scholarly journals Optimising the Australian Wave Observation Network

2018 ◽  
Vol 68 (1) ◽  
pp. 184
Author(s):  
Diana J.M. Greenslade ◽  
Adriana Zanca ◽  
Stefan Zieger ◽  
Mark A. Hemer
Keyword(s):  
Population Distribution ◽  
Spatial Coherence ◽  
Wave Climate ◽  
Spatial Density ◽  
Maritime Industry ◽  
Data Set ◽  
Wave Data ◽  
Observation Network ◽  
Australian Coastline ◽  
Climatological Variability

The Australian national in situ wave data network currently consists of 35 platforms distributed around the Australian coastline. At present, all except for five are directional waverider buoys. The spatial density of the observation locations is variable – at a glance, density is higher on the east coast compared to the rest of the coastline. This variability has resulted in some areas of the coastline being well observed and well accounted for in models and wave climate studies and other areas not being observed at all. This work aims to identify potential gaps in the existing wave observing network in order to provide guidance for prospective future deployments. In addition, the technique used allows us to easily identify which are the key locations in the existing network. The method is based on considering the spatial coherence of the wave field determined from a multi-decadal hindcast wave data set. For each modelled data point, correlations between monthly statistics (means and 95th percentiles) of modelled variables (significant wave height, mean period and mean direction) at that location and corresponding modelled variables at each observation site are calculated. Areas of low correlation provide an indication of the key network gaps, i.e. areas where climatological variability of the wave fields is poorly captured by existing observations. Removing locations individually from the network and repeating the analysis can also provide an indication of which are the most important locations in the network (and conversely, which are the least important) to capture the regional climatological variability. Several key gaps are identified, suggesting that most value can be gained by placing additional buoys in these areas. However, it is noted that other factors such as accessibility, areas of maritime industry, and population distribution are also important in selecting sites for new buoy deployments.

scholarly journals A multi-decadal wind-wave hindcast for the North Sea 1949–2014: coastDat2

2017 ◽  
Vol 9 (2) ◽  
pp. 955-968 ◽  
Author(s):  
Nikolaus Groll ◽  
Ralf Weisse
Keyword(s):  
Climate Variability ◽  
North Sea ◽  
Wind Wave ◽  
Wave Climate ◽  
Climate Variability And Change ◽  
Data Set ◽  
Wave Hindcast ◽  
Wave Data ◽  
The North ◽  
The North Sea

Abstract. Long and consistent wave data are important for analysing wave climate variability and change. Moreover, such wave data are also needed in coastal and offshore design and for addressing safety-related issues at sea. Using the third-generation spectral wave model WAM a multi-decadal wind-wave hindcast for the North Sea covering the period 1949–2014 was produced. The hindcast is part of the coastDat database representing a consistent and homogeneous met-ocean data set. It is shown that despite not being perfect, data from the wave hindcast are generally suitable for wave climate analysis. In particular, comparisons of hindcast data with in situ and satellite observations show on average a reasonable agreement, while a tendency towards overestimation of the highest waves could be inferred. Despite these limitations, the wave hindcast still provides useful data for assessing wave climate variability and change as well as for risk analysis, in particular when conservative estimates are needed. Hindcast data are stored at the World Data Center for Climate (WDCC) and can be freely accessed using the doi:10.1594/WDCC/coastDat-2_WAM–North_Sea Groll and Weisse(2016) or via the coastDat web-page http://www.coastdat.de.

scholarly journals Regional Downscaling of Copernicus ERA5 Wave Data for Coastal Engineering Activities and Operational Coastal Services

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 859
Author(s):  
Giorgio Bellotti ◽  
Leopoldo Franco ◽  
Claudia Cecioni
Keyword(s):  
Time Series ◽  
National Level ◽  
Wave Model ◽  
Wave Climate ◽  
Tyrrhenian Sea ◽  
Computational Domain ◽  
Multiple Wave ◽  
Space Resolution ◽  
Wave Data ◽  
Fundamental Information

