scholarly journals How Can Existing Ground-Based Profiling Instruments Improve European Weather Forecasts?

2019 ◽  
Vol 100 (4) ◽  
pp. 605-619 ◽  
Author(s):  
A. J. Illingworth ◽  
D. Cimini ◽  
A. Haefele ◽  
M. Haeffelin ◽  
M. Hervo ◽  
...  
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Positive Impact ◽  
Weather Forecast ◽  
Weather Conditions ◽  
Time Data ◽  
Weather Forecasts ◽  
Forecast Models ◽  
Wind Lidar ◽  
Microwave Radiometers

Abstract To realize the promise of improved predictions of hazardous weather such as flash floods, wind storms, fog, and poor air quality from high-resolution mesoscale models, the forecast models must be initialized with an accurate representation of the current state of the atmosphere, but the lowest few kilometers are hardly accessible by satellite, especially in dynamically active conditions. We report on recent European developments in the exploitation of existing ground-based profiling instruments so that they are networked and able to send data in real time to forecast centers. The three classes of instruments are i) automatic lidars and ceilometers providing backscatter profiles of clouds, aerosols, dust, fog, and volcanic ash, the last two being especially important for air traffic control; ii) Doppler wind lidars deriving profiles of wind, turbulence, wind shear, wind gusts, and low-level jets; and iii) microwave radiometers estimating profiles of temperature and humidity in nearly all weather conditions. The project includes collaboration from 22 European countries and 15 European national weather services, which involves the implementation of common operating procedures, instrument calibrations, data formats, and retrieval algorithms. Currently, data from 265 ceilometers in 19 countries are being distributed in near–real time to national weather forecast centers; this should soon rise to many hundreds. One wind lidar is currently delivering real time data rising to 5 by the end of 2019, and the plan is to incorporate radiometers in 2020. Initial data assimilation tests indicate a positive impact of the new data.

scholarly journals Scientific motivation for ADM/Aeolus mission

2018 ◽  
Vol 176 ◽  
pp. 02008
Author(s):  
Erland Källén
Keyword(s):  
Weather Forecast ◽  
Weather Forecasts ◽  
Weather And Climate ◽  
Medium Range ◽  
Forecast Models ◽  
Tropical Areas ◽  
Global Coverage ◽  
Wind Lidar ◽  
Wind Profiles

The ADM/Aeolus wind lidar mission will provide a global coverage of atmospheric wind profiles. Atmospheric wind observations are required for initiating weather forecast models and for predicting and monitoring long term climate change. Improved knowledge of the global wind field is widely recognised as fundamental to advancing the understanding and prediction of weather and climate. In particular over tropical areas there is a need for better wind data leading to improved medium range (3-10 days) weather forecasts over the whole globe.

scholarly journals REGULATING WATER SUPPLY AND WATER CUTBACKS IN AGRICULTURE USING IOT BASED SMART IRRIGATION SYSTEM

2021 ◽  
Vol 9 (1) ◽  
pp. 629-633
Author(s):  
Prabhas Kumar Gupta, Dr. Nagendra Tripathi
Keyword(s):  
Data Analysis ◽  
Real Time ◽  
Irrigation System ◽  
Weather Forecast ◽  
Weather Conditions ◽  
Underground Water ◽  
Contributing Factors ◽  
Time Data ◽  
Cloud Data ◽  
Real Time Data

Involvement of Machine Learning, Real-time Data Analysis and IOT are critically contributing factors in contemporary technical scenarios. Utilization of these three technologies can play a major role in the success of farming thereby modernizing the irrigation system. This paper is focused on the Smart Irrigation System which draws a lot from real-time data analysis, IOT and Machine Leaning. It also presents a study of a system that processes real time data and takes decision about to what extent the field needs to be irrigated. In this way water is saved, its misuse regulated and can be restored for future use if required. Here we rely on cloud data and some other agri-factors which help in decision making.  The Smart Irrigation System discussed here shall also regulate the use of underground water my incorporating IOT and weather forecast. The system will also contribute to effective irrigation taking in view the contemporary weather conditions and the requirement of water in the crop.

