scholarly journals Evaluation of numerical models by FerryBox and fixed platform in situ data in the southern North Sea

Ocean Science ◽  
2015 ◽  
Vol 11 (6) ◽  
pp. 879-896 ◽  
Author(s):  
M. Haller ◽  
F. Janssen ◽  
J. Siddorn ◽  
W. Petersen ◽  
S. Dick
Keyword(s):  
North Sea ◽  
High Frequency ◽  
Numerical Models ◽  
Spatial Scales ◽  
Model Performance ◽  
Eastern Coast ◽  
Southern North Sea ◽  
In Situ Data ◽  
Oyster Ground ◽  
Study Results

Abstract. For understanding and forecasting of hydrodynamics in coastal regions, numerical models have served as an important tool for many years. In order to assess the model performance, we compared simulations to observational data of water temperature and salinity. Observations were available from FerryBox transects in the southern North Sea and, additionally, from a fixed platform of the MARNET network. More detailed analyses have been made at three different stations, located off the English eastern coast, at the Oyster Ground and in the German Bight. FerryBoxes installed on ships of opportunity (SoO) provide high-frequency surface measurements along selected tracks on a regular basis. The results of two operational hydrodynamic models have been evaluated for two different time periods: BSHcmod v4 (January 2009 to April 2012) and FOAM AMM7 NEMO (April 2011 to April 2012). While they adequately simulate temperature, both models underestimate salinity, especially near the coast in the southern North Sea. Statistical errors differ between the two models and between the measured parameters. The root mean square error (RMSE) of water temperatures amounts to 0.72 °C (BSHcmod v4) and 0.44 °C (AMM7), while for salinity the performance of BSHcmod is slightly better (0.68 compared to 1.1). The study results reveal weaknesses in both models, in terms of variability, absolute levels and limited spatial resolution. Simulation of the transition zone between the coasts and the open sea is still a demanding task for operational modelling. Thus, FerryBox data, combined with other observations with differing temporal and spatial scales, can serve as an invaluable tool not only for model evaluation, but also for model optimization by assimilation of such high-frequency observations.

scholarly journals Evaluation of numerical models by FerryBox and Fixed Platform in-situ data in the southern North Sea

2015 ◽  
Vol 12 (1) ◽  
pp. 355-401
Author(s):  
M. Haller ◽  
F. Janssen ◽  
J. Siddorn ◽  
W. Petersen ◽  
S. Dick
Keyword(s):  
North Sea ◽  
High Frequency ◽  
Numerical Models ◽  
Spatial Scales ◽  
Southern North Sea ◽  
In Situ Data ◽  
The North ◽  
Oyster Ground ◽  
Study Results ◽  
Operational Modelling

Abstract. FerryBoxes installed on ships of opportunity (SoO) provide high-frequency surface biogeochemical measurements along selected tracks on a regular basis. Within the European FerryBox Community, several FerryBoxes are operated by different institutions. Here we present a comparison of model simulations applied to the North Sea with FerryBox temperature and salinity data from a transect along the southern North Sea and a more detailed analysis at three different positions located off the English East coast, at the Oyster Ground and in the German Bight. In addition to the FerryBox data, data from a Fixed Platform of the MARNET network are applied. Two operational hydrodynamic models have been evaluated for different time periods: results of BSHcmod v4 are analysed for 2009–2012, while simulations of FOAM AMM7 NEMO have been available from MyOcean data base for 2011 and 2012. The simulation of water temperatures is satisfying; however, limitations of the models exist, especially near the coast in the southern North Sea, where both models are underestimating salinity. Statistical errors differ between the models and the measured parameters, as the root mean square error (rmse) accounts for BSHcmod v4 to 0.92 K, for AMM7 only to 0.44 K. For salinity, BSHcmod is slightly better than AMM7 (0.98 and 1.1 psu, respectively). The study results reveal weaknesses of both models, in terms of variability, absolute levels and limited spatial resolution. In coastal areas, where the simulation of the transition zone between the coasts and the open ocean is still a demanding task for operational modelling, FerryBox data, combined with other observations with differing temporal and spatial scales serve as an invaluable tool for model evaluation and optimization. The optimization of hydrodynamical models with high frequency regional datasets, like the FerryBox data, is beneficial for their subsequent integration in ecosystem modelling.

