This article is focused on the algorithm developments however, it is also the first study of sea state retrieval in the Baltic Sea using high-resolution satellite-based techniques. The wind speed, estimated from SAR images, was compared to measurements from 14 collocated in situ stations, yielding a high agreement with an r value of 0.90. The comparison of SAR-derived wave heights with measured wave heights shows high agreement with a correlation coefficient r of 0.88. The analysed data include both high and low windsea conditions. The wave height results from SAR images were compared with collocated in situ data from 11 available buoys. In total, 95 TS-X/TD-X StripMap scenes between 20 were acquired in Eastern Baltic Sea, processed and analysed. A term to compensate spectral distortions triggered by windsea waves moving in SAR flight direction has also been introduced. For the empirical XWAVE_C model function, based on the spectral analysis of subscenes as well as on local wind information, an additional term was incorporated for assessment the minimal windsea significant wave height by applying JONSWAP wave spectra. The XWAVE_C algorithm, developed for the North Sea, where the long swell waves coming from the Atlantic Ocean are present during storms, was further enhanced for the short steep windsea which dominates under ordinary storm conditions in the Baltics. Since the moving targets can be defocused and shifted in SAR images, sea state consisting of short windsea waves with strong local orbital velocities and wave breaking needs additional effort for accurate estimation of the total significant wave height that consists of swell and windsea parts. If you are not sure how to accomplish the above feel free to send me an experiment with the relevant data and I'd help.In this work, remote sensing synthetic aperture radar (SAR) data from X-band TerraSAR-X and TanDEM-X (TS-X and TD-X) satellites have been used to adopt the algorithms for estimating sea state parameters in the specific condition of the Baltic Sea with archipelago islands and where short steep sea state dominates. You can find (and modify) that value by choosing Axis Range from Gizmo menu. Since cylinders are drawing objects (not data objects) their scale is such that a cylinder of height 1 extends half the global delta-z of the plot. Now return to the scatter dialog and set scale1 as the Marker Size Wave. Now assign the delta-z values by modifying the scaling of the third column of the wave. For example, if your scatter triplet was in tripletWave1 use: Open the scatter object properties and select Fixed Shape -> Object and from the Object menu select cylinder0.Ĭreate a scale wave that has the same number of rows as your scatter data. Set the cylinder radii to a small value, e.g., 0.01 and leave the default height. If your error bars have only delta-z variation you can, for example, choose a cylinder object as a marker. Next use the same triplet waves to create scatter objects that will be used to add error bars as markers at each data point. You would then add these to Gizmo as Path objects and obtain the basic curves. In order to display this in Gizmo you need to start by creating a triplet wave for each one of your curves. I have tried gizmo scattered plots, but I cant add the error bars to each point. I was wondering what is the "best" way to deal with this issue. I want to generate a 3D plot for this 5 curves as is displayed in the attached curve. Total I have x and y wave, fitwave, and error bars waves. I have five 2D plots each consisting of several points with an error associated to them in addition of a curve fit, specified as a separate wave each. I have a related question while trying to generate a 3D plot. If this does not help I suggest you send an IGOR experiment containing sample data to Inc. You can still provide an easy projection onto the XY plane (at minimum z) by duplicating your triplet wave and setting the third column to the minimum z value. There is really no meaning to a contour plot in this context (simply because one of your parameters is an fake index). Now plot the triplets as scatter in Gizmo. For example, suppose you have multiple XY pairs of waves: xwave_i and ywave_i (where i is an integer index) then, create a set of triplet waves triplet_i with: If you just have multiple XY sets of data, there is nothing that keeps you from converting them to triplet waves. The plots do however depict true 3-axis variation, something that is not apparent from your description of your data as an XY set. The plots you referenced are closer to what you would generate with Gizmo which brings me back to my earlier suggestion. Wide-Angle Neutron Spin Echo Spectroscopy.
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