scholarly journals Fundamental Study of Real-time Short-term Rainfall Prediction System in Watershed: Case Study of Kinu Watershed in Japan

2016 ◽  
Vol 154 ◽  
pp. 88-93
Author(s):  
Tao Lu ◽  
Tomohito Yamada ◽  
Tadashi Yamada
Keyword(s):  
Real Time ◽  
Prediction System ◽  
Short Term ◽  
Fundamental Study ◽  
Rainfall Prediction

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>

HailTrack - Improving Radar-Based Hailfall Estimates by Modeling Hail Trajectories

2021 ◽  
Author(s):  
Jordan P. Brook ◽  
Alain Protat ◽  
Joshua Soderholm ◽  
Jacob T. Carlin ◽  
Hamish McGowan ◽  
...  
Keyword(s):  
Real Time ◽  
Doppler Radar ◽  
Public Awareness ◽  
Impact Angle ◽  
Short Term ◽  
Detection Techniques ◽  
Skill Scores ◽  
Insurance Claims Data ◽  
Differential Reflectivity

AbstractA spatial mismatch between radar-based hail swaths and surface hail reports is commonly noted in meteorological literature. The discrepancy is partly due to hailstone advection and melting between detection aloft and observation at the ground. The present study aims to mitigate this problem by introducing a model named HailTrack, which estimates hailfall at the surface using radar observations. The model operates by detecting, tracking and collating hailstone trajectories using dual-polarised, dual-Doppler radar retrievals. Notable improvements in hailfall forecasts were observed through the use of HailTrack, and initialising the model with hail differential reflectivity (HDR) radar retrievals was found to produce the most accurate hailfall estimates. The analysis of a case study in Brisbane, Australia demonstrated that trajectory modelling significantly improved the correlation between hail swaths and hail-related insurance losses, increasing Heidke skill scores from 0.48 to 0.58. The accumulated kinetic energy of hailstone impacts also showed some skill in identifying areas that were exposed to particularly severe hailfall. Other unique impact estimates are presented such as hailstone advection information and hailstone impact angle statistics. The potential to run the model in real time and produce short-term (10-15 minute) nowcasts is also introduced. Model applications include improving radar-based hail climatologies, validating hail detection techniques and insurance claims data, and providing real-time hail impact maps to improve public awareness of hail risk.

scholarly journals Using Digital Surveillance Tools for Near Real-Time Mapping of the Risk of International Infectious Disease Spread: Ebola as a Case Study

2019 ◽  
Author(s):  
Sangeeta Bhatia ◽  
Britta Lassmann ◽  
Emily Cohn ◽  
Malwina Carrion ◽  
Moritz U. G. Kraemer ◽  
...  
Keyword(s):  
Real Time ◽  
Disease Surveillance ◽  
Early Warning Systems ◽  
West African ◽  
Disease Spread ◽  
Warning Systems ◽  
Short Term ◽  
Incidence Trends ◽  
Digital Surveillance

AbstractIn our increasingly interconnected world, it is crucial to understand the risk of an outbreak originating in one country or region and spreading to the rest of the world. Digital disease surveillance tools such as ProMED and HealthMap have the potential to serve as important early warning systems as well as complement the field surveillance during an ongoing outbreak. Here we present a flexible statistical model that uses data produced from digital surveillance tools (ProMED and HealthMap) to forecast short term incidence trends in a spatially explicit manner. The model was applied to data collected by ProMED and HealthMap during the 2013-2016 West African Ebola epidemic. The model was able to predict each instance of international spread 1 to 4 weeks in advance. Our study highlights the potential and limitations of using publicly available digital surveillance data for assessing outbreak dynamics in real-time.

scholarly journals Deep BLSTM-GRU Model for Monthly Rainfall Prediction: A Case Study of Simtokha, Bhutan

2020 ◽  
Vol 12 (19) ◽  
pp. 3174
Author(s):  
Manoj Chhetri ◽  
Sudhanshu Kumar ◽  
Partha Pratim Roy ◽  
Byung-Gyu Kim
Keyword(s):  
Short Term Memory ◽  
Monthly Rainfall ◽  
Short Term ◽  
Term Memory ◽  
Rainfall Prediction ◽  
Agriculture Sector ◽  
Proposed Model ◽  
Long Short Term Memory ◽  
Prediction Systems

