scholarly journals An upcoming European network of microwave radiometers for operational temperature profiling and humidity observations

2021 ◽  
Author(s):  
Rolf Rüfenacht ◽  
Simone Bircher-Ardot ◽  
Bernhard Pospichal ◽  
Domenico Cimini ◽  
Christine Knist ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Weather Prediction ◽  
Absolute Calibration ◽  
60 Ghz ◽  
European Network ◽  
Cost Action ◽  
V Band ◽  
Brightness Temperatures ◽  
Microwave Radiometers

<p>From the perspective of numerical weather prediction and nowcasting, the atmospheric boundary layer (ABL) is one of the most undersampled regions of the atmosphere due to difficulties of spaceborne remote sensing at these altitudes. Ground-based microwave radiometers (MWR) have the potential to contribute to the closing of this gap. Indeed, commercial K- and V-band (20-60 GHz) radiometers provide observations of temperature profile, water vapour and liquid water and are most sensitive to the ABL due to their choice of spectral channels and observation geometry.<br>EUMETNET's E-PROFILE observation programme has thus evaluated the potential for a European network of ground-based microwave radiometers. The stakeholder needs were inferred from WMO and EUMETNET Statements of Guidance, OSCAR and a dedicated user survey. The maturity and effectivity of the technology was assessed through a literature review and experts judgements comprising recent large-scale campaigns, experiences with long-term usage and assimilation trials and outcomes of the recent COST action TOPROF. Last but not least, the availability of existing instrumentation from which a European network could be built up was investigated. <br>Based on this study, EUMETNET decided to establish an operational MWR network by 2023 with continuous near real-time provision of brightness temperatures, humidity and temperature information from a centralised retrieval as well as forecast indices for fore- and nowcasting. The products will come along with different monitoring quality control stages at timescales from near real-time to monthly. Special care will be dedicated to ensure reliable absolute calibration results by accounting for the recent developments and recommendations from TOPROF. In the setting up and operation of the network as well as in the implementation of retrievals and monitoring, important synergies with the ACTRIS programme and the scientific community gathered in the COST action PROBE are expected.<br>The presentation will briefly outline the reasoning for setting up the network but mainly focusses on the operational aspects and services that E-PROFILE MWR will provide. Moreover, the first steps taken towards an operational network will be discussed and the general roadmap outlined.</p>

Real Time Rainfall Monitoring for Pipeline Geohazards

2021 ◽  
Author(s):  
Gerry Ferris ◽  
Patrick Grover ◽  
Aron Zahradka
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Oil And Gas ◽  
Weather Prediction ◽  
Extreme Rainfall ◽  
Rainfall Data ◽  
Annual Rainfall ◽  
Extreme Rainfall Event ◽  
Pipeline Failures

Abstract Oil and gas pipelines are subjected to multiple types of geohazards which cause pipeline failures (loss of containment); two of the most common types occur at watercourse crossings and at landslides. At watercourse crossings, the most common geohazard which causes pipeline failures is flooding during which excessive scour may result in the exposure of the buried pipeline and if the exposure results in a free spanning pipeline, then this may fail due to fatigue caused by cyclic loading from vortex-induced vibration. Fortunately the free span length and water velocity combinations that lead to failure can be defined and can be used to identify the flood discharge that should be monitored for in order to trigger actions to manage the hazard and avoid failure. Most watercourse crossings in a pipeline network are on ungauged watercourses and necessitate the use of a proxy gauged watercourse. The “proxy” gauged watercourse is used to infer whether flooding is occurring on the ungauged crossing, and the owner can take appropriate actions. Often the proxy gauged watercourse is too far away or the watercourse may not be representative of the crossing of concern (e.g. large difference in the drainage areas). Real-time rainfall data can be used in conjunction with streamflow monitoring to determine when extreme precipitation has occurred within the ungauged watercourses catchment which may result in flooding. Where pipelines cross landslide prone areas, large scale movements can be initiated, or slow on-going movement rates increased when extreme rainfall occurs. The definition of the extreme rainfall event for slope sites is the key component of providing a suitable warning of potentially dangerous conditions; shallow slides can be caused by short term events from sub-hourly to 3 day duration precipitation events whereas large deep seated (creeping) landslides can be driven by annual and intra-annual rainfall amounts. Monitoring of real time rainfall can be used to determine when extreme rainfall occurs at a landslide site. The density of in-situ weather stations collecting real-time rainfall data prevents the application along remote sections of pipeline routes and within large sections of Canada. Gridded real time rainfall from quantitative precipitation estimations which integrate a multiple data sources including in-situ, numerical weather prediction, satellite and weather radar, can be used to overcome this problem and provide warnings when pre-determined rainfall thresholds are exceeded on a site-specific basis.

scholarly journals A Real-Time Evaporation Correction Scheme for Radar-Derived Mosaicked Precipitation Estimations

2018 ◽  
Vol 19 (1) ◽  
pp. 87-111 ◽  
Author(s):  
Steven M. Martinaitis ◽  
Heather M. Grams ◽  
Carrie Langston ◽  
Jian Zhang ◽  
Kenneth Howard
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Weather Prediction ◽  
Horizontal Resolution ◽  
Surface Precipitation ◽  
Correction Scheme ◽  
Evaporation Technique ◽  
Microphysical Processes ◽  
Rain Events ◽  
2D And 3D

