Improved Attenuation-Based Radar Precipitation Estimation Considering the Azimuthal Variabilities of Microphysical Properties

AbstractThe attenuation-based rainfall estimator is less sensitive to the variability of raindrop size distributions (DSDs) than conventional radar rainfall estimators. For the attenuation-based quantitative precipitation estimation (QPE), the key is to accurately estimate the horizontal specific attenuation AH, which requires a good estimate of the ray-averaged ratio between AH and specific differential phase KDP, also known as the coefficient α. In this study, a variational approach is proposed to optimize the coefficient α for better estimates of AH and rainfall. The performance of the variational approach is illustrated using observations from an S-band operational weather radar with rigorous quality control in south China, by comparing against the α optimization approach using a slope of differential reflectivity ZDR dependence on horizontal reflectivity factor ZH. Similar to the ZDR-slope approach, the variational approach can obtain the optimized α consistent with the DSD properties of precipitation on a sweep-to-sweep basis. The attenuation-based hourly rainfall estimates using the sweep-averaged α values from these two approaches show comparable accuracy when verified against the gauge measurements. One advantage of the variational approach is its feasibility to optimize α for each radar ray, which mitigates the impact of the azimuthal DSD variabilities on rainfall estimation. It is found that, based on the optimized α for radar rays, the hourly rainfall amounts derived from the variational approach are consistent with gauge measurements, showing lower bias (1.0%), higher correlation coefficient (0.92), and lower root-mean-square error (2.35 mm) than the results based on the sweep-averaged α.

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Observed Response of the Raindrop Size Distribution to Changes in the Melting Layer

Atmosphere ◽

10.3390/atmos9080319 ◽

2018 ◽

Vol 9 (8) ◽

pp. 319 ◽

Cited By ~ 7

Author(s):

Patrick Gatlin ◽

Walter Petersen ◽

Kevin Knupp ◽

Lawrence Carey

Keyword(s):

Size Distribution ◽

Basic Assumption ◽

Weighted Mean ◽

Radar Rainfall ◽

Melting Layer ◽

Quantitative Precipitation Estimation ◽

Raindrop Size Distribution ◽

Precipitation Estimation ◽

Raindrop Size ◽

Stratiform Rainfall

Vertical variability in the raindrop size distribution (RSD) can disrupt the basic assumption of a constant rain profile that is customarily parameterized in radar-based quantitative precipitation estimation (QPE) techniques. This study investigates the utility of melting layer (ML) characteristics to help prescribe the RSD, in particular the mass-weighted mean diameter (Dm), of stratiform rainfall. We utilize ground-based polarimetric radar to map the ML and compare it with Dm observations from the ground upwards to the bottom of the ML. The results show definitive proof that a thickening, and to a lesser extent a lowering, of the ML causes an increase in raindrop diameter below the ML that extends to the surface. The connection between rainfall at the ground and the overlying microphysics in the column provide a means for improving radar QPE at far distances from a ground-based radar or close to the ground where satellite-based radar rainfall retrievals can be ill-defined.

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An Improved Dual-Polarization Radar Rainfall Algorithm (DROPS2.0): Application in NASA IFloodS Field Campaign

Journal of Hydrometeorology ◽

10.1175/jhm-d-16-0124.1 ◽

2017 ◽

Vol 18 (4) ◽

pp. 917-937 ◽

Cited By ~ 48

Author(s):

Haonan Chen ◽

V. Chandrasekar ◽

Renzo Bechini

Keyword(s):

Spatial Coherence ◽

Dual Polarization ◽

Radar Rainfall ◽

Field Campaign ◽

Quantitative Precipitation Estimation ◽

Precipitation Estimation ◽

Ground Validation ◽

Single Polarization ◽

The Impact ◽

Self Aggregation

Abstract Compared to traditional single-polarization radar, dual-polarization radar has a number of advantages for quantitative precipitation estimation because more information about the drop size distribution and hydrometeor type can be gleaned. In this paper, an improved dual-polarization rainfall methodology is proposed, which is driven by a region-based hydrometeor classification mechanism. The objective of this study is to incorporate the spatial coherence and self-aggregation of dual-polarization observables in hydrometeor classification and to produce robust rainfall estimates for operational applications. The S-band dual-polarization data collected from the NASA Polarimetric (NPOL) radar during the GPM Iowa Flood Studies (IFloodS) ground validation field campaign are used to demonstrate and evaluate the proposed rainfall algorithm. Results show that the improved rainfall method provides better performance than a few single- and dual-polarization algorithms in previous studies. This paper also investigates the impact of radar beam broadening on various rainfall algorithms. It is found that the radar-based rainfall products are less correlated with ground disdrometer measurements as the distance from the radar increases.

