Modeling probabilistic radar rainfall estimation at ungauged locations based on spatiotemporal errors which correspond to gauged data

2013 ◽  
Vol 46 (1) ◽  
pp. 39-59 ◽  
Author(s):  
Shiang-Jen Wu ◽  
Ho-Cheng Lien ◽  
Chih-Tsung Hsu ◽  
Che-Hao Chang ◽  
Jhih-Cyuan Shen
Keyword(s):  
Model Development ◽  
Temporal Variations ◽  
Cumulative Probability ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Multiple Sensors ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Reservoir Watershed ◽  
Rainfall Estimates

This study presents a probabilistic radar rainfall estimation (PRRE) model to quantify the reliability and accuracy of the resulting radar rainfall estimates at ungauged locations from a radar-based quantitative precipitation estimation (QPE) model. This model primarily estimates the quantiles of the radar rainfall errors at ungauged locations by incorporating seven spatiotemporal variogram models with a nonparametric sample quantile estimate method based on the radar rainfall errors at rain gauges. Then, by adding the resulting error quantiles to the radar rainfall estimates, the corresponding radar rainfall quantiles can be obtained. The QPE system Quantitative Precipitation Estimation Using Multiple Sensors (QPESUMS) provides hourly observed and radar precipitation for three typhoons in the Shinmen reservoir watershed in Northern Taiwan, which are used in the model development and validation. The results indicate that the proposed PRRE model can quantify the spatial and temporal variations of radar rainfall estimates at ungauged locations provided by the QPESUMS system. Also, its reliability and accuracy could be evaluated based on a 95% confidence interval and occurrence probability resulting from the cumulative probability distribution established by the proposed PRRE model.

A Study of the Error Covariance Matrix of Radar Rainfall Estimates in Stratiform Rain. Part II: Scale Dependence

2009 ◽  
Vol 24 (3) ◽  
pp. 800-811 ◽  
Author(s):  
Marc Berenguer ◽  
Isztar Zawadzki
Keyword(s):  
Strong Dependence ◽  
Large Dataset ◽  
Radar Rainfall ◽  
Sources Of Uncertainty ◽  
Quantitative Precipitation Estimation ◽  
Error Covariance ◽  
Precipitation Estimation ◽  
Grid Points ◽  
Dependent Error ◽  
Rainfall Estimates

Abstract The contribution of various physical sources of uncertainty affecting radar rainfall estimates at the ground has been recently quantified at a resolution typically used in schemes assimilating rainfall at the ground onto mesoscale models. Here, the contribution of the two most important sources of uncertainty at nonattenuating wavelengths (the range-dependent error and the uncertainty due to the Z–R transformation) and their interaction are studied as a function of the resolution of radar observations. The analysis is carried out using a large dataset of collocated reflectivity profiles from the McGill S-band radar and disdrometric measurements obtained in stratiform rainfall at resolutions of 1 × 1, 5 × 5, and 15 × 15 km2. Results show that the errors affecting radar quantitative precipitation estimation (QPE) have a strong dependence with range, and that their structure is scale dependent. At the analyzed resolutions, QPE errors are significantly correlated in time and over several grid points.

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

Atmosphere ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 319 ◽  
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.

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

2020 ◽  
Vol 21 (7) ◽  
pp. 1605-1620
Author(s):  
Hao Huang ◽  
Kun Zhao ◽  
Haonan Chen ◽  
Dongming Hu ◽  
Peiling Fu ◽  
...  
Keyword(s):  
Variational Approach ◽  
Optimization Approach ◽  
Radar Rainfall ◽  
Microphysical Properties ◽  
Quantitative Precipitation Estimation ◽  
Hourly Rainfall ◽  
Precipitation Estimation ◽  
Raindrop Size ◽  
Differential Reflectivity ◽  
The Impact

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.

Estimation of Rainfall Based on the Results of Polarimetric Echo Classification

2008 ◽  
Vol 47 (9) ◽  
pp. 2445-2462 ◽  
Author(s):  
Scott E. Giangrande ◽  
Alexander V. Ryzhkov
Keyword(s):  
Type A ◽  
New Method ◽  
Polarimetric Radar ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Different Types ◽  
Rms Error ◽  
Radar Resolution ◽  
Rainfall Estimates

Abstract The quality of polarimetric radar rainfall estimation is investigated for a broad range of distances from the polarimetric prototype of the Weather Surveillance Radar-1988 Doppler (WSR-88D). The results of polarimetric echo classification have been integrated into the study to investigate the performance of radar rainfall estimation contingent on hydrometeor type. A new method for rainfall estimation that capitalizes on the results of polarimetric echo classification (EC method) is suggested. According to the EC method, polarimetric rainfall relations are utilized if the radar resolution volume is filled with rain (or rain and hail), and multiple R(Z) relations are used for different types of frozen hydrometeors. The intercept parameters in the R(Z) relations for each class are determined empirically from comparisons with gauges. It is shown that the EC method exhibits better performance than the conventional WSR-88D algorithm with a reduction by a factor of 1.5–2 in the rms error of 1-h rainfall estimates up to distances of 150 km from the radar.

