46 research outputs found
A multi-collocation method for coastal zone observations with applications to Sentinel-3A altimeter wave height data
In many coastal areas there is an increasing number and variety
of observation data available, which are often very heterogeneous in their temporal and
spatial sampling characteristics. With the advent of new systems, like the radar
altimeter on board the Sentinel-3A satellite, a lot of questions arise concerning the
accuracy and added value of different instruments and numerical models. Quantification of
errors is a key factor for applications, like data assimilation and forecast improvement.
In the past, the triple collocation method to estimate systematic and stochastic
errors of measurements and numerical models was successfully applied to different data
sets.
This method relies on the assumption that three independent data sets provide estimates of the same quantity.
In coastal areas with strong gradients even small distances between measurements can lead
to larger differences and this assumption can become critical. In this study the
triple collocation method is extended in different ways with the specific
problems of the coast in mind. In addition to nearest-neighbour approximations considered
so far, the presented method allows for use of a large variety of interpolation
approaches to take spatial variations in the observed area into account. Observation and
numerical model errors can therefore be estimated, even if the distance between the
different data sources is too large to assume that they measure the same quantity. If the
number of observations is sufficient, the method can also be used to estimate error
correlations between certain data source components. As a second novelty, an estimator
for the uncertainty in the derived observation errors is derived as a function of the
covariance matrices of the input data and the number of available samples.
In the first step, the method is assessed using synthetic observations and Monte Carlo
simulations. The technique is then applied to a data set of Sentinel-3A altimeter
measurements, in situ wave observations, and numerical wave model data with a focus on
the North Sea. Stochastic observation errors for the significant wave height, as well as
bias and calibration errors, are derived for the model and the altimeter. The analysis
indicates a slight overestimation of altimeter wave heights, which become more pronounced
at higher sea
states. The smallest stochastic errors are found for the in situ measurements.
Different observation geometries of in situ data and altimeter tracks are furthermore
analysed, considering 1-D and 2-D interpolation approaches. For example, the geometry of
an altimeter track passing between two in situ wave instruments is considered with model
data being available at the in situ locations. It is shown that for a sufficiently large
sample, the errors of all data sources, as well as the error correlations of the model,
can be estimated with the new method.</p
Investigation of inhomogeneity parameters of ERS-2 wave mode image
In order to classify different types of globally distributed synthetic aperture radar (SAR) wave mode images, which are acquired over the ocean for wind speed and sea state measurements, we develop a new scheme to differentiate images showing ocean wave, sea ice and surface slicks. A new classification parameter has been developed using 1535 SAR wave mode images to differentiate homogeneous and inhomoge-neous images. The new parameter is applied to two years of images. Comparison of the performance using the new parameter and inhomogeneity parameter (IH) defined in [1] are given. In the Arctic area the results of two parameters are compared to Special Sensor Microwave Imager (SSM/I) ice concentration data. The global distribution of inhomogeneous images is analyzed. Inhomegeneity in ice-free SAR images was found to be mainly due to low wind speed
Statistical analysis of ocean wave and wind parameters retrieved with an empirical SAR algorithum
A global dataset of two years (September 1998 to December 2000) of ERS SAR data was reprocessed to more than one million SAR imagettes. Met ocean Parameters like significant ocean wave height (H s), wind speed (U 10) and mean wave period (T m-10) are derived from the SAR images using a new empirical algorithm CWAVE [1]. The results are compared to collocated ERS altimeter data and in Situ measurements from NOAA buoys and observations taken onboard the vessel Polarstern. It is shown that the SAR derived H s is comparable in quality to altimeter measurements and can thus be used for real time assimilation
Synergy of wind wave model simulations and satellite observations during extreme events
In this study, the quality of wave data provided by the
new Sentinel-3A satellite is evaluated and the sensitivity of the wave model
to wind forcing is tested. We focus on coastal areas, where altimeter data
are of lower quality and wave modelling is more complex than for the open
ocean. In the first part of the study, the sensitivity of the wave model to
wind forcing is evaluated using data with different temporal and spatial
resolution, such as ERA-Interim and ERA5 reanalyses, the European Centre for
Medium-Range Weather Forecasts (ECMWF) operational analysis and short-range
forecasts, German Weather Service (DWD) forecasts and regional atmospheric
model simulations (coastDat). Numerical simulations show that
the wave model forced using the ERA5 reanalyses and that forced using the
ECMWF operational analysis/forecast demonstrate the best capability over the
whole study period, as well as during extreme events. To further estimate the
variance of the significant wave height of ensemble members for different
wind forcings, especially during extreme events, an empirical orthogonal
function (EOF) analysis is performed. In the second part of the study, the
satellite data of Sentinel-3A, Jason-2 and CryoSat-2 are assessed in
comparison with in situ measurements and spectral wave model (WAM)
simulations. Intercomparisons between remote sensing and in situ observations
demonstrate that the overall quality of the former is good over the North Sea
and Baltic Sea throughout the study period, although the significant wave
heights estimated based on satellite data tend to be greater than the in situ
measurements by 7 to 26 cm. The quality of all satellite data near
the coastal area decreases; however, within 10 km off the coast,
Sentinel-3A performs better than the other two satellites. Analyses in which
data from satellite tracks are separated in terms of onshore and offshore
flights have been carried out. No substantial differences are found when
comparing the statistics for onshore and offshore flights. Moreover, no
substantial differences are found between satellite tracks under various
metocean conditions. Furthermore, the satellite data quality does not depend
on the wind direction relative to the flight direction. Thus, the quality of
the data obtained by the new Sentinel-3A satellite over coastal areas
is improved compared to that of older satellites.</p
Linear and Nonlinear Rogue Wave Statistics in the Presence of Random Currents
We review recent progress in modeling the probability distribution of wave
heights in the deep ocean as a function of a small number of parameters
describing the local sea state. Both linear and nonlinear mechanisms of rogue
wave formation are considered. First, we show that when the average wave
steepness is small and nonlinear wave effects are subleading, the wave height
distribution is well explained by a single "freak index" parameter, which
describes the strength of (linear) wave scattering by random currents relative
to the angular spread of the incoming random sea. When the average steepness is
large, the wave height distribution takes a very similar functional form, but
the key variables determining the probability distribution are the steepness,
and the angular and frequency spread of the incoming waves. Finally, even
greater probability of extreme wave formation is predicted when linear and
nonlinear effects are acting together.Comment: 25 pages, 12 figure