On the initial-state assimilation for limited-area air-quality forecasts

2020 ◽  
Author(s):  
Rostislav Kouznetsov ◽  
Mikhail Sofiev
Keyword(s):  
Air Quality ◽  
Real Time ◽  
Air Pollutant ◽  
Optimal Interpolation ◽  
Limited Area ◽  
Time Data ◽  
Initial State ◽  
Skill Scores ◽  
Forecast Lead Time ◽  
Set Up

<p>An ensemble of 9 regional Air Quality models is being run operationally within CAMS-50 project providing the 3D fields of air-pollutant distribution over Europe. The models are initialized from their previous-day's forecasts for 00Z and run for 4 days forward. The same models are used for near-real-time reanalysis of the previous day involving the air-quality observations to adjust the modelled  fields via data assimilation methods, such as 3D-var or optimal-interpolation procedures.  In this set-up the observed near-real-time data do not affect the forecasts.  Development of a method to improve the forecast quality by using the assimilated fields from the previous-day analysis is one of the goals for the CAMS-61 project.</p><p>As a prototype evaluation for this study, we made several tests with SILAM model (http://silam.fmi.fi) initializing the simulations from the assimilated or non-assimilated states and evaluated the evolution of the model skill scores along the forecast lead time. The tests were made for summer and winter seasons and for initialization time of 00Z vs 12Z.  In order to generalize the results, and make them independent on particular implementation of 3D-VAR in SILAM, the tests were made also with initialization from the analyses made with other CAMS-50 models.  That experiment utilized the list of species and vertical available in the CAMS-50 product catalog. </p><p>The results of the simulation corroborated with our earlier studies that showed a quite quick relaxation of the scores for runs initialized from analyses to the free-run state: with certain variability between the species, the runs converged to the free-run trajectory generally within several hours.  We also investigated the issues connected with initialization from the incomplete set of species and sparse vertical, which might make the scores of the forecast initialized from the incomplete assimilated model state being worse than the ones from the free-run model.</p><p> </p>

scholarly journals Real Time Air Quality Index from Various Locations

2020 ◽  
Vol 9 (2) ◽  
pp. 368-372
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Real Time ◽  
Environmental Protection Agency ◽  
Quality Index ◽  
Ground Level ◽  
Air Quality Index ◽  
Time Data ◽  
Open Government Data ◽  
Government Data

The surveys regarding air pollution shows that there has been a hasty growth due to the emission of fuels and exhaust gases from factories. The Air Quality Index (AQI) has been launched to note the contemporary status of the air quality. The intent of AQI is to aid every individual know how the regional air quality will make an impact on them. The Environmental Protection Agency assess the AQI for five major air pollutants namely Nitrogen dioxide (NO2), ground-level ozone (O3), particle pollution (PM10, PM2.5), carbon monoxide (CO), and sulphur dioxide (SO2). The intent of the project is to congregate real-time Air Quality Index from distinct monitoring stations across India, analysing the data and reporting on it. Collect the real-time data using the API key provided by Open Government Data (OGD) platform India. This is done by making use of Microsoft Business Intelligence (MSBI) and Power BI Tools to transform, analyse and visualize the data. This project can be utilized to develop various programs like Ozone today in Europe and in mobile applications which acts as an alert system that can protect people from air pollution.

Operational Readiness Case Study for Accessibility and Mobility of Wells Real Time Centre System and Applications During Movement Control Order

2021 ◽  
Author(s):  
Mohd Ridzuan Hamid ◽  
Meor M. Meor Hashim ◽  
Lokman Norhashimi ◽  
Muhammad Faris Arriffin ◽  
Azlan Mohamad
Keyword(s):  
Real Time ◽  
Movement Control ◽  
Time Data ◽  
Work From Home ◽  
Global Pandemic ◽  
Machine Learning Applications ◽  
Full Capacity ◽  
Set Up ◽  
Control Order ◽  
Remote Operations

