scholarly journals Unprecedented Vessel-Icing Climatology based on Spray-Icing Modelling and Reanalysis Data: A Risk-Based Decision-Making Input for Arctic Offshore Industries

Atmosphere ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 197
Author(s):  
Naseri ◽  
Samuelsen
Keyword(s):  
Decision Making ◽  
Accretion Rate ◽  
Reanalysis Data ◽  
Coast Guard ◽  
Decision Makers ◽  
The Arctic ◽  
Spatial And Temporal Variation ◽  
Data Sets ◽  
Ice Accretion ◽  
Temporal And Spatial

Marine icing is considered a major concern for vessels operating in the Arctic Ocean. Interaction between vessels and waves is the major source of sea spray that, under certain conditions, can lead to ice accretion on the vessels and thus create hazardous situations. Various models have been developed for the estimation of ice accretion rate using meteorological and oceanographic parameters. Various data sets are also available containing observations of spray icing events for different Arctic offshore regions. However, there is limited climatological information that can be used for providing decision-makers with the necessary information on optimal options and solutions in advance for assessing, managing, and mitigating the risks imposed by spray icing. In this study, a Marine-Icing model for the Norwegian Coast Guard (MINCOG) is adapted to study and analyze ice accretion on vessels operating in sea areas between Northern Norway and Spitsbergen, their temporal and spatial variations, as well as their statistical distributions over the region. This study uses NOrwegian ReAnalysis 10 km data (NORA10) of atmosphere and ocean parameters as input to the icing model from 1980 to 2012. The developed spray icing maps representing spatial and temporal variation of icing severity and spray-ice accretion rate, as well as the probability of the occurrence of icing events at different junctures and periods, can be used for risk-based decision-making tasks involved in industrial activities including shipping and offshore logistics operations in these sea areas.

POTENTIAL PLACES OF REFUGE FOR SHIPS IN DISTRESS–THE CALIFORNIA APPROACH TO DEVELOPING DATA CAPTURE AND DECISION-MAKING PROCESSES

2008 ◽  
Vol 2008 (1) ◽  
pp. 1067-1070 ◽  
Author(s):  
Ellen Faurot-Daniels ◽  
Kelly Dietrich
Keyword(s):  
Decision Making ◽  
Coastal Area ◽  
Stakeholder Involvement ◽  
Data Gathering ◽  
Coast Guard ◽  
Decision Makers ◽  
Contingency Planning ◽  
Response Team ◽  
Decision Making Processes ◽  
Unified Command

ABSTRACT California'S coastal Area Contingency Planning Committees began the process to develop “California Distressed Vessel/Potential Places of Refuge (PPOR)” data-gathering and decision-making tools in July 2006. The first step in this process was for members of California'S statewide Area Contingency Plan (ACP) Committee to be open to the possibility they may allow a distressed vessels into their backyard. Next, they were challenged with representing non-situational data in a common data collection format for use by all six California coastal Area Committees. Modeled largely on the PPOR products developed in Alaska, the committee relied on the Regional Response Team IX Guidelines, and the Commandant Instruction (COMDTINST) 16451.9 U.S. Coast Guard Places of Refuge Policy Enclosure (2) (POR Job Aid) resources. Stakeholder involvement throughout this process helps to establish realistic expectations in advance and build trust between stakeholders and decision makers. The populated databases, located in the ACPs, will support incident-specific decision-making and risk assessment anywhere in California by any California Federal On-Scene Coordinator or Unified Command during an actual Places of Refuge (POR) event.

scholarly journals HOAPS and ERA-Interim precipitation over sea: Validation against shipboard in-situ measurements

2016 ◽  
Author(s):  
Karl Bumke ◽  
Gert König-Langlo ◽  
Julian Kinzel ◽  
Marc Schröder
Keyword(s):  
Reanalysis Data ◽  
Data Sets ◽  
Weather Forecasts ◽  
Rain Gauges ◽  
Medium Range ◽  
Temporal And Spatial ◽  
The Tropics ◽  
Along Track ◽  
Spatial Threshold

