scholarly journals Demonstration of Stormwater Management Technology by Short-Term Rainfall Prediction and Real-Time Runoff Analysis System Using Small X-Band Radar

2021 ◽  
Vol 16 (3) ◽  
pp. 403-409
Author(s):  
Ryo Matsuoka ◽  
◽  
Shinichiro Oki
Keyword(s):  
Real Time ◽  
Flood Damage ◽  
Dual Polarization ◽  
Short Term ◽  
Radar Rainfall ◽  
Flood Prediction ◽  
Rainfall Prediction ◽  
X Band ◽  
Small X ◽  
Runoff Analysis

We developed a system that combines urban area rainfall radar (small X-band, dual-polarization radar), short-term rainfall prediction model, and real-time runoff analysis technology, and the demonstration study was conducted on the drainage districts in Fukui City and Toyama City. We demonstrated the effectiveness of the flood damage, by providing the real-time information on rainfall prediction, water level in sewerage pipes, and inland flood prediction to the operators of drainage pump of stormwater storage pipes, and residents in flood-prone areas. During the study for about two years, it was confirmed that the accuracy of the radar rainfall observation was comparable to that of the X-band dual-polarization Doppler weather radar managed by the Ministry of Land, Infrastructure, Transport and Tourism. In the operation of the drainage pump for the Tsukimiminori Stormwater Storage Pipe in Fukui City, we were able to secure the storage capacity for the next rainfall based on the forecast information by maximizing the drainage capacity of the discharge destination. In addition, it was also confirmed that the residents themselves could secure the lead time for setting up water-stop sandbags and moving their vehicles to higher ground.

Very short-term radar rainfall prediction using deep neural network for hydropower dam operation

2021 ◽  
Author(s):  
Seongsim Yoon ◽  
Hongjoon Shin
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Deep Neural Network ◽  
Rainfall Intensity ◽  
Short Term ◽  
Radar Rainfall ◽  
Rainfall Prediction ◽  
Flood Season ◽  
Short Period ◽  
Dam Operation

<p>It is important to utilize various hydrological and weather information and accurate real-time forecasts to understand the hydrological conditions of the dam in order to make decisions of dam operation. In particular, due to rainfall concentrated in a short period of time during the flood season, it is necessary to plan the exact amount of dam discharge using real-time rainfall forecasting information. Compared to the ground rain gauge network, the radar has a high resolution of time and space, which enables the continuous expression of rainfall, which is very advantageous for very short-term prediction. Especially, In particular, the radar is capable of three-dimensional observation of the atmosphere, which has an advantage in understanding the vertical development and structure of clouds and rainfall, which can be used to observe torrential rain in the dam basin and to anticipate future rainfall intensity changes, rainfall movement and duration time. This study aims to develop a suitable radar-based very short-term rainfall prediction technique and to produce rainfall prediction information of the dam basin for stable dam operation and water disaster prevention. The radar-based rainfall prediction in this study is to be performed using a convolutional deep neural network with the 8 years weather radar data of the Korea Meteorological Administration. And, we select rainfall cases with high rainfall intensity and train the deep neural network to ensure the accuracy of flood season rainfall prediction. In addition, we intend to perform the accuracy evaluation with extrapolation-based rainfall prediction results for the dam basin.</p><p> </p><p>This work was supported by KOREA HYDRO & NUCLEAR POWER CO., LTD (No. 2018-Tech-20)</p>

scholarly journals Evaluation of X-Band Dual-Polarization Radar-Rainfall Estimates from OLYMPEX

2019 ◽  
Vol 20 (9) ◽  
pp. 1941-1959 ◽  
Author(s):  
Yagmur Derin ◽  
Emmanouil Anagnostou ◽  
Marios Anagnostou ◽  
John Kalogiros
Keyword(s):  
Rainfall Variability ◽  
Dual Polarization ◽  
Significant Information ◽  
Radar Rainfall ◽  
Rain Gauges ◽  
X Band ◽  
Error Characterization ◽  
Median Volume ◽  
Multiparameter Measurements