Abstract The recent global pandemic is an unprecedented event and took the world by storm. The Movement Control Order (MCO) issued by Malaysia's government to halt the spread of the deadly infection has changed the landscape of work via a flexible working arrangement. The Wells Real Time Centre (WRTC) is not an exception and is also subjected to the change. WRTC is an in-house proactive monitoring hub, built to handle massive real-time drilling data, to support and guide wells delivery effectiveness and excellence. The functionality of the WRTC system and applications are embedded in the wells delivery workflow. The centre houses drilling specialists who are responsible for observing the smooth sailing of well construction and are tasked to intervene when necessary to avoid any unintended incidents. WRTC is equipped with myriads of engineering applications and drilling software that are vital for the operations. Such applications include monitoring software, machine learning applications, engineering modules, real-time data acquisition, and database management. These applications are mostly cloud-based and Internet-facing, hence it is accessible and agile as an infrastructure that is ready to be deployed anytime anywhere when it is required. The strategy for WRTC mobility started as soon as the MCO was announced. This announcement mandated the WRTC to operate outside of the office and required the staff to work from home. The careful coordination and preparation to transform and adapt WRTC to a new norm was greatly assisted by the infrastructure readiness. All of these factors contributed greatly to a successful arrangement with zero to minimal downtime where a workstation was set up in each personnel's home, running at full capacity. This transformation was done within one day of the notice and completed within hours of activation. Despite the successful move, few rooms for improvements such as redundancy of VPN use to access applications and limited access to some proprietary software can be enhanced in the future. WRTC is ready to be mobile and agile to support the drilling operations remotely either in the office or from home. The quick turnaround is a major indicator that WRTC infrastructure and personnel are ready and capable for remote operations without interruption.

scholarly journals Air quality modelling using the Met Office Unified Model (AQUM OS24-26): model description and initial evaluation

2013 ◽  
Vol 6 (2) ◽  
pp. 353-372 ◽  
Author(s):  
N. H. Savage ◽  
P. Agnew ◽  
L. S. Davis ◽  
C. Ordóñez ◽  
R. Thorpe ◽  
...  
Keyword(s):  
Air Quality ◽  
Evaluation Studies ◽  
Unified Model ◽  
Atmospheric Composition ◽  
Model Description ◽  
Limited Area ◽  
Negative Bias ◽  
Air Quality Model ◽  
Air Quality Modelling ◽  
Forecast Lead Time

Abstract. The on-line air quality model AQUM (Air Quality in the Unified Model) is a limited-area forecast configuration of the Met Office Unified Model which uses the UKCA (UK Chemistry and Aerosols) sub-model. AQUM has been developed with two aims: as an operational system to deliver regional air quality forecasts and as a modelling system to conduct air quality studies to inform policy decisions on emissions controls. This paper presents a description of the model and the methods used to evaluate the performance of the forecast system against the automated UK surface network of air quality monitors. Results are presented of evaluation studies conducted for a year-long period of operational forecast trials and several past cases of poor air quality episodes. The results demonstrate that AQUM tends to over-predict ozone (~8 μg m−3 mean bias for the year-long forecast), but has a good level of responsiveness to elevated ozone episode conditions – a characteristic which is essential for forecasting poor air quality episodes. AQUM is shown to have a negative bias for PM10, while for PM2.5 the negative bias is much smaller in magnitude. An analysis of speciated PM2.5 data during an episode of elevated particulate matter (PM) suggests that the PM bias occurs mainly in the coarse component. The sensitivity of model predictions to lateral boundary conditions (LBCs) has been assessed by using LBCs from two different global reanalyses and by comparing the standard, single-nested configuration with a configuration having an intermediate European nest. We conclude that, even with a much larger regional domain, the LBCs remain an important source of model error for relatively long-lived pollutants such as ozone. To place the model performance in context we compare AQUM ozone forecasts with those of another forecasting system, the MACC (Monitoring Atmospheric Composition and Climate) ensemble, for a 5-month period. An analysis of the variation of model skill with forecast lead time is presented and the insights this provides to the relative sources of error in air quality modelling are discussed.

scholarly journals Semantic Traffic Sensor Data: The TRAFAIR Experience

2020 ◽  
Vol 10 (17) ◽  
pp. 5882
Author(s):  
Federico Desimoni ◽  
Sergio Ilarri ◽  
Laura Po ◽  
Federica Rollo ◽  
Raquel Trillo-Lado
Keyword(s):  
Air Quality ◽  
Real Time ◽  
Smart Cities ◽  
Low Cost ◽  
Semantic Annotation ◽  
Open Data ◽  
Transportation Systems ◽  
Sensor Data ◽  
Time Data ◽  
Future Evolution