Abstract. The satellite derived HOAPS (Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite data) and ECMWF (European Centre for Medium-Range Weather Forecasts) ERA-Interim reanalysis data sets have been validated against in-situ precipitation measurements from ship rain gauges and optical disdrometers over the open-ocean by applying a statistical analysis for binary forecasts. For this purpose collocated pairs of data were merged within a certain temporal and spatial threshold into single events, according to the satellites' overpass, the observation and the forecast times. HOAPS detects the frequency of precipitation well, while ERA-Interim strongly overestimates it, especially in the tropics and subtropics. Although precipitation rates are difficult to compare because along-track point measurements are collocated with areal estimates and the numbers of available data are limited, we find that HOAPS underestimates precipitation rates, while ERA-Interim's Atlantic-wide average precipitation rate is close to measurements. However, regionally averaged over latitudinal belts, there are deviations between the observed mean precipitation rates and ERA-Interim. The most obvious ERA-Interim feature is an overestimation of precipitation in the area of the intertropical convergence zone and the southern sub-tropics over the Atlantic Ocean. For a limited number of snow measurements by optical disdrometers it can be concluded that both HOAPS and ERA-Interim are suitable to detect the occurrence of solid precipitation.

scholarly journals Synthesis and evaluation of historical meridional heat transport from midlatitudes towards the Arctic

2020 ◽  
Vol 11 (1) ◽  
pp. 77-96
Author(s):  
Yang Liu ◽  
Jisk Attema ◽  
Ben Moat ◽  
Wilco Hazeleger
Keyword(s):  
Climate Variability ◽  
Heat Transport ◽  
Climate Models ◽  
Energy Transport ◽  
Reanalysis Data ◽  
Arctic Climate ◽  
The Arctic ◽  
Global Ocean ◽  
Data Sets ◽  
Sea Ice Concentration

Abstract. Meridional energy transport (MET), both in the atmosphere (AMET) and ocean (OMET), has significant impact on the climate in the Arctic. In this study, we quantify AMET and OMET at subpolar latitudes from six reanalysis data sets. We investigate the differences between the data sets and we check the coherence between MET and the Arctic climate variability at interannual timescales. The results indicate that, although the mean transport in all data sets agrees well, the spatial distributions and temporal variations of AMET and OMET differ substantially among the reanalysis data sets. For the ocean, only after 2007, the low-frequency signals in all reanalysis products agree well. A further comparison with observed heat transport at 26.5∘ N and the subpolar Atlantic, and a high-resolution ocean model hindcast confirms that the OMET estimated from the reanalysis data sets are consistent with the observations. For the atmosphere, the differences between ERA-Interim and the Japanese 55-year Reanalysis (JRA-55) are small, while the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) differs from them. An extended analysis of linkages between Arctic climate variability and AMET shows that atmospheric reanalyses differ substantially from each other. Among the chosen atmospheric products, ERA-Interim and JRA-55 results are most consistent with those from coupled climate models. For the ocean, the Ocean Reanalysis System 4 (ORAS4) and Simple Ocean Data Assimilation version 3 (SODA3) agree well on the relation between OMET and sea ice concentration (SIC), while the GLobal Ocean reanalyses and Simulations version 3 (GLORYS2V3) deviates from those data sets. The regressions of multiple fields in the Arctic on both AMET and OMET suggest that the Arctic climate is sensitive to changes of meridional energy transport at subpolar latitudes in winter. Given the good agreement on the diagnostics among assessed reanalysis products, our study suggests that the reanalysis products are useful for the evaluation of energy transport. However, assessments of products with the AMET and OMET estimated from reanalysis data sets beyond interannual timescales should be conducted with great care and the robustness of results should be evaluated through intercomparison, especially when studying variability and interactions between the Arctic and midlatitudes.

