scholarly journals Realized ecological forecast through interactive Ecological Platform for Assimilating Data into model (EcoPAD)

2018 ◽  
Author(s):  
Yuanyuan Huang ◽  
Mark Stacy ◽  
Jiang Jiang ◽  
Nilutpal Sundi ◽  
Shuang Ma ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Methane Production ◽  
Data Transfer ◽  
Model Simulation ◽  
Meteorological Data ◽  
Sensor Data ◽  
Model Structure ◽  
Web Based ◽  
Ecological Forecasting

Abstract. Predicting future changes in ecosystem services is not only highly desirable but also becomes feasible as several forces (e.g., available big data, developed data assimilation (DA) techniques, and advanced cyberinfrastructure) are converging to transform ecological research to quantitative forecasting. To realize ecological forecasting, we have developed an Ecological Platform for Assimilating Data (EcoPAD) into models. EcoPAD is a web-based software system that automates data transfer and processes from sensor networks to ecological forecasting through data management, model simulation, data assimilation, and visualization. It facilitates interactive data-model integration from which model is recursively improved through updated data while data is systematically refined under the guidance of model. EcoPAD relies on data from observations, process-oriented models, DA techniques, and web-based workflow. We applied EcoPAD to the Spruce and Peatland Responses Under Climatic and Environmental change (SPRUCE) experiment at North Minnesota. The EcoPAD-SPRUCE realizes fully automated data transfer, feeds meteorological data to drive model simulations, assimilates both manually measured and automated sensor data into Terrestrial ECOsystem (TECO) model, and recursively forecast responses of various biophysical and biogeochemical processes to five temperature and two CO2 treatments in near real-time (weekly). The near real-time forecasting with EcoPAD-SPRUCE has revealed that uncertainties or mismatches in forecasting carbon pool dynamics are more related to model (e.g., model structure, parameter, and initial value) than forcing variables, opposite to forecasting flux variables. EcoPAD-SPRUCE quantified acclimations of methane production in response to warming treatments through shifted posterior distributions of the CH4:CO2 ratio and temperature sensitivity (Q10) of methane production towards lower values. Different case studies indicated that realistic forecasting of carbon dynamics relies on appropriate model structure, correct parameterization and accurate external forcing. Moreover, EcoPAD-SPRUCE stimulated active feedbacks between experimenters and modelers so as to identify model components to be improved and additional measurements to be made. It becomes the first interactive model-experiment (ModEx) system and opens a novel avenue for interactive dialogue between modelers and experimenters. EcoPAD also has the potential to become an interactive tool for resource management, to stimulate citizen science in ecology, and transform environmental education with its easily accessible web interface.

scholarly journals Realized ecological forecast through an interactive Ecological Platform for Assimilating Data (EcoPAD, v1.0) into models

2019 ◽  
Vol 12 (3) ◽  
pp. 1119-1137 ◽  
Author(s):  
Yuanyuan Huang ◽  
Mark Stacy ◽  
Jiang Jiang ◽  
Nilutpal Sundi ◽  
Shuang Ma ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Methane Production ◽  
Data Transfer ◽  
Model Simulation ◽  
Meteorological Data ◽  
Sensor Data ◽  
Model Structure ◽  
Sources Of Information ◽  
Web Based ◽  
Ecological Forecasting

Abstract. Predicting future changes in ecosystem services is not only highly desirable but is also becoming feasible as several forces (e.g., available big data, developed data assimilation (DA) techniques, and advanced cyber-infrastructure) are converging to transform ecological research into quantitative forecasting. To realize ecological forecasting, we have developed an Ecological Platform for Assimilating Data (EcoPAD, v1.0) into models. EcoPAD (v1.0) is a web-based software system that automates data transfer and processing from sensor networks to ecological forecasting through data management, model simulation, data assimilation, forecasting, and visualization. It facilitates interactive data–model integration from which the model is recursively improved through updated data while data are systematically refined under the guidance of model. EcoPAD (v1.0) relies on data from observations, process-oriented models, DA techniques, and the web-based workflow. We applied EcoPAD (v1.0) to the Spruce and Peatland Responses Under Climatic and Environmental change (SPRUCE) experiment in northern Minnesota. The EcoPAD-SPRUCE realizes fully automated data transfer, feeds meteorological data to drive model simulations, assimilates both manually measured and automated sensor data into the Terrestrial ECOsystem (TECO) model, and recursively forecasts the responses of various biophysical and biogeochemical processes to five temperature and two CO2 treatments in near-real time (weekly). Forecasting with EcoPAD-SPRUCE has revealed that mismatches in forecasting carbon pool dynamics are more related to model (e.g., model structure, parameter, and initial value) than forcing variables, opposite to forecasting flux variables. EcoPAD-SPRUCE quantified acclimations of methane production in response to warming treatments through shifted posterior distributions of the CH4:CO2 ratio and the temperature sensitivity (Q10) of methane production towards lower values. Different case studies indicated that realistic forecasting of carbon dynamics relies on appropriate model structure, correct parameterization, and accurate external forcing. Moreover, EcoPAD-SPRUCE stimulated active feedbacks between experimenters and modelers to identify model components to be improved and additional measurements to be taken. It has become an interactive model–experiment (ModEx) system and opens a novel avenue for interactive dialogue between modelers and experimenters. Altogether, EcoPAD (v1.0) acts to integrate multiple sources of information and knowledge to best inform ecological forecasting.