Abstract The difficulty of representing high rainfall variability over mountainous areas using ground-based sensors is an open problem in hydrometeorology. Observations from locally deployed dual-polarization X-band radar have the advantage of providing multiparameter measurements near ground that carry significant information useful for estimating drop size distribution (DSD) and surface rainfall rate. Although these measurements are at fine spatiotemporal scale and are less inhibited by complex topography than operational radar network observations, uncertainties in their estimates necessitate error characterization based upon in situ measurements. During November 2015–February 2016, a dual-polarized Doppler on Wheels (DOW) X-band radar was deployed on the Olympic Peninsula of Washington State as part of NASA’s Olympic Mountain Experiment (OLYMPEX). In this study, rain gauges and disdrometers from a dense network positioned within 40 km of DOW are used to evaluate the self-consistency and accuracy of the attenuation and brightband/vertical profile corrections, and rain microphysics estimation by SCOP-ME, an algorithm that uses optimal parameterization and best-fitted functions of specific attenuation coefficients and DSD parameters with radar polarimetric measurements. In addition, the SCOP-ME precipitation microphysical retrievals of median volume diameter D0 and normalized intercept parameter NW are evaluated against corresponding parameters derived from the in situ disdrometer spectra observations.

scholarly journals Short-term forecasting of urban storm water runoff in real-time using extrapolated radar rainfall data

2013 ◽  
Vol 15 (3) ◽  
pp. 897-912 ◽  
Author(s):  
S. Thorndahl ◽  
M. R. Rasmussen
Keyword(s):  
Real Time ◽  
Water Levels ◽  
Rainfall Data ◽  
Storm Water ◽  
Water Runoff ◽  
Short Term ◽  
Radar Rainfall ◽  
Storm Water Runoff ◽  
Short Term Forecasting ◽  
Urban Storm

Model-based short-term forecasting of urban storm water runoff can be applied in real-time control of drainage systems in order to optimize system capacity during rain and minimize combined sewer overflows, improve wastewater treatment or activate alarms if local flooding is impending. A novel online system, which forecasts flows and water levels in real-time with inputs from extrapolated radar rainfall data, has been developed. The fully distributed urban drainage model includes auto-calibration using online in-sewer measurements which is seen to improve forecast skills significantly. The radar rainfall extrapolation (nowcast) limits the lead time of the system to 2 hours. In this paper, the model set-up is tested on a small urban catchment for a period of 1.5 years. The 50 largest events are presented.

X-band Polarimetric Radar Rainfall Measurements in Keys Area Microphysics Project

2006 ◽  
Vol 63 (1) ◽  
pp. 187-203 ◽  
Author(s):  
Emmanouil N. Anagnostou ◽  
Mircea Grecu ◽  
Marios N. Anagnostou
Keyword(s):  
Phase Shift ◽  
Attenuation Correction ◽  
Doppler Radar ◽  
Model Parameters ◽  
Dual Polarization ◽  
Radar Rainfall ◽  
Differential Phase Shift ◽  
Differential Phase ◽  
X Band ◽  
Filter Noise

