scholarly journals An Early Performance Evaluation of the NEXRAD Dual-Polarization Radar Rainfall Estimates for Urban Flood Applications

2013 ◽  
Vol 28 (6) ◽  
pp. 1478-1497 ◽  
Author(s):  
Luciana K. Cunha ◽  
James A. Smith ◽  
Mary Lynn Baeck ◽  
Witold F. Krajewski
Keyword(s):  
Kansas City ◽  
Spatial Scales ◽  
Rain Gauge ◽  
Dual Polarization ◽  
Radar Rainfall ◽  
Urban Flood ◽  
Temporal And Spatial ◽  
Single Polarization ◽  
Early Performance ◽  
Rainfall Estimates

Abstract Dual-polarization radars are expected to provide better rainfall estimates than single-polarization radars because of their ability to characterize hydrometeor type. The goal of this study is to evaluate single- and dual-polarization radar rainfall fields based on two overlapping radars (Kansas City, Missouri, and Topeka, Kansas) and a dense rain gauge network in Kansas City. The study area is located at different distances from the two radars (23–72 km for Kansas City and 104–157 km for Topeka), allowing for the investigation of radar range effects. The temporal and spatial scales of radar rainfall uncertainty based on three significant rainfall events are also examined. It is concluded that the improvements in rainfall estimation achieved by polarimetric radars are not consistent for all events or radars. The nature of the improvement depends fundamentally on range-dependent sampling of the vertical structure of the storms and hydrometeor types. While polarimetric algorithms reduce range effects, they are not able to completely resolve issues associated with range-dependent sampling. Radar rainfall error is demonstrated to decrease as temporal and spatial scales increase. However, errors in the estimation of total storm accumulations based on polarimetric radars remain significant (up to 25%) for scales of approximately 650 km2.

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.

scholarly journals Scale Dependence of Radar Rainfall Uncertainty: Initial Evaluation of NEXRAD’s New Super-Resolution Data for Hydrologic Applications

2010 ◽  
Vol 11 (5) ◽  
pp. 1191-1198 ◽  
Author(s):  
Bong-Chul Seo ◽  
Witold F. Krajewski
Keyword(s):  
Scale Effects ◽  
Spatial Scales ◽  
Super Resolution ◽  
Rain Gauge ◽  
Radar Rainfall ◽  
Weather Radars ◽  
Level Ii ◽  
Simple Scaling ◽  
Resolution Level ◽  
Reflectivity Data

Abstract This study explores the scale effects of radar rainfall accumulation fields generated using the new super-resolution level II radar reflectivity data acquired by the Next Generation Weather Radar (NEXRAD) network of the Weather Surveillance Radar-1988 Doppler (WSR-88D) weather radars. Eleven months (May 2008–August 2009, exclusive of winter months) of high-density rain gauge network data are used to describe the uncertainty structure of radar rainfall and rain gauge representativeness with respect to five spatial scales (0.5, 1, 2, 4, and 8 km). While both uncertainties of gauge representativeness and radar rainfall show simple scaling behavior, the uncertainty of radar rainfall is characterized by an almost 3 times greater standard error at higher temporal and spatial resolutions (15 min and 0.5 km) than at lower resolutions (1 h and 8 km). These results may have implications for error propagation through distributed hydrologic models that require high-resolution rainfall input. Another interesting result of the study is that uncertainty obtained by averaging rainfall products produced from the super-resolution reflectivity data is slightly lower at smaller scales than the uncertainty of the corresponding resolution products produced using averaged (recombined) reflectivity data.

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.

Radar-based rainfall nowcasting for flash flood hazard assessment: recent results at regional and Continental scales

2020 ◽  
Author(s):  
Marc Berenguer ◽  
Shinju Park ◽  
Daniel Sempere-Torres
Keyword(s):  
Real Time ◽  
Hazard Assessment ◽  
Flash Flood ◽  
Flood Hazard ◽  
Spatial Scales ◽  
Flash Floods ◽  
Radar Rainfall ◽  
European Projects ◽  
Rainfall Estimates ◽  
Ne Spain

<p>Radar rainfall estimates and nowcasts have been used in Catalonia (NE Spain) for real-time flash flood hazard nowcasting based on the basin-aggregated rainfall for several years. This approach has been further developed within the European Projects ERICHA (www.ericha.eu) and ANYWHERE (www.anywhere-h2020.eu), where it has been demonstrated to monitor flash floods in real time in several locations and at different spatial scales (from regional to Continental coverage).</p><p>The work summarizes the main results of the recent projects, analysing the performance of the flash flood nowcasting system. The results obtained on recent events  show the main advantages and some of the limitations of the system.</p>

A convolutional neural network for nowcasting rainfall intensities at fine temporal and spatial scales