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

2017 ◽  
Vol 18 (4) ◽  
pp. 917-937 ◽  
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.

Comparison of Single- and Dual-Polarization–Based Rainfall Estimates Using NEXRAD Data for the NASA Iowa Flood Studies Project

2015 ◽  
Vol 16 (4) ◽  
pp. 1658-1675 ◽  
Author(s):  
Bong-Chul Seo ◽  
Brenda Dolan ◽  
Witold F. Krajewski ◽  
Steven A. Rutledge ◽  
Walter Petersen
Keyword(s):  
Rain Rate ◽  
Heavy Rain ◽  
Rain Gauge ◽  
Superior Performance ◽  
Dual Polarization ◽  
Radar Rainfall ◽  
Colorado State University ◽  
Precipitation Estimation ◽  
Improved Performance ◽  
Rainfall Estimates

Abstract This study compares and evaluates single-polarization (SP)- and dual-polarization (DP)-based radar-rainfall (RR) estimates using NEXRAD data acquired during Iowa Flood Studies (IFloodS), a NASA GPM ground validation field campaign carried out in May–June 2013. The objective of this study is to understand the potential benefit of the DP quantitative precipitation estimation, which selects different rain-rate estimators according to radar-identified precipitation types, and to evaluate RR estimates generated by the recent research SP and DP algorithms. The Iowa Flood Center SP (IFC-SP) and Colorado State University DP (CSU-DP) products are analyzed and assessed using two high-density, high-quality rain gauge networks as ground reference. The CSU-DP algorithm shows superior performance to the IFC-SP algorithm, especially for heavy convective rains. We verify that dynamic changes in the proportion of heavy rain during the convective period are associated with the improved performance of CSU-DP rainfall estimates. For a lighter rain case, the IFC-SP and CSU-DP products are not significantly different in statistical metrics and visual agreement with the rain gauge data. This is because both algorithms use the identical NEXRAD reflectivity–rain rate (Z–R) relation that might lead to substantial underestimation for the presented case.

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

2011 ◽  
Vol 28 (3) ◽  
pp. 352-364 ◽  
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.

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

2014 ◽  
Vol 15 (1) ◽  
pp. 393-410 ◽  
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.

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

2007 ◽  
Vol 8 (6) ◽  
pp. 1325-1347 ◽  
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.

scholarly journals Application of bias correction methods to improve the accuracy of quantitative radar rainfall in Korea

2015 ◽  
Vol 8 (11) ◽  
pp. 11429-11465
Author(s):  
J.-K. Lee ◽  
J.-H. Kim ◽  
M.-K. Suk
Keyword(s):  
Bias Correction ◽  
Mean Field ◽  
Correction Method ◽  
Estimation Bias ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Reflectivity Measurement ◽  
Estimation Process ◽  
Post Process ◽  
Precipitation Estimation

Abstract. There are many potential sources of the biases in the radar rainfall estimation process. This study classified the biases from the rainfall estimation process into the reflectivity measurement bias and the rainfall estimation bias by the Quantitative Precipitation Estimation (QPE) model and also conducted the bias correction methods to improve the accuracy of the Radar-AWS Rainrate (RAR) calculation system operated by the Korea Meteorological Administration (KMA). In the Z bias correction for the reflectivity biases occurred by measuring the rainfalls, this study utilized the bias correction algorithm. The concept of this algorithm is that the reflectivity of the target single-pol radars is corrected based on the reference dual-pol radar corrected in the hardware and software bias. This study, and then, dealt with two post-process methods, the Mean Field Bias Correction (MFBC) method and the Local Gauge Correction method (LGC), to correct the rainfall estimation bias by the QPE model. The Z bias and rainfall estimation bias correction methods were applied to the RAR system. The accuracy of the RAR system was improved after correcting Z bias. For the rainfall types, although the accuracy of the Changma front and the local torrential cases was slightly improved without the Z bias correction the accuracy of the typhoon cases got worse than the existing results in particular. As a result of the rainfall estimation bias correction, the Z bias_LGC was especially superior to the MFBC method because the different rainfall biases were applied to each grid rainfall amount in the LGC method. For the rainfall types, the results of the Z bias_LGC showed that the rainfall estimates for all types was more accurate than only the Z bias and, especially, the outcomes in the typhoon cases was vastly superior to the others.

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