scholarly journals USE OF DATA MINING TECHNIQUES IN ADVANCE DECISION MAKING PROCESSES IN A LOCAL FIRM

2015 ◽  
Vol 10 (2) ◽  
Author(s):  
Onur Doğan ◽  
Hakan  Aşan ◽  
Ejder Ayç
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Big Data ◽  
Data Cleaning ◽  
Decision Makers ◽  
Data Sets ◽  
Data Set ◽  
Scientific Methods ◽  
Local Firm ◽  
Data Mining Techniques

In today’s competitive world, organizations need to make the right decisions to prolong their existence. Using non-scientific methods and making emotional decisions gave way to the use of scientific methods in the decision making process in this competitive area. Within this scope, many decision support models are still being developed in order to assist the decision makers and owners of organizations. It is easy to collect massive amount of data for organizations, but generally the problem is using this data to achieve economic advances. There is a critical need for specialization and automation to transform the data into the knowledge in big data sets. Data mining techniques are capable of providing description, estimation, prediction, classification, clustering, and association. Recently, many data mining techniques have been developed in order to find hidden patterns and relations in big data sets. It is important to obtain new correlations, patterns, and trends, which are understandable and useful to the decision makers. There have been many researches and applications focusing on different data mining techniques and methodologies.In this study, we aim to obtain understandable and applicable results from a large volume of record set that belong to a firm, which is active in the meat processing industry, by using data mining techniques. In the application part, firstly, data cleaning and data integration, which are the first steps of data mining process, are performed on the data in the database. With the aid of data cleaning and data integration, the data set was obtained, which is suitable for data mining. Then, various association rule algorithms were applied to this data set. This analysis revealed that finding unexplored patterns in the set of data would be beneficial for the decision makers of the firm. Finally, many association rules are obtained, which are useful for decision makers of the local firm. 

scholarly journals Near-real-time adjusted reanalysis forcing data for hydrology

2018 ◽  
Vol 22 (2) ◽  
pp. 989-1000 ◽  
Author(s):  
Peter Berg ◽  
Chantal Donnelly ◽  
David Gustafsson
Keyword(s):  
Real Time ◽  
Global Scale ◽  
Reanalysis Data ◽  
The Arctic ◽  
Data Sets ◽  
Initial State ◽  
Time Step ◽  
Data Set ◽  
Global Data Sets ◽  
Global Data

Abstract. Extending climatological forcing data to current and real-time forcing is a necessary task for hydrological forecasting. While such data are often readily available nationally, it is harder to find fit-for-purpose global data sets that span long climatological periods through to near-real time. Hydrological simulations are generally sensitive to bias in the meteorological forcing data, especially relative to the data used for the calibration of the model. The lack of high-quality daily resolution data on a global scale has previously been solved by adjusting reanalysis data with global gridded observations. However, existing data sets of this type have been produced for a fixed past time period determined by the main global observational data sets. Long delays between updates of these data sets leaves a data gap between the present day and the end of the data set. Further, hydrological forecasts require initializations of the current state of the snow, soil and lake (and sometimes river) storage. This is normally conceived by forcing the model with observed meteorological conditions for an extended spin-up period, typically at a daily time step, to calculate the initial state. Here, we present and evaluate a method named HydroGFD (Hydrological Global Forcing Data) to combine different data sets in order to produce near-real-time updated hydrological forcing data of temperature and precipitation that are compatible with the products covering the climatological period. HydroGFD resembles the already established WFDEI (WATCH Forcing Data–ERA-Interim) method (Weedon et al., 2014) closely but uses updated climatological observations, and for the near-real time it uses interim products that apply similar methods. This allows HydroGFD to produce updated forcing data including the previous calendar month around the 10th of each month. We present the HydroGFD method and therewith produced data sets, which are evaluated against global data sets, as well as with hydrological simulations with the HYPE (Hydrological Predictions for the Environment) model over Europe and the Arctic regions. We show that HydroGFD performs similarly to WFDEI and that the updated period significantly reduces the bias of the reanalysis data. For real-time updates until the current day, extending HydroGFD with operational meteorological forecasts, a large drift is present in the hydrological simulations due to the bias of the meteorological forecasting model.

scholarly journals Unraveling driving forces explaining significant reduction in satellite-inferred Arctic surface albedo since the 1980s

