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

2017 ◽  
Author(s):  
Peter Berg ◽  
Chantal Donnelly ◽  
David Gustafsson
Keyword(s):  
Real Time ◽  
Arctic Region ◽  
Analysis Data ◽  
Global Scale ◽  
Current Data ◽  
The Arctic ◽  
Data Sets ◽  
Initial State ◽  
Time Step ◽  
Data Set

Abstract. Updating climatological forcing data to near current data are compelling for impact modelling, e.g. to update model simulations or to simulate recent extreme events. 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 daily resolution data at a global scale has previously been solved by adjusting re-analysis data 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 present and the end of the data set. Further, hydrological forecasts require initialisations of the current state of the snow, soil, 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 a method named GFD (Global Forcing Data) to combine different data sets in order to produce near real-time updated hydrological forcing data that are compatible with the products covering the climatological period. GFD resembles the already established WFDEI 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 GFD to produce updated forcing data including the previous calendar month around the 10th of each month. We present the GFD method and different produced data sets, which are evaluated against the WFDEI data set, as well as with hydrological simulations with the HYPE model over Europe and the Arctic region. We show that GFD performs similarly to WFDEI and that the updated period significantly reduces the bias of the reanalysis data, although less well for the last two months of the updating cycle. For real-time updates until the current day, extending GFD with operational meteorological forecasts, a large drift is present in the hydrological simulations due to the bias of the meteorological forecasting model.

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.

HydroGFD3: a climatological and real-time updated hydrological forcing dataset

2020 ◽  
Author(s):  
Peter Berg ◽  
Fredrik Almén ◽  
Denica Bozhinova ◽  
Riejanne Mook
Keyword(s):  
Real Time ◽  
Observational Data ◽  
Bias Correction ◽  
Hydrological Model ◽  
Initial Conditions ◽  
Correction Method ◽  
Reanalysis Data ◽  
Data Sets ◽  
Initial State ◽  
Data Set

<p>Hydrological forecasting benefits substantially from good initial conditions, which translate information into the forecast. It is therefore important to perform frequent updates of the initial state of the model before the forecast, which demands good meteorological forcing data. For a continental or global hydrological model, it is difficult to find observational data sets which fulfill the requirements of (i) long time series for calibration and spin up, (ii) consistent quality, (iii) at least daily time steps, and (iv) at least data for temperature and precipitation. HydroGFD3 is a new data set that fulfills all the criteria and provides real-time updated data.</p><p>HydroGFD3 builds upon the ERA5 reanalysis data set, and performs a bias correction for each new produced month. In contrast to earlier versions (Berg et al., 2018), HydroGFD3 is based on a multi-source climatological background, upon which individual days are produced by adding anomalies from different freely available monthly global observational data sets. These are then disaggregated based on the ERA5 reanalysis. For production redundancy and local tailoring, HydroGFD3 is produced in several tiers, each using different observational data sets originating from GPCC and CPC. Further, intermediate daily updates of the reanalysis through the source ERA5T allow the data set to be updated to within a few days of real-time.</p><p>To reach actual real-time, one tier is based on a bias correction method calibrated on the period 1980-2009, which is applied on ERA5T, and further prolonged to current day using the ECMWF deterministic forecasts. The assumption for this to work is that the forecasts have a similar bias as the reanalysis model, which is currently the case. The method also allows bias correction of the forecasts themselves; solving the issue of “drift” in the forecasts as the hydrological model adjusts to the (biased) climatological state of the forcing data.</p><p>Berg, Peter, Chantal Donnelly, and David Gustafsson. "Near-real-time adjusted reanalysis forcing data for hydrology." Hydrology and Earth System Sciences 22.2 (2018): 989-1000.</p><p> </p>

scholarly journals Temperature and Relative Humidity Profile Retrieval from Fengyun-3D/HIRAS in the Arctic Region

2021 ◽  
Vol 13 (10) ◽  
pp. 1884
Author(s):  
Jingjing Hu ◽  
Yansong Bao ◽  
Jian Liu ◽  
Hui Liu ◽  
George P. Petropoulos ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Real Time ◽  
Wide Spectrum ◽  
Warm Season ◽  
Arctic Region ◽  
Cold Season ◽  
The Arctic ◽  
Practical Applications ◽  
A New Technique ◽  
The Arctic Region

