相关论文: Detiding DART buoy data for real-time extraction o…
A performance measure for a DART tsunami buoy network has been developed. The measure is based on a statistical analysis of simulated forecasts of wave heights outside an impact site and how much the forecasts are degraded in accuracy when…
Accurate analysis and forecasting of tidal level are very important tasks for human activities in oceanic and coastal areas. They can be crucial in catastrophic situations like occurrences of Tsunamis in order to provide a rapid alerting to…
Observational data from buoys are of primary importance during the development, calibration, and evaluation of ocean wave models, and these data are also used to make real-time corrections to operational models via data assimilation. By…
Current evaluation metrics for deep learning weather models create a "Statistical Similarity Trap", rewarding blurry predictions while missing rare, high-impact events. We provide quantitative evidence of this trap, showing sophisticated…
Estimation of nearshore bathymetry is important for accurate prediction of nearshore wave conditions. However, direct data collection is expensive and time-consuming while accurate airborne lidar-based survey is limited by breaking waves…
In-situ ocean wave observations are critical to improve model skill and validate remote sensing wave measurements. Historically, such observations are extremely sparse due to the large costs and complexity of traditional wave buoys and…
Geomagnetic storms resulting from high-speed streams can have significant negative impacts on modern infrastructure due to complex interactions between the solar wind and geomagnetic field. One measure of the extent of this effect is the…
Although the sequential tsunami scenario detection framework was validated in our previous work, several tasks remain to be resolved from a practical point of view. This study aims to evaluate the performance of the previous tsunami…
The accurate measurement of waves underpins marine energy resource characterization, device design, and project development. Datawell wave buoys are widely deployed around the world and have long served as a trusted standard for wave…
Sea wave monitoring is key in many applications in oceanography such as the validation of weather and wave models. Conventional in situ solutions are based on moored buoys whose measurements are often recognized as a standard. However,…
Fragments of deep-ocean tidal records up to 3 days long belong to the same functional sub-space, regardless of the record's origin. The tidal sub-space basis can be derived via Empirical Orthogonal Function (EOF) analysis of a tidal record…
The 4D-Var method for filtering partially observed nonlinear chaotic dynamical systems consists of finding the maximum a-posteriori (MAP) estimator of the initial condition of the system given observations over a time window, and…
This paper proposes a machine learning method based on the Extra Trees (ET) algorithm for forecasting Significant Wave Heights in oceanic waters. To derive multiple features from the CDIP buoys, which make point measurements, we first…
A comparison is carried out between two operational wave forecasting/assimilation models for the North Sea, with the emphasis on the assimilation schemes. One model is the WAM model, in combination with an optimal interpolation method…
Given a distribution of earthquake-induced seafloor elevations, we present a method to compute the probability of the resulting tsunamis reaching a certain size on shore. Instead of sampling, the proposed method relies on optimization to…
With the aim of assessing the merits of the new source function proposed earlier in Polnikov (2005),it was tested and validated by means of the modification of the well known model WAVEWATCH-III. Assessment was done on the basis of…
In this paper, we predict severity of extreme weather events (tropical storms, hurricanes, etc.) using buoy data time series variables such as wind speed and air temperature. The prediction/forecasting method is based on various forecasting…
Time-Series (TS) exhibits pronounced non-stationarity. Consequently, most forecasting methods display compromised robustness to concept drift, despite the prevalent application of instance normalization. We tackle this challenge by first…
In this paper, we propose a Light Gradient Boosting (LightGBM) to forecast dominant wave periods in oceanic waters. First, we use the data collected from CDIP buoys and apply various data filtering methods. The data filtering methods allow…
We propose and compare methods for the analysis of extreme events in complex systems governed by PDEs that involve random parameters, in situations where we are interested in quantifying the probability that a scalar function of the…