Hindcasted wind and wave data, available on a coarse resolution global grid (Copernicus ERA5 dataset), are downscaled by means of the numerical model SWAN (simulating waves in the nearshore) to produce time series of wave conditions at a high resolution along the Italian coasts in the central Tyrrhenian Sea. In order to achieve the proper spatial resolution along the coast, the finite element version of the model is used. Wave data time series at the ERA5 grid are used to specify boundary conditions for the wave model at the offshore sides of the computational domain. The wind field is fed to the model to account for local wave generation. The modeled sea states are compared against the multiple wave records available in the area, in order to calibrate and validate the model. The model results are in quite good agreement with direct measurements, both in terms of wave climate and wave extremes. The results show that using the present modeling chain, it is possible to build a reliable nearshore wave parameters database with high space resolution. Such a database, once prepared for coastal areas, possibly at the national level, can be of high value for many engineering activities related to coastal area management, and can be useful to provide fundamental information for the development of operational coastal services.

The climatic, topographic and litho-tectonic characteristics of badlands in Turkey

2021 ◽  
Author(s):  
Aydogan Avcioglu ◽  
Tolga Gorum ◽  
Abdullah Akbas ◽  
Mariano Moreno de las Heras ◽  
Omer Yetemen
Keyword(s):  
Critical Role ◽  
Sedimentary Rocks ◽  
Spatial Coherence ◽  
Google Earth ◽  
Controlling Factors ◽  
Mean Annual Precipitation ◽  
Rainfall Erosivity ◽  
Data Set ◽  
Anatolian Plateau ◽  
Precipitation Seasonality

<p>Badland areas are present in all continents, excluding Antarctica, and play a critical role in establishing local erosion and sedimentation rates. The presence of unconsolidated rocks (e.g., marls, sandstone, mudstone etc.) is a major driver controlling the distribution of badlands, which together with other environmental components, such as climate, tectonics, vegetation, and topography, determine their forms and processes. The mutual interaction of controlling factors in badlands areas provides a basis for developing a holistic approach to clarify their distribution patterns. Turkey's geodynamic evolution has led to the emergence of marine sedimentary rocks, pyroclastics, and continental clastics, especially in line with the uplift of the Anatolian Plateau and volcanism during the last 8 Ma.</p><p>This study aims to explore the country-scale distribution of badlands and the controlling factors of this badland distribution in Turkey. Remarkably wide badlands landscapes (4494 km<sup>2</sup>) have been visually inspected using Google Earth Pro<sup>TM</sup> to further digitize and extract geomorphological units by applying high-resolution multispectral images provided by WorldView-4/Maxar Technology and CNES/Airbus. To obtain exact boundaries, we eliminated contiguous flat areas surrounding the identified badlands by using red relief image map (RRIM) mosaics that express surface concavity and convexity combined with topographic slope derived from a digital elevation model of 5-m spatial resolution. Last, to determine the controlling factors of badlands distribution, we have compiled a global data set comprising 1-km resolution layers of mean annual precipitation, temperature and precipitation seasonality, aridity, NDVI, rainfall erosivity factor, elevation, and majority values of regional lithology in sub-catchments units. The enhanced investigation of the complex relationship that expresses the controlling factors of badlands distribution, has been conducted by K-means unsupervised cluster analysis.</p><p>Our comprehensive regional analyses exploring the distribution and environmental attributes of major Turkish badlands identified five different groups or clusters of badlands that display spatial coherence with climatic and tectonic settings. We argue that Turkey's climatic and topographic transition zones, varying from Mediterranean climate dominated areas to the more arid Central Anatolian Plateau, and tectonically‑induced topographic barriers play a relevant role in discriminating these groups of badlands. Moreover, the Anatolian diversity of sedimentary rocks, which consists of Neogene and Paleogene continental clastics, volcano clastics & pyroclastics, and lacustrine deposits, makes an essential contribution to the identified, extensive badland distribution.</p><p>This study has been produced benefiting from the 2232 International Fellowship for Outstanding Researchers Program of the Scientific and Technological Research Council of Turkey (TUBITAK) through grant 118C329. The financial support received from TUBITAK does not mean that the content of the publication is approved in a scientific sense by TUBITAK.</p>

scholarly journals The BernClim plant phenological data set from the canton of Bern (Switzerland) 1970–2018