scholarly journals Advanced Flight Planning and the Benefit of In-Flight Aircraft Trajectory Optimization

2021 ◽  
Vol 13 (3) ◽  
pp. 1383
Author(s):  
Judith Rosenow ◽  
Martin Lindner ◽  
Joachim Scheiderer
Keyword(s):  
New York ◽  
Traffic Control ◽  
Traffic Management ◽  
Trajectory Optimization ◽  
Service Providers ◽  
Weather Conditions ◽  
Air Traffic ◽  
Weather Data ◽  
Weather Forecasts ◽  
Aircraft Trajectory

The implementation of Trajectory-Based Operations, invented by the Single European Sky Air Traffic Management Research program SESAR, enables airlines to fly along optimized waypoint-less trajectories and accordingly to significantly increase the sustainability of the air transport system in a business with increasing environmental awareness. However, unsteady weather conditions and uncertain weather forecasts might induce the necessity to re-optimize the trajectory during the flight. By considering a re-optimization of the trajectory during the flight they further support air traffic control towards achieving precise air traffic flow management and, in consequence, an increase in airspace and airport capacity. However, the re-optimization leads to an increase in the operator and controller’s task loads which must be balanced with the benefit of the re-optimization. From this follows that operators need a decision support under which circumstances and how often a trajectory re-optimization should be carried out. Local numerical weather service providers issue hourly weather forecasts for the coming hour. Such weather data sets covering three months were used to re-optimize a daily A320 flight from Seattle to New York every hour and to calculate the effects of this re-optimization on fuel consumption and deviation from the filed path. Therefore, a simulation-based trajectory optimization tool was used. Fuel savings between 0.5% and 7% per flight were achieved despite minor differences in wind speed between two consecutive weather forecasts in the order of 0.5 m s−1. The calculated lateral deviations from the filed path within 1 nautical mile were always very small. Thus, the method could be easily implemented in current flight operations. The developed performance indicators could help operators to evaluate the re-optimization and to initiate its activation as a new flight plan accordingly.

scholarly journals IMPLEMENTASI BACKPROPAGATION NEURAL NETWORK DALAM PRAKIRAAN CUACA DI DAERAH BALI SELATAN

2016 ◽  
Vol 5 (4) ◽  
pp. 126 ◽  
Author(s):  
I MADE DWI UDAYANA PUTRA ◽  
G. K. GANDHIADI ◽  
LUH PUTU IDA HARINI
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Learning Algorithm ◽  
Weather Forecast ◽  
Weather Forecasts ◽  
Forecast Models ◽  
Artificial Neural Network Method ◽  
Artificial Neural ◽  
Hidden Layer ◽  
Backpropagation Learning

Weather information has an important role in human life in various fields, such as agriculture, marine, and aviation. The accurate weather forecasts are needed in order to improve the performance of various fields. In this study, use artificial neural network method with backpropagation learning algorithm to create a model of weather forecasting in the area of ??South Bali. The aim of this study is to determine the effect of the number of neurons in the hidden layer and to determine the level of accuracy of the method of artificial neural network with backpropagation learning algorithm in weather forecast models. Weather forecast models in this study use input of the factors that influence the weather, namely air temperature, dew point, wind speed, visibility, and barometric pressure.The results of testing the network with a different number of neurons in the hidden layer of artificial neural network method with backpropagation learning algorithms show that the increase in the number of neurons in the hidden layer is not directly proportional to the value of the accuracy of the weather forecasts, the increase in the number of neurons in the hidden layer does not necessarily increase or decrease value accuracy of weather forecasts we obtain the best accuracy rate of 51.6129% on a network model with three neurons in the hidden layer.

scholarly journals Construction worker productivity in hot and humid weather conditions

