scholarly journals An <i>N</i>-dimensional Fortran interpolation programme (NterGeo.v2020a) for geophysics sciences – application to a back-trajectory programme (Backplumes.v2020r1) using CHIMERE or WRF outputs

2021 ◽  
Vol 14 (1) ◽  
pp. 91-106
Author(s):  
Bertrand Bessagnet ◽  
Laurent Menut ◽  
Maxime Beauchamp
Keyword(s):  
Input Data ◽  
Weather Research And Forecasting ◽  
Back Trajectory ◽  
Meteorological Model ◽  
Back Trajectories ◽  
Interface Models ◽  
Geophysical Application ◽  
Different Types ◽  
Irregular Meshes ◽  
Standard Fortran

Abstract. An interpolation programme coded in Fortran for irregular N-dimensional cases is presented and freely available. The need for interpolation procedures over irregular meshes or matrixes with interdependent input data dimensions is frequent in geophysical models. Also, these models often embed look-up tables of physics or chemistry modules. Fortran is a fast and powerful language and is highly portable. It is easy to interface models written in Fortran with each other. Our programme does not need any libraries; it is written in standard Fortran and tested with two usual compilers. The programme is fast and competitive compared to current Python libraries. A normalization option parameter is provided when considering different types of units on each dimension. Some tests and examples are provided and available in the code package. Moreover, a geophysical application embedding this interpolation programme is provided and discussed; it consists in determining back trajectories using chemistry-transport or mesoscale meteorological model outputs, respectively, from the widely used CHIMERE and Weather Research and Forecasting (WRF) models.

scholarly journals A N-dimensional Fortran Interpolation Program (NterGeo.v2020a) for Geophysics Sciences – Application to a back-trajectory program (BACKPLUMES.v2020r1) using CHIMERE or WRF outputs

2020 ◽  
Author(s):  
Bertrand Bessagnet ◽  
Laurent Menut ◽  
Maxime Beauchamp
Keyword(s):  
Atmospheric Dispersion ◽  
Back Trajectory ◽  
Meteorological Model ◽  
Back Trajectories ◽  
Different Types ◽  
Weight Calculation ◽  
Interpolation Procedure ◽  
General Program ◽  
Irregular Meshes ◽  
Inverse Distance

Abstract. An interpolation program coded in Fortran for irregular N-dimensional cases is presented and freely available. Needs of interpolation procedure over irregular meshes or matrixes with interdependent input data dimensions is frequent in geophysical models. Also, these models often embed look-up tables of physics/chemistry modules. Fortran is a powerful and fast language, highly portable and easy to interface with other existing Fortran models. Our program does not need any libraries and can be compiled with any Fortran compiler. The program is fast and competitive compared to current Python libraries. A novel optional parameter (normalisation option) is provided when considering different types of units on each dimension. For the general program, the inverse distance is used for the weight calculation with a distance defined as a p-distance. Some tests and examples are provided and available in the code package. Moreover, a real case of geophysics application embedding this interpolation program is provided and discussed, it consists in determining back-trajectories using atmospheric dispersion or mesoscale meteorological model outputs, respectively from the widely used models CHIMERE and WRF.

scholarly journals Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 47
Author(s):  
Vasyl Teslyuk ◽  
Artem Kazarian ◽  
Natalia Kryvinska ◽  
Ivan Tsmots
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Input Data ◽  
Optimization Criterion ◽  
Fuzzy Input ◽  
Different Types ◽  
Smart House ◽  
Artificial Neural

In the process of the “smart” house systems work, there is a need to process fuzzy input data. The models based on the artificial neural networks are used to process fuzzy input data from the sensors. However, each artificial neural network has a certain advantage and, with a different accuracy, allows one to process different types of data and generate control signals. To solve this problem, a method of choosing the optimal type of artificial neural network has been proposed. It is based on solving an optimization problem, where the optimization criterion is an error of a certain type of artificial neural network determined to control the corresponding subsystem of a “smart” house. In the process of learning different types of artificial neural networks, the same historical input data are used. The research presents the dependencies between the types of neural networks, the number of inner layers of the artificial neural network, the number of neurons on each inner layer, the error of the settings parameters calculation of the relative expected results.

