We are working toward the development of a low‐cost, light‐weight, and disposable ballooning hydrometeor imaging probe with DSD measurement capabilities. This paper describes the performance of a new line‐scanning laser hydrometeor imaging probe (called PROBE) and validates the DSD using a commercially available impact‐type disdrometer. PROBE captures raindrop images with 0.125 mm horizontal resolution and more than six slices at the 33‐kHz slice rate, providing a reasonable raindrop diameter. DSD validation shows that PROBE acquires adequate DSD shapes during every 2‐min interval. These results demonstrate PROBE's performance in hydrometeor imaging and DSD measurement capabilities in a rainfall. © 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Classification of Precipitation Types during Transitional Winter Weather Using the RUC Model and Polarimetric Radar Retrievals

AbstractA new hydrometeor classification algorithm that combines thermodynamic output from the Rapid Update Cycle (RUC) model with polarimetric radar observations is introduced. The algorithm improves upon existing classification techniques that rely solely on polarimetric radar observations by using thermodynamic information to help to diagnose microphysical processes (such as melting or refreezing) that might occur aloft. This added information is especially important for transitional weather events for which past studies have shown radar-only techniques to be deficient. The algorithm first uses vertical profiles of wet-bulb temperature derived from the RUC model output to provide a background precipitation classification type. According to a set of empirical rules, polarimetric radar data are then used to refine precipitation-type categories when the observations are found to be inconsistent with the background classification. Using data from the polarimetric KOUN Weather Surveillance Radar-1988 Doppler (WSR-88D) located in Norman, Oklahoma, the algorithm is tested on a transitional winter-storm event that produced a combination of rain, freezing rain, ice pellets, and snow as it passed over central Oklahoma on 30 November 2006. Examples are presented in which the presence of a radar bright band (suggesting an elevated warm layer) is observed immediately above a background classification of dry snow (suggesting the absence of an elevated warm layer in the model output). Overall, the results demonstrate the potential benefits of combining polarimetric radar data with thermodynamic information from numerical models, with model output providing widespread coverage and polarimetric radar data providing an observation-based modification of the derived precipitation type at closer ranges.

Assimilation of Reflectivity Data in a Convective-Scale, Cycled 3DVAR Framework with Hydrometeor Classification

Abstract The impact of assimilating radar reflectivity and radial velocity data with an intermittent, cycled three-dimensional variational assimilation (3DVAR) system is explored using an idealized thunderstorm case and a real data case on 8 May 2003. A new forward operator for radar reflectivity is developed that uses a background temperature field provided by a numerical weather prediction model for automatic hydrometeor classification. Three types of experiments are performed on both the idealized and real data cases. The first experiment uses radial velocity data only, the second experiment uses both radial velocity and reflectivity data without hydrometeor classification, and the final experiment uses both radial velocity and reflectivity data with hydrometeor classification. All experiments advance the analysis state to the next observation time using a numerical model prediction, which is then used as the background for the next analysis. Results from both the idealized and real data cases show that, assimilating only radial velocity data, the model can reconstruct the supercell thunderstorm after several cycles, but the development of precipitation is delayed because of the well-known spinup problem. The spinup problem is reduced dramatically when assimilating reflectivity without hydrometeor classification. The analyses are further improved using the new reflectivity formulation with hydrometeor classification. This study represents a successful first effort in variational convective-scale data assimilation to partition hydrometeors using a background temperature field from a numerical weather prediction model.

An Application of Fuzzy Logic Method to Cloud Hydrometeor Classifications Using the ARM WACR Data

An Application of Fuzzy Logic Method to Cloud Hydrometeor Classifications Using the ARM WACR Data

The NSSL Hydrometeor Classification Algorithm in Winter Surface Precipitation: Evaluation and Future Development

Abstract The National Severe Storms Laboratory (NSSL) has developed a hydrometeor classification algorithm (HCA) for use with the polarimetric upgrade of the current Weather Surveillance Radar-1988 Doppler (WSR-88D) network. The algorithm was developed specifically for warm-season convection, but it will run regardless of season, and so its performance on surface precipitation type during winter events is examined here. The HCA output is compared with collocated (in time and space) observations of precipitation type provided by the public. The Peirce skill score (PSS) shows that the NSSL HCA applied to winter surface precipitation displays little skill, with a PSS of only 0.115. Further analysis indicates that HCA failures are strongly linked to the inability of HCA to accommodate refreezing below the first freezing level and to errors in the melting-level detection algorithm. Entrants in the 2009 American Meteorological Society second annual artificial intelligence competition developed classification methods that yield a PSS of 0.35 using a subset of available radar data merged with limited environmental data. Thus, when polarimetric radar data and environmental data are appropriately combined, more information about winter surface precipitation type is available than from either data source alone.

Recent advances in classification of observations from dual polarization weather radars

It has been a decade since some of the early papers on dual-polarization radar based hydrometeor classification were published. Subsequently this topic has seen rapid advances especially due to adaptation of this technology for operational applications. Since the early papers on hydrometeor classification, additional characterization in terms of echo classification, data quality and hydrometeor identification have been researched, and products produced some distinctly, some with all three procedures combined. This paper provides a summary of the advances since the early papers in this area, especially with an emphasis on how the successful operational applications have influenced this field. Principles of the various types of classification and examples of characterizations such as data quality, echo and hydrometeor classification are also described.