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Quantitative Precipitation Estimation with Operational Polarimetric Radar Measurements in Southern China: A Differential Phase–Based Variational Approach

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-17-0142.1 ◽

2018 ◽

Vol 35 (6) ◽

pp. 1253-1271 ◽

Cited By ~ 8

Author(s):

Hao Huang ◽

Kun Zhao ◽

Guifu Zhang ◽

Qing Lin ◽

Long Wen ◽

...

Keyword(s):

Variational Approach ◽

Southern China ◽

A Priori ◽

Polarimetric Radar ◽

Random Errors ◽

Differential Phase ◽

Quantitative Precipitation Estimation ◽

Precipitation Estimation ◽

Radar Measurements ◽

The Impact

AbstractQuantitative precipitation estimation (QPE) with polarimetric radar measurements suffers from different sources of uncertainty. The variational approach appears to be a promising way to optimize the radar QPE statistically. In this study a variational approach is developed to quantitatively estimate the rainfall rate (R) from the differential phase (ΦDP). A spline filter is utilized in the optimization procedures to eliminate the impact of the random errors in ΦDP, which can be a major source of error in the specific differential phase (KDP)-based QPE. In addition, R estimated from the horizontal reflectivity factor (ZH) is used in the a priori with the error covariance matrix statistically determined. The approach is evaluated by an idealized case and multiple real rainfall cases observed by an operational S-band polarimetric radar in southern China. The comparative results demonstrate that with a proper range filter, the proposed variational radar QPE with the a priori included agrees well with the rain gauge measurements and proves to have better performance than the other three approaches, that is, the proposed variational approach without the a priori included, the variational approach proposed by Hogan, and the conventional power-law estimator-based approach.

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An evaluation framework for identifying the optimal raingauge network based on spatiotemporal variation in quantitative precipitation estimation

Hydrology Research ◽

10.2166/nh.2016.169 ◽

2016 ◽

Vol 48 (1) ◽

pp. 77-98 ◽

Cited By ~ 7

Author(s):

Che-Hao Chang ◽

Shiang-Jen Wu ◽

Chih-Tsung Hsu ◽

Jhih-Cyuan Shen ◽

Ho-Cheng Lien

Keyword(s):

Temporal Variation ◽

Evaluation Framework ◽

Spatial And Temporal Variation ◽

Cluster Number ◽

Data Set ◽

Rainfall Characteristics ◽

Quantitative Precipitation Estimation ◽

Hourly Rainfall ◽

Precipitation Estimation ◽

Grid Based

This study proposes an evaluation framework to identify the optimal raingauge network in a watershed using grid-based quantitative precipitation estimation (QPE) with high spatial and temporal resolution. The proposed evaluation framework is based on comparison of the spatial and temporal variation in rainfall characteristics (i.e. rainfall depth and storm pattern) from the gauged data compared with those from QPE. The proposed framework first utilizes cluster analysis to separate raingauges into various clusters based on the locations and rainfall characteristics. Then, a cross-validation algorithm is used to identify the influential raingauge in each cluster based on evaluating performance of fitting weighted spatiotemporal semivariograms of rainfall characteristics from the gauged rainfall to the QPE data. Thus, the influential raingauges for a specific cluster number form the representative network. The optimal raingauge network is the one corresponding to the best fitness performance among the representative networks considered. The study area and data set are the hourly rainfall from 26 raingauges and 1,336 QPE grids for 10 typhoons in the Wu River watershed located in central Taiwan. The proposed evaluation framework suggests that a 10-gauge network is the optimal and can describe a good spatial and temporal variation in the rain field similar to the grid-based QPE from two additional typhoon events.

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Analysis of Dual-Polarimetric Radar Variables and Quantitative Precipitation Estimators for Landfall Typhoons and Squall Lines Based on Disdrometer Data in Southern China

Atmosphere ◽

10.3390/atmos10010030 ◽

2019 ◽

Vol 10 (1) ◽

pp. 30 ◽

Cited By ~ 3

Author(s):

Yonghua Zhang ◽

Liping Liu ◽

Shuoben Bi ◽

Zhifang Wu ◽

Ping Shen ◽

...

