Basin Hydrometeorology Real-Time Forecast System

This paper presents a Debris Flow Disaster Faster-than-early Forecast System (DFS) with wireless sensor networks. Debris flows carrying saturated solid materials in water flowing downslope often cause severe damage to the lives and properties in their path. Faster-than-early or faster-than-real-time forecasts are imperative to save lives and reduce damage. This paper presents a novel multi-sensor networks for monitoring debris flows. The main idea is to let these sensors drift with the debris flow, to collect flow information as they move along, and to transmit the collected data to base stations in real time. The Raw data are sent to the cloud processing center from the base station. And the processed data and the video of the debris flow are display on the remote PC. The design of the system address many challenging issues, including cost, deployment efforts, and fast reaction.

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An automatic lake-model application using near real-time data forcing: Development of an operational forecast model for Lake Erie

10.5194/gmd-2021-34 ◽

2021 ◽

Author(s):

Shuqi Lin ◽

Leon Boegman ◽

Shiliang Shan ◽

Ryan Mulligan

Keyword(s):

Open Access ◽

Real Time ◽

Public Safety ◽

Lake Erie ◽

Water Resource Management ◽

Computational Models ◽

Fishery Management ◽

Water Levels ◽

Time Data ◽

Forecast System

Abstract. For enhanced public safety and water resource management, a three-dimensional operational lake hydrodynamic forecast system called COASTLINES (Canadian cOASTal and Lake forecastINg modEl System) was developed. The modelling system is built upon the Aquatic Ecosystem Model (AEM3D) model, with predictive simulation capabilities developed and tested for a large lake (i.e., Lake Erie). The open-access web-based platform derives model forcing, code execution, post-processing and visualization of the model outputs, including water level elevations and temperature, is in near real-time. COASTLINES currently generates 240-h predictions using atmospheric forcing from 15 km and 25 km horizontal-resolution operational meteorological products from the Environment Canada Global Deterministic Forecast System (GDPS). Simulated water levels were validated against observations from 6 gauge stations, with model error increasing for longer forecast times. Satellite images and lake buoys were applied to validate forecast lake surface temperature (LST) and the water column thermal stratification. The forecast LST is as accurate as hindcasts, with a root-mean-square-deviation < 2 ℃. COASTLINES predicts storm-surge events and up-/down-welling events that are important for flood water and drinking water/fishery management, respectively. Model forecasts are available in real-time at https://coastlines.engineering.queensu.ca/. This study provides an example of the successful development of an operational forecasting system, entirely driven by open-access data, that may be easily adapted to simulate aquatic systems or to drive other computational models, as required for management and public safety.

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Possible hydrologic forecasting improvements resulting from advancements in precipitation estimation and forecasting for a real-time flood forecast system in the Ohio River Valley, USA

Journal of Hydrology ◽

10.1016/j.jhydrol.2019.124138 ◽

2019 ◽

Vol 579 ◽

pp. 124138

Author(s):

Thomas E. Adams, III ◽

Randel L. Dymond

Keyword(s):

Real Time ◽

River Valley ◽

Ohio River ◽

Ohio River Valley ◽

Flood Forecast ◽

Forecast System ◽

Precipitation Estimation

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Real-Time Performance of a Multi-Model Ensemble-Based Extended Range Forecast System in Predicting the 2014 Monsoon Season Based on NCEP-CFSv2

Current Science ◽

10.18520/cs/v109/i10/1802-1813 ◽

2015 ◽

Vol 109 (10) ◽

pp. 1802 ◽

Cited By ~ 11

Author(s):

A. K. Sahai ◽

R. Chattopadhyay ◽

S. Joseph ◽

R. Mandal ◽

A. Dey ◽

...

Keyword(s):

Real Time ◽

Monsoon Season ◽

Model Ensemble ◽

Forecast System ◽

Time Performance ◽

Extended Range

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A Real-Time Tsunami Inundation Forecast System Using Vector Supercomputer SX-ACE

Journal of Disaster Research ◽

10.20965/jdr.2018.p0234 ◽

2018 ◽

Vol 13 (2) ◽

pp. 234-244 ◽

Cited By ~ 4

Author(s):

Akihiro Musa ◽

Takashi Abe ◽

Takuya Inoue ◽

Hiroaki Hokari ◽

Yoichi Murashima ◽

...

Keyword(s):

Real Time ◽

Local Governments ◽

High Performance ◽

Polygonal Domain ◽

Computational Domain ◽

Xeon Phi ◽

Forecast System ◽

Tsunami Inundation ◽

City Regions ◽

Efficient Vector

Tsunami disasters can cause serious casualties and damage to social infrastructures. An early understanding of disaster states is required in order to advise evacuations and plan rescues and recoveries. We have developed a real-time tsunami inundation forecast system using a vector supercomputer SX-ACE. The system can complete a tsunami inundation and damage estimation for coastal city regions at the resolution of a 10 m grid size in under 20 minutes, and distribute tsunami inundation and infrastructure damage information to local governments in Japan. We also develop a new configuration for the computational domain, which is changed from rectangles to polygons and called a polygonal domain, in order to effectively simulate in the entire coast of Japan. Meanwhile, new supercomputers have been developed, and their peak performances have increased year by year. In 2016, a new Xeon Phi processor calledKnights Landingwas released for high-performance computing. In this paper, we present an overview of our real-time tsunami inundation forecast system and the polygonal domain, which can decrease the amount of computation in a simulation, and then discuss its performance on a vector supercomputer SX-ACE and a supercomputer system based on Intel Xeon Phi. We also clarify that the real-time tsunami inundation forecast system requires the efficient vector processing of a supercomputer with high-performance cores.

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A Dynamical Initialization Scheme for Real-Time Forecasts of Tropical Cyclones Using the WRF Model*

Monthly Weather Review ◽

10.1175/mwr-d-12-00077.1 ◽

2013 ◽

Vol 141 (3) ◽

pp. 964-986 ◽

Cited By ~ 50

Author(s):

Dong-Hyun Cha ◽

Yuqing Wang

Keyword(s):

Real Time ◽

Large Scale ◽

Initial Conditions ◽

Wrf Model ◽

Forecast Model ◽

Initial Position ◽

Northwest Pacific ◽

Forecast System ◽

Positive Effects ◽

Forecast Models

Abstract To improve the initial conditions of tropical cyclone (TC) forecast models, a dynamical initialization (DI) scheme using cycle runs is developed and implemented into a real-time forecast system for northwest Pacific TCs based on the Weather Research and Forecasting (WRF) Model. In this scheme, cycle runs with a 6-h window before the initial forecast time are repeatedly conducted to spin up the axisymmetric component of the TC vortex until the model TC intensity is comparable to the observed. This is followed by a 72-h forecast using the Global Forecast System (GFS) prediction as lateral boundary conditions. In the DI scheme, the spectral nudging technique is employed during each cycle run to reduce bias in the large-scale environmental field, and the relocation method is applied after the last cycle run to reduce the initial position error. To demonstrate the effectiveness of the proposed DI scheme, 69 forecast experiments with and without the DI are conducted for 13 TCs over the northwest Pacific in 2010 and 2011. The DI shows positive effects on both track and intensity forecasts of TCs, although its overall skill depends strongly on the performance of the GFS forecasts. Compared to the forecasts without the DI, on average, forecasts with the DI reduce the position and intensity errors by 10% and 30%, respectively. The results demonstrate that the proposed DI scheme improves the initial TC vortex structure and intensity and provides warm physics spinup, producing initial states consistent with the forecast model, thus achieving improved track and intensity forecasts.

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