2019 ◽  
Vol 116 (48) ◽  
pp. 23947-23953 ◽  
Author(s):  
Rudong Zhang ◽  
Hailong Wang ◽  
Qiang Fu ◽  
Philip J. Rasch ◽  
Xuanji Wang
Keyword(s):  
Sea Ice ◽  
Snow Cover ◽  
Surface Albedo ◽  
Driving Forces ◽  
Surface Air Temperature ◽  
Reanalysis Data ◽  
The Arctic ◽  
Data Sets ◽  
Sea Ice Extent ◽  
Snow Cover Fraction

The Arctic has warmed significantly since the early 1980s and much of this warming can be attributed to the surface albedo feedback. In this study, satellite observations reveal a 1.25 to 1.51% per decade absolute reduction in the Arctic mean surface albedo in spring and summer during 1982 to 2014. Results from a global model and reanalysis data are used to unravel the causes of this albedo reduction. We find that reductions of terrestrial snow cover, snow cover fraction over sea ice, and sea ice extent appear to contribute equally to the Arctic albedo decline. We show that the decrease in snow cover fraction is primarily driven by the increase in surface air temperature, followed by declining snowfall. Although the total precipitation has increased as the Arctic warms, Arctic snowfall is reduced substantially in all analyzed data sets. Light-absorbing soot in snow has been decreasing in past decades over the Arctic, indicating that soot heating has not been the driver of changes in the Arctic snow cover, ice cover, and surface albedo since the 1980s.

scholarly journals HOAPS and ERA-Interim precipitation over the sea: validation against shipboard in situ measurements

2016 ◽  
Vol 9 (5) ◽  
pp. 2409-2423 ◽  
Author(s):  
Karl Bumke ◽  
Gert König-Langlo ◽  
Julian Kinzel ◽  
Marc Schröder
Keyword(s):  
Reanalysis Data ◽  
Data Sets ◽  
Weather Forecasts ◽  
Rain Gauges ◽  
Medium Range ◽  
Temporal And Spatial ◽  
The Tropics ◽  
Along Track ◽  
Spatial Threshold

Abstract. The satellite-derived HOAPS (Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data) and ECMWF (European Centre for Medium-Range Weather Forecasts) ERA-Interim reanalysis data sets have been validated against in situ precipitation measurements from ship rain gauges and optical disdrometers over the open ocean by applying a statistical analysis for binary estimates. For this purpose collocated pairs of data were merged within a certain temporal and spatial threshold into single events, according to the satellites' overpass, the observation and the ERA-Interim times. HOAPS detects the frequency of precipitation well, while ERA-Interim strongly overestimates it, especially in the tropics and subtropics. Although precipitation rates are difficult to compare because along-track point measurements are collocated with areal estimates and the number of available data are limited, we find that HOAPS underestimates precipitation rates, while ERA-Interim's Atlantic-wide average precipitation rate is close to measurements. However, when regionally averaged over latitudinal belts, deviations between the observed mean precipitation rates and ERA-Interim exist. The most obvious ERA-Interim feature is an overestimation of precipitation in the area of the intertropical convergence zone and the southern subtropics over the Atlantic Ocean. For a limited number of snow measurements by optical disdrometers, it can be concluded that both HOAPS and ERA-Interim are suitable for detecting the occurrence of solid precipitation.

scholarly journals INVESTIGATION OF WINTER ARCTIC STRATOSPHERIC DYNAMICS IN PRESENT AND FUTURE CLIMATE

2021 ◽  
Vol 1 (6) ◽  
pp. 21-28
Author(s):  
P.N. Vargin ◽  
◽  
S.V. Коstrykin ◽  
, N.D. Tsvetkova ◽  
, A.N. Lukyanov ◽  
...  
Keyword(s):  
Climate Model ◽  
Polar Vortex ◽  
Mean Amplitude ◽  
Reanalysis Data ◽  
Future Climate ◽  
The Arctic ◽  
Data Sets ◽  
Stratospheric Polar Vortex ◽  
Stratospheric Dynamics ◽  
Stratospheric Clouds

