[MUSIC] Hello, in this coming sessions we are going to analyze large sets of wind data to investigate the characteristics of the wind resource at a given location. First, we will focus on the concept of atmospheric that was previously introduced. You are going to study the vertical wind shear ability on the basis of real wind speed measurements. These measurements have been collected on the site of Sirta located at about 20 kilometer to the south of Paris. Let's go down now to see what instrument we have used to measure the wind velocity. Hello, we are here on the site of Sirta on the compose of Ecole Polytechnique where a wide range of instruments are dedicated to wind measurements. The projects on which you are going to work in the next quiz makes use of real data that has been measured here. For this project, you will be given measurements that have been performed over one year from August, 2014 to July, 2015, with the wind instruments. I recall you that the wind [INAUDIBLE] instruments makes remote measure of the [INAUDIBLE] the vertical confidence of the wind velocity at various heights ranging from 40 meters to 250 meters. The wind instrument other special resolution of 20 meters and makes averages of the wind velocity over ten minutes. Here is a sample of the text file Lidar_wind_vertical_profiles.txt that you will use in the next quiz. It contains the wind measures of the Lidar instrument averaged over periods of an hour. Let's see how to read it. In the first three columns you have the date in the format year, month, day. Then the fourth column corresponds to the hour of the day. And the last 12 columns are the horizontal wind velocity at 12 different altitudes ranging between 40 meters and 250 meters. Note that sometimes there is no measure. This maybe due to the meteorological condition that disturb the radar such as strong fog or simply a temporary malfunction of the instrument. To describe quantitatively, the shape of the atmospheric boundary layer, you are going to fit the vertical profile of wind velocity to a power law. The power law exponent, alpha, accounts for how much shear is the vertical wind profile. For instance, here is the power law fit to the wind vertical profile measured on August 24, 2014 at midnight. The referenced height z1 is chosen arbitrarily and the two independent parameters to be computed are alpha and V1. Note the the power exponent of alpha does not depend on the reference height that you choose. To compute the parameters of the power law, you can do a fitting using the numerical analysis software. Or smarter and easier, you can plot the wind velocity measures in a graph. In that case, you expect the rough linear distribution and by doing a linear outfit, you compute the value of V1 from the y-intercept and the value of alpha from the slope of the linear. Now, you are going to do yourself the power law fit in a practice quiz. It is important that you do it carefully and check that you obtain the correct fit as you will need it for the following graded quiz. For completing that excercise, you will need the software of numerical analysis. For instance, you might use Scilab, Octave, Matlab or Python, or any other equivalent software. So far, we have computed the power law for a particular date and the particular hour. However, remember, in the data you have there are thousands of vertical profiles corresponding to the 24 hours of the day, and these for one year. Thus, you can write a code that computes the power law for all the wind vertical profiles of the year. An interesting approach to understand the variability of the wind shear is to consider that distribution of alpha over the year. On this histogram, you can see the statistical distribution of power law exponents and it reveals the bar distribution with two distinct peaks. The project that you're going to complete now consists in analyzing the distribution by investigating first, the diurnal variability of the power law exponent, which means how it varies throughout the day, and second, it's seasonal variability. That is to say, how it evolves throughout the year. You will see that the statistical distribution of power law exponents can be decomposed into physically meaningful simpler distributions. Now, you can go on with the quiz, where you will download the data we have presented. And analyze, by yourself, the characteristics of the boundary layer as observed on the site of Sirta.