2019 ◽  
Vol 11 (4) ◽  
pp. 1645-1654 ◽  
Author(s):  
This Rutishauser ◽  
François Jeanneret ◽  
Robert Brügger ◽  
Yuri Brugnara ◽  
Christian Röthlisberger ◽  
...  
Keyword(s):  
Growing Season ◽  
Data Set ◽  
Urban And Regional Planning ◽  
Phenological Data ◽  
Observation Network ◽  
Good Internal Consistency ◽  
Change Impact ◽  
University Of Bern ◽  
The University

Abstract. In 1970, the Institute of Geography of the University of Bern initiated the phenological observation network BernClim. Seasonality information from plants, fog and snow was originally available for applications in urban and regional planning and agricultural and touristic suitability and is now a valuable data set for climate change impact studies. Covering the growing season, volunteer observers record the dates of key development stages of hazel (Corylus avellana), dandelion (Taraxacum officinale), apple tree (Pyrus malus) and beech (Fagus sylvatica). All observations consist of detailed site information, including location, altitude, exposition (aspect) and inclination, that makes BernClim unique in its richness in detail on decadal timescales. Quality control (QC) by experts and statistical analyses of the data have been performed to flag impossible dates, dates outside the biologically plausible range, repeated dates in the same year, stretches of consecutive identical dates and statistically inconsistent dates (outliers in time or in space). Here, we report BernClim data of 7414 plant phenological observations from 1970 to 2018 from 1304 sites at 110 stations, the QC procedure and selected applications (Rutishauser et al., 2019: https://doi.org/10.1594/PANGAEA.900102). The QC points to very good internal consistency (only 0.2 % were flagged as internally inconsistent) and likely high quality of the data. BernClim data indicate a trend towards an extended growing season. They also track the regime shift in the late 1980s well to pronounced earlier dates like numerous other phenological records across the Northern Hemisphere.

Prévision des vagues dans l'estuaire du Saint-Laurent à l'aide d'un modèle bidimensionnel

1999 ◽  
Vol 26 (6) ◽  
pp. 713-723 ◽  
Author(s):  
L Dupuis ◽  
Y Ouellet
Keyword(s):  
Water Waves ◽  
Wave Climate ◽  
Wave Direction ◽  
Limited Region ◽  
Lawrence Estuary ◽  
Wave Data ◽  
Modèle Bidimensionnel ◽  
Wave Heights ◽  
Wave Hindcasting ◽  
2D Model

Until now, wave hindcasting in the Estuary and Gulf of St. Lawrence has been done with one-dimensional models. The objective of the present paper is to verify if the two-dimensional model WAWSP, developed to predict waves on the Great Lakes, could be used in the St. Lawrence estuary, a semi-open fetch limited region. Waves (significant wave heights, peak periods, and directions) hindcast by this 2D model are compared with wave data observed at two buoys in 1991, 1992, and 1993, as well as with the ones obtained with 1D models SPM-77 and SPM-84. As a whole, the 2D model gives better results than 1D models. Wave heights are well reproduced, as long as wind data are well represented. However, wave periods are much smaller than those measured, and wave directions are not accurate, mainly because of the presence of swell in the estuary. This study shows the need to obtain more wave data with better quality in order to validate wave hindcasting models.Key words: water waves, numerical modeling, wave hindcasting, 2D model, wave climate, wave height, wave period, wave direction, calculated versus measured waves.