Keyword(s):

Drop Size ◽

Southern China ◽

Average Diameter ◽

Squall Line ◽

Polarimetric Radar ◽

Quantitative Precipitation Estimation ◽

Squall Lines ◽

Fitting Method ◽

Precipitation Estimation ◽

Differential Reflectivity

Typhoon rainstorms often cause disasters in southern China. Quantitative precipitation estimation (QPE) with the use of polarimetric radar can improve the accuracy of precipitation estimation and enhance typhoon defense ability. On the basis of the observed drop size distribution (DSD) of raindrops, a comparison is conducted among the DSD parameters and the polarimetric radar observation retrieved from DSD in five typhoon and three squall line events that occurred in southern China from 2016 to 2017. A new piecewise fitting method (PFM) is used to develop the QPE estimators for landfall typhoons and squall lines. The performance of QPE is evaluated by two fitting methods for two precipitation types using DSD data collected. Findings indicate that the number concentration of raindrops in typhoon precipitation is large and the average diameter is small, while the raindrops in squall line rain have opposite characteristics. The differential reflectivity (ZDR) and specific differential phase (KDP) in these two precipitation types increase slowly with the reflectivity factor (ZH), whereas the two precipitation types have different ZDR and KDP in the same ZH. Thus, it is critical to fit the rainfall estimator for different precipitation types. Enhanced estimation can be obtained using the estimators for specific precipitation types, whether the estimators are derived from the conventional fitting method (CFM) or PFM, and the estimators fitted using the PFM can produce better results. The estimators for the developed polarimetric radar can be used in operational QPE and quantitative precipitation foresting, and they can improve disaster defense against typhoons and heavy rains.

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A Variational Approach for Retrieving Raindrop Size Distribution from Polarimetric Radar Measurements in the Presence of Attenuation

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-12-0101.1 ◽

2013 ◽

Vol 52 (1) ◽

pp. 169-185 ◽

Cited By ~ 12

Author(s):

Qing Cao ◽

Guifu Zhang ◽

Ming Xue

Keyword(s):

Variational Approach ◽

Simulated Data ◽

Real Data ◽

Radar Data ◽

Polarimetric Radar ◽

University Of Oklahoma ◽

Raindrop Size Distribution ◽

Radar Measurements ◽

Raindrop Size ◽

Differential Reflectivity

AbstractThis study presents a two-dimensional variational approach to retrieving raindrop size distributions (DSDs) from polarimetric radar data in the presence of attenuation. A two-parameter DSD model, the constrained-gamma model, is used to represent rain DSDs. Three polarimetric radar measurements—reflectivity ZH, differential reflectivity ZDR, and specific differential phase KDP—are optimally used to correct for the attenuation and retrieve DSDs by taking into account measurement error effects. Retrieval results with simulated data demonstrate that the proposed algorithm performs well. Applications to real data collected by the X-band Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) radars and the C-band University of Oklahoma–Polarimetric Radar for Innovations in Meteorology and Engineering (OU-PRIME) also demonstrate the efficacy of this approach.

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

Journal of Atmospheric and Oceanic Technology ◽

10.1175/2010jtecha1488.1 ◽

2011 ◽

Vol 28 (3) ◽

pp. 352-364 ◽

Cited By ~ 110

Author(s):

R. Cifelli ◽

V. Chandrasekar ◽

S. Lim ◽

P. C. Kennedy ◽

Y. Wang ◽

...

Keyword(s):

Estimation Algorithm ◽

State University ◽

Dual Polarization ◽

Rainfall Estimation ◽

Radar Rainfall ◽

Colorado State University ◽

Differential Phase ◽

Precipitation Estimation ◽

The One ◽

Differential Reflectivity

Abstract 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? At Colorado State University (CSU), an optimization algorithm has been developed and used for a number of 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.

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Quantitative Assessment of Operational Weather Radar Rainfall Estimates over California’s Northern Sonoma County Using HMT-West Data

Journal of Hydrometeorology ◽

10.1175/jhm-d-13-045.1 ◽

2014 ◽

Vol 15 (1) ◽

pp. 393-410 ◽

Cited By ~ 23

Author(s):

Sergey Y. Matrosov ◽

F. Martin Ralph ◽

Paul J. Neiman ◽

Allen B. White

Keyword(s):

Total Precipitation ◽

Radar Rainfall ◽

Quantitative Precipitation Estimation ◽

Precipitation Estimation ◽

Sonoma County ◽

Adequate Coverage ◽

Russian River ◽

A Site ◽

High Clouds

Abstract An evaluation of Weather Surveillance Radar-1988 Doppler (WSR-88D) KMUX and KDAX radar quantitative precipitation estimation (QPE) over a site in California’s northern Sonoma County is performed and rain type climatology is presented. This site is next to the flood-prone Russian River basin and, because of the mountainous terrain and remoteness from operational radars, is generally believed to lack adequate coverage. QPE comparisons were conducted for multiyear observations with concurrent classification of rainfall structure using measurements from a gauge and an S-band profiler deployed at the location of interest. The radars were able to detect most of the brightband (BB) rain, which contributed over half of the total precipitation. For this rain type hourly radar-based QPE obtained with a default vertical profile of reflectivity correction provided results with errors of about 50%–60%. The operational radars did not detect precipitation during about 30% of the total rainy hours with mostly shallow nonbrightband (NBB) rain, which, depending on the radar, provided ~(12%–15%) of the total precipitation. The accuracy of radar-based QPE for the detected fraction of NBB rain was rather poor with large negative biases and characteristic errors of around 80%. On some occasions, radars falsely detected precipitation when observing high clouds, which did not precipitate or coexisted with shallow rain (less than 10% of total accumulation). For heavier rain with a significant fraction of BB hourly periods, radar QPE for event totals showed relatively good agreement with gauge data. Cancelation of errors of opposite signs contributed, in part, to such agreement. On average, KDAX-based QPE was biased low compared to KMUX.