The acquisition of real-time temperature and relative humidity (RH) profiles in the Arctic is of great significance for the study of the Arctic’s climate and Arctic scientific research. However, the operational algorithm of Fengyun-3D only takes into account areas within 60°N, the innovation of this work is that a new technique based on Neural Network (NN) algorithm was proposed, which can retrieve these parameters in real time from the Fengyun-3D Hyperspectral Infrared Radiation Atmospheric Sounding (HIRAS) observations in the Arctic region. Considering the difficulty of obtaining a large amount of actual observation (such as radiosonde) in the Arctic region, collocated ERA5 data from European Centre for Medium-Range Weather Forecasts (ECMWF) and HIRAS observations were used to train the neural networks (NNs). Brightness temperature and training targets were classified using two variables: season (warm season and cold season) and surface type (ocean and land). NNs-based retrievals were compared with ERA5 data and radiosonde observations (RAOBs) independent of the NN training sets. Results showed that (1) the NNs retrievals accuracy is generally higher on warm season and ocean; (2) the root-mean-square error (RMSE) of retrieved profiles is generally slightly higher in the RAOB comparisons than in the ERA5 comparisons, but the variation trend of errors with height is consistent; (3) the retrieved profiles by the NN method are closer to ERA5, comparing with the AIRS products. All the results demonstrated the potential value in time and space of NN algorithm in retrieving temperature and relative humidity profiles of the Arctic region from HIRAS observations under clear-sky conditions. As such, the proposed NN algorithm provides a valuable pathway for retrieving reliably temperature and RH profiles from HIRAS observations in the Arctic region, providing information of practical value in a wide spectrum of practical applications and research investigations alike.All in all, our work has important implications in broadening Fengyun-3D’s operational implementation range from within 60°N to the Arctic region.

Fast and accurate detection of surface defect based on improved YOLOv4

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jiawei Lian ◽  
Junhong He ◽  
Yun Niu ◽  
Tianze Wang
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Surface Defect ◽  
Steel Ingot ◽  
Industrial Applications ◽  
Data Sets ◽  
Data Set ◽  
Processing Technologies ◽  
Content Type ◽  
Public Data

Purpose The current popular image processing technologies based on convolutional neural network have the characteristics of large computation, high storage cost and low accuracy for tiny defect detection, which is contrary to the high real-time and accuracy, limited computing resources and storage required by industrial applications. Therefore, an improved YOLOv4 named as YOLOv4-Defect is proposed aim to solve the above problems. Design/methodology/approach On the one hand, this study performs multi-dimensional compression processing on the feature extraction network of YOLOv4 to simplify the model and improve the feature extraction ability of the model through knowledge distillation. On the other hand, a prediction scale with more detailed receptive field is added to optimize the model structure, which can improve the detection performance for tiny defects. Findings The effectiveness of the method is verified by public data sets NEU-CLS and DAGM 2007, and the steel ingot data set collected in the actual industrial field. The experimental results demonstrated that the proposed YOLOv4-Defect method can greatly improve the recognition efficiency and accuracy and reduce the size and computation consumption of the model. Originality/value This paper proposed an improved YOLOv4 named as YOLOv4-Defect for the detection of surface defect, which is conducive to application in various industrial scenarios with limited storage and computing resources, and meets the requirements of high real-time and precision.

Multi-Scale Virtual Reality of Sintering

2007 ◽  
Vol 534-536 ◽  
pp. 573-576
Author(s):  
Eugene Olevsky
Keyword(s):  
Global Scale ◽  
Initial State ◽  
Model Framework ◽  
Time Step ◽  
Computational Framework ◽  
Multi Scale ◽  
Scale Modeling ◽  
History Of ◽  
On Line ◽  
Damage Criteria

The directions of further developments in the modeling of sintering are pointed out, including multi-scale modeling of sintering, on-line sintering damage criteria, particle agglomeration, sintering with phase transformations. A true multi-scale approach is applied for the development of a new meso-macro methodology for modeling of sintering. The developed macroscopic level computational framework envelopes the mesoscopic simulators. No closed forms of constitutive relationships are assumed for the parameters of the material. When a time-step of the calculations is finished for one macroscopic element, the mesostructures of the next element are restored from the initial state according to the history of loading. The model framework is able to predict the final dimensions of the sintered specimen on a global scale and identify the granular structure in any localized area for prediction of the material properties.

