NEXRAD Radar Rainfall: Meeting User Requirements for Design and Real-Time Hydrologic Applications

2004 ◽  
Author(s):  
J. E. Vieux ◽  
B. E. Vieux
Keyword(s):  
Real Time ◽  
User Requirements ◽  
Radar Rainfall ◽  
Nexrad Radar

scholarly journals Proposal of a Conceptual Architecture System for Informing the User in the IoT Environment

2019 ◽  
Vol 31 (1) ◽  
pp. 37-47 ◽  
Author(s):  
Marko Periša ◽  
Goran Marković ◽  
Peter Kolarovszki ◽  
Radovan Madleňák
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
The Internet ◽  
User Requirements ◽  
Elderly Persons ◽  
System Operation ◽  
Persons With Disabilities ◽  
Real Time Information ◽  
Time Information ◽  
The Internet Of Things

Design and development of systems for delivering real-time information to people with disabilities and elderly persons need to be based on defined user requirements. For this purpose, the user requirements have been defined in this paper according to the everyday needs of people who use traffic networks and move in closed spaces. The logical presentation of the functionality of the informing system operation and its subsystems includes all the information (data) important for designing a user information delivery system. The paper presents a conceptual architecture system for delivering user informing services related to the environment based on the Internet of Things concept. The aim of the user informing service is an increase in the level of mobility of persons with disabilities and the senior age groups of users. In order to check the operation of the proposed architecture, the informing system operation was monitored on Arduino Uno and Raspberry Pi platforms in laboratory conditions. A simulation confirmed the interdependence of individual data from different subsystems in order to provide real-time information to the system user. The proposed conceptual architecture can contribute to a more efficient approach to the modeling of assistive technologies (with the aim of informing the users) based on dew/fog/cloud technologies in the Internet of Things  environment.

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>

Online Information as Real-Time Big Data About Heavy Rain Disasters and its Limitations: Case Study of Miyagi Prefecture, Japan, During Typhoons 17 and 18 in 2015

2017 ◽  
Vol 12 (2) ◽  
pp. 335-346 ◽  
Author(s):  
Shosuke Sato ◽  
◽  
Shuichi Kure ◽  
Shuji Moriguchi ◽  
Keiko Udo ◽  
...  
Keyword(s):  
Social Media ◽  
Real Time ◽  
Local Governments ◽  
Expert Knowledge ◽  
Water Levels ◽  
Rainfall Data ◽  
Online Information ◽  
Radar Rainfall ◽  
Cumulative Rainfall ◽  
Miyagi Prefecture

The role of public online information in helping to reduce disaster damage is expected to become increasingly important since it can be used for decision making about disaster response. This paper aims to discuss the effectiveness and limitations of real-time online information about heavy rainfall based on an analysis of data on the disaster caused by Typhoons 17 and 18 in 2015 in Miyagi prefecture, Japan, and on a focus group interview survey with four experts on natural disasters. The results from the interviews showed the following: (1) Landslide alert information is reliable for prediction purposes. However, many people did not monitor it because it was released around midnight. (2) Areas of landslide occurrence and river flooding correspond to areas with heavy cumulative rainfall. Yet cumulative rainfall data are not available on the web. (3) The available radar-rainfall data can be used to predict the situation one hour from the present as long as the person has expert knowledge. (4) It is possible to monitor river water levels at many points. Yet, about half of the observation points have no established “flood danger water level.” (5) Local governments released a great amount of disaster information through social media before flooding occurred on some rivers. However, one must monitor multiple social media accounts and not just the account of one’s hometown.

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.

Comparison of short-term rainfall forecasts for model-based flow prediction in urban drainage systems

2013 ◽  
Vol 68 (2) ◽  
pp. 472-478 ◽  
Author(s):  
Søren Thorndahl ◽  
Troels Sander Poulsen ◽  
Thomas Bøvith ◽  
Morten Borup ◽  
Malte Ahm ◽  
...  
Keyword(s):  
Real Time ◽  
Weather Prediction ◽  
Waste Water Treatment ◽  
Treatment Plant ◽  
Waste Water Treatment Plant ◽  
Lead Times ◽  
Radar Rainfall ◽  
Drainage Systems ◽  
Flow Prediction ◽  
Time Flow

Forecast-based flow prediction in drainage systems can be used to implement real-time control of drainage systems. This study compares two different types of rainfall forecast – a radar rainfall extrapolation-based nowcast model and a numerical weather prediction model. The models are applied as input to an urban runoff model predicting the inlet flow to a waste water treatment plant. The modelled flows are auto-calibrated against real-time flow observations in order to certify the best possible forecast. Results show that it is possible to forecast flows with a lead time of 24 h. The best performance of the system is found using the radar nowcast for the short lead times and the weather model for larger lead times.

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.

Dynamic rainfall mapping using multi-radar multi-gauge observations in changing synoptic environments

2021 ◽  
Author(s):  
Yabin Gou ◽  
Haonan Chen
Keyword(s):  
Real Time ◽  
Physical Model ◽  
Feedback Mechanism ◽  
Radar Rainfall ◽  
Weather Radars ◽  
Raindrop Size Distribution ◽  
Precipitation Estimates ◽  
Storm Cells ◽  
Raindrop Size ◽  
Quantitative Precipitation Estimates

<p>It is well known that the performance of radar-derived quantitative precipitation estimates greatly relies on the physical model of the raindrop size distribution (DSD) and the relation between the physical model and radar parameters. However, incorporating changing precipitation microphysics to dynamically adjust the radar reflectivity (Z) and rain rate (R) relations can be challenging for real-time applications. In this study, two adaptive radar rainfall approaches are developed based on the radar-gauge feedback mechanism using 16 S-band Doppler weather radars and 4579 surface rain gauges deployed over the Eastern JiangHuai River Basin (EJRB) in China. Although the Z–R relations in both approaches are dynamically adjusted within a single precipitation system, one is using a single global optimal (SGO) Z–R relation, whereas the other is using different Z–R relations for different storm cells identified by a storm cell identification and tracking (SCIT) algorithm. Four precipitation events featured by different rainfall microphysical characteristics are investigated to demonstrate the performances of these two rainfall mapping methodologies. In addition, the short-term vertical profile of reflectivity (VPR) clusters are extensively analyzed to resolve the storm-scale characteristics of different storm cells. The verification results based on independent gauge observations show that both rainfall estimation approaches with dynamic Z–R relations perform much better than fixed Z–R relations. The adaptive approach incorporating the SCIT algorithm and real-time gauge measurements performs best since it can better capture the spatial variability and temporal evolution of precipitation.</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>

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