scholarly journals Potential Source Areas for Atmospheric Lead Reaching Ny-Ålesund from 2010 to 2018

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 388
Author(s):  
Andrea Bazzano ◽  
Stefano Bertinetti ◽  
Francisco Ardini ◽  
David Cappelletti ◽  
Marco Grotti
Keyword(s):  
North American ◽  
Enrichment Factors ◽  
Atmospheric Particulate Matter ◽  
Back Trajectory ◽  
Pb Isotope ◽  
Back Trajectories ◽  
Source Areas ◽  
The North ◽  
Central Asian ◽  
Pb Concentration

Lead content, enrichment factors, and isotopic composition (208Pb/206Pb and 207Pb/206Pb) measured in atmospheric particulate matter (PM10) samples collected for nine years at Ny-Ålesund (Svalbard islands, Norwegian Arctic) during spring and summer are presented and discussed. The possible source areas (PSA) for particulate inferred from Pb isotope ratio values were compared to cluster analysis of back-trajectories. Results show that anthropogenic Pb dominates over natural crustal Pb, with a recurring higher influence in spring, compared to summer. Crustal Pb accounted for 5–16% of the measured Pb concentration. Anthropogenic Pb was affected by (i) a Central Asian PSA with Pb isotope signature compatible with ores smelted in the Rudny Altai region, at the Russian and Kazakhstan border, which accounted for 85% of the anthropogenic Pb concentration, and (ii) a weaker North American PSA, contributing for the remaining 15%. Central Asian PSA exerted an influence on 71–86% of spring samples, without any significant interannual variation. On the contrary, 59–87% of summer samples were influenced by the North American PSA, with higher contributions during 2015 and 2018. Back-trajectory analysis agreed on the seasonal difference in PSA and highlighted a possible increased influence for North American air masses during summer 2010 and 2018, but not for summer 2015.

scholarly journals A new moisture tagging capability in the Weather Research and Forecasting Model: formulation, validation and application to the 2014 Great Lake-effect snowstorm

2017 ◽  
Author(s):  
Damián Insua-Costa ◽  
Gonzalo Miguez-Macho
Keyword(s):  
Great Lake ◽  
Snow Water Equivalent ◽  
Precipitable Water ◽  
Weather Research And Forecasting ◽  
Atmospheric Conditions ◽  
Meteorological Model ◽  
Moisture Sources ◽  
Lake Effect ◽  
Snow Water ◽  
Weather Research

Abstract. A new moisture-tagging tool, usually known as water vapor tracer (WVT) method or online Eulerian method, has been implemented into the Weather Research and Forecasting (WRF) regional meteorological model, enabling it for precise studies on atmospheric moisture sources and pathways. We present here the method and its formulation, along with details of the implementation into WRF. We perform an in-depth validation with monthly long simulations over North America at 20 km resolution, tagging all possible moisture sources: lateral boundaries, continental, maritime or lake surfaces and initial atmospheric conditions. We estimate errors as the moisture or precipitation amounts that cannot be traced back to any source. Validation results indicate that the method exhibits high precision, with errors considerably lower than 1 % during the entire simulation period, for both precipitation and total precipitable water. We apply the method to the Great Lake-effect snowstorm of November 2014, aiming at quantifying the contribution of lake evaporation to the large snow accumulations observed in the event. We perform simulations in a nested domain at 5 km resolution with the tagging technique, demonstrating that about 30–50 % of precipitation in the regions immediately downwind, originated from evaporated moisture in the Great Lakes. This contribution increases to between 50–60 % of the snow water equivalent in the most severely affected areas, which suggests that evaporative fluxes from the lakes have a fundamental role in producing the most extreme accumulations in these episodes, resulting in the highest socio-economic impacts.