scholarly journals The Water and Energy Budget of the Arctic Atmosphere

2005 ◽  
Vol 18 (13) ◽  
pp. 2515-2530 ◽  
Author(s):  
Tido Semmler ◽  
Daniela Jacob ◽  
K. Heinke Schlünzen ◽  
Ralf Podzun
Keyword(s):  
Energy Budget ◽  
Climate Model ◽  
Small Sample Size ◽  
Regional Climate ◽  
Arctic Region ◽  
Analysis Data ◽  
Small Sample ◽  
The Arctic ◽  
Energy Fluxes ◽  
Arctic Atmosphere

Abstract The Arctic plays a major role in the global circulation, and its water and energy budget is not as well explored as that in other regions of the world. The aim of this study is to calculate the climatological mean water and energy fluxes depending on the season and on the North Atlantic Oscillation (NAO) through the lower, lateral, and upper boundaries of the Arctic atmosphere north of 70°N. The relevant fluxes are derived from results of the regional climate model (REMO 5.1), which is applied to the Arctic region for the time period 1979–2000. Model forcing data are a combination of 15-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-15) data and analysis data. The annual and seasonal total water and energy fluxes derived from REMO 5.1 results are very similar to the fluxes calculated from observational and reanalysis data, although there are some differences in the components. The agreement between simulated and observed total fluxes shows that these fluxes are reliable. Even if differences between high and low NAO situations occur in our simulation consistent with previous studies, these differences are mostly smaller than the large uncertainties due to a small sample size of the NAO high and low composites.

scholarly journals A Distributed Content-Based Video Retrieval System for Large Data-sets

2021 ◽  
Author(s):  
ElMehdi SAOUDI ◽  
Said Jai Andaloussi
Keyword(s):  
Real Time ◽  
Retrieval System ◽  
Video Retrieval ◽  
Multimedia Databases ◽  
Video Data ◽  
Multimedia Data ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Data Set ◽  
Content Based Video Retrieval

Abstract With the rapid growth of the volume of video data and the development of multimedia technologies, it has become necessary to have the ability to accurately and quickly browse and search through information stored in large multimedia databases. For this purpose, content-based video retrieval ( CBVR ) has become an active area of research over the last decade. In this paper, We propose a content-based video retrieval system providing similar videos from a large multimedia data-set based on a query video. The approach uses vector motion-based signatures to describe the visual content and uses machine learning techniques to extract key-frames for rapid browsing and efficient video indexing. We have implemented the proposed approach on both, single machine and real-time distributed cluster to evaluate the real-time performance aspect, especially when the number and size of videos are large. Experiments are performed using various benchmark action and activity recognition data-sets and the results reveal the effectiveness of the proposed method in both accuracy and processing time compared to state-of-the-art methods.

scholarly journals Nordic EPOS - A FAIR Nordic EPOS Data Hub

2021 ◽  
Author(s):  
Annakaisa Korja ◽  
Kuvvet Atakan ◽  
Peter H. Voss ◽  
Michael Roth ◽  
Kristin Vogfjord ◽  
...  
Keyword(s):  
Solid Earth ◽  
Arctic Region ◽  
Research Community ◽  
The Arctic ◽  
Data Sets ◽  
On Line ◽  
Scientific Expertise ◽  
Sustainable Societies ◽  
And Training ◽  
The Arctic Region

