scholarly journals Directional Calibration of Wave Reanalysis Databases Using Instrumental Data

2011 ◽  
Vol 28 (11) ◽  
pp. 1466-1485 ◽  
Author(s):  
R. Mínguez ◽  
A. Espejo ◽  
A. Tomás ◽  
F. J. Méndez ◽  
I. J. Losada
Keyword(s):  
Time Series ◽  
Calibration Method ◽  
Wave Climate ◽  
Wind Fields ◽  
Regression Problem ◽  
Statistical Characterization ◽  
Instrumental Data ◽  
Spatial Coverage ◽  
Long Time ◽  
Wave Reanalysis

Abstract Wave reanalysis databases (WRDBs) offer important advantages for the statistical characterization of wave climate (continuous time series, good spatial coverage, constant time span, homogeneous forcing, and more than a 40-yr-long time series) and for this reason, they have become a powerful tool for the design of offshore and coastal structures. However, WRDBs are not quantitatively perfect and corrections using instrumental observations must be addressed before they are used; this process is called calibration. The calibration is especially relevant near the coast and in areas where the orography is complex, since in these places the inaccuracy of WRDB is evident because of the bad description of the wind fields (i.e., insufficient forcing resolution). The quantitative differences between numerical and instrumental data suggest that different corrections should be applied depending on the mean direction of the sea state. This paper proposes a calibration method based on a nonlinear regression problem, where the corresponding correction parameters vary smoothly along the possible wave directions by means of cubic splines. The correction of significant wave height is performed using instrumental data: (i) buoy records and/or (ii) satellite data. The performance of the method is illustrated considering data from different locations around Spain.

scholarly journals Regression Models for Outlier Identification (Hurricanes and Typhoons) in Wave Hindcast Databases

2012 ◽  
Vol 29 (2) ◽  
pp. 267-285 ◽  
Author(s):  
R. Mínguez ◽  
B. G. Reguero ◽  
A. Luceño ◽  
F. J. Méndez
Keyword(s):  
Time Series ◽  
Prediction Models ◽  
Numerical Models ◽  
Wave Climate ◽  
Spatiotemporal Resolution ◽  
Statistical Characterization ◽  
Outlier Identification ◽  
Wave Hindcast ◽  
Spatial Coverage ◽  
Instrumental Records

Abstract The development of numerical wave prediction models for hindcast applications allows a detailed description of wave climate in locations where long-term instrumental records are not available. Wave hindcast databases (WHDBs) have become a powerful tool for the design of offshore and coastal structures, offering important advantages for the statistical characterization of wave climate all over the globe (continuous time series, wide spatial coverage, constant time span, homogeneous forcing, and more than 60-yr-long time series). However, WHDBs present several deficiencies reported in the literature. One of these deficiencies is related to typhoons and hurricanes, which are inappropriately reproduced by numerical models. The main reasons are (i) the difficulty of specifying accurate wind fields during these events and (ii) the insufficient spatiotemporal resolution used. These difficulties make the data related to these events appear as “outliers” when compared with instrumental records. These bad data distort results from calibration and/or correction techniques. In this paper, several methods for detecting the presence of typhoons and/or hurricane data are presented, and their automatic outlier identification capabilities are analyzed and compared. All the methods are applied to a global wave hindcast database and results are compared with existing hurricane and buoy databases in the Gulf of Mexico, Caribbean Sea, and North Atlantic Ocean.

scholarly journals RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series

2019 ◽  
Vol 33 ◽  
pp. 5409-5416 ◽  
Author(s):  
Qingsong Wen ◽  
Jingkun Gao ◽  
Xiaomin Song ◽  
Liang Sun ◽  
Huan Xu ◽  
...  
Keyword(s):  
Time Series ◽  
Real World ◽  
Abrupt Change ◽  
Decomposition Algorithm ◽  
Real World Data ◽  
Regression Problem ◽  
Sparse Regularization ◽  
Least Absolute Deviations ◽  
Long Time ◽  
Non Local

Decomposing complex time series into trend, seasonality, and remainder components is an important task to facilitate time series anomaly detection and forecasting. Although numerous methods have been proposed, there are still many time series characteristics exhibiting in real-world data which are not addressed properly, including 1) ability to handle seasonality fluctuation and shift, and abrupt change in trend and reminder; 2) robustness on data with anomalies; 3) applicability on time series with long seasonality period. In the paper, we propose a novel and generic time series decomposition algorithm to address these challenges. Specifically, we extract the trend component robustly by solving a regression problem using the least absolute deviations loss with sparse regularization. Based on the extracted trend, we apply the the non-local seasonal filtering to extract the seasonality component. This process is repeated until accurate decomposition is obtained. Experiments on different synthetic and real-world time series datasets demonstrate that our method outperforms existing solutions.

Time Series Analysis of Significant Wave Height Data for Identification of Trends in the Ocean Wave Climate

2013 ◽  
Author(s):  
Erik Vanem ◽  
Sam-Erik Walker
Keyword(s):  
Time Series ◽  
Wave Height ◽  
Significant Wave Height ◽  
Wave Climate ◽  
Ocean Wave ◽  
Significant Wave ◽  
Long Time ◽  
Long Term Trends ◽  
Ocean Wave Climate