Modern cities face pressing problems with transportation systems including, but not limited to, traffic congestion, safety, health, and pollution. To tackle them, public administrations have implemented roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. In the case of traffic sensor data not only the real-time data are essential, but also historical values need to be preserved and published. When real-time and historical data of smart cities become available, everyone can join an evidence-based debate on the city’s future evolution. The TRAFAIR (Understanding Traffic Flows to Improve Air Quality) project seeks to understand how traffic affects urban air quality. The project develops a platform to provide real-time and predicted values on air quality in several cities in Europe, encompassing tasks such as the deployment of low-cost air quality sensors, data collection and integration, modeling and prediction, the publication of open data, and the development of applications for end-users and public administrations. This paper explicitly focuses on the modeling and semantic annotation of traffic data. We present the tools and techniques used in the project and validate our strategies for data modeling and its semantic enrichment over two cities: Modena (Italy) and Zaragoza (Spain). An experimental evaluation shows that our approach to publish Linked Data is effective.

scholarly journals HYBRIDJOIN for Near-Real-Time Data Warehousing

2011 ◽  
Vol 7 (4) ◽  
pp. 21-42 ◽  
Author(s):  
M. Asif Naeem ◽  
Gillian Dobbie ◽  
Gerald Weber
Keyword(s):  
Real Time ◽  
Data Stream ◽  
Time Integration ◽  
Synthetic Data ◽  
Performance Measurements ◽  
Time Data ◽  
Join Algorithm ◽  
Real Time Data ◽  
Set Up ◽  
Nested Loop

An important component of near-real-time data warehouses is the near-real-time integration layer. One important element in near-real-time data integration is the join of a continuous input data stream with a disk-based relation. For high-throughput streams, stream-based algorithms, such as Mesh Join (MESHJOIN), can be used. However, in MESHJOIN the performance of the algorithm is inversely proportional to the size of disk-based relation. The Index Nested Loop Join (INLJ) can be set up so that it processes stream input, and can deal with intermittences in the update stream but it has low throughput. This paper introduces a robust stream-based join algorithm called Hybrid Join (HYBRIDJOIN), which combines the two approaches. A theoretical result shows that HYBRIDJOIN is asymptotically as fast as the fastest of both algorithms. The authors present performance measurements of the implementation. In experiments using synthetic data based on a Zipfian distribution, HYBRIDJOIN performs significantly better for typical parameters of the Zipfian distribution, and in general performs in accordance with the theoretical model while the other two algorithms are unacceptably slow under different settings.

scholarly journals A Methodology for Heterogeneous Sensor Data Organization and Near Real-Time Data Sharing by Adopting OGC SWE Standards

2019 ◽  
Vol 8 (4) ◽  
pp. 167 ◽  
Author(s):  
Bartolomeo Ventura ◽  
Andrea Vianello ◽  
Daniel Frisinghelli ◽  
Mattia Rossi ◽  
Roberto Monsorno ◽  
...  
Keyword(s):  
Real Time ◽  
Sensor Data ◽  
Weather Data ◽  
Time Data ◽  
Heterogeneous Sensors ◽  
Heterogeneous Datasets ◽  
Real Time Data ◽  
Sensor Registration ◽  
Set Up ◽  
Minimum Interaction

Finding a solution to collect, analyze, and share, in near real-time, data acquired by heterogeneous sensors, such as traffic, air pollution, soil moisture, or weather data, represents a great challenge. This paper describes the solution developed at Eurac Research to automatically upload data, in near real-time, by adopting Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) standards to guarantee interoperability. We set up a methodology capable of ingesting heterogeneous datasets to automatize observation uploading and sensor registration, with minimum interaction required of the user. This solution has been successfully tested and applied in the Long Term (Socio-)Ecological Research (LT(S)ER) Matsch-Mazia initiative, and the code is accessible under the CC BY 4.0 license.