Design and Performance Characteristics of the New 8.5-m Dual-Offset Gregorian Antenna for the CSU–CHILL Radar

Abstract The Colorado State University–University of Chicago–Illinois State Water Survey (CSU–CHILL) national weather radar facility has been operated by the Colorado State University under a cooperative agreement with the U.S. National Science Foundation from 1990 to the present. The radar is configured to measure the elements of the 3 × 3 polarimetric covariance matrix based on using a two-transmitter and two-receiver system in the horizontal–vertical polarization basis. This S-band Doppler, dual-polarized radar facility is used for observations of precipitation with the highest possible data quality. To achieve this, a new dual-offset 8.5-m Gregorian antenna was custom designed and built by VertexRSI (now General Dynamics SATCOM) in Kilgore, Texas, to replace the circa 1994 center-fed parabolic reflector antenna. Here, the design features used to achieve the stringent specifications in terms of the sidelobe envelope and off-axis cross-polar levels are described, and the way in which they were validated at the manufacturer’s long- and short-range pattern measurement facility. Measurements in several different storm types, including stratiform rain and an intense hailstorm, and ground clutter (from mountains) are used to illustrate the new antenna performance. The linear depolarization ratio (LDR) system limit is shown to be −40 dB or better, which should lead to more insights into the microphysics of convective precipitation at subfreezing temperatures (e.g., hail formation, improved hydrometeor-type classification), and in winter precipitation in general (e.g., aggregation processes, rimed versus unrimed particles). In the case of the intense hailstorm, it is shown that measurement artifacts resulting from strong cross-beam gradients of reflectivity, up to 40 dB km−1 at 40-km range, have been greatly reduced or eliminated. Previously noted measurement artifacts with the 1994 antenna at storm tops in intense convection have been eliminated with the dual-offset antenna. The ground (mountain) clutter example shows greatly reduced returns (in terms of near-zero mean Doppler velocity areas) because of rapid falloff in the sidelobe levels with increasing elevation angle. The greatly improved antenna performance as compared with the 1994 antenna are expected to result in corresponding data quality improvements leading to more accurate measurement of rain rate and hydrometeor classification.

Cross Validation of Spaceborne Radar and Ground Polarimetric Radar Aided by Polarimetric Echo Classification of Hydrometeor Types

AbstractGround-based polarimetric weather radar is arguably the most powerful validation tool that provides physical insight into the development and interpretation of spaceborne weather radar algorithms and observations. This study aims to compare and resolve discrepancies in hydrometeor retrievals and reflectivity observations between the NOAA/National Severe Storm Laboratory “proof of concept” KOUN polarimetric Weather Surveillance Radar-1988 Doppler (WSR-88D) and the spaceborne precipitation radar (PR) on board NASA’s Tropical Rainfall Measuring Mission (TRMM) platform. An intercomparison of PR and KOUN melting-layer heights retrieved from 2 to 5 km MSL shows a high correlation coefficient of 0.88 with relative bias of 5.9%. A resolution volume–matching technique is used to compare simultaneous TRMM PR and KOUN reflectivity observations. The comparisons reveal an overall bias of <0.2% between PR and KOUN. The bias is hypothesized to be from non-Rayleigh scattering effects and/or errors in attenuation correction procedures applied to Ku-band PR measurements. By comparing reflectivity with respect to different hydrometeor types (as determined by KOUN’s hydrometeor classification algorithm), it is found that the bias is from echoes that are classified as rain–hail mixture, wet snow, graupel, and heavy rain. These results agree with expectations from backscattering calculations at Ku and S bands, but with the notable exception of dry snow. Comparison of vertical reflectivity profiles shows that PR suffers significant attenuation at lower altitudes, especially in convective rain and in the melting layer. The attenuation correction performs very well for both stratiform and convective rain, however. In light of the imminent upgrade of the U.S. national weather radar network to include polarimetric capabilities, the findings in this study will potentially serve as the basis for nationwide validation of space-based precipitation products and also invite synergistic development of coordinated space–ground multisensor precipitation products.

Bias in Copolar Correlation Coefficient Caused by Antenna Radiation Patterns

We present a theoretical study of the bias in the copolar correlation coefficient caused by cross-polar radiation patterns and by unmatched horizontal and vertical copolar radiation patterns. The analysis of the bias induced by cross-polarization radiation is carried out for both modes of operation of polarimetric radars, designated as the simultaneous transmission and reception of horizontally and vertically polarized waves and the alternate transmission of horizontally and vertically polarized waves, respectively. The bias caused by unmatched horizontal and vertical copolar radiation patterns as a function of slight differences in pointing angles and beamwidths is also analyzed. In well-designed weather radars, for the purpose of hydrometeor classification, the overall acceptable bias in the copolar correlation coefficient should be less than about 0.01. The levels of cross-to-copolar gain ratios for acceptable performance are indicated. Ultimately, pointing angle and beamwidth tolerances are indicated for horizontal and vertical copolar antenna patterns.

Effects of Charge Distribution in Thunderstorms on Lightning Propagation Paths in Darwin, Australia

Abstract The charge distributions in a thundercloud play an important role in the initiation and propagation of lightning discharges. To further understand the effects of charge distributions on lightning discharge, the authors conducted a very high-frequency (VHF) lightning observation campaign during the 2006/07 monsoon in Darwin, Australia, using a VHF broadband digital interferometer (DITF). A C-band polarimetric weather radar to estimate the precipitation profiles such as hydrometeor classification was operated by the Bureau of Meteorology (BOM) Research Centre. Cloud-to-ground (CG) and intracloud (IC) flashes were initiated from the outer and the inner parts of the upper side of the graupel regions, respectively. In the cases of CG flashes, the negative leaders travel first about 10 km horizontally through positive charge regions and then begin to bend toward the ground when they reach the edge of the positive charge regions where there is no graupel region underneath. In contrast, in the cases of the IC flashes the negatively charged graupel regions block the downward developments of negative leaders. It is noted that positive charge regions could facilitate the extension of the horizontal negative leader. These results may suggest that lightning flash types are closely dependent on their initiation locations and the surrounding charge distributions. The experimental results are consistent with other previous observation results and charge model simulations.