2019 ◽  
Vol 27 (1) ◽  
pp. 83-108
Author(s):  
Ammar Saeed Mohammed Moohialdin ◽  
Fiona Lamari ◽  
Marc Miska ◽  
Bambang Trigunarsyah
Keyword(s):  
Real Time ◽  
Construction Industry ◽  
Productivity Loss ◽  
Weather Conditions ◽  
Research Field ◽  
Future Research ◽  
Time Data ◽  
Worker Productivity ◽  
Content Type ◽  
The Impact

Purpose The purpose of this paper shows the effect of hot and humid weather conditions (HHWCs) on workers that has resulted in considerable loss in the construction industry, especially during the hottest periods due to decline in worker productivity (WP). Until the last few decades, there is very limited research on construction WP in HHWCs. Nevertheless, these studies have sparked interests on seeking for the most appropriate methods to assess the impact of HHWCs on construction workers. Design/methodology/approach This paper begins by reviewing the current measuring methods on WP in HHWCs, follows by presenting the potential impact of HHWCs on WP. The paper highlights the methodological deficiencies, which consequently provides a platform for scholars and practitioners to direct future research to resolve the significant productivity loss due to global warming. This paper highlights the need to identify the limitations and advantages of the current methods to formulate a framework of new approaches to measure the WP in HHWCs. Findings Results show that the methods used in providing real-time response on the effects of HHWCs on WP in construction at project, task and crew levels are limited. An integration of nonintrusive real-time monitoring system and local weather measurement with real-time data synchronisation and analysis is required to produce suitable information to determine worker health- and safety-related decisions in HHWCs. Originality/value The comprehensive literature review makes an original contribution to WP measurements filed in HHWCs in the construction industry. Results of this review provide researchers and practitioners with an insight into challenges associated with the measurements methods and solving practical site measurements issues. The findings will also enable the researchers and practitioners to bridge the identified research gaps in this research field and enhance the ability to provide accurate measures in HHWCs. The proposed research framework may promote potential improvements in the productivity measurements methods, which support researchers and practitioners in developing new innovative methods in HHWCs with the integration of the most recent monitoring technologies.

Study on Urban Intersection Traffic Flow Forecasting and Traffic Signal Control

2011 ◽  
Vol 135-136 ◽  
pp. 969-974 ◽  
Author(s):  
Yong Feng Ju ◽  
Xiao Wei Wei
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Traffic Control ◽  
Road Network ◽  
Intelligent Transportation ◽  
Traffic Signal Control ◽  
Time Data ◽  
Urban Road ◽  
Traffic Flow Forecasting ◽  
Short Time

Short-traffic flow forecasting is an important part of ITS, and its accuracy and real-time is directly related to the effect of traffic control and traffic induce. Gathering and analyzing the real-time data of urban road network ,short-time traffic flow forecasting could estimate the state of traffic flow for a few minutes in future and provide support to intelligent transportation control, so it is one of the important premise for ITS.

Fully Automated and Wirelessly Enabled Drillstem Tests: Seven-Zones Campaign Case Study in Sakhalin

2021 ◽  
Author(s):  
Elias Temer ◽  
Deiveindran Subramaniam
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Time Window ◽  
Weather Conditions ◽  
Testing Time ◽  
Well Testing ◽  
Well Test ◽  
Time Data ◽  
Operations Control ◽  
Bad Weather

Abstract Well test is one of the crucial steps required to forecast production investments of their fields. However, the operators face many challenges such as reduced capex, exploration budgets, and bad weather conditions that limit the well testing time window. To overcome these challenges, an automated well testing platform enabled a real time monitoring and controlling more zones in a single run for appraisal wells in the Sea of Okhotsk, Russia. This article highlights the test objectives, the job planning, and automated execution of wirelessly enabled operations in very hostile conditions and limited time period. The use of a telemetry system to well test seven zones allowed real-time data acquisition, control of critical downhole equipment, data transmission to the operator's office in town. Various operational cases will be discussed to demonstrate how automated data acquisition and downhole operations control has optimized operations for both the service company and the operator.