Uncertainty in Operational Analysis of Two-Lane Highways

2002 ◽  
Vol 1802 (1) ◽  
pp. 105-114 ◽  
Author(s):  
R. Tapio Luttinen
Keyword(s):  
Input Data ◽  
Travel Speed ◽  
Level Of Service ◽  
Model Parameters ◽  
Highway Capacity ◽  
Highway Capacity Manual ◽  
Operational Analysis ◽  
Percent Time ◽  
Different Types ◽  
Percent Time Spent Following

The Highway Capacity Manual (HCM) 2000 provides methods to estimate performance measures and the level of service for different types of traffic facilities. Because neither the input data nor the model parameters are totally accurate, there is an element of uncertainty in the results. An analytical method was used to estimate the uncertainty in the service measures of two-lane highways. The input data and the model parameters were considered as random variables. The propagation of error through the arithmetic operations in the HCM 2000 methodology was estimated. Finally, the uncertainty in the average travel speed and percent time spent following was analyzed, and four approaches were considered to deal with uncertainty in the level of service.

scholarly journals Calibrating Partial Factors – Methodology, Input Data and Case Study of Steel Structures

2019 ◽  
Author(s):  
Vitali Nadolski ◽  
Árpád Rózsás ◽  
Miroslav Sýkora
Keyword(s):  
Input Data ◽  
Structural Reliability ◽  
Construction Materials ◽  
Steel Structures ◽  
Structural Performance ◽  
Limit States ◽  
Different Types ◽  
Load Effects ◽  
Previous Design

Partial factors are commonly based on expert judgements and on calibration to previous design formats. This inevitably results in unbalanced structural reliability for different types of construction materials, loads and limit states. Probabilistic calibration makes it possible to account for plentiful requirements on structural performance, environmental conditions, production and execution quality etc. In the light of ongoing revisions of Eurocodes and the development of National Annexes, the study overviews the methodology of probabilistic calibration, provides input data for models of basic variables and illustrates the application by a case study. It appears that the partial factors recommended in the current standards provide for a lower reliability level than that indicated in EN 1990. Different values should be considered for the partial factors for imposed, wind and snow loads, appreciating the distinct nature of uncertainties in their load effects.

scholarly journals Directional Biases in Back Trajectories Caused by Model and Input Data

2005 ◽  
Vol 55 (11) ◽  
pp. 1649-1662 ◽  
Author(s):  
Kristi A. Gebhart ◽  
Bret A. Schichtel ◽  
Michael G. Barna
Keyword(s):  
Input Data ◽  
Back Trajectories

scholarly journals Meteorological and Back Trajectory Modeling for the Rocky Mountain Atmospheric Nitrogen and Sulfur Study II

2014 ◽  
Vol 2014 ◽  
pp. 1-19 ◽  
Author(s):  
Kristi A. Gebhart ◽  
William C. Malm ◽  
Marco A. Rodriguez ◽  
Michael G. Barna ◽  
Bret A. Schichtel ◽  
...  
Keyword(s):  
Complex Terrain ◽  
Rocky Mountain ◽  
Rocky Mountain National Park ◽  
Back Trajectory ◽  
Atmospheric Nitrogen ◽  
Transport Models ◽  
Meteorological Model ◽  
Chemical Transport Models ◽  
Linear Regression Technique ◽  
Transport Patterns

The Rocky Mountain Atmospheric Nitrogen and Sulfur (RoMANS II) study with field operations during November 2008 through November 2009 was designed to evaluate the composition and sources of reactive nitrogen in Rocky Mountain National Park, Colorado, USA. As part of RoMANS II, a mesoscale meteorological model was utilized to provide input for back trajectory and chemical transport models. Evaluation of the model's ability to capture important transport patterns in this region of complex terrain is discussed. Previous source-receptor studies of nitrogen in this region are also reviewed. Finally, results of several back trajectory analyses for RoMANS II are presented. The trajectory mass balance (TrMB) model, a receptor-based linear regression technique, was used to estimate mean source attributions of airborne ammonia concentrations during RoMANS II. Though ammonia concentrations are usually higher when there is transport from the east, the TrMB model estimates that, on average, areas to the west contribute a larger mean fraction of the ammonia. Possible reasons for this are discussed and include the greater frequency of westerly versus easterly winds, the possibility that ammonia is transported long distances as ammonium nitrate, and the difficulty of correctly modeling the transport winds in this area.