Rainfall prediction is an important task due to the dependence of many people on it, especially in the agriculture sector. Prediction is difficult and even more complex due to the dynamic nature of rainfalls. In this study, we carry out monthly rainfall prediction over Simtokha a region in the capital of Bhutan, Thimphu. The rainfall data were obtained from the National Center of Hydrology and Meteorology Department (NCHM) of Bhutan. We study the predictive capability with Linear Regression, Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional Long Short Term Memory (BLSTM) based on the parameters recorded by the automatic weather station in the region. Furthermore, this paper proposes a BLSTM-GRU based model which outperforms the existing machine and deep learning models. From the six different existing models under study, LSTM recorded the best Mean Square Error (MSE) score of 0.0128. The proposed BLSTM-GRU model outperformed LSTM by 41.1% with a MSE score of 0.0075. Experimental results are encouraging and suggest that the proposed model can achieve lower MSE in rainfall prediction systems.

scholarly journals Adaptive Blending Method of Radar-Based and Numerical Weather Prediction QPFs for Urban Flood Forecasting

2019 ◽  
Vol 11 (6) ◽  
pp. 642 ◽  
Author(s):  
Seong-Sim Yoon
Keyword(s):  
Real Time ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Harmony Search ◽  
Flood Forecasting ◽  
Prediction System ◽  
Short Term ◽  
Urban Flood ◽  
Numerical Weather ◽  
Blending Method

Preparing proper disaster prevention measures is important for decreasing the casualties and property losses resulting from floods. One of the most efficient measures in this regard is real-time flood forecasting using quantitative precipitation forecasts (QPFs) based on either short-term radar-based extrapolation or longer-term numerical weather prediction. As both methods have individual advantages and limitations, in this study we developed a new real-time blending technique to improve the accuracy of rainfall forecasts for hydrological applications. We tested the hydrological applicability of six QPFs used for urban flood forecasting in Seoul, South Korea: the McGill Algorithm for Prediction Nowcasting by Lagrangian Extrapolation (MAPLE), KOrea NOwcasting System (KONOS), Spatial-scale Decomposition method (SCDM), Unified Model Local Data Assimilation and Prediction System (UM LDAPS), and Advanced Storm-scale Analysis and Prediction System (ASAPS), as well as our proposed blended approach based on the assumption that the error of the previously predicted rainfall is similar to that of current predicted rainfall. We used the harmony search algorithm to optimize real-time weights that would minimize the root mean square error between predicted and observed rainfall for a 1 h lead time at 10 min intervals. We tested these models using the Storm Water Management Model (SWMM) and Grid-based Inundation Analysis Model (GIAM) to estimate urban flood discharge and inundation using rainfall from the QPFs as input. Although the blended QPF did not always have the highest correlation coefficient, its accuracy varied less than that of the other QPFs. In addition, its simulated water depth in pipe and spatial extent were most similar to observed inundated areas, demonstrating the value of this new approach for short-term flood forecasting.

Emplacing Berlin: Digital Itineraries and Short-term Study Abroad in the City

2018 ◽  
Vol 12 (1) ◽  
pp. 26-36 ◽  
Author(s):  
Richard B. Apgar
Keyword(s):  
Study Abroad ◽  
Urban Landscape ◽  
Physical Space ◽  
Short Term ◽  
Study Abroad Programs ◽  
Term Study ◽  
Digital Maps ◽  
Short Term Study ◽  
The City

As destination of choice for many short-term study abroad programs, Berlin offers students of German language, culture and history a number of sites richly layered with significance. The complexities of these sites and the competing narratives that surround them are difficult for students to grasp in a condensed period of time. Using approaches from the spatial humanities, this article offers a case study for enhancing student learning through the creation of digital maps and itineraries in a campus-based course for subsequent use during a three-week program in Berlin. In particular, the concept of deep mapping is discussed as a means of augmenting understanding of the city and its history from a narrative across time to a narrative across the physical space of the city. As itineraries, these course-based projects were replicated on site. In moving from the digital environment to the urban landscape, this article concludes by noting meanings uncovered and narratives formed as we moved through the physical space of the city.

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