scholarly journals Mobile technologies for disease surveillance in humans and animals

2014 ◽  
Vol 81 (2) ◽  
Author(s):  
Mpoki Mwabukusi ◽  
Esron D. Karimuribo ◽  
Mark M. Rweyemamu ◽  
Eric Beda
Keyword(s):  
Developing Countries ◽  
Human Health ◽  
Real Time ◽  
Disease Surveillance ◽  
Open Data ◽  
Weather Conditions ◽  
Mobile Technologies ◽  
Surveillance Systems ◽  
Time Data ◽  
Real Time Data

A paper-based disease reporting system has been associated with a number of challenges. These include difficulties to submit hard copies of the disease surveillance forms because of poor road infrastructure, weather conditions or challenging terrain, particularly in the developing countries. The system demands re-entry of the data at data processing and analysis points, thus making it prone to introduction of errors during this process. All these challenges contribute to delayed acquisition, processing and response to disease events occurring in remote hard to reach areas. Our study piloted the use of mobile phones in order to transmit near to real-time data from remote districts in Tanzania (Ngorongoro and Ngara), Burundi (Muyinga) and Zambia (Kazungula and Sesheke). Two technologies namely, digital and short messaging services were used to capture and transmit disease event data in the animal and human health sectors in the study areas based on a server–client model. Smart phones running the Android operating system (minimum required version: Android 1.6), and which supported open source application, Epicollect, as well as the Open Data Kit application, were used in the study. These phones allowed collection of geo-tagged data, with the opportunity of including static and moving images related to disease events. The project supported routine disease surveillance systems in the ministries responsible for animal and human health in Burundi, Tanzania and Zambia, as well as data collection for researchers at the Sokoine University of Agriculture, Tanzania. During the project implementation period between 2011 and 2013, a total number of 1651 diseases event-related forms were submitted, which allowed reporters to include GPS coordinates and photographs related to the events captured. It was concluded that the new technology-based surveillance system is useful in providing near to real-time data, with potential for enhancing timely response in rural remote areas of Africa. We recommended adoption of the proven technologies to improve disease surveillance, particularly in the developing countries.

scholarly journals Overview of the first HyMeX Special Observation Period over Italy: observations and model results

2013 ◽  
Vol 10 (9) ◽  
pp. 11643-11710 ◽  
Author(s):  
R. Ferretti ◽  
E. Pichelli ◽  
S. Gentile ◽  
I. Maiello ◽  
D. Cimini ◽  
...  
Keyword(s):  
Central Italy ◽  
Weather Forecast ◽  
Heavy Precipitation ◽  
Western Mediterranean ◽  
Target Area ◽  
Weather Forecasts ◽  
Weather Events ◽  
North Eastern ◽  
Forecast Models ◽  
Observation Period

Abstract. During the first Hymex campaign (5 September–6 November 2012) referred to as Special Observation Period (SOP-1), dedicated to heavy precipitation events and flash floods in Western Mediterranean, three Italian hydro-meteorological monitoring sites were activated: Liguria-Tuscany, North-Eastern Italy and Central Italy. The extraordinary deployment of advanced instrumentation, including instrumented aircrafts, and the use of several different operational weather forecast models has allowed an unprecedented monitoring and analysis of high impact weather events around the Italian hydro-meteorological sites. This activity has seen the strict collaboration between the Italian scientific and operational communities. In this paper, an overview of the Italian organization during the SOP-1 is provided, and selected Intensive Observation Periods (IOPs) are described. A significant event for each Italian target area is chosen for this analysis: IOP2 (12–13 September 2012) in North-Eastern Italy, IOP13 (15–16 October 2012) in Central Italy and IOP19 (3–5 November 2012) in Liguria and Tuscany. For each IOP the meteorological characteristics, together with special observations and weather forecasts, are analyzed with the aim of highlighting strengths and weaknesses of the forecast modeling systems. Moreover, using one of the three events, the usefulness of different operational chains is highlighted.

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