Design and Application of a Communication System Based on Codesys

2014 ◽  
Vol 1044-1045 ◽  
pp. 1113-1117 ◽  
Author(s):  
Jia Liu ◽  
Wei Ping Fu ◽  
Lei Zhou ◽  
Na Qie ◽  
Wen Yun Wang
Keyword(s):  
Real Time ◽  
Communication System ◽  
Data Exchange ◽  
Intelligent Robot ◽  
Real Time Control ◽  
Time Data ◽  
Time Control ◽  
Physical Channel ◽  
Soft Plc ◽  
Set Up

A model of wireless network data interchange was built to solve cross-platform exchange data in intelligent robot. In the framework of embedded Soft-PLC - Codesys real-time operating system, we apply Socket network programming technology based on TCP/IP communication protocol to set up a physical channel between VC++ platform and the Codesys platform. It realizes the real-time data exchange between host-computer and slave-computer uploaded embedded operation system of robot. A special multi-threaded processing class was developed to enhance the multi-tasking allocation ability of the system. Exchanged data was packaged and analyzed to ensure the accuracy of transmitted data. The experiment shows built communication system platform is justifiable, and data transmission speed is less than 10ms. It is able to meet the needs of real-time control in intelligent robot.

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 Enhancement of real-time IoT-based air quality monitoring system

2020 ◽  
Vol 11 (1) ◽  
pp. 390
Author(s):  
Wei Jian Ng ◽  
Zuraini Dahari
Keyword(s):  
Air Pollution ◽  
Carbon Monoxide ◽  
Air Quality ◽  
Real Time ◽  
Monitoring System ◽  
Atmospheric Pollutants ◽  
Time Data ◽  
Real Time System ◽  
The Real ◽  
Environmental Monitoring System

Air pollution is one of the biggest threat for the environment and the human’s health. The monitoring of air pollution based on several atmospheric pollutants is becoming critical in most countries including Malaysia. This paper presents a development and enhancement features of real-time Internet of Things (IoT)-based environmental monitoring system for air quality. The proposed system will be beneficial to monitor the real-time data for a specific set of air quality parameters such as temperature, humidity and concentration of carbon monoxide, liquified petroleum gas (LPG) and smoke. An alarm system will be triggered if the concentration of carbon monoxide exceeds 50 ppm. Users can use their smartphone to view these data via Wi-Fi by installing an application called “AirProp”. Based on the collected data, this paper also analyses other contributing factors such as time and traffic condition on the temperature, humidity and concentration of pollutant gases at different locations. The advantage of the real-time system is it serves as the data base platform to store data up to certain duration of time. The data can be further analysed and leveraged by governments and researchers to mitigate air pollution.

scholarly journals Improving NOAA NAQFC PM2.5 Predictions with a Bias Correction Approach

2017 ◽  
Vol 32 (2) ◽  
pp. 407-421 ◽  
Author(s):  
Jianping Huang ◽  
Jeffery McQueen ◽  
James Wilczak ◽  
Irina Djalalova ◽  
Ivanka Stajner ◽  
...  
Keyword(s):  
United States ◽  
Air Quality ◽  
Bias Correction ◽  
The United States ◽  
Correction Method ◽  
Ensemble Member ◽  
Air Pollutant ◽  
Wildfire Smoke ◽  
Skill Scores ◽  
Forecast Guidance

Abstract Particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5) is a critical air pollutant with important impacts on human health. It is essential to provide accurate air quality forecasts to alert people to avoid or reduce exposure to high ambient levels of PM2.5. The NOAA National Air Quality Forecasting Capability (NAQFC) provides numerical forecast guidance of surface PM2.5 for the United States. However, the NAQFC forecast guidance for PM2.5 has exhibited substantial seasonal biases, with overpredictions in winter and underpredictions in summer. To reduce these biases, an analog ensemble bias correction approach is being integrated into the NAQFC to improve experimental PM2.5 predictions over the contiguous United States. Bias correction configurations with varying lengths of training periods (i.e., the time period over which searches for weather or air quality scenario analogs are made) and differing ensemble member size are evaluated for July, August, September, and November 2015. The analog bias correction approach yields substantial improvement in hourly time series and diurnal variation patterns of PM2.5 predictions as well as forecast skill scores. However, two prominent issues appear when the analog ensemble bias correction is applied to the NAQFC for operational forecast guidance. First, day-to-day variability is reduced after using bias correction. Second, the analog bias correction method can be limited in improving PM2.5 predictions for extreme events such as Fourth of July Independence Day firework emissions and wildfire smoke events. The use of additional predictors and longer training periods for analog searches is recommended for future studies.

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