Building a Global Resource for Rapid Assessment of the Wave Climate

2004 ◽  
Author(s):  
Cees de Valk ◽  
Peter Groenewoud ◽  
Sander Hulst ◽  
Gert Klopman
Keyword(s):  
Rapid Assessment ◽  
Wave Climate ◽  
Offshore Wind ◽  
Wave Transformation ◽  
Extreme Value Analysis ◽  
Radar Altimeter ◽  
Spectra Analysis ◽  
Value Analysis ◽  
Wave Hindcast ◽  
Wave Data

In order to provide rapid access to reliable wave and wind climate information worldwide, a resource has been created combining: • a global offshore wind- and wave data-base, currently containing calibrated and validated spectral wave data from a wave hindcast model as well data from several satellite microwave sensors; • a simple but effective numerical model to predict nearshore wave conditions from the offshore spectra; • analysis tools to extract various climate parameters from the data such as scatter tables, extreme value analysis and persistency; • a web interface giving instantaneous access to the most commonly needed information. The resource is primarily intended for use in planning and design of operations typically requiring five years of data, but it an also be used for the design of certain structures, as there are now 16 years of significant wave height data from satellite radar altimeter available. This paper describes the components of the system and discusses their merits and limitations. We also present some results of the validation of the global satellite wave and wind data, of the global and regional wave model hindcasts, and of the nearshore wave transformation employed to obtain wave climate at sheltered or shallow-water sites.

scholarly journals Water vapor mapping by fusing InSAR and GNSS remote sensing data and atmospheric simulations

2015 ◽  
Vol 19 (12) ◽  
pp. 4747-4764 ◽  
Author(s):  
F. Alshawaf ◽  
B. Fersch ◽  
S. Hinz ◽  
H. Kunstmann ◽  
M. Mayer ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Vapor ◽  
Data Fusion ◽  
Spatial Resolution ◽  
Heterogeneous Data ◽  
Spatial Density ◽  
Data Sets ◽  
Data Set ◽  
Fixed Rank Kriging ◽  
Fixed Rank

Abstract. Data fusion aims at integrating multiple data sources that can be redundant or complementary to produce complete, accurate information of the parameter of interest. In this work, data fusion of precipitable water vapor (PWV) estimated from remote sensing observations and data from the Weather Research and Forecasting (WRF) modeling system are applied to provide complete grids of PWV with high quality. Our goal is to correctly infer PWV at spatially continuous, highly resolved grids from heterogeneous data sets. This is done by a geostatistical data fusion approach based on the method of fixed-rank kriging. The first data set contains absolute maps of atmospheric PWV produced by combining observations from the Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR). These PWV maps have a high spatial density and a millimeter accuracy; however, the data are missing in regions of low coherence (e.g., forests and vegetated areas). The PWV maps simulated by the WRF model represent the second data set. The model maps are available for wide areas, but they have a coarse spatial resolution and a still limited accuracy. The PWV maps inferred by the data fusion at any spatial resolution show better qualities than those inferred from single data sets. In addition, by using the fixed-rank kriging method, the computational burden is significantly lower than that for ordinary kriging.

scholarly journals Dynamic spatio-temporal generation of large-scale synthetic gridded precipitation: with improved spatial coherence of extremes

2019 ◽  
Vol 34 (9) ◽  
pp. 1369-1383 ◽  
Author(s):  
Dirk Diederen ◽  
Ye Liu
Keyword(s):  
Large Scale ◽  
Spatial Coherence ◽  
Original Data ◽  
Return Level ◽  
Data Sets ◽  
Large Set ◽  
Precipitation Data ◽  
Data Set ◽  
Spatio Temporal ◽  
Synthetic Precipitation