scholarly journals Comparison of Hydrologic Model Performance Statistics Using Rain Gauge and NEXRAD Precipitation Input at Different Watershed Spatial Scales and Rainfall Return Frequencies for the Upper St. Johns River, Florida USA

Proceedings ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. 11
Author(s):  
Amanda Bredesen ◽  
Christopher J. Brown
Keyword(s):  
Spatial Scale ◽  
Model Simulation ◽  
Numerical Models ◽  
Spatial Scales ◽  
Model Performance ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Precipitation Data ◽  
Remotely Sensed Data ◽  
Model Input

Water resources numerical models are dependent upon various input hydrologic field data. As models become increasingly complex and model simulation times expand, it is critical to understand the inherent value in using different input datasets available. One important category of model input is precipitation data. For hydrologic models, the precipitation data inputs are perhaps the most critical. Common precipitation model input includes either rain gauge or remotely-sensed data such next-generation radar-based (NEXRAD) data. NEXRAD data provides a higher level of spatial resolution than point rain gauge coverage, but is subject to more extensive data pre and post processing along with additional computational requirements. This study first documents the development and initial calibration of a HEC-HMS model of a subtropical watershed in the Upper St. Johns River Basin in Florida, USA. Then, the study compares calibration performance of the same HEC-HMS model using either rain gauge or NEXRAD precipitation inputs. The results are further discretized by comparing key calibration statistics such as Nash–Sutcliffe Efficiency for different spatial scale and at different rainfall return frequencies. The study revealed that at larger spatial scale, the calibration performance of the model was about the same for the two different precipitation datasets while the study showed some benefit of NEXRAD for smaller watersheds. Similarly, the study showed that for smaller return frequency precipitation events, NEXRAD data was superior.

scholarly journals Temporal variability in southern North Sea epifauna communities after the cold winter of 1995/1996

2009 ◽  
Vol 66 (10) ◽  
pp. 2233-2243 ◽  
Author(s):  
Hermann Neumann ◽  
Henning Reiss ◽  
Sebastian Rakers ◽  
Siegfried Ehrich ◽  
Ingrid Kröncke
Keyword(s):  
Community Structure ◽  
North Sea ◽  
Food Supply ◽  
Temporal Variability ◽  
Cold Winter ◽  
Widespread Species ◽  
Southern North Sea ◽  
Oyster Ground ◽  
North Frisian ◽  
Hydroclimatic Change

Abstract Neumann, H., Reiss, H., Rakers, S., Ehrich, S., and Kröncke, I. 2009. Temporal variability in southern North Sea epifauna communities after the cold winter of 1995/1996. – ICES Journal of Marine Science, 66: 2233–2243. Epifauna communities in the southeastern North Sea were studied from 1998 to 2008 to evaluate the effect of hydroclimatic change in community structure. The spatial analysis revealed four communities along the West and North Frisian coasts, on the Oyster Ground, and on the Dogger Bank. The variability between communities was caused mainly by differing abundance of widespread species such as Asterias rubens, probably reflecting differences in environmental conditions, e.g. temperature variation and food supply. Community structure varied between 1998–2000 and 2003–2008 at the shallow West and North Frisian coasts. The hypothesis is that epibenthic communities in these areas were severely affected by the cold winter of 1995/1996, resulting in the outbreak of the opportunistic brittlestar Ophiura albida and followed by characteristic post-disturbance succession stages from 1998 to 2000. The period between 2003 and 2008 was characterized by a continuous decrease in O. albida and by an increase in other species and diversity in the coastal areas. In contrast, secondary production increased in all four areas after 2003, probably because of an increase in sea surface temperature (SST) and in the length of the warming season. We conclude that the cold winter affected epifauna mainly in shallow areas and that the increasing SST influenced the epifauna in the entire southeastern North Sea mainly through an increased food supply.

scholarly journals Combining TRMM and Surface Observations of Precipitation: Technique and Validation over South America

2010 ◽  
Vol 25 (3) ◽  
pp. 885-894 ◽  
Author(s):  
José Roberto Rozante ◽  
Demerval Soares Moreira ◽  
Luis Gustavo G. de Goncalves ◽  
Daniel A. Vila
Keyword(s):  
Large Scale ◽  
Numerical Models ◽  
Spatial Scales ◽  
Model Performance ◽  
Evaluation Process ◽  
Tropical Rainfall Measuring Mission ◽  
Atmospheric Model ◽  
Continuous Assessment ◽  
Precipitation Estimates ◽  
Surface Observations