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Product-Error-Driven Uncertainty Model for Probabilistic Quantitative Precipitation Estimation with NEXRAD Data

Journal of Hydrometeorology ◽

10.1175/2007jhm814.1 ◽

2007 ◽

Vol 8 (6) ◽

pp. 1325-1347 ◽

Cited By ~ 163

Author(s):

Grzegorz J. Ciach ◽

Witold F. Krajewski ◽

Gabriele Villarini

Keyword(s):

Agricultural Research ◽

Central Element ◽

Processing System ◽

Rain Gauge ◽

Random Errors ◽

Radar Rainfall ◽

Quantitative Precipitation Estimation ◽

Uncertainty Model ◽

Precipitation Estimation ◽

The U.S

Abstract Although it is broadly acknowledged that the radar-rainfall (RR) estimates based on the U.S. national network of Weather Surveillance Radar-1988 Doppler (WSR-88D) stations contain a high degree of uncertainty, no methods currently exist to inform users about its quantitative characteristics. The most comprehensive characterization of this uncertainty can be achieved by delivering the products in a probabilistic rather than the traditional deterministic form. The authors are developing a methodology for probabilistic quantitative precipitation estimation (PQPE) based on weather radar data. In this study, they present the central element of this methodology: an empirically based error structure model for the RR products. The authors apply a product-error-driven (PED) approach to obtain a realistic uncertainty model. It is based on the analyses of six years of data from the Oklahoma City, Oklahoma, WSR-88D radar (KTLX) processed with the Precipitation Processing System algorithm of the NEXRAD system. The modeled functional-statistical relationship between RR estimates and corresponding true rainfall consists of two components: a systematic distortion function and a stochastic factor quantifying remaining random errors. The two components are identified using a nonparametric functional estimation apparatus. The true rainfall is approximated with rain gauge data from the Oklahoma Mesonet and the U.S. Department of Agriculture (USDA) Agricultural Research Service Micronet networks. The RR uncertainty model presented here accounts for different time scales, synoptic regimes, and distances from the radar. In addition, this study marks the first time in which results on RR error correlation in space and time are presented.

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Comparative Evaluation of the OTT PARSIVEL2 Using a Collocated Two-Dimensional Video Disdrometer

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-16-0256.1 ◽

2017 ◽

Vol 34 (9) ◽

pp. 2059-2082 ◽

Cited By ~ 22

Author(s):

S.-G. Park ◽

Hae-Lim Kim ◽

Young-Woong Ham ◽

Sung-Hwa Jung

Keyword(s):

Heavy Rainfall ◽

Drop Diameter ◽

Two Dimensional ◽

Radar Rainfall ◽

Rain Gauges ◽

Empirical Relations ◽

Raindrop Size ◽

Rain Events ◽

Using Data ◽

Differential Reflectivity

AbstractThe performance of the OTT second-generation Particle Size Velocity (PARSIVEL2) laser weather sensor is evaluated by comparing it with a collocated two-dimensional video disdrometer (2DVD) and rain gauges using data collected over a total of 36 rain events. A comparison of raindrop size distributions (DSDs) between the 2DVD and two PARSIVEL2 reveals good agreement for weak rainfall rates below approximately 10 mm h−1 and for midsize drops with diameters between 0.6 and 4.0 mm irrespective of rainfall rates, whereas the PARSIVEL2 produces overestimations of large drops with diameters above 4 mm during heavy rainfall above approximately 20 mm h−1. The resultant DSD parameters of the PARSIVEL2 present overestimations of the mean diameter Dm in the normalized gamma function and the maximum drop diameter Dmax, and underestimations of the intercept parameter Nw and total number of drops NT. Furthermore, how the characteristics of DSDs from the PARSIVEL2 affect the polarimetric radar variables, such as differential reflectivity ZDR and specific differential phase KDP, is examined, as well as how these characteristics affect empirical relations required in radar hydrometeorological applications such as quantitative rainfall estimations. Based on these examinations, it can be concluded that the OTT PARSIVEL2 still produces overestimations of large drops and underestimations of small drops during heavy rainfall, similar to older models of PARSIVEL, despite significant improvements to the PARSIVEL2 system, and furthermore that the uses of PARSIVEL2 measurements can act as a source of error in radar hydrometeorological applications such as radar rainfall estimations.

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