scholarly journals Description of the NASA GEOS Composition Forecast Modeling System GEOS‐CF v1.0

2021 ◽  
Author(s):  
Christoph A. Keller ◽  
K. Emma Knowland ◽  
Bryan N. Duncan ◽  
Junhua Liu ◽  
Daniel C. Anderson ◽  
...  
Keyword(s):  
Forecast Modeling

Description of the NASA GEOS Composition Forecast Modeling System GEOS-CF v1.0

2020 ◽  
Author(s):  
Christoph A. Keller ◽  
K. Emma Knowland ◽  
Bryan N Duncan ◽  
Junhua Liu ◽  
Daniel C Anderson ◽  
...  
Keyword(s):  
Forecast Modeling

scholarly journals Model Results and Software Comparisons in Myrtle Beach, SC Using Virtual Beach and R Regression Toolboxes

2015 ◽  
pp. 80-85
Author(s):  
Matthew J. Neet ◽  
R. Heath Kelsey ◽  
Dwayne E. Porter ◽  
Dan W. Ramage ◽  
Adrian B. Jones
Keyword(s):  
Environmental Protection Agency ◽  
Ease Of Use ◽  
Statistical Interpretation ◽  
Protection Agency ◽  
R Software ◽  
Robust Model ◽  
Myrtle Beach ◽  
Model Setup ◽  
User Friendly ◽  
Forecast Modeling

Utilizing R software and a variety of data sources, daily forecasts of bacteria levels were developed and automated for beach waters in Myrtle Beach, SC. Modeled results are then shown for beach locations via a website and mobile device app. While R provides a robust set of tools for use in forecast modeling, the software has an extensive learning curve and requires skilled statistical interpretation of results. The Environmental Protection Agency (EPA) created the “Virtual Beach” software package to address these concerns. To evaluate the utility of the more user-friendly Virtual Beach modeling toolbox, predictive models were developed and model results were analyzed using the two software suites. Recommendations were made based on ease of use and several performance measures. Model results indicate the two software toolboxes yield comparable outputs. However, Virtual Beach tends to create more robust model forecasts, while R provides more options for model setup and outputs.

scholarly journals Designing problems of oil fields infrustructre in the Arctic under the climate change

2021 ◽  
Vol 6 (3) ◽  
pp. 130-135
Author(s):  
Elena A. Poskonina ◽  
Anna N. Kurchatova
Keyword(s):  
Climate Change ◽  
Oil And Gas ◽  
Thermal Stabilization ◽  
Oil Fields ◽  
The Arctic ◽  
Soil Conditions ◽  
Industrial Facilities ◽  
Oil And Gas Fields ◽  
Gas Fields ◽  
Forecast Modeling

Background. Designing problems of oil fields infrastructure in the Arctic under climate change, namely, applying of temperature coefficient when calculating bearing capacity, heaving of lightly loaded foundations, optimization of thermal stabilization solutions are presented in the article. Aim. To change the strategy for designing foundations on permafrost by choosing the worst soil conditions to the implementation of an invariant matrix for designing and construction of soil bases and foundations considering specifics of industrial facilities of oil and gas fields based on unified numerical calculations (regulations). Materials and methods. An overview of the current regulatory requirements to the design of foundations on permafrost is made. The analysis of forecast modeling of the temperature of soil bases of typical industrial facilities of oil and gas fields to justify design solutions and also the use of thermal stabilization systems is done. Results. It is proposed to develop a regional directory of weather stations with long observation period based on updated climate data to decrease the volume of designing work and the amount of mistakes in applying of thermal stabilization systems. It is necessary to create regional dynamic models of permafrost geosystems, implement forecast modeling of seasonal thawing potential depth and frozen ground temperature in natural landscapes on the base of geotechnical monitoring data and select adaptation methods to existing or expecting climate change trends. Conclusions. Regulations on designing and construction of soil bases and foundations on permafrost considering specifics of industrial facilities of oil and gas fields is an effective solution. It allows moving on the strategy implementation of uniform approaches to oil fields development on permafrost: from designing for every structure on the base of typical solutions and results of engineering surveys to invariant matrix of project solutions.

scholarly journals Prediction of Muddy Floods Using High-Resolution Radar Precipitation Forecasts and Physically-Based Erosion Modeling in Agricultural Landscapes

Geosciences ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 401
Author(s):  
Phoebe Hänsel ◽  
Stefan Langel ◽  
Marcus Schindewolf ◽  
Andreas Kaiser ◽  
Arno Buchholz ◽  
...  
Keyword(s):  
High Resolution ◽  
Interdisciplinary Approach ◽  
Agricultural Landscapes ◽  
Transport Infrastructure ◽  
Data Series ◽  
Storm Events ◽  
Erosion Modeling ◽  
Physically Based ◽  
High Resolution Radar ◽  
Forecast Modeling

The monitoring, modeling, and prediction of storm events and accompanying heavy rain is crucial for intensively used agricultural landscapes and its settlements and transport infrastructure. In Saxony, Germany, repeated and numerous storm events triggered muddy floods from arable fields in May 2016. They caused severe devastation to settlements and transport infrastructure. This interdisciplinary approach investigates three muddy floods, which developed on silty soils of loess origin tending to soil surface sealing. To achieve this, the study focuses on the test of a historical forecast modeling of three muddy floods in ungauged agricultural landscapes. Therefore, this approach firstly illustrates the reconstruction of the muddy floods, which was performed by high-resolution radar precipitation data, physically-based erosion modeling, and the qualitative validation by unmanned aerial vehicle-based orthophotos. Subsequently, historical radar precipitation forecasts served as input data for the physically-based erosion model to test the forecast modeling retrospectively. The model results indicate a possible warning for two of the three muddy floods. This method of a historical forecast modeling of muddy floods seems particularly promising. Naturally, the data series of three muddy floods should be extended to more reliable data and statistical statements. Finally, this approach assesses the feasibility of a real-time muddy flood early warning system in ungauged agricultural landscapes by high-resolution radar precipitation forecasts and physically-based erosion modeling.

scholarly journals Ensemble forecast modeling for the design of COVID-19 vaccine efficacy trials

Vaccine ◽  
2020 ◽  
Vol 38 (46) ◽  
pp. 7213-7216 ◽  
Author(s):  
Natalie E. Dean ◽  
Ana Pastore y Piontti ◽  
Zachary J. Madewell ◽  
Derek A.T Cummings ◽  
Matthew D.T. Hitchings ◽  
...  
Keyword(s):  
Vaccine Efficacy ◽  
Ensemble Forecast ◽  
Efficacy Trials ◽  
Forecast Modeling
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