Abstract The measure of atmospheric model performance is highly dependent on the quality of the observations used in the evaluation process. In the particular case of operational forecast centers, large-scale datasets must be made available in a timely manner for continuous assessment of model results. Numerical models and surface observations usually work at distinct spatial scales (i.e., areal average in a regular grid versus point measurements), making direct comparison difficult. Alternatively, interpolation methods are employed for mapping observational data to regular grids and vice versa. A new technique (hereafter called MERGE) to combine Tropical Rainfall Measuring Mission (TRMM) satellite precipitation estimates with surface observations over the South American continent is proposed and its performance is evaluated for the 2007 summer and winter seasons. Two different approaches for the evaluation of the performance of this product against observations were tested: a cross-validation subsampling of the entire continent and another subsampling of only areas with sparse observations. Results show that over areas with a high density of observations, the MERGE technique’s performance is equivalent to that of simply averaging the stations within the grid boxes. However, over areas with sparse observations, MERGE shows superior results.

scholarly journals Optimizing the Environmental and Economic Sustainability of Remote Community Infrastructure

2020 ◽  
Vol 12 (6) ◽  
pp. 2208 ◽  
Author(s):  
Jamie E. Filer ◽  
Justin D. Delorit ◽  
Andrew J. Hoisington ◽  
Steven J. Schuldt
Keyword(s):  
Environmental Impacts ◽  
Sustainability Assessment ◽  
Model Performance ◽  
Assessment Model ◽  
Remote Communities ◽  
Remote Community ◽  
Rural Villages ◽  
Sustainable Technologies ◽  
Study Results

Remote communities such as rural villages, post-disaster housing camps, and military forward operating bases are often located in remote and hostile areas with limited or no access to established infrastructure grids. Operating these communities with conventional assets requires constant resupply, which yields a significant logistical burden, creates negative environmental impacts, and increases costs. For example, a 2000-member isolated village in northern Canada relying on diesel generators required 8.6 million USD of fuel per year and emitted 8500 tons of carbon dioxide. Remote community planners can mitigate these negative impacts by selecting sustainable technologies that minimize resource consumption and emissions. However, the alternatives often come at a higher procurement cost and mobilization requirement. To assist planners with this challenging task, this paper presents the development of a novel infrastructure sustainability assessment model capable of generating optimal tradeoffs between minimizing environmental impacts and minimizing life-cycle costs over the community’s anticipated lifespan. Model performance was evaluated using a case study of a hypothetical 500-person remote military base with 864 feasible infrastructure portfolios and 48 procedural portfolios. The case study results demonstrated the model’s novel capability to assist planners in identifying optimal combinations of infrastructure alternatives that minimize negative sustainability impacts, leading to remote communities that are more self-sufficient with reduced emissions and costs.

scholarly journals Two-Dimensional Numerical Modeling of Flow in Physical Models of Rock Vane and Bendway Weir Configurations

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 458
Author(s):  
Drew C. Baird ◽  
Benjamin Abban ◽  
S. Michael Scurlock ◽  
Steven B. Abt ◽  
Christopher I. Thornton
Keyword(s):  
Numerical Modeling ◽  
Physical Model ◽  
Numerical Models ◽  
Hydraulic Performance ◽  
Physical Models ◽  
Protection Measures ◽  
Design Engineers ◽  
Model Study ◽  
Study Results ◽  
Wide Range

While there are a wide range of design recommendations for using rock vanes and bendway weirs as streambank protection measures, no comprehensive, standard approach is currently available for design engineers to evaluate their hydraulic performance before construction. This study investigates using 2D numerical modeling as an option for predicting the hydraulic performance of rock vane and bendway weir structure designs for streambank protection. We used the Sedimentation and River Hydraulics (SRH)-2D depth-averaged numerical model to simulate flows around rock vane and bendway weir installations that were previously examined as part of a physical model study and that had water surface elevation and velocity observations. Overall, SRH-2D predicted the same general flow patterns as the physical model, but over- and underpredicted the flow velocity in some areas. These over- and underpredictions could be primarily attributed to the assumption of negligible vertical velocities. Nonetheless, the point differences between the predicted and observed velocities generally ranged from 15 to 25%, with some exceptions. The results showed that 2D numerical models could provide adequate insight into the hydraulic performance of rock vanes and bendway weirs. Accordingly, design guidance and implications of the study results are presented for design engineers.