Abstract With the ongoing development of distributed hydrological models, flood risk analysis calls for synthetic, gridded precipitation data sets. The availability of large, coherent, gridded re-analysis data sets in combination with the increase in computational power, accommodates the development of new methodology to generate such synthetic data. We tracked moving precipitation fields and classified them using self-organising maps. For each class, we fitted a multivariate mixture model and generated a large set of synthetic, coherent descriptors, which we used to reconstruct moving synthetic precipitation fields. We introduced randomness in the original data set by replacing the observed precipitation fields in the original data set with the synthetic precipitation fields. The output is a continuous, gridded, hourly precipitation data set of a much longer duration, containing physically plausible and spatio-temporally coherent precipitation events. The proposed methodology implicitly provides an important improvement in the spatial coherence of precipitation extremes. We investigate the issue of unrealistic, sudden changes on the grid and demonstrate how a dynamic spatio-temporal generator can provide spatial smoothness in the probability distribution parameters and hence in the return level estimates.

scholarly journals GAUSSPY+: A fully automated Gaussian decomposition package for emission line spectra

2019 ◽  
Vol 628 ◽  
pp. A78 ◽  
Author(s):  
M. Riener ◽  
J. Kainulainen ◽  
J. D. Henshaw ◽  
J. H. Orkisz ◽  
C. E. Murray ◽  
...  
Keyword(s):  
Emission Line ◽  
Velocity Structure ◽  
Galactic Plane ◽  
Spatial Coherence ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Test Field ◽  
Data Set ◽  
Gaussian Decomposition

Our understanding of the dynamics of the interstellar medium is informed by the study of the detailed velocity structure of emission line observations. One approach to study the velocity structure is to decompose the spectra into individual velocity components; this leads to a description of the data set that is significantly reduced in complexity. However, this decomposition requires full automation lest it become prohibitive for large data sets, such as Galactic plane surveys. We developed GAUSSPY+, a fully automated Gaussian decomposition package that can be applied to emission line data sets, especially large surveys of HI and isotopologues of CO. We built our package upon the existing GAUSSPY algorithm and significantly improved its performance for noisy data. New functionalities of GAUSSPY+ include: (i) automated preparatory steps, such as an accurate noise estimation, which can also be used as stand-alone applications; (ii) an improved fitting routine; (iii) an automated spatial refitting routine that can add spatial coherence to the decomposition results by refitting spectra based on neighbouring fit solutions. We thoroughly tested the performance of GAUSSPY+ on synthetic spectra and a test field from the Galactic Ring Survey. We found that GAUSSPY+ can deal with cases of complex emission and even low to moderate signal-to-noise values.

Predictive processing of array acoustic waveform data

Geophysics ◽  
1997 ◽  
Vol 62 (6) ◽  
pp. 1710-1714 ◽  
Author(s):  
Xiao Ming Tang
Keyword(s):  
Time Domain ◽  
Predictive Processing ◽  
Multiple Wave ◽  
Data Set ◽  
Waveform Data ◽  
Array Data ◽  
Wave Data ◽  
Minimization Procedure ◽  
The Time Domain ◽  
Wave Modes

Estimation of wave velocity (or slowness) from array waveform data is a basic and very important process in acoustic logging and seismic processing. A predictive method is developed to process array waveform data containing multiple wave modes. These wave modes may overlap in both time and frequency and are inseparable using conventional techniques. In this new technique, the waveform at a receiver is modeled by a combination of wave data at other receivers using a time‐domain prediction theory. It is assumed that the array data contain a number of propagating modes. A minimization procedure is formulated to optimize the match between the predicted and measured waveforms, yielding slowness estimates of the wave modes across the array. Most important, the optimization is performed directly in the time domain using the entire array wave data set, including all possible data combinations. This strategy effectively reduces the noise effects and enhances the robustness of the estimation. Furthermore, the estimated slowness values can be used in formulating a procedure to split the array data into individual wave modes, allowing their behavior to be analyzed. Examples are shown to demonstrate the ability of the technique to extract wave slowness from multiple wavemode data.

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