Basin-Scale, Real-Time Salinity Management Using Telemetered Sensor Networks and Model-Based Salt Assimilative Capacity Forecasts

2016 ◽  
pp. 193-220
Author(s):  
Nigel W.T. Quinn ◽  
Roberta Tassey ◽  
Jun Wang
Keyword(s):  
Real Time ◽  
Software Tool ◽  
Sensor Data ◽  
Quality Data ◽  
Monitoring Site ◽  
Data Quality Management ◽  
Web Based ◽  
Web Access ◽  
State Water ◽  
Salinity Management

This chapter describes a new approach to environmental decision support for salinity management in the San Joaquin Basin that focuses on Web-based data sharing using tools such as YSI Econet and continuous data quality management using an enterprise-level software tool WISKI. These tools offer real-time Web-access to sensor data as well as providing the owner full control over the way the data is visualized. The same websites use GIS to superimpose the monitoring site locations on maps of local hydrography and allow point and click access to the data collected at each environmental monitoring site. This information technology suite of software and hardware work together with a watershed simulation model WARMF-SJR to provide timely, reliable, and high quality data and forecasts of river salinity that can used by stakeholder decision makers to ensure compliance with state water quality objectives.

Basin-Scale, Real-Time Salinity Management Using Telemetered Sensor Networks and Model-Based Salt Assimilative Capacity Forecasts

2015 ◽  
pp. 89-117
Author(s):  
Nigel W.T. Quinn ◽  
Roberta Tassey ◽  
Jun Wang
Keyword(s):  
Real Time ◽  
Software Tool ◽  
Sensor Data ◽  
Quality Data ◽  
Monitoring Site ◽  
Data Quality Management ◽  
Web Based ◽  
Web Access ◽  
State Water ◽  
Salinity Management

This chapter describes a new approach to environmental decision support for salinity management in the San Joaquin Basin that focuses on Web-based data sharing using tools such as YSI Econet and continuous data quality management using an enterprise-level software tool WISKI. These tools offer real-time Web-access to sensor data as well as providing the owner full control over the way the data is visualized. The same websites use GIS to superimpose the monitoring site locations on maps of local hydrography and allow point and click access to the data collected at each environmental monitoring site. This information technology suite of software and hardware work together with a watershed simulation model WARMF-SJR to provide timely, reliable, and high quality data and forecasts of river salinity that can used by stakeholder decision makers to ensure compliance with state water quality objectives.

scholarly journals A study on WRF radar data assimilation for hydrological rainfall prediction

2013 ◽  
Vol 17 (8) ◽  
pp. 3095-3110 ◽  
Author(s):  
J. Liu ◽  
M. Bray ◽  
D. Han
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Meteorological Data ◽  
Weather Radar ◽  
Radar Data ◽  
Rain Gauge ◽  
Radar Reflectivity ◽  
Storm Event ◽  
Hydrological Applications ◽  
Radar Data Assimilation

Abstract. Mesoscale numerical weather prediction (NWP) models are gaining more attention in providing high-resolution rainfall forecasts at the catchment scale for real-time flood forecasting. The model accuracy is however negatively affected by the "spin-up" effect and errors in the initial and lateral boundary conditions. Synoptic studies in the meteorological area have shown that the assimilation of operational observations, especially the weather radar data, can improve the reliability of the rainfall forecasts from the NWP models. This study aims at investigating the potential of radar data assimilation in improving the NWP rainfall forecasts that have direct benefits for hydrological applications. The Weather Research and Forecasting (WRF) model is adopted to generate 10 km rainfall forecasts for a 24 h storm event in the Brue catchment (135.2 km2) located in southwest England. Radar reflectivity from the lowest scan elevation of a C-band weather radar is assimilated by using the three-dimensional variational (3D-Var) data-assimilation technique. Considering the unsatisfactory quality of radar data compared to the rain gauge observations, the radar data are assimilated in both the original form and an improved form based on a real-time correction ratio developed according to the rain gauge observations. Traditional meteorological observations including the surface and upper-air measurements of pressure, temperature, humidity and wind speed are also assimilated as a bench mark to better evaluate and test the potential of radar data assimilation. Four modes of data assimilation are thus carried out on different types/combinations of observations: (1) traditional meteorological data; (2) radar reflectivity; (3) corrected radar reflectivity; (4) a combination of the original reflectivity and meteorological data; and (5) a combination of the corrected reflectivity and meteorological data. The WRF rainfall forecasts before and after different modes of data assimilation are evaluated by examining the rainfall temporal variations and total amounts which have direct impacts on rainfall–runoff transformation in hydrological applications. It is found that by solely assimilating radar data, the improvement of rainfall forecasts are not as obvious as assimilating meteorological data; whereas the positive effect of radar data can be seen when combined with the traditional meteorological data, which leads to the best rainfall forecasts among the five modes. To further improve the effect of radar data assimilation, limitations of the radar correction ratio developed in this study are discussed and suggestions are made on more efficient utilisation of radar data in NWP data assimilation.