scholarly journals Modelling of butylated hydroxytoluene (BHT) transport in water in PE-HD pipe including chemical reaction of BHT oxidation

2018 ◽  
Vol 44 ◽  
pp. 00118
Author(s):  
Anna Musz-Pomorska ◽  
Beata Kowalska ◽  
Marcin K. Widomski
Keyword(s):  
Input Data ◽  
Calculated Data ◽  
Butylated Hydroxytoluene ◽  
Pipe Material ◽  
Laboratory Measurements ◽  
Different Types ◽  
Laminar And Turbulent Flow ◽  
Modelling Studies ◽  
Acceptable Agreement ◽  
Pipe Materials

This paper presents laboratory and modelling studies of antioxidant butylated hydroxytoluene leaching from a water supply PE-HD pipe for laminar and turbulent flow. The reaction of BHT oxidation to BHT-OH was included in our calculations. Required input data, initial and boundary conditions were based on laboratory measurements performed for a new PE-HD pipe. Laboratory measurements covered tests of BHT content and homogeneity in pipe material as well as measurements of BHT concentration in water flowing inside the pipe loop with different velocities. The BHT content of the PE-HD pipe and its concentration in water were determined by GC-MS method. Modelling calculations of BHT concentration in water for two different types of flow were performed using the commercial CDF software Fluent (Ansys Inc.). Non-homogeneous BHT distribution in pipe material and leaching of the antioxidant to the water were observed during laboratory tests. Numerical prediction of BHT concentration in water showed acceptable agreement between measured and calculated data. Nonetheless, the universality of the developed model is limited by the great diversity of available plastic pipe materials with various contents and technological amendments.

scholarly journals Interaction between Atlantic cyclones and Eurasian atmospheric blocking drives wintertime warm extremes in the high Arctic

2022 ◽  
Vol 3 (1) ◽  
pp. 21-44
Author(s):  
Sonja Murto ◽  
Rodrigo Caballero ◽  
Gunilla Svensson ◽  
Lukas Papritz
Keyword(s):  
North Atlantic ◽  
High Latitude ◽  
Diabatic Heating ◽  
High Arctic ◽  
Back Trajectory ◽  
Time Of Arrival ◽  
Atmospheric Blocking ◽  
Ural Mountains ◽  
Back Trajectories ◽  
Maximum Heating

Abstract. Atmospheric blocking can influence Arctic weather by diverting the mean westerly flow and steering cyclones polewards, bringing warm, moist air to high latitudes. Recent studies have shown that diabatic heating processes in the ascending warm conveyor belt branch of extratropical cyclones are relevant to blocking dynamics. This leads to the question of the extent to which diabatic heating associated with mid-latitude cyclones may influence high-latitude blocking and drive Arctic warm events. In this study we investigate the dynamics behind 50 extreme warm events of wintertime high-Arctic temperature anomalies during 1979–2016. Classifying the warm events based on blocking occurrence within three selected sectors, we find that 30 of these events are associated with a block over the Urals, featuring negative upper-level potential vorticity (PV) anomalies over central Siberia north of the Ural Mountains. Lagrangian back-trajectory calculations show that almost 60 % of the air parcels making up these negative PV anomalies experience lifting and diabatic heating (median 11 K) in the 6 d prior to the block. Further, almost 70 % of the heated trajectories undergo maximum heating in a compact region of the mid-latitude North Atlantic, temporally taking place between 6 and 1 d before arriving in the blocking region. We also find anomalously high cyclone activity (on average five cyclones within this 5 d heating window) within a sector northwest of the main heating domain. In addition, 10 of the 50 warm events are associated with blocking over Scandinavia. Around 60 % of the 6 d back trajectories started from these blocks experience diabatic heating, of which 60 % undergo maximum heating over the North Atlantic but generally closer to the time of arrival in the block and further upstream relative to heated trajectories associated with Ural blocking. This study suggests that, in addition to the ability of blocks to guide cyclones northwards, Atlantic cyclones play a significant role in the dynamics of high-latitude blocking by providing low-PV air via moist-diabatic processes. This emphasizes the importance of the mutual interactions between mid-latitude cyclones and Eurasian blocking for wintertime Arctic warm extremes.

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