<p>Nordic EPOS - A FAIR Nordic EPOS Data Hub – is a consortium of the Nordic geophysical observatories financed by NordForsk. It is delivering on-line data to European Plate Observing System’s Thematic Core Services (EPOS’s TCSs). Nordic EPOS consortium comprises of the Universities of Helsinki, Bergen, Uppsala, Oulu and GEUS and Icelandic Meteorological Office. Nordic EPOS enhances and stimulates the ongoing active Nordic interactions related to Solid Earth Research Infrastructures (RIs) in general and EPOS in particular. Nordic EPOS develops expertise and tools designed to integrate Nordic RI data and to enhance their accessibility and usefulness to the Nordic research community. Together we can address global challenges in Norden and with Nordic data.</p><p>The Nordic EPOS’s main tasks are to advance the usage of multi-disciplinary Solid Earth data sets on scientific and societal problem solving, increase the amount of open, shared homogenized data sets, and increase the scientific expertise in creating sustainable societies in Nordic countries and especially in the Arctic region. In addition to developing services better suited for Nordic interest for EPOS, Nordic EPOS will also try to bring forward Nordic research interest, such as research of Arctic areas in TCSs and EPOS-ERIC governance and scientific boards.</p><p>The Nordic EPOS is organized into Tasks and Activities. The project has six main infrastructure TASKs: I - Training in usage of EPOS-RI data and services; II - Nordic data integration and FAIRness; III - Nordic station management of seismological networks, IV - Induced seismicity, safe society; V - Ash and gas monitoring; and VI- Geomagnetic hazards. In addition, the project has one transversal TASK VII on Communication and dissemination. The activities within the TASKs are workshops, tutorials, demos and training sessions (virtual and on-site), and communication and dissemination of EPOS data and metadata information at local, national and international workshops, meetings, and conferences.</p>

scholarly journals From pole to pole: 33 years of physical oceanography onboard R/V <i>Polarstern</i>

2017 ◽  
Vol 9 (1) ◽  
pp. 211-220 ◽  
Author(s):  
Amelie Driemel ◽  
Eberhard Fahrbach ◽  
Gerd Rohardt ◽  
Agnieszka Beszczynska-Möller ◽  
Antje Boetius ◽  
...  
Keyword(s):  
Data Collection ◽  
Water Cycle ◽  
Heat Budget ◽  
Ocean Dynamics ◽  
The Arctic ◽  
Sensor Calibration ◽  
Data Sets ◽  
Calibration Data ◽  
Data Set ◽  
Different Characteristics

Abstract. Measuring temperature and salinity profiles in the world's oceans is crucial to understanding ocean dynamics and its influence on the heat budget, the water cycle, the marine environment and on our climate. Since 1983 the German research vessel and icebreaker Polarstern has been the platform of numerous CTD (conductivity, temperature, depth instrument) deployments in the Arctic and the Antarctic. We report on a unique data collection spanning 33 years of polar CTD data. In total 131 data sets (1 data set per cruise leg) containing data from 10 063 CTD casts are now freely available at doi:10.1594/PANGAEA.860066. During this long period five CTD types with different characteristics and accuracies have been used. Therefore the instruments and processing procedures (sensor calibration, data validation, etc.) are described in detail. This compilation is special not only with regard to the quantity but also the quality of the data – the latter indicated for each data set using defined quality codes. The complete data collection includes a number of repeated sections for which the quality code can be used to investigate and evaluate long-term changes. Beginning with 2010, the salinity measurements presented here are of the highest quality possible in this field owing to the introduction of the OPTIMARE Precision Salinometer.

Descriptive Statistics and Cross-National Comparisons of Arctic Power Projection

2020 ◽  
pp. 56-80
Author(s):  
Jonathan N. Markowitz
Keyword(s):  
Descriptive Statistics ◽  
The Arctic ◽  
Military Force ◽  
Data Sets ◽  
Data Set ◽  
Power Projection ◽  
Resource Rents ◽  
Before And After ◽  
Military Activity ◽  
Events Data

Chapter 4 employs data from three new data sets, the Arctic Military Activity Events Data Set, the Arctic Bases Data Set, and the Icebreaker and Ice-Hardened Warships Data Set. These new data enable a systematic comparison of each state’s Arctic military forces and deployments before and after the 2007 climate shock. The data offer a corrective to both sensationalist media accounts that suggest that all states are scrambling to fight over Arctic resources and those who downplay real changes in states’ Arctic military capabilities and presence. Confirming Rent-Addition’s Theory’s predictions, the descriptive statistical comparisons reveal that the states that were most economically dependent on resource rents, Norway and Russia, were the most willing to back their claims by projecting military force to disputed areas and investing in Arctic bases, ice-hardened warships, and icebreakers.

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