Reliable return period estimates of sea state parameters such as the significant wave height is of great importance in marine structural design and ocean engineering. Hence, time series of significant wave height have been extensively studied in recent years. However, with the possibility of an ongoing change in the global climate, this might influence the ocean wave climate as well and it would be of great interest to analyze long time series to see if any long-term trends can be detected. In this paper, long time series of significant wave height stemming from the ERA-40 reanalysis project, containing 6-hourly data over a period of more than 44 years are investigated with the purpose of identifying long term trends. Different time series analysis methods are employed, i.e. seasonal ARIMA, multiple linear regression, the Theil-Sen estimator and generalized additive models, and the results are discussed. These results are then compared to previous studies; in particular results are compared to a recent study where a spatio-temporal stochastic model was applied to the same data. However, in the current analysis, the spatial dimension has been reduced and spatial minima, mean and maxima have been analysed for temporal trends. Overall, increasing trends in the wave climate have been identified by most of the modelling approaches explored in the paper, although some of the trends are not statistically significant at the 95% level. Based on the results presented in this paper, it may be argued that there is evidence of a roughening trend in the recent ocean wave climate, and more detailed analyses of individual months and seasons indicate that these trends might be mostly due to trends during the winter months.

scholarly journals On the feasibility of the use of wind SAR to downscale waves on shallow water

2015 ◽  
Vol 12 (4) ◽  
pp. 1567-1593
Author(s):  
O. Q. Gutiérrez ◽  
F. Filipponi ◽  
A. Taramelli ◽  
E. Valentini ◽  
P. Camus ◽  
...  
Keyword(s):  
Shallow Water ◽  
Spatial Resolution ◽  
Water Waves ◽  
Wave Climate ◽  
Wind Fields ◽  
Northern Adriatic Sea ◽  
Northern Adriatic ◽  
Wave Fields ◽  
Offshore Wave ◽  
Wave Reanalysis

Abstract. On the recent years wave reanalysis have become popular as a powerful source of information for wave climate research and engineering applications. These wave reanalysis provide continuous time-series of offshore wave parameters, nevertheless on coastal areas or shallow water waves are poorly described because spatial resolution is not detailed. By means of wave downscaling it is possible to increase spatial resolution in high temporal coverage simulations, using forcing from wind and offshore wave databases. Meanwhile the reanalysis wave databases are enough to describe the wave climate on the limit of simulations, wind reanalysis at an adequate spatial resolution to describe the wind structure near the coast are not frequently available. Remote Sensing Synthetic Aperture Radar (SAR) has the ability to detect sea surface signatures and estimate wind field at high resolution (up to 300 m) and high frequency. In this work a wave downscaling is done on the northern Adriatic sea, using an hybrid methodology and Global wave and wind reanalysis as forcing. The wave fields produced were compared to wave fields produced with SAR winds that represent the two dominant wind regimes in the area: the Bora (ENE direction) and Sirocco (SE direction). Results show a good correlation between the waves forced with reanalysis wind and SAR wind. In addition, a validation of reanalysis is shown. This research demonstrates how Earth Observation products, as SAR wind fields, can be successfully up-taken into oceanographic modeling, producing similar downscaled wave field when compared to waves forced with reanalysis wind.

scholarly journals Sea Ice Concentration Products over Polar Regions with Chinese FY3C/MWRI Data

2021 ◽  
Vol 13 (11) ◽  
pp. 2174
Author(s):  
Lijian Shi ◽  
Sen Liu ◽  
Yingni Shi ◽  
Xue Ao ◽  
Bin Zou ◽  
...  
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Ocean Circulation ◽  
The Arctic ◽  
Retrieval Algorithm ◽  
Atmospheric Effects ◽  
Observation Data ◽  
Long Time Series ◽  
Ice Concentration ◽  
Long Time

Polar sea ice affects atmospheric and ocean circulation and plays an important role in global climate change. Long time series sea ice concentrations (SIC) are an important parameter for climate research. This study presents an SIC retrieval algorithm based on brightness temperature (Tb) data from the FY3C Microwave Radiation Imager (MWRI) over the polar region. With the Tb data of Special Sensor Microwave Imager/Sounder (SSMIS) as a reference, monthly calibration models were established based on time–space matching and linear regression. After calibration, the correlation between the Tb of F17/SSMIS and FY3C/MWRI at different channels was improved. Then, SIC products over the Arctic and Antarctic in 2016–2019 were retrieved with the NASA team (NT) method. Atmospheric effects were reduced using two weather filters and a sea ice mask. A minimum ice concentration array used in the procedure reduced the land-to-ocean spillover effect. Compared with the SIC product of National Snow and Ice Data Center (NSIDC), the average relative difference of sea ice extent of the Arctic and Antarctic was found to be acceptable, with values of −0.27 ± 1.85 and 0.53 ± 1.50, respectively. To decrease the SIC error with fixed tie points (FTPs), the SIC was retrieved by the NT method with dynamic tie points (DTPs) based on the original Tb of FY3C/MWRI. The different SIC products were evaluated with ship observation data, synthetic aperture radar (SAR) sea ice cover products, and the Round Robin Data Package (RRDP). In comparison with the ship observation data, the SIC bias of FY3C with DTP is 4% and is much better than that of FY3C with FTP (9%). Evaluation results with SAR SIC data and closed ice data from RRDP show a similar trend between FY3C SIC with FTPs and FY3C SIC with DTPs. Using DTPs to present the Tb seasonal change of different types of sea ice improved the SIC accuracy, especially for the sea ice melting season. This study lays a foundation for the release of long time series operational SIC products with Chinese FY3 series satellites.

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.

scholarly journals Performance stability of the MODIS and VIIRS LAI algorithms inferred from analysis of long time series of products

2021 ◽  
Vol 260 ◽  
pp. 112438
Author(s):  
Kai Yan ◽  
Jiabin Pu ◽  
Taejin Park ◽  
Baodong Xu ◽  
Yelu Zeng ◽  
...  
Keyword(s):  
Time Series ◽  
Long Time Series ◽  
Long Time
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