Abstract The Keys Area Microphysics Project (KAMP), conducted as part of NASA’s Fourth Convective and Moisture Experiment (CAMEX-4) in the lower Keys area, deployed a number of ground radars and four arrays of rain gauge and disdrometer clusters. Among the various instruments is an X-band dual-polarization Doppler radar on wheels (XPOL), contributed by the University of Connecticut. XPOL was used to retrieve rainfall rate and raindrop size distribution (DSD) parameters to be used in support of KAMP science objectives. This paper presents the XPOL measurements in KAMP and the algorithm developed for attenuation correction and estimation of DSD model parameters. XPOL observations include the horizontal polarization reflectivity ZH, differential reflectivity ZDR, and differential phase shift ΦDP. Here, ZH and ZDR were determined to be positively biased by 3 and 0.3 dB, respectively. A technique was also applied to filter noise and correct for potential phase folding in ΦDP profiles. The XPOL attenuation correction uses parameterizations that relate the path-integrated specific (differential) attenuation along a radar ray to the filtered-ΦDP (specific attenuation) profile. Attenuation-corrected ZH and specific differential phase shift (derived from filtered ΦDP profiles) data are then used to derive two parameters of the normalized gamma DSD model, that is, intercept (Nw) and mean drop diameter (D0). The third parameter (shape parameter μ) is calculated using a constrained μ–Λ relationship derived from the measured raindrop spectra. The XPOL attenuation correction is evaluated using coincidental nonattenuated reflectivity fields from the Key West Weather Surveillance Radar-1988 Doppler (WSR-88D), while the DSD parameter retrievals are statistically assessed using DSD parameters calculated from the measured raindrop spectra. Statistics show that XPOL DSD parameter estimation is consistent with independent observations. XPOL estimates of water content and Nw are also shown to be consistent with corresponding retrievals from matched ER-2 Doppler radar (EDOP) profiling observations from the 19 September airborne campaign. Results shown in this paper strengthen the applicability of X-band dual-polarization high resolution observations in cloud modeling and precipitation remote sensing studies.

scholarly journals Dual-Polarization radar rainfall prediction and rain gauge data

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Tyson H. Walsh ◽  
Jesse W. Lansford ◽  
T. V. Hromadka ◽  
Prasada Rao
Keyword(s):  
Weather Forecasting ◽  
Rain Gauge ◽  
Dual Polarization ◽  
Multiple Sources ◽  
Data Set ◽  
Radar Rainfall ◽  
Rainfall Prediction ◽  
Rain Gauges ◽  
Precipitation Estimates ◽  
Data Points

Abstract Objective Reported rainfall data from multiple rain gauges and its corresponding estimate from Dual-Polarization (Dual-Pol) radar is presented here. The ordered set of data pairs were collected from multiple peer reviewed publications spanning across the last decade. Data description Taken from multiple sources, the data set represents several radar sites and rain gauge sites combined for 12,734 data points. The data is relevant in various applications of hydrometeorology and engineering as well as weather forecasting. Further, the importance of accuracy in radar precipitation estimates continues to increase, necessitating the incorporation of as much data as possible.

Urban pluvial flood prediction: a case study evaluating radar rainfall nowcasts and numerical weather prediction models as model inputs

2016 ◽  
Vol 74 (11) ◽  
pp. 2599-2610 ◽  
Author(s):  
Søren Thorndahl ◽  
Jesper Ellerbæk Nielsen ◽  
David Getreuer Jensen
Keyword(s):  
Real Time ◽  
Prediction Models ◽  
Weather Prediction ◽  
Drainage System ◽  
System Capacity ◽  
Radar Rainfall ◽  
Flood Prediction ◽  
Forecast Performance ◽  
Numerical Weather ◽  
Systems Model

Flooding produced by high-intensive local rainfall and drainage system capacity exceedance can have severe impacts in cities. In order to prepare cities for these types of flood events – especially in the future climate – it is valuable to be able to simulate these events numerically, both historically and in real-time. There is a rather untested potential in real-time prediction of urban floods. In this paper, radar data observations with different spatial and temporal resolution, radar nowcasts of 0–2 h leadtime, and numerical weather models with leadtimes up to 24 h are used as inputs to an integrated flood and drainage systems model in order to investigate the relative difference between different inputs in predicting future floods. The system is tested on the small town of Lystrup in Denmark, which was flooded in 2012 and 2014. Results show it is possible to generate detailed flood maps in real-time with high resolution radar rainfall data, but rather limited forecast performance in predicting floods with leadtimes more than half an hour.

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