Abstract Precipitation values estimated by radar are assumed to be the amount of precipitation that occurred at the surface, yet this notion is inaccurate. Numerous atmospheric and microphysical processes can alter the precipitation rate between the radar beam elevation and the surface. One such process is evaporation. This study determines the applicability of integrating an evaporation correction scheme for real-time radar-derived mosaicked precipitation rates to reduce quantitative precipitation estimate (QPE) overestimation and to reduce the coverage of false surface precipitation. An evaporation technique previously developed for large-scale numerical modeling is applied to Multi-Radar Multi-Sensor (MRMS) precipitation rates through the use of 2D and 3D numerical weather prediction (NWP) atmospheric parameters as well as basic radar properties. Hourly accumulated QPE with evaporation adjustment compared against gauge observations saw an average reduction of the overestimation bias by 57%–76% for rain events and 42%–49% for primarily snow events. The removal of false surface precipitation also reduced the number of hourly gauge observations that were considered as “false zero” observations by 52.1% for rain and 38.2% for snow. Optimum computational efficiency was achieved through the use of simplified equations and hourly 10-km horizontal resolution NWP data. The run time for the evaporation correction algorithm is 6–7 s.

Methods for the Quantification of Salivary Cortisol and of a-amylase in Biosensors and Portable Devices

2018 ◽  
Vol 68 (12) ◽  
pp. 2857-2859
Author(s):  
Cristina Mihaela Ghiciuc ◽  
Andreea Silvana Szalontay ◽  
Luminita Radulescu ◽  
Sebastian Cozma ◽  
Catalina Elena Lupusoru ◽  
...  
Keyword(s):  
Real Time ◽  
Medical Practice ◽  
Salivary Cortisol ◽  
Clinical Studies ◽  
Large Scale ◽  
Psychological Research ◽  
Portable Devices ◽  
Salivary Amylase ◽  
Specificity And Sensitivity

There is an increasing interest in the analysis of salivary biomarkers for medical practice. The objective of this article was to identify the specificity and sensitivity of quantification methods used in biosensors or portable devices for the determination of salivary cortisol and salivary a-amylase. There are no biosensors and portable devices for salivary amylase and cortisol that are used on a large scale in clinical studies. These devices would be useful in assessing more real-time psychological research in the future.

scholarly journals BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13849-13850
Author(s):  
Donghyeon Lee ◽  
Man-Je Kim ◽  
Chang Wook Ahn
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Real Time ◽  
Large Scale ◽  
Outcome Predictor ◽  
Short Term ◽  
Rts Game

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

scholarly journals Modeling Fused Filament Fabrication using Artificial Neural Networks

2021 ◽  
Author(s):  
Paul Oehlmann ◽  
Paul Osswald ◽  
Juan Camilo Blanco ◽  
Martin Friedrich ◽  
Dominik Rietzel ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Cost Effective ◽  
Print Quality ◽  
Ann Model ◽  
Production Environment ◽  
Fused Filament Fabrication ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

scholarly journals A Framework for International Collaboration on ITER Using Large-Scale Data Transfer to Enable Near-Real-Time Analysis

2021 ◽  
Vol 77 (2) ◽  
pp. 98-108
Author(s):  
R. M. Churchill ◽  
C. S. Chang ◽  
J. Choi ◽  
J. Wong ◽  
S. Klasky ◽  
...  
Keyword(s):  
Real Time ◽  
International Collaboration ◽  
Large Scale ◽  
Data Transfer ◽  
Time Analysis ◽  
Real Time Analysis ◽  
Large Scale Data ◽  
Scale Data

scholarly journals HGPT2: An ERA5-Based Global Model to Estimate Relative Humidity

2021 ◽  
Vol 13 (11) ◽  
pp. 2179
Author(s):  
Pedro Mateus ◽  
Virgílio B. Mendes ◽  
Sandra M. Plecha
Keyword(s):  
Water Vapor ◽  
Relative Humidity ◽  
Real Time ◽  
Prediction Models ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Global Navigation Satellite Systems ◽  
International Gnss Service ◽  
Weighted Mean Temperature

The neutral atmospheric delay is one of the major error sources in Space Geodesy techniques such as Global Navigation Satellite Systems (GNSS), and its modeling for high accuracy applications can be challenging. Improving the modeling of the atmospheric delays (hydrostatic and non-hydrostatic) also leads to a more accurate and precise precipitable water vapor estimation (PWV), mostly in real-time applications, where models play an important role, since numerical weather prediction models cannot be used for real-time processing or forecasting. This study developed an improved version of the Hourly Global Pressure and Temperature (HGPT) model, the HGPT2. It is based on 20 years of ERA5 reanalysis data at full spatial (0.25° × 0.25°) and temporal resolution (1-h). Apart from surface air temperature, surface pressure, zenith hydrostatic delay, and weighted mean temperature, the updated model also provides information regarding the relative humidity, zenith non-hydrostatic delay, and precipitable water vapor. The HGPT2 is based on the time-segmentation concept and uses the annual, semi-annual, and quarterly periodicities to calculate the relative humidity anywhere on the Earth’s surface. Data from 282 moisture sensors located close to GNSS stations during 1 year (2020) were used to assess the model coefficients. The HGPT2 meteorological parameters were used to process 35 GNSS sites belonging to the International GNSS Service (IGS) using the GAMIT/GLOBK software package. Results show a decreased root-mean-square error (RMSE) and bias values relative to the most used zenith delay models, with a significant impact on the height component. The HGPT2 was developed to be applied in the most diverse areas that can significantly benefit from an ERA5 full-resolution model.

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