2020 ◽  
Author(s):  
Bora Shehu ◽  
Malkin Gerchow ◽  
Uwe Haberlandt
Keyword(s):  
Neural Network ◽  
Spatial Scales ◽  
Radar Data ◽  
Data Driven ◽  
Training Dataset ◽  
Lead Times ◽  
Linear Transformations ◽  
Urban Flood ◽  
Deep Cnn ◽  
Temporal And Spatial

<p>The short term forecast of rainfall intensities for fine temporal and spatial resolutions, has always been challenging due to the unpredictable nature of rainfall. Commonly at such scales, radar data are employed to track and extrapolate rainfall storms in the future. For very short lead times, the Lagrange persistence can produce reliable results up to 20 min whilst for longer lead times hybrid models are necessary, in order to account for the birth, death and non-linear transformations of storms that might increase the predictability of rainfall. Recently, data driven techniques, are gaining popularity due to their high learning skills, although their performance is highly dependent on the size of the training dataset and don’t include any physical background. Thus the aim of this study is to investigate the use of data driven techniques in increasing the predictability of rainfall forecast at very fine scales.</p><p>For this purpose, a deep convolutional artificial neural network (CNN) is employed to predict rainfall intensities at 5min and 1km<sup>2</sup> resolutions for the Hannover radar range area at lead times from 5min to 3 hours. The deep CNN is trained for each lead time based on a past window of 15 minutes. The training dataset consist of 93 events (convective, stratiform and mixed events) from the period 2003-2012 and the validating dataset of 17 convective events from the period 2013-2018. The performance is assessed by computing the correlation and the root mean square error of the forecasted fields from the observed radar field, and is compared against the performance of an existing Lagrange-based nowcast method; the Lucas-Kanade optical flow. Special attention is given to the quality of the radar input by using a merged product between radar and gauge data (100 recording stations are used) instead of the raw radar one.  </p><p>The results of this study reveal that the deep CNN is able to learn complex relationship and improve the nowcast for short lead times. However there is a limit that a CNN cannot pass; for those lead times a blending of the radar based nowcast with NWP might be more desirable. Moreover, since most urban models are validated on gauge observations, forecasting on merged data yields more reliable results for urban flood forecasting as the forecast agrees better with the gauge observation.</p>

A climatological approach for operational radar rainfall bias correction 

2021 ◽  
Author(s):  
Ruben Imhoff ◽  
Claudia Brauer ◽  
Klaas-Jan van Heeringen ◽  
Hidde Leijnse ◽  
Aart Overeem ◽  
...  
Keyword(s):  
The Netherlands ◽  
Real Time ◽  
Grid Cell ◽  
Rain Gauge ◽  
Bias Reduction ◽  
Training Dataset ◽  
Correction Factors ◽  
Spatial And Temporal Patterns ◽  
Radar Rainfall ◽  
Rainfall Estimates

<p>Most radar quantitative precipitation estimation (QPE) products systematically deviate from the true rainfall amount. This makes radar QPE adjustments unavoidable for operational use in hydro-meteorological (forecasting) models. Most correction methods require a timely available, high-density network of quality-controlled rain gauge observations. Here, we introduce a set of fixed bias reduction factors for the Netherlands, which vary per grid cell and day of the year. With this approach, we aim to provide an alternative to current practice, because the climatological factors are both operationally available and independent of the real-time rain gauge availability.</p><p>The correction factors were based on 10 years of 5-min radar QPE and reference rainfall data. We tested this method on the resulting rainfall estimates and subsequent discharge simulations for twelve Dutch catchment and polder areas. In addition, we compared the results to the operational mean field bias (MFB) corrected rainfall estimates and a reference dataset. This reference consisted of the radar QPE, spatially adjusted with a network of 356 validated rain gauge observations. Of this network, only 31 are automatic gauges. Hence, only these were available in real-time for the operational MFB corrections.</p><p>The climatological correction factors show clear spatial and temporal patterns. The factors are higher far from the radars and higher during winter than in summer. The latter pattern is likely a result of sampling above the melting layer during the months December–March, which causes higher underestimations. Estimated yearly rainfall sums are generally comparable to the reference and outperform the MFB corrected rainfall estimates for catchments far from the radars (south and east of the country). This difference is absent for catchments closer to the radars, where both products tend to marginally overestimate the rainfall sums. The differences amplify when both QPE products are used to force the hydrologic models. Discharge simulations based on the proposed QPE product outperform the MFB corrected rainfall estimates for all but one basin. Moreover, the climatological factor derivation shows little sensitivity to the moving window length and to leaving individual years out of the training dataset. The presented method provides a robust and straightforward operational alternative. It can serve as a benchmark for further QPE algorithm development in the Netherlands and elsewhere.</p>

Evaluation of GPM-IMERG rainfall estimates at multiple temporal and spatial scales over Greece