Web-Based Data Inquiry and Real-Time Control of Structural Health Monitoring Sensor Networks

2009 ◽  
Author(s):  
Bo Chen ◽  
Wenjia Liu ◽  
Jinjiang Wang ◽  
Justin Slepak
Keyword(s):  
Real Time ◽  
Operating Mode ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Data Repository ◽  
Real Time Control ◽  
Web Based ◽  
Structural Health ◽  
Time Control ◽  
Data Inquiry

This paper presents a Web-based data inquiry and real-time control of sensor’s operating mode for structural health monitoring sensor networks. The main objective of the presented system is to provide a Web interface for real-time sensor data visualization, sensor-level damage diagnosis, and control of sensor’s operating mode. Web services are available both on distributed sensor nodes and a data repository machine. Users can request Web pages hosted on the sensor nodes or the data repository machine by specifying corresponding sensor IDs. The ability of directly accessing data on sensor nodes via internet allows users to monitor a structure’s performance in a timely manner. The damage diagnosis algorithms implemented on the sensor nodes help users to assess the structural health conditions without the need of transmitting sensor data to a central data station. The presented system also provides the capability of dynamically changing sensor’s operating mode through the Web interface. This feature greatly enhances the flexibility of the system to accommodate different sensing needs and achieve a long lifespan. The system has been tested in the Laboratory to validate its capabilities.

A web based tool for operational real-time flood forecasting using data assimilation to update hydraulic states

2016 ◽  
Vol 84 ◽  
pp. 35-49 ◽  
Author(s):  
Mathieu Mure-Ravaud ◽  
Guillaume Binet ◽  
Michael Bracq ◽  
Jean-Jacques Perarnaud ◽  
Antonin Fradin ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Flood Forecasting ◽  
Web Based ◽  
Using Data

scholarly journals Real-time monitoring system for weather and air pollutant measurement with HTML-based UI application

2021 ◽  
Vol 10 (3) ◽  
pp. 1669-1677
Author(s):  
Prisma Megantoro ◽  
Brahmantya Aji Pramudita ◽  
P. Vigneshwaran ◽  
Abdufattah Yurianta ◽  
Hendra Ari Winarno
Keyword(s):  
Real Time ◽  
Web Application ◽  
Barometric Pressure ◽  
Air Pollutant ◽  
Sensor Data ◽  
Time Data ◽  
Web Based ◽  
Uv Index ◽  
Weather Parameters ◽  
Real Time Database

This article discusses devising an IoT system to monitor weather parameters and gas pollutants in the air along with anHTML web-based application. Weather parameters measured include; speed and direction of the wind, rainfall, air temperature and humidity, barometric pressure, and UV index. On the other side, the gases measured are; ammonia, hydrogen, methane, ozone, carbon monoxide, and carbon dioxide. This article is introducing a technique to send all parameter data. All parameters read by each sensor are converted into a string then joined into a string dataset, where this dataset is sent to the server periodically. On the UI side, the dataset that has been downloaded from the server-parsed for processing and then displayed. This system uses Google Firebase as a real-time database server for sensor data. Also, using the GitHub platform as a web hosting. The web application uses the HTML programming platform. The results of this study indicate that the device operates successfully to provide information about the weather and gases condition as real-time data.

scholarly journals Evaluation of the Impacts of Assimilating the TAMDAR Data on 12/4 km Grid WRF-Based RTFDDA Simulations over the CONUS

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Yongxin Zhang ◽  
Yubao Liu ◽  
Thomas Nipen
Keyword(s):  
Wind Speed ◽  
Data Assimilation ◽  
Real Time ◽  
Mean Squared Error ◽  
Meteorological Data ◽  
Horizontal Resolution ◽  
Weather Research And Forecasting ◽  
Squared Error ◽  
Forecasting System ◽  
Four Dimensional Data Assimilation

An analysis of the impacts of assimilating the Tropospheric Airborne Meteorological Data Report (TAMDAR) data with the Weather Research and Forecasting- (WRF-) real-time four-dimensional data assimilation (RTFDDA) and forecasting system over the Contiguous US (CONUS) is presented. The impacts of the horizontal resolution increase from 12 km to 4 km on the WRF-RTFDDA simulations are also examined in conjunction with the TAMDAR data impacts. The assimilation of the TAMDAR data reduces the root mean squared error of the moisture field predictions and increases the correlation between the predictions and the observations for both domains with 12 km and 4 km grid spacings. The TAMDAR data reduce the model dry biases in the middle and lower levels by adding moisture at those levels. Assimilating the TAMDAR data improves temperature predictions at middle to high levels and wind speed predictions at all levels especially for the 12 km domain. Increasing the horizontal resolution from 12 km to 4 km results in significantly larger impacts on surface variables than assimilating the TAMDAR data.

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