. Using reanalysis data sets variability of temperature, zonal mean, amplitude-planetary waves, as well as the influence of the Arctic stratospheric polar vortex changes on the circulation of troposphere from 2016 to 2021 are studied. The results of calculations of the climate model of the INM RAS CM5 for the current and future climate are used to analyze changes in the volume of air masses inside the stratospheric polar vortex with temperatures sufficient for the formation of polar stratospheric clouds necessary for the destruction of the ozone layer.

scholarly journals All the world’s polar research data in one place

2019 ◽  
Author(s):  
Tamer Abu-Alam
Keyword(s):  
Research Data ◽  
Decision Makers ◽  
The Arctic ◽  
Data Sets ◽  
Polar Regions ◽  
Knowledge Based ◽  
Discovery Service ◽  
Teachers And Students ◽  
Norwegian Polar Institute ◽  
Under Construction

Data from the Polar Regions are of critical importance to modern research. Regardless of their disciplinary and institutional affiliations, researchers rely heavily on the comparison of existing data with new data sets to assess changes that are taking effect. In turn, knowledge based on as broad and comprehensive a selection of polar data sets as possible is used to inform politicians and decision makers. Although individual researchers and their institutions are aware of the importance of making collected data openly available through institutional websites, the infrastructures that are used for these purposes at many institutions, are often poorly interoperable, and therefore make valuable data difficult to find and reuse. In a recent survey of 113 major polar data providers, we found that an estimated 60% of the existing polar research data is unfindable through common search engines and can only be accessed through an institutional webpage. This findability gap limits the ability of researchers to establish robust models by which changes in the polar regions can be predicted. In this contribution, we present a new, free-to-use discovery service covering the global output of openly accessible polar research data and publications, with the purpose of rendering polar research more visible and retrievable to the research community as well as to the interested public, teachers and students and public services. The new service is currently under construction and will be hosted by UiT The Arctic University of Norway in close collaboration with the Norwegian Polar Institute.

scholarly journals Accessing Artificial Intelligence for Clinical Decision-Making

2021 ◽  
Vol 3 ◽  
Author(s):  
Chris Giordano ◽  
Meghan Brennan ◽  
Basma Mohamed ◽  
Parisa Rashidi ◽  
François Modave ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Patient Care ◽  
Clinical Decision Making ◽  
Clinical Medicine ◽  
Clinical Decision ◽  
Disruptive Innovation ◽  
Decision Makers ◽  
Data Sets ◽  
Sequential Decision

Advancements in computing and data from the near universal acceptance and implementation of electronic health records has been formative for the growth of personalized, automated, and immediate patient care models that were not previously possible. Artificial intelligence (AI) and its subfields of machine learning, reinforcement learning, and deep learning are well-suited to deal with such data. The authors in this paper review current applications of AI in clinical medicine and discuss the most likely future contributions that AI will provide to the healthcare industry. For instance, in response to the need to risk stratify patients, appropriately cultivated and curated data can assist decision-makers in stratifying preoperative patients into risk categories, as well as categorizing the severity of ailments and health for non-operative patients admitted to hospitals. Previous overt, traditional vital signs and laboratory values that are used to signal alarms for an acutely decompensating patient may be replaced by continuously monitoring and updating AI tools that can pick up early imperceptible patterns predicting subtle health deterioration. Furthermore, AI may help overcome challenges with multiple outcome optimization limitations or sequential decision-making protocols that limit individualized patient care. Despite these tremendously helpful advancements, the data sets that AI models train on and develop have the potential for misapplication and thereby create concerns for application bias. Subsequently, the mechanisms governing this disruptive innovation must be understood by clinical decision-makers to prevent unnecessary harm. This need will force physicians to change their educational infrastructure to facilitate understanding AI platforms, modeling, and limitations to best acclimate practice in the age of AI. By performing a thorough narrative review, this paper examines these specific AI applications, limitations, and requisites while reviewing a few examples of major data sets that are being cultivated and curated in the US.

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