2022 ◽  
pp. 106014
Author(s):  
Anastasios-Petros Kazamias ◽  
Marios Sapountzis ◽  
Konstantinos Lagouvardos
Keyword(s):  
Spatial Scales ◽  
Temporal And Spatial ◽  
Rainfall Estimates

scholarly journals Impact of Gauge Representative Error on a Radar Rainfall Uncertainty Model

2018 ◽  
Vol 57 (12) ◽  
pp. 2769-2787 ◽  
Author(s):  
Qiang Dai ◽  
Qiqi Yang ◽  
Jun Zhang ◽  
Shuliang Zhang
Keyword(s):  
Spatial Scales ◽  
Radar Data ◽  
Rain Gauge ◽  
Considerable Effect ◽  
Temporal Dependence ◽  
Radar Rainfall ◽  
The United Kingdom ◽  
Uncertainty Model ◽  
Uncertainty Band ◽  
Uncertainty Models

AbstractIn modeling the radar rainfall uncertainty, rain gauge measurement is generally regarded as the areal “true” rainfall. However, the inconsistent scales between radar and gauge may introduce a new uncertainty (also known as gauge representative uncertainty), which is erroneously identified as radar rainfall uncertainty and therefore called pseudouncertainty. It is crucial to comprehend what percentage of the estimated radar rainfall uncertainty actually stems from such pseudouncertainty rather than radar rainfall itself. For this reason, based on a fully formulated radar rainfall uncertainty model, this study aims to explore how the gauge representative error affects the distribution, spatial dependence, and temporal dependence of hourly accumulated radar rainfall uncertainty, and consequently affects the produced radar rainfall uncertainty band. Three group scenarios that delineate various degrees of gauge representative errors were designed to configure and run the uncertainty model. In the setting of a long-term analysis (almost 7 years) of the Brue catchment in the United Kingdom, we found that the gauge representative error affected the simulation of the marginal distribution of radar rainfall error, and had a considerable effect on temporal dependence estimation of radar rainfall uncertainty. The spread of the rainfall uncertainty band decreased with the growth of the gauge density in a radar pixel. The scenario with the lowest representative error only had 78% uncertainty spread of the scenario that has the largest error. This indicated there was a large impact of the representative error on radar rainfall uncertainty models. It is hoped that more catchments with diverse climate and geographical conditions and more radar data with various spatial scales could be explored by the research community to further investigate this crucial issue.

scholarly journals Correction of Radar Reflectivity and Differential Reflectivity for Rain Attenuation at X Band. Part II: Evaluation and Application

2005 ◽  
Vol 22 (11) ◽  
pp. 1633-1655 ◽  
Author(s):  
S-G. Park ◽  
M. Maki ◽  
K. Iwanami ◽  
V. N. Bringi ◽  
V. Chandrasekar
Keyword(s):  
Drop Size ◽  
Earth Science ◽  
Radar Data ◽  
Rain Gauge ◽  
Radar Rainfall ◽  
X Band ◽  
Rain Events ◽  
Differential Reflectivity ◽  
Rainfall Estimates ◽  
Good Agreement

Abstract In this paper, the attenuation-correction methodology presented in Part I is applied to radar measurements observed by the multiparameter radar at the X-band wavelength (MP-X) of the National Research Institute for Earth Science and Disaster Prevention (NIED), and is evaluated by comparison with scattering simulations using ground-based disdrometer data. Further, effects of attenuation on the estimation of rainfall amounts and drop size distribution parameters are also investigated. The joint variability of the corrected reflectivity and differential reflectivity show good agreement with scattering simulations. In addition, specific attenuation and differential attenuation, which are derived in the correction procedure, show good agreement with scattering simulations. In addition, a composite rainfall-rate algorithm is proposed and evaluated by comparison with eight gauges. The radar-rainfall estimates from the uncorrected (or observed) ZH produce severe underestimation, even at short ranges from the radar and for stratiform rain events. On the contrary, the reflectivity-based rainfall estimates from the attenuation-corrected ZH does not show such severe underestimation and does show better agreement with rain gauge measurements. More accurate rainfall amounts can be obtained from a simple composite algorithm based on specific differential phase KDP, with the R(ZH_cor) estimates being used for low rainfall rates (KDP ≤ 0.3° km−1 or ZH_cor ≤ 35 dBZ). This improvement in accuracy of rainfall estimation based on KDP is a result of the insensitivity of the rainfall algorithm to natural variations of drop size distributions (DSDs). The ZH, ZDR, and KDP data are also used to infer the parameters (median volume diameter D0 and normalized intercept parameter Nw) of a normalized gamma DSD. The retrieval of D0 and Nw from the corrected radar data show good agreement with those from disdrometer data in terms of the respective relative frequency histograms. The results of this study demonstrate that high-quality hydrometeorological information on rain events such as rainfall amounts and DSDs can be derived from X-band polarimetric radars.

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