A scatterometer is a side-looking radar that sends a pulse to a direction of the vertical and records the return signal. This is like the synthetic-aperture radar we saw before. But the difference is that we don't want with this radar to form an image of the earth and surface, but what we want is to record information on the wind speed and direction. Then like we saw for synthetic-aperture radar in this scatterometers, the surface roughness is modulating the intensity of the signal that goes back to the radar antenna. For a flat sea, there is a perfect reflection, no signal returns to the antenna while the roughness starts to develop, the signal is scattered into different directions and it starts to go back to the radar. This is more intense and this is for sure much more. Then we use this information because this surface [inaudible] is produced by the wind. So as we are getting to different directions, this is the case that we have according to the vertical poles, but we start going to different directions. What we recover are different signals. Here you have the intensity of the signal in function of the wind speed for the different inclinations. So far as scatterometers that has a range in the direction is between 30 and 60 degrees, this is the best configuration because it provides a higher variation with wind speed. So using this information, we will be able to derive the wind speed according to the amount of radiation that is going back to the radar antenna. But this also changes not only with the inclination of the signal with respect to the vertical and with the wind speed, but also depends on the duration of the propagating waves to the duration of the radar signal. So in this case, what we saw here on the right is that for different wind speeds, this is the lowest, this is the highest. This is how the signal received by the radar is varying according to what we call the azimuth angle. This is not the incidence angle with respect to this surface, this is the azimuth angle, how the angle varies according to the direction of propagation of the wind wave. So there is an influence of this direction that can be used to retrieve the wind direction. So scatterometers is able to provide us the wind speed but also the wind direction. This is one of the scatterometers that was flying between 1999 and 2009. It's QuickScat, a satellite that was carrying a scatterometer named SeaWinds, and this was able to provide a distribution of what we call the vector wind fill. That means not only the speed, but also the direction of the wind with a special resolution of 25 kilometers. One of the big advantages of scatterometers is that they are able to locate precisely storms in the ocean. The effect of storms on the ocean surface roughness, and this is very vulnerable, for example, for hurricanes. In this case here, we see a QuickScat image of Hurricane Katrina, the one that produced a very strong devastation in the US in 2005. So detect that these are able to locate precisely the storms, is an advantage with comparison to numerical weather predictions, the predictions that are made by the weather forecast centers that not always have the capability of locating precisely these kinds of storms, and scatterometers are able to do so. This is an example of what we were saying just now. This is the output of a model. Numerical weather prediction model is indicating that in this area, we have an empty cyclonic, sorry, this is cyclonic we have in the Southern Ocean. This is a clockwise storm. While the observation of the scatterometer, QuickScat scatterometer is this image here. First thing we can see the center of the storm is located here according to the model and it is located here according to the observation of the radar, and this is not the same. Look at where the model was predicting the center of the storm and where the center of the storm impact was. So these radar scatterometers are able to improve the information that the numerical forecasts are able to give us. Here we, have an example of the impact of simulating scatterometers data into a numerical prediction model. On the top, you see the evolution of the storm in the Indian Ocean made with a model that does not assimilate scatterometer data. You can see how the center of the storm evolves both in intensity and in location. When we use scatterometer data into the model, what we call the assimilation of data into the model, the output of the model is modified by this. So you see compared to this that the location of the center of the storm is not the same and this is very important, the intensity of the storm is not the same. This is very important for warnings on the effects of these huge storms in different areas.