scholarly journals Evaluating the accuracy and uncertainty of atmospheric and wave model hindcasts during severe events using model ensembles

2021 ◽  
Author(s):  
Ali Abdolali ◽  
Andre van der Westhuysen ◽  
Zaizhong Ma ◽  
Avichal Mehra ◽  
Aron Roland ◽  
...  
Keyword(s):  
Extreme Event ◽  
Numerical Models ◽  
Model Performance ◽  
Ground Truth ◽  
Wave Model ◽  
Atmospheric Model ◽  
Stationary Source ◽  
Source Point ◽  
Spectral Wave Model ◽  
Model Ensembles

AbstractVarious uncertainties exist in a hindcast due to the inabilities of numerical models to resolve all the complicated atmosphere-sea interactions, and the lack of certain ground truth observations. Here, a comprehensive analysis of an atmospheric model performance in hindcast mode (Hurricane Weather and Research Forecasting model—HWRF) and its 40 ensembles during severe events is conducted, evaluating the model accuracy and uncertainty for hurricane track parameters, and wind speed collected along satellite altimeter tracks and at stationary source point observations. Subsequently, the downstream spectral wave model WAVEWATCH III is forced by two sets of wind field data, each includes 40 members. The first ones are randomly extracted from original HWRF simulations and the second ones are based on spread of best track parameters. The atmospheric model spread and wave model error along satellite altimeters tracks and at stationary source point observations are estimated. The study on Hurricane Irma reveals that wind and wave observations during this extreme event are within ensemble spreads. While both Models have wide spreads over areas with landmass, maximum uncertainty in the atmospheric model is at hurricane eye in contrast to the wave model.

scholarly journals 8-Day and Daily Maximum and Minimum Air Temperature Estimation via Machine Learning Method on a Climate Zone to Global Scale

2021 ◽  
Vol 13 (12) ◽  
pp. 2355
Author(s):  
Linglin Zeng ◽  
Yuchao Hu ◽  
Rui Wang ◽  
Xiang Zhang ◽  
Guozhang Peng ◽  
...  
Keyword(s):  
Spatial Scale ◽  
Air Temperature ◽  
Spatial Scales ◽  
Model Performance ◽  
Global Scale ◽  
Scale Model ◽  
Validation Dataset ◽  
Daily Maximum ◽  
Climate Zone ◽  
Temporal And Spatial

Air temperature (Ta) is a required input in a wide range of applications, e.g., agriculture. Land Surface Temperature (LST) products from Moderate Resolution Imaging Spectroradiometer (MODIS) are widely used to estimate Ta. Previous studies of these products in Ta estimation, however, were generally applied in small areas and with a small number of meteorological stations. This study designed both temporal and spatial experiments to estimate 8-day and daily maximum and minimum Ta (Tmax and Tmin) on three spatial scales: climate zone, continental and global scales from 2009 to 2018, using the Random Forest (RF) method based on MODIS LST products and other auxiliary data. Factors contributing to the relation between LST and Ta were determined based on physical models and equations. Temporal and spatial experiments were defined by the rules of dividing the training and validation datasets for the RF method, in which the stations selected in the training dataset were all included or not in the validation dataset. The RF model was first trained and validated on each spatial scale, respectively. On a global scale, model accuracy with a determination coefficient (R2) > 0.96 and root mean square error (RMSE) < 1.96 °C and R2 > 0.95 and RMSE < 2.55 °C was achieved for 8-day and daily Ta estimations, respectively, in both temporal and spatial experiments. Then the model was trained and cross-validated on each spatial scale. The results showed that the data size and station distribution of the study area were the main factors influencing the model performance at different spatial scales. Finally, the spatial patterns of the model performance and variable importance were analyzed. Both daytime and nighttime LST had a significant contribution in the 8-day Tmax estimation on all the three spatial scales; while their contribution in daily Tmax estimation varied over different continents or climate zones. This study was expected to improve our understanding of Ta estimation in terms of accuracy variations and influencing variables on different spatial and temporal scales. The future work mainly includes identifying underlying mechanisms of estimation errors and the uncertainty sources